From 8a5298654d4596ba6ed676729f99fc81d0abf81c Mon Sep 17 00:00:00 2001
From: BenoitDherin <dherin@google.com>
Date: Thu, 18 Jan 2024 19:59:02 +0000
Subject: [PATCH 1/2] remove unpaired lab/solution notebooks

---
 ..._dataset_api.ipynb => 2_dataset_api.ipynb} |    0
 .../labs/adv_logistic_reg_TF2.0.ipynb         | 2859 -----------------
 .../labs/adv_tfdv_facets.ipynb                |  595 ----
 .../basic_intro_logistic_regression.ipynb     | 1104 -------
 .../labs/feat.cols_tf.data.ipynb              | 1083 -------
 .../labs/int_logistic_regression.ipynb        | 1460 ---------
 .../intro_logistic_regression_TF2.0.ipynb     |  481 ---
 .../labs/load_diff_filedata.ipynb             | 1327 --------
 .../labs/load_images_tf.data.ipynb            |  744 -----
 .../labs/tfrecord-tf.example.ipynb            | 1670 ----------
 .../labs/what_if_mortgage.ipynb               |  766 -----
 .../labs/write_low_level_code.ipynb           |  760 -----
 ..._dataset_api.ipynb => 2_dataset_api.ipynb} |    0
 .../solutions/2b_loading_filedata.ipynb       |  992 ------
 .../solutions/2c_loading_images.ipynb         |  608 ----
 .../solutions/2d_loading_tfrecords.ipynb      | 1259 --------
 16 files changed, 15708 deletions(-)
 rename notebooks/introduction_to_tensorflow/labs/{2a_dataset_api.ipynb => 2_dataset_api.ipynb} (100%)
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/adv_logistic_reg_TF2.0.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/adv_tfdv_facets.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/basic_intro_logistic_regression.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/feat.cols_tf.data.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/int_logistic_regression.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/intro_logistic_regression_TF2.0.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/load_diff_filedata.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/load_images_tf.data.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/tfrecord-tf.example.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/what_if_mortgage.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/labs/write_low_level_code.ipynb
 rename notebooks/introduction_to_tensorflow/solutions/{2a_dataset_api.ipynb => 2_dataset_api.ipynb} (100%)
 delete mode 100644 notebooks/introduction_to_tensorflow/solutions/2b_loading_filedata.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/solutions/2c_loading_images.ipynb
 delete mode 100644 notebooks/introduction_to_tensorflow/solutions/2d_loading_tfrecords.ipynb

diff --git a/notebooks/introduction_to_tensorflow/labs/2a_dataset_api.ipynb b/notebooks/introduction_to_tensorflow/labs/2_dataset_api.ipynb
similarity index 100%
rename from notebooks/introduction_to_tensorflow/labs/2a_dataset_api.ipynb
rename to notebooks/introduction_to_tensorflow/labs/2_dataset_api.ipynb
diff --git a/notebooks/introduction_to_tensorflow/labs/adv_logistic_reg_TF2.0.ipynb b/notebooks/introduction_to_tensorflow/labs/adv_logistic_reg_TF2.0.ipynb
deleted file mode 100644
index 832745d5..00000000
--- a/notebooks/introduction_to_tensorflow/labs/adv_logistic_reg_TF2.0.ipynb
+++ /dev/null
@@ -1,2859 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "dUeKVCYTbcyT"
-   },
-   "source": [
-    "#  Advanced Logistic Regression in TensorFlow 2.0 \n",
-    "\n",
-    "\n",
-    "\n",
-    "## Learning Objectives\n",
-    "\n",
-    "1. Load a CSV file using Pandas\n",
-    "2. Create train, validation, and test sets\n",
-    "3. Define and train a model using Keras (including setting class weights)\n",
-    "4. Evaluate the model using various metrics (including precision and recall)\n",
-    "5. Try common techniques for dealing with imbalanced data like:\n",
-    "    Class weighting and\n",
-    "    Oversampling\n",
-    "\n",
-    "\n",
-    "\n",
-    "## Introduction \n",
-    "This lab how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. You will work with the [Credit Card Fraud Detection](https://www.kaggle.com/mlg-ulb/creditcardfraud) dataset hosted on Kaggle. The aim is to detect a mere 492 fraudulent transactions from 284,807 transactions in total. You will use [Keras](../../guide/keras/overview.ipynb) to define the model and [class weights](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model) to help the model learn from the imbalanced data. \n",
-    "\n",
-    "PENDING LINK UPDATE:  Each learning objective will correspond to a __#TODO__ in the [student lab notebook](https://training-data-analyst/courses/machine_learning/deepdive2/image_classification/labs/5_fashion_mnist_class.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "kRHmSyHxEIhN"
-   },
-   "source": [
-    "Start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "JM7hDSNClfoK"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow version:  2.1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import tempfile\n",
-    "\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import sklearn\n",
-    "import tensorflow as tf\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from tensorflow import keras\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next cell, we're going to customize our Matplot lib visualization figure size and colors. Note that each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element we create. This configuration can be adjusted at any time using the plt.rc convenience routine. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "c8o1FHzD-_y_"
-   },
-   "outputs": [],
-   "source": [
-    "mpl.rcParams[\"figure.figsize\"] = (12, 10)\n",
-    "colors = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Z3iZVjziKHmX"
-   },
-   "source": [
-    "## Data processing and exploration"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "4sA9WOcmzH2D"
-   },
-   "source": [
-    "### Download the Kaggle Credit Card Fraud data set\n",
-    "\n",
-    "Pandas is a Python library with many helpful utilities for loading and working with structured data and can be used to download CSVs into a dataframe.\n",
-    "\n",
-    "Note: This dataset has been collected and analysed during a research collaboration of Worldline and the [Machine Learning Group](http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available [here](https://www.researchgate.net/project/Fraud-detection-5) and the page of the [DefeatFraud](https://mlg.ulb.ac.be/wordpress/portfolio_page/defeatfraud-assessment-and-validation-of-deep-feature-engineering-and-learning-solutions-for-fraud-detection/) project"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "pR_SnbMArXr7"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Time</th>\n",
-       "      <th>V1</th>\n",
-       "      <th>V2</th>\n",
-       "      <th>V3</th>\n",
-       "      <th>V4</th>\n",
-       "      <th>V5</th>\n",
-       "      <th>V6</th>\n",
-       "      <th>V7</th>\n",
-       "      <th>V8</th>\n",
-       "      <th>V9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>V21</th>\n",
-       "      <th>V22</th>\n",
-       "      <th>V23</th>\n",
-       "      <th>V24</th>\n",
-       "      <th>V25</th>\n",
-       "      <th>V26</th>\n",
-       "      <th>V27</th>\n",
-       "      <th>V28</th>\n",
-       "      <th>Amount</th>\n",
-       "      <th>Class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.359807</td>\n",
-       "      <td>-0.072781</td>\n",
-       "      <td>2.536347</td>\n",
-       "      <td>1.378155</td>\n",
-       "      <td>-0.338321</td>\n",
-       "      <td>0.462388</td>\n",
-       "      <td>0.239599</td>\n",
-       "      <td>0.098698</td>\n",
-       "      <td>0.363787</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.018307</td>\n",
-       "      <td>0.277838</td>\n",
-       "      <td>-0.110474</td>\n",
-       "      <td>0.066928</td>\n",
-       "      <td>0.128539</td>\n",
-       "      <td>-0.189115</td>\n",
-       "      <td>0.133558</td>\n",
-       "      <td>-0.021053</td>\n",
-       "      <td>149.62</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.191857</td>\n",
-       "      <td>0.266151</td>\n",
-       "      <td>0.166480</td>\n",
-       "      <td>0.448154</td>\n",
-       "      <td>0.060018</td>\n",
-       "      <td>-0.082361</td>\n",
-       "      <td>-0.078803</td>\n",
-       "      <td>0.085102</td>\n",
-       "      <td>-0.255425</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.225775</td>\n",
-       "      <td>-0.638672</td>\n",
-       "      <td>0.101288</td>\n",
-       "      <td>-0.339846</td>\n",
-       "      <td>0.167170</td>\n",
-       "      <td>0.125895</td>\n",
-       "      <td>-0.008983</td>\n",
-       "      <td>0.014724</td>\n",
-       "      <td>2.69</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-1.358354</td>\n",
-       "      <td>-1.340163</td>\n",
-       "      <td>1.773209</td>\n",
-       "      <td>0.379780</td>\n",
-       "      <td>-0.503198</td>\n",
-       "      <td>1.800499</td>\n",
-       "      <td>0.791461</td>\n",
-       "      <td>0.247676</td>\n",
-       "      <td>-1.514654</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.247998</td>\n",
-       "      <td>0.771679</td>\n",
-       "      <td>0.909412</td>\n",
-       "      <td>-0.689281</td>\n",
-       "      <td>-0.327642</td>\n",
-       "      <td>-0.139097</td>\n",
-       "      <td>-0.055353</td>\n",
-       "      <td>-0.059752</td>\n",
-       "      <td>378.66</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.966272</td>\n",
-       "      <td>-0.185226</td>\n",
-       "      <td>1.792993</td>\n",
-       "      <td>-0.863291</td>\n",
-       "      <td>-0.010309</td>\n",
-       "      <td>1.247203</td>\n",
-       "      <td>0.237609</td>\n",
-       "      <td>0.377436</td>\n",
-       "      <td>-1.387024</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.108300</td>\n",
-       "      <td>0.005274</td>\n",
-       "      <td>-0.190321</td>\n",
-       "      <td>-1.175575</td>\n",
-       "      <td>0.647376</td>\n",
-       "      <td>-0.221929</td>\n",
-       "      <td>0.062723</td>\n",
-       "      <td>0.061458</td>\n",
-       "      <td>123.50</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>-1.158233</td>\n",
-       "      <td>0.877737</td>\n",
-       "      <td>1.548718</td>\n",
-       "      <td>0.403034</td>\n",
-       "      <td>-0.407193</td>\n",
-       "      <td>0.095921</td>\n",
-       "      <td>0.592941</td>\n",
-       "      <td>-0.270533</td>\n",
-       "      <td>0.817739</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.009431</td>\n",
-       "      <td>0.798278</td>\n",
-       "      <td>-0.137458</td>\n",
-       "      <td>0.141267</td>\n",
-       "      <td>-0.206010</td>\n",
-       "      <td>0.502292</td>\n",
-       "      <td>0.219422</td>\n",
-       "      <td>0.215153</td>\n",
-       "      <td>69.99</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
-       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
-       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
-       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
-       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
-       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
-       "\n",
-       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
-       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
-       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
-       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
-       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
-       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
-       "\n",
-       "        V26       V27       V28  Amount  Class  \n",
-       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
-       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
-       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
-       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
-       "4  0.502292  0.219422  0.215153   69.99      0  \n",
-       "\n",
-       "[5 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "file = tf.keras.utils\n",
-    "raw_df = pd.read_csv(\n",
-    "    \"https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv\"\n",
-    ")\n",
-    "raw_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's view the statistics of the raw dataframe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "-fgdQgmwUFuj"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Time</th>\n",
-       "      <th>V1</th>\n",
-       "      <th>V2</th>\n",
-       "      <th>V3</th>\n",
-       "      <th>V4</th>\n",
-       "      <th>V5</th>\n",
-       "      <th>V26</th>\n",
-       "      <th>V27</th>\n",
-       "      <th>V28</th>\n",
-       "      <th>Amount</th>\n",
-       "      <th>Class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>284807.000000</td>\n",
-       "      <td>2.848070e+05</td>\n",
-       "      <td>2.848070e+05</td>\n",
-       "      <td>2.848070e+05</td>\n",
-       "      <td>2.848070e+05</td>\n",
-       "      <td>2.848070e+05</td>\n",
-       "      <td>2.848070e+05</td>\n",
-       "      <td>2.848070e+05</td>\n",
-       "      <td>2.848070e+05</td>\n",
-       "      <td>284807.000000</td>\n",
-       "      <td>284807.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>94813.859575</td>\n",
-       "      <td>1.165980e-15</td>\n",
-       "      <td>3.416908e-16</td>\n",
-       "      <td>-1.373150e-15</td>\n",
-       "      <td>2.086869e-15</td>\n",
-       "      <td>9.604066e-16</td>\n",
-       "      <td>1.687098e-15</td>\n",
-       "      <td>-3.666453e-16</td>\n",
-       "      <td>-1.220404e-16</td>\n",
-       "      <td>88.349619</td>\n",
-       "      <td>0.001727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>47488.145955</td>\n",
-       "      <td>1.958696e+00</td>\n",
-       "      <td>1.651309e+00</td>\n",
-       "      <td>1.516255e+00</td>\n",
-       "      <td>1.415869e+00</td>\n",
-       "      <td>1.380247e+00</td>\n",
-       "      <td>4.822270e-01</td>\n",
-       "      <td>4.036325e-01</td>\n",
-       "      <td>3.300833e-01</td>\n",
-       "      <td>250.120109</td>\n",
-       "      <td>0.041527</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-5.640751e+01</td>\n",
-       "      <td>-7.271573e+01</td>\n",
-       "      <td>-4.832559e+01</td>\n",
-       "      <td>-5.683171e+00</td>\n",
-       "      <td>-1.137433e+02</td>\n",
-       "      <td>-2.604551e+00</td>\n",
-       "      <td>-2.256568e+01</td>\n",
-       "      <td>-1.543008e+01</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>54201.500000</td>\n",
-       "      <td>-9.203734e-01</td>\n",
-       "      <td>-5.985499e-01</td>\n",
-       "      <td>-8.903648e-01</td>\n",
-       "      <td>-8.486401e-01</td>\n",
-       "      <td>-6.915971e-01</td>\n",
-       "      <td>-3.269839e-01</td>\n",
-       "      <td>-7.083953e-02</td>\n",
-       "      <td>-5.295979e-02</td>\n",
-       "      <td>5.600000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>84692.000000</td>\n",
-       "      <td>1.810880e-02</td>\n",
-       "      <td>6.548556e-02</td>\n",
-       "      <td>1.798463e-01</td>\n",
-       "      <td>-1.984653e-02</td>\n",
-       "      <td>-5.433583e-02</td>\n",
-       "      <td>-5.213911e-02</td>\n",
-       "      <td>1.342146e-03</td>\n",
-       "      <td>1.124383e-02</td>\n",
-       "      <td>22.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>139320.500000</td>\n",
-       "      <td>1.315642e+00</td>\n",
-       "      <td>8.037239e-01</td>\n",
-       "      <td>1.027196e+00</td>\n",
-       "      <td>7.433413e-01</td>\n",
-       "      <td>6.119264e-01</td>\n",
-       "      <td>2.409522e-01</td>\n",
-       "      <td>9.104512e-02</td>\n",
-       "      <td>7.827995e-02</td>\n",
-       "      <td>77.165000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>172792.000000</td>\n",
-       "      <td>2.454930e+00</td>\n",
-       "      <td>2.205773e+01</td>\n",
-       "      <td>9.382558e+00</td>\n",
-       "      <td>1.687534e+01</td>\n",
-       "      <td>3.480167e+01</td>\n",
-       "      <td>3.517346e+00</td>\n",
-       "      <td>3.161220e+01</td>\n",
-       "      <td>3.384781e+01</td>\n",
-       "      <td>25691.160000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Time            V1            V2            V3            V4  \\\n",
-       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
-       "mean    94813.859575  1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-15   \n",
-       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
-       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
-       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
-       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
-       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
-       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
-       "\n",
-       "                 V5           V26           V27           V28         Amount  \\\n",
-       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
-       "mean   9.604066e-16  1.687098e-15 -3.666453e-16 -1.220404e-16      88.349619   \n",
-       "std    1.380247e+00  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
-       "min   -1.137433e+02 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
-       "25%   -6.915971e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
-       "50%   -5.433583e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
-       "75%    6.119264e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
-       "max    3.480167e+01  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
-       "\n",
-       "               Class  \n",
-       "count  284807.000000  \n",
-       "mean        0.001727  \n",
-       "std         0.041527  \n",
-       "min         0.000000  \n",
-       "25%         0.000000  \n",
-       "50%         0.000000  \n",
-       "75%         0.000000  \n",
-       "max         1.000000  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df[\n",
-    "    [\n",
-    "        \"Time\",\n",
-    "        \"V1\",\n",
-    "        \"V2\",\n",
-    "        \"V3\",\n",
-    "        \"V4\",\n",
-    "        \"V5\",\n",
-    "        \"V26\",\n",
-    "        \"V27\",\n",
-    "        \"V28\",\n",
-    "        \"Amount\",\n",
-    "        \"Class\",\n",
-    "    ]\n",
-    "].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "xWKB_CVZFLpB"
-   },
-   "source": [
-    "### Examine the class label imbalance\n",
-    "\n",
-    "Let's look at the dataset imbalance:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "HCJFrtuY2iLF"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Examples:\n",
-      "    Total: 284807\n",
-      "    Positive: 492 (0.17% of total)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "neg, pos = np.bincount(raw_df[\"Class\"])\n",
-    "total = neg + pos\n",
-    "print(\n",
-    "    \"Examples:\\n    Total: {}\\n    Positive: {} ({:.2f}% of total)\\n\".format(\n",
-    "        total, pos, 100 * pos / total\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "KnLKFQDsCBUg"
-   },
-   "source": [
-    "This shows the small fraction of positive samples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6qox6ryyzwdr"
-   },
-   "source": [
-    "### Clean, split and normalize the data\n",
-    "\n",
-    "The raw data has a few issues. First the `Time` and `Amount` columns are too variable to use directly. Drop the `Time` column (since it's not clear what it means) and take the log of the `Amount` column to reduce its range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Ef42jTuxEjnj"
-   },
-   "outputs": [],
-   "source": [
-    "cleaned_df = raw_df.copy()\n",
-    "\n",
-    "# You don't want the `Time` column.\n",
-    "cleaned_df.pop(\"Time\")\n",
-    "\n",
-    "# The `Amount` column covers a huge range. Convert to log-space.\n",
-    "eps = 0.001  # 0 => 0.1¢\n",
-    "cleaned_df[\"Log Ammount\"] = np.log(cleaned_df.pop(\"Amount\") + eps)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "uSNgdQFFFQ6u"
-   },
-   "source": [
-    "Split the dataset into train, validation, and test sets. The validation set is used during the model fitting to evaluate the loss and any metrics, however the model is not fit with this data. The test set is completely unused during the training phase and is only used at the end to evaluate how well the model generalizes to new data. This is especially important with imbalanced datasets where [overfitting](https://developers.google.com/machine-learning/crash-course/generalization/peril-of-overfitting) is a significant concern from the lack of training data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "xfxhKg7Yr1-b"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "# Use a utility from sklearn to split and shuffle our dataset.\n",
-    "train_df, test_df = #TODO: Your code goes here.\n",
-    "train_df, val_df = #TODO: Your code goes here.\n",
-    "\n",
-    "# Form np arrays of labels and features.\n",
-    "train_labels = #TODO: Your code goes here.\n",
-    "bool_train_labels = #TODO: Your code goes here.\n",
-    "val_labels = #TODO: Your code goes here.\n",
-    "test_labels = #TODO: Your code goes here.\n",
-    "\n",
-    "train_features = np.array(train_df)\n",
-    "val_features = np.array(val_df)\n",
-    "test_features = np.array(test_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "8a_Z_kBmr7Oh"
-   },
-   "source": [
-    "Normalize the input features using the sklearn StandardScaler.\n",
-    "This will set the mean to 0 and standard deviation to 1.\n",
-    "\n",
-    "Note: The `StandardScaler` is only fit using the `train_features` to be sure the model is not peeking at the validation or test sets. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "IO-qEUmJ5JQg"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training labels shape: (182276,)\n",
-      "Validation labels shape: (45569,)\n",
-      "Test labels shape: (56962,)\n",
-      "Training features shape: (182276, 29)\n",
-      "Validation features shape: (45569, 29)\n",
-      "Test features shape: (56962, 29)\n"
-     ]
-    }
-   ],
-   "source": [
-    "scaler = StandardScaler()\n",
-    "train_features = scaler.fit_transform(train_features)\n",
-    "\n",
-    "val_features = scaler.transform(val_features)\n",
-    "test_features = scaler.transform(test_features)\n",
-    "\n",
-    "train_features = np.clip(train_features, -5, 5)\n",
-    "val_features = np.clip(val_features, -5, 5)\n",
-    "test_features = np.clip(test_features, -5, 5)\n",
-    "\n",
-    "\n",
-    "print(\"Training labels shape:\", train_labels.shape)\n",
-    "print(\"Validation labels shape:\", val_labels.shape)\n",
-    "print(\"Test labels shape:\", test_labels.shape)\n",
-    "\n",
-    "print(\"Training features shape:\", train_features.shape)\n",
-    "print(\"Validation features shape:\", val_features.shape)\n",
-    "print(\"Test features shape:\", test_features.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "XF2nNfWKJ33w"
-   },
-   "source": [
-    "Caution: If you want to deploy a model, it's critical that you preserve the preprocessing calculations. The easiest way to implement them as layers, and attach them to your model before export.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "uQ7m9nqDC3W6"
-   },
-   "source": [
-    "### Look at the data distribution\n",
-    "\n",
-    "Next compare the distributions of the positive and negative examples over a few features. Good questions to ask yourself at this point are:\n",
-    "\n",
-    "* Do these distributions make sense? \n",
-    "    * Yes. You've normalized the input and these are mostly concentrated in the `+/- 2` range.\n",
-    "* Can you see the difference between the ditributions?\n",
-    "    * Yes the positive examples contain a much higher rate of extreme values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "raK7hyjd_vf6"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pos_df = pd.DataFrame(\n",
-    "    train_features[bool_train_labels], columns=train_df.columns\n",
-    ")\n",
-    "neg_df = pd.DataFrame(\n",
-    "    train_features[~bool_train_labels], columns=train_df.columns\n",
-    ")\n",
-    "\n",
-    "sns.jointplot(\n",
-    "    pos_df[\"V5\"], pos_df[\"V6\"], kind=\"hex\", xlim=(-5, 5), ylim=(-5, 5)\n",
-    ")\n",
-    "plt.suptitle(\"Positive distribution\")\n",
-    "\n",
-    "sns.jointplot(\n",
-    "    neg_df[\"V5\"], neg_df[\"V6\"], kind=\"hex\", xlim=(-5, 5), ylim=(-5, 5)\n",
-    ")\n",
-    "_ = plt.suptitle(\"Negative distribution\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "qFK1u4JX16D8"
-   },
-   "source": [
-    "## Define the model and metrics\n",
-    "\n",
-    "Define a function that creates a simple neural network with a densly connected hidden layer, a [dropout](https://developers.google.com/machine-learning/glossary/#dropout_regularization) layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction being fraudulent: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3JQDzUqT3UYG"
-   },
-   "outputs": [],
-   "source": [
-    "METRICS = [\n",
-    "    keras.metrics.TruePositives(name=\"tp\"),\n",
-    "    keras.metrics.FalsePositives(name=\"fp\"),\n",
-    "    keras.metrics.TrueNegatives(name=\"tn\"),\n",
-    "    keras.metrics.FalseNegatives(name=\"fn\"),\n",
-    "    keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
-    "    keras.metrics.Precision(name=\"precision\"),\n",
-    "    keras.metrics.Recall(name=\"recall\"),\n",
-    "    keras.metrics.AUC(name=\"auc\"),\n",
-    "]\n",
-    "\n",
-    "\n",
-    "def make_model(metrics=METRICS, output_bias=None):\n",
-    "    if output_bias is not None:\n",
-    "        output_bias = tf.keras.initializers.Constant(output_bias)\n",
-    "    # TODO 1\n",
-    "    model = keras.Sequential(\n",
-    "        # TODO: Your code goes here.\n",
-    "        # TODO: Your code goes here.\n",
-    "        # TODO: Your code goes here.\n",
-    "        # TODO: Your code goes here.\n",
-    "    )\n",
-    "\n",
-    "    model.compile(\n",
-    "        optimizer=keras.optimizers.Adam(lr=1e-3),\n",
-    "        loss=keras.losses.BinaryCrossentropy(),\n",
-    "        metrics=metrics,\n",
-    "    )\n",
-    "\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "SU0GX6E6mieP"
-   },
-   "source": [
-    "### Understanding useful metrics\n",
-    "\n",
-    "Notice that there are a few metrics defined above that can be computed by the model that will be helpful when evaluating the performance.\n",
-    "\n",
-    "\n",
-    "\n",
-    "*   **False** negatives and **false** positives are samples that were **incorrectly** classified\n",
-    "*   **True** negatives and **true** positives are samples that were **correctly** classified\n",
-    "*   **Accuracy** is the percentage of examples correctly classified\n",
-    ">   $\\frac{\\text{true samples}}{\\text{total samples}}$\n",
-    "*   **Precision** is the percentage of **predicted** positives that were correctly classified\n",
-    ">   $\\frac{\\text{true positives}}{\\text{true positives + false positives}}$\n",
-    "*   **Recall** is the percentage of **actual** positives that were correctly classified\n",
-    ">   $\\frac{\\text{true positives}}{\\text{true positives + false negatives}}$\n",
-    "*   **AUC** refers to the Area Under the Curve of a Receiver Operating Characteristic curve (ROC-AUC). This metric is equal to the probability that a classifier will rank a random positive sample higher than than a random negative sample.\n",
-    "\n",
-    "Note: Accuracy is not a helpful metric for this task. You can 99.8%+ accuracy on this task by predicting False all the time.  \n",
-    "\n",
-    "Read more:\n",
-    "*  [True vs. False and Positive vs. Negative](https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative)\n",
-    "*  [Accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy)\n",
-    "*   [Precision and Recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)\n",
-    "*   [ROC-AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "FYdhSAoaF_TK"
-   },
-   "source": [
-    "## Baseline model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IDbltVPg2m2q"
-   },
-   "source": [
-    "### Build the model\n",
-    "\n",
-    "Now create and train your model using the function that was defined earlier. Notice that the model is fit using a larger than default batch size of 2048, this is important to ensure that each batch has a decent chance of containing a few positive samples. If the batch size was too small, they would likely have no fraudulent transactions to learn from.\n",
-    "\n",
-    "\n",
-    "Note: this model will not handle the class imbalance well. You will improve it later in this tutorial."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "ouUkwPcGQsy3"
-   },
-   "outputs": [],
-   "source": [
-    "EPOCHS = 100\n",
-    "BATCH_SIZE = 2048\n",
-    "\n",
-    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
-    "    monitor=\"val_auc\",\n",
-    "    verbose=1,\n",
-    "    patience=10,\n",
-    "    mode=\"max\",\n",
-    "    restore_best_weights=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "1xlR_dekzw7C"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_8\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "dense_16 (Dense)             (None, 16)                480       \n",
-      "_________________________________________________________________\n",
-      "dropout_8 (Dropout)          (None, 16)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_17 (Dense)             (None, 1)                 17        \n",
-      "=================================================================\n",
-      "Total params: 497\n",
-      "Trainable params: 497\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = make_model()\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Wx7ND3_SqckO"
-   },
-   "source": [
-    "Test run the model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "LopSd-yQqO3a"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.89924395],\n",
-       "       [0.7323974 ],\n",
-       "       [0.9322966 ],\n",
-       "       [0.8881701 ],\n",
-       "       [0.88115484],\n",
-       "       [0.6485833 ],\n",
-       "       [0.79132897],\n",
-       "       [0.7073316 ],\n",
-       "       [0.8343261 ],\n",
-       "       [0.8008822 ]], dtype=float32)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.predict(train_features[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "YKIgWqHms_03"
-   },
-   "source": [
-    "### Optional: Set the correct initial bias."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "qk_3Ry6EoYDq"
-   },
-   "source": [
-    "These are initial guesses are not great. You know the dataset is imbalanced. Set the output layer's bias to reflect that (See: [A Recipe for Training Neural Networks: \"init well\"](http://karpathy.github.io/2019/04/25/recipe/#2-set-up-the-end-to-end-trainingevaluation-skeleton--get-dumb-baselines)). This can help with initial convergence."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "PdbfWDuVpo6k"
-   },
-   "source": [
-    "With the default bias initialization the loss should be about `math.log(2) = 0.69314` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "H-oPqh3SoGXk"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 1.7441\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = model.evaluate(\n",
-    "    train_features, train_labels, batch_size=BATCH_SIZE, verbose=0\n",
-    ")\n",
-    "print(f\"Loss: {results[0]:0.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "hE-JRzfKqfhB"
-   },
-   "source": [
-    "The correct bias to set can be derived from:\n",
-    "\n",
-    "$$ p_0 = pos/(pos + neg) = 1/(1+e^{-b_0}) $$\n",
-    "$$ b_0 = -log_e(1/p_0 - 1) $$\n",
-    "$$ b_0 = log_e(pos/neg)$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "F5KWPSjjstUS"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-6.35935934])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "initial_bias = np.log([pos / neg])\n",
-    "initial_bias"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "d1juXI9yY1KD"
-   },
-   "source": [
-    "Set that as the initial bias, and the model will give much more reasonable initial guesses. \n",
-    "\n",
-    "It should be near: `pos/total = 0.0018`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "50oyu1uss0i-"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.00196099],\n",
-       "       [0.00737071],\n",
-       "       [0.00182639],\n",
-       "       [0.00342294],\n",
-       "       [0.00442886],\n",
-       "       [0.00714428],\n",
-       "       [0.0061818 ],\n",
-       "       [0.00631511],\n",
-       "       [0.0088356 ],\n",
-       "       [0.01214694]], dtype=float32)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = make_model(output_bias=initial_bias)\n",
-    "model.predict(train_features[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "4xqFYb2KqRHQ"
-   },
-   "source": [
-    "With this initialization the initial loss should be approximately:\n",
-    "\n",
-    "$$-p_0log(p_0)-(1-p_0)log(1-p_0) = 0.01317$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "xVDqCWXDqHSc"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 0.0275\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = model.evaluate(\n",
-    "    train_features, train_labels, batch_size=BATCH_SIZE, verbose=0\n",
-    ")\n",
-    "print(f\"Loss: {results[0]:0.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "FrDC8hvNr9yw"
-   },
-   "source": [
-    "This initial loss is about 50 times less than if would have been with naive initilization.\n",
-    "\n",
-    "This way the model doesn't need to spend the first few epochs just learning that positive examples are unlikely. This also makes it easier to read plots of the loss during training."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "0EJj9ixKVBMT"
-   },
-   "source": [
-    "### Checkpoint the initial weights\n",
-    "\n",
-    "To make the various training runs more comparable, keep this initial model's weights in a checkpoint file, and load them into each model before training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "_tSUm4yAVIif"
-   },
-   "outputs": [],
-   "source": [
-    "initial_weights = os.path.join(tempfile.mkdtemp(), \"initial_weights\")\n",
-    "model.save_weights(initial_weights)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "EVXiLyqyZ8AX"
-   },
-   "source": [
-    "### Confirm that the bias fix helps\n",
-    "\n",
-    "Before moving on, confirm quick that the careful bias initialization actually helped.\n",
-    "\n",
-    "Train the model for 20 epochs, with and without this careful initialization, and compare the losses: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Dm4-4K5RZ63Q"
-   },
-   "outputs": [],
-   "source": [
-    "model = make_model()\n",
-    "model.load_weights(initial_weights)\n",
-    "model.layers[-1].bias.assign([0.0])\n",
-    "zero_bias_history = model.fit(\n",
-    "    train_features,\n",
-    "    train_labels,\n",
-    "    batch_size=BATCH_SIZE,\n",
-    "    epochs=20,\n",
-    "    validation_data=(val_features, val_labels),\n",
-    "    verbose=0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "j8DsLXHQaSql"
-   },
-   "outputs": [],
-   "source": [
-    "model = make_model()\n",
-    "model.load_weights(initial_weights)\n",
-    "careful_bias_history = model.fit(\n",
-    "    train_features,\n",
-    "    train_labels,\n",
-    "    batch_size=BATCH_SIZE,\n",
-    "    epochs=20,\n",
-    "    validation_data=(val_features, val_labels),\n",
-    "    verbose=0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "E3XsMBjhauFV"
-   },
-   "outputs": [],
-   "source": [
-    "def plot_loss(history, label, n):\n",
-    "    # Use a log scale to show the wide range of values.\n",
-    "    plt.semilogy(\n",
-    "        history.epoch,\n",
-    "        history.history[\"loss\"],\n",
-    "        color=colors[n],\n",
-    "        label=\"Train \" + label,\n",
-    "    )\n",
-    "    plt.semilogy(\n",
-    "        history.epoch,\n",
-    "        history.history[\"val_loss\"],\n",
-    "        color=colors[n],\n",
-    "        label=\"Val \" + label,\n",
-    "        linestyle=\"--\",\n",
-    "    )\n",
-    "    plt.xlabel(\"Epoch\")\n",
-    "    plt.ylabel(\"Loss\")\n",
-    "\n",
-    "    plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "dxFaskm7beC7"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_loss(zero_bias_history, \"Zero Bias\", 0)\n",
-    "plot_loss(careful_bias_history, \"Careful Bias\", 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "fKMioV0ddG3R"
-   },
-   "source": [
-    "The above figure makes it clear: In terms of validation loss, on this problem, this careful initialization gives a clear advantage. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "RsA_7SEntRaV"
-   },
-   "source": [
-    "### Train the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "yZKAc8NCDnoR"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 182276 samples, validate on 45569 samples\n",
-      "Epoch 1/100\n",
-      "182276/182276 [==============================] - 3s 16us/sample - loss: 0.0256 - tp: 64.0000 - fp: 745.0000 - tn: 181227.0000 - fn: 240.0000 - accuracy: 0.9946 - precision: 0.0791 - recall: 0.2105 - auc: 0.8031 - val_loss: 0.0079 - val_tp: 17.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 66.0000 - val_accuracy: 0.9984 - val_precision: 0.7083 - val_recall: 0.2048 - val_auc: 0.9377\n",
-      "Epoch 2/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0100 - tp: 111.0000 - fp: 131.0000 - tn: 181841.0000 - fn: 193.0000 - accuracy: 0.9982 - precision: 0.4587 - recall: 0.3651 - auc: 0.8758 - val_loss: 0.0056 - val_tp: 40.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 43.0000 - val_accuracy: 0.9989 - val_precision: 0.8511 - val_recall: 0.4819 - val_auc: 0.9422\n",
-      "Epoch 3/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0075 - tp: 148.0000 - fp: 57.0000 - tn: 181915.0000 - fn: 156.0000 - accuracy: 0.9988 - precision: 0.7220 - recall: 0.4868 - auc: 0.9206 - val_loss: 0.0048 - val_tp: 52.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 31.0000 - val_accuracy: 0.9992 - val_precision: 0.8814 - val_recall: 0.6265 - val_auc: 0.9382\n",
-      "Epoch 4/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0065 - tp: 157.0000 - fp: 48.0000 - tn: 181924.0000 - fn: 147.0000 - accuracy: 0.9989 - precision: 0.7659 - recall: 0.5164 - auc: 0.9210 - val_loss: 0.0045 - val_tp: 52.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 31.0000 - val_accuracy: 0.9992 - val_precision: 0.8814 - val_recall: 0.6265 - val_auc: 0.9387\n",
-      "Epoch 5/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0058 - tp: 172.0000 - fp: 43.0000 - tn: 181929.0000 - fn: 132.0000 - accuracy: 0.9990 - precision: 0.8000 - recall: 0.5658 - auc: 0.9246 - val_loss: 0.0042 - val_tp: 51.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 32.0000 - val_accuracy: 0.9991 - val_precision: 0.8793 - val_recall: 0.6145 - val_auc: 0.9390\n",
-      "Epoch 6/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0054 - tp: 169.0000 - fp: 28.0000 - tn: 181944.0000 - fn: 135.0000 - accuracy: 0.9991 - precision: 0.8579 - recall: 0.5559 - auc: 0.9210 - val_loss: 0.0039 - val_tp: 56.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 27.0000 - val_accuracy: 0.9993 - val_precision: 0.8889 - val_recall: 0.6747 - val_auc: 0.9391\n",
-      "Epoch 7/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0054 - tp: 167.0000 - fp: 33.0000 - tn: 181939.0000 - fn: 137.0000 - accuracy: 0.9991 - precision: 0.8350 - recall: 0.5493 - auc: 0.9224 - val_loss: 0.0038 - val_tp: 60.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 23.0000 - val_accuracy: 0.9993 - val_precision: 0.8955 - val_recall: 0.7229 - val_auc: 0.9392\n",
-      "Epoch 8/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0050 - tp: 182.0000 - fp: 28.0000 - tn: 181944.0000 - fn: 122.0000 - accuracy: 0.9992 - precision: 0.8667 - recall: 0.5987 - auc: 0.9215 - val_loss: 0.0038 - val_tp: 62.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 21.0000 - val_accuracy: 0.9994 - val_precision: 0.8986 - val_recall: 0.7470 - val_auc: 0.9332\n",
-      "Epoch 9/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0047 - tp: 186.0000 - fp: 36.0000 - tn: 181936.0000 - fn: 118.0000 - accuracy: 0.9992 - precision: 0.8378 - recall: 0.6118 - auc: 0.9238 - val_loss: 0.0036 - val_tp: 63.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_precision: 0.9000 - val_recall: 0.7590 - val_auc: 0.9332\n",
-      "Epoch 10/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0048 - tp: 176.0000 - fp: 33.0000 - tn: 181939.0000 - fn: 128.0000 - accuracy: 0.9991 - precision: 0.8421 - recall: 0.5789 - auc: 0.9208 - val_loss: 0.0036 - val_tp: 63.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_precision: 0.9000 - val_recall: 0.7590 - val_auc: 0.9332\n",
-      "Epoch 11/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0045 - tp: 180.0000 - fp: 32.0000 - tn: 181940.0000 - fn: 124.0000 - accuracy: 0.9991 - precision: 0.8491 - recall: 0.5921 - auc: 0.9341 - val_loss: 0.0035 - val_tp: 64.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 19.0000 - val_accuracy: 0.9994 - val_precision: 0.9014 - val_recall: 0.7711 - val_auc: 0.9331\n",
-      "Epoch 12/100\n",
-      "169984/182276 [==========================>...] - ETA: 0s - loss: 0.0045 - tp: 175.0000 - fp: 30.0000 - tn: 169674.0000 - fn: 105.0000 - accuracy: 0.9992 - precision: 0.8537 - recall: 0.6250 - auc: 0.9306Restoring model weights from the end of the best epoch.\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0045 - tp: 188.0000 - fp: 31.0000 - tn: 181941.0000 - fn: 116.0000 - accuracy: 0.9992 - precision: 0.8584 - recall: 0.6184 - auc: 0.9326 - val_loss: 0.0034 - val_tp: 63.0000 - val_fp: 6.0000 - val_tn: 45480.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_precision: 0.9130 - val_recall: 0.7590 - val_auc: 0.9332\n",
-      "Epoch 00012: early stopping\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = make_model()\n",
-    "model.load_weights(initial_weights)\n",
-    "baseline_history = model.fit(\n",
-    "    train_features,\n",
-    "    train_labels,\n",
-    "    batch_size=BATCH_SIZE,\n",
-    "    epochs=EPOCHS,\n",
-    "    callbacks=[early_stopping],\n",
-    "    validation_data=(val_features, val_labels),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "iSaDBYU9xtP6"
-   },
-   "source": [
-    "### Check training history\n",
-    "In this section, you will produce plots of your model's accuracy and loss on the training and validation set. These are useful to check for overfitting, which you can learn more about in this [tutorial](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit).\n",
-    "\n",
-    "Additionally, you can produce these plots for any of the metrics you created above. False negatives are included as an example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "WTSkhT1jyGu6"
-   },
-   "outputs": [],
-   "source": [
-    "def plot_metrics(history):\n",
-    "    metrics = [\"loss\", \"auc\", \"precision\", \"recall\"]\n",
-    "    for n, metric in enumerate(metrics):\n",
-    "        name = metric.replace(\"_\", \" \").capitalize()\n",
-    "        plt.subplot(2, 2, n + 1)\n",
-    "        plt.plot(\n",
-    "            history.epoch,\n",
-    "            history.history[metric],\n",
-    "            color=colors[0],\n",
-    "            label=\"Train\",\n",
-    "        )\n",
-    "        plt.plot(\n",
-    "            history.epoch,\n",
-    "            history.history[\"val_\" + metric],\n",
-    "            color=colors[0],\n",
-    "            linestyle=\"--\",\n",
-    "            label=\"Val\",\n",
-    "        )\n",
-    "        plt.xlabel(\"Epoch\")\n",
-    "        plt.ylabel(name)\n",
-    "        if metric == \"loss\":\n",
-    "            plt.ylim([0, plt.ylim()[1]])\n",
-    "        elif metric == \"auc\":\n",
-    "            plt.ylim([0.8, 1])\n",
-    "        else:\n",
-    "            plt.ylim([0, 1])\n",
-    "\n",
-    "        plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "u6LReDsqlZlk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x720 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_metrics(baseline_history)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "UCa4iWo6WDKR"
-   },
-   "source": [
-    "Note: That the validation curve generally performs better than the training curve. This is mainly caused by the fact that the dropout layer is not active when evaluating the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "aJC1booryouo"
-   },
-   "source": [
-    "### Evaluate metrics\n",
-    "\n",
-    "You can use a [confusion matrix](https://developers.google.com/machine-learning/glossary/#confusion_matrix) to summarize the actual vs. predicted labels where the X axis is the predicted label and the Y axis is the actual label."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "aNS796IJKrev"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "train_predictions_baseline = #TODO: Your code goes here.\n",
-    "test_predictions_baseline = #TODO: Your code goes here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "MVWBGfADwbWI"
-   },
-   "outputs": [],
-   "source": [
-    "def plot_cm(labels, predictions, p=0.5):\n",
-    "    cm = confusion_matrix(labels, predictions > p)\n",
-    "    plt.figure(figsize=(5, 5))\n",
-    "    sns.heatmap(cm, annot=True, fmt=\"d\")\n",
-    "    plt.title(f\"Confusion matrix @{p:.2f}\")\n",
-    "    plt.ylabel(\"Actual label\")\n",
-    "    plt.xlabel(\"Predicted label\")\n",
-    "\n",
-    "    print(\"Legitimate Transactions Detected (True Negatives): \", cm[0][0])\n",
-    "    print(\n",
-    "        \"Legitimate Transactions Incorrectly Detected (False Positives): \",\n",
-    "        cm[0][1],\n",
-    "    )\n",
-    "    print(\"Fraudulent Transactions Missed (False Negatives): \", cm[1][0])\n",
-    "    print(\"Fraudulent Transactions Detected (True Positives): \", cm[1][1])\n",
-    "    print(\"Total Fraudulent Transactions: \", np.sum(cm[1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "nOTjD5Z5Wp1U"
-   },
-   "source": [
-    "Evaluate your model on the test dataset and display the results for the metrics you created above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "poh_hZngt2_9"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loss :  0.005941324691873794\n",
-      "tp :  55.0\n",
-      "fp :  12.0\n",
-      "tn :  56845.0\n",
-      "fn :  50.0\n",
-      "accuracy :  0.99891156\n",
-      "precision :  0.8208955\n",
-      "recall :  0.52380955\n",
-      "auc :  0.9390888\n",
-      "\n",
-      "Legitimate Transactions Detected (True Negatives):  56845\n",
-      "Legitimate Transactions Incorrectly Detected (False Positives):  12\n",
-      "Fraudulent Transactions Missed (False Negatives):  50\n",
-      "Fraudulent Transactions Detected (True Positives):  55\n",
-      "Total Fraudulent Transactions:  105\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "baseline_results = model.evaluate(\n",
-    "    test_features, test_labels, batch_size=BATCH_SIZE, verbose=0\n",
-    ")\n",
-    "for name, value in zip(model.metrics_names, baseline_results):\n",
-    "    print(name, \": \", value)\n",
-    "print()\n",
-    "\n",
-    "plot_cm(test_labels, test_predictions_baseline)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "PyZtSr1v6L4t"
-   },
-   "source": [
-    "If the model had predicted everything perfectly, this would be a [diagonal matrix](https://en.wikipedia.org/wiki/Diagonal_matrix) where values off the main diagonal, indicating incorrect predictions, would be zero. In this case the matrix shows that you have relatively few false positives, meaning that there were relatively few legitimate transactions that were incorrectly flagged. However, you would likely want to have even fewer false negatives despite the cost of increasing the number of false positives. This trade off may be preferable because false negatives would allow fraudulent transactions to go through, whereas false positives may cause an email to be sent to a customer to ask them to verify their card activity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "P-QpQsip_F2Q"
-   },
-   "source": [
-    "### Plot the ROC\n",
-    "\n",
-    "Now plot the [ROC](https://developers.google.com/machine-learning/glossary#ROC). This plot is useful because it shows, at a glance, the range of performance the model can reach just by tuning the output threshold."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "lhaxsLSvANF9"
-   },
-   "outputs": [],
-   "source": [
-    "def plot_roc(name, labels, predictions, **kwargs):\n",
-    "    fp, tp, _ = sklearn.metrics.roc_curve(labels, predictions)\n",
-    "\n",
-    "    plt.plot(100 * fp, 100 * tp, label=name, linewidth=2, **kwargs)\n",
-    "    plt.xlabel(\"False positives [%]\")\n",
-    "    plt.ylabel(\"True positives [%]\")\n",
-    "    plt.xlim([-0.5, 20])\n",
-    "    plt.ylim([80, 100.5])\n",
-    "    plt.grid(True)\n",
-    "    ax = plt.gca()\n",
-    "    ax.set_aspect(\"equal\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "DfHHspttKJE0"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f5f28134cf8>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_roc(\n",
-    "    \"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0]\n",
-    ")\n",
-    "plot_roc(\n",
-    "    \"Test Baseline\",\n",
-    "    test_labels,\n",
-    "    test_predictions_baseline,\n",
-    "    color=colors[0],\n",
-    "    linestyle=\"--\",\n",
-    ")\n",
-    "plt.legend(loc=\"lower right\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "gpdsFyp64DhY"
-   },
-   "source": [
-    "It looks like the precision is relatively high, but the recall and the area under the ROC curve (AUC) aren't as high as you might like. Classifiers often face challenges when trying to maximize both precision and recall, which is especially true when working with imbalanced datasets. It is important to consider the costs of different types of errors in the context of the problem you care about. In this example, a false negative (a fraudulent transaction is missed) may have a financial cost, while a false positive (a transaction is incorrectly flagged as fraudulent) may decrease user happiness."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "cveQoiMyGQCo"
-   },
-   "source": [
-    "## Class weights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ePGp6GUE1WfH"
-   },
-   "source": [
-    "### Calculate class weights\n",
-    "\n",
-    "The goal is to identify fradulent transactions, but you don't have very many of those positive samples to work with, so you would want to have the classifier heavily weight the few examples that are available. You can do this by passing Keras weights for each class through a parameter. These will cause the model to \"pay more attention\" to examples from an under-represented class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "qjGWErngGny7"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Weight for class 0: 0.50\n",
-      "Weight for class 1: 289.44\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Scaling by total/2 helps keep the loss to a similar magnitude.\n",
-    "# The sum of the weights of all examples stays the same.\n",
-    "# TODO 1\n",
-    "weight_for_0 = #TODO: Your code goes here.\n",
-    "weight_for_1 = #TODO: Your code goes here.\n",
-    "\n",
-    "class_weight = #TODO: Your code goes here.\n",
-    "\n",
-    "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n",
-    "print('Weight for class 1: {:.2f}'.format(weight_for_1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Mk1OOE2ZSHzy"
-   },
-   "source": [
-    "### Train a model with class weights\n",
-    "\n",
-    "Now try re-training and evaluating the model with class weights to see how that affects the predictions.\n",
-    "\n",
-    "Note: Using `class_weights` changes the range of the loss. This may affect the stability of the training depending on the optimizer. Optimizers whose step size is dependent on the magnitude of the gradient, like `optimizers.SGD`, may fail. The optimizer used here, `optimizers.Adam`, is unaffected by the scaling change. Also note that because of the weighting, the total losses are not comparable between the two models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "UJ589fn8ST3x"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "Train on 182276 samples, validate on 45569 samples\n",
-      "Epoch 1/100\n",
-      "182276/182276 [==============================] - 3s 19us/sample - loss: 1.0524 - tp: 138.0000 - fp: 2726.0000 - tn: 179246.0000 - fn: 166.0000 - accuracy: 0.9841 - precision: 0.0482 - recall: 0.4539 - auc: 0.8321 - val_loss: 0.4515 - val_tp: 59.0000 - val_fp: 432.0000 - val_tn: 45054.0000 - val_fn: 24.0000 - val_accuracy: 0.9900 - val_precision: 0.1202 - val_recall: 0.7108 - val_auc: 0.9492\n",
-      "Epoch 2/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.5537 - tp: 216.0000 - fp: 3783.0000 - tn: 178189.0000 - fn: 88.0000 - accuracy: 0.9788 - precision: 0.0540 - recall: 0.7105 - auc: 0.9033 - val_loss: 0.3285 - val_tp: 69.0000 - val_fp: 514.0000 - val_tn: 44972.0000 - val_fn: 14.0000 - val_accuracy: 0.9884 - val_precision: 0.1184 - val_recall: 0.8313 - val_auc: 0.9605\n",
-      "Epoch 3/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.4178 - tp: 238.0000 - fp: 4540.0000 - tn: 177432.0000 - fn: 66.0000 - accuracy: 0.9747 - precision: 0.0498 - recall: 0.7829 - auc: 0.9237 - val_loss: 0.2840 - val_tp: 69.0000 - val_fp: 570.0000 - val_tn: 44916.0000 - val_fn: 14.0000 - val_accuracy: 0.9872 - val_precision: 0.1080 - val_recall: 0.8313 - val_auc: 0.9669\n",
-      "Epoch 4/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.3848 - tp: 247.0000 - fp: 5309.0000 - tn: 176663.0000 - fn: 57.0000 - accuracy: 0.9706 - precision: 0.0445 - recall: 0.8125 - auc: 0.9292 - val_loss: 0.2539 - val_tp: 71.0000 - val_fp: 622.0000 - val_tn: 44864.0000 - val_fn: 12.0000 - val_accuracy: 0.9861 - val_precision: 0.1025 - val_recall: 0.8554 - val_auc: 0.9709\n",
-      "Epoch 5/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.3596 - tp: 254.0000 - fp: 6018.0000 - tn: 175954.0000 - fn: 50.0000 - accuracy: 0.9667 - precision: 0.0405 - recall: 0.8355 - auc: 0.9323 - val_loss: 0.2363 - val_tp: 72.0000 - val_fp: 713.0000 - val_tn: 44773.0000 - val_fn: 11.0000 - val_accuracy: 0.9841 - val_precision: 0.0917 - val_recall: 0.8675 - val_auc: 0.9725\n",
-      "Epoch 6/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.3115 - tp: 255.0000 - fp: 6366.0000 - tn: 175606.0000 - fn: 49.0000 - accuracy: 0.9648 - precision: 0.0385 - recall: 0.8388 - auc: 0.9477 - val_loss: 0.2243 - val_tp: 72.0000 - val_fp: 768.0000 - val_tn: 44718.0000 - val_fn: 11.0000 - val_accuracy: 0.9829 - val_precision: 0.0857 - val_recall: 0.8675 - val_auc: 0.9728\n",
-      "Epoch 7/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.3179 - tp: 258.0000 - fp: 6804.0000 - tn: 175168.0000 - fn: 46.0000 - accuracy: 0.9624 - precision: 0.0365 - recall: 0.8487 - auc: 0.9435 - val_loss: 0.2165 - val_tp: 72.0000 - val_fp: 812.0000 - val_tn: 44674.0000 - val_fn: 11.0000 - val_accuracy: 0.9819 - val_precision: 0.0814 - val_recall: 0.8675 - val_auc: 0.9739\n",
-      "Epoch 8/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2880 - tp: 260.0000 - fp: 6669.0000 - tn: 175303.0000 - fn: 44.0000 - accuracy: 0.9632 - precision: 0.0375 - recall: 0.8553 - auc: 0.9530 - val_loss: 0.2122 - val_tp: 72.0000 - val_fp: 783.0000 - val_tn: 44703.0000 - val_fn: 11.0000 - val_accuracy: 0.9826 - val_precision: 0.0842 - val_recall: 0.8675 - val_auc: 0.9769\n",
-      "Epoch 9/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2676 - tp: 262.0000 - fp: 6904.0000 - tn: 175068.0000 - fn: 42.0000 - accuracy: 0.9619 - precision: 0.0366 - recall: 0.8618 - auc: 0.9594 - val_loss: 0.2056 - val_tp: 72.0000 - val_fp: 855.0000 - val_tn: 44631.0000 - val_fn: 11.0000 - val_accuracy: 0.9810 - val_precision: 0.0777 - val_recall: 0.8675 - val_auc: 0.9750\n",
-      "Epoch 10/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2498 - tp: 266.0000 - fp: 6833.0000 - tn: 175139.0000 - fn: 38.0000 - accuracy: 0.9623 - precision: 0.0375 - recall: 0.8750 - auc: 0.9593 - val_loss: 0.2001 - val_tp: 73.0000 - val_fp: 840.0000 - val_tn: 44646.0000 - val_fn: 10.0000 - val_accuracy: 0.9813 - val_precision: 0.0800 - val_recall: 0.8795 - val_auc: 0.9761\n",
-      "Epoch 11/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2681 - tp: 262.0000 - fp: 6845.0000 - tn: 175127.0000 - fn: 42.0000 - accuracy: 0.9622 - precision: 0.0369 - recall: 0.8618 - auc: 0.9559 - val_loss: 0.1964 - val_tp: 73.0000 - val_fp: 865.0000 - val_tn: 44621.0000 - val_fn: 10.0000 - val_accuracy: 0.9808 - val_precision: 0.0778 - val_recall: 0.8795 - val_auc: 0.9768\n",
-      "Epoch 12/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2406 - tp: 268.0000 - fp: 7070.0000 - tn: 174902.0000 - fn: 36.0000 - accuracy: 0.9610 - precision: 0.0365 - recall: 0.8816 - auc: 0.9646 - val_loss: 0.1940 - val_tp: 73.0000 - val_fp: 848.0000 - val_tn: 44638.0000 - val_fn: 10.0000 - val_accuracy: 0.9812 - val_precision: 0.0793 - val_recall: 0.8795 - val_auc: 0.9771\n",
-      "Epoch 13/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2285 - tp: 269.0000 - fp: 6976.0000 - tn: 174996.0000 - fn: 35.0000 - accuracy: 0.9615 - precision: 0.0371 - recall: 0.8849 - auc: 0.9680 - val_loss: 0.1930 - val_tp: 73.0000 - val_fp: 857.0000 - val_tn: 44629.0000 - val_fn: 10.0000 - val_accuracy: 0.9810 - val_precision: 0.0785 - val_recall: 0.8795 - val_auc: 0.9772\n",
-      "Epoch 14/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2322 - tp: 268.0000 - fp: 6718.0000 - tn: 175254.0000 - fn: 36.0000 - accuracy: 0.9629 - precision: 0.0384 - recall: 0.8816 - auc: 0.9644 - val_loss: 0.1915 - val_tp: 73.0000 - val_fp: 808.0000 - val_tn: 44678.0000 - val_fn: 10.0000 - val_accuracy: 0.9820 - val_precision: 0.0829 - val_recall: 0.8795 - val_auc: 0.9781\n",
-      "Epoch 15/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2631 - tp: 267.0000 - fp: 6578.0000 - tn: 175394.0000 - fn: 37.0000 - accuracy: 0.9637 - precision: 0.0390 - recall: 0.8783 - auc: 0.9551 - val_loss: 0.1900 - val_tp: 73.0000 - val_fp: 803.0000 - val_tn: 44683.0000 - val_fn: 10.0000 - val_accuracy: 0.9822 - val_precision: 0.0833 - val_recall: 0.8795 - val_auc: 0.9781\n",
-      "Epoch 16/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2314 - tp: 266.0000 - fp: 6644.0000 - tn: 175328.0000 - fn: 38.0000 - accuracy: 0.9633 - precision: 0.0385 - recall: 0.8750 - auc: 0.9672 - val_loss: 0.1882 - val_tp: 73.0000 - val_fp: 806.0000 - val_tn: 44680.0000 - val_fn: 10.0000 - val_accuracy: 0.9821 - val_precision: 0.0830 - val_recall: 0.8795 - val_auc: 0.9784\n",
-      "Epoch 17/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2152 - tp: 271.0000 - fp: 6663.0000 - tn: 175309.0000 - fn: 33.0000 - accuracy: 0.9633 - precision: 0.0391 - recall: 0.8914 - auc: 0.9687 - val_loss: 0.1895 - val_tp: 73.0000 - val_fp: 754.0000 - val_tn: 44732.0000 - val_fn: 10.0000 - val_accuracy: 0.9832 - val_precision: 0.0883 - val_recall: 0.8795 - val_auc: 0.9785\n",
-      "Epoch 18/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2420 - tp: 264.0000 - fp: 6535.0000 - tn: 175437.0000 - fn: 40.0000 - accuracy: 0.9639 - precision: 0.0388 - recall: 0.8684 - auc: 0.9610 - val_loss: 0.1895 - val_tp: 73.0000 - val_fp: 749.0000 - val_tn: 44737.0000 - val_fn: 10.0000 - val_accuracy: 0.9833 - val_precision: 0.0888 - val_recall: 0.8795 - val_auc: 0.9786\n",
-      "Epoch 19/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2279 - tp: 268.0000 - fp: 6443.0000 - tn: 175529.0000 - fn: 36.0000 - accuracy: 0.9645 - precision: 0.0399 - recall: 0.8816 - auc: 0.9672 - val_loss: 0.1895 - val_tp: 73.0000 - val_fp: 763.0000 - val_tn: 44723.0000 - val_fn: 10.0000 - val_accuracy: 0.9830 - val_precision: 0.0873 - val_recall: 0.8795 - val_auc: 0.9788\n",
-      "Epoch 20/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2247 - tp: 267.0000 - fp: 6596.0000 - tn: 175376.0000 - fn: 37.0000 - accuracy: 0.9636 - precision: 0.0389 - recall: 0.8783 - auc: 0.9684 - val_loss: 0.1896 - val_tp: 73.0000 - val_fp: 760.0000 - val_tn: 44726.0000 - val_fn: 10.0000 - val_accuracy: 0.9831 - val_precision: 0.0876 - val_recall: 0.8795 - val_auc: 0.9797\n",
-      "Epoch 21/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2296 - tp: 269.0000 - fp: 6562.0000 - tn: 175410.0000 - fn: 35.0000 - accuracy: 0.9638 - precision: 0.0394 - recall: 0.8849 - auc: 0.9656 - val_loss: 0.1889 - val_tp: 73.0000 - val_fp: 750.0000 - val_tn: 44736.0000 - val_fn: 10.0000 - val_accuracy: 0.9833 - val_precision: 0.0887 - val_recall: 0.8795 - val_auc: 0.9797\n",
-      "Epoch 22/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1982 - tp: 271.0000 - fp: 6583.0000 - tn: 175389.0000 - fn: 33.0000 - accuracy: 0.9637 - precision: 0.0395 - recall: 0.8914 - auc: 0.9756 - val_loss: 0.1879 - val_tp: 73.0000 - val_fp: 764.0000 - val_tn: 44722.0000 - val_fn: 10.0000 - val_accuracy: 0.9830 - val_precision: 0.0872 - val_recall: 0.8795 - val_auc: 0.9777\n",
-      "Epoch 23/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2154 - tp: 273.0000 - fp: 6552.0000 - tn: 175420.0000 - fn: 31.0000 - accuracy: 0.9639 - precision: 0.0400 - recall: 0.8980 - auc: 0.9682 - val_loss: 0.1882 - val_tp: 73.0000 - val_fp: 762.0000 - val_tn: 44724.0000 - val_fn: 10.0000 - val_accuracy: 0.9831 - val_precision: 0.0874 - val_recall: 0.8795 - val_auc: 0.9779\n",
-      "Epoch 24/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1861 - tp: 272.0000 - fp: 6248.0000 - tn: 175724.0000 - fn: 32.0000 - accuracy: 0.9655 - precision: 0.0417 - recall: 0.8947 - auc: 0.9779 - val_loss: 0.1885 - val_tp: 73.0000 - val_fp: 772.0000 - val_tn: 44714.0000 - val_fn: 10.0000 - val_accuracy: 0.9828 - val_precision: 0.0864 - val_recall: 0.8795 - val_auc: 0.9785\n",
-      "Epoch 25/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1953 - tp: 270.0000 - fp: 6501.0000 - tn: 175471.0000 - fn: 34.0000 - accuracy: 0.9641 - precision: 0.0399 - recall: 0.8882 - auc: 0.9751 - val_loss: 0.1877 - val_tp: 73.0000 - val_fp: 768.0000 - val_tn: 44718.0000 - val_fn: 10.0000 - val_accuracy: 0.9829 - val_precision: 0.0868 - val_recall: 0.8795 - val_auc: 0.9786\n",
-      "Epoch 26/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1704 - tp: 277.0000 - fp: 6215.0000 - tn: 175757.0000 - fn: 27.0000 - accuracy: 0.9658 - precision: 0.0427 - recall: 0.9112 - auc: 0.9808 - val_loss: 0.1903 - val_tp: 73.0000 - val_fp: 698.0000 - val_tn: 44788.0000 - val_fn: 10.0000 - val_accuracy: 0.9845 - val_precision: 0.0947 - val_recall: 0.8795 - val_auc: 0.9788\n",
-      "Epoch 27/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1946 - tp: 271.0000 - fp: 6036.0000 - tn: 175936.0000 - fn: 33.0000 - accuracy: 0.9667 - precision: 0.0430 - recall: 0.8914 - auc: 0.9748 - val_loss: 0.1908 - val_tp: 73.0000 - val_fp: 692.0000 - val_tn: 44794.0000 - val_fn: 10.0000 - val_accuracy: 0.9846 - val_precision: 0.0954 - val_recall: 0.8795 - val_auc: 0.9786\n",
-      "Epoch 28/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2115 - tp: 271.0000 - fp: 5873.0000 - tn: 176099.0000 - fn: 33.0000 - accuracy: 0.9676 - precision: 0.0441 - recall: 0.8914 - auc: 0.9688 - val_loss: 0.1914 - val_tp: 73.0000 - val_fp: 691.0000 - val_tn: 44795.0000 - val_fn: 10.0000 - val_accuracy: 0.9846 - val_precision: 0.0955 - val_recall: 0.8795 - val_auc: 0.9785\n",
-      "Epoch 29/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2237 - tp: 266.0000 - fp: 6047.0000 - tn: 175925.0000 - fn: 38.0000 - accuracy: 0.9666 - precision: 0.0421 - recall: 0.8750 - auc: 0.9672 - val_loss: 0.1909 - val_tp: 73.0000 - val_fp: 698.0000 - val_tn: 44788.0000 - val_fn: 10.0000 - val_accuracy: 0.9845 - val_precision: 0.0947 - val_recall: 0.8795 - val_auc: 0.9784\n",
-      "Epoch 30/100\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2232 - tp: 272.0000 - fp: 5990.0000 - tn: 175982.0000 - fn: 32.0000 - accuracy: 0.9670 - precision: 0.0434 - recall: 0.8947 - auc: 0.9668 - val_loss: 0.1919 - val_tp: 73.0000 - val_fp: 642.0000 - val_tn: 44844.0000 - val_fn: 10.0000 - val_accuracy: 0.9857 - val_precision: 0.1021 - val_recall: 0.8795 - val_auc: 0.9785\n",
-      "Epoch 31/100\n",
-      "178176/182276 [============================>.] - ETA: 0s - loss: 0.2022 - tp: 273.0000 - fp: 5659.0000 - tn: 172216.0000 - fn: 28.0000 - accuracy: 0.9681 - precision: 0.0460 - recall: 0.9070 - auc: 0.9705Restoring model weights from the end of the best epoch.\n",
-      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1989 - tp: 276.0000 - fp: 5796.0000 - tn: 176176.0000 - fn: 28.0000 - accuracy: 0.9680 - precision: 0.0455 - recall: 0.9079 - auc: 0.9708 - val_loss: 0.1920 - val_tp: 73.0000 - val_fp: 626.0000 - val_tn: 44860.0000 - val_fn: 10.0000 - val_accuracy: 0.9860 - val_precision: 0.1044 - val_recall: 0.8795 - val_auc: 0.9788\n",
-      "Epoch 00031: early stopping\n"
-     ]
-    }
-   ],
-   "source": [
-    "weighted_model = make_model()\n",
-    "weighted_model.load_weights(initial_weights)\n",
-    "\n",
-    "weighted_history = weighted_model.fit(\n",
-    "    train_features,\n",
-    "    train_labels,\n",
-    "    batch_size=BATCH_SIZE,\n",
-    "    epochs=EPOCHS,\n",
-    "    callbacks=[early_stopping],\n",
-    "    validation_data=(val_features, val_labels),\n",
-    "    # The class weights go here\n",
-    "    class_weight=class_weight,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "R0ynYRO0G3Lx"
-   },
-   "source": [
-    "### Check training history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "BBe9FMO5ucTC"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x720 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_metrics(weighted_history)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "REy6WClTZIwQ"
-   },
-   "source": [
-    "### Evaluate metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nifqscPGw-5w"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "train_predictions_weighted = #TODO: Your code goes here.\n",
-    "test_predictions_weighted = #TODO: Your code goes here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "owKL2vdMBJr6"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loss :  0.06950428275801711\n",
-      "tp :  94.0\n",
-      "fp :  905.0\n",
-      "tn :  55952.0\n",
-      "fn :  11.0\n",
-      "accuracy :  0.9839191\n",
-      "precision :  0.0940941\n",
-      "recall :  0.8952381\n",
-      "auc :  0.9844724\n",
-      "\n",
-      "Legitimate Transactions Detected (True Negatives):  55952\n",
-      "Legitimate Transactions Incorrectly Detected (False Positives):  905\n",
-      "Fraudulent Transactions Missed (False Negatives):  11\n",
-      "Fraudulent Transactions Detected (True Positives):  94\n",
-      "Total Fraudulent Transactions:  105\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU4AAAFNCAYAAABvx4bHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xm8VXW9//HXG1DEkVFUINHEnG6aGpqZl1IRNIPKch5JyjS18pdmlqZWWnrNKbuYA2qJmnlFr0pc1NKUBOdABCIHCENlUBkUOJ/fH+t7aHM857DXZu+zz2a/nzzW4+z1XdN3n8P5nM93WGsrIjAzs+J1qHYFzMxqjQOnmVlODpxmZjk5cJqZ5eTAaWaWkwOnmVlODpxmZjk5cLZDkrpIuk/SIkl3rcV5jpb0x3LWrVokfUbSy9Wuhxk4cK4VSUdJmizpPUlzJT0oad8ynPowoDfQIyK+UupJIuK3ETG4DPWpKEkhabvW9omIxyLiY2t5ncHpD9Ibkt6U9LikkyR1aLJfd0n3SFos6VVJR7VyzgskLU//BxqXbQu27ybpaUlL0tfd1uY9WPvgwFkiSd8Bfgn8lCzIfQT4FTCsDKffGpgeESvKcK6aJ6lTGc7xc7Kf1W+AHYAtgNOAzwH3S+pcsPu1wAdkP9ejgesk7dzK6e+IiI0LllnpmusD9wK3Ad2A0cC9qdxqWUR4ybkAmwHvAV9pZZ/OZIH1n2n5JdA5bRsEzAa+C8wD5gInpm0/JvulXZ6uMQK4ALit4Nz9gQA6pfUTgFnAu8A/gKMLyh8vOG4fYBKwKH3dp2Dbo8BFwF/Sef4I9GzhvTXW/3sF9R8OHAxMB+YD5xbsPxB4EliY9r0GWD9t+3N6L4vT+z284PxnA28AtzaWpWM+mq6xe1rfCngTGNRCfY9L76dzC9t/Afwovd4off+3L9h+K3BJC8eu9rNpsm0wMAdQQdlrwJBq/x/2snZL1StQiwswBFjRGLha2OdCYCKwOdALeAK4KG0blI6/EFgvBZwlQLe0vWmgbDFwpl/0d4CPpW1bAjun16sCJ9AdWAAcm447Mq33SNsfBf4ObA90SestBYvG+v8o1f/kFLh+B2wC7AwsBbZJ++8B7J2u2x94CTiz4HwBbNfM+S8l+wPUpTBwpn1OBqYCGwLjgMta+VnMAPql15eSBeNngCvS96ML8Pe0/RPAkibHnwXc18K5LyD7QzQfmAKcUrDt28CDTfa/H/hutf8Pe1m7xU310vQA3orWm9JHAxdGxLyIeJMskzy2YPvytH15RDxAlm2V2ofXAOwiqUtEzI2IKc3scwgwIyJujYgVEXE7MA04tGCfmyJiekQsBe4EWuuPWw78JCKWA2OAnsCVEfFuuv5UYFeAiHg6Iiam674C/Dfwn0W8p/Mj4v1Un9VExPXATOCvZH8sftDcSVLf6T8j4nVJQ4GhwMfJ/vjtD3RM558vqSewMdkfokKLyP4gNOdOYEeyP44nAz+SdGTatnE6tthzWY1w4CzN20DPNfS9bQW8WrD+aipbdY4mgXcJ2S9aLhGxmKx5+w1grqT/lbRDEfVprFOfgvU3ctTn7YhYmV43BrZ/FWxf2ni8pO0l3Z8GZd4h62vs2cq5Ad6MiGVr2Od6YBfg6oh4v4V9NidrLgP8B/BQ+mM2D3go1a8DWR/kfLI/YJs2OcemZN0XHxIRUyPinxGxMiKeAK4kG9wj77msdjhwluZJ4H2yfr2W/JNskKfRR1JZKRaTNUkbbVG4MSLGRcSBZJnXNLKAsqb6NNZpTjP7ltt1ZPUaEBGbAucCWsMxrT7vUNLGZP3GNwAXSOrewq5vkX1fAF4EDpK0uaTNybLOjYCfAQ9ERANZH20nSQMKzrErWTO8GMG/39sU4OOSCt/rx3Ocy9opB84SRMQisv69ayUNl7ShpPUkDU2jtwC3A+dJ6pWagD8iG10txXPAfpI+Imkz4PuNGyT1ljRM0kZkwfw9smZuUw8A26cpVJ0kHQ7sRNbnVmmbkDV/30vZ8ClNtv8L2PZDR7XuSmByRHwN+F/g183tFBHTgX6StoyIB8myzOeBsWQDU6eQZYBnpf0XA38ALpS0kaRPk82UuLW586fvfTdlBgKnk42kQ9ZPvBI4XVJnSael8odzvldrb6rdyVrLC1k/5mSyjPANsl/gfdK2DYCryEaR56bXG6RtgygY6EhlrwAHpNcX0GSklmyKzEKyfr2T+ffg0JbAn8j6zhaS/bLulI45gdVH1fcFnk77Pg3sW7DtUeBrBeurHdukLqvVP9UjgP4FZY8Dx6TX+5FlnO8Bj5ENihXW6xvpe7QQ+GoL359VZWSBbA7QPa1vnL4vR7dQ35HpZ/OhwbwWyroD/5N+rq8BRxVs+wzwXsH67WRdN++l93h6k3N9In2vl5INSH2i2v9vvaz9ovTDNVunSbqGrMn9I7Kulg5k04UuBg6JiKb9v2YtcuC0uiHpi8CppNF+silil0Y2qGNWNAdOM7OcPDhkZpaTA6eZWU5r/fCESln+1iz3IdSoTfoOqnYVbC0sW/bamubYNqvU39n1em5b0vWqyRmnmVlO7TbjNLMa07ByzfusIxw4zaw8orkb1tZNDpxmVh4NDpxmZrmEM04zs5yccZqZ5eSM08wsJ4+qm5nl5IzTzCwn93GameXjUXUzs7yccZqZ5eSM08wsJ4+qm5nl5IzTzCwn93GameVURxmnH2RsZpaTM04zKw831c3M8onwqLqZWT511MfpwGlm5eGmuplZTs44zcxyqqM7hzwdyczKIxpKW4og6RVJL0p6TtLkVNZd0nhJM9LXbqlckq6SNFPSC5J2LzjP8Wn/GZKOLyjfI51/ZjpWrdXHgdPMyqOhobSleJ+NiN0iYs+0fg4wISIGABPSOsBQYEBaRgLXQRZogfOBvYCBwPmNwTbtc3LBcUNaq4gDp5mVRwUzzhYMA0an16OB4QXlt0RmItBV0pbAQcD4iJgfEQuA8cCQtG3TiJgYEQHcUnCuZjlwmll5VDbjDOCPkp6WNDKV9Y6Iuen1G0Dv9LoP8HrBsbNTWWvls5spb5EHh8ysPEqcjpQC4ciColERMarJbvtGxBxJmwPjJU0r3BgRISlKqkAJHDjNrCxKvXMoBcmmgbLpPnPS13mS7iHro/yXpC0jYm5qbs9Lu88B+hUc3jeVzQEGNSl/NJX3bWb/FrmpbmblUaGmuqSNJG3S+BoYDPwNGAs0jowfD9ybXo8Fjkuj63sDi1KTfhwwWFK3NCg0GBiXtr0jae80mn5cwbma5YzTzMqjchPgewP3pBlCnYDfRcRDkiYBd0oaAbwKfDXt/wBwMDATWAKcCBAR8yVdBExK+10YEfPT628CNwNdgAfT0iJlg0jtz/K3ZrXPitkabdJ3ULWrYGth2bLXWp3D2JKlE0aV9DvbZf+RJV2vmpxxmll51NEtl+7jNDPLyRmnmZWHn45kZpZTHTXVHTjNrDyccZqZ5eTAaWaWk5vqZmY5OeM0M8vJGaeZWU7OOM3McnLGaWaWkzNOM7OcHDjNzHJqp09aqwQHTjMrD2ecZmY5OXCameXkUXUzs5zqKOP0g4zNzHJyxmlm5eFRdTOznOqoqe7AaWbl4cBpZpaTR9XNzPKJBvdxmpnl46a6mVlObqqbmeXkprqZWU5uqpuZ5eTAacUa/OXj2WjDDenQoQMdO3bkzhuv4tobbuPusQ/RretmAJzx9ePZb5+BLF++nB///GqmTJuBOohzzvgGA3f/OAAnnPY93nprPp07dwZg1C9/Qo9uXRk95g/cfd9DdOzYke5dN+Oic7/NVlv0rtr7rRennnoSJ510JJK48cbbueaaG+jWbTNuu+1XbL11X159dTZHH/1NFi5cxH777c1dd/2GV155HYB7732In/70yiq/gyrwnUOWx41XX7IqSDY69vDhnHjUYauV/X7sQwDcc+t1vL1gIad894eM+c2VdOiQPTLgkvO/xy47br/aMTsO+Ch33HAVXTbYgDH33M/l197I5Rd9v4LvxnbaaXtOOulI9t33UD74YDn33XcrDzzwf4wYcTSPPPIXLrvsV5x11jc566xvct55PwPgL3+ZxJe+dGKVa15ldZRxVuwhH5J2kHS2pKvScrakHSt1vVrw91deY+AeuwLQo1tXNtl4I6ZMm9HqMQP32JUuG2wAwK4778C/3nyr4vWsdzvsMIBJk55l6dJlrFy5kscem8jw4UM59NADue223wNw222/5wtfGFzlmrYzDVHaUoMqEjglnQ2MAQQ8lRYBt0s6pxLXrBZJjPz2D/jqSd/irnsfWFV++9338cXjTuG8n/4Xi955F4CPbbcNjz4+kRUrVjL7n28w9eWZvPGvN1cd88OfXsGXjz+VX9/0O6KZZs8f7vsjn9l7z8q/qTo3ZcrLfPrTA+nevStdumzAQQd9lr59t2TzzXvyxhvzAHjjjXlsvnnPVcfstdfuPPXUQ9x772h2bNJqqBvRUNpSgyrVVB8B7BwRywsLJf0XMAW4pELXbXO3XHcZvXv15O0FCzn5zHPZZut+HP7FQ/jGCVn/2NXX38Ivrrmei8/9Dl885CBmvfI6h484na222JzddtmRDh2zv12Xnv89evfqyeLFSzjzBxcz9qEJDBt6wKrr3DfuYaZMm87N1/68Wm+1brz88kwuv/w67r//tyxZsoQXXpjKypUf/gVv/Nv27LN/Y/vtP8XixUs46KDPctdd17PLLv/ZxrVuB2o0eyxFpZrqDcBWzZRvmbY1S9JISZMlTf7NLbdXqGrl1btXlnX06NaV/ffbhxenvkzP7t3o2LEjHTp04LAvDOVvU6cD0KlTR84+4+vcPfparr70fN55bzH9+/VZ7TwbbbQhhxz42VXHADw56VlGjR7D1T+/gPXXX7+N32F9uvnmO9hnn0M44ICvsHDhImbMmMW8eW+xxRabA7DFFpvzZuo2effd91i8eAkA48Y9wnrrdaJHj25Vq3u1RENDSUstqlTgPBOYIOlBSaPS8hAwATijpYMiYlRE7BkRe37tuCMrVLXyWbJ02apfmCVLl/HEU88wYNv+vPnW/FX7TPjTE2y37dYALF22jCVLlwHwxFPP0KljRz66zdasWLGSBQsXAbB8xQr+9MRfVx3z0vSZ/PjnV3HNpefTo1vXtnx7da1Xrx4A9Ou3FcOGDeGOO+7l/vvHc8wx2YDfMcccxn33jQegd+9eq47bc89d6dChA2+/vaDtK21tpiJN9Yh4SNL2wECgTyqeA0yKiJWVuGY1vD1/AWecexEAK1es5ODBg9h37z0558Jf8PKMWSDos0Vvzv/e6QDMX7CIr3/7B6hDB3r36sHPfnQWAB8sX87Xv3Mey1esoGFlA3t/8hMc9oUhAFx+7Q0sWbqM75z3UwC27N2La35+Qdu/2TozZsx/0717N5YvX86ZZ/6QRYve4bLLfsVvf3sdJ5xwOK+9Noejjz4FgC9+8WBGjjyWFStWsHTpMo499rQq175K6qipruYGIdqD5W/Nap8VszXapO+galfB1sKyZa+plOMWX3xMSb+zG513W0nXqybP4zSz8qijjNOB08zKo0YHekrhT7k0s/Ko4AR4SR0lPSvp/rS+jaS/Spop6Q5J66fyzml9Ztrev+Ac30/lL0s6qKB8SCqbWew8cwdOMyuPyk6APwN4qWD9UuCKiNgOWEA2d5z0dUEqvyLth6SdgCOAnYEhwK9SMO4IXAsMBXYCjkz7tsqB08zKo0IZp6S+wCHAb9K6gM8Bv0+7jAaGp9fD0jpp+/5p/2HAmIh4PyL+Acwkm/UzEJgZEbMi4gOyOx6HralO7uM0s7Ko4GT2XwLfAzZJ6z2AhRGxIq3P5t/THvsArwNExApJi9L+fYCJBecsPOb1JuV7ralCzjjNrDxKzDgL7xhMy8jGU0r6PDAvIp6u4jv7EGecZlYeJU5HiohRwKgWNn8a+IKkg4ENgE2BK4GukjqlrLMv2Q02pK/9gNmSOgGbAW8XlDcqPKal8hY54zSz8qjA4FBEfD8i+kZEf7LBnYcj4mjgEaDxgbfHA/em12PTOmn7w5Hd5TMWOCKNum8DDCB7atskYEAapV8/XWPsmt6qM04zK4+2nQB/NjBG0sXAs8ANqfwG4FZJM4H5ZIGQiJgi6U5gKrACOLXx9m9JpwHjgI7AjRExZU0X9y2XVna+5bK2lXrL5btnHlrS7+wmv7zPt1yaWZ3yLZdmZjnV0S2XDpxmVh7OOM3McqqjwOnpSGZmOTnjNLOyaK8zdCrBgdPMyqOOmuoOnGZWHg6cZmb5hAOnmVlODpxmZjnVz/x3B04zKw831c3M8nLgNDPLyU11M7N83FQ3M8vLGaeZWT7OOM3M8nLGaWaWzxo+d22d4sBpZuXhwGlmlk89ZZx+kLGZWU7OOM2sPOoo43TgNLOyqKemugOnmZWFAycgqXtrB0bE/PJXx8xqlQNn5mkgADWzLYBtK1IjM6tN0VyoWDe1GDgjYpu2rIiZ1bZ6yjjXOB1JmWMk/TCtf0TSwMpXzcxqSTSopKUWFTOP81fAp4Cj0vq7wLUVq5GZ1aRoKG2pRcWMqu8VEbtLehYgIhZIWr/C9TKzGhPu41zNckkdyQaEkNSLuprqambFqNXssRTFBM6rgHuA3pJ+AhwGnFfRWplZzanV/spSrDFwRsRvJT0N7J+KhkfES5WtlpnVmqif5xgXfefQhkBjc71L5apjZrWqnjLOYqYj/QgYDXQHegI3SXJT3cxWU0/TkYrJOI8Gdo2IZQCSLgGeAy6uZMXMrLa4qb66fwIbAMvSemdgTsVqZGY1qVazx1K09pCPq8n6NBcBUySNT+sHAk+1TfXMzNqf1jLOyenr02TTkRo9WrHamFnNqtQEeEkbAH8ma+12An4fEedL2gYYA/Qgi1PHRsQHkjoDtwB7AG8Dh0fEK+lc3wdGACuB0yNiXCofAlxJNgj+m4i4pLU6tfaQj9Fr8V7NrM5UcAL8+8DnIuI9SesBj0t6EPgOcEVEjJH0a7KAeF36uiAitpN0BHApcLiknYAjgJ2BrYD/k7R9usa1ZK3p2cAkSWMjYmpLFSpmVH2ApN9LmippVuNS6nfAzNZNDaGSljWJzHtpdb20BPA54PepfDQwPL0eltZJ2/eXpFQ+JiLej4h/ADOBgWmZGRGzIuIDsix2WGt1KuYhHzeRRfEVwGfJUuDbijjOzOpIhEpaiiGpo6TngHnAeODvwMKIWJF2mQ30Sa/7AK9ndYoVZOM0PQrLmxzTUnmLigmcXSJiAqCIeDUiLgAOKeI4M6sjpc7jlDRS0uSCZeSHzh2xMiJ2A/qSZYg7tPkbLFDMdKT3JXUAZkg6jWwq0saVrZaZ1ZpS53FGxChgVJH7LpT0CNmjLrtK6pSyyr78e5rkHKAfMFtSJ2AzskGixvJGhce0VN6sYjLOM8huuTydbJTqWOD4Io4zszpSqTuHJPWS1DW97kI2iPMS8AjZQ4cgi0n3ptdj+XeMOgx4OCIilR8hqXMakR9ANrVyEjBA0jbpkZlHpH1bVMxDPiall+8BJ67xXZpZXSpmoKdEWwKj0+MtOwB3RsT9kqYCYyRdDDwL3JD2vwG4VdJMYD5ZICQipki6E5hKNmZzakSsBEit6XFk05FujIgprVVI0UJ+Lek+0jM4mxMRXyjuPZdm+Vuz6ugGrnXLJn0HVbsKthaWLXutpAj44jaHlvQ7+x//uK/mbjlqLeO8rM1qYWY1z/eqAxHxp7asiJnVtgo21dudYp/HaWbWKn/mkJlZTm6qtwNdtvpMtatgZjm4qU71R9XNrLa4qZ7xqLqZFc0ZJx5VNzNryRr7OCUNAH4G7ET2ERoARMS2FayXmdWYOhobKmpw6CbgfOAKssfKnUhx97ibWR2pp6a6HytnZmVRyedxtjd+rJyZlUXlPjmj/fFj5cysLAKVtNQiP1bOzMqioY5Gh4oZVX+EZgbMIuJzFamRmdWkhhrNHktRTB/nWQWvNwC+TPYQUDOzVWq12V2KYprqTzcp+oukpypUHzOrUfU0OFRMU717wWoHsgGizSpWIzOrSc44V/c0WR+nyJro/wBGVLJSZlZ7nHGubseIWFZYIKlzhepjZjWqngJnMfM4n2im7MlyV8TMapvncQKStgD6AF0kfQJWvcNNySbEm5mtUsRHpK8zWmuqHwScAPQFLuffgfMd4NzKVsvMao3ncQIRMZrsQ+C/HBF3t2GdzKwG1dGNQ0X1ce4hqWvjiqRuki6uYJ3MzNq1YgLn0IhY2LgSEQuAgytXJTOrRQ0lLrWomOlIHSV1joj3ASR1ATwdycxW0yD3cRb6LTBB0k1p/UTglspVycxqUT31cRZzr/qlkp4HDkhFF0XEuMpWy8xqTa02u0tRTMZJRDwEPAQgaV9J10bEqRWtmZnVFM/jbCJNgD8S+CrZvep/qGSlzKz2eB4nIGl7smB5JPAWcAfZB7Z9to3qZmY1xH2cmWnAY8DnI2ImgKRvt0mtzKzm1FNTvbV5nF8C5gKPSLpe0v5QR7m4meVST/M4WwycEfE/EXEEsAPwCHAmsLmk6yQNbqsKmlltiBKXWrTGO4ciYnFE/C4iDiV74MezwNkVr5mZ1ZQGlbbUomJuuVwlIhZExKiI2L9SFTKz2lRPTfWipiOZma1JrQbBUjhwmllZRI02u0vhwGlmZVFPGWeuPk4zs5ZUqo9TUj9Jj0iaKmmKpDNSeXdJ4yXNSF+7pXJJukrSTEkvSNq94FzHp/1nSDq+oHwPSS+mY66SWn/UkwOnmZVFBacjrQC+GxE7AXsDp0raCTgHmBARA4AJaR1gKDAgLSOB6yALtMD5wF7AQOD8xmCb9jm54LghrVXIgdPM2rWImBsRz6TX7wIvkX2Q5DBgdNptNDA8vR4G3BKZiUBXSVuSfY7a+IiYnx7IPh4YkrZtGhETIyLIHpvZeK5muY/TzMqiLeZkSuoPfAL4K9A7IuamTW8AvdPrPsDrBYfNTmWtlc9uprxFzjjNrCxK7eOUNFLS5IJlZHPnl7QxcDdwZkS8U7gtZYptdiOSM04zK4tSR9UjYhQwqrV9JK1HFjR/GxGNj7X8l6QtI2Juam7PS+VzgH4Fh/dNZXOAQU3KH03lfZvZv0XOOM2sLCo1OJRGuG8AXoqI/yrYNBZoHBk/Hri3oPy4NLq+N7AoNenHAYPTJ/V2AwYD49K2dyTtna51XMG5muWM08zKooJ9nJ8GjgVelPRcKjsXuAS4U9II4FWyB60DPED2SbwzgSVkn5NGRMyXdBEwKe13YUTMT6+/CdwMdAEeTEuLHDjNrCwqNQE+Ih6n5Udafui5Gam/s9mP9omIG4EbmymfDOxSbJ0cOM2sLGr1EXGlcOA0s7JoqKPQ6cBpZmVRT/eqO3CaWVnUT77pwGlmZeKM08wsp1r9GIxSOHCaWVl4cMjMLKf6CZsOnGZWJu7jNDPLqZ6a6n7Ih5lZTs44zaws6iffdOA0szJxH6eZWU711MfpwGlmZVE/YdOB08zKxE11M7Ocoo5yTgdOMysLZ5xmZjnV0+CQJ8C3getHXc4/Zz/Pc89OWFX25S9/nuefe5gPlr3OHrt/vIq1szX51mkjeO7ZCTz/3MOc/q2vrbbt22d+nRUfzKFHj25Vql37UalPuWyPHDjbwC233Mkhnz96tbIpU6bxla+ezGOPTaxSrawYO+/8MUaMOIpP7XMIu+9xIIccfAAf/Wh/APr23YoDD9iPV1+dXd1KthMNRElLLXLgbAOPPf5X5i9YuFrZtGkzmT7971WqkRVrhx0G8NRTz7J06TJWrlzJnx+byBeHDwXg8ssu4Jxzf0L2oYrWUOJSi9o8cEo6sa2vaVaqKVOmse++e9G9eze6dNmAoUM+R9++W3HooYOZM2cuL7wwtdpVbDeixH+1qBqDQz8GbqrCdc1ymzZtJr/4xbU8+MDvWLJ4Cc89P4XOndfn+2d/iyEHH1Xt6rUrtZo9lqIiGaekF1pYXgR6t3LcSEmTJU1uaFhciaqZ5XbTzWPYa++hfHb/L7Nw4SKmTp1O//4f4ZnJ45k5fSJ9+27JpL+Oo3fvXtWualU541x7vYGDgAVNygU80dJBETEKGAXQaf0+tfkdtXVOr149ePPNt+nXbyuGDx/Kp/c9lKuvuWHV9pnTJ7LXp4by9ttN/7vXl3rKOCsVOO8HNo6I55pukPRoha7Zbt1267X8536fomfP7rwyazI/vvAy5i9YyJVXXEyvXt0Ze+8tPP/8FA5uMvJu7cNdd1xP9x7dWL58Baef/gMWLXqn2lVqlxrqaJBM7XVE0BmnWXWs+GBOSZ9XeezWXyrpd/bWV/9Qc5+P6TuHzKws6inTceA0s7Ko1cnspXDgNLOyqNUR8lI4cJpZWXhU3cwsJzfVzcxyclPdzCwnN9XNzHJqr3PCK8GB08zKwn2cZmY5ualuZpZTPQ0O+QnwZlYWlfroDEk3Spon6W8FZd0ljZc0I33tlsol6SpJM9OjLHcvOOb4tP8MSccXlO8h6cV0zFWS1njvvAOnmZVFRJS0FOFmYEiTsnOACRExAJiQ1gGGAgPSMhK4DrJAC5wP7AUMBM5vDLZpn5MLjmt6rQ9x4DSzsqjUZw5FxJ+B+U2KhwGj0+vRwPCC8lsiMxHoKmlLsucDj4+I+RGxABgPDEnbNo2IiZFF8VsKztUi93GaWVm0cR9n74iYm16/wb8/WaIP8HrBfrNTWWvls5spb5UzTjMri1L7OAs/MictI/NcN2WKbRq1nXGaWVUVfmRODv+StGVEzE3N7XmpfA7Qr2C/vqlsDjCoSfmjqbxvM/u3yhmnmZVFBQeHmjMWaBwZPx64t6D8uDS6vjewKDXpxwGDJXVLg0KDgXFp2zuS9k6j6ccVnKtFzjjNrCwqdeeQpNvJssWekmaTjY5fAtwpaQTwKvDVtPsDwMHATGAJcCJARMyXdBEwKe13YUQ0Djh9k2zkvgvwYFpar1N7vb/UnzlkVh2lfubQoL4HlPQ7++js//NnDplZfaqnT7l04DSzsqifsOnAaWZl4qcjmZnl5MBpZpZTex1orgQHTjMrC2ecZmY51dPzOB04zaws3FQ3M8vJTXUzs5wsrngYAAAFc0lEQVSccZqZ5eSM08wsJw8OmZnlVE/3qvt5nGZmOTnjNLOycFPdzCynemqqO3CaWVk44zQzy8kZp5lZTs44zcxycsZpZpaTM04zs5wiGqpdhTbjwGlmZeF71c3McvLTkczMcnLGaWaWkzNOM7OcPB3JzCwnT0cyM8vJTXUzs5w8OGRmllM9ZZx+AryZWU7OOM2sLDyqbmaWUz011R04zawsPDhkZpaTM04zs5zcx2lmlpPvHDIzy8kZp5lZTvXUx+kJ8GZWFlHiv2JIGiLpZUkzJZ1T4beyRs44zawsKpVxSuoIXAscCMwGJkkaGxFTK3LBIjhwmllZVLCpPhCYGRGzACSNAYYBVQucbqqbWVlEiUsR+gCvF6zPTmVV024zzhUfzFG161BJkkZGxKhq18NK45/fh5X6OytpJDCyoGhUe//eOuOsnpFr3sXaMf/8yiQiRkXEngVL06A5B+hXsN43lVWNA6eZtXeTgAGStpG0PnAEMLaaFWq3TXUzM4CIWCHpNGAc0BG4MSKmVLNODpzV0677cGyN/PNrQxHxAPBAtevRSPU029/MrBzcx2lmlpMDZxW0t9vHrHiSbpQ0T9Lfql0Xqx4HzjZWcPvYUGAn4EhJO1W3VpbDzcCQalfCqsuBs+2tun0sIj4AGm8fsxoQEX8G5le7HlZdDpxtr93dPmZm+Thwmpnl5MDZ9trd7WNmlo8DZ9trd7ePmVk+DpxtLCJWAI23j70E3Fnt28eseJJuB54EPiZptqQR1a6TtT3fOWRmlpMzTjOznBw4zcxycuA0M8vJgdPMLCcHTjOznBw41wGSVkp6TtLfJN0lacO1ONcgSfen119o7elNkrpK+mYJ17hA0lnFljfZ52ZJh+W4Vn8/ycjKzYFz3bA0InaLiF2AD4BvFG5UJvfPOiLGRsQlrezSFcgdOM1qnQPnuucxYLuUab0s6Rbgb0A/SYMlPSnpmZSZbgyrng86TdIzwJcaTyTpBEnXpNe9Jd0j6fm07ANcAnw0Zbu/SPv9P0mTJL0g6ccF5/qBpOmSHgc+tqY3IenkdJ7nJd3dJIs+QNLkdL7Pp/07SvpFwbW/vrbfSLOWOHCuQyR1InvO54upaADwq4jYGVgMnAccEBG7A5OB70jaALgeOBTYA9iihdNfBfwpInYFdgemAOcAf0/Z7v+TNDhdcyCwG7CHpP0k7UF2a+luwMHAJ4t4O3+IiE+m670EFN6h0z9d4xDg1+k9jAAWRcQn0/lPlrRNEdcxy80f1rZu6CLpufT6MeAGYCvg1YiYmMr3Jntw8l8kAaxPduvgDsA/ImIGgKTbaP4zwz8HHAcQESuBRZK6NdlncFqeTesbkwXSTYB7ImJJukYx9+bvIulisu6AjcluUW10Z0Q0ADMkzUrvYTDw8YL+z83StacXcS2zXBw41w1LI2K3woIUHBcXFgHjI+LIJvutdtxaEvCziPjvJtc4s4Rz3QwMj4jnJZ0ADCrY1vQ+4UjX/lZEFAZYJPUv4dpmrXJTvX5MBD4taTsASRtJ2h6YBvSX9NG035EtHD8BOCUd21HSZsC7ZNlko3HASQV9p30kbQ78GRguqYukTci6BdZkE2CupPWAo5ts+4qkDqnO2wIvp2ufkvZH0vaSNiriOma5OeOsExHxZsrcbpfUORWfFxHTJY0E/lfSErKm/ibNnOIMYFR6GtBK4JSIeFLSX9J0nwdTP+eOwJMp430POCYinpF0B/A8MI/s0Xpr8kPgr8Cb6WthnV4DngI2Bb4REcsk/Yas7/MZZRd/Exhe3HfHLB8/HcnMLCc31c3McnLgNDPLyYHTzCwnB04zs5wcOM3McnLgNDPLyYHTzCwnB04zs5z+Pw7aaQbBEXQZAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 360x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "weighted_results = weighted_model.evaluate(\n",
-    "    test_features, test_labels, batch_size=BATCH_SIZE, verbose=0\n",
-    ")\n",
-    "for name, value in zip(weighted_model.metrics_names, weighted_results):\n",
-    "    print(name, \": \", value)\n",
-    "print()\n",
-    "\n",
-    "plot_cm(test_labels, test_predictions_weighted)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "PTh1rtDn8r4-"
-   },
-   "source": [
-    "Here you can see that with class weights the accuracy and precision are lower because there are more false positives, but conversely the recall and AUC are higher because the model also found more true positives. Despite having lower accuracy, this model has higher recall (and identifies more fraudulent transactions). Of course, there is a cost to both types of error (you wouldn't want to bug users by flagging too many legitimate transactions as fraudulent, either). Carefully consider the trade offs between these different types of errors for your application."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "hXDAwyr0HYdX"
-   },
-   "source": [
-    "### Plot the ROC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3hzScIVZS1Xm"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f5f2017f7b8>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_roc(\n",
-    "    \"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0]\n",
-    ")\n",
-    "plot_roc(\n",
-    "    \"Test Baseline\",\n",
-    "    test_labels,\n",
-    "    test_predictions_baseline,\n",
-    "    color=colors[0],\n",
-    "    linestyle=\"--\",\n",
-    ")\n",
-    "\n",
-    "plot_roc(\n",
-    "    \"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1]\n",
-    ")\n",
-    "plot_roc(\n",
-    "    \"Test Weighted\",\n",
-    "    test_labels,\n",
-    "    test_predictions_weighted,\n",
-    "    color=colors[1],\n",
-    "    linestyle=\"--\",\n",
-    ")\n",
-    "\n",
-    "\n",
-    "plt.legend(loc=\"lower right\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "5ysRtr6xHnXP"
-   },
-   "source": [
-    "## Oversampling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "18VUHNc-UF5w"
-   },
-   "source": [
-    "### Oversample the minority class\n",
-    "\n",
-    "A related approach would be to resample the dataset by oversampling the minority class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "sHirNp6u7OWp"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "pos_features = #TODO: Your code goes here.\n",
-    "neg_features = train_features[~bool_train_labels]\n",
-    "\n",
-    "pos_labels = #TODO: Your code goes here.\n",
-    "neg_labels = #TODO: Your code goes here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "WgBVbX7P7QrL"
-   },
-   "source": [
-    "#### Using NumPy\n",
-    "\n",
-    "You can balance the dataset manually by choosing the right number of random \n",
-    "indices from the positive examples:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "BUzGjSkwqT88"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(181972, 29)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = np.arange(len(pos_features))\n",
-    "choices = np.random.choice(ids, len(neg_features))\n",
-    "\n",
-    "res_pos_features = pos_features[choices]\n",
-    "res_pos_labels = pos_labels[choices]\n",
-    "\n",
-    "res_pos_features.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "7ie_FFet6cep"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(363944, 29)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resampled_features = np.concatenate([res_pos_features, neg_features], axis=0)\n",
-    "resampled_labels = np.concatenate([res_pos_labels, neg_labels], axis=0)\n",
-    "\n",
-    "order = np.arange(len(resampled_labels))\n",
-    "np.random.shuffle(order)\n",
-    "resampled_features = resampled_features[order]\n",
-    "resampled_labels = resampled_labels[order]\n",
-    "\n",
-    "resampled_features.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IYfJe2Kc-FAz"
-   },
-   "source": [
-    "#### Using `tf.data`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "usyixaST8v5P"
-   },
-   "source": [
-    "If you're using `tf.data` the easiest way to produce balanced examples is to start with a `positive` and a `negative` dataset, and merge them. See [the tf.data guide](../../guide/data.ipynb) for more examples."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "yF4OZ-rI6xb6"
-   },
-   "outputs": [],
-   "source": [
-    "BUFFER_SIZE = 100000\n",
-    "\n",
-    "\n",
-    "def make_ds(features, labels):\n",
-    "    ds = tf.data.Dataset.from_tensor_slices((features, labels))  # .cache()\n",
-    "    ds = ds.shuffle(BUFFER_SIZE).repeat()\n",
-    "    return ds\n",
-    "\n",
-    "\n",
-    "pos_ds = make_ds(pos_features, pos_labels)\n",
-    "neg_ds = make_ds(neg_features, neg_labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "RNQUx-OA-oJc"
-   },
-   "source": [
-    "Each dataset provides `(feature, label)` pairs:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "llXc9rNH7Fbz"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Features:\n",
-      " [-2.46955933  3.42534191 -4.42937043  3.70651659 -3.17895499 -1.30458304\n",
-      " -5.          2.86676917 -4.9308611  -5.          3.58555137 -5.\n",
-      "  1.51535494 -5.          0.01049775 -5.         -5.         -5.\n",
-      "  2.02380731  0.36595419  1.61836304 -1.16743779  0.31324117 -0.35515978\n",
-      " -0.62579636 -0.55952005  0.51255883  1.15454727  0.87478003]\n",
-      "\n",
-      "Label:  1\n"
-     ]
-    }
-   ],
-   "source": [
-    "for features, label in pos_ds.take(1):\n",
-    "    print(\"Features:\\n\", features.numpy())\n",
-    "    print()\n",
-    "    print(\"Label: \", label.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "sLEfjZO0-vbN"
-   },
-   "source": [
-    "Merge the two together using `experimental.sample_from_datasets`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "e7w9UQPT9wzE"
-   },
-   "outputs": [],
-   "source": [
-    "resampled_ds = tf.data.experimental.sample_from_datasets(\n",
-    "    [pos_ds, neg_ds], weights=[0.5, 0.5]\n",
-    ")\n",
-    "resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "EWXARdTdAuQK"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.48974609375\n"
-     ]
-    }
-   ],
-   "source": [
-    "for features, label in resampled_ds.take(1):\n",
-    "    print(label.numpy().mean())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "irgqf3YxAyN0"
-   },
-   "source": [
-    "To use this dataset, you'll need the number of steps per epoch.\n",
-    "\n",
-    "The definition of \"epoch\" in this case is less clear. Say it's the number of batches required to see each negative example once:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "xH-7K46AAxpq"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "278.0"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resampled_steps_per_epoch = np.ceil(2.0 * neg / BATCH_SIZE)\n",
-    "resampled_steps_per_epoch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "XZ1BvEpcBVHP"
-   },
-   "source": [
-    "### Train on the oversampled data\n",
-    "\n",
-    "Now try training the model with the resampled data set instead of using class weights to see how these methods compare.\n",
-    "\n",
-    "Note: Because the data was balanced by replicating the positive examples, the total dataset size is larger, and each epoch runs for more training steps. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "soRQ89JYqd6b"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train for 278.0 steps, validate for 23 steps\n",
-      "Epoch 1/100\n",
-      "278/278 [==============================] - 13s 48ms/step - loss: 0.4624 - tp: 267186.0000 - fp: 124224.0000 - tn: 160439.0000 - fn: 17495.0000 - accuracy: 0.7511 - precision: 0.6826 - recall: 0.9385 - auc: 0.9268 - val_loss: 0.3299 - val_tp: 79.0000 - val_fp: 2825.0000 - val_tn: 42661.0000 - val_fn: 4.0000 - val_accuracy: 0.9379 - val_precision: 0.0272 - val_recall: 0.9518 - val_auc: 0.9799\n",
-      "Epoch 2/100\n",
-      "278/278 [==============================] - 11s 39ms/step - loss: 0.2362 - tp: 264077.0000 - fp: 26654.0000 - tn: 257570.0000 - fn: 21043.0000 - accuracy: 0.9162 - precision: 0.9083 - recall: 0.9262 - auc: 0.9708 - val_loss: 0.1926 - val_tp: 75.0000 - val_fp: 1187.0000 - val_tn: 44299.0000 - val_fn: 8.0000 - val_accuracy: 0.9738 - val_precision: 0.0594 - val_recall: 0.9036 - val_auc: 0.9779\n",
-      "Epoch 3/100\n",
-      "278/278 [==============================] - 11s 40ms/step - loss: 0.1887 - tp: 263490.0000 - fp: 12935.0000 - tn: 271381.0000 - fn: 21538.0000 - accuracy: 0.9395 - precision: 0.9532 - recall: 0.9244 - auc: 0.9804 - val_loss: 0.1373 - val_tp: 75.0000 - val_fp: 1064.0000 - val_tn: 44422.0000 - val_fn: 8.0000 - val_accuracy: 0.9765 - val_precision: 0.0658 - val_recall: 0.9036 - val_auc: 0.9778\n",
-      "Epoch 4/100\n",
-      "278/278 [==============================] - 11s 41ms/step - loss: 0.1605 - tp: 263933.0000 - fp: 10513.0000 - tn: 274505.0000 - fn: 20393.0000 - accuracy: 0.9457 - precision: 0.9617 - recall: 0.9283 - auc: 0.9866 - val_loss: 0.1078 - val_tp: 75.0000 - val_fp: 1070.0000 - val_tn: 44416.0000 - val_fn: 8.0000 - val_accuracy: 0.9763 - val_precision: 0.0655 - val_recall: 0.9036 - val_auc: 0.9783\n",
-      "Epoch 5/100\n",
-      "278/278 [==============================] - 11s 39ms/step - loss: 0.1423 - tp: 265715.0000 - fp: 9592.0000 - tn: 275145.0000 - fn: 18892.0000 - accuracy: 0.9500 - precision: 0.9652 - recall: 0.9336 - auc: 0.9901 - val_loss: 0.0928 - val_tp: 75.0000 - val_fp: 1051.0000 - val_tn: 44435.0000 - val_fn: 8.0000 - val_accuracy: 0.9768 - val_precision: 0.0666 - val_recall: 0.9036 - val_auc: 0.9762\n",
-      "Epoch 6/100\n",
-      "278/278 [==============================] - 11s 40ms/step - loss: 0.1297 - tp: 267181.0000 - fp: 8944.0000 - tn: 275445.0000 - fn: 17774.0000 - accuracy: 0.9531 - precision: 0.9676 - recall: 0.9376 - auc: 0.9920 - val_loss: 0.0847 - val_tp: 75.0000 - val_fp: 1077.0000 - val_tn: 44409.0000 - val_fn: 8.0000 - val_accuracy: 0.9762 - val_precision: 0.0651 - val_recall: 0.9036 - val_auc: 0.9748\n",
-      "Epoch 7/100\n",
-      "278/278 [==============================] - 11s 39ms/step - loss: 0.1203 - tp: 267440.0000 - fp: 8606.0000 - tn: 276459.0000 - fn: 16839.0000 - accuracy: 0.9553 - precision: 0.9688 - recall: 0.9408 - auc: 0.9933 - val_loss: 0.0775 - val_tp: 75.0000 - val_fp: 1003.0000 - val_tn: 44483.0000 - val_fn: 8.0000 - val_accuracy: 0.9778 - val_precision: 0.0696 - val_recall: 0.9036 - val_auc: 0.9742\n",
-      "Epoch 8/100\n",
-      "278/278 [==============================] - 11s 40ms/step - loss: 0.1132 - tp: 268799.0000 - fp: 8165.0000 - tn: 276260.0000 - fn: 16120.0000 - accuracy: 0.9573 - precision: 0.9705 - recall: 0.9434 - auc: 0.9941 - val_loss: 0.0716 - val_tp: 75.0000 - val_fp: 927.0000 - val_tn: 44559.0000 - val_fn: 8.0000 - val_accuracy: 0.9795 - val_precision: 0.0749 - val_recall: 0.9036 - val_auc: 0.9713\n",
-      "Epoch 9/100\n",
-      "278/278 [==============================] - 11s 40ms/step - loss: 0.1074 - tp: 269627.0000 - fp: 7971.0000 - tn: 276559.0000 - fn: 15187.0000 - accuracy: 0.9593 - precision: 0.9713 - recall: 0.9467 - auc: 0.9947 - val_loss: 0.0670 - val_tp: 75.0000 - val_fp: 880.0000 - val_tn: 44606.0000 - val_fn: 8.0000 - val_accuracy: 0.9805 - val_precision: 0.0785 - val_recall: 0.9036 - val_auc: 0.9713\n",
-      "Epoch 10/100\n",
-      "278/278 [==============================] - 11s 39ms/step - loss: 0.1017 - tp: 270359.0000 - fp: 7590.0000 - tn: 277311.0000 - fn: 14084.0000 - accuracy: 0.9619 - precision: 0.9727 - recall: 0.9505 - auc: 0.9952 - val_loss: 0.0629 - val_tp: 75.0000 - val_fp: 848.0000 - val_tn: 44638.0000 - val_fn: 8.0000 - val_accuracy: 0.9812 - val_precision: 0.0813 - val_recall: 0.9036 - val_auc: 0.9717\n",
-      "Epoch 11/100\n",
-      "276/278 [============================>.] - ETA: 0s - loss: 0.0977 - tp: 269672.0000 - fp: 7408.0000 - tn: 274621.0000 - fn: 13547.0000 - accuracy: 0.9629 - precision: 0.9733 - recall: 0.9522 - auc: 0.9955Restoring model weights from the end of the best epoch.\n",
-      "278/278 [==============================] - 11s 39ms/step - loss: 0.0978 - tp: 271609.0000 - fp: 7474.0000 - tn: 276625.0000 - fn: 13636.0000 - accuracy: 0.9629 - precision: 0.9732 - recall: 0.9522 - auc: 0.9955 - val_loss: 0.0615 - val_tp: 75.0000 - val_fp: 841.0000 - val_tn: 44645.0000 - val_fn: 8.0000 - val_accuracy: 0.9814 - val_precision: 0.0819 - val_recall: 0.9036 - val_auc: 0.9637\n",
-      "Epoch 00011: early stopping\n"
-     ]
-    }
-   ],
-   "source": [
-    "resampled_model = make_model()\n",
-    "resampled_model.load_weights(initial_weights)\n",
-    "\n",
-    "# Reset the bias to zero, since this dataset is balanced.\n",
-    "output_layer = resampled_model.layers[-1]\n",
-    "output_layer.bias.assign([0])\n",
-    "\n",
-    "val_ds = tf.data.Dataset.from_tensor_slices((val_features, val_labels)).cache()\n",
-    "val_ds = val_ds.batch(BATCH_SIZE).prefetch(2)\n",
-    "\n",
-    "resampled_history = resampled_model.fit(\n",
-    "    resampled_ds,\n",
-    "    epochs=EPOCHS,\n",
-    "    steps_per_epoch=resampled_steps_per_epoch,\n",
-    "    callbacks=[early_stopping],\n",
-    "    validation_data=val_ds,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "avALvzUp3T_c"
-   },
-   "source": [
-    "If the training process were considering the whole dataset on each gradient update, this oversampling would be basically identical to the class weighting.\n",
-    "\n",
-    "But when training the model batch-wise, as you did here, the oversampled data provides a smoother gradient signal: Instead of each positive example being shown in one batch with a large weight, they're shown in many different batches each time with a small weight. \n",
-    "\n",
-    "This smoother gradient signal makes it easier to train the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "klHZ0HV76VC5"
-   },
-   "source": [
-    "### Check training history\n",
-    "\n",
-    "Note that the distributions of metrics will be different here, because the training data has a totally different distribution from the validation and test data. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "YoUGfr1vuivl"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x720 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_metrics(resampled_history)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "1PuH3A2vnwrh"
-   },
-   "source": [
-    "### Re-train\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "KFLxRL8eoDE5"
-   },
-   "source": [
-    "Because training is easier on the balanced data, the above training procedure may overfit quickly. \n",
-    "\n",
-    "So break up the epochs to give the `callbacks.EarlyStopping` finer control over when to stop training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "e_yn9I26qAHU"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train for 20 steps, validate for 23 steps\n",
-      "Epoch 1/1000\n",
-      "20/20 [==============================] - 4s 181ms/step - loss: 0.8800 - tp: 18783.0000 - fp: 16378.0000 - tn: 4036.0000 - fn: 1763.0000 - accuracy: 0.5571 - precision: 0.5342 - recall: 0.9142 - auc: 0.7752 - val_loss: 1.3661 - val_tp: 83.0000 - val_fp: 40065.0000 - val_tn: 5421.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.1208 - val_precision: 0.0021 - val_recall: 1.0000 - val_auc: 0.9425\n",
-      "Epoch 2/1000\n",
-      "20/20 [==============================] - 1s 35ms/step - loss: 0.7378 - tp: 19613.0000 - fp: 15282.0000 - tn: 5187.0000 - fn: 878.0000 - accuracy: 0.6055 - precision: 0.5621 - recall: 0.9572 - auc: 0.8680 - val_loss: 1.1629 - val_tp: 83.0000 - val_fp: 36851.0000 - val_tn: 8635.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.1913 - val_precision: 0.0022 - val_recall: 1.0000 - val_auc: 0.9580\n",
-      "Epoch 3/1000\n",
-      "20/20 [==============================] - 1s 39ms/step - loss: 0.6431 - tp: 19522.0000 - fp: 13990.0000 - tn: 6558.0000 - fn: 890.0000 - accuracy: 0.6367 - precision: 0.5825 - recall: 0.9564 - auc: 0.8950 - val_loss: 0.9853 - val_tp: 82.0000 - val_fp: 32268.0000 - val_tn: 13218.0000 - val_fn: 1.0000 - val_accuracy: 0.2919 - val_precision: 0.0025 - val_recall: 0.9880 - val_auc: 0.9660\n",
-      "Epoch 4/1000\n",
-      "20/20 [==============================] - 1s 39ms/step - loss: 0.5563 - tp: 19488.0000 - fp: 12475.0000 - tn: 8032.0000 - fn: 965.0000 - accuracy: 0.6719 - precision: 0.6097 - recall: 0.9528 - auc: 0.9135 - val_loss: 0.8430 - val_tp: 82.0000 - val_fp: 26633.0000 - val_tn: 18853.0000 - val_fn: 1.0000 - val_accuracy: 0.4155 - val_precision: 0.0031 - val_recall: 0.9880 - val_auc: 0.9713\n",
-      "Epoch 5/1000\n",
-      "20/20 [==============================] - 1s 37ms/step - loss: 0.4984 - tp: 19489.0000 - fp: 11049.0000 - tn: 9377.0000 - fn: 1045.0000 - accuracy: 0.7047 - precision: 0.6382 - recall: 0.9491 - auc: 0.9242 - val_loss: 0.7307 - val_tp: 82.0000 - val_fp: 20850.0000 - val_tn: 24636.0000 - val_fn: 1.0000 - val_accuracy: 0.5424 - val_precision: 0.0039 - val_recall: 0.9880 - val_auc: 0.9753\n",
-      "Epoch 6/1000\n",
-      "20/20 [==============================] - 1s 39ms/step - loss: 0.4463 - tp: 19305.0000 - fp: 9622.0000 - tn: 10895.0000 - fn: 1138.0000 - accuracy: 0.7373 - precision: 0.6674 - recall: 0.9443 - auc: 0.9336 - val_loss: 0.6405 - val_tp: 82.0000 - val_fp: 15843.0000 - val_tn: 29643.0000 - val_fn: 1.0000 - val_accuracy: 0.6523 - val_precision: 0.0051 - val_recall: 0.9880 - val_auc: 0.9773\n",
-      "Epoch 7/1000\n",
-      "20/20 [==============================] - 1s 40ms/step - loss: 0.4121 - tp: 19365.0000 - fp: 8524.0000 - tn: 11931.0000 - fn: 1140.0000 - accuracy: 0.7641 - precision: 0.6944 - recall: 0.9444 - auc: 0.9411 - val_loss: 0.5691 - val_tp: 82.0000 - val_fp: 11981.0000 - val_tn: 33505.0000 - val_fn: 1.0000 - val_accuracy: 0.7371 - val_precision: 0.0068 - val_recall: 0.9880 - val_auc: 0.9787\n",
-      "Epoch 8/1000\n",
-      "20/20 [==============================] - 1s 39ms/step - loss: 0.3784 - tp: 19242.0000 - fp: 7375.0000 - tn: 13072.0000 - fn: 1271.0000 - accuracy: 0.7889 - precision: 0.7229 - recall: 0.9380 - auc: 0.9461 - val_loss: 0.5120 - val_tp: 80.0000 - val_fp: 9309.0000 - val_tn: 36177.0000 - val_fn: 3.0000 - val_accuracy: 0.7957 - val_precision: 0.0085 - val_recall: 0.9639 - val_auc: 0.9794\n",
-      "Epoch 9/1000\n",
-      "20/20 [==============================] - 1s 45ms/step - loss: 0.3551 - tp: 19106.0000 - fp: 6529.0000 - tn: 13989.0000 - fn: 1336.0000 - accuracy: 0.8080 - precision: 0.7453 - recall: 0.9346 - auc: 0.9495 - val_loss: 0.4657 - val_tp: 80.0000 - val_fp: 7354.0000 - val_tn: 38132.0000 - val_fn: 3.0000 - val_accuracy: 0.8386 - val_precision: 0.0108 - val_recall: 0.9639 - val_auc: 0.9799\n",
-      "Epoch 10/1000\n",
-      "20/20 [==============================] - 1s 38ms/step - loss: 0.3350 - tp: 19149.0000 - fp: 5794.0000 - tn: 14698.0000 - fn: 1319.0000 - accuracy: 0.8263 - precision: 0.7677 - recall: 0.9356 - auc: 0.9535 - val_loss: 0.4275 - val_tp: 80.0000 - val_fp: 5832.0000 - val_tn: 39654.0000 - val_fn: 3.0000 - val_accuracy: 0.8720 - val_precision: 0.0135 - val_recall: 0.9639 - val_auc: 0.9802\n",
-      "Epoch 11/1000\n",
-      "20/20 [==============================] - 1s 40ms/step - loss: 0.3168 - tp: 19224.0000 - fp: 5013.0000 - tn: 15322.0000 - fn: 1401.0000 - accuracy: 0.8434 - precision: 0.7932 - recall: 0.9321 - auc: 0.9552 - val_loss: 0.3969 - val_tp: 80.0000 - val_fp: 4730.0000 - val_tn: 40756.0000 - val_fn: 3.0000 - val_accuracy: 0.8961 - val_precision: 0.0166 - val_recall: 0.9639 - val_auc: 0.9805\n",
-      "Epoch 12/1000\n",
-      "20/20 [==============================] - 1s 40ms/step - loss: 0.3077 - tp: 19028.0000 - fp: 4564.0000 - tn: 16058.0000 - fn: 1310.0000 - accuracy: 0.8566 - precision: 0.8065 - recall: 0.9356 - auc: 0.9593 - val_loss: 0.3695 - val_tp: 80.0000 - val_fp: 3819.0000 - val_tn: 41667.0000 - val_fn: 3.0000 - val_accuracy: 0.9161 - val_precision: 0.0205 - val_recall: 0.9639 - val_auc: 0.9804\n",
-      "Epoch 13/1000\n",
-      "20/20 [==============================] - 1s 40ms/step - loss: 0.2936 - tp: 19047.0000 - fp: 4028.0000 - tn: 16444.0000 - fn: 1441.0000 - accuracy: 0.8665 - precision: 0.8254 - recall: 0.9297 - auc: 0.9597 - val_loss: 0.3461 - val_tp: 79.0000 - val_fp: 3149.0000 - val_tn: 42337.0000 - val_fn: 4.0000 - val_accuracy: 0.9308 - val_precision: 0.0245 - val_recall: 0.9518 - val_auc: 0.9802\n",
-      "Epoch 14/1000\n",
-      "20/20 [==============================] - 1s 38ms/step - loss: 0.2829 - tp: 19087.0000 - fp: 3596.0000 - tn: 16855.0000 - fn: 1422.0000 - accuracy: 0.8775 - precision: 0.8415 - recall: 0.9307 - auc: 0.9619 - val_loss: 0.3266 - val_tp: 79.0000 - val_fp: 2691.0000 - val_tn: 42795.0000 - val_fn: 4.0000 - val_accuracy: 0.9409 - val_precision: 0.0285 - val_recall: 0.9518 - val_auc: 0.9803\n",
-      "Epoch 15/1000\n",
-      "20/20 [==============================] - 1s 39ms/step - loss: 0.2748 - tp: 19020.0000 - fp: 3174.0000 - tn: 17283.0000 - fn: 1483.0000 - accuracy: 0.8863 - precision: 0.8570 - recall: 0.9277 - auc: 0.9627 - val_loss: 0.3095 - val_tp: 79.0000 - val_fp: 2360.0000 - val_tn: 43126.0000 - val_fn: 4.0000 - val_accuracy: 0.9481 - val_precision: 0.0324 - val_recall: 0.9518 - val_auc: 0.9797\n",
-      "Epoch 16/1000\n",
-      "20/20 [==============================] - 1s 40ms/step - loss: 0.2666 - tp: 18890.0000 - fp: 2889.0000 - tn: 17757.0000 - fn: 1424.0000 - accuracy: 0.8947 - precision: 0.8673 - recall: 0.9299 - auc: 0.9653 - val_loss: 0.2945 - val_tp: 78.0000 - val_fp: 2101.0000 - val_tn: 43385.0000 - val_fn: 5.0000 - val_accuracy: 0.9538 - val_precision: 0.0358 - val_recall: 0.9398 - val_auc: 0.9796\n",
-      "Epoch 17/1000\n",
-      "20/20 [==============================] - 1s 38ms/step - loss: 0.2583 - tp: 18959.0000 - fp: 2517.0000 - tn: 17973.0000 - fn: 1511.0000 - accuracy: 0.9017 - precision: 0.8828 - recall: 0.9262 - auc: 0.9657 - val_loss: 0.2817 - val_tp: 78.0000 - val_fp: 1929.0000 - val_tn: 43557.0000 - val_fn: 5.0000 - val_accuracy: 0.9576 - val_precision: 0.0389 - val_recall: 0.9398 - val_auc: 0.9794\n",
-      "Epoch 18/1000\n",
-      "20/20 [==============================] - 1s 46ms/step - loss: 0.2511 - tp: 19104.0000 - fp: 2344.0000 - tn: 18043.0000 - fn: 1469.0000 - accuracy: 0.9069 - precision: 0.8907 - recall: 0.9286 - auc: 0.9678 - val_loss: 0.2704 - val_tp: 78.0000 - val_fp: 1787.0000 - val_tn: 43699.0000 - val_fn: 5.0000 - val_accuracy: 0.9607 - val_precision: 0.0418 - val_recall: 0.9398 - val_auc: 0.9793\n",
-      "Epoch 19/1000\n",
-      "20/20 [==============================] - 1s 40ms/step - loss: 0.2445 - tp: 19183.0000 - fp: 2087.0000 - tn: 18215.0000 - fn: 1475.0000 - accuracy: 0.9130 - precision: 0.9019 - recall: 0.9286 - auc: 0.9693 - val_loss: 0.2598 - val_tp: 78.0000 - val_fp: 1665.0000 - val_tn: 43821.0000 - val_fn: 5.0000 - val_accuracy: 0.9634 - val_precision: 0.0448 - val_recall: 0.9398 - val_auc: 0.9791\n",
-      "Epoch 20/1000\n",
-      "20/20 [==============================] - 1s 39ms/step - loss: 0.2373 - tp: 18995.0000 - fp: 1906.0000 - tn: 18602.0000 - fn: 1457.0000 - accuracy: 0.9179 - precision: 0.9088 - recall: 0.9288 - auc: 0.9712 - val_loss: 0.2500 - val_tp: 78.0000 - val_fp: 1587.0000 - val_tn: 43899.0000 - val_fn: 5.0000 - val_accuracy: 0.9651 - val_precision: 0.0468 - val_recall: 0.9398 - val_auc: 0.9788\n",
-      "Epoch 21/1000\n",
-      "19/20 [===========================>..] - ETA: 0s - loss: 0.2378 - tp: 18121.0000 - fp: 1821.0000 - tn: 17599.0000 - fn: 1371.0000 - accuracy: 0.9180 - precision: 0.9087 - recall: 0.9297 - auc: 0.9714Restoring model weights from the end of the best epoch.\n",
-      "20/20 [==============================] - 1s 40ms/step - loss: 0.2376 - tp: 19083.0000 - fp: 1918.0000 - tn: 18513.0000 - fn: 1446.0000 - accuracy: 0.9179 - precision: 0.9087 - recall: 0.9296 - auc: 0.9714 - val_loss: 0.2401 - val_tp: 78.0000 - val_fp: 1485.0000 - val_tn: 44001.0000 - val_fn: 5.0000 - val_accuracy: 0.9673 - val_precision: 0.0499 - val_recall: 0.9398 - val_auc: 0.9785\n",
-      "Epoch 00021: early stopping\n"
-     ]
-    }
-   ],
-   "source": [
-    "resampled_model = make_model()\n",
-    "resampled_model.load_weights(initial_weights)\n",
-    "\n",
-    "# Reset the bias to zero, since this dataset is balanced.\n",
-    "output_layer = resampled_model.layers[-1]\n",
-    "output_layer.bias.assign([0])\n",
-    "\n",
-    "resampled_history = resampled_model.fit(\n",
-    "    resampled_ds,\n",
-    "    # These are not real epochs\n",
-    "    steps_per_epoch=20,\n",
-    "    epochs=10 * EPOCHS,\n",
-    "    callbacks=[early_stopping],\n",
-    "    validation_data=(val_ds),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "UuJYKv0gpBK1"
-   },
-   "source": [
-    "### Re-check training history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "FMycrpJwn39w"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x720 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_metrics(resampled_history)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "bUuE5HOWZiwP"
-   },
-   "source": [
-    "### Evaluate metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "C0fmHSgXxFdW"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "train_predictions_resampled = #TODO: Your code goes here.\n",
-    "test_predictions_resampled = #TODO: Your code goes here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "FO0mMOYUDWFk"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loss :  0.3960801533448772\n",
-      "tp :  99.0\n",
-      "fp :  5892.0\n",
-      "tn :  50965.0\n",
-      "fn :  6.0\n",
-      "accuracy :  0.8964573\n",
-      "precision :  0.016524788\n",
-      "recall :  0.94285715\n",
-      "auc :  0.9804354\n",
-      "\n",
-      "Legitimate Transactions Detected (True Negatives):  50965\n",
-      "Legitimate Transactions Incorrectly Detected (False Positives):  5892\n",
-      "Fraudulent Transactions Missed (False Negatives):  6\n",
-      "Fraudulent Transactions Detected (True Positives):  99\n",
-      "Total Fraudulent Transactions:  105\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "resampled_results = resampled_model.evaluate(\n",
-    "    test_features, test_labels, batch_size=BATCH_SIZE, verbose=0\n",
-    ")\n",
-    "for name, value in zip(resampled_model.metrics_names, resampled_results):\n",
-    "    print(name, \": \", value)\n",
-    "print()\n",
-    "\n",
-    "plot_cm(test_labels, test_predictions_resampled)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "_xYozM1IIITq"
-   },
-   "source": [
-    "### Plot the ROC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "fye_CiuYrZ1U"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f5eebd220b8>"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_roc(\n",
-    "    \"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0]\n",
-    ")\n",
-    "plot_roc(\n",
-    "    \"Test Baseline\",\n",
-    "    test_labels,\n",
-    "    test_predictions_baseline,\n",
-    "    color=colors[0],\n",
-    "    linestyle=\"--\",\n",
-    ")\n",
-    "\n",
-    "plot_roc(\n",
-    "    \"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1]\n",
-    ")\n",
-    "plot_roc(\n",
-    "    \"Test Weighted\",\n",
-    "    test_labels,\n",
-    "    test_predictions_weighted,\n",
-    "    color=colors[1],\n",
-    "    linestyle=\"--\",\n",
-    ")\n",
-    "\n",
-    "plot_roc(\n",
-    "    \"Train Resampled\",\n",
-    "    train_labels,\n",
-    "    train_predictions_resampled,\n",
-    "    color=colors[2],\n",
-    ")\n",
-    "plot_roc(\n",
-    "    \"Test Resampled\",\n",
-    "    test_labels,\n",
-    "    test_predictions_resampled,\n",
-    "    color=colors[2],\n",
-    "    linestyle=\"--\",\n",
-    ")\n",
-    "plt.legend(loc=\"lower right\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3o3f0ywl8uqW"
-   },
-   "source": [
-    "## Applying this tutorial to your problem\n",
-    "\n",
-    "Imbalanced data classification is an inherantly difficult task since there are so few samples to learn from. You should always start with the data first and do your best to collect as many samples as possible and give substantial thought to what features may be relevant so the model can get the most out of your minority class. At some point your model may struggle to improve and yield the results you want, so it is important to keep in mind the context of your problem and the trade offs between different types of errors."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "imbalanced_data.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/adv_tfdv_facets.ipynb b/notebooks/introduction_to_tensorflow/labs/adv_tfdv_facets.ipynb
deleted file mode 100644
index 6667d22e..00000000
--- a/notebooks/introduction_to_tensorflow/labs/adv_tfdv_facets.ipynb
+++ /dev/null
@@ -1,595 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "HnU0fNSuG2aD"
-   },
-   "source": [
-    "# Lab: Feature Analysis Using TensorFlow Data Validation and Facets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "iVkPBosnIFlu"
-   },
-   "source": [
-    "**Learning Objectives:**\n",
-    "1. Use TFRecords to load record-oriented binary format data\n",
-    "2. Use TFDV to generate statistics and Facets to visualize the data\n",
-    "3. Use the TFDV widget to answer questions\n",
-    "4. Analyze label distribution for subset groups \n",
-    " \n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "Bias can manifest in any part of a typical machine learning pipeline, from an unrepresentative dataset, to learned model representations, to the way in which the results are presented to the user. Errors that result from this bias can disproportionately impact some users more than others.\n",
-    "\n",
-    "[TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started) (TFDV) is one tool you can use to analyze your data to find potential problems in your data, such as missing values and data imbalances - that can lead to Fairness disparities.  The TFDV tool analyzes training and serving data to compute descriptive statistics, infer a schema, and detect data anomalies.  [Facets Overview](https://pair-code.github.io/facets/) provides a succinct visualization of these statistics for easy browsing. Both the TFDV and Facets are tools that are part of the [Fairness Indicators](https://www.tensorflow.org/tfx/fairness_indicators).\n",
-    "\n",
-    "In this notebook, we use TFDV to compute descriptive statistics that provide a quick overview of the data in terms of the features that are present and the shapes of their value distributions.  We use Facets Overview to visualize these statistics using the Civil Comments dataset.  \n",
-    "\n",
-    "Each learning objective will correspond to a __#TODO__ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](../solutions/adv_tfdv_facets.ipynb). \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set up environment variables and load necessary libraries \n",
-    "We will start by importing the necessary dependencies for the libraries we'll be using in this exercise.  First, run the cell below to install Fairness Indicators. \n",
-    "\n",
-    "**NOTE:**  You can ignore the \"pip\" being invoked by an old script wrapper, as it will not affect the lab's functionality.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip3 install fairness-indicators --user"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<strong>Restart the kernel</strong> after you do a pip3 install (click on the <strong>Restart the kernel</strong> button above)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "mdLlKWbIlxYH"
-   },
-   "source": [
-    "Next, import all the dependencies we'll use in this exercise, which include Fairness Indicators, TensorFlow Data Validation (tfdv), and the What-If tool (WIT) Facets Overview."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "6E__x2XkJDFW"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.0.0\n",
-      "<module 'tensorflow' from '/home/jupyter/.local/lib/python3.5/site-packages/tensorflow/__init__.py'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "# %tensorflow_version 2.x\n",
-    "import sys\n",
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # Ignore deprecation warnings\n",
-    "import tempfile\n",
-    "import warnings\n",
-    "from datetime import datetime\n",
-    "\n",
-    "import apache_beam as beam\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import tensorflow as tf\n",
-    "import tensorflow_data_validation as tfdv\n",
-    "import tensorflow_hub as hub\n",
-    "import tensorflow_model_analysis as tfma\n",
-    "from fairness_indicators.examples import util\n",
-    "from tensorflow_model_analysis.addons.fairness.post_export_metrics import (\n",
-    "    fairness_indicators,\n",
-    ")\n",
-    "from tensorflow_model_analysis.addons.fairness.view import widget_view\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "from witwidget.notebook.visualization import WitConfigBuilder, WitWidget\n",
-    "\n",
-    "print(tf.version.VERSION)\n",
-    "print(\n",
-    "    tf\n",
-    ")  # This statement shows us what version of Python we are currently running."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "J3R2QWkru1WN"
-   },
-   "source": [
-    "### About the Civil Comments dataset\n",
-    "\n",
-    "Click below to learn more about the Civil Comments dataset, and how we've preprocessed it for this exercise."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ZZswcJJMCDjU"
-   },
-   "source": [
-    "The Civil Comments dataset comprises approximately 2 million public comments that were submitted to the Civil Comments platform. [Jigsaw](https://jigsaw.google.com/) sponsored the effort to compile and annotate these comments for ongoing [research](https://arxiv.org/abs/1903.04561); they've also hosted competitions on [Kaggle](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) to help classify toxic comments as well as minimize unintended model bias. \n",
-    "\n",
-    "#### Features\n",
-    "\n",
-    "Within the Civil Comments data, a subset of comments are tagged with a variety of identity attributes pertaining to gender, sexual orientation, religion, race, and ethnicity. Each identity annotation column contains a value that represents the percentage of annotators who categorized a comment as containing references to that identity. Multiple identities may be present in a comment.\n",
-    "\n",
-    "**NOTE:** These identity attributes are intended *for evaluation purposes only*, to assess how well a classifier trained solely on the comment text performs on different tag sets.\n",
-    "\n",
-    "To collect these identity labels, each comment was reviewed by up to 10 annotators, who were asked to indicate all identities that were mentioned in the comment. For example, annotators were posed the question: \"What genders are mentioned in the comment?\", and asked to choose all of the following categories that were applicable.\n",
-    "\n",
-    "* Male\n",
-    "* Female\n",
-    "* Transgender\n",
-    "* Other gender\n",
-    "* No gender mentioned\n",
-    "\n",
-    "**NOTE:** *We recognize the limitations of the categories used in the original dataset, and acknowledge that these terms do not encompass the full range of vocabulary used in describing gender.*\n",
-    "\n",
-    "Jigsaw used these ratings to generate an aggregate score for each identity attribute representing the percentage of raters who said the identity was mentioned in the comment. For example, if 10 annotators reviewed a comment, and 6 said that the comment mentioned the identity \"female\" and 0 said that the comment mentioned the identity \"male,\" the comment would receive a `female` score of `0.6` and a `male` score of `0.0`.\n",
-    "\n",
-    "**NOTE:** For the purposes of annotation, a comment was considered to \"mention\" gender if it contained a comment about gender issues (e.g., a discussion about feminism, wage gap between men and women, transgender rights, etc.), gendered language, or gendered insults. Use of \"he,\" \"she,\" or gendered names (e.g., Donald, Margaret) did not require a gender label. \n",
-    "\n",
-    "#### Label\n",
-    "\n",
-    "Each comment was rated by up to 10 annotators for toxicity, who each classified it with one of the following ratings.\n",
-    "\n",
-    "* Very Toxic\n",
-    "* Toxic\n",
-    "* Hard to Say\n",
-    "* Not Toxic\n",
-    "\n",
-    "Again, Jigsaw used these ratings to generate an aggregate toxicity \"score\" for each comment (ranging from `0.0` to `1.0`) to serve as the [label](https://developers.google.com/machine-learning/glossary?utm_source=Colab&utm_medium=fi-colab&utm_campaign=fi-practicum&utm_content=glossary&utm_term=label#label), representing the fraction of annotators who labeled the comment either \"Very Toxic\" or \"Toxic.\" For example, if 10 annotators rated a comment, and 3 of them labeled it \"Very Toxic\" and 5 of them labeled it \"Toxic\", the comment would receive a toxicity score of `0.8`.\n",
-    "\n",
-    "**NOTE:** For more information on the Civil Comments labeling schema, see the [Data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data) section of the Jigsaw Untended Bias in Toxicity Classification Kaggle competition.\n",
-    "\n",
-    "### Preprocessing the data\n",
-    "For the purposes of this exercise, we converted toxicity and identity columns to booleans in order to work with our neural net and metrics calculations. In the preprocessed dataset, we considered any value ≥ 0.5 as True (i.e., a comment is considered toxic if 50% or more crowd raters labeled it as toxic).\n",
-    "\n",
-    "For identity labels, the threshold 0.5 was chosen and the identities were grouped together by their categories. For example, if one comment has `{ male: 0.3, female: 1.0, transgender: 0.0, heterosexual: 0.8, homosexual_gay_or_lesbian: 1.0 }`, after processing, the data will be `{ gender: [female], sexual_orientation: [heterosexual, homosexual_gay_or_lesbian] }`.\n",
-    "\n",
-    "**NOTE:** Missing identity fields were converted to False.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "0YNqAJW5JjZD"
-   },
-   "source": [
-    "### Use TFRecords to load record-oriented binary format data\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "-------------------------------------------------------------------------------------------------------\n",
-    "\n",
-    "The [TFRecord format](https://www.tensorflow.org/tutorials/load_data/tfrecord) is a simple [Protobuf](https://developers.google.com/protocol-buffers)-based format for storing a sequence of binary records. It gives you and your machine learning models to handle arbitrarily large datasets over the network because it:\n",
-    "1. Splits up large files into 100-200MB chunks\n",
-    "2. Stores the results as serialized binary messages for faster ingestion\n",
-    "\n",
-    "If you already have a dataset in TFRecord format, you can use the tf.keras.utils functions for accessing the data (as you will below!). If you want to practice creating your own TFRecord datasets you can do so outside of this lab by [viewing the documentation here](https://www.tensorflow.org/tutorials/load_data/tfrecord). \n",
-    "\n",
-    "#### TODO 1: Use the utility functions tf.keras to download and import our datasets\n",
-    "Run the following cell to download and import the training and validation preprocessed datasets. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "duPWGTQAvYKK"
-   },
-   "outputs": [],
-   "source": [
-    "download_original_data = False  # @param {type:\"boolean\"}\n",
-    "\n",
-    "\n",
-    "# TODO 1\n",
-    "\n",
-    "# TODO: Your code goes here\n",
-    "\n",
-    "# The identity terms list will be grouped together by their categories\n",
-    "# (see 'IDENTITY_COLUMNS') on threshould 0.5. Only the identity term column,\n",
-    "# text column and label column will be kept after processing.\n",
-    "# TODO: Your code goes here\n",
-    "\n",
-    "# TODO 1a\n",
-    "\n",
-    "# TODO: Your code goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "aLup7wY0_Q3K"
-   },
-   "source": [
-    "### Use TFDV to generate statistics and Facets to visualize the data\n",
-    "  \n",
-    "\n",
-    "TensorFlow Data Validation supports data stored in a TFRecord file, a CSV input format, with extensibility for other common formats. You can find the available data decoders [here](https://github.com/tensorflow/data-validation/tree/master/tensorflow_data_validation/coders). In addition, TFDV provides the  [tfdv.generate_statistics_from_dataframe](https://www.tensorflow.org/tfx/data_validation/api_docs/python/tfdv/generate_statistics_from_dataframe) utility function for users with in-memory data represented as a pandas DataFrame.\n",
-    "\n",
-    "In addition to computing a default set of data statistics, TFDV can also compute statistics for semantic domains (e.g., images, text). To enable computation of semantic domain statistics, pass a tfdv.StatsOptions object with enable_semantic_domain_stats set to True to tfdv.generate_statistics_from_tfrecord.Before we train the model, let's do a quick audit of our training data using [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started), so we can better understand our data distribution.  \n",
-    "\n",
-    "#### TODO 2: Use TFDV to get quick statistics on your dataset\n",
-    "\n",
-    "The following cell may take 2–3 minutes to run.  **NOTE:** Please ignore the deprecation warnings.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "vkzcE_g8_m_h"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id='facets-iframe' width=\"100%\" height=\"500px\"></iframe>\n",
-       "        <script>\n",
-       "        facets_iframe = document.getElementById('facets-iframe');\n",
-       "        facets_html = '<script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"><\\/script><link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/master/facets-dist/facets-jupyter.html\"><facets-overview proto-input=\"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\"></facets-overview>';\n",
-       "        facets_iframe.srcdoc = facets_html;\n",
-       "         facets_iframe.id = \"\";\n",
-       "         setTimeout(() => {\n",
-       "           facets_iframe.setAttribute('height', facets_iframe.contentWindow.document.body.offsetHeight + 'px')\n",
-       "         }, 1500)\n",
-       "         </script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# TODO 2\n",
-    "\n",
-    "# The computation of statistics using TFDV.  The returned value is a DatasetFeatureStatisticsList protocol buffer.\n",
-    "# TODO: Your code goes here\n",
-    "\n",
-    "# TODO 2a\n",
-    "\n",
-    "# A visualization of the statistics using Facets Overview.\n",
-    "# TODO: Your code goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wZU1Djze6E-s"
-   },
-   "source": [
-    "### TODO 3:  Use the TensorFlow Data Validation widget above to answer the following questions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ne2_vKAb-XGD"
-   },
-   "source": [
-    "#### **1. How many total examples are in the training dataset?**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "UFBqqnRD-Zkj"
-   },
-   "source": [
-    "#### Solution\n",
-    "\n",
-    "See below solution.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "XSkOfchI-arC"
-   },
-   "source": [
-    "**There are 1.08 million total examples in the training dataset.**\n",
-    "\n",
-    "  The count column tells us how many examples there are for a given feature.  Each feature (`sexual_orientation`, `comment_text`, `gender`, etc.) has 1.08 million examples. The missing column tells us what percentage of examples are missing that feature. \n",
-    "\n",
-    "![Screenshot of first row of Categorical Features table in the TFDV widget, with 1.08 million count of examples and 0% missing examples highlighted](https://developers.google.com/machine-learning/practica/fairness-indicators/colab-images/tfdv_screenshot_exercise1.png)  \n",
-    "  \n",
-    "Each feature is missing from 0% of examples, so we know that the per-feature example count of 1.08 million is also the total number of examples in the dataset."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "_PgFNm6sAZB2"
-   },
-   "source": [
-    "#### **2. How many unique values are there for the `gender` feature? What are they, and what are the frequencies of each of these values?**\n",
-    "\n",
-    "**NOTE #1:** `gender` and the other identity features (`sexual_orientation`, `religion`, `disability`, and `race`) are included in this dataset for evaluation purposes only, so we can assess model performance on different identity slices. The only feature we will use for model training is `comment_text`.\n",
-    "\n",
-    "**NOTE #2:** *We recognize the limitations of the categories used in the original dataset, and acknowledge that these terms do not encompass the full range of vocabulary used in describing gender.*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6KmrCS-uAz0s"
-   },
-   "source": [
-    "#### Solution\n",
-    "\n",
-    "See below solution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wkc7P1nvA4cw"
-   },
-   "source": [
-    "The **unique** column of the **Categorical Features** table tells us that there are 4 unique values for the `gender` feature.\n",
-    "\n",
-    "To view the 4 values and their frequencies, we can click on the **SHOW RAW DATA** button:\n",
-    "\n",
-    "![\"gender\" row of the \"Categorical Data\" table in the TFDV widget, with raw data highlighted.](https://developers.google.com/machine-learning/practica/fairness-indicators/colab-images/tfdv_screenshot_exercise2.png)\n",
-    "\n",
-    "The raw data table shows that there are 32,208 examples with a gender value of `female`, 26,758 examples with a value of `male`, 1,551 examples with a value of `transgender`, and 4 examples with a value of `other gender`.\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "NDUO57bdNUQR"
-   },
-   "source": [
-    "**NOTE:** As described [earlier](#scrollTo=J3R2QWkru1WN), a `gender` feature can contain zero or more of these 4 values, depending on the content of the comment. For example, a comment containing the text \"I am a transgender man\" will have both `transgender` and `male` as `gender` values, whereas a comment that does not reference gender at all will have an empty/false `gender` value."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wX62Ktwp-qoF"
-   },
-   "source": [
-    "#### **3. What percentage of total examples are labeled toxic? Overall, is this a class-balanced dataset (relatively even split of examples between positive and negative classes) or a class-imbalanced dataset (majority of examples are in one class)?**\n",
-    "\n",
-    "**NOTE:** In this dataset, a `toxicity` value of `0` signifies \"not toxic,\"  and a `toxicity` value of `1` signifies \"toxic.\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IvvxNMgM-6A2"
-   },
-   "source": [
-    "#### Solution\n",
-    "\n",
-    "See below solution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "QmCtkzZqOvC2"
-   },
-   "source": [
-    "**7.98 percent of examples are toxic.**\n",
-    "\n",
-    "Under **Numeric Features**, we can see the distribution of values for the `toxicity` feature. 92.02% of examples have a value of 0 (which signifies \"non-toxic\"), so 7.98% of examples are toxic.\n",
-    "\n",
-    "![Screenshot of the \"toxicity\" row in the Numeric Features table in the TFDV widget, highlighting the \"zeros\" column showing that 92.01% of examples have a toxicity value of 0.](https://developers.google.com/machine-learning/practica/fairness-indicators/colab-images/tfdv_screenshot_exercise3.png)\n",
-    "\n",
-    "This is a [**class-imbalanced dataset**](https://developers.google.com/machine-learning/glossary?utm_source=Colab&utm_medium=fi-colab&utm_campaign=fi-practicum&utm_content=glossary&utm_term=class-imbalanced-dataset#class-imbalanced-dataset), as the overwhelming majority of examples (over 90%) are classified as nontoxic."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that there is one numeric feature (count of toxic comments) and six categorical features."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### TODO 4: Analyze label distribution for subset groups "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "9MGLCsVhGWz0"
-   },
-   "source": [
-    "Run the following code to analyze label distribution for the subset of examples that contain a `gender` value**\n",
-    "\n",
-    "\n",
-    "**NOTE:** *The cell run should for just a few minutes*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "cellView": "form",
-    "colab": {},
-    "colab_type": "code",
-    "id": "f5pEWIkgLTKz"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Toxic Gender Examples: 7189\n",
-      "Nontoxic Gender Examples: 41572\n"
-     ]
-    }
-   ],
-   "source": [
-    "# @title Calculate label distribution for gender-related examples\n",
-    "raw_dataset = tf.data.TFRecordDataset(train_tf_file)\n",
-    "\n",
-    "toxic_gender_examples = 0\n",
-    "nontoxic_gender_examples = 0\n",
-    "\n",
-    "# TODO 4\n",
-    "\n",
-    "# There are 1,082,924 examples in the dataset\n",
-    "# TODO: Your code goes here\n",
-    "\n",
-    "# TODO 4a\n",
-    "\n",
-    "# TODO: Your code goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "WJag4cEKNINy"
-   },
-   "source": [
-    "#### **What percentage of `gender` examples are labeled toxic? Compare this percentage to the percentage of total examples that are labeled toxic from #3 above. What, if any, fairness concerns can you identify based on this comparison?**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "-J4hbOhgHZid"
-   },
-   "source": [
-    "#### Solution\n",
-    "\n",
-    "Click below for one possible solution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2KK3VWzkHmJ7"
-   },
-   "source": [
-    "There are 7,189 gender-related examples that are labeled toxic, which represent 14.7% of all gender-related examples.\n",
-    "\n",
-    "The percentage of gender-related examples that are toxic (14.7%) is nearly double the percentage of toxic examples overall (7.98%). In other words, in our dataset, gender-related comments are almost two times more likely than comments overall to be labeled as toxic.\n",
-    "\n",
-    "This skew suggests that a model trained on this dataset might learn a correlation between gender-related content and toxicity. This raises fairness considerations, as the model might be more likely to classify nontoxic comments as toxic if they contain gender terminology, which could lead to [disparate impact](https://developers.google.com/machine-learning/glossary?utm_source=Colab&utm_medium=fi-colab&utm_campaign=fi-practicum&utm_content=glossary&utm_term=disparate-impact#disparate-impact) for gender subgroups. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc.\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-    "http://www.apache.org/licenses/LICENSE-2.0\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [
-    "J3R2QWkru1WN",
-    "UFBqqnRD-Zkj",
-    "6KmrCS-uAz0s",
-    "IvvxNMgM-6A2",
-    "-J4hbOhgHZid",
-    "tGyACRd8oFwP",
-    "FQGWSdrJy08B",
-    "LlkfgynX0yfF",
-    "FBhBsevUOinO",
-    "P5MBQR7EF6ny",
-    "OaL3qgHCcmwG"
-   ],
-   "name": "Copy of Fairness Exercise 1: Explore the Model",
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/basic_intro_logistic_regression.ipynb b/notebooks/introduction_to_tensorflow/labs/basic_intro_logistic_regression.ipynb
deleted file mode 100644
index 8c6c9dc3..00000000
--- a/notebooks/introduction_to_tensorflow/labs/basic_intro_logistic_regression.ipynb
+++ /dev/null
@@ -1,1104 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
-  "colab": {
-   "name": "03_int_logistic_regression (2).ipynb",
-   "provenance": [],
-   "private_outputs": true,
-   "collapsed_sections": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "LDrzLFXE8T1l"
-   },
-   "source": [
-    "# Basic Introduction to Logistic Regression\n",
-    "\n",
-    "## Learning Objectives\n",
-    "\n",
-    "1. Build a model\n",
-    "2. Train this model on example data\n",
-    "3. Use the model to make predictions about unknown data\n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "In this notebook, you use machine learning to *categorize* Iris flowers by species.  It uses TensorFlow to:\n",
-    "\n",
-    "* Use TensorFlow's default eager execution development environment\n",
-    "* Import data with the Datasets API\n",
-    "* Build models and layers with TensorFlow's Keras API\n",
-    "\n",
-    "Here firstly we will Import and parse the dataset, then select the type of model. After that Train the model.\n",
-    "\n",
-    "At last we will Evaluate the model's effectiveness and then use the trained model to make predictions.\n",
-    "\n",
-    "Each learning objective will correspond to a _#TODO_ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/basic_intro_logistic_regression.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ensure the right version of Tensorflow is installed.\n",
-    "!pip freeze | grep tensorflow==2.1 || pip install tensorflow==2.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "1J3AuPBT9gyR"
-   },
-   "source": [
-    "### Configure imports\n",
-    "\n",
-    "Import TensorFlow and the other required Python modules. By default, TensorFlow uses eager execution to evaluate operations immediately, returning concrete values instead of creating a computational graph that is executed later. If you are used to a REPL or the `python` interactive console, this feels familiar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "jElLULrDhQZR",
-    "colab": {}
-   },
-   "source": [
-    "import os\n",
-    "\n",
-    "import matplotlib.pyplot as plt"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "bfV2Dai0Ow2o",
-    "colab": {}
-   },
-   "source": [
-    "import tensorflow as tf"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "g4Wzg69bnwK2",
-    "colab": {}
-   },
-   "source": [
-    "print(f\"TensorFlow version: {tf.__version__}\")\n",
-    "print(f\"Eager execution: {tf.executing_eagerly()}\")"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Zx7wc0LuuxaJ"
-   },
-   "source": [
-    "## The Iris classification problem\n",
-    "\n",
-    "Imagine you are a botanist seeking an automated way to categorize each Iris flower you find. Machine learning provides many algorithms to classify flowers statistically. For instance, a sophisticated machine learning program could classify flowers based on photographs. Our ambitions are more modest—we're going to classify Iris flowers based on the length and width measurements of their [sepals](https://en.wikipedia.org/wiki/Sepal) and [petals](https://en.wikipedia.org/wiki/Petal).\n",
-    "\n",
-    "The Iris genus entails about 300 species, but our program will only classify the following three:\n",
-    "\n",
-    "* Iris setosa\n",
-    "* Iris virginica\n",
-    "* Iris versicolor\n",
-    "\n",
-    "<table>\n",
-    "  <tr><td>\n",
-    "    <img src=\"https://www.tensorflow.org/images/iris_three_species.jpg\"\n",
-    "         alt=\"Petal geometry compared for three iris species: Iris setosa, Iris virginica, and Iris versicolor\">\n",
-    "  </td></tr>\n",
-    "  <tr><td align=\"center\">\n",
-    "    <b>Figure 1.</b> <a href=\"https://commons.wikimedia.org/w/index.php?curid=170298\">Iris setosa</a> (by <a href=\"https://commons.wikimedia.org/wiki/User:Radomil\">Radomil</a>, CC BY-SA 3.0), <a href=\"https://commons.wikimedia.org/w/index.php?curid=248095\">Iris versicolor</a>, (by <a href=\"https://commons.wikimedia.org/wiki/User:Dlanglois\">Dlanglois</a>, CC BY-SA 3.0), and <a href=\"https://www.flickr.com/photos/33397993@N05/3352169862\">Iris virginica</a> (by <a href=\"https://www.flickr.com/photos/33397993@N05\">Frank Mayfield</a>, CC BY-SA 2.0).<br/>&nbsp;\n",
-    "  </td></tr>\n",
-    "</table>\n",
-    "\n",
-    "Fortunately, someone has already created a [dataset of 120 Iris flowers](https://en.wikipedia.org/wiki/Iris_flower_data_set) with the sepal and petal measurements. This is a classic dataset that is popular for beginner machine learning classification problems."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3Px6KAg0Jowz"
-   },
-   "source": [
-    "## Import and parse the training dataset\n",
-    "\n",
-    "Download the dataset file and convert it into a structure that can be used by this Python program.\n",
-    "\n",
-    "### Download the dataset\n",
-    "\n",
-    "Download the training dataset file using the `tf.keras.utils.get_file` function. This returns the file path of the downloaded file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "J6c7uEU9rjRM",
-    "colab": {}
-   },
-   "source": [
-    "train_dataset_url = \"https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv\"\n",
-    "\n",
-    "train_dataset_fp = tf.keras.utils.get_file(\n",
-    "    fname=os.path.basename(train_dataset_url), origin=train_dataset_url\n",
-    ")\n",
-    "\n",
-    "print(f\"Local copy of the dataset file: {train_dataset_fp}\")"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "qnX1-aLors4S"
-   },
-   "source": [
-    "### Inspect the data\n",
-    "\n",
-    "This dataset, `iris_training.csv`, is a plain text file that stores tabular data formatted as comma-separated values (CSV). Use the `head -n5` command to take a peek at the first five entries:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "FQvb_JYdrpPm",
-    "colab": {}
-   },
-   "source": [
-    "!head -n5 {train_dataset_fp}"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "kQhzD6P-uBoq"
-   },
-   "source": [
-    "From this view of the dataset, notice the following:\n",
-    "\n",
-    "1. The first line is a header containing information about the dataset:\n",
-    "  * There are 120 total examples. Each example has four features and one of three possible label names.\n",
-    "2. Subsequent rows are data records, one [example](https://developers.google.com/machine-learning/glossary/#example) per line, where:\n",
-    "  * The first four fields are [features](https://developers.google.com/machine-learning/glossary/#feature): these are the characteristics of an example. Here, the fields hold float numbers representing flower measurements.\n",
-    "  * The last column is the [label](https://developers.google.com/machine-learning/glossary/#label): this is the value we want to predict. For this dataset, it's an integer value of 0, 1, or 2 that corresponds to a flower name.\n",
-    "\n",
-    "Let's write that out in code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "9Edhevw7exl6",
-    "colab": {}
-   },
-   "source": [
-    "# column order in CSV file\n",
-    "column_names = [\n",
-    "    \"sepal_length\",\n",
-    "    \"sepal_width\",\n",
-    "    \"petal_length\",\n",
-    "    \"petal_width\",\n",
-    "    \"species\",\n",
-    "]\n",
-    "\n",
-    "feature_names = column_names[:-1]\n",
-    "label_name = column_names[-1]\n",
-    "\n",
-    "print(f\"Features: {feature_names}\")\n",
-    "print(f\"Label: {label_name}\")"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "CCtwLoJhhDNc"
-   },
-   "source": [
-    "Each label is associated with string name (for example, \"setosa\"), but machine learning typically relies on numeric values. The label numbers are mapped to a named representation, such as:\n",
-    "\n",
-    "* `0`: Iris setosa\n",
-    "* `1`: Iris versicolor\n",
-    "* `2`: Iris virginica\n",
-    "\n",
-    "For more information about features and labels, see the [ML Terminology section of the Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/framing/ml-terminology)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "sVNlJlUOhkoX",
-    "colab": {}
-   },
-   "source": [
-    "class_names = [\"Iris setosa\", \"Iris versicolor\", \"Iris virginica\"]"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "dqPkQExM2Pwt"
-   },
-   "source": [
-    "### Create a `tf.data.Dataset`\n",
-    "\n",
-    "TensorFlow's Dataset API handles many common cases for loading data into a model. This is a high-level API for reading data and transforming it into a form used for training.\n",
-    "\n",
-    "\n",
-    "Since the dataset is a CSV-formatted text file, use the `tf.data.experimental.make_csv_dataset` function to parse the data into a suitable format. Since this function generates data for training models, the default behavior is to shuffle the data (`shuffle=True, shuffle_buffer_size=10000`), and repeat the dataset forever (`num_epochs=None`). We also set the [batch_size](https://developers.google.com/machine-learning/glossary/#batch_size) parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "WsxHnz1ebJ2S",
-    "colab": {}
-   },
-   "source": [
-    "batch_size = 32\n",
-    "\n",
-    "train_dataset = tf.data.experimental.make_csv_dataset(\n",
-    "    train_dataset_fp,\n",
-    "    batch_size,\n",
-    "    column_names=column_names,\n",
-    "    label_name=label_name,\n",
-    "    num_epochs=1,\n",
-    ")"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "gB_RSn62c-3G"
-   },
-   "source": [
-    "The `make_csv_dataset` function returns a `tf.data.Dataset` of `(features, label)` pairs, where `features` is a dictionary: `{'feature_name': value}`\n",
-    "\n",
-    "These `Dataset` objects are iterable. Let's look at a batch of features:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "iDuG94H-C122",
-    "colab": {}
-   },
-   "source": [
-    "features, labels = next(iter(train_dataset))\n",
-    "\n",
-    "print(features)"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "E63mArnQaAGz"
-   },
-   "source": [
-    "Notice that like-features are grouped together, or *batched*. Each example row's fields are appended to the corresponding feature array. Change the `batch_size` to set the number of examples stored in these feature arrays.\n",
-    "\n",
-    "You can start to see some clusters by plotting a few features from the batch:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "me5Wn-9FcyyO",
-    "colab": {}
-   },
-   "source": [
-    "plt.scatter(\n",
-    "    features[\"petal_length\"], features[\"sepal_length\"], c=labels, cmap=\"viridis\"\n",
-    ")\n",
-    "\n",
-    "plt.xlabel(\"Petal length\")\n",
-    "plt.ylabel(\"Sepal length\")\n",
-    "plt.show()"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "YlxpSyHlhT6M"
-   },
-   "source": [
-    "To simplify the model building step, create a function to repackage the features dictionary into a single array with shape: `(batch_size, num_features)`.\n",
-    "\n",
-    "This function uses the `tf.stack` method which takes values from a list of tensors and creates a combined tensor at the specified dimension:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "jm932WINcaGU",
-    "colab": {}
-   },
-   "source": [
-    "def pack_features_vector(features, labels):\n",
-    "    \"\"\"Pack the features into a single array.\"\"\"\n",
-    "    features = tf.stack(list(features.values()), axis=1)\n",
-    "    return features, labels"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "V1Vuph_eDl8x"
-   },
-   "source": [
-    "Then use the `tf.data.Dataset#map` method to pack the `features` of each `(features,label)` pair into the training dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "ZbDkzGZIkpXf",
-    "colab": {}
-   },
-   "source": [
-    "train_dataset = train_dataset.map(pack_features_vector)"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "NLy0Q1xCldVO"
-   },
-   "source": [
-    "The features element of the `Dataset` are now arrays with shape `(batch_size, num_features)`. Let's look at the first few examples:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "kex9ibEek6Tr",
-    "colab": {}
-   },
-   "source": [
-    "features, labels = next(iter(train_dataset))\n",
-    "\n",
-    "print(features[:5])"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "LsaVrtNM3Tx5"
-   },
-   "source": [
-    "## Select the type of model\n",
-    "\n",
-    "### Why model?\n",
-    "\n",
-    "A [model](https://developers.google.com/machine-learning/crash-course/glossary#model) is a relationship between features and the label.  For the Iris classification problem, the model defines the relationship between the sepal and petal measurements and the predicted Iris species. Some simple models can be described with a few lines of algebra, but complex machine learning models have a large number of parameters that are difficult to summarize.\n",
-    "\n",
-    "Could you determine the relationship between the four features and the Iris species *without* using machine learning?  That is, could you use traditional programming techniques (for example, a lot of conditional statements) to create a model?  Perhaps—if you analyzed the dataset long enough to determine the relationships between petal and sepal measurements to a particular species. And this becomes difficult—maybe impossible—on more complicated datasets. A good machine learning approach *determines the model for you*. If you feed enough representative examples into the right machine learning model type, the program will figure out the relationships for you.\n",
-    "\n",
-    "### Select the model\n",
-    "\n",
-    "We need to select the kind of model to train. There are many types of models and picking a good one takes experience. This tutorial uses a neural network to solve the Iris classification problem. [Neural networks](https://developers.google.com/machine-learning/glossary/#neural_network) can find complex relationships between features and the label. It is a highly-structured graph, organized into one or more [hidden layers](https://developers.google.com/machine-learning/glossary/#hidden_layer). Each hidden layer consists of one or more [neurons](https://developers.google.com/machine-learning/glossary/#neuron). There are several categories of neural networks and this program uses a dense, or [fully-connected neural network](https://developers.google.com/machine-learning/glossary/#fully_connected_layer): the neurons in one layer receive input connections from *every* neuron in the previous layer. For example, Figure 2 illustrates a dense neural network consisting of an input layer, two hidden layers, and an output layer:\n",
-    "\n",
-    "<table>\n",
-    "  <tr><td>\n",
-    "    <img src=\"https://www.tensorflow.org/images/custom_estimators/full_network.png\"\n",
-    "         alt=\"A diagram of the network architecture: Inputs, 2 hidden layers, and outputs\">\n",
-    "  </td></tr>\n",
-    "  <tr><td align=\"center\">\n",
-    "    <b>Figure 2.</b> A neural network with features, hidden layers, and predictions.<br/>&nbsp;\n",
-    "  </td></tr>\n",
-    "</table>\n",
-    "\n",
-    "When the model from Figure 2 is trained and fed an unlabeled example, it yields three predictions: the likelihood that this flower is the given Iris species. This prediction is called [inference](https://developers.google.com/machine-learning/crash-course/glossary#inference). For this example, the sum of the output predictions is 1.0. In Figure 2, this prediction breaks down as: `0.02` for *Iris setosa*, `0.95` for *Iris versicolor*, and `0.03` for *Iris virginica*. This means that the model predicts—with 95% probability—that an unlabeled example flower is an *Iris versicolor*."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "W23DIMVPQEBt"
-   },
-   "source": [
-    "### Create a model using Keras\n",
-    "\n",
-    "The TensorFlow `tf.keras` API is the preferred way to create models and layers. This makes it easy to build models and experiment while Keras handles the complexity of connecting everything together.\n",
-    "\n",
-    "The `tf.keras.Sequential` model is a linear stack of layers. Its constructor takes a list of layer instances, in this case, two `tf.keras.layers.Dense` layers with 10 nodes each, and an output layer with 3 nodes representing our label predictions. The first layer's `input_shape` parameter corresponds to the number of features from the dataset, and is required:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Lab Task #1:** Building the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "2fZ6oL2ig3ZK",
-    "colab": {}
-   },
-   "source": [
-    "# TODO 1\n",
-    "# TODO -- Your code here."
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "FHcbEzMpxbHL"
-   },
-   "source": [
-    "The [activation function](https://developers.google.com/machine-learning/crash-course/glossary#activation_function) determines the output shape of each node in the layer. These non-linearities are important—without them the model would be equivalent to a single layer. There are many `tf.keras.activations`, but [ReLU](https://developers.google.com/machine-learning/crash-course/glossary#ReLU) is common for hidden layers.\n",
-    "\n",
-    "The ideal number of hidden layers and neurons depends on the problem and the dataset. Like many aspects of machine learning, picking the best shape of the neural network requires a mixture of knowledge and experimentation. As a rule of thumb, increasing the number of hidden layers and neurons typically creates a more powerful model, which requires more data to train effectively."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2wFKnhWCpDSS"
-   },
-   "source": [
-    "### Using the model\n",
-    "\n",
-    "Let's have a quick look at what this model does to a batch of features:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "xe6SQ5NrpB-I",
-    "colab": {}
-   },
-   "source": [
-    "predictions = model(features)\n",
-    "predictions[:5]"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wxyXOhwVr5S3"
-   },
-   "source": [
-    "Here, each example returns a [logit](https://developers.google.com/machine-learning/crash-course/glossary#logits) for each class.\n",
-    "\n",
-    "To convert these logits to a probability for each class, use the [softmax](https://developers.google.com/machine-learning/crash-course/glossary#softmax) function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "_tRwHZmTNTX2",
-    "colab": {}
-   },
-   "source": [
-    "tf.nn.softmax(predictions[:5])"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "uRZmchElo481"
-   },
-   "source": [
-    "Taking the `tf.argmax` across classes gives us the predicted class index. But, the model hasn't been trained yet, so these aren't good predictions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "-Jzm_GoErz8B",
-    "colab": {}
-   },
-   "source": [
-    "print(f\"Prediction: {tf.argmax(predictions, axis=1)}\")\n",
-    "print(f\"Labels: {labels}\")"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Vzq2E5J2QMtw"
-   },
-   "source": [
-    "## Train the model\n",
-    "\n",
-    "[Training](https://developers.google.com/machine-learning/crash-course/glossary#training) is the stage of machine learning when the model is gradually optimized, or the model *learns* the dataset. The goal is to learn enough about the structure of the training dataset to make predictions about unseen data. If you learn *too much* about the training dataset, then the predictions only work for the data it has seen and will not be generalizable. This problem is called [overfitting](https://developers.google.com/machine-learning/crash-course/glossary#overfitting)—it's like memorizing the answers instead of understanding how to solve a problem.\n",
-    "\n",
-    "The Iris classification problem is an example of [supervised machine learning](https://developers.google.com/machine-learning/glossary/#supervised_machine_learning): the model is trained from examples that contain labels. In [unsupervised machine learning](https://developers.google.com/machine-learning/glossary/#unsupervised_machine_learning), the examples don't contain labels. Instead, the model typically finds patterns among the features."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "RaKp8aEjKX6B"
-   },
-   "source": [
-    "### Define the loss and gradient function\n",
-    "\n",
-    "Both training and evaluation stages need to calculate the model's [loss](https://developers.google.com/machine-learning/crash-course/glossary#loss). This measures how off a model's predictions are from the desired label, in other words, how bad the model is performing. We want to minimize, or optimize, this value.\n",
-    "\n",
-    "Our model will calculate its loss using the `tf.keras.losses.SparseCategoricalCrossentropy` function which takes the model's class probability predictions and the desired label, and returns the average loss across the examples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Lab Task #2:** Training Model on example data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "QOsi6b-1CXIn",
-    "colab": {}
-   },
-   "source": [
-    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "tMAT4DcMPwI-",
-    "colab": {}
-   },
-   "source": [
-    "def loss(model, x, y, training):\n",
-    "# TODO 2\n",
-    "# TODO -- Your code here."
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3IcPqA24QM6B"
-   },
-   "source": [
-    "Use the `tf.GradientTape` context to calculate the [gradients](https://developers.google.com/machine-learning/crash-course/glossary#gradient) used to optimize your model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "x57HcKWhKkei",
-    "colab": {}
-   },
-   "source": [
-    "def grad(model, inputs, targets):\n",
-    "    with tf.GradientTape() as tape:\n",
-    "        loss_value = loss(model, inputs, targets, training=True)\n",
-    "    return loss_value, tape.gradient(loss_value, model.trainable_variables)"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "lOxFimtlKruu"
-   },
-   "source": [
-    "### Create an optimizer\n",
-    "\n",
-    "An [optimizer](https://developers.google.com/machine-learning/crash-course/glossary#optimizer) applies the computed gradients to the model's variables to minimize the `loss` function. You can think of the loss function as a curved surface (see Figure 3) and we want to find its lowest point by walking around. The gradients point in the direction of steepest ascent—so we'll travel the opposite way and move down the hill. By iteratively calculating the loss and gradient for each batch, we'll adjust the model during training. Gradually, the model will find the best combination of weights and bias to minimize loss. And the lower the loss, the better the model's predictions.\n",
-    "\n",
-    "<table>\n",
-    "  <tr><td>\n",
-    "    <img src=\"https://cs231n.github.io/assets/nn3/opt1.gif\" width=\"70%\"\n",
-    "         alt=\"Optimization algorithms visualized over time in 3D space.\">\n",
-    "  </td></tr>\n",
-    "  <tr><td align=\"center\">\n",
-    "    <b>Figure 3.</b> Optimization algorithms visualized over time in 3D space.<br/>(Source: <a href=\"http://cs231n.github.io/neural-networks-3/\">Stanford class CS231n</a>, MIT License, Image credit: <a href=\"https://twitter.com/alecrad\">Alec Radford</a>)\n",
-    "  </td></tr>\n",
-    "</table>\n",
-    "\n",
-    "TensorFlow has many optimization algorithms available for training. This model uses the `tf.keras.optimizers.SGD` that implements the [stochastic gradient descent](https://developers.google.com/machine-learning/crash-course/glossary#gradient_descent) (SGD) algorithm. The `learning_rate` sets the step size to take for each iteration down the hill. This is a *hyperparameter* that you'll commonly adjust to achieve better results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "XkUd6UiZa_dF"
-   },
-   "source": [
-    "Let's setup the optimizer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "8xxi2NNGKwG_",
-    "colab": {}
-   },
-   "source": [
-    "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "pJVRZ0hP52ZB"
-   },
-   "source": [
-    "We'll use this to calculate a single optimization step:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "rxRNTFVe56RG",
-    "colab": {}
-   },
-   "source": [
-    "loss_value, grads = grad(model, features, labels)\n",
-    "\n",
-    "print(\n",
-    "    \"Step: {}, Initial Loss: {}\".format(\n",
-    "        optimizer.iterations.numpy(), loss_value.numpy()\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
-    "\n",
-    "print(\n",
-    "    \"Step: {},Loss: {}\".format(\n",
-    "        optimizer.iterations.numpy(),\n",
-    "        loss(model, features, labels, training=True).numpy(),\n",
-    "    )\n",
-    ")"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "7Y2VSELvwAvW"
-   },
-   "source": [
-    "### Training loop\n",
-    "\n",
-    "With all the pieces in place, the model is ready for training! A training loop feeds the dataset examples into the model to help it make better predictions. The following code block sets up these training steps:\n",
-    "\n",
-    "1. Iterate each *epoch*. An epoch is one pass through the dataset.\n",
-    "2. Within an epoch, iterate over each example in the training `Dataset` grabbing its *features* (`x`) and *label* (`y`).\n",
-    "3. Using the example's features, make a prediction and compare it with the label. Measure the inaccuracy of the prediction and use that to calculate the model's loss and gradients.\n",
-    "4. Use an `optimizer` to update the model's variables.\n",
-    "5. Keep track of some stats for visualization.\n",
-    "6. Repeat for each epoch.\n",
-    "\n",
-    "The `num_epochs` variable is the number of times to loop over the dataset collection. Counter-intuitively, training a model longer does not guarantee a better model. `num_epochs` is a [hyperparameter](https://developers.google.com/machine-learning/glossary/#hyperparameter) that you can tune. Choosing the right number usually requires both experience and experimentation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "AIgulGRUhpto",
-    "colab": {}
-   },
-   "source": [
-    "## Note: Rerunning this cell uses the same model variables\n",
-    "\n",
-    "# Keep results for plotting\n",
-    "train_loss_results = []\n",
-    "train_accuracy_results = []\n",
-    "\n",
-    "num_epochs = 201\n",
-    "\n",
-    "for epoch in range(num_epochs):\n",
-    "    epoch_loss_avg = tf.keras.metrics.Mean()\n",
-    "    epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\n",
-    "\n",
-    "    # Training loop - using batches of 32\n",
-    "    for x, y in train_dataset:\n",
-    "        # Optimize the model\n",
-    "        loss_value, grads = grad(model, x, y)\n",
-    "        optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
-    "\n",
-    "        # Track progress\n",
-    "        epoch_loss_avg.update_state(loss_value)  # Add current batch loss\n",
-    "        # Compare predicted label to actual label\n",
-    "        # training=True is needed only if there are layers with different\n",
-    "        # behavior during training versus inference (e.g. Dropout).\n",
-    "        epoch_accuracy.update_state(y, model(x, training=True))\n",
-    "\n",
-    "    # End epoch\n",
-    "    train_loss_results.append(epoch_loss_avg.result())\n",
-    "    train_accuracy_results.append(epoch_accuracy.result())\n",
-    "\n",
-    "    if epoch % 50 == 0:\n",
-    "        print(\n",
-    "            \"Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}\".format(\n",
-    "                epoch, epoch_loss_avg.result(), epoch_accuracy.result()\n",
-    "            )\n",
-    "        )"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2FQHVUnm_rjw"
-   },
-   "source": [
-    "### Visualize the loss function over time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "j3wdbmtLVTyr"
-   },
-   "source": [
-    "While it's helpful to print out the model's training progress, it's often *more* helpful to see this progress. [TensorBoard](https://www.tensorflow.org/tensorboard) is a nice visualization tool that is packaged with TensorFlow, but we can create basic charts using the `matplotlib` module.\n",
-    "\n",
-    "Interpreting these charts takes some experience, but you really want to see the *loss* go down and the *accuracy* go up:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "agjvNd2iUGFn",
-    "colab": {}
-   },
-   "source": [
-    "fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))\n",
-    "fig.suptitle(\"Training Metrics\")\n",
-    "\n",
-    "axes[0].set_ylabel(\"Loss\", fontsize=14)\n",
-    "axes[0].plot(train_loss_results)\n",
-    "\n",
-    "axes[1].set_ylabel(\"Accuracy\", fontsize=14)\n",
-    "axes[1].set_xlabel(\"Epoch\", fontsize=14)\n",
-    "axes[1].plot(train_accuracy_results)\n",
-    "plt.show()"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Zg8GoMZhLpGH"
-   },
-   "source": [
-    "## Evaluate the model's effectiveness\n",
-    "\n",
-    "Now that the model is trained, we can get some statistics on its performance.\n",
-    "\n",
-    "*Evaluating* means determining how effectively the model makes predictions. To determine the model's effectiveness at Iris classification, pass some sepal and petal measurements to the model and ask the model to predict what Iris species they represent. Then compare the model's predictions against the actual label.  For example, a model that picked the correct species on half the input examples has an [accuracy](https://developers.google.com/machine-learning/glossary/#accuracy) of `0.5`. Figure 4 shows a slightly more effective model, getting 4 out of 5 predictions correct at 80% accuracy:\n",
-    "\n",
-    "<table cellpadding=\"8\" border=\"0\">\n",
-    "  <colgroup>\n",
-    "    <col span=\"4\" >\n",
-    "    <col span=\"1\" bgcolor=\"lightblue\">\n",
-    "    <col span=\"1\" bgcolor=\"lightgreen\">\n",
-    "  </colgroup>\n",
-    "  <tr bgcolor=\"lightgray\">\n",
-    "    <th colspan=\"4\">Example features</th>\n",
-    "    <th colspan=\"1\">Label</th>\n",
-    "    <th colspan=\"1\" >Model prediction</th>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>5.9</td><td>3.0</td><td>4.3</td><td>1.5</td><td align=\"center\">1</td><td align=\"center\">1</td>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>6.9</td><td>3.1</td><td>5.4</td><td>2.1</td><td align=\"center\">2</td><td align=\"center\">2</td>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>5.1</td><td>3.3</td><td>1.7</td><td>0.5</td><td align=\"center\">0</td><td align=\"center\">0</td>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>6.0</td> <td>3.4</td> <td>4.5</td> <td>1.6</td> <td align=\"center\">1</td><td align=\"center\" bgcolor=\"red\">2</td>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>5.5</td><td>2.5</td><td>4.0</td><td>1.3</td><td align=\"center\">1</td><td align=\"center\">1</td>\n",
-    "  </tr>\n",
-    "  <tr><td align=\"center\" colspan=\"6\">\n",
-    "    <b>Figure 4.</b> An Iris classifier that is 80% accurate.<br/>&nbsp;\n",
-    "  </td></tr>\n",
-    "</table>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "z-EvK7hGL0d8"
-   },
-   "source": [
-    "### Setup the test dataset\n",
-    "\n",
-    "Evaluating the model is similar to training the model. The biggest difference is the examples come from a separate [test set](https://developers.google.com/machine-learning/crash-course/glossary#test_set) rather than the training set. To fairly assess a model's effectiveness, the examples used to evaluate a model must be different from the examples used to train the model.\n",
-    "\n",
-    "The setup for the test `Dataset` is similar to the setup for training `Dataset`. Download the CSV text file and parse that values, then give it a little shuffle:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "Ps3_9dJ3Lodk",
-    "colab": {}
-   },
-   "source": [
-    "test_url = (\n",
-    "    \"https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv\"\n",
-    ")\n",
-    "\n",
-    "test_fp = tf.keras.utils.get_file(\n",
-    "    fname=os.path.basename(test_url), origin=test_url\n",
-    ")"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "SRMWCu30bnxH",
-    "colab": {}
-   },
-   "source": [
-    "test_dataset = tf.data.experimental.make_csv_dataset(\n",
-    "    test_fp,\n",
-    "    batch_size,\n",
-    "    column_names=column_names,\n",
-    "    label_name=\"species\",\n",
-    "    num_epochs=1,\n",
-    "    shuffle=False,\n",
-    ")\n",
-    "\n",
-    "test_dataset = test_dataset.map(pack_features_vector)"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "HFuOKXJdMAdm"
-   },
-   "source": [
-    "### Evaluate the model on the test dataset\n",
-    "\n",
-    "Unlike the training stage, the model only evaluates a single [epoch](https://developers.google.com/machine-learning/glossary/#epoch) of the test data. In the following code cell, we iterate over each example in the test set and compare the model's prediction against the actual label. This is used to measure the model's accuracy across the entire test set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "Tw03-MK1cYId",
-    "colab": {}
-   },
-   "source": [
-    "test_accuracy = tf.keras.metrics.Accuracy()\n",
-    "\n",
-    "for x, y in test_dataset:\n",
-    "    # training=False is needed only if there are layers with different\n",
-    "    # behavior during training versus inference (e.g. Dropout).\n",
-    "    logits = model(x, training=False)\n",
-    "    prediction = tf.argmax(logits, axis=1, output_type=tf.int32)\n",
-    "    test_accuracy(prediction, y)\n",
-    "\n",
-    "print(f\"Test set accuracy: {test_accuracy.result():.3%}\")"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "HcKEZMtCOeK-"
-   },
-   "source": [
-    "We can see on the last batch, for example, the model is usually correct:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "uNwt2eMeOane",
-    "colab": {}
-   },
-   "source": [
-    "tf.stack([y, prediction], axis=1)"
-   ],
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "7Li2r1tYvW7S"
-   },
-   "source": [
-    "## Use the trained model to make predictions\n",
-    "\n",
-    "We've trained a model and \"proven\" that it's good—but not perfect—at classifying Iris species. Now let's use the trained model to make some predictions on [unlabeled examples](https://developers.google.com/machine-learning/glossary/#unlabeled_example); that is, on examples that contain features but not a label.\n",
-    "\n",
-    "In real-life, the unlabeled examples could come from lots of different sources including apps, CSV files, and data feeds. For now, we're going to manually provide three unlabeled examples to predict their labels. Recall, the label numbers are mapped to a named representation as:\n",
-    "\n",
-    "* `0`: Iris setosa\n",
-    "* `1`: Iris versicolor\n",
-    "* `2`: Iris virginica"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Lab Task #3:** Use model to make predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab_type": "code",
-    "id": "kesTS5Lzv-M2",
-    "colab": {}
-   },
-   "source": [
-    "# TODO 3\n",
-    "# TODO -- Your code here."
-   ],
-   "execution_count": null,
-   "outputs": []
-  }
- ]
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/feat.cols_tf.data.ipynb b/notebooks/introduction_to_tensorflow/labs/feat.cols_tf.data.ipynb
deleted file mode 100644
index 3244685f..00000000
--- a/notebooks/introduction_to_tensorflow/labs/feat.cols_tf.data.ipynb
+++ /dev/null
@@ -1,1083 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rNdWfPXCjTjY"
-   },
-   "source": [
-    "# Introduction to Feature Columns \n",
-    "**Learning Objectives**\n",
-    "\n",
-    "\n",
-    "1. Load a CSV file using [Pandas](https://pandas.pydata.org/)\n",
-    "2. Create an input pipeline using tf.data\n",
-    "3. Create multiple types of feature columns\n",
-    "\n",
-    " \n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "In this notebook, you classify structured data (e.g. tabular data in a CSV file) using [feature columns](https://www.tensorflow.org/guide/feature_columns).  Feature columns serve as a bridge to map from columns in a CSV file to features used to train a model. In a subsequent lab, we will use [Keras](https://www.tensorflow.org/guide/keras) to define the model.\n",
-    "\n",
-    "Each learning objective will correspond to a __#TODO__ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](../solutions/feat.cols_tf.data.ipynb). \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "K1y4OHpGgss7"
-   },
-   "source": [
-    "## The Dataset\n",
-    "\n",
-    "We will use a small [dataset](https://archive.ics.uci.edu/ml/datasets/heart+Disease) provided by the Cleveland Clinic Foundation for Heart Disease. There are several hundred rows in the CSV. Each row describes   a patient, and each column describes an attribute. We will use this information to predict whether a patient has heart disease, which in this dataset is a binary classification task.\n",
-    "\n",
-    "Following is a [description](https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/heart-disease.names) of this dataset. Notice there are both numeric and categorical columns.\n",
-    "\n",
-    ">Column| Description| Feature Type | Data Type\n",
-    ">------------|--------------------|----------------------|-----------------\n",
-    ">Age | Age in years | Numerical | integer\n",
-    ">Sex | (1 = male; 0 = female) | Categorical | integer\n",
-    ">CP | Chest pain type (0, 1, 2, 3, 4) | Categorical | integer\n",
-    ">Trestbpd | Resting blood pressure (in mm Hg on admission to the hospital) | Numerical | integer\n",
-    ">Chol | Serum cholestoral in mg/dl | Numerical | integer\n",
-    ">FBS | (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) | Categorical | integer\n",
-    ">RestECG | Resting electrocardiographic results (0, 1, 2) | Categorical | integer\n",
-    ">Thalach | Maximum heart rate achieved | Numerical | integer\n",
-    ">Exang | Exercise induced angina (1 = yes; 0 = no) | Categorical | integer\n",
-    ">Oldpeak | ST depression induced by exercise relative to rest | Numerical | float\n",
-    ">Slope | The slope of the peak exercise ST segment | Numerical | integer\n",
-    ">CA | Number of major vessels (0-3) colored by flourosopy | Numerical | integer\n",
-    ">Thal | 3 = normal; 6 = fixed defect; 7 = reversable defect | Categorical | string\n",
-    ">Target | Diagnosis of heart disease (1 = true; 0 = false) | Classification | integer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "VxyBFc_kKazA"
-   },
-   "source": [
-    "## Import TensorFlow and other libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "9dEreb4QKizj"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow version:  2.1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from tensorflow import feature_column\n",
-    "from tensorflow.keras import layers\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "KCEhSZcULZ9n"
-   },
-   "source": [
-    "## Lab Task 1: Use Pandas to create a dataframe\n",
-    "\n",
-    "[Pandas](https://pandas.pydata.org/) is a Python library with many helpful utilities for loading and working with structured data. We will use Pandas to download the dataset from a URL, and load it into a dataframe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "REZ57BXCLdfG"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>cp</th>\n",
-       "      <th>trestbps</th>\n",
-       "      <th>chol</th>\n",
-       "      <th>fbs</th>\n",
-       "      <th>restecg</th>\n",
-       "      <th>thalach</th>\n",
-       "      <th>exang</th>\n",
-       "      <th>oldpeak</th>\n",
-       "      <th>slope</th>\n",
-       "      <th>ca</th>\n",
-       "      <th>thal</th>\n",
-       "      <th>target</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>63</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>145</td>\n",
-       "      <td>233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>150</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>fixed</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>67</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>160</td>\n",
-       "      <td>286</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>108</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>67</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>120</td>\n",
-       "      <td>229</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>129</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>reversible</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>37</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>130</td>\n",
-       "      <td>250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>187</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>41</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>130</td>\n",
-       "      <td>204</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>172</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
-       "0   63    1   1       145   233    1        2      150      0      2.3      3   \n",
-       "1   67    1   4       160   286    0        2      108      1      1.5      2   \n",
-       "2   67    1   4       120   229    0        2      129      1      2.6      2   \n",
-       "3   37    1   3       130   250    0        0      187      0      3.5      3   \n",
-       "4   41    0   2       130   204    0        2      172      0      1.4      1   \n",
-       "\n",
-       "   ca        thal  target  \n",
-       "0   0       fixed       0  \n",
-       "1   3      normal       1  \n",
-       "2   2  reversible       0  \n",
-       "3   0      normal       0  \n",
-       "4   0      normal       0  "
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "URL = \"https://storage.googleapis.com/applied-dl/heart.csv\"\n",
-    "dataframe = pd.read_csv(URL)\n",
-    "dataframe.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 303 entries, 0 to 302\n",
-      "Data columns (total 14 columns):\n",
-      "age         303 non-null int64\n",
-      "sex         303 non-null int64\n",
-      "cp          303 non-null int64\n",
-      "trestbps    303 non-null int64\n",
-      "chol        303 non-null int64\n",
-      "fbs         303 non-null int64\n",
-      "restecg     303 non-null int64\n",
-      "thalach     303 non-null int64\n",
-      "exang       303 non-null int64\n",
-      "oldpeak     303 non-null float64\n",
-      "slope       303 non-null int64\n",
-      "ca          303 non-null int64\n",
-      "thal        303 non-null object\n",
-      "target      303 non-null int64\n",
-      "dtypes: float64(1), int64(12), object(1)\n",
-      "memory usage: 33.3+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "dataframe.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Split the dataframe into train, validation, and test\n",
-    "\n",
-    "The dataset we downloaded was a single CSV file. As a best practice, Complete the below TODO by splitting this into train, validation, and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "193 train examples\n",
-      "49 validation examples\n",
-      "61 test examples\n"
-     ]
-    }
-   ],
-   "source": [
-    "# TODO 1a\n",
-    "# TODO: Your code goes here\n",
-    "print(len(train), \"train examples\")\n",
-    "print(len(val), \"validation examples\")\n",
-    "print(len(test), \"test examples\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lab Task 2: Create an input pipeline using tf.data\n",
-    "\n",
-    "Next, we will wrap the dataframes with [tf.data](https://www.tensorflow.org/guide/datasets). This will enable us  to use feature columns as a bridge to map from the columns in the Pandas dataframe to features used to train a model. If we were working with a very large CSV file (so large that it does not fit into memory), we would use tf.data to read it from disk directly. That is not covered in this lab."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Complete the `TODOs` in the below cells using `df_to_dataset` function. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# A utility method to create a tf.data dataset from a Pandas Dataframe\n",
-    "def df_to_dataset(dataframe, shuffle=True, batch_size=32):\n",
-    "  dataframe = dataframe.copy()\n",
-    "  labels = dataframe.pop('target')\n",
-    "  ds = # TODO 2a: Your code goes here\n",
-    "  if shuffle:\n",
-    "    ds = ds.shuffle(buffer_size=len(dataframe))\n",
-    "  ds = ds.batch(batch_size)\n",
-    "  return ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 5  # A small batch sized is used for demonstration purposes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO 2b\n",
-    "train_ds = # Your code goes here\n",
-    "val_ds = # Your code goes here\n",
-    "test_ds = # Your code goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Understand the input pipeline\n",
-    "\n",
-    "Now that we have created the input pipeline, let's call it to see the format of the data it returns. We have used a small batch size to keep the output readable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Every feature: ['ca', 'thal', 'trestbps', 'restecg', 'oldpeak', 'exang', 'sex', 'age', 'slope', 'chol', 'fbs', 'thalach', 'cp']\n",
-      "A batch of ages: tf.Tensor([49 68 41 51 63], shape=(5,), dtype=int32)\n",
-      "A batch of targets: tf.Tensor([0 0 0 0 0], shape=(5,), dtype=int32)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for feature_batch, label_batch in train_ds.take(1):\n",
-    "    print(\"Every feature:\", list(feature_batch.keys()))\n",
-    "    print(\"A batch of ages:\", feature_batch[\"age\"])\n",
-    "    print(\"A batch of targets:\", label_batch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ttIvgLRaNoOQ"
-   },
-   "source": [
-    "## Lab Task 3: Demonstrate several types of feature column\n",
-    "TensorFlow provides many types of feature columns. In this section, we will create several types of feature columns, and demonstrate how they transform a column from the dataframe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We will use this batch to demonstrate several types of feature columns\n",
-    "example_batch = next(iter(train_ds))[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# A utility method to create a feature column\n",
-    "# and to transform a batch of data\n",
-    "def demo(feature_column):\n",
-    "    feature_layer = layers.DenseFeatures(feature_column)\n",
-    "    print(feature_layer(example_batch).numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Q7OEKe82N-Qb"
-   },
-   "source": [
-    "### Numeric columns\n",
-    "The output of a feature column becomes the input to the model. A [numeric column](https://www.tensorflow.org/api_docs/python/tf/feature_column/numeric_column) is the simplest type of column. It is used to represent real valued features. When using this column, your model will receive the column value from the dataframe unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NumericColumn(key='age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)\n"
-     ]
-    }
-   ],
-   "source": [
-    "age = feature_column.numeric_column(\"age\")\n",
-    "tf.feature_column.numeric_column\n",
-    "print(age)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "7a6ddSyzOKpq"
-   },
-   "source": [
-    "### Let's have a look at the output:\n",
-    "\n",
-    "#### key='age'\n",
-    "A unique string identifying the input feature. It is used as the column name and the dictionary key for feature parsing configs, feature Tensor objects, and feature columns.\n",
-    "\n",
-    "#### shape=(1,)\n",
-    "In the heart disease dataset, most columns from the dataframe are numeric.  Recall that tensors have a rank.  \"Age\" is a \"vector\" or \"rank-1\" tensor, which is like a list of values. A vector has 1-axis, thus the shape will always look like this:  shape=(3,), where 3 is a scalar (or single number) and with 1-axis.  \n",
-    "\n",
-    "#### default_value=None\n",
-    "A single value compatible with dtype or an iterable of values compatible with dtype which the column takes on during tf.Example parsing if data is missing. A default value of None will cause tf.io.parse_example to fail if an example does not contain this column. If a single value is provided, the same value will be applied as the default value for every item. If an iterable of values is provided, the shape of the default_value should be equal to the given shape.\n",
-    "\n",
-    "#### dtype=tf.float32\n",
-    "defines the type of values. Default value is tf.float32. Must be a non-quantized, real integer or floating point type.\n",
-    "\n",
-    "\n",
-    "#### normalizer_fn=None\n",
-    "If not None, a function that can be used to normalize the value of the tensor after default_value is applied for parsing. Normalizer function takes the input Tensor as its argument, and returns the output Tensor. (e.g. lambda x: (x - 3.0) / 4.2). Please note that even though the most common use case of this function is normalization, it can be used for any kind of Tensorflow transformations.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer dense_features_22 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
-      "\n",
-      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
-      "\n",
-      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
-      "\n",
-      "[[60.]\n",
-      " [58.]\n",
-      " [55.]\n",
-      " [54.]\n",
-      " [51.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "demo(age)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IcSxUoYgOlA1"
-   },
-   "source": [
-    "### Bucketized columns\n",
-    "Often, you don't want to feed a number directly into the model, but instead split its value into different categories based on numerical ranges. Consider raw data that represents a person's age. Instead of representing age as a numeric column, we could split the age into several buckets using a [bucketized column](https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column). Notice the one-hot values below describe which age range each row matches."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "wJ4Wt3SAOpTQ"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer dense_features_23 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
-      "\n",
-      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
-      "\n",
-      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
-      "\n",
-      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "age_buckets = tf.feature_column.bucketized_column(\n",
-    "    age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]\n",
-    ")\n",
-    "demo(____)  # TODO 3a: Replace the blanks with a correct value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "r1tArzewPb-b"
-   },
-   "source": [
-    "### Categorical columns\n",
-    "In this dataset, thal is represented as a string (e.g. 'fixed', 'normal', or 'reversible'). We cannot feed strings directly to a model. Instead, we must first map them to numeric values. The categorical vocabulary columns provide a way to represent strings as a one-hot vector (much like you have seen above with age buckets). The vocabulary can be passed as a list using [categorical_column_with_vocabulary_list](https://www.tensorflow.org/api_docs/python/tf/feature_column/categorical_column_with_vocabulary_list), or loaded from a file using [categorical_column_with_vocabulary_file](https://www.tensorflow.org/api_docs/python/tf/feature_column/categorical_column_with_vocabulary_file)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "DJ6QnSHkPtOC"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer dense_features_24 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
-      "\n",
-      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
-      "\n",
-      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
-      "\n",
-      "[[0. 0. 1.]\n",
-      " [0. 1. 0.]\n",
-      " [0. 0. 1.]\n",
-      " [0. 1. 0.]\n",
-      " [0. 1. 0.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "thal = tf.feature_column.categorical_column_with_vocabulary_list(\n",
-    "    \"thal\", [\"fixed\", \"normal\", \"reversible\"]\n",
-    ")\n",
-    "\n",
-    "thal_one_hot = tf.feature_column.indicator_column(thal)\n",
-    "demo(thal_one_hot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "dxQloQ9jOoXL"
-   },
-   "source": [
-    "In a more complex dataset, many columns would be categorical (e.g. strings). Feature columns are most valuable when working with categorical data. Although there is only one categorical column in this dataset, we will use it to demonstrate several important types of feature columns that you could use when working with other datasets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "LEFPjUr6QmwS"
-   },
-   "source": [
-    "### Embedding columns\n",
-    "Suppose instead of having just a few possible strings, we have thousands (or more) values per category. For a number of reasons, as the number of categories grow large, it becomes infeasible to train a neural network using one-hot encodings. We can use an embedding column to overcome this limitation. Instead of representing the data as a one-hot vector of many dimensions, an [embedding column](https://www.tensorflow.org/api_docs/python/tf/feature_column/embedding_column) represents that data as a lower-dimensional, dense vector in which each cell can contain any number, not just 0 or 1. The size of the embedding (8, in the example below) is a parameter that must be tuned.\n",
-    "\n",
-    "Key point: using an embedding column is best when a categorical column has many possible values. We are using one here for demonstration purposes, so you have a complete example you can modify for a different dataset in the future."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "hSlohmr2Q_UU"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer dense_features_25 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
-      "\n",
-      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
-      "\n",
-      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
-      "\n",
-      "[[ 0.26216975 -0.66194284  0.33328214 -0.09756625  0.20408471  0.57926923\n",
-      "  -0.07685163  0.4386801 ]\n",
-      " [-0.24602154  0.0877578   0.07975551  0.34634778  0.2708743  -0.6707659\n",
-      "  -0.15825593 -0.08179379]\n",
-      " [ 0.26216975 -0.66194284  0.33328214 -0.09756625  0.20408471  0.57926923\n",
-      "  -0.07685163  0.4386801 ]\n",
-      " [-0.24602154  0.0877578   0.07975551  0.34634778  0.2708743  -0.6707659\n",
-      "  -0.15825593 -0.08179379]\n",
-      " [-0.24602154  0.0877578   0.07975551  0.34634778  0.2708743  -0.6707659\n",
-      "  -0.15825593 -0.08179379]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Notice the input to the embedding column is the categorical column\n",
-    "# we previously created\n",
-    "thal_embedding = tf.feature_column.embedding_column(thal, dimension=8)\n",
-    "demo(thal_embedding)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "urFCAvTVRMpB"
-   },
-   "source": [
-    "### Hashed feature columns\n",
-    "\n",
-    "Another way to represent a categorical column with a large number of values is to use a [categorical_column_with_hash_bucket](https://www.tensorflow.org/api_docs/python/tf/feature_column/categorical_column_with_hash_bucket). This feature column calculates a hash value of the input, then selects one of the `hash_bucket_size` buckets to encode a string. When using this column, you do not need to provide the vocabulary, and you can choose to make the number of hash_buckets significantly smaller than the number of actual categories to save space.\n",
-    "\n",
-    "Key point: An important downside of this technique is that there may be collisions in which different strings are mapped to the same bucket. In practice, this can work well for some datasets regardless."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "YHU_Aj2nRRDC"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer dense_features_26 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
-      "\n",
-      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
-      "\n",
-      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
-      "\n",
-      "[[0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "thal_hashed = tf.feature_column.categorical_column_with_hash_bucket(\n",
-    "    \"thal\", hash_bucket_size=1000\n",
-    ")\n",
-    "demo(tf.feature_column.indicator_column(thal_hashed))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "fB94M27DRXtZ"
-   },
-   "source": [
-    "### Crossed feature columns\n",
-    "Combining features into a single feature, better known as [feature crosses](https://developers.google.com/machine-learning/glossary/#feature_cross), enables a model to learn separate weights for each combination of features. Here, we will create a new feature that is the cross of age and thal. Note that `crossed_column` does not build the full table of all possible combinations (which could be very large). Instead, it is backed by a `hashed_column`, so you can choose how large the table is."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "oaPVERd9Rep6"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer dense_features_27 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
-      "\n",
-      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
-      "\n",
-      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
-      "\n",
-      "[[0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "crossed_feature = tf.feature_column.crossed_column(\n",
-    "    [age_buckets, thal], hash_bucket_size=1000\n",
-    ")\n",
-    "demo(tf.feature_column.indicator_column(crossed_feature))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ypkI9zx6Rj1q"
-   },
-   "source": [
-    "## Choose which columns to use\n",
-    "We have seen how to use several types of feature columns. Now we will use them to train a model. The goal of this tutorial is to show you the complete code (e.g. mechanics) needed to work with feature columns. We have selected a few columns to train our model below arbitrarily.\n",
-    "\n",
-    "Key point: If your aim is to build an accurate model, try a larger dataset of your own, and think carefully about which features are the most meaningful to include, and how they should be represented."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4PlLY7fORuzA"
-   },
-   "outputs": [],
-   "source": [
-    "feature_columns = []\n",
-    "\n",
-    "# numeric cols\n",
-    "for header in [\"age\", \"trestbps\", \"chol\", \"thalach\", \"oldpeak\", \"slope\", \"ca\"]:\n",
-    "    feature_columns.append(feature_column.numeric_column(header))\n",
-    "\n",
-    "# bucketized cols\n",
-    "age_buckets = feature_column.bucketized_column(\n",
-    "    age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]\n",
-    ")\n",
-    "feature_columns.append(age_buckets)\n",
-    "\n",
-    "# indicator cols\n",
-    "thal = feature_column.categorical_column_with_vocabulary_list(\n",
-    "    \"thal\", [\"fixed\", \"normal\", \"reversible\"]\n",
-    ")\n",
-    "thal_one_hot = feature_column.indicator_column(thal)\n",
-    "feature_columns.append(thal_one_hot)\n",
-    "\n",
-    "# embedding cols\n",
-    "thal_embedding = feature_column.embedding_column(thal, dimension=8)\n",
-    "feature_columns.append(thal_embedding)\n",
-    "\n",
-    "# crossed cols\n",
-    "crossed_feature = feature_column.crossed_column(\n",
-    "    [age_buckets, thal], hash_bucket_size=1000\n",
-    ")\n",
-    "crossed_feature = feature_column.indicator_column(crossed_feature)\n",
-    "feature_columns.append(crossed_feature)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "M-nDp8krS_ts"
-   },
-   "source": [
-    "### How to Input Feature Columns to a Keras Model\n",
-    "Now that we have defined our feature columns, we now use a [DenseFeatures](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/DenseFeatures) layer to input them to a Keras model.  Don't worry if you have not used Keras before.  There is a more detailed video and lab introducing the Keras Sequential and Functional models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "6o-El1R2TGQP"
-   },
-   "outputs": [],
-   "source": [
-    "feature_layer = tf.keras.layers.DenseFeatures(feature_columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "8cf6vKfgTH0U"
-   },
-   "source": [
-    "Earlier, we used a small batch size to demonstrate how feature columns worked. We create a new input pipeline with a larger batch size."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gcemszoGSse_"
-   },
-   "outputs": [],
-   "source": [
-    "batch_size = 32\n",
-    "train_ds = df_to_dataset(train, batch_size=batch_size)\n",
-    "val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)\n",
-    "test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "bBx4Xu0eTXWq"
-   },
-   "source": [
-    "## Create, compile, and train the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "_YJPPb3xTPeZ"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train for 7 steps, validate for 2 steps\n",
-      "Epoch 1/5\n",
-      "7/7 [==============================] - 1s 157ms/step - loss: 1.1446 - accuracy: 0.6580 - val_loss: 1.4723 - val_accuracy: 0.4694\n",
-      "Epoch 2/5\n",
-      "7/7 [==============================] - 0s 10ms/step - loss: 0.7330 - accuracy: 0.6632 - val_loss: 0.5254 - val_accuracy: 0.7143\n",
-      "Epoch 3/5\n",
-      "7/7 [==============================] - 0s 10ms/step - loss: 0.4610 - accuracy: 0.7565 - val_loss: 0.4916 - val_accuracy: 0.7755\n",
-      "Epoch 4/5\n",
-      "7/7 [==============================] - 0s 10ms/step - loss: 0.4359 - accuracy: 0.7617 - val_loss: 0.5403 - val_accuracy: 0.7551\n",
-      "Epoch 5/5\n",
-      "7/7 [==============================] - 0s 10ms/step - loss: 0.5650 - accuracy: 0.7409 - val_loss: 0.6612 - val_accuracy: 0.7551\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = tf.keras.Sequential(\n",
-    "    [\n",
-    "        feature_layer,\n",
-    "        layers.Dense(128, activation=\"relu\"),\n",
-    "        layers.Dense(128, activation=\"relu\"),\n",
-    "        layers.Dense(1),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "\n",
-    "history = model.fit(train_ds, validation_data=val_ds, epochs=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "GnFmMOW0Tcaa"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2/2 [==============================] - 0s 4ms/step - loss: 0.4773 - accuracy: 0.7705\n",
-      "Accuracy 0.7704918\n"
-     ]
-    }
-   ],
-   "source": [
-    "loss, accuracy = model.evaluate(test_ds)\n",
-    "print(\"Accuracy\", accuracy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize the model loss curve\n",
-    "\n",
-    "Next, we will use Matplotlib to draw the model's loss curves for training and validation.  A line plot is also created showing the accuracy over the training epochs for both the train (blue) and test (orange) sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_curves(history, metrics):\n",
-    "    nrows = 1\n",
-    "    ncols = 2\n",
-    "    fig = plt.figure(figsize=(10, 5))\n",
-    "\n",
-    "    for idx, key in enumerate(metrics):\n",
-    "        ax = fig.add_subplot(nrows, ncols, idx + 1)\n",
-    "        plt.plot(history.history[key])\n",
-    "        plt.plot(history.history[f\"val_{key}\"])\n",
-    "        plt.title(f\"model {key}\")\n",
-    "        plt.ylabel(key)\n",
-    "        plt.xlabel(\"epoch\")\n",
-    "        plt.legend([\"train\", \"validation\"], loc=\"upper left\")\n",
-    "\n",
-    "\n",
-    "plot_curves(history, [\"loss\", \"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3bdfbq20V6zu"
-   },
-   "source": [
-    "You can see that accuracy is at 77% for both the training and validation data, while loss bottoms out at about .477 after four epochs.\n",
-    "\n",
-    "Key point: You will typically see best results with deep learning with much larger and more complex datasets. When working with a small dataset like this one, we recommend using a decision tree or random forest as a strong baseline. The goal of this tutorial is not to train an accurate model, but to demonstrate the mechanics of working with structured data, so you have code to use as a starting point when working with your own datasets in the future."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "SotnhVWuHQCw"
-   },
-   "source": [
-    "## Next steps\n",
-    "The best way to learn more about classifying structured data is to try it yourself. We suggest finding another dataset to work with, and training a model to classify it using code similar to the above. To improve accuracy, think carefully about which features to include in your model, and how they should be represented."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc.\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-    "http://www.apache.org/licenses/LICENSE-2.0\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "feature_columns.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/int_logistic_regression.ipynb b/notebooks/introduction_to_tensorflow/labs/int_logistic_regression.ipynb
deleted file mode 100644
index 77b8aeb8..00000000
--- a/notebooks/introduction_to_tensorflow/labs/int_logistic_regression.ipynb
+++ /dev/null
@@ -1,1460 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rwxGnsA92emp"
-   },
-   "source": [
-    "##### Copyright 2018 The TensorFlow Authors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "cellView": "form",
-    "colab": {},
-    "colab_type": "code",
-    "id": "CPII1rGR2rF9"
-   },
-   "outputs": [],
-   "source": [
-    "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-    "# you may not use this file except in compliance with the License.\n",
-    "# You may obtain a copy of the License at\n",
-    "#\n",
-    "# https://www.apache.org/licenses/LICENSE-2.0\n",
-    "#\n",
-    "# Unless required by applicable law or agreed to in writing, software\n",
-    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-    "# See the License for the specific language governing permissions and\n",
-    "# limitations under the License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "JtEZ1pCPn--z"
-   },
-   "source": [
-    "# Custom training: walkthrough"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "GV1F7tVTN3Dn"
-   },
-   "source": [
-    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
-    "  <td>\n",
-    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
-    "  </td>\n",
-    "  <td>\n",
-    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/customization/custom_training_walkthrough.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
-    "  </td>\n",
-    "  <td>\n",
-    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/customization/custom_training_walkthrough.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
-    "  </td>\n",
-    "  <td>\n",
-    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/customization/custom_training_walkthrough.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
-    "  </td>\n",
-    "</table>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "LDrzLFXE8T1l"
-   },
-   "source": [
-    "This guide uses machine learning to *categorize* Iris flowers by species. It uses TensorFlow to:\n",
-    "1. Build a model,\n",
-    "2. Train this model on example data, and\n",
-    "3. Use the model to make predictions about unknown data.\n",
-    "\n",
-    "## TensorFlow programming\n",
-    "\n",
-    "This guide uses these high-level TensorFlow concepts:\n",
-    "\n",
-    "* Use TensorFlow's default [eager execution](../../guide/eager.ipynb) development environment,\n",
-    "* Import data with the [Datasets API](../../guide/datasets.ipynb),\n",
-    "* Build models and layers with TensorFlow's [Keras API](../../guide/keras/overview.ipynb).\n",
-    "\n",
-    "This tutorial is structured like many TensorFlow programs:\n",
-    "\n",
-    "1. Import and parse the dataset.\n",
-    "2. Select the type of model.\n",
-    "3. Train the model.\n",
-    "4. Evaluate the model's effectiveness.\n",
-    "5. Use the trained model to make predictions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "yNr7H-AIoLOR"
-   },
-   "source": [
-    "## Setup program"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "1J3AuPBT9gyR"
-   },
-   "source": [
-    "### Configure imports\n",
-    "\n",
-    "Import TensorFlow and the other required Python modules. By default, TensorFlow uses [eager execution](../../guide/eager.ipynb) to evaluate operations immediately, returning concrete values instead of creating a computational graph that is executed later. If you are used to a REPL or the `python` interactive console, this feels familiar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "jElLULrDhQZR"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "bfV2Dai0Ow2o"
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "g4Wzg69bnwK2"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow version: 2.1.0\n",
-      "Eager execution: True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"TensorFlow version: {tf.__version__}\")\n",
-    "print(f\"Eager execution: {tf.executing_eagerly()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Zx7wc0LuuxaJ"
-   },
-   "source": [
-    "## The Iris classification problem\n",
-    "\n",
-    "Imagine you are a botanist seeking an automated way to categorize each Iris flower you find. Machine learning provides many algorithms to classify flowers statistically. For instance, a sophisticated machine learning program could classify flowers based on photographs. Our ambitions are more modest—we're going to classify Iris flowers based on the length and width measurements of their [sepals](https://en.wikipedia.org/wiki/Sepal) and [petals](https://en.wikipedia.org/wiki/Petal).\n",
-    "\n",
-    "The Iris genus entails about 300 species, but our program will only classify the following three:\n",
-    "\n",
-    "* Iris setosa\n",
-    "* Iris virginica\n",
-    "* Iris versicolor\n",
-    "\n",
-    "<table>\n",
-    "  <tr><td>\n",
-    "    <img src=\"https://www.tensorflow.org/images/iris_three_species.jpg\"\n",
-    "         alt=\"Petal geometry compared for three iris species: Iris setosa, Iris virginica, and Iris versicolor\">\n",
-    "  </td></tr>\n",
-    "  <tr><td align=\"center\">\n",
-    "    <b>Figure 1.</b> <a href=\"https://commons.wikimedia.org/w/index.php?curid=170298\">Iris setosa</a> (by <a href=\"https://commons.wikimedia.org/wiki/User:Radomil\">Radomil</a>, CC BY-SA 3.0), <a href=\"https://commons.wikimedia.org/w/index.php?curid=248095\">Iris versicolor</a>, (by <a href=\"https://commons.wikimedia.org/wiki/User:Dlanglois\">Dlanglois</a>, CC BY-SA 3.0), and <a href=\"https://www.flickr.com/photos/33397993@N05/3352169862\">Iris virginica</a> (by <a href=\"https://www.flickr.com/photos/33397993@N05\">Frank Mayfield</a>, CC BY-SA 2.0).<br/>&nbsp;\n",
-    "  </td></tr>\n",
-    "</table>\n",
-    "\n",
-    "Fortunately, someone has already created a [dataset of 120 Iris flowers](https://en.wikipedia.org/wiki/Iris_flower_data_set) with the sepal and petal measurements. This is a classic dataset that is popular for beginner machine learning classification problems."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3Px6KAg0Jowz"
-   },
-   "source": [
-    "## Import and parse the training dataset\n",
-    "\n",
-    "Download the dataset file and convert it into a structure that can be used by this Python program.\n",
-    "\n",
-    "### Download the dataset\n",
-    "\n",
-    "Download the training dataset file using the `tf.keras.utils.get_file` function. This returns the file path of the downloaded file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "J6c7uEU9rjRM"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "8192/2194 [================================================================================================================] - 0s 0us/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Local copy of the dataset file: /home/kbuilder/.keras/datasets/iris_training.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_dataset_url = \"https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv\"\n",
-    "\n",
-    "train_dataset_fp = tf.keras.utils.get_file(\n",
-    "    fname=os.path.basename(train_dataset_url), origin=train_dataset_url\n",
-    ")\n",
-    "\n",
-    "print(f\"Local copy of the dataset file: {train_dataset_fp}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "qnX1-aLors4S"
-   },
-   "source": [
-    "### Inspect the data\n",
-    "\n",
-    "This dataset, `iris_training.csv`, is a plain text file that stores tabular data formatted as comma-separated values (CSV). Use the `head -n5` command to take a peek at the first five entries:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "FQvb_JYdrpPm"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "120,4,setosa,versicolor,virginica\r\n",
-      "6.4,2.8,5.6,2.2,2\r\n",
-      "5.0,2.3,3.3,1.0,1\r\n",
-      "4.9,2.5,4.5,1.7,2\r\n",
-      "4.9,3.1,1.5,0.1,0\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!head -n5 {train_dataset_fp}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "kQhzD6P-uBoq"
-   },
-   "source": [
-    "From this view of the dataset, notice the following:\n",
-    "\n",
-    "1. The first line is a header containing information about the dataset:\n",
-    "  * There are 120 total examples. Each example has four features and one of three possible label names.\n",
-    "2. Subsequent rows are data records, one *[example](https://developers.google.com/machine-learning/glossary/#example)* per line, where:\n",
-    "  * The first four fields are *[features](https://developers.google.com/machine-learning/glossary/#feature)*: these are the characteristics of an example. Here, the fields hold float numbers representing flower measurements.\n",
-    "  * The last column is the *[label](https://developers.google.com/machine-learning/glossary/#label)*: this is the value we want to predict. For this dataset, it's an integer value of 0, 1, or 2 that corresponds to a flower name.\n",
-    "\n",
-    "Let's write that out in code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "9Edhevw7exl6"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n",
-      "Label: species\n"
-     ]
-    }
-   ],
-   "source": [
-    "# column order in CSV file\n",
-    "column_names = [\n",
-    "    \"sepal_length\",\n",
-    "    \"sepal_width\",\n",
-    "    \"petal_length\",\n",
-    "    \"petal_width\",\n",
-    "    \"species\",\n",
-    "]\n",
-    "\n",
-    "feature_names = column_names[:-1]\n",
-    "label_name = column_names[-1]\n",
-    "\n",
-    "print(f\"Features: {feature_names}\")\n",
-    "print(f\"Label: {label_name}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "CCtwLoJhhDNc"
-   },
-   "source": [
-    "Each label is associated with string name (for example, \"setosa\"), but machine learning typically relies on numeric values. The label numbers are mapped to a named representation, such as:\n",
-    "\n",
-    "* `0`: Iris setosa\n",
-    "* `1`: Iris versicolor\n",
-    "* `2`: Iris virginica\n",
-    "\n",
-    "For more information about features and labels, see the [ML Terminology section of the Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/framing/ml-terminology)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "sVNlJlUOhkoX"
-   },
-   "outputs": [],
-   "source": [
-    "class_names = [\"Iris setosa\", \"Iris versicolor\", \"Iris virginica\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "dqPkQExM2Pwt"
-   },
-   "source": [
-    "### Create a `tf.data.Dataset`\n",
-    "\n",
-    "TensorFlow's [Dataset API](../../guide/data.ipynb) handles many common cases for loading data into a model. This is a high-level API for reading data and transforming it into a form used for training.\n",
-    "\n",
-    "\n",
-    "Since the dataset is a CSV-formatted text file, use the `tf.data.experimental.make_csv_dataset` function to parse the data into a suitable format. Since this function generates data for training models, the default behavior is to shuffle the data (`shuffle=True, shuffle_buffer_size=10000`), and repeat the dataset forever (`num_epochs=None`). We also set the [batch_size](https://developers.google.com/machine-learning/glossary/#batch_size) parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "WsxHnz1ebJ2S"
-   },
-   "outputs": [],
-   "source": [
-    "batch_size = 32\n",
-    "\n",
-    "train_dataset = tf.data.experimental.make_csv_dataset(\n",
-    "    train_dataset_fp,\n",
-    "    batch_size,\n",
-    "    column_names=column_names,\n",
-    "    label_name=label_name,\n",
-    "    num_epochs=1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "gB_RSn62c-3G"
-   },
-   "source": [
-    "The `make_csv_dataset` function returns a `tf.data.Dataset` of `(features, label)` pairs, where `features` is a dictionary: `{'feature_name': value}`\n",
-    "\n",
-    "These `Dataset` objects are iterable. Let's look at a batch of features:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "iDuG94H-C122"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OrderedDict([('sepal_length', <tf.Tensor: shape=(32,), dtype=float32, numpy=\n",
-      "array([6.5, 6.6, 4.7, 5.7, 4.8, 5.5, 6.3, 6.5, 5.9, 5.2, 4.8, 6.3, 4.9,\n",
-      "       6.2, 6.7, 5.8, 5.6, 6.3, 7.2, 6.7, 6.4, 7.7, 4.9, 6. , 5.7, 4.9,\n",
-      "       5.5, 4.4, 5.7, 5.4, 6.2, 7.7], dtype=float32)>), ('sepal_width', <tf.Tensor: shape=(32,), dtype=float32, numpy=\n",
-      "array([3.2, 2.9, 3.2, 4.4, 3.4, 3.5, 3.4, 3. , 3.2, 3.4, 3. , 2.3, 3.1,\n",
-      "       3.4, 3.3, 2.6, 2.7, 3.3, 3.2, 3. , 2.7, 2.6, 3.1, 2.7, 2.8, 2.4,\n",
-      "       2.6, 3. , 3.8, 3.4, 2.2, 3.8], dtype=float32)>), ('petal_length', <tf.Tensor: shape=(32,), dtype=float32, numpy=\n",
-      "array([5.1, 4.6, 1.3, 1.5, 1.6, 1.3, 5.6, 5.8, 4.8, 1.4, 1.4, 4.4, 1.5,\n",
-      "       5.4, 5.7, 4. , 4.2, 6. , 6. , 5. , 5.3, 6.9, 1.5, 5.1, 4.1, 3.3,\n",
-      "       4.4, 1.3, 1.7, 1.5, 4.5, 6.7], dtype=float32)>), ('petal_width', <tf.Tensor: shape=(32,), dtype=float32, numpy=\n",
-      "array([2. , 1.3, 0.2, 0.4, 0.2, 0.2, 2.4, 2.2, 1.8, 0.2, 0.1, 1.3, 0.1,\n",
-      "       2.3, 2.1, 1.2, 1.3, 2.5, 1.8, 1.7, 1.9, 2.3, 0.1, 1.6, 1.3, 1. ,\n",
-      "       1.2, 0.2, 0.3, 0.4, 1.5, 2.2], dtype=float32)>)])\n"
-     ]
-    }
-   ],
-   "source": [
-    "features, labels = next(iter(train_dataset))\n",
-    "\n",
-    "print(features)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "E63mArnQaAGz"
-   },
-   "source": [
-    "Notice that like-features are grouped together, or *batched*. Each example row's fields are appended to the corresponding feature array. Change the `batch_size` to set the number of examples stored in these feature arrays.\n",
-    "\n",
-    "You can start to see some clusters by plotting a few features from the batch:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "me5Wn-9FcyyO"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(\n",
-    "    features[\"petal_length\"], features[\"sepal_length\"], c=labels, cmap=\"viridis\"\n",
-    ")\n",
-    "\n",
-    "plt.xlabel(\"Petal length\")\n",
-    "plt.ylabel(\"Sepal length\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "YlxpSyHlhT6M"
-   },
-   "source": [
-    "To simplify the model building step, create a function to repackage the features dictionary into a single array with shape: `(batch_size, num_features)`.\n",
-    "\n",
-    "This function uses the `tf.stack` method which takes values from a list of tensors and creates a combined tensor at the specified dimension:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "jm932WINcaGU"
-   },
-   "outputs": [],
-   "source": [
-    "def pack_features_vector(features, labels):\n",
-    "    \"\"\"Pack the features into a single array.\"\"\"\n",
-    "    features = tf.stack(list(features.values()), axis=1)\n",
-    "    return features, labels"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "V1Vuph_eDl8x"
-   },
-   "source": [
-    "Then use the `tf.data.Dataset#map` method to pack the `features` of each `(features,label)` pair into the training dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "ZbDkzGZIkpXf"
-   },
-   "outputs": [],
-   "source": [
-    "train_dataset = train_dataset.map(pack_features_vector)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "NLy0Q1xCldVO"
-   },
-   "source": [
-    "The features element of the `Dataset` are now arrays with shape `(batch_size, num_features)`. Let's look at the first few examples:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kex9ibEek6Tr"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tf.Tensor(\n",
-      "[[5.8 2.6 4.  1.2]\n",
-      " [6.9 3.1 4.9 1.5]\n",
-      " [5.  3.4 1.5 0.2]\n",
-      " [4.8 3.  1.4 0.1]\n",
-      " [5.5 2.4 3.8 1.1]], shape=(5, 4), dtype=float32)\n"
-     ]
-    }
-   ],
-   "source": [
-    "features, labels = next(iter(train_dataset))\n",
-    "\n",
-    "print(features[:5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "LsaVrtNM3Tx5"
-   },
-   "source": [
-    "## Select the type of model\n",
-    "\n",
-    "### Why model?\n",
-    "\n",
-    "A *[model](https://developers.google.com/machine-learning/crash-course/glossary#model)* is a relationship between features and the label.  For the Iris classification problem, the model defines the relationship between the sepal and petal measurements and the predicted Iris species. Some simple models can be described with a few lines of algebra, but complex machine learning models have a large number of parameters that are difficult to summarize.\n",
-    "\n",
-    "Could you determine the relationship between the four features and the Iris species *without* using machine learning?  That is, could you use traditional programming techniques (for example, a lot of conditional statements) to create a model?  Perhaps—if you analyzed the dataset long enough to determine the relationships between petal and sepal measurements to a particular species. And this becomes difficult—maybe impossible—on more complicated datasets. A good machine learning approach *determines the model for you*. If you feed enough representative examples into the right machine learning model type, the program will figure out the relationships for you.\n",
-    "\n",
-    "### Select the model\n",
-    "\n",
-    "We need to select the kind of model to train. There are many types of models and picking a good one takes experience. This tutorial uses a neural network to solve the Iris classification problem. *[Neural networks](https://developers.google.com/machine-learning/glossary/#neural_network)* can find complex relationships between features and the label. It is a highly-structured graph, organized into one or more *[hidden layers](https://developers.google.com/machine-learning/glossary/#hidden_layer)*. Each hidden layer consists of one or more *[neurons](https://developers.google.com/machine-learning/glossary/#neuron)*. There are several categories of neural networks and this program uses a dense, or *[fully-connected neural network](https://developers.google.com/machine-learning/glossary/#fully_connected_layer)*: the neurons in one layer receive input connections from *every* neuron in the previous layer. For example, Figure 2 illustrates a dense neural network consisting of an input layer, two hidden layers, and an output layer:\n",
-    "\n",
-    "<table>\n",
-    "  <tr><td>\n",
-    "    <img src=\"https://www.tensorflow.org/images/custom_estimators/full_network.png\"\n",
-    "         alt=\"A diagram of the network architecture: Inputs, 2 hidden layers, and outputs\">\n",
-    "  </td></tr>\n",
-    "  <tr><td align=\"center\">\n",
-    "    <b>Figure 2.</b> A neural network with features, hidden layers, and predictions.<br/>&nbsp;\n",
-    "  </td></tr>\n",
-    "</table>\n",
-    "\n",
-    "When the model from Figure 2 is trained and fed an unlabeled example, it yields three predictions: the likelihood that this flower is the given Iris species. This prediction is called *[inference](https://developers.google.com/machine-learning/crash-course/glossary#inference)*. For this example, the sum of the output predictions is 1.0. In Figure 2, this prediction breaks down as: `0.02` for *Iris setosa*, `0.95` for *Iris versicolor*, and `0.03` for *Iris virginica*. This means that the model predicts—with 95% probability—that an unlabeled example flower is an *Iris versicolor*."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "W23DIMVPQEBt"
-   },
-   "source": [
-    "### Create a model using Keras\n",
-    "\n",
-    "The TensorFlow `tf.keras` API is the preferred way to create models and layers. This makes it easy to build models and experiment while Keras handles the complexity of connecting everything together.\n",
-    "\n",
-    "The `tf.keras.Sequential` model is a linear stack of layers. Its constructor takes a list of layer instances, in this case, two `tf.keras.layers.Dense` layers with 10 nodes each, and an output layer with 3 nodes representing our label predictions. The first layer's `input_shape` parameter corresponds to the number of features from the dataset, and is required:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "2fZ6oL2ig3ZK"
-   },
-   "outputs": [],
-   "source": [
-    "model = tf.keras.Sequential(\n",
-    "    [\n",
-    "        tf.keras.layers.Dense(\n",
-    "            10, activation=tf.nn.relu, input_shape=(4,)\n",
-    "        ),  # input shape required\n",
-    "        tf.keras.layers.Dense(10, activation=tf.nn.relu),\n",
-    "        tf.keras.layers.Dense(3),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "FHcbEzMpxbHL"
-   },
-   "source": [
-    "The *[activation function](https://developers.google.com/machine-learning/crash-course/glossary#activation_function)* determines the output shape of each node in the layer. These non-linearities are important—without them the model would be equivalent to a single layer. There are many `tf.keras.activations`, but [ReLU](https://developers.google.com/machine-learning/crash-course/glossary#ReLU) is common for hidden layers.\n",
-    "\n",
-    "The ideal number of hidden layers and neurons depends on the problem and the dataset. Like many aspects of machine learning, picking the best shape of the neural network requires a mixture of knowledge and experimentation. As a rule of thumb, increasing the number of hidden layers and neurons typically creates a more powerful model, which requires more data to train effectively."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2wFKnhWCpDSS"
-   },
-   "source": [
-    "### Using the model\n",
-    "\n",
-    "Let's have a quick look at what this model does to a batch of features:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "xe6SQ5NrpB-I"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(5, 3), dtype=float32, numpy=\n",
-       "array([[-0.24120538, -6.219783  , -0.28271127],\n",
-       "       [-0.3727467 , -7.456743  , -0.2836677 ],\n",
-       "       [ 0.29496336, -5.0277514 , -1.2696607 ],\n",
-       "       [ 0.27997503, -4.6681266 , -1.181448  ],\n",
-       "       [-0.23913011, -5.8720627 , -0.23690107]], dtype=float32)>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = model(features)\n",
-    "predictions[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wxyXOhwVr5S3"
-   },
-   "source": [
-    "Here, each example returns a [logit](https://developers.google.com/machine-learning/crash-course/glossary#logits) for each class.\n",
-    "\n",
-    "To convert these logits to a probability for each class, use the [softmax](https://developers.google.com/machine-learning/crash-course/glossary#softmax) function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "_tRwHZmTNTX2"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(5, 3), dtype=float32, numpy=\n",
-       "array([[5.0971615e-01, 1.2908182e-03, 4.8899299e-01],\n",
-       "       [4.7755370e-01, 4.0038861e-04, 5.2204597e-01],\n",
-       "       [8.2369196e-01, 4.0191957e-03, 1.7228886e-01],\n",
-       "       [8.0710059e-01, 5.7278876e-03, 1.8717149e-01],\n",
-       "       [4.9855182e-01, 1.7838521e-03, 4.9966437e-01]], dtype=float32)>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tf.nn.softmax(predictions[:5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "uRZmchElo481"
-   },
-   "source": [
-    "Taking the `tf.argmax` across classes gives us the predicted class index. But, the model hasn't been trained yet, so these aren't good predictions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "-Jzm_GoErz8B"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prediction: [0 2 0 0 2 2 0 0 0 0 0 0 2 0 2 0 2 0 2 2 0 0 0 2 2 0 0 0 2 0 0 2]\n",
-      "    Labels: [1 1 0 0 1 1 1 0 1 0 0 0 2 0 1 0 1 0 1 2 0 1 0 2 1 0 1 0 2 1 0 2]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Prediction: {tf.argmax(predictions, axis=1)}\")\n",
-    "print(f\"    Labels: {labels}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Vzq2E5J2QMtw"
-   },
-   "source": [
-    "## Train the model\n",
-    "\n",
-    "*[Training](https://developers.google.com/machine-learning/crash-course/glossary#training)* is the stage of machine learning when the model is gradually optimized, or the model *learns* the dataset. The goal is to learn enough about the structure of the training dataset to make predictions about unseen data. If you learn *too much* about the training dataset, then the predictions only work for the data it has seen and will not be generalizable. This problem is called *[overfitting](https://developers.google.com/machine-learning/crash-course/glossary#overfitting)*—it's like memorizing the answers instead of understanding how to solve a problem.\n",
-    "\n",
-    "The Iris classification problem is an example of *[supervised machine learning](https://developers.google.com/machine-learning/glossary/#supervised_machine_learning)*: the model is trained from examples that contain labels. In *[unsupervised machine learning](https://developers.google.com/machine-learning/glossary/#unsupervised_machine_learning)*, the examples don't contain labels. Instead, the model typically finds patterns among the features."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "RaKp8aEjKX6B"
-   },
-   "source": [
-    "### Define the loss and gradient function\n",
-    "\n",
-    "Both training and evaluation stages need to calculate the model's *[loss](https://developers.google.com/machine-learning/crash-course/glossary#loss)*. This measures how off a model's predictions are from the desired label, in other words, how bad the model is performing. We want to minimize, or optimize, this value.\n",
-    "\n",
-    "Our model will calculate its loss using the `tf.keras.losses.SparseCategoricalCrossentropy` function which takes the model's class probability predictions and the desired label, and returns the average loss across the examples."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "QOsi6b-1CXIn"
-   },
-   "outputs": [],
-   "source": [
-    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "tMAT4DcMPwI-"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss test: 3.012593984603882\n"
-     ]
-    }
-   ],
-   "source": [
-    "def loss(model, x, y, training):\n",
-    "    # training=training is needed only if there are layers with different\n",
-    "    # behavior during training versus inference (e.g. Dropout).\n",
-    "    y_ = model(x, training=training)\n",
-    "\n",
-    "    return loss_object(y_true=y, y_pred=y_)\n",
-    "\n",
-    "\n",
-    "l = loss(model, features, labels, training=False)\n",
-    "print(f\"Loss test: {l}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3IcPqA24QM6B"
-   },
-   "source": [
-    "Use the `tf.GradientTape` context to calculate the *[gradients](https://developers.google.com/machine-learning/crash-course/glossary#gradient)* used to optimize your model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "x57HcKWhKkei"
-   },
-   "outputs": [],
-   "source": [
-    "def grad(model, inputs, targets):\n",
-    "    with tf.GradientTape() as tape:\n",
-    "        loss_value = loss(model, inputs, targets, training=True)\n",
-    "    return loss_value, tape.gradient(loss_value, model.trainable_variables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "lOxFimtlKruu"
-   },
-   "source": [
-    "### Create an optimizer\n",
-    "\n",
-    "An *[optimizer](https://developers.google.com/machine-learning/crash-course/glossary#optimizer)* applies the computed gradients to the model's variables to minimize the `loss` function. You can think of the loss function as a curved surface (see Figure 3) and we want to find its lowest point by walking around. The gradients point in the direction of steepest ascent—so we'll travel the opposite way and move down the hill. By iteratively calculating the loss and gradient for each batch, we'll adjust the model during training. Gradually, the model will find the best combination of weights and bias to minimize loss. And the lower the loss, the better the model's predictions.\n",
-    "\n",
-    "<table>\n",
-    "  <tr><td>\n",
-    "    <img src=\"https://cs231n.github.io/assets/nn3/opt1.gif\" width=\"70%\"\n",
-    "         alt=\"Optimization algorithms visualized over time in 3D space.\">\n",
-    "  </td></tr>\n",
-    "  <tr><td align=\"center\">\n",
-    "    <b>Figure 3.</b> Optimization algorithms visualized over time in 3D space.<br/>(Source: <a href=\"http://cs231n.github.io/neural-networks-3/\">Stanford class CS231n</a>, MIT License, Image credit: <a href=\"https://twitter.com/alecrad\">Alec Radford</a>)\n",
-    "  </td></tr>\n",
-    "</table>\n",
-    "\n",
-    "TensorFlow has many optimization algorithms available for training. This model uses the `tf.keras.optimizers.SGD` that implements the *[stochastic gradient descent](https://developers.google.com/machine-learning/crash-course/glossary#gradient_descent)* (SGD) algorithm. The `learning_rate` sets the step size to take for each iteration down the hill. This is a *hyperparameter* that you'll commonly adjust to achieve better results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "XkUd6UiZa_dF"
-   },
-   "source": [
-    "Let's setup the optimizer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "8xxi2NNGKwG_"
-   },
-   "outputs": [],
-   "source": [
-    "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "pJVRZ0hP52ZB"
-   },
-   "source": [
-    "We'll use this to calculate a single optimization step:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "rxRNTFVe56RG"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step: 0, Initial Loss: 3.012593984603882\n",
-      "Step: 1,         Loss: 2.4900705814361572\n"
-     ]
-    }
-   ],
-   "source": [
-    "loss_value, grads = grad(model, features, labels)\n",
-    "\n",
-    "print(\n",
-    "    \"Step: {}, Initial Loss: {}\".format(\n",
-    "        optimizer.iterations.numpy(), loss_value.numpy()\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
-    "\n",
-    "print(\n",
-    "    \"Step: {},         Loss: {}\".format(\n",
-    "        optimizer.iterations.numpy(),\n",
-    "        loss(model, features, labels, training=True).numpy(),\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "7Y2VSELvwAvW"
-   },
-   "source": [
-    "### Training loop\n",
-    "\n",
-    "With all the pieces in place, the model is ready for training! A training loop feeds the dataset examples into the model to help it make better predictions. The following code block sets up these training steps:\n",
-    "\n",
-    "1. Iterate each *epoch*. An epoch is one pass through the dataset.\n",
-    "2. Within an epoch, iterate over each example in the training `Dataset` grabbing its *features* (`x`) and *label* (`y`).\n",
-    "3. Using the example's features, make a prediction and compare it with the label. Measure the inaccuracy of the prediction and use that to calculate the model's loss and gradients.\n",
-    "4. Use an `optimizer` to update the model's variables.\n",
-    "5. Keep track of some stats for visualization.\n",
-    "6. Repeat for each epoch.\n",
-    "\n",
-    "The `num_epochs` variable is the number of times to loop over the dataset collection. Counter-intuitively, training a model longer does not guarantee a better model. `num_epochs` is a *[hyperparameter](https://developers.google.com/machine-learning/glossary/#hyperparameter)* that you can tune. Choosing the right number usually requires both experience and experimentation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "AIgulGRUhpto"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 000: Loss: 1.594, Accuracy: 70.000%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 050: Loss: 0.286, Accuracy: 97.500%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 100: Loss: 0.172, Accuracy: 97.500%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 150: Loss: 0.144, Accuracy: 98.333%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 200: Loss: 0.122, Accuracy: 98.333%\n"
-     ]
-    }
-   ],
-   "source": [
-    "## Note: Rerunning this cell uses the same model variables\n",
-    "\n",
-    "# Keep results for plotting\n",
-    "train_loss_results = []\n",
-    "train_accuracy_results = []\n",
-    "\n",
-    "num_epochs = 201\n",
-    "\n",
-    "for epoch in range(num_epochs):\n",
-    "    epoch_loss_avg = tf.keras.metrics.Mean()\n",
-    "    epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\n",
-    "\n",
-    "    # Training loop - using batches of 32\n",
-    "    for x, y in train_dataset:\n",
-    "        # Optimize the model\n",
-    "        loss_value, grads = grad(model, x, y)\n",
-    "        optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
-    "\n",
-    "        # Track progress\n",
-    "        epoch_loss_avg.update_state(loss_value)  # Add current batch loss\n",
-    "        # Compare predicted label to actual label\n",
-    "        # training=True is needed only if there are layers with different\n",
-    "        # behavior during training versus inference (e.g. Dropout).\n",
-    "        epoch_accuracy.update_state(y, model(x, training=True))\n",
-    "\n",
-    "    # End epoch\n",
-    "    train_loss_results.append(epoch_loss_avg.result())\n",
-    "    train_accuracy_results.append(epoch_accuracy.result())\n",
-    "\n",
-    "    if epoch % 50 == 0:\n",
-    "        print(\n",
-    "            \"Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}\".format(\n",
-    "                epoch, epoch_loss_avg.result(), epoch_accuracy.result()\n",
-    "            )\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2FQHVUnm_rjw"
-   },
-   "source": [
-    "### Visualize the loss function over time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "j3wdbmtLVTyr"
-   },
-   "source": [
-    "While it's helpful to print out the model's training progress, it's often *more* helpful to see this progress. [TensorBoard](https://www.tensorflow.org/tensorboard) is a nice visualization tool that is packaged with TensorFlow, but we can create basic charts using the `matplotlib` module.\n",
-    "\n",
-    "Interpreting these charts takes some experience, but you really want to see the *loss* go down and the *accuracy* go up:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "agjvNd2iUGFn"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))\n",
-    "fig.suptitle(\"Training Metrics\")\n",
-    "\n",
-    "axes[0].set_ylabel(\"Loss\", fontsize=14)\n",
-    "axes[0].plot(train_loss_results)\n",
-    "\n",
-    "axes[1].set_ylabel(\"Accuracy\", fontsize=14)\n",
-    "axes[1].set_xlabel(\"Epoch\", fontsize=14)\n",
-    "axes[1].plot(train_accuracy_results)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Zg8GoMZhLpGH"
-   },
-   "source": [
-    "## Evaluate the model's effectiveness\n",
-    "\n",
-    "Now that the model is trained, we can get some statistics on its performance.\n",
-    "\n",
-    "*Evaluating* means determining how effectively the model makes predictions. To determine the model's effectiveness at Iris classification, pass some sepal and petal measurements to the model and ask the model to predict what Iris species they represent. Then compare the model's predictions against the actual label.  For example, a model that picked the correct species on half the input examples has an *[accuracy](https://developers.google.com/machine-learning/glossary/#accuracy)* of `0.5`. Figure 4 shows a slightly more effective model, getting 4 out of 5 predictions correct at 80% accuracy:\n",
-    "\n",
-    "<table cellpadding=\"8\" border=\"0\">\n",
-    "  <colgroup>\n",
-    "    <col span=\"4\" >\n",
-    "    <col span=\"1\" bgcolor=\"lightblue\">\n",
-    "    <col span=\"1\" bgcolor=\"lightgreen\">\n",
-    "  </colgroup>\n",
-    "  <tr bgcolor=\"lightgray\">\n",
-    "    <th colspan=\"4\">Example features</th>\n",
-    "    <th colspan=\"1\">Label</th>\n",
-    "    <th colspan=\"1\" >Model prediction</th>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>5.9</td><td>3.0</td><td>4.3</td><td>1.5</td><td align=\"center\">1</td><td align=\"center\">1</td>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>6.9</td><td>3.1</td><td>5.4</td><td>2.1</td><td align=\"center\">2</td><td align=\"center\">2</td>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>5.1</td><td>3.3</td><td>1.7</td><td>0.5</td><td align=\"center\">0</td><td align=\"center\">0</td>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>6.0</td> <td>3.4</td> <td>4.5</td> <td>1.6</td> <td align=\"center\">1</td><td align=\"center\" bgcolor=\"red\">2</td>\n",
-    "  </tr>\n",
-    "  <tr>\n",
-    "    <td>5.5</td><td>2.5</td><td>4.0</td><td>1.3</td><td align=\"center\">1</td><td align=\"center\">1</td>\n",
-    "  </tr>\n",
-    "  <tr><td align=\"center\" colspan=\"6\">\n",
-    "    <b>Figure 4.</b> An Iris classifier that is 80% accurate.<br/>&nbsp;\n",
-    "  </td></tr>\n",
-    "</table>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "z-EvK7hGL0d8"
-   },
-   "source": [
-    "### Setup the test dataset\n",
-    "\n",
-    "Evaluating the model is similar to training the model. The biggest difference is the examples come from a separate *[test set](https://developers.google.com/machine-learning/crash-course/glossary#test_set)* rather than the training set. To fairly assess a model's effectiveness, the examples used to evaluate a model must be different from the examples used to train the model.\n",
-    "\n",
-    "The setup for the test `Dataset` is similar to the setup for training `Dataset`. Download the CSV text file and parse that values, then give it a little shuffle:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Ps3_9dJ3Lodk"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "8192/573 [============================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 0us/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_url = (\n",
-    "    \"https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv\"\n",
-    ")\n",
-    "\n",
-    "test_fp = tf.keras.utils.get_file(\n",
-    "    fname=os.path.basename(test_url), origin=test_url\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "SRMWCu30bnxH"
-   },
-   "outputs": [],
-   "source": [
-    "test_dataset = tf.data.experimental.make_csv_dataset(\n",
-    "    test_fp,\n",
-    "    batch_size,\n",
-    "    column_names=column_names,\n",
-    "    label_name=\"species\",\n",
-    "    num_epochs=1,\n",
-    "    shuffle=False,\n",
-    ")\n",
-    "\n",
-    "test_dataset = test_dataset.map(pack_features_vector)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "HFuOKXJdMAdm"
-   },
-   "source": [
-    "### Evaluate the model on the test dataset\n",
-    "\n",
-    "Unlike the training stage, the model only evaluates a single [epoch](https://developers.google.com/machine-learning/glossary/#epoch) of the test data. In the following code cell, we iterate over each example in the test set and compare the model's prediction against the actual label. This is used to measure the model's accuracy across the entire test set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Tw03-MK1cYId"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test set accuracy: 96.667%\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_accuracy = tf.keras.metrics.Accuracy()\n",
-    "\n",
-    "for x, y in test_dataset:\n",
-    "    # training=False is needed only if there are layers with different\n",
-    "    # behavior during training versus inference (e.g. Dropout).\n",
-    "    logits = model(x, training=False)\n",
-    "    prediction = tf.argmax(logits, axis=1, output_type=tf.int32)\n",
-    "    test_accuracy(prediction, y)\n",
-    "\n",
-    "print(f\"Test set accuracy: {test_accuracy.result():.3%}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "HcKEZMtCOeK-"
-   },
-   "source": [
-    "We can see on the last batch, for example, the model is usually correct:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "uNwt2eMeOane"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(30, 2), dtype=int32, numpy=\n",
-       "array([[1, 1],\n",
-       "       [2, 2],\n",
-       "       [0, 0],\n",
-       "       [1, 1],\n",
-       "       [1, 1],\n",
-       "       [1, 1],\n",
-       "       [0, 0],\n",
-       "       [2, 1],\n",
-       "       [1, 1],\n",
-       "       [2, 2],\n",
-       "       [2, 2],\n",
-       "       [0, 0],\n",
-       "       [2, 2],\n",
-       "       [1, 1],\n",
-       "       [1, 1],\n",
-       "       [0, 0],\n",
-       "       [1, 1],\n",
-       "       [0, 0],\n",
-       "       [0, 0],\n",
-       "       [2, 2],\n",
-       "       [0, 0],\n",
-       "       [1, 1],\n",
-       "       [2, 2],\n",
-       "       [1, 1],\n",
-       "       [1, 1],\n",
-       "       [1, 1],\n",
-       "       [0, 0],\n",
-       "       [1, 1],\n",
-       "       [2, 2],\n",
-       "       [1, 1]], dtype=int32)>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tf.stack([y, prediction], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "7Li2r1tYvW7S"
-   },
-   "source": [
-    "## Use the trained model to make predictions\n",
-    "\n",
-    "We've trained a model and \"proven\" that it's good—but not perfect—at classifying Iris species. Now let's use the trained model to make some predictions on [unlabeled examples](https://developers.google.com/machine-learning/glossary/#unlabeled_example); that is, on examples that contain features but not a label.\n",
-    "\n",
-    "In real-life, the unlabeled examples could come from lots of different sources including apps, CSV files, and data feeds. For now, we're going to manually provide three unlabeled examples to predict their labels. Recall, the label numbers are mapped to a named representation as:\n",
-    "\n",
-    "* `0`: Iris setosa\n",
-    "* `1`: Iris versicolor\n",
-    "* `2`: Iris virginica"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kesTS5Lzv-M2"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Example 0 prediction: Iris setosa (98.6%)\n",
-      "Example 1 prediction: Iris versicolor (96.7%)\n",
-      "Example 2 prediction: Iris virginica (57.1%)\n"
-     ]
-    }
-   ],
-   "source": [
-    "predict_dataset = tf.convert_to_tensor(\n",
-    "    [\n",
-    "        [\n",
-    "            5.1,\n",
-    "            3.3,\n",
-    "            1.7,\n",
-    "            0.5,\n",
-    "        ],\n",
-    "        [\n",
-    "            5.9,\n",
-    "            3.0,\n",
-    "            4.2,\n",
-    "            1.5,\n",
-    "        ],\n",
-    "        [6.9, 3.1, 5.4, 2.1],\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "# training=False is needed only if there are layers with different\n",
-    "# behavior during training versus inference (e.g. Dropout).\n",
-    "predictions = model(predict_dataset, training=False)\n",
-    "\n",
-    "for i, logits in enumerate(predictions):\n",
-    "    class_idx = tf.argmax(logits).numpy()\n",
-    "    p = tf.nn.softmax(logits)[class_idx]\n",
-    "    name = class_names[class_idx]\n",
-    "    print(f\"Example {i} prediction: {name} ({100 * p:4.1f}%)\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "custom_training_walkthrough.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true,
-   "version": "0.3.2"
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/intro_logistic_regression_TF2.0.ipynb b/notebooks/introduction_to_tensorflow/labs/intro_logistic_regression_TF2.0.ipynb
deleted file mode 100644
index 1fb08434..00000000
--- a/notebooks/introduction_to_tensorflow/labs/intro_logistic_regression_TF2.0.ipynb
+++ /dev/null
@@ -1,481 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "04QgGZc9bF5D"
-   },
-   "source": [
-    "# Introduction to Logistic Regression Using TF 2.0\n",
-    "\n",
-    "**Learning Objectives**\n",
-    "\n",
-    "\n",
-    "1. Build a neural network that classifies images.\n",
-    "2. Train this neural network.\n",
-    "3. Evaluate the accuracy of the model.\n",
-    "\n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "This short introduction uses [Keras](https://keras.io/), a high-level API to build and train models in TensoFlow.  In this lab, you Load and prepare the MNIST dataset, convert the samples from integers to floating-point numbers, build and train a neural network that classifies images and the evaluate then accuracy of the model.\n",
-    "\n",
-    "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/intro_logistic_regression_TF2.0.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "nnrWf3PCEzXL"
-   },
-   "source": [
-    "## Load necessary libraries \n",
-    "We will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "0trJmd6DjqBZ"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow version:  2.1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "7NAbSZiaoJ4z"
-   },
-   "source": [
-    "Load and prepare the [MNIST dataset](http://yann.lecun.com/exdb/mnist/). Convert the samples from integers to floating-point numbers:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "7FP5258xjs-v"
-   },
-   "outputs": [],
-   "source": [
-    "mnist = tf.keras.datasets.mnist\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "x_train, x_test = x_train / 255.0, x_test / 255.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "BPZ68wASog_I"
-   },
-   "source": [
-    "**Lab Task 1:** Build the `tf.keras.Sequential` model by stacking layers. Choose an optimizer and loss function for training:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "h3IKyzTCDNGo"
-   },
-   "outputs": [],
-   "source": [
-    "model = # TODO 1 -- Your code here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "l2hiez2eIUz8"
-   },
-   "source": [
-    "For each example the model returns a vector of \"[logits](https://developers.google.com/machine-learning/glossary#logits)\" or \"[log-odds](https://developers.google.com/machine-learning/glossary#log-odds)\" scores, one for each class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "OeOrNdnkEEcR"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Layer flatten_2 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
-      "\n",
-      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
-      "\n",
-      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.06166657,  0.07144614, -0.07372011,  0.3451226 , -0.06205732,\n",
-       "        -0.23894641, -0.00426888,  0.38629198,  0.11753443,  0.21888584]],\n",
-       "      dtype=float32)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = model(x_train[:1]).numpy()\n",
-    "predictions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "tgjhDQGcIniO"
-   },
-   "source": [
-    "The `tf.nn.softmax` function converts these logits to \"probabilities\" for each class: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "zWSRnQ0WI5eq"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.09631761, 0.09726418, 0.0841217 , 0.1278819 , 0.08510853,\n",
-       "        0.07131012, 0.09017171, 0.1332566 , 0.10185182, 0.11271589]],\n",
-       "      dtype=float32)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tf.nn.softmax(predictions).numpy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "he5u_okAYS4a"
-   },
-   "source": [
-    "Note: It is possible to bake this `tf.nn.softmax` in as the activation function for the last layer of the network. While this can make the model output more directly interpretable, this approach is discouraged as it's impossible to\n",
-    "provide an exact and numerically stable loss calculation for all models when using a softmax output. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "hQyugpgRIyrA"
-   },
-   "source": [
-    "The `losses.SparseCategoricalCrossentropy` loss takes a vector of logits and a `True` index and returns a scalar loss for each example."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Lab Task #2:** Usage of losses.SparseCategoricalCrossentropy with logits vectors and a True index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "RSkzdv8MD0tT"
-   },
-   "outputs": [],
-   "source": [
-    "loss_fn = # TODO 2 -- Your code here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "SfR4MsSDU880"
-   },
-   "source": [
-    "This loss is equal to the negative log probability of the true class:\n",
-    "It is zero if the model is sure of the correct class.\n",
-    "\n",
-    "This untrained model gives probabilities close to random (1/10 for each class), so the initial loss should be close to `-tf.log(1/10) ~= 2.3`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "NJWqEVrrJ7ZB"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.6407173"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loss_fn(y_train[:1], predictions).numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "9foNKHzTD2Vo"
-   },
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer=\"adam\", loss=loss_fn, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ix4mEL65on-w"
-   },
-   "source": [
-    "The `Model.fit` method adjusts the model parameters to minimize the loss: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "y7suUbJXVLqP"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples\n",
-      "Epoch 1/5\n",
-      "60000/60000 [==============================] - 4s 74us/sample - loss: 0.2948 - accuracy: 0.9159\n",
-      "Epoch 2/5\n",
-      "60000/60000 [==============================] - 4s 68us/sample - loss: 0.1449 - accuracy: 0.9575\n",
-      "Epoch 3/5\n",
-      "60000/60000 [==============================] - 4s 67us/sample - loss: 0.1086 - accuracy: 0.9669\n",
-      "Epoch 4/5\n",
-      "60000/60000 [==============================] - 4s 67us/sample - loss: 0.0890 - accuracy: 0.9722\n",
-      "Epoch 5/5\n",
-      "60000/60000 [==============================] - 4s 67us/sample - loss: 0.0760 - accuracy: 0.9761\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x7fb6b435e898>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.fit(x_train, y_train, epochs=5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "4mDAAPFqVVgn"
-   },
-   "source": [
-    "The `Model.evaluate` method checks the models performance, usually on a \"[Validation-set](https://developers.google.com/machine-learning/glossary#validation-set)\" or \"[Test-set](https://developers.google.com/machine-learning/glossary#test-set)\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "F7dTAzgHDUh7"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000/10000 - 0s - loss: 0.0789 - accuracy: 0.9762\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[0.07894639570089057, 0.9762]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.evaluate(x_test, y_test, verbose=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "T4JfEh7kvx6m"
-   },
-   "source": [
-    "The image classifier is now trained to ~98% accuracy on this dataset. To learn more, read the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Aj8NrlzlJqDG"
-   },
-   "source": [
-    "If you want your model to return a probability, you can wrap the trained model, and attach the softmax to it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "rYb6DrEH0GMv"
-   },
-   "outputs": [],
-   "source": [
-    "probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "cnqOZtUp1YR_"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(5, 10), dtype=float32, numpy=\n",
-       "array([[4.16968859e-08, 6.23608756e-08, 5.28075470e-05, 2.82978057e-03,\n",
-       "        1.16726082e-11, 1.71175685e-07, 3.04637446e-13, 9.97084558e-01,\n",
-       "        4.06323215e-06, 2.85600290e-05],\n",
-       "       [1.44917021e-08, 2.85007991e-05, 9.99968171e-01, 2.84016392e-06,\n",
-       "        5.33308461e-15, 2.64569479e-07, 3.80620824e-09, 1.71865629e-11,\n",
-       "        2.15737089e-07, 1.31360444e-14],\n",
-       "       [4.27655920e-08, 9.99692202e-01, 3.80819870e-06, 4.24063728e-06,\n",
-       "        1.78703067e-05, 1.56593114e-06, 4.08388269e-06, 2.54444923e-04,\n",
-       "        2.10298167e-05, 6.26707333e-07],\n",
-       "       [9.99903679e-01, 5.74413441e-08, 6.42219311e-05, 2.98295333e-07,\n",
-       "        6.06606605e-08, 2.66647930e-05, 7.69448434e-07, 3.18793695e-06,\n",
-       "        2.18482214e-07, 8.51092807e-07],\n",
-       "       [5.69916847e-06, 1.11318251e-07, 1.25890219e-04, 7.61389970e-07,\n",
-       "        9.85019803e-01, 1.97083864e-06, 1.77188417e-06, 1.21961719e-04,\n",
-       "        3.18770253e-05, 1.46901319e-02]], dtype=float32)>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "probability_model(x_test[:5])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [
-    "rX8mhOLljYeM"
-   ],
-   "name": "beginner.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/load_diff_filedata.ipynb b/notebooks/introduction_to_tensorflow/labs/load_diff_filedata.ipynb
deleted file mode 100644
index ddaf1a5d..00000000
--- a/notebooks/introduction_to_tensorflow/labs/load_diff_filedata.ipynb
+++ /dev/null
@@ -1,1327 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "sUtoed20cRJJ"
-   },
-   "source": [
-    "# How to Load CSV and Numpy File Types in TensorFlow 2.0\n",
-    "\n",
-    "\n",
-    "\n",
-    "## Learning Objectives\n",
-    "\n",
-    "1. Load a CSV file into a `tf.data.Dataset`. \n",
-    "2. Load Numpy data\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "In this lab, you load CSV data from a file into a `tf.data.Dataset`.  This tutorial provides an example of loading data from NumPy arrays into a `tf.data.Dataset` you also load text data.\n",
-    "\n",
-    "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/ml_on_gcloud_v2/labs/03_load_diff_filedata.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "fgZ9gjmPfSnK"
-   },
-   "source": [
-    "## Load necessary libraries \n",
-    "We will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "baYFZMW_bJHh"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow version:  2.3.0-dev20200613\n"
-     ]
-    }
-   ],
-   "source": [
-    "import functools\n",
-    "\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Ncf5t6tgL5ZI"
-   },
-   "outputs": [],
-   "source": [
-    "TRAIN_DATA_URL = \"https://storage.googleapis.com/tf-datasets/titanic/train.csv\"\n",
-    "TEST_DATA_URL = \"https://storage.googleapis.com/tf-datasets/titanic/eval.csv\"\n",
-    "\n",
-    "train_file_path = tf.keras.utils.get_file(\"train.csv\", TRAIN_DATA_URL)\n",
-    "test_file_path = tf.keras.utils.get_file(\"eval.csv\", TEST_DATA_URL)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4ONE94qulk6S"
-   },
-   "outputs": [],
-   "source": [
-    "# Make numpy values easier to read.\n",
-    "np.set_printoptions(precision=3, suppress=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Wuqj601Qw0Ml"
-   },
-   "source": [
-    "## Load data\n",
-    "\n",
-    "This section provides an example of how to load CSV data from a file into a `tf.data.Dataset`.  The data used in this tutorial are taken from the Titanic passenger list. The model will predict the likelihood a passenger survived based on characteristics like age, gender, ticket class, and whether the person was traveling alone.\n",
-    "\n",
-    "To start, let's look at the top of the CSV file to see how it is formatted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "54Dv7mCrf9Yw"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "survived,sex,age,n_siblings_spouses,parch,fare,class,deck,embark_town,alone\n",
-      "0,male,22.0,1,0,7.25,Third,unknown,Southampton,n\n",
-      "1,female,38.0,1,0,71.2833,First,C,Cherbourg,n\n",
-      "1,female,26.0,0,0,7.925,Third,unknown,Southampton,y\n",
-      "1,female,35.0,1,0,53.1,First,C,Southampton,n\n",
-      "0,male,28.0,0,0,8.4583,Third,unknown,Queenstown,y\n",
-      "0,male,2.0,3,1,21.075,Third,unknown,Southampton,n\n",
-      "1,female,27.0,0,2,11.1333,Third,unknown,Southampton,n\n",
-      "1,female,14.0,1,0,30.0708,Second,unknown,Cherbourg,n\n",
-      "1,female,4.0,1,1,16.7,Third,G,Southampton,n\n"
-     ]
-    }
-   ],
-   "source": [
-    "!head {train_file_path}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "jC9lRhV-q_R3"
-   },
-   "source": [
-    "You can [load this using pandas](pandas_dataframe.ipynb), and pass the NumPy arrays to TensorFlow. If you need to scale up to a large set of files, or need a loader that integrates with [TensorFlow and tf.data](../../guide/data.ipynb) then use the `tf.data.experimental.make_csv_dataset` function:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "67mfwr4v-mN_"
-   },
-   "source": [
-    "The only column you need to identify explicitly is the one with the value that the model is intended to predict. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "iXROZm5f3V4E"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1: Add string name for label column\n",
-    "LABEL_COLUMN = \"\"\n",
-    "LABELS = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "t4N-plO4tDXd"
-   },
-   "source": [
-    "Now read the CSV data from the file and create a dataset. \n",
-    "\n",
-    "(For the full documentation, see `tf.data.experimental.make_csv_dataset`)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "yIbUscB9sqha"
-   },
-   "outputs": [],
-   "source": [
-    "def get_dataset(file_path, **kwargs):\n",
-    "# TODO 2\n",
-    "# TODO: Read the CSV data from the file and create a dataset \n",
-    "dataset = tf.data.experimental.make_csv_dataset( \n",
-    "# TODO: Your code goes here.\n",
-    "# TODO: Your code goes here.\n",
-    "# TODO: Your code goes here.\n",
-    "# TODO: Your code goes here.\n",
-    ") \n",
-    "  return dataset\n",
-    "\n",
-    "raw_train_data = # TODO: Your code goes here.\n",
-    "raw_test_data = # TODO: Your code goes here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "v4oMO9MIxgTG"
-   },
-   "outputs": [],
-   "source": [
-    "def show_batch(dataset):\n",
-    "    for batch, label in dataset.take(1):\n",
-    "        for key, value in batch.items():\n",
-    "            print(f\"{key:20s}: {value.numpy()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "vHUQFKoQI6G7"
-   },
-   "source": [
-    "Each item in the dataset is a batch, represented as a tuple of (*many examples*, *many labels*). The data from the examples is organized in column-based tensors (rather than row-based tensors), each with as many elements as the batch size (5 in this case).\n",
-    "\n",
-    "It might help to see this yourself."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "HjrkJROoxoll"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sex                 : [b'male' b'male' b'male' b'male' b'male']\n",
-      "age                 : [34. 18. 45. 46. 29.]\n",
-      "n_siblings_spouses  : [1 0 1 1 1]\n",
-      "parch               : [0 0 0 0 0]\n",
-      "fare                : [26.     8.3   83.475 61.175  7.046]\n",
-      "class               : [b'Second' b'Third' b'First' b'First' b'Third']\n",
-      "deck                : [b'unknown' b'unknown' b'C' b'E' b'unknown']\n",
-      "embark_town         : [b'Southampton' b'Southampton' b'Southampton' b'Southampton'\n",
-      " b'Southampton']\n",
-      "alone               : [b'n' b'y' b'n' b'n' b'n']\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_batch(raw_train_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "YOYKQKmMj3D6"
-   },
-   "source": [
-    "As you can see, the columns in the CSV are named. The dataset constructor will pick these names up automatically. If the file you are working with does not contain the column names in the first line, pass them in a list of strings to  the `column_names` argument in the `make_csv_dataset` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "2Av8_9L3tUg1"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sex                 : [b'male' b'female' b'male' b'male' b'male']\n",
-      "age                 : [30. 50. 18. 51. 28.]\n",
-      "n_siblings_spouses  : [1 0 1 0 0]\n",
-      "parch               : [0 1 1 0 0]\n",
-      "fare                : [ 16.1   247.521   7.854   8.05    7.05 ]\n",
-      "class               : [b'Third' b'First' b'Third' b'Third' b'Third']\n",
-      "deck                : [b'unknown' b'B' b'unknown' b'unknown' b'unknown']\n",
-      "embark_town         : [b'Southampton' b'Cherbourg' b'Southampton' b'Southampton' b'Southampton']\n",
-      "alone               : [b'n' b'n' b'n' b'y' b'y']\n"
-     ]
-    }
-   ],
-   "source": [
-    "CSV_COLUMNS = [\n",
-    "    \"survived\",\n",
-    "    \"sex\",\n",
-    "    \"age\",\n",
-    "    \"n_siblings_spouses\",\n",
-    "    \"parch\",\n",
-    "    \"fare\",\n",
-    "    \"class\",\n",
-    "    \"deck\",\n",
-    "    \"embark_town\",\n",
-    "    \"alone\",\n",
-    "]\n",
-    "\n",
-    "temp_dataset = get_dataset(train_file_path, column_names=CSV_COLUMNS)\n",
-    "\n",
-    "show_batch(temp_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "gZfhoX7bR9u4"
-   },
-   "source": [
-    "This example is going to use all the available columns. If you need to omit some columns from the dataset, create a list of just the columns you plan to use, and pass it into the (optional) `select_columns` argument of the constructor.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "S1TzSkUKwsNP"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "age                 : [28. 34. 28. 50.  2.]\n",
-      "n_siblings_spouses  : [0 0 0 2 1]\n",
-      "class               : [b'Third' b'First' b'Third' b'First' b'Second']\n",
-      "deck                : [b'unknown' b'unknown' b'unknown' b'unknown' b'unknown']\n",
-      "alone               : [b'y' b'y' b'y' b'n' b'n']\n"
-     ]
-    }
-   ],
-   "source": [
-    "SELECT_COLUMNS = [\n",
-    "    \"survived\",\n",
-    "    \"age\",\n",
-    "    \"n_siblings_spouses\",\n",
-    "    \"class\",\n",
-    "    \"deck\",\n",
-    "    \"alone\",\n",
-    "]\n",
-    "\n",
-    "temp_dataset = get_dataset(train_file_path, select_columns=SELECT_COLUMNS)\n",
-    "\n",
-    "show_batch(temp_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "9cryz31lxs3e"
-   },
-   "source": [
-    "## Data preprocessing\n",
-    "\n",
-    "A CSV file can contain a variety of data types. Typically you want to convert from those mixed types to a fixed length vector before feeding the data into your model.\n",
-    "\n",
-    "TensorFlow has a built-in system for describing common input conversions: `tf.feature_column`, see [this tutorial](../keras/feature_columns) for details.\n",
-    "\n",
-    "\n",
-    "You can preprocess your data using any tool you like (like [nltk](https://www.nltk.org/) or [sklearn](https://scikit-learn.org/stable/)), and just pass the processed output to TensorFlow. \n",
-    "\n",
-    "\n",
-    "The primary advantage of doing the preprocessing inside your model is that when you export the model it includes the preprocessing. This way you can pass the raw data directly to your model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "9AsbaFmCeJtF"
-   },
-   "source": [
-    "### Continuous data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Xl0Q0DcfA_rt"
-   },
-   "source": [
-    "If your data is already in an appropriate numeric format, you can pack the data into a vector before passing it off to the model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4Yfji3J5BMxz"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "age                 : [28.  32.5 28.  32.  28. ]\n",
-      "n_siblings_spouses  : [0. 1. 0. 0. 0.]\n",
-      "parch               : [0. 0. 0. 0. 0.]\n",
-      "fare                : [26.55  30.071  7.829 13.     7.75 ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "SELECT_COLUMNS = [\"survived\", \"age\", \"n_siblings_spouses\", \"parch\", \"fare\"]\n",
-    "DEFAULTS = [0, 0.0, 0.0, 0.0, 0.0]\n",
-    "temp_dataset = get_dataset(\n",
-    "    train_file_path, select_columns=SELECT_COLUMNS, column_defaults=DEFAULTS\n",
-    ")\n",
-    "\n",
-    "show_batch(temp_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "zEUhI8kZCfq8"
-   },
-   "outputs": [],
-   "source": [
-    "example_batch, labels_batch = next(iter(temp_dataset))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IP45_2FbEKzn"
-   },
-   "source": [
-    "Here's a simple function that will pack together all the columns:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "JQ0hNSL8CC3a"
-   },
-   "outputs": [],
-   "source": [
-    "def pack(features, label):\n",
-    "    return tf.stack(list(features.values()), axis=-1), label"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "75LA9DisEIoE"
-   },
-   "source": [
-    "Apply this to each element of the dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "VnP2Z2lwCTRl"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:AutoGraph could not transform <function pack at 0x7f52c0743ea0> and will run it as-is.\n",
-      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
-      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
-      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
-      "WARNING: AutoGraph could not transform <function pack at 0x7f52c0743ea0> and will run it as-is.\n",
-      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
-      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
-      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
-      "[[ 18.      1.      0.    108.9  ]\n",
-      " [ 31.      0.      0.     50.496]\n",
-      " [ 70.      1.      1.     71.   ]\n",
-      " [ 24.      1.      0.     16.1  ]\n",
-      " [ 31.      1.      1.     37.004]]\n",
-      "\n",
-      "[0 0 0 0 0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "packed_dataset = temp_dataset.map(pack)\n",
-    "\n",
-    "for features, labels in packed_dataset.take(1):\n",
-    "    print(features.numpy())\n",
-    "    print()\n",
-    "    print(labels.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "1VBvmaFrFU6J"
-   },
-   "source": [
-    "If you have mixed datatypes you may want to separate out these simple-numeric fields. The `tf.feature_column` api can handle them, but this incurs some overhead and should be avoided unless really necessary. Switch back to the mixed dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "ad-IQ_JPFQge"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sex                 : [b'male' b'female' b'male' b'male' b'male']\n",
-      "age                 : [18. 28. 28. 28. 28.]\n",
-      "n_siblings_spouses  : [0 0 0 3 1]\n",
-      "parch               : [0 0 0 1 1]\n",
-      "fare                : [ 7.75   7.879  7.75  25.467 15.246]\n",
-      "class               : [b'Third' b'Third' b'Third' b'Third' b'Third']\n",
-      "deck                : [b'unknown' b'unknown' b'unknown' b'unknown' b'unknown']\n",
-      "embark_town         : [b'Southampton' b'Queenstown' b'Queenstown' b'Southampton' b'Cherbourg']\n",
-      "alone               : [b'y' b'y' b'y' b'n' b'n']\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_batch(raw_train_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "HSrYNKKcIdav"
-   },
-   "outputs": [],
-   "source": [
-    "example_batch, labels_batch = next(iter(temp_dataset))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "p5VtThKfGPaQ"
-   },
-   "source": [
-    "So define a more general preprocessor that selects a list of numeric features and packs them into a single column:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "5DRishYYGS-m"
-   },
-   "outputs": [],
-   "source": [
-    "class PackNumericFeatures:\n",
-    "    def __init__(self, names):\n",
-    "        self.names = names\n",
-    "\n",
-    "    def __call__(self, features, labels):\n",
-    "        numeric_features = [features.pop(name) for name in self.names]\n",
-    "        numeric_features = [\n",
-    "            tf.cast(feat, tf.float32) for feat in numeric_features\n",
-    "        ]\n",
-    "        numeric_features = tf.stack(numeric_features, axis=-1)\n",
-    "        features[\"numeric\"] = numeric_features\n",
-    "\n",
-    "        return features, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "1SeZka9AHfqD"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:AutoGraph could not transform <__main__.PackNumericFeatures object at 0x7f52c06f77b8> and will run it as-is.\n",
-      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
-      "Cause: module 'gast' has no attribute 'Constant'\n",
-      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
-      "WARNING: AutoGraph could not transform <__main__.PackNumericFeatures object at 0x7f52c06f77b8> and will run it as-is.\n",
-      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
-      "Cause: module 'gast' has no attribute 'Constant'\n",
-      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
-      "WARNING:tensorflow:AutoGraph could not transform <__main__.PackNumericFeatures object at 0x7f52c06f7438> and will run it as-is.\n",
-      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
-      "Cause: module 'gast' has no attribute 'Constant'\n",
-      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
-      "WARNING: AutoGraph could not transform <__main__.PackNumericFeatures object at 0x7f52c06f7438> and will run it as-is.\n",
-      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
-      "Cause: module 'gast' has no attribute 'Constant'\n",
-      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
-     ]
-    }
-   ],
-   "source": [
-    "NUMERIC_FEATURES = [\"age\", \"n_siblings_spouses\", \"parch\", \"fare\"]\n",
-    "\n",
-    "packed_train_data = raw_train_data.map(PackNumericFeatures(NUMERIC_FEATURES))\n",
-    "\n",
-    "packed_test_data = raw_test_data.map(PackNumericFeatures(NUMERIC_FEATURES))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "wFrw0YobIbUB"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sex                 : [b'male' b'male' b'male' b'female' b'male']\n",
-      "class               : [b'Third' b'Second' b'Third' b'First' b'First']\n",
-      "deck                : [b'unknown' b'unknown' b'unknown' b'B' b'B']\n",
-      "embark_town         : [b'Southampton' b'Southampton' b'Southampton' b'Southampton' b'Cherbourg']\n",
-      "alone               : [b'n' b'y' b'y' b'n' b'y']\n",
-      "numeric             : [[ 4.     4.     2.    31.275]\n",
-      " [16.     0.     0.    26.   ]\n",
-      " [25.     0.     0.     7.05 ]\n",
-      " [36.     0.     2.    71.   ]\n",
-      " [32.     0.     0.    30.5  ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_batch(packed_train_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "_EPUS8fPLUb1"
-   },
-   "outputs": [],
-   "source": [
-    "example_batch, labels_batch = next(iter(packed_train_data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "o2maE8d2ijsq"
-   },
-   "source": [
-    "#### Data Normalization\n",
-    "\n",
-    "Continuous data should always be normalized."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "WKT1ASWpwH46"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>n_siblings_spouses</th>\n",
-       "      <th>parch</th>\n",
-       "      <th>fare</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>627.000000</td>\n",
-       "      <td>627.000000</td>\n",
-       "      <td>627.000000</td>\n",
-       "      <td>627.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>29.631308</td>\n",
-       "      <td>0.545455</td>\n",
-       "      <td>0.379585</td>\n",
-       "      <td>34.385399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>12.511818</td>\n",
-       "      <td>1.151090</td>\n",
-       "      <td>0.792999</td>\n",
-       "      <td>54.597730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.895800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>28.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>15.045800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>35.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>31.387500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>80.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>512.329200</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              age  n_siblings_spouses       parch        fare\n",
-       "count  627.000000          627.000000  627.000000  627.000000\n",
-       "mean    29.631308            0.545455    0.379585   34.385399\n",
-       "std     12.511818            1.151090    0.792999   54.597730\n",
-       "min      0.750000            0.000000    0.000000    0.000000\n",
-       "25%     23.000000            0.000000    0.000000    7.895800\n",
-       "50%     28.000000            0.000000    0.000000   15.045800\n",
-       "75%     35.000000            1.000000    0.000000   31.387500\n",
-       "max     80.000000            8.000000    5.000000  512.329200"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "desc = pd.read_csv(train_file_path)[NUMERIC_FEATURES].describe()\n",
-    "desc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "cHHstcKPsMXM"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "MEAN = # TODO: Your code goes here.\n",
-    "STD = # TODO: Your code goes here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "REKqO_xHPNx0"
-   },
-   "outputs": [],
-   "source": [
-    "def normalize_numeric_data(data, mean, std):\n",
-    "  # Center the data\n",
-    "  # TODO 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[29.631  0.545  0.38  34.385] [12.512  1.151  0.793 54.598]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(MEAN, STD)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "VPsoMUgRCpUM"
-   },
-   "source": [
-    "Now create a numeric column. The `tf.feature_columns.numeric_column` API accepts a `normalizer_fn` argument, which will be run on each batch.\n",
-    "\n",
-    "Bind the `MEAN` and `STD` to the normalizer fn using [`functools.partial`](https://docs.python.org/3/library/functools.html#functools.partial)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Bw0I35xRS57V"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "NumericColumn(key='numeric', shape=(4,), default_value=None, dtype=tf.float32, normalizer_fn=functools.partial(<function normalize_numeric_data at 0x7f52c066f488>, mean=array([29.631,  0.545,  0.38 , 34.385]), std=array([12.512,  1.151,  0.793, 54.598])))"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# See what you just created.\n",
-    "normalizer = functools.partial(normalize_numeric_data, mean=MEAN, std=STD)\n",
-    "\n",
-    "numeric_column = tf.feature_column.numeric_column(\n",
-    "    \"numeric\", normalizer_fn=normalizer, shape=[len(NUMERIC_FEATURES)]\n",
-    ")\n",
-    "numeric_columns = [numeric_column]\n",
-    "numeric_column"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "HZxcHXc6LCa7"
-   },
-   "source": [
-    "When you train the model, include this feature column to select and center this block of numeric data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "b61NM76Ot_kb"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(5, 4), dtype=float32, numpy=\n",
-       "array([[ 2.   ,  3.   ,  1.   , 21.075],\n",
-       "       [28.   ,  1.   ,  0.   , 16.1  ],\n",
-       "       [28.   ,  1.   ,  0.   , 19.967],\n",
-       "       [16.   ,  0.   ,  1.   , 39.4  ],\n",
-       "       [24.   ,  2.   ,  0.   , 24.15 ]], dtype=float32)>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_batch[\"numeric\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "j-r_4EAJAZoI"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-2.208,  2.132,  0.782, -0.244],\n",
-       "       [-0.13 ,  0.395, -0.479, -0.335],\n",
-       "       [-0.13 ,  0.395, -0.479, -0.264],\n",
-       "       [-1.089, -0.474,  0.782,  0.092],\n",
-       "       [-0.45 ,  1.264, -0.479, -0.187]], dtype=float32)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numeric_layer = tf.keras.layers.DenseFeatures(numeric_columns)\n",
-    "numeric_layer(example_batch).numpy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "M37oD2VcCO4R"
-   },
-   "source": [
-    "The mean based normalization used here requires knowing the means of each column ahead of time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "tSyrkSQwYHKi"
-   },
-   "source": [
-    "### Categorical data\n",
-    "\n",
-    "Some of the columns in the CSV data are categorical columns. That is, the content should be one of a limited set of options.\n",
-    "\n",
-    "Use the `tf.feature_column` API to create a collection with a `tf.feature_column.indicator_column` for each categorical column.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "mWDniduKMw-C"
-   },
-   "outputs": [],
-   "source": [
-    "CATEGORIES = {\n",
-    "    \"sex\": [\"male\", \"female\"],\n",
-    "    \"class\": [\"First\", \"Second\", \"Third\"],\n",
-    "    \"deck\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\"],\n",
-    "    \"embark_town\": [\"Cherbourg\", \"Southhampton\", \"Queenstown\"],\n",
-    "    \"alone\": [\"y\", \"n\"],\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kkxLdrsLwHPT"
-   },
-   "outputs": [],
-   "source": [
-    "categorical_columns = []\n",
-    "for feature, vocab in CATEGORIES.items():\n",
-    "    cat_col = tf.feature_column.categorical_column_with_vocabulary_list(\n",
-    "        key=feature, vocabulary_list=vocab\n",
-    "    )\n",
-    "    categorical_columns.append(tf.feature_column.indicator_column(cat_col))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "H18CxpHY_Nma"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='class', vocabulary_list=('First', 'Second', 'Third'), dtype=tf.string, default_value=-1, num_oov_buckets=0)),\n",
-       " IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='embark_town', vocabulary_list=('Cherbourg', 'Southhampton', 'Queenstown'), dtype=tf.string, default_value=-1, num_oov_buckets=0)),\n",
-       " IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='deck', vocabulary_list=('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'), dtype=tf.string, default_value=-1, num_oov_buckets=0)),\n",
-       " IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='sex', vocabulary_list=('male', 'female'), dtype=tf.string, default_value=-1, num_oov_buckets=0)),\n",
-       " IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='alone', vocabulary_list=('y', 'n'), dtype=tf.string, default_value=-1, num_oov_buckets=0))]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# See what you just created.\n",
-    "categorical_columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "p7mACuOsArUH"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n"
-     ]
-    }
-   ],
-   "source": [
-    "categorical_layer = tf.keras.layers.DenseFeatures(categorical_columns)\n",
-    "print(categorical_layer(example_batch).numpy()[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "R7-1QG99_1sN"
-   },
-   "source": [
-    "This will be become part of a data processing input later when you build the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "kPWkC4_1l3IG"
-   },
-   "source": [
-    "### Combined preprocessing layer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "R3QAjo1qD4p9"
-   },
-   "source": [
-    "Add the two feature column collections and pass them to a `tf.keras.layers.DenseFeatures` to create an input layer that will extract and preprocess both input types:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3-OYK7GnaH0r"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "preprocessing_layer = # TODO: Your code goes here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "m7_U_K0UMSVS"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.     1.     0.     0.     1.     0.     0.     0.     0.     0.\n",
-      "  0.     0.     0.     0.     0.     0.     0.     0.    -2.208  2.132\n",
-      "  0.782 -0.244  1.     0.   ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(preprocessing_layer(example_batch).numpy()[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "DlF_omQqtnOP"
-   },
-   "source": [
-    "### Next Step\n",
-    "\n",
-    "A next step would be to build a build a `tf.keras.Sequential`, starting with the `preprocessing_layer`, which is beyond the scope of this lab.  We will cover the Keras Sequential API in the next Lesson."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load NumPy data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load necessary libraries \n",
-    "First, restart the Kernel.  Then, we will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow version:  2.3.0-dev20200613\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load data from `.npz` file\n",
-    "\n",
-    "We use the MNIST dataset in Keras."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DATA_URL = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz'\n",
-    "\n",
-    "path = tf.keras.utils.get_file('mnist.npz', DATA_URL)\n",
-    "with np.load(path) as data:\n",
-    "# TODO 1\n",
-    "  train_examples = # TODO: Your code goes here.\n",
-    "  train_labels = # TODO: Your code goes here.\n",
-    "  test_examples = # TODO: Your code goes here.\n",
-    "  test_labels = # TODO: Your code goes here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load NumPy arrays with `tf.data.Dataset`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Assuming you have an array of examples and a corresponding array of labels, pass the two arrays as a tuple into `tf.data.Dataset.from_tensor_slices` to create a `tf.data.Dataset`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO 2\n",
-    "train_dataset = # TODO: Your code goes here.\n",
-    "test_dataset = # TODO: Your code goes here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Next Step\n",
-    "\n",
-    "A next step would be to build a build a `tf.keras.Sequential`, starting with the `preprocessing_layer`, which is beyond the scope of this lab.  We will cover the Keras Sequential API in the next Lesson."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Resources \n",
-    "1. Load text data - this link: https://www.tensorflow.org/tutorials/load_data/text\n",
-    "2. TF.text - this link:  https://www.tensorflow.org/tutorials/tensorflow_text/intro\n",
-    "3. Load image daeta - https://www.tensorflow.org/tutorials/load_data/images\n",
-    "4. Read data into a Pandas DataFrame - https://www.tensorflow.org/tutorials/load_data/pandas_dataframe\n",
-    "5. How to represent Unicode strings in TensorFlow - https://www.tensorflow.org/tutorials/load_data/unicode\n",
-    "6. TFRecord and tf.Example -  https://www.tensorflow.org/tutorials/load_data/tfrecord                  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc.\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-    "http://www.apache.org/licenses/LICENSE-2.0\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "csv.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/load_images_tf.data.ipynb b/notebooks/introduction_to_tensorflow/labs/load_images_tf.data.ipynb
deleted file mode 100644
index 0af2c94b..00000000
--- a/notebooks/introduction_to_tensorflow/labs/load_images_tf.data.ipynb
+++ /dev/null
@@ -1,744 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ucMoYase6URl"
-   },
-   "source": [
-    "# Loading Images Using tf.Data.Dataset\n",
-    "\n",
-    "**Learning Objectives**\n",
-    "\n",
-    "1. Retrieve Images using tf.keras.utils.get_file\n",
-    "2. Load Images using Keras Pre-Processing\n",
-    "3. Load Images using tf.Data.Dataset\n",
-    "4. Understand basic Methods for Training\n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "In this notebook, we load an image dataset using tf.data.  The dataset used in this example is distributed as directories of images, with one class of image per directory.\n",
-    "\n",
-    "\n",
-    "Each learning objective will correspond to a **#TODO** in the [student lab notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/load_images_tf.data.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "hoQQiZDB6URn"
-   },
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3vhAMaIOBIee"
-   },
-   "source": [
-    "## Load necessary libraries \n",
-    "We will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gIksPgtT8B6B"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow version:  2.3.0-dev20200613\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "import IPython.display as display\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from PIL import Image\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "KT6CcaqgQewg"
-   },
-   "outputs": [],
-   "source": [
-    "AUTOTUNE = tf.data.experimental.AUTOTUNE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wO0InzL66URu"
-   },
-   "source": [
-    "### Retrieve the images\n",
-    "\n",
-    "Before you start any training, you will need a set of images to teach the network about the new classes you want to recognize. You can use an archive of creative-commons licensed flower photos from Google.\n",
-    "\n",
-    "Note: all images are licensed CC-BY, creators are listed in the `LICENSE.txt` file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "rN-Pc6Zd6awg"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\n",
-      "228818944/228813984 [==============================] - 2s 0us/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pathlib\n",
-    "\n",
-    "data_dir = tf.keras.utils.get_file(\n",
-    "    origin=\"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\",\n",
-    "    fname=\"flower_photos\",\n",
-    "    untar=True,\n",
-    ")\n",
-    "data_dir = pathlib.Path(data_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rFkFK74oO--g"
-   },
-   "source": [
-    "After downloading (218MB), you should now have a copy of the flower photos available.\n",
-    "\n",
-    "The directory contains 5 sub-directories, one per class:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "QhewYCxhXQBX"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3670"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "image_count = len(list(data_dir.glob(\"*/*.jpg\")))\n",
-    "image_count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "sJ1HKKdR4A7c"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['daisy', 'tulips', 'roses', 'dandelion', 'sunflowers'],\n",
-       "      dtype='<U10')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "CLASS_NAMES = np.array(\n",
-    "    [item.name for item in data_dir.glob(\"*\") if item.name != \"LICENSE.txt\"]\n",
-    ")\n",
-    "CLASS_NAMES"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IVxsk4OW61TY"
-   },
-   "source": [
-    "Each directory contains images of that type of flower. Here are some roses:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "crs7ZjEp60Ot"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at 0x7FB25DBE1A58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=185x240 at 0x7FB225B83B38>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at 0x7FB225B83898>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "roses = list(data_dir.glob(\"roses/*\"))\n",
-    "\n",
-    "for image_path in roses[:3]:\n",
-    "    display.display(Image.open(str(image_path)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6jobDTUs8Wxu"
-   },
-   "source": [
-    "## Load using `keras.preprocessing`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ehhW308g8soJ"
-   },
-   "source": [
-    "A simple way to load images is to use `tf.keras.preprocessing`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Lab Task #1:** load your images using tf.keras.preprocessing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "syDdF_LWVrWE"
-   },
-   "outputs": [],
-   "source": [
-    "# The 1./255 is to convert from uint8 to float32 in range [0,1].\n",
-    "# TODO 1a\n",
-    "# TODO -- Your code here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "lAmtzsnjDNhB"
-   },
-   "source": [
-    "Define some parameters for the loader:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "1zf695or-Flq"
-   },
-   "outputs": [],
-   "source": [
-    "BATCH_SIZE = 32\n",
-    "IMG_HEIGHT = 224\n",
-    "IMG_WIDTH = 224\n",
-    "STEPS_PER_EPOCH = np.ceil(image_count / BATCH_SIZE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Pw94ajOOVrWI"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 3670 images belonging to 5 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_data_gen = image_generator.flow_from_directory(\n",
-    "    directory=str(data_dir),\n",
-    "    batch_size=BATCH_SIZE,\n",
-    "    shuffle=True,\n",
-    "    target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
-    "    classes=list(CLASS_NAMES),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2ZgIZeXaDUsF"
-   },
-   "source": [
-    "Inspect a batch for image processing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nLp0XVG_Vgi2"
-   },
-   "outputs": [],
-   "source": [
-    "def show_batch(image_batch, label_batch):\n",
-    "  plt.figure(figsize=(10,10))\n",
-    "  for n in range(25):\n",
-    "  # TODO 1b\n",
-    "      ax = # TODO -- Your code here.\n",
-    "      # TODO -- Your code here.\n",
-    "      plt.title(CLASS_NAMES[label_batch[n]==1][0].title())\n",
-    "      plt.axis('off')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "suh6Sjv68rY3"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 25 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "image_batch, label_batch = next(train_data_gen)\n",
-    "show_batch(image_batch, label_batch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "AxS1cLzM8mEp"
-   },
-   "source": [
-    "## Load using `tf.data`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Ylj9fgkamgWZ"
-   },
-   "source": [
-    "The above `keras.preprocessing` method is convienient, but has three downsides: \n",
-    "\n",
-    "1. It's slow. See the performance section below.\n",
-    "1. It lacks fine-grained control.\n",
-    "1. It is not well integrated with the rest of TensorFlow."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IIG5CPaULegg"
-   },
-   "source": [
-    "To load the files as a `tf.data.Dataset` first create a dataset of the file paths:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "lAkQp5uxoINu"
-   },
-   "outputs": [],
-   "source": [
-    "list_ds = tf.data.Dataset.list_files(str(data_dir / \"*/*\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "coORvEH-NGwc"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "b'/home/jupyter/.keras/datasets/flower_photos/tulips/2351637471_5dd34fd3ac_n.jpg'\n",
-      "b'/home/jupyter/.keras/datasets/flower_photos/tulips/16711791713_e54bc9c1af_n.jpg'\n",
-      "b'/home/jupyter/.keras/datasets/flower_photos/roses/229488796_21ac6ee16d_n.jpg'\n",
-      "b'/home/jupyter/.keras/datasets/flower_photos/roses/2535466393_6556afeb2f_m.jpg'\n",
-      "b'/home/jupyter/.keras/datasets/flower_photos/dandelion/14070457521_8eb41f65fa.jpg'\n"
-     ]
-    }
-   ],
-   "source": [
-    "for f in list_ds.take(5):\n",
-    "    print(f.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "91CPfUUJ_8SZ"
-   },
-   "source": [
-    "**Lab Task #2:** Write a short pure-tensorflow function that converts a file path to an `(img, label)` pair:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "arSQzIey-4D4"
-   },
-   "outputs": [],
-   "source": [
-    "def get_label(file_path):\n",
-    "  # TODO 2a\n",
-    "  # convert the path to a list of path components\n",
-    "  # TODO -- Your code here.\n",
-    "  # The second to last is the class-directory\n",
-    "  # TODO -- Your code here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "MGlq4IP4Aktb"
-   },
-   "outputs": [],
-   "source": [
-    "def decode_img(img):\n",
-    "    # TODO 2b\n",
-    "    # convert the compressed string to a 3D uint8 tensor\n",
-    "    # TODO -- Your code here.\n",
-    "    # Use `convert_image_dtype` to convert to floats in the [0,1] range.\n",
-    "    # TODO -- Your code here.\n",
-    "    # resize the image to the desired size.\n",
-    "    return tf.image.resize(img, [IMG_WIDTH, IMG_HEIGHT])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "-xhBRgvNqRRe"
-   },
-   "outputs": [],
-   "source": [
-    "def process_path(file_path):\n",
-    "    label = get_label(file_path)\n",
-    "    # TODO 2c\n",
-    "    # load the raw data from the file as a string\n",
-    "    # TODO -- Your code here.\n",
-    "    img = decode_img(img)\n",
-    "    return img, label"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "S9a5GpsUOBx8"
-   },
-   "source": [
-    "Use `Dataset.map` to create a dataset of `image, label` pairs:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3SDhbo8lOBQv"
-   },
-   "outputs": [],
-   "source": [
-    "# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\n",
-    "labeled_ds = list_ds.map(process_path, num_parallel_calls=AUTOTUNE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kxrl0lGdnpRz"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Image shape:  (224, 224, 3)\n",
-      "Label:  [False False False  True False]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for image, label in labeled_ds.take(1):\n",
-    "    print(\"Image shape: \", image.numpy().shape)\n",
-    "    print(\"Label: \", label.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "vYGCgJuR_9Qp"
-   },
-   "source": [
-    "### Next Steps:  Basic methods for training"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wwZavzgsIytz"
-   },
-   "source": [
-    "To train a model with this dataset you will want the data:\n",
-    "\n",
-    "* To be well shuffled.\n",
-    "* To be batched.\n",
-    "* Batches to be available as soon as possible.\n",
-    "\n",
-    "These features can be easily added using the `tf.data` api."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Lab Task #3:** Adding features using the tf.data api. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "uZmZJx8ePw_5"
-   },
-   "outputs": [],
-   "source": [
-    "def prepare_for_training(ds, cache=True, shuffle_buffer_size=1000):\n",
-    "    # This is a small dataset, only load it once, and keep it in memory.\n",
-    "    # use `.cache(filename)` to cache preprocessing work for datasets that don't\n",
-    "    # fit in memory.\n",
-    "    # TODO 3a\n",
-    "    if cache:\n",
-    "        if isinstance(cache, str):\n",
-    "            ds = ds.cache(cache)\n",
-    "        else:\n",
-    "            ds = ds.cache()\n",
-    "\n",
-    "    # TODO -- Your code here.\n",
-    "\n",
-    "    # Repeat forever\n",
-    "    ds = ds.repeat()\n",
-    "\n",
-    "    ds = ds.batch(BATCH_SIZE)\n",
-    "\n",
-    "    # `prefetch` lets the dataset fetch batches in the background while the model\n",
-    "    # is training.\n",
-    "    ds = ds.prefetch(buffer_size=AUTOTUNE)\n",
-    "\n",
-    "    return ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "-YKnrfAeZV10"
-   },
-   "outputs": [],
-   "source": [
-    "train_ds = prepare_for_training(labeled_ds)\n",
-    "\n",
-    "image_batch, label_batch = next(iter(train_ds))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "UN_Dnl72YNIj"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 25 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_batch(image_batch.numpy(), label_batch.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc.\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-    "http://www.apache.org/licenses/LICENSE-2.0\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "images.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/tfrecord-tf.example.ipynb b/notebooks/introduction_to_tensorflow/labs/tfrecord-tf.example.ipynb
deleted file mode 100644
index 972292ff..00000000
--- a/notebooks/introduction_to_tensorflow/labs/tfrecord-tf.example.ipynb
+++ /dev/null
@@ -1,1670 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3pkUd_9IZCFO"
-   },
-   "source": [
-    "# TFRecord and tf.Example\n",
-    "\n",
-    "**Learning Objectives**\n",
-    "\n",
-    "1. Understand the TFRecord format for storing data\n",
-    "2. Understand the tf.Example message type\n",
-    "3. Read and Write a TFRecord file\n",
-    "\n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "In this notebook, you create, parse, and use the `tf.Example` message, and then serialize, write, and read `tf.Example` messages to and from `.tfrecord` files.  To read data efficiently it can be helpful to serialize your data and store it in a set of files (100-200MB each) that can each be read linearly. This is especially true if the data is being streamed over a network. This can also be useful for caching any data-preprocessing.\n",
-    "\n",
-    "\n",
-    "Each learning objective will correspond to a __#TODO__ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](../solutions/tfrecord-tf.example.ipynb). \n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Ac83J0QxjhFt"
-   },
-   "source": [
-    "### The TFRecord format \n",
-    "\n",
-    "The TFRecord format is a simple format for storing a sequence of binary records.  [Protocol buffers](https://developers.google.com/protocol-buffers/) are a cross-platform, cross-language library for efficient serialization of structured data.  Protocol messages are defined by `.proto` files, these are often the easiest way to understand a message type.\n",
-    "\n",
-    "The `tf.Example` message (or protobuf) is a flexible message type that represents a `{\"string\": value}` mapping. It is designed for use with TensorFlow and is used throughout the higher-level APIs such as [TFX](https://www.tensorflow.org/tfx/).\n",
-    "Note: While useful, these structures are optional. There is no need to convert existing code to use TFRecords, unless you are using [`tf.data`](https://www.tensorflow.org/guide/datasets) and reading data is still the bottleneck to training. See [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets) for dataset performance tips."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "WkRreBf1eDVc"
-   },
-   "source": [
-    "## Load necessary libraries \n",
-    "We will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Ja7sezsmnXph"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[31mERROR: tensorflow 2.1.0 has requirement gast==0.2.2, but you'll have gast 0.3.3 which is incompatible.\u001b[0m\n",
-      "\u001b[31mERROR: witwidget 1.6.0 has requirement oauth2client>=4.1.3, but you'll have oauth2client 3.0.0 which is incompatible.\u001b[0m\n",
-      "\u001b[31mERROR: tensorflow-probability 0.8.0 has requirement cloudpickle==1.1.1, but you'll have cloudpickle 1.3.0 which is incompatible.\u001b[0m\n",
-      "\u001b[31mERROR: tensorflow-probability 0.8.0 has requirement gast<0.3,>=0.2, but you'll have gast 0.3.3 which is incompatible.\u001b[0m\n",
-      "\u001b[31mERROR: tensorflow-io 0.9.10 has requirement tensorflow==2.1.0rc0, but you'll have tensorflow 2.1.0 which is incompatible.\u001b[0m\n",
-      "\u001b[33mWARNING: You are using pip version 20.1; however, version 20.1.1 is available.\n",
-      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "TensorFlow version:  2.3.0-dev20200613\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install -q tf-nightly\n",
-    "import IPython.display as display\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Please ignore any incompatibility warnings and errors.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "e5Kq88ccUWQV"
-   },
-   "source": [
-    "## `tf.Example`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "VrdQHgvNijTi"
-   },
-   "source": [
-    "### Data types for `tf.Example`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "lZw57Qrn4CTE"
-   },
-   "source": [
-    "Fundamentally, a `tf.Example` is a `{\"string\": tf.train.Feature}` mapping.\n",
-    "\n",
-    "The `tf.train.Feature` message type can accept one of the following three types (See the [`.proto` file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto) for reference). Most other generic types can be coerced into one of these:\n",
-    "\n",
-    "1. `tf.train.BytesList` (the following types can be coerced)\n",
-    "\n",
-    "  - `string`\n",
-    "  - `byte`\n",
-    "\n",
-    "1. `tf.train.FloatList` (the following types can be coerced)\n",
-    "\n",
-    "  - `float` (`float32`)\n",
-    "  - `double` (`float64`)\n",
-    "\n",
-    "1. `tf.train.Int64List` (the following types can be coerced)\n",
-    "\n",
-    "  - `bool`\n",
-    "  - `enum`\n",
-    "  - `int32`\n",
-    "  - `uint32`\n",
-    "  - `int64`\n",
-    "  - `uint64`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "_e3g9ExathXP"
-   },
-   "source": [
-    "**Lab Task #1a:** In order to convert a standard TensorFlow type to a `tf.Example`-compatible `tf.train.Feature`, you can use the shortcut functions below. Note that each function takes a scalar input value and returns a `tf.train.Feature` containing one of the three `list` types above. Complete the `TODOs` below using these types."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "mbsPOUpVtYxA"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1a\n",
-    "# The following functions can be used to convert a value to a type compatible\n",
-    "# with tf.Example.\n",
-    "\n",
-    "\n",
-    "def _bytes_feature(value):\n",
-    "    \"\"\"Returns a bytes_list from a string / byte.\"\"\"\n",
-    "    if isinstance(value, type(tf.constant(0))):\n",
-    "        value = (\n",
-    "            value.numpy()\n",
-    "        )  # BytesList won't unpack a string from an EagerTensor.\n",
-    "    return  # TODO: Complete the code here.\n",
-    "\n",
-    "\n",
-    "def _float_feature(value):\n",
-    "    \"\"\"Returns a float_list from a float / double.\"\"\"\n",
-    "    return  # TODO: Complete the code here.\n",
-    "\n",
-    "\n",
-    "def _int64_feature(value):\n",
-    "    \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n",
-    "    return  # TODO: Complete the code here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Wst0v9O8hgzy"
-   },
-   "source": [
-    "Note: To stay simple, this example only uses scalar inputs. The simplest way to handle non-scalar features is to use `tf.serialize_tensor` to convert tensors to binary-strings. Strings are scalars in tensorflow. Use `tf.parse_tensor` to convert the binary-string back to a tensor."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "vsMbkkC8xxtB"
-   },
-   "source": [
-    "Below are some examples of how these functions work. Note the varying input types and the standardized output types. If the input type for a function does not match one of the coercible types stated above, the function will raise an exception (e.g. `_int64_feature(1.0)` will error out, since `1.0` is a float, so should be used with the `_float_feature` function instead):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "hZzyLGr0u73y"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bytes_list {\n",
-      "  value: \"test_string\"\n",
-      "}\n",
-      "\n",
-      "bytes_list {\n",
-      "  value: \"test_bytes\"\n",
-      "}\n",
-      "\n",
-      "float_list {\n",
-      "  value: 2.7182817459106445\n",
-      "}\n",
-      "\n",
-      "int64_list {\n",
-      "  value: 1\n",
-      "}\n",
-      "\n",
-      "int64_list {\n",
-      "  value: 1\n",
-      "}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(_bytes_feature(b\"test_string\"))\n",
-    "print(_bytes_feature(b\"test_bytes\"))\n",
-    "\n",
-    "print(_float_feature(np.exp(1)))\n",
-    "\n",
-    "print(_int64_feature(True))\n",
-    "print(_int64_feature(1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "nj1qpfQU5qmi"
-   },
-   "source": [
-    "**Lab Task #1b:** All proto messages can be serialized to a binary-string using the `.SerializeToString` method. Use this method to complete the below `TODO`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "5afZkORT5pjm"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "b'\\x12\\x06\\n\\x04T\\xf8-@'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "feature = _float_feature(np.exp(1))\n",
-    "\n",
-    "# TODO 1b\n",
-    "# TODO: Complete the code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "laKnw9F3hL-W"
-   },
-   "source": [
-    "### Creating a `tf.Example` message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "b_MEnhxchQPC"
-   },
-   "source": [
-    "Suppose you want to create a `tf.Example` message from existing data. In practice, the dataset may come from anywhere, but the procedure of creating the `tf.Example` message from a single observation will be the same:\n",
-    "\n",
-    "1. Within each observation, each value needs to be converted to a `tf.train.Feature` containing one of the 3 compatible types, using one of the functions above.\n",
-    "\n",
-    "1. You create a map (dictionary) from the feature name string to the encoded feature value produced in #1.\n",
-    "\n",
-    "1. The map produced in step 2 is converted to a [`Features` message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "4EgFQ2uHtchc"
-   },
-   "source": [
-    "In this notebook, you will create a dataset using NumPy.\n",
-    "\n",
-    "This dataset will have 4 features:\n",
-    "\n",
-    "* a boolean feature, `False` or `True` with equal probability\n",
-    "* an integer feature uniformly randomly chosen from `[0, 5]`\n",
-    "* a string feature generated from a string table by using the integer feature as an index\n",
-    "* a float feature from a standard normal distribution\n",
-    "\n",
-    "Consider a sample consisting of 10,000 independently and identically distributed observations from each of the above distributions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "CnrguFAy3YQv"
-   },
-   "outputs": [],
-   "source": [
-    "# The number of observations in the dataset.\n",
-    "n_observations = int(1e4)\n",
-    "\n",
-    "# Boolean feature, encoded as False or True.\n",
-    "feature0 = np.random.choice([False, True], n_observations)\n",
-    "\n",
-    "# Integer feature, random from 0 to 4.\n",
-    "feature1 = np.random.randint(0, 5, n_observations)\n",
-    "\n",
-    "# String feature\n",
-    "strings = np.array([b\"cat\", b\"dog\", b\"chicken\", b\"horse\", b\"goat\"])\n",
-    "feature2 = strings[feature1]\n",
-    "\n",
-    "# Float feature, from a standard normal distribution\n",
-    "feature3 = np.random.randn(n_observations)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "aGrscehJr7Jd"
-   },
-   "source": [
-    "Each of these features can be coerced into a `tf.Example`-compatible type using one of `_bytes_feature`, `_float_feature`, `_int64_feature`. You can then create a `tf.Example` message from these encoded features:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "RTCS49Ij_kUw"
-   },
-   "outputs": [],
-   "source": [
-    "def serialize_example(feature0, feature1, feature2, feature3):\n",
-    "    \"\"\"\n",
-    "    Creates a tf.Example message ready to be written to a file.\n",
-    "    \"\"\"\n",
-    "    # Create a dictionary mapping the feature name to the tf.Example-compatible\n",
-    "    # data type.\n",
-    "    feature = {\n",
-    "        \"feature0\": _int64_feature(feature0),\n",
-    "        \"feature1\": _int64_feature(feature1),\n",
-    "        \"feature2\": _bytes_feature(feature2),\n",
-    "        \"feature3\": _float_feature(feature3),\n",
-    "    }\n",
-    "\n",
-    "    # Create a Features message using tf.train.Example.\n",
-    "\n",
-    "    example_proto = tf.train.Example(\n",
-    "        features=tf.train.Features(feature=feature)\n",
-    "    )\n",
-    "    return example_proto.SerializeToString()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "XftzX9CN_uGT"
-   },
-   "source": [
-    "For example, suppose you have a single observation from the dataset, `[False, 4, bytes('goat'), 0.9876]`. You can create and print the `tf.Example` message for this observation using `create_message()`. Each single observation will be written as a `Features` message as per the above. Note that the `tf.Example` [message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88) is just a wrapper around the `Features` message:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "N8BtSx2RjYcb"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "b'\\nR\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x04\\n\\x14\\n\\x08feature2\\x12\\x08\\n\\x06\\n\\x04goat\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04[\\xd3|?'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This is an example observation from the dataset.\n",
-    "\n",
-    "example_observation = []\n",
-    "\n",
-    "serialized_example = serialize_example(False, 4, b\"goat\", 0.9876)\n",
-    "serialized_example"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "_pbGATlG6u-4"
-   },
-   "source": [
-    "**Lab Task #1c:** To decode the message use the `tf.train.Example.FromString` method and complete the below TODO"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "dGim-mEm6vit"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "features {\n",
-       "  feature {\n",
-       "    key: \"feature0\"\n",
-       "    value {\n",
-       "      int64_list {\n",
-       "        value: 0\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  feature {\n",
-       "    key: \"feature1\"\n",
-       "    value {\n",
-       "      int64_list {\n",
-       "        value: 4\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  feature {\n",
-       "    key: \"feature2\"\n",
-       "    value {\n",
-       "      bytes_list {\n",
-       "        value: \"goat\"\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  feature {\n",
-       "    key: \"feature3\"\n",
-       "    value {\n",
-       "      float_list {\n",
-       "        value: 0.9876000285148621\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# TODO 1c\n",
-    "example_proto = # TODO: Complete the code here\n",
-    "example_proto"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "o6qxofy89obI"
-   },
-   "source": [
-    "## TFRecords format details\n",
-    "\n",
-    "A TFRecord file contains a sequence of records. The file can only be read sequentially.\n",
-    "\n",
-    "Each record contains a byte-string, for the data-payload, plus the data-length, and  CRC32C (32-bit CRC using the Castagnoli polynomial) hashes for integrity checking.\n",
-    "\n",
-    "Each record is stored in the following formats:\n",
-    "\n",
-    "    uint64 length\n",
-    "    uint32 masked_crc32_of_length\n",
-    "    byte   data[length]\n",
-    "    uint32 masked_crc32_of_data\n",
-    "\n",
-    "The records are concatenated together to produce the file. CRCs are\n",
-    "[described here](https://en.wikipedia.org/wiki/Cyclic_redundancy_check), and\n",
-    "the mask of a CRC is:\n",
-    "\n",
-    "    masked_crc = ((crc >> 15) | (crc << 17)) + 0xa282ead8ul\n",
-    "\n",
-    "Note: There is no requirement to use `tf.Example` in TFRecord files. `tf.Example` is just a method of serializing dictionaries to byte-strings. Lines of text, encoded  image data, or serialized tensors (using `tf.io.serialize_tensor`, and\n",
-    "`tf.io.parse_tensor` when loading). See the `tf.io` module for more options."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "y-Hjmee-fbLH"
-   },
-   "source": [
-    "## TFRecord files using `tf.data`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "GmehkCCT81Ez"
-   },
-   "source": [
-    "The `tf.data` module also provides tools for reading and writing data in TensorFlow."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "1FISEuz8ubu3"
-   },
-   "source": [
-    "### Writing a TFRecord file\n",
-    "\n",
-    "The easiest way to get the data into a dataset is to use the `from_tensor_slices` method.\n",
-    "\n",
-    "Applied to an array, it returns a dataset of scalars:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "mXeaukvwu5_-"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<TensorSliceDataset shapes: (), types: tf.int64>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tf.data.Dataset.from_tensor_slices(feature1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "f-q0VKyZvcad"
-   },
-   "source": [
-    "Applied to a tuple of arrays, it returns a dataset of tuples:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "H5sWyu1kxnvg"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<TensorSliceDataset shapes: ((), (), (), ()), types: (tf.bool, tf.int64, tf.string, tf.float64)>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features_dataset = tf.data.Dataset.from_tensor_slices(\n",
-    "    (feature0, feature1, feature2, feature3)\n",
-    ")\n",
-    "features_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "m1C-t71Nywze"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tf.Tensor(False, shape=(), dtype=bool)\n",
-      "tf.Tensor(1, shape=(), dtype=int64)\n",
-      "tf.Tensor(b'dog', shape=(), dtype=string)\n",
-      "tf.Tensor(-0.6086492521118764, shape=(), dtype=float64)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Use `take(1)` to only pull one example from the dataset.\n",
-    "for f0, f1, f2, f3 in features_dataset.take(1):\n",
-    "    print(f0)\n",
-    "    print(f1)\n",
-    "    print(f2)\n",
-    "    print(f3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "mhIe63awyZYd"
-   },
-   "source": [
-    "Use the `tf.data.Dataset.map` method to apply a function to each element of a `Dataset`.\n",
-    "\n",
-    "The mapped function must operate in TensorFlow graph mode—it must operate on and return `tf.Tensors`. A non-tensor function, like `serialize_example`, can be wrapped with `tf.py_function` to make it compatible.\n",
-    "\n",
-    "**Lab Task 2a:** Using `tf.py_function` requires to specify the shape and type information that is otherwise unavailable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "apB5KYrJzjPI"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 2a\n",
-    "# TODO: Your code goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "lHFjW4u4Npz9"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03dog\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04p\\xd0\\x1b\\xbf'>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tf_serialize_example(f0, f1, f2, f3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "CrFZ9avE3HUF"
-   },
-   "source": [
-    "**Lab Task 2b:** Apply this function to each element in the features_dataset using the map function and complete below `TODO`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "VDeqYVbW3ww9"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<MapDataset shapes: (), types: tf.string>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# TODO 2b\n",
-    "serialized_features_dataset = #TODO : Complete the code here.\n",
-    "serialized_features_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "DlDfuh46bRf6"
-   },
-   "outputs": [],
-   "source": [
-    "def generator():\n",
-    "    for features in features_dataset:\n",
-    "        yield serialize_example(*features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "iv9oXKrcbhvX"
-   },
-   "outputs": [],
-   "source": [
-    "serialized_features_dataset = tf.data.Dataset.from_generator(\n",
-    "    generator, output_types=tf.string, output_shapes=()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Dqz8C4D5cIj9"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<FlatMapDataset shapes: (), types: tf.string>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "serialized_features_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "p6lw5VYpjZZC"
-   },
-   "source": [
-    "And write them to a TFRecord file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "vP1VgTO44UIE"
-   },
-   "outputs": [],
-   "source": [
-    "filename = \"test.tfrecord\"\n",
-    "writer = tf.data.experimental.TFRecordWriter(filename)\n",
-    "writer.write(serialized_features_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6aV0GQhV8tmp"
-   },
-   "source": [
-    "### Reading a TFRecord file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "o3J5D4gcSy8N"
-   },
-   "source": [
-    "You can also read the TFRecord file using the `tf.data.TFRecordDataset` class.\n",
-    "\n",
-    "More information on consuming TFRecord files using `tf.data` can be found [here](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data).\n",
-    "\n",
-    "**Lab Task 2c:** Complete the below TODO by using `TFRecordDataset`s which is useful for standardizing input data and optimizing performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "6OjX6UZl-bHC"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<TFRecordDatasetV2 shapes: (), types: tf.string>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# TODO 2c\n",
-    "# TODO: Your code goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6_EQ9i2E_-Fz"
-   },
-   "source": [
-    "At this point the dataset contains serialized `tf.train.Example` messages. When iterated over it returns these as scalar string tensors.\n",
-    "\n",
-    "Use the `.take` method to only show the first 10 records.\n",
-    "\n",
-    "Note: iterating over a `tf.data.Dataset` only works with eager execution enabled."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "hxVXpLz_AJlm"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03dog\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04p\\xd0\\x1b\\xbf\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x01'>\n",
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nS\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x03\\n\\x15\\n\\x08feature2\\x12\\t\\n\\x07\\n\\x05horse\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\xa6\\xbf\\xba\\xbe'>\n",
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nS\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x03\\n\\x15\\n\\x08feature2\\x12\\t\\n\\x07\\n\\x05horse\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\xaa\\x05/@'>\n",
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03dog\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04C\\x96\\n?\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x01'>\n",
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03cat\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04^\\x06\\x96>\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x00'>\n",
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03dog\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\x057\\x8c\\xbe'>\n",
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nS\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x03\\n\\x15\\n\\x08feature2\\x12\\t\\n\\x07\\n\\x05horse\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\xbco\\xab\\xbe\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00'>\n",
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03cat\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04p[|\\xbd'>\n",
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nU\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x02\\n\\x17\\n\\x08feature2\\x12\\x0b\\n\\t\\n\\x07chicken\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\xba.\\xb6\\xbf'>\n",
-      "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\x96tf?\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03cat'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "for raw_record in raw_dataset.take(10):\n",
-    "    print(repr(raw_record))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "W-6oNzM4luFQ"
-   },
-   "source": [
-    "These tensors can be parsed using the function below. Note that the `feature_description` is necessary here because datasets use graph-execution, and need this description to build their shape and type signature:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "zQjbIR1nleiy"
-   },
-   "outputs": [],
-   "source": [
-    "# Create a description of the features.\n",
-    "feature_description = {\n",
-    "    \"feature0\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
-    "    \"feature1\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
-    "    \"feature2\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n",
-    "    \"feature3\": tf.io.FixedLenFeature([], tf.float32, default_value=0.0),\n",
-    "}\n",
-    "\n",
-    "\n",
-    "def _parse_function(example_proto):\n",
-    "    # Parse the input `tf.Example` proto using the dictionary above.\n",
-    "    return tf.io.parse_single_example(example_proto, feature_description)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "gWETjUqhEQZf"
-   },
-   "source": [
-    "Alternatively, use `tf.parse example` to parse the whole batch at once. Apply this function to each item in the dataset using the `tf.data.Dataset.map` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "6Ob7D-zmBm1w"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<MapDataset shapes: {feature0: (), feature2: (), feature1: (), feature3: ()}, types: {feature0: tf.int64, feature2: tf.string, feature1: tf.int64, feature3: tf.float32}>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "parsed_dataset = raw_dataset.map(_parse_function)\n",
-    "parsed_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "sNV-XclGnOvn"
-   },
-   "source": [
-    "Use eager execution to display the observations in the dataset. There are 10,000 observations in this dataset, but you will only display the first 10. The data is displayed as a dictionary of features. Each item is a `tf.Tensor`, and the `numpy` element of this tensor displays the value of the feature:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "x2LT2JCqhoD_"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'dog'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.60864925>}\n",
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'horse'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.3647434>}\n",
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'horse'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=2.7347207>}\n",
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'dog'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=0.5413553>}\n",
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'cat'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=0.29301733>}\n",
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'dog'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.27385727>}\n",
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'horse'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.33483684>}\n",
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'cat'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.06161064>}\n",
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'chicken'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=2>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-1.423301>}\n",
-      "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'cat'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=0.90021646>}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for parsed_record in parsed_dataset.take(10):\n",
-    "    print(repr(parsed_record))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Cig9EodTlDmg"
-   },
-   "source": [
-    "Here, the `tf.parse_example` function unpacks the `tf.Example` fields into standard tensors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "jyg1g3gU7DNn"
-   },
-   "source": [
-    "## TFRecord files in Python"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3FXG3miA7Kf1"
-   },
-   "source": [
-    "The `tf.io` module also contains pure-Python functions for reading and writing TFRecord files."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "CKn5uql2lAaN"
-   },
-   "source": [
-    "### Writing a TFRecord file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "LNW_FA-GQWXs"
-   },
-   "source": [
-    "Next, write the 10,000 observations to the file `test.tfrecord`. Each observation is converted to a `tf.Example` message, then written to file. You can then verify that the file `test.tfrecord` has been created:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "MKPHzoGv7q44"
-   },
-   "outputs": [],
-   "source": [
-    "# Write the `tf.Example` observations to the file.\n",
-    "with tf.io.TFRecordWriter(filename) as writer:\n",
-    "    for i in range(n_observations):\n",
-    "        example = serialize_example(\n",
-    "            feature0[i], feature1[i], feature2[i], feature3[i]\n",
-    "        )\n",
-    "        writer.write(example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "EjdFHHJMpUUo"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "984K\ttest.tfrecord\n"
-     ]
-    }
-   ],
-   "source": [
-    "!du -sh {filename}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2osVRnYNni-E"
-   },
-   "source": [
-    "### Reading a TFRecord file\n",
-    "\n",
-    "These serialized tensors can be easily parsed using `tf.train.Example.ParseFromString`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "U3tnd3LerOtV"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<TFRecordDatasetV2 shapes: (), types: tf.string>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filenames = [filename]\n",
-    "raw_dataset = tf.data.TFRecordDataset(filenames)\n",
-    "raw_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nsEAACHcnm3f"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "features {\n",
-      "  feature {\n",
-      "    key: \"feature0\"\n",
-      "    value {\n",
-      "      int64_list {\n",
-      "        value: 0\n",
-      "      }\n",
-      "    }\n",
-      "  }\n",
-      "  feature {\n",
-      "    key: \"feature1\"\n",
-      "    value {\n",
-      "      int64_list {\n",
-      "        value: 1\n",
-      "      }\n",
-      "    }\n",
-      "  }\n",
-      "  feature {\n",
-      "    key: \"feature2\"\n",
-      "    value {\n",
-      "      bytes_list {\n",
-      "        value: \"dog\"\n",
-      "      }\n",
-      "    }\n",
-      "  }\n",
-      "  feature {\n",
-      "    key: \"feature3\"\n",
-      "    value {\n",
-      "      float_list {\n",
-      "        value: -0.6086492538452148\n",
-      "      }\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for raw_record in raw_dataset.take(1):\n",
-    "    example = tf.train.Example()\n",
-    "    example.ParseFromString(raw_record.numpy())\n",
-    "    print(example)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "S0tFDrwdoj3q"
-   },
-   "source": [
-    "## Walkthrough: Reading and writing image data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rjN2LFxFpcR9"
-   },
-   "source": [
-    "This is an end-to-end example of how to read and write image data using TFRecords. Using an image as input data, you will write the data as a TFRecord file, then read the file back and display the image.\n",
-    "\n",
-    "This can be useful if, for example, you want to use several models on the same input dataset. Instead of storing the image data raw, it can be preprocessed into the TFRecords format, and that can be used in all further processing and modelling.\n",
-    "\n",
-    "First, let's download [this image](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg) of a cat in the snow and [this photo](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg) of the Williamsburg Bridge, NYC under construction."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "5Lk2qrKvN0yu"
-   },
-   "source": [
-    "### Fetch the images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3a0fmwg8lHdF"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg\n",
-      "24576/17858 [=========================================] - 0s 0us/step\n",
-      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\n",
-      "16384/15477 [===============================] - 0s 0us/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "cat_in_snow = tf.keras.utils.get_file(\n",
-    "    \"320px-Felis_catus-cat_on_snow.jpg\",\n",
-    "    \"https://storage.googleapis.com/download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg\",\n",
-    ")\n",
-    "williamsburg_bridge = tf.keras.utils.get_file(\n",
-    "    \"194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\",\n",
-    "    \"https://storage.googleapis.com/download.tensorflow.org/example_images/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "7aJJh7vENeE4"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Image cc-by: <a \"href=https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg\">Von.grzanka</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display.display(display.Image(filename=cat_in_snow))\n",
-    "display.display(\n",
-    "    display.HTML(\n",
-    "        'Image cc-by: <a \"href=https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg\">Von.grzanka</a>'\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "KkW0uuhcXZqA"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<a \"href=https://commons.wikimedia.org/wiki/File:New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\">From Wikimedia</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display.display(display.Image(filename=williamsburg_bridge))\n",
-    "display.display(\n",
-    "    display.HTML(\n",
-    "        '<a \"href=https://commons.wikimedia.org/wiki/File:New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\">From Wikimedia</a>'\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "VSOgJSwoN5TQ"
-   },
-   "source": [
-    "### Write the TFRecord file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Azx83ryQEU6T"
-   },
-   "source": [
-    "As before, encode the features as types compatible with `tf.Example`. This stores the raw image string feature, as well as the height, width, depth, and arbitrary `label` feature. The latter is used when you write the file to distinguish between the cat image and the bridge image. Use `0` for the cat_in_snow image, and `1` for the williamsburg_bridge image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kC4TS1ZEONHr"
-   },
-   "outputs": [],
-   "source": [
-    "image_labels = {\n",
-    "    cat_in_snow: 0,\n",
-    "    williamsburg_bridge: 1,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "c5njMSYNEhNZ"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "features {\n",
-      "  feature {\n",
-      "    key: \"depth\"\n",
-      "    value {\n",
-      "      int64_list {\n",
-      "        value: 3\n",
-      "      }\n",
-      "    }\n",
-      "  }\n",
-      "  feature {\n",
-      "    key: \"height\"\n",
-      "    value {\n",
-      "      int64_list {\n",
-      "        value: 213\n",
-      "      }\n",
-      "...\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This is an example, just using the cat image.\n",
-    "image_string = open(cat_in_snow, \"rb\").read()\n",
-    "\n",
-    "label = image_labels[cat_in_snow]\n",
-    "\n",
-    "\n",
-    "# Create a dictionary with features that may be relevant.\n",
-    "def image_example(image_string, label):\n",
-    "    image_shape = tf.image.decode_jpeg(image_string).shape\n",
-    "\n",
-    "    feature = {\n",
-    "        \"height\": _int64_feature(image_shape[0]),\n",
-    "        \"width\": _int64_feature(image_shape[1]),\n",
-    "        \"depth\": _int64_feature(image_shape[2]),\n",
-    "        \"label\": _int64_feature(label),\n",
-    "        \"image_raw\": _bytes_feature(image_string),\n",
-    "    }\n",
-    "\n",
-    "    return tf.train.Example(features=tf.train.Features(feature=feature))\n",
-    "\n",
-    "\n",
-    "for line in str(image_example(image_string, label)).split(\"\\n\")[:15]:\n",
-    "    print(line)\n",
-    "print(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2G_o3O9MN0Qx"
-   },
-   "source": [
-    "Notice that all of the features are now stored in the `tf.Example` message. Next, functionalize the code above and write the example messages to a file named `images.tfrecords`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "qcw06lQCOCZU"
-   },
-   "outputs": [],
-   "source": [
-    "# Write the raw image files to `images.tfrecords`.\n",
-    "# First, process the two images into `tf.Example` messages.\n",
-    "# Then, write to a `.tfrecords` file.\n",
-    "record_file = \"images.tfrecords\"\n",
-    "with tf.io.TFRecordWriter(record_file) as writer:\n",
-    "    for filename, label in image_labels.items():\n",
-    "        image_string = open(filename, \"rb\").read()\n",
-    "        tf_example = image_example(image_string, label)\n",
-    "        writer.write(tf_example.SerializeToString())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "yJrTe6tHPCfs"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "36K\timages.tfrecords\n"
-     ]
-    }
-   ],
-   "source": [
-    "!du -sh {record_file}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "jJSsCkZLPH6K"
-   },
-   "source": [
-    "### Read the TFRecord file\n",
-    "\n",
-    "You now have the file—`images.tfrecords`—and can now iterate over the records in it to read back what you wrote. Given that in this example you will only reproduce the image, the only feature you will need is the raw image string. Extract it using the getters described above, namely `example.features.feature['image_raw'].bytes_list.value[0]`. You can also use the labels to determine which record is the cat and which one is the bridge:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "M6Cnfd3cTKHN"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<MapDataset shapes: {depth: (), height: (), width: (), image_raw: (), label: ()}, types: {depth: tf.int64, height: tf.int64, width: tf.int64, image_raw: tf.string, label: tf.int64}>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_image_dataset = tf.data.TFRecordDataset(\"images.tfrecords\")\n",
-    "\n",
-    "# Create a dictionary describing the features.\n",
-    "image_feature_description = {\n",
-    "    \"height\": tf.io.FixedLenFeature([], tf.int64),\n",
-    "    \"width\": tf.io.FixedLenFeature([], tf.int64),\n",
-    "    \"depth\": tf.io.FixedLenFeature([], tf.int64),\n",
-    "    \"label\": tf.io.FixedLenFeature([], tf.int64),\n",
-    "    \"image_raw\": tf.io.FixedLenFeature([], tf.string),\n",
-    "}\n",
-    "\n",
-    "\n",
-    "def _parse_image_function(example_proto):\n",
-    "    # Parse the input tf.Example proto using the dictionary above.\n",
-    "    return tf.io.parse_single_example(example_proto, image_feature_description)\n",
-    "\n",
-    "\n",
-    "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n",
-    "parsed_image_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "0PEEFPk4NEg1"
-   },
-   "source": [
-    "Recover the images from the TFRecord file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "yZf8jOyEIjSF"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/jpeg": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for image_features in parsed_image_dataset:\n",
-    "    image_raw = image_features[\"image_raw\"].numpy()\n",
-    "    display.display(display.Image(data=image_raw))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc.\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-    "http://www.apache.org/licenses/LICENSE-2.0\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [
-    "pL--_KGdYoBz"
-   ],
-   "name": "tfrecord.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/what_if_mortgage.ipynb b/notebooks/introduction_to_tensorflow/labs/what_if_mortgage.ipynb
deleted file mode 100644
index f94860ea..00000000
--- a/notebooks/introduction_to_tensorflow/labs/what_if_mortgage.ipynb
+++ /dev/null
@@ -1,766 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# LABXX: What-if Tool: Model Interpretability Using Mortgage Data \n",
-    "\n",
-    "**Learning Objectives**\n",
-    "\n",
-    "1. Create a What-if Tool visualization\n",
-    "2. What-if Tool exploration using the XGBoost Model\n",
-    " \n",
-    " \n",
-    "## Introduction \n",
-    "\n",
-    "This notebook shows how to use the [What-if Tool (WIT)](https://pair-code.github.io/what-if-tool/) on a deployed [Cloud AI Platform](https://cloud.google.com/ai-platform/) model. The What-If Tool provides an easy-to-use interface for expanding understanding of black-box classification and regression ML models. With the plugin, you can perform inference on a large set of examples and immediately visualize the results in a variety of ways. Additionally, examples can be edited manually or programmatically and re-run through the model in order to see the results of the changes. It contains tooling for investigating model performance and fairness over subsets of a dataset.  The purpose of the tool is to give people a simple, intuitive, and powerful way to explore and investigate trained ML models through a visual interface with absolutely no code required.\n",
-    "\n",
-    "[Extreme Gradient Boosting (XGBoost)](https://xgboost.ai/) is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree based algorithms are considered best-in-class right now. Please see the chart below for the evolution of tree-based algorithms over the years.\n",
-    "\n",
-    "*You don't need your own cloud project* to run this notebook. \n",
-    "\n",
-    "** UPDATE LINK BEFORE PRODUCTION **:  Each learning objective will correspond to a __#TODO__ in the [student lab notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/gwendolyn-dev/courses/machine_learning/deepdive2/ml_on_gc/what_if_mortgage.ipynb)) -- try to complete that notebook first before reviewing this solution notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set up environment variables and load necessary libraries \n",
-    "We will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Y93EHw56Vtid"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Python Version:  3\n"
-     ]
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "\n",
-    "python_version = sys.version_info[0]\n",
-    "print(\"Python Version: \", python_version)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip3 install witwidget"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "CosDxuLy7M4Q"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import witwidget\n",
-    "from witwidget.notebook.visualization import WitConfigBuilder, WitWidget"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "bFIxtguO1In_"
-   },
-   "source": [
-    "## Loading the mortgage test dataset\n",
-    "\n",
-    "The model we'll be exploring here is a binary classification model built with XGBoost and trained on a [mortgage dataset](https://www.ffiec.gov/hmda/hmdaflat.htm). It predicts whether or not a mortgage application will be approved. In this section we'll:\n",
-    "\n",
-    "* Download some test data from Cloud Storage and load it into a numpy array + Pandas DataFrame\n",
-    "* Preview the features for our model in Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "9BngZjdsO6Mr"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Copying gs://mortgage_dataset_files/data.pkl...\n",
-      "| [1 files][104.0 MiB/104.0 MiB]                                                \n",
-      "Operation completed over 1 objects/104.0 MiB.                                    \n",
-      "Copying gs://mortgage_dataset_files/x_test.npy...\n",
-      "/ [1 files][172.0 KiB/172.0 KiB]                                                \n",
-      "Operation completed over 1 objects/172.0 KiB.                                    \n",
-      "Copying gs://mortgage_dataset_files/y_test.npy...\n",
-      "/ [1 files][  628.0 B/  628.0 B]                                                \n",
-      "Operation completed over 1 objects/628.0 B.                                      \n"
-     ]
-    }
-   ],
-   "source": [
-    "# Download our Pandas dataframe and our test features and labels\n",
-    "!gsutil cp gs://mortgage_dataset_files/data.pkl .\n",
-    "!gsutil cp gs://mortgage_dataset_files/x_test.npy .\n",
-    "!gsutil cp gs://mortgage_dataset_files/y_test.npy ."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preview the Features \n",
-    "\n",
-    "Preview the features from our model as a pandas DataFrame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "GkHavVlmGYlk"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>as_of_year</th>\n",
-       "      <th>occupancy</th>\n",
-       "      <th>loan_amt_thousands</th>\n",
-       "      <th>county_code</th>\n",
-       "      <th>applicant_income_thousands</th>\n",
-       "      <th>population</th>\n",
-       "      <th>ffiec_median_fam_income</th>\n",
-       "      <th>tract_to_msa_income_pct</th>\n",
-       "      <th>num_owner_occupied_units</th>\n",
-       "      <th>num_1_to_4_family_units</th>\n",
-       "      <th>...</th>\n",
-       "      <th>purchaser_type_Life insurance company, credit union, mortgage bank, or finance company</th>\n",
-       "      <th>purchaser_type_Loan was not originated or was not sold in calendar year covered by register</th>\n",
-       "      <th>purchaser_type_Other type of purchaser</th>\n",
-       "      <th>purchaser_type_Private securitization</th>\n",
-       "      <th>hoepa_status_HOEPA loan</th>\n",
-       "      <th>hoepa_status_Not a HOEPA loan</th>\n",
-       "      <th>lien_status_Not applicable (purchased loans)</th>\n",
-       "      <th>lien_status_Not secured by a lien</th>\n",
-       "      <th>lien_status_Secured by a first lien</th>\n",
-       "      <th>lien_status_Secured by a subordinate lien</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>310650</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>1</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>119.0</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>5930.0</td>\n",
-       "      <td>64100.0</td>\n",
-       "      <td>98.81</td>\n",
-       "      <td>1305.0</td>\n",
-       "      <td>1631.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>630129</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>1</td>\n",
-       "      <td>480.0</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>270.0</td>\n",
-       "      <td>4791.0</td>\n",
-       "      <td>90300.0</td>\n",
-       "      <td>144.06</td>\n",
-       "      <td>1420.0</td>\n",
-       "      <td>1450.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>715484</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>2</td>\n",
-       "      <td>240.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>3439.0</td>\n",
-       "      <td>105700.0</td>\n",
-       "      <td>104.62</td>\n",
-       "      <td>853.0</td>\n",
-       "      <td>1076.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>887708</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>1</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>3952.0</td>\n",
-       "      <td>61300.0</td>\n",
-       "      <td>90.93</td>\n",
-       "      <td>1272.0</td>\n",
-       "      <td>1666.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>719598</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>1</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>2422.0</td>\n",
-       "      <td>46400.0</td>\n",
-       "      <td>88.37</td>\n",
-       "      <td>650.0</td>\n",
-       "      <td>1006.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 44 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        as_of_year  occupancy  loan_amt_thousands  county_code  \\\n",
-       "310650        2016          1               110.0        119.0   \n",
-       "630129        2016          1               480.0         33.0   \n",
-       "715484        2016          2               240.0         59.0   \n",
-       "887708        2016          1                76.0         65.0   \n",
-       "719598        2016          1               100.0        127.0   \n",
-       "\n",
-       "        applicant_income_thousands  population  ffiec_median_fam_income  \\\n",
-       "310650                        55.0      5930.0                  64100.0   \n",
-       "630129                       270.0      4791.0                  90300.0   \n",
-       "715484                        96.0      3439.0                 105700.0   \n",
-       "887708                        85.0      3952.0                  61300.0   \n",
-       "719598                        70.0      2422.0                  46400.0   \n",
-       "\n",
-       "        tract_to_msa_income_pct  num_owner_occupied_units  \\\n",
-       "310650                    98.81                    1305.0   \n",
-       "630129                   144.06                    1420.0   \n",
-       "715484                   104.62                     853.0   \n",
-       "887708                    90.93                    1272.0   \n",
-       "719598                    88.37                     650.0   \n",
-       "\n",
-       "        num_1_to_4_family_units  ...  \\\n",
-       "310650                   1631.0  ...   \n",
-       "630129                   1450.0  ...   \n",
-       "715484                   1076.0  ...   \n",
-       "887708                   1666.0  ...   \n",
-       "719598                   1006.0  ...   \n",
-       "\n",
-       "        purchaser_type_Life insurance company, credit union, mortgage bank, or finance company  \\\n",
-       "310650                                                  0                                        \n",
-       "630129                                                  0                                        \n",
-       "715484                                                  0                                        \n",
-       "887708                                                  0                                        \n",
-       "719598                                                  0                                        \n",
-       "\n",
-       "        purchaser_type_Loan was not originated or was not sold in calendar year covered by register  \\\n",
-       "310650                                                  0                                             \n",
-       "630129                                                  1                                             \n",
-       "715484                                                  0                                             \n",
-       "887708                                                  1                                             \n",
-       "719598                                                  1                                             \n",
-       "\n",
-       "        purchaser_type_Other type of purchaser  \\\n",
-       "310650                                       0   \n",
-       "630129                                       0   \n",
-       "715484                                       0   \n",
-       "887708                                       0   \n",
-       "719598                                       0   \n",
-       "\n",
-       "        purchaser_type_Private securitization  hoepa_status_HOEPA loan  \\\n",
-       "310650                                      0                        0   \n",
-       "630129                                      0                        0   \n",
-       "715484                                      0                        0   \n",
-       "887708                                      0                        0   \n",
-       "719598                                      0                        0   \n",
-       "\n",
-       "        hoepa_status_Not a HOEPA loan  \\\n",
-       "310650                              1   \n",
-       "630129                              1   \n",
-       "715484                              1   \n",
-       "887708                              1   \n",
-       "719598                              1   \n",
-       "\n",
-       "        lien_status_Not applicable (purchased loans)  \\\n",
-       "310650                                             0   \n",
-       "630129                                             0   \n",
-       "715484                                             0   \n",
-       "887708                                             0   \n",
-       "719598                                             0   \n",
-       "\n",
-       "        lien_status_Not secured by a lien  \\\n",
-       "310650                                  0   \n",
-       "630129                                  0   \n",
-       "715484                                  0   \n",
-       "887708                                  0   \n",
-       "719598                                  0   \n",
-       "\n",
-       "        lien_status_Secured by a first lien  \\\n",
-       "310650                                    1   \n",
-       "630129                                    1   \n",
-       "715484                                    1   \n",
-       "887708                                    0   \n",
-       "719598                                    1   \n",
-       "\n",
-       "        lien_status_Secured by a subordinate lien  \n",
-       "310650                                          0  \n",
-       "630129                                          0  \n",
-       "715484                                          0  \n",
-       "887708                                          1  \n",
-       "719598                                          0  \n",
-       "\n",
-       "[5 rows x 44 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = pd.read_pickle(\"data.pkl\")\n",
-    "features.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 999999 entries, 310650 to 875688\n",
-      "Data columns (total 44 columns):\n",
-      "as_of_year                                                                                     999999 non-null int16\n",
-      "occupancy                                                                                      999999 non-null int8\n",
-      "loan_amt_thousands                                                                             999999 non-null float64\n",
-      "county_code                                                                                    999999 non-null float64\n",
-      "applicant_income_thousands                                                                     999999 non-null float64\n",
-      "population                                                                                     999999 non-null float64\n",
-      "ffiec_median_fam_income                                                                        999999 non-null float64\n",
-      "tract_to_msa_income_pct                                                                        999999 non-null float64\n",
-      "num_owner_occupied_units                                                                       999999 non-null float64\n",
-      "num_1_to_4_family_units                                                                        999999 non-null float64\n",
-      "agency_code_Consumer Financial Protection Bureau (CFPB)                                        999999 non-null uint8\n",
-      "agency_code_Department of Housing and Urban Development (HUD)                                  999999 non-null uint8\n",
-      "agency_code_Federal Deposit Insurance Corporation (FDIC)                                       999999 non-null uint8\n",
-      "agency_code_Federal Reserve System (FRS)                                                       999999 non-null uint8\n",
-      "agency_code_National Credit Union Administration (NCUA)                                        999999 non-null uint8\n",
-      "agency_code_Office of the Comptroller of the Currency (OCC)                                    999999 non-null uint8\n",
-      "loan_type_Conventional (any loan other than FHA, VA, FSA, or RHS loans)                        999999 non-null uint8\n",
-      "loan_type_FHA-insured (Federal Housing Administration)                                         999999 non-null uint8\n",
-      "loan_type_FSA/RHS (Farm Service Agency or Rural Housing Service)                               999999 non-null uint8\n",
-      "loan_type_VA-guaranteed (Veterans Administration)                                              999999 non-null uint8\n",
-      "property_type_Manufactured housing                                                             999999 non-null uint8\n",
-      "property_type_One to four-family (other than manufactured housing)                             999999 non-null uint8\n",
-      "loan_purpose_Home improvement                                                                  999999 non-null uint8\n",
-      "loan_purpose_Home purchase                                                                     999999 non-null uint8\n",
-      "loan_purpose_Refinancing                                                                       999999 non-null uint8\n",
-      "preapproval_Not applicable                                                                     999999 non-null uint8\n",
-      "preapproval_Preapproval was not requested                                                      999999 non-null uint8\n",
-      "preapproval_Preapproval was requested                                                          999999 non-null uint8\n",
-      "purchaser_type_Affiliate institution                                                           999999 non-null uint8\n",
-      "purchaser_type_Commercial bank, savings bank or savings association                            999999 non-null uint8\n",
-      "purchaser_type_Fannie Mae (FNMA)                                                               999999 non-null uint8\n",
-      "purchaser_type_Farmer Mac (FAMC)                                                               999999 non-null uint8\n",
-      "purchaser_type_Freddie Mac (FHLMC)                                                             999999 non-null uint8\n",
-      "purchaser_type_Ginnie Mae (GNMA)                                                               999999 non-null uint8\n",
-      "purchaser_type_Life insurance company, credit union, mortgage bank, or finance company         999999 non-null uint8\n",
-      "purchaser_type_Loan was not originated or was not sold in calendar year covered by register    999999 non-null uint8\n",
-      "purchaser_type_Other type of purchaser                                                         999999 non-null uint8\n",
-      "purchaser_type_Private securitization                                                          999999 non-null uint8\n",
-      "hoepa_status_HOEPA loan                                                                        999999 non-null uint8\n",
-      "hoepa_status_Not a HOEPA loan                                                                  999999 non-null uint8\n",
-      "lien_status_Not applicable (purchased loans)                                                   999999 non-null uint8\n",
-      "lien_status_Not secured by a lien                                                              999999 non-null uint8\n",
-      "lien_status_Secured by a first lien                                                            999999 non-null uint8\n",
-      "lien_status_Secured by a subordinate lien                                                      999999 non-null uint8\n",
-      "dtypes: float64(8), int16(1), int8(1), uint8(34)\n",
-      "memory usage: 104.0 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "features.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the test features and labels into numpy arrays"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Developing machine learning models in Python often requires the use of NumPy arrays.  Recall that NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays.  NumPy arrays are efficient data structures for working with data in Python, and machine learning models like those in the scikit-learn library, and deep learning models like those in the Keras library, expect input data in the format of NumPy arrays and make predictions in the format of NumPy arrays.  As such, it is common to need to save NumPy arrays to file.  Note that the data info reveals the following datatypes dtypes: float64(8), int16(1), int8(1), uint8(34) -- and no strings or \"objects\". So, let's now load the features and labels into numpy arrays.   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "57KQ_XX2FEdl"
-   },
-   "outputs": [],
-   "source": [
-    "x_test = np.load(\"x_test.npy\")\n",
-    "y_test = np.load(\"y_test.npy\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at the contents of the 'x_test.npy' file.  You can see the \"array\" structure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[2.016e+03 1.000e+00 4.170e+02 ... 0.000e+00 1.000e+00 0.000e+00]\n",
-      " [2.016e+03 1.000e+00 2.760e+02 ... 0.000e+00 1.000e+00 0.000e+00]\n",
-      " [2.016e+03 1.000e+00 6.000e+01 ... 0.000e+00 1.000e+00 0.000e+00]\n",
-      " ...\n",
-      " [2.016e+03 1.000e+00 5.000e+02 ... 0.000e+00 0.000e+00 0.000e+00]\n",
-      " [2.016e+03 1.000e+00 1.100e+02 ... 0.000e+00 1.000e+00 0.000e+00]\n",
-      " [2.016e+03 1.000e+00 3.680e+02 ... 0.000e+00 1.000e+00 0.000e+00]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(x_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Combine the features and labels into one array for the What-if Tool\n",
-    "\n",
-    "Note that the numpy.hstack() function is used to stack the sequence of input arrays horizontally (i.e. column wise) to make a single array.  In the following example, the numpy matrix is reshaped into a vector using the reshape function with .reshape((-1, 1) to convert the array into a single column matrix."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "hoFIrCQfFgvm"
-   },
-   "outputs": [],
-   "source": [
-    "test_examples = np.hstack((x_test, y_test.reshape(-1, 1)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "-8xNn8EhgUi7"
-   },
-   "source": [
-    "## Using the What-if Tool to interpret our model\n",
-    "With our test examples ready, we can now connect our model to the What-if Tool using the `WitWidget`. To use the What-if Tool with Cloud AI Platform, we need to send it:\n",
-    "* A Python list of our test features + ground truth labels\n",
-    "* Optionally, the names of our columns\n",
-    "* Our Cloud project, model, and version name (we've created a public one for you to play around with)\n",
-    "\n",
-    "See the next cell for some exploration ideas in the What-if Tool."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  Create a What-if Tool visualization\n",
-    "\n",
-    "This prediction adjustment function is needed as this xgboost model's prediction returns just a score for the positive class of the binary classification, whereas the What-If Tool expects a list of scores for each class (in this case, both the negative class and the positive class).  \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**NOTE:** The WIT may take a minute to load.  While it is loading, review the parameters that are defined in the next cell, BUT NOT RUN IT, it is simply for reference."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ******** DO NOT RUN THIS CELL ********\n",
-    "\n",
-    "# TODO 1\n",
-    "\n",
-    "PROJECT_ID = \"YOUR_PROJECT_ID\"\n",
-    "MODEL_NAME = \"YOUR_MODEL_NAME\"\n",
-    "VERSION_NAME = \"YOUR_VERSION_NAME\"\n",
-    "TARGET_FEATURE = \"mortgage_status\"\n",
-    "LABEL_VOCAB = [\"denied\", \"approved\"]\n",
-    "\n",
-    "# TODO 1a\n",
-    "\n",
-    "config_builder = (\n",
-    "    WitConfigBuilder(\n",
-    "        test_examples.tolist(), features.columns.tolist() + [\"mortgage_status\"]\n",
-    "    )\n",
-    "    .set_ai_platform_model(\n",
-    "        PROJECT_ID,\n",
-    "        MODEL_NAME,\n",
-    "        VERSION_NAME,\n",
-    "        adjust_prediction=adjust_prediction,\n",
-    "    )\n",
-    "    .set_target_feature(TARGET_FEATURE)\n",
-    "    .set_label_vocab(LABEL_VOCAB)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run this cell to load the WIT config builder.  **NOTE:** The WIT may take a minute to load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "dqAbAmxkgW4p"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.container { width:100% !important; }</style>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "654bf83aca0642c78308122d31fc000f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "WitWidget(config={'use_aip': True, 'model_name': 'xgb_mortgage', 'uses_json_list': True, 'get_explanations': T…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# TODO 1b\n",
-    "\n",
-    "\n",
-    "def adjust_prediction(pred):\n",
-    "    return [1 - pred, pred]\n",
-    "\n",
-    "\n",
-    "config_builder = (\n",
-    "    WitConfigBuilder(\n",
-    "        test_examples.tolist(), features.columns.tolist() + [\"mortgage_status\"]\n",
-    "    )\n",
-    "    .set_ai_platform_model(\n",
-    "        \"wit-caip-demos\",\n",
-    "        \"xgb_mortgage\",\n",
-    "        \"v1\",\n",
-    "        adjust_prediction=adjust_prediction,\n",
-    "    )\n",
-    "    .set_target_feature(\"mortgage_status\")\n",
-    "    .set_label_vocab([\"denied\", \"approved\"])\n",
-    ")\n",
-    "WitWidget(config_builder, height=800)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "_B2BskDk55rk"
-   },
-   "source": [
-    "## What-if Tool exploration using the XGBoost Model\n",
-    "\n",
-    "#### TODO 2\n",
-    "\n",
-    "* **Individual data points**: The default graph shows all data points from the test set, colored by their ground truth label (approved or denied)\n",
-    "  * Try selecting data points close to the middle and tweaking some of their feature values. Then run inference again to see if the model prediction changes\n",
-    "  * Select a data point and then move the \"Show nearest counterfactual datapoint\" slider to the right. This will highlight a data point with feature values closest to your original one, but with a different prediction\n",
-    "\n",
-    "####  TODO 2a\n",
-    "\n",
-    "* **Binning data**: Create separate graphs for individual features\n",
-    "  * From the \"Binning - X axis\" dropdown, try selecting one of the agency codes, for example \"Department of Housing and Urban Development (HUD)\". This will create 2 separate graphs, one for loan applications from the HUD (graph labeled 1), and one for all other agencies (graph labeled 0). This shows us that loans from this agency are more likely to be denied\n",
-    "\n",
-    "####  TODO 2b\n",
-    "\n",
-    "* **Exploring overall performance**: Click on the \"Performance & Fairness\" tab to view overall performance statistics on the model's results on the provided dataset, including confusion matrices, PR curves, and ROC curves.\n",
-    "   * Experiment with the threshold slider, raising and lowering the positive classification score the model needs to return before it decides to predict \"approved\" for the loan, and see how it changes accuracy, false positives, and false negatives.\n",
-    "   * On the left side \"Slice by\" menu, select \"loan_purpose_Home purchase\". You'll now see performance on the two subsets of your data: the \"0\" slice shows when the loan is not for a home purchase, and the \"1\" slice is for when the loan is for a home purchase. Notice that the model's false positive rate is much higher on loans for home purchases. If you expand the rows to look at the confusion matrices, you can see that the model predicts \"approved\" more often for home purchase loans.\n",
-    "   * You can use the optimization buttons on the left side to have the tool auto-select different positive classification thresholds for each slice in order to achieve different goals. If you select the \"Demographic parity\" button, then the two thresholds will be adjusted so that the model predicts \"approved\" for a similar percentage of applicants in both slices. What does this do to the accuracy, false positives and false negatives for each slice?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc.\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-    "http://www.apache.org/licenses/LICENSE-2.0\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "What-If Tool with XGBoost Cloud AI Platform Model",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/labs/write_low_level_code.ipynb b/notebooks/introduction_to_tensorflow/labs/write_low_level_code.ipynb
deleted file mode 100644
index 7c791adb..00000000
--- a/notebooks/introduction_to_tensorflow/labs/write_low_level_code.ipynb
+++ /dev/null
@@ -1,760 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Writing Low-Level TensorFlow Code\n",
-    "\n",
-    "\n",
-    "**Learning Objectives**\n",
-    "\n",
-    " 1. Practice defining and performing basic operations on constant Tensors\n",
-    " 2. Use Tensorflow's automatic differentiation capability\n",
-    " 3. Learn how to train a linear regression from scratch with TensorFLow\n",
-    "\n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "In this notebook, we will start by reviewing the main operations on Tensors in TensorFlow and understand how to manipulate TensorFlow Variables. We explain how these are compatible with python built-in list and numpy arrays. \n",
-    "\n",
-    "Then we will jump to the problem of training a linear regression from scratch with gradient descent. The first order of business will be to understand how to compute the gradients of a function (the loss here) with respect to some of its arguments (the model weights here). The TensorFlow construct allowing us to do that is `tf.GradientTape`, which we will describe. \n",
-    "\n",
-    "At last we will create a simple training loop to learn the weights of a 1-dim linear regression using synthetic data generated from a linear model. \n",
-    "\n",
-    "As a bonus exercise, we will do the same for data generated from a non linear model, forcing us to manual engineer non-linear features to improve our linear model performance.\n",
-    "\n",
-    "Each learning objective will correspond to a #TODO in the [student lab notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/write_low_level_code.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ensure the right version of Tensorflow is installed.\n",
-    "!pip freeze | grep tensorflow==2.1 || pip install tensorflow==2.1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(tf.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Operations on Tensors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Variables and Constants"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tensors in TensorFlow are either contant (`tf.constant`) or variables (`tf.Variable`).\n",
-    "Constant values can not be changed, while variables values can be.\n",
-    "\n",
-    "The main difference is that instances of `tf.Variable` have methods allowing us to change \n",
-    "their values while tensors constructed with `tf.constant` don't have these methods, and\n",
-    "therefore their values can not be changed. When you want to change the value of a `tf.Variable`\n",
-    "`x` use one of the following method: \n",
-    "\n",
-    "* `x.assign(new_value)`\n",
-    "* `x.assign_add(value_to_be_added)`\n",
-    "* `x.assign_sub(value_to_be_subtracted`\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = tf.constant([2, 3, 4])\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = tf.Variable(2.0, dtype=tf.float32, name=\"my_variable\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x.assign(45.8)\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x.assign_add(4)\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x.assign_sub(3)\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Point-wise operations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tensorflow offers similar point-wise tensor operations as numpy does:\n",
-    "    \n",
-    "* `tf.add` allows to add the components of a tensor \n",
-    "* `tf.multiply` allows us to multiply the components of a tensor\n",
-    "* `tf.subtract` allow us to substract the components of a tensor\n",
-    "* `tf.math.*` contains the usual math operations to be applied on the components of a tensor\n",
-    "* and many more...\n",
-    "\n",
-    "Most of the standard aritmetic operations (`tf.add`, `tf.substrac`, etc.) are overloaded by the usual corresponding arithmetic symbols (`+`, `-`, etc.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Lab Task #1:** Performing basic operations on Tensors \n",
-    "1. Compute the sum of the constants `a` and `b` below using `tf.add` and `+` and verify both operations produce the same values.\n",
-    "2. Compute the product of the constants `a` and `b` below using `tf.multiply` and `*` and verify both operations produce the same values.\n",
-    "3. Compute the exponential of the constant `a` using `tf.math.exp`. Note, you'll need to specify the type for this operation.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO 1a\n",
-    "a = # TODO -- Your code here.\n",
-    "b = # TODO -- Your code here.\n",
-    "c = # TODO -- Your code here.\n",
-    "d = # TODO -- Your code here.\n",
-    "\n",
-    "print(\"c:\", c)\n",
-    "print(\"d:\", d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO 1b\n",
-    "a = # TODO -- Your code here.\n",
-    "b = # TODO -- Your code here.\n",
-    "c = # TODO -- Your code here.\n",
-    "d = # TODO -- Your code here.\n",
-    "\n",
-    "print(\"c:\", c)\n",
-    "print(\"d:\", d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO 1c\n",
-    "# tf.math.exp expects floats so we need to explicitly give the type\n",
-    "a = # TODO -- Your code here.\n",
-    "b = # TODO -- Your code here.\n",
-    "\n",
-    "print(\"b:\", b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### NumPy Interoperability\n",
-    "\n",
-    "In addition to native TF tensors, tensorflow operations can take native python types and NumPy arrays as operands. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# native python list\n",
-    "a_py = [1, 2]\n",
-    "b_py = [3, 4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf.add(a_py, b_py)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# numpy arrays\n",
-    "a_np = np.array([1, 2])\n",
-    "b_np = np.array([3, 4])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf.add(a_np, b_np)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# native TF tensor\n",
-    "a_tf = tf.constant([1, 2])\n",
-    "b_tf = tf.constant([3, 4])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf.add(a_tf, b_tf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can convert a native TF tensor to a NumPy array using .numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a_tf.numpy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Linear Regression\n",
-    "\n",
-    "Now let's use low level tensorflow operations to implement linear regression.\n",
-    "\n",
-    "Later in the course you'll see abstracted ways to do this using high level TensorFlow."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Toy Dataset\n",
-    "\n",
-    "We'll model the following function:\n",
-    "\n",
-    "\\begin{equation}\n",
-    "y= 2x + 10\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = tf.constant(range(10), dtype=tf.float32)\n",
-    "Y = 2 * X + 10\n",
-    "\n",
-    "print(f\"X:{X}\")\n",
-    "print(f\"Y:{Y}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's also create a test dataset to evaluate our models:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_test = tf.constant(range(10, 20), dtype=tf.float32)\n",
-    "Y_test = 2 * X_test + 10\n",
-    "\n",
-    "print(f\"X_test:{X_test}\")\n",
-    "print(f\"Y_test:{Y_test}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Loss Function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The simplest model we can build is a model that for each value of x returns the sample mean of the training set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_mean = Y.numpy().mean()\n",
-    "\n",
-    "\n",
-    "def predict_mean(X):\n",
-    "    y_hat = [y_mean] * len(X)\n",
-    "    return y_hat\n",
-    "\n",
-    "\n",
-    "Y_hat = predict_mean(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using mean squared error, our loss is:\n",
-    "\\begin{equation}\n",
-    "MSE = \\frac{1}{m}\\sum_{i=1}^{m}(\\hat{Y}_i-Y_i)^2\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this simple model the loss is then:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "errors = (Y_hat - Y) ** 2\n",
-    "loss = tf.reduce_mean(errors)\n",
-    "loss.numpy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This values for the MSE loss above will give us a baseline to compare how a more complex model is doing."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, if $\\hat{Y}$ represents the vector containing our model's predictions when we use a linear regression model\n",
-    "\\begin{equation}\n",
-    "\\hat{Y} = w_0X + w_1\n",
-    "\\end{equation}\n",
-    "\n",
-    "we can write a loss function taking as arguments the coefficients of the model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def loss_mse(X, Y, w0, w1):\n",
-    "    Y_hat = w0 * X + w1\n",
-    "    errors = (Y_hat - Y) ** 2\n",
-    "    return tf.reduce_mean(errors)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Gradient Function\n",
-    "\n",
-    "To use gradient descent we need to take the partial derivatives of the loss function with respect to each of the weights. We could manually compute the derivatives, but with Tensorflow's automatic differentiation capabilities we don't have to!\n",
-    "\n",
-    "During gradient descent we think of the loss as a function of the parameters $w_0$ and $w_1$. Thus, we want to compute the partial derivative with respect to these variables. \n",
-    "\n",
-    "For that we need to wrap our loss computation within the context of `tf.GradientTape` instance which will reccord gradient information:\n",
-    "\n",
-    "```python\n",
-    "with tf.GradientTape() as tape:\n",
-    "    loss = # computation \n",
-    "```\n",
-    "\n",
-    "This will allow us to later compute the gradients of any tensor computed within the `tf.GradientTape` context with respect to instances of `tf.Variable`:\n",
-    "\n",
-    "```python\n",
-    "gradients = tape.gradient(loss, [w0, w1])\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We illustrate this procedure with by computing the loss gradients with respect to the model weights:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Lab Task #2:** Complete the function below to compute the loss gradients with respect to the model weights `w0` and `w1`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO 2\n",
-    "def compute_gradients(X, Y, w0, w1):\n",
-    "    # TODO -- Your code here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w0 = tf.Variable(0.0)\n",
-    "w1 = tf.Variable(0.0)\n",
-    "\n",
-    "dw0, dw1 = compute_gradients(X, Y, w0, w1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"dw0:\", dw0.numpy())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"dw1\", dw1.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training Loop\n",
-    "\n",
-    "Here we have a very simple training loop that converges. Note we are ignoring best practices like batching, creating a separate test set, and random weight initialization for the sake of simplicity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Lab Task #3:** Complete the `for` loop below to train a linear regression. \n",
-    "1. Use `compute_gradients` to compute `dw0` and `dw1`.\n",
-    "2. Then, re-assign the value of `w0` and `w1` using the `.assign_sub(...)` method with the computed gradient values and the `LEARNING_RATE`.\n",
-    "3. Finally, for every 100th step , we'll compute and print the `loss`. Use the `loss_mse` function we created above to compute the `loss`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO 3\n",
-    "STEPS = 1000\n",
-    "LEARNING_RATE = .02\n",
-    "MSG = \"STEP {step} - loss: {loss}, w0: {w0}, w1: {w1}\\n\"\n",
-    "\n",
-    "\n",
-    "w0 = tf.Variable(0.0)\n",
-    "w1 = tf.Variable(0.0)\n",
-    "\n",
-    "\n",
-    "for step in range(0, STEPS + 1):\n",
-    "\n",
-    "    dw0, dw1 = # TODO -- Your code here.\n",
-    "\n",
-    "    if step % 100 == 0:\n",
-    "        loss = # TODO -- Your code here.\n",
-    "        print(MSG.format(step=step, loss=loss, w0=w0.numpy(), w1=w1.numpy()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's compare the test loss for this linear regression to the test loss from the baseline model that outputs always the mean of the training set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loss = loss_mse(X_test, Y_test, w0, w1)\n",
-    "loss.numpy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is indeed much better!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bonus"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Try modelling a non-linear function such as: $y=xe^{-x^2}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = tf.constant(np.linspace(0, 2, 1000), dtype=tf.float32)\n",
-    "Y = X * tf.exp(-(X**2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "plt.plot(X, Y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_features(X):\n",
-    "    f1 = tf.ones_like(X)  # Bias.\n",
-    "    f2 = X\n",
-    "    f3 = tf.square(X)\n",
-    "    f4 = tf.sqrt(X)\n",
-    "    f5 = tf.exp(X)\n",
-    "    return tf.stack([f1, f2, f3, f4, f5], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def predict(X, W):\n",
-    "    return tf.squeeze(X @ W, -1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def loss_mse(X, Y, W):\n",
-    "    Y_hat = predict(X, W)\n",
-    "    errors = (Y_hat - Y) ** 2\n",
-    "    return tf.reduce_mean(errors)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_gradients(X, Y, W):\n",
-    "    with tf.GradientTape() as tape:\n",
-    "        loss = loss_mse(Xf, Y, W)\n",
-    "    return tape.gradient(loss, W)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "STEPS = 2000\n",
-    "LEARNING_RATE = 0.02\n",
-    "\n",
-    "\n",
-    "Xf = make_features(X)\n",
-    "n_weights = Xf.shape[1]\n",
-    "\n",
-    "W = tf.Variable(np.zeros((n_weights, 1)), dtype=tf.float32)\n",
-    "\n",
-    "# For plotting\n",
-    "steps, losses = [], []\n",
-    "plt.figure()\n",
-    "\n",
-    "\n",
-    "for step in range(1, STEPS + 1):\n",
-    "    dW = compute_gradients(X, Y, W)\n",
-    "    W.assign_sub(dW * LEARNING_RATE)\n",
-    "\n",
-    "    if step % 100 == 0:\n",
-    "        loss = loss_mse(Xf, Y, W)\n",
-    "        steps.append(step)\n",
-    "        losses.append(loss)\n",
-    "        plt.clf()\n",
-    "        plt.plot(steps, losses)\n",
-    "\n",
-    "\n",
-    "print(f\"STEP: {STEPS} MSE: {loss_mse(Xf, Y, W)}\")\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.plot(X, Y, label=\"actual\")\n",
-    "plt.plot(X, predict(Xf, W), label=\"predicted\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/solutions/2a_dataset_api.ipynb b/notebooks/introduction_to_tensorflow/solutions/2_dataset_api.ipynb
similarity index 100%
rename from notebooks/introduction_to_tensorflow/solutions/2a_dataset_api.ipynb
rename to notebooks/introduction_to_tensorflow/solutions/2_dataset_api.ipynb
diff --git a/notebooks/introduction_to_tensorflow/solutions/2b_loading_filedata.ipynb b/notebooks/introduction_to_tensorflow/solutions/2b_loading_filedata.ipynb
deleted file mode 100644
index 6497df97..00000000
--- a/notebooks/introduction_to_tensorflow/solutions/2b_loading_filedata.ipynb
+++ /dev/null
@@ -1,992 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "sUtoed20cRJJ"
-   },
-   "source": [
-    "# How to Load CSV and Numpy File Types in TensorFlow 2.0\n",
-    "\n",
-    "\n",
-    "\n",
-    "## Learning Objectives\n",
-    "\n",
-    "1. Load a CSV file into a `tf.data.Dataset`. \n",
-    "2. Load Numpy data\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "In this lab, you load CSV data from a file into a `tf.data.Dataset`.  This tutorial provides an example of loading data from NumPy arrays into a `tf.data.Dataset` you also load text data.\n",
-    "\n",
-    "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/ml_on_gcloud_v2/labs/03_load_diff_filedata.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "fgZ9gjmPfSnK"
-   },
-   "source": [
-    "## Load necessary libraries \n",
-    "We will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "baYFZMW_bJHh"
-   },
-   "outputs": [],
-   "source": [
-    "import functools\n",
-    "import os\n",
-    "\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
-    "\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Ncf5t6tgL5ZI"
-   },
-   "outputs": [],
-   "source": [
-    "TRAIN_DATA_URL = \"https://storage.googleapis.com/tf-datasets/titanic/train.csv\"\n",
-    "TEST_DATA_URL = \"https://storage.googleapis.com/tf-datasets/titanic/eval.csv\"\n",
-    "\n",
-    "train_file_path = tf.keras.utils.get_file(\"train.csv\", TRAIN_DATA_URL)\n",
-    "test_file_path = tf.keras.utils.get_file(\"eval.csv\", TEST_DATA_URL)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4ONE94qulk6S"
-   },
-   "outputs": [],
-   "source": [
-    "# Make numpy values easier to read.\n",
-    "np.set_printoptions(precision=3, suppress=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Wuqj601Qw0Ml"
-   },
-   "source": [
-    "## Load data\n",
-    "\n",
-    "This section provides an example of how to load CSV data from a file into a `tf.data.Dataset`.  The data used in this tutorial are taken from the Titanic passenger list. The model will predict the likelihood a passenger survived based on characteristics like age, gender, ticket class, and whether the person was traveling alone.\n",
-    "\n",
-    "To start, let's look at the top of the CSV file to see how it is formatted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "54Dv7mCrf9Yw"
-   },
-   "outputs": [],
-   "source": [
-    "!head {train_file_path}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "jC9lRhV-q_R3"
-   },
-   "source": [
-    "You can [load this using pandas](pandas_dataframe.ipynb), and pass the NumPy arrays to TensorFlow. If you need to scale up to a large set of files, or need a loader that integrates with [TensorFlow and tf.data](../../guide/data.ipynb) then use the `tf.data.experimental.make_csv_dataset` function:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "67mfwr4v-mN_"
-   },
-   "source": [
-    "The only column you need to identify explicitly is the one with the value that the model is intended to predict. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "iXROZm5f3V4E"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "LABEL_COLUMN = \"survived\"\n",
-    "LABELS = [0, 1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "t4N-plO4tDXd"
-   },
-   "source": [
-    "Now read the CSV data from the file and create a dataset. \n",
-    "\n",
-    "(For the full documentation, see `tf.data.experimental.make_csv_dataset`)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "yIbUscB9sqha"
-   },
-   "outputs": [],
-   "source": [
-    "def get_dataset(file_path, **kwargs):\n",
-    "    # TODO 2\n",
-    "    dataset = tf.data.experimental.make_csv_dataset(\n",
-    "        file_path,\n",
-    "        batch_size=5,  # Artificially small to make examples easier to show.\n",
-    "        label_name=LABEL_COLUMN,\n",
-    "        na_value=\"?\",\n",
-    "        num_epochs=1,\n",
-    "        ignore_errors=True,\n",
-    "        **kwargs,\n",
-    "    )\n",
-    "    return dataset\n",
-    "\n",
-    "\n",
-    "raw_train_data = get_dataset(train_file_path)\n",
-    "raw_test_data = get_dataset(test_file_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "v4oMO9MIxgTG"
-   },
-   "outputs": [],
-   "source": [
-    "def show_batch(dataset):\n",
-    "    for batch, label in dataset.take(1):\n",
-    "        for key, value in batch.items():\n",
-    "            print(f\"{key:20s}: {value.numpy()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "vHUQFKoQI6G7"
-   },
-   "source": [
-    "Each item in the dataset is a batch, represented as a tuple of (*many examples*, *many labels*). The data from the examples is organized in column-based tensors (rather than row-based tensors), each with as many elements as the batch size (5 in this case).\n",
-    "\n",
-    "It might help to see this yourself."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "HjrkJROoxoll"
-   },
-   "outputs": [],
-   "source": [
-    "show_batch(raw_train_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "YOYKQKmMj3D6"
-   },
-   "source": [
-    "As you can see, the columns in the CSV are named. The dataset constructor will pick these names up automatically. If the file you are working with does not contain the column names in the first line, pass them in a list of strings to  the `column_names` argument in the `make_csv_dataset` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "2Av8_9L3tUg1"
-   },
-   "outputs": [],
-   "source": [
-    "CSV_COLUMNS = [\n",
-    "    \"survived\",\n",
-    "    \"sex\",\n",
-    "    \"age\",\n",
-    "    \"n_siblings_spouses\",\n",
-    "    \"parch\",\n",
-    "    \"fare\",\n",
-    "    \"class\",\n",
-    "    \"deck\",\n",
-    "    \"embark_town\",\n",
-    "    \"alone\",\n",
-    "]\n",
-    "\n",
-    "temp_dataset = get_dataset(train_file_path, column_names=CSV_COLUMNS)\n",
-    "\n",
-    "show_batch(temp_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "gZfhoX7bR9u4"
-   },
-   "source": [
-    "This example is going to use all the available columns. If you need to omit some columns from the dataset, create a list of just the columns you plan to use, and pass it into the (optional) `select_columns` argument of the constructor.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "S1TzSkUKwsNP"
-   },
-   "outputs": [],
-   "source": [
-    "SELECT_COLUMNS = [\n",
-    "    \"survived\",\n",
-    "    \"age\",\n",
-    "    \"n_siblings_spouses\",\n",
-    "    \"class\",\n",
-    "    \"deck\",\n",
-    "    \"alone\",\n",
-    "]\n",
-    "\n",
-    "temp_dataset = get_dataset(train_file_path, select_columns=SELECT_COLUMNS)\n",
-    "\n",
-    "show_batch(temp_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "9cryz31lxs3e"
-   },
-   "source": [
-    "## Data preprocessing\n",
-    "\n",
-    "A CSV file can contain a variety of data types. Typically you want to convert from those mixed types to a fixed length vector before feeding the data into your model.\n",
-    "\n",
-    "TensorFlow has a built-in system for describing common input conversions: `tf.feature_column`, see [this tutorial](../keras/feature_columns) for details.\n",
-    "\n",
-    "\n",
-    "You can preprocess your data using any tool you like (like [nltk](https://www.nltk.org/) or [sklearn](https://scikit-learn.org/stable/)), and just pass the processed output to TensorFlow. \n",
-    "\n",
-    "\n",
-    "The primary advantage of doing the preprocessing inside your model is that when you export the model it includes the preprocessing. This way you can pass the raw data directly to your model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "9AsbaFmCeJtF"
-   },
-   "source": [
-    "### Continuous data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Xl0Q0DcfA_rt"
-   },
-   "source": [
-    "If your data is already in an appropriate numeric format, you can pack the data into a vector before passing it off to the model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4Yfji3J5BMxz"
-   },
-   "outputs": [],
-   "source": [
-    "SELECT_COLUMNS = [\"survived\", \"age\", \"n_siblings_spouses\", \"parch\", \"fare\"]\n",
-    "DEFAULTS = [0, 0.0, 0.0, 0.0, 0.0]\n",
-    "temp_dataset = get_dataset(\n",
-    "    train_file_path, select_columns=SELECT_COLUMNS, column_defaults=DEFAULTS\n",
-    ")\n",
-    "\n",
-    "show_batch(temp_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "zEUhI8kZCfq8"
-   },
-   "outputs": [],
-   "source": [
-    "example_batch, labels_batch = next(iter(temp_dataset))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IP45_2FbEKzn"
-   },
-   "source": [
-    "Here's a simple function that will pack together all the columns:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "JQ0hNSL8CC3a"
-   },
-   "outputs": [],
-   "source": [
-    "def pack(features, label):\n",
-    "    return tf.stack(list(features.values()), axis=-1), label"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "75LA9DisEIoE"
-   },
-   "source": [
-    "Apply this to each element of the dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "VnP2Z2lwCTRl"
-   },
-   "outputs": [],
-   "source": [
-    "packed_dataset = temp_dataset.map(pack)\n",
-    "\n",
-    "for features, labels in packed_dataset.take(1):\n",
-    "    print(features.numpy())\n",
-    "    print()\n",
-    "    print(labels.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "1VBvmaFrFU6J"
-   },
-   "source": [
-    "If you have mixed datatypes you may want to separate out these simple-numeric fields. The `tf.feature_column` api can handle them, but this incurs some overhead and should be avoided unless really necessary. Switch back to the mixed dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "ad-IQ_JPFQge"
-   },
-   "outputs": [],
-   "source": [
-    "show_batch(raw_train_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "HSrYNKKcIdav"
-   },
-   "outputs": [],
-   "source": [
-    "example_batch, labels_batch = next(iter(temp_dataset))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "p5VtThKfGPaQ"
-   },
-   "source": [
-    "So define a more general preprocessor that selects a list of numeric features and packs them into a single column:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "5DRishYYGS-m"
-   },
-   "outputs": [],
-   "source": [
-    "class PackNumericFeatures:\n",
-    "    def __init__(self, names):\n",
-    "        self.names = names\n",
-    "\n",
-    "    def __call__(self, features, labels):\n",
-    "        numeric_features = [features.pop(name) for name in self.names]\n",
-    "        numeric_features = [\n",
-    "            tf.cast(feat, tf.float32) for feat in numeric_features\n",
-    "        ]\n",
-    "        numeric_features = tf.stack(numeric_features, axis=-1)\n",
-    "        features[\"numeric\"] = numeric_features\n",
-    "\n",
-    "        return features, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "1SeZka9AHfqD"
-   },
-   "outputs": [],
-   "source": [
-    "NUMERIC_FEATURES = [\"age\", \"n_siblings_spouses\", \"parch\", \"fare\"]\n",
-    "\n",
-    "packed_train_data = raw_train_data.map(PackNumericFeatures(NUMERIC_FEATURES))\n",
-    "\n",
-    "packed_test_data = raw_test_data.map(PackNumericFeatures(NUMERIC_FEATURES))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "wFrw0YobIbUB"
-   },
-   "outputs": [],
-   "source": [
-    "show_batch(packed_train_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "_EPUS8fPLUb1"
-   },
-   "outputs": [],
-   "source": [
-    "example_batch, labels_batch = next(iter(packed_train_data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "o2maE8d2ijsq"
-   },
-   "source": [
-    "#### Data Normalization\n",
-    "\n",
-    "Continuous data should always be normalized."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "WKT1ASWpwH46"
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "desc = pd.read_csv(train_file_path)[NUMERIC_FEATURES].describe()\n",
-    "desc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "cHHstcKPsMXM"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "MEAN = np.array(desc.T[\"mean\"])\n",
-    "STD = np.array(desc.T[\"std\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "REKqO_xHPNx0"
-   },
-   "outputs": [],
-   "source": [
-    "def normalize_numeric_data(data, mean, std):\n",
-    "    # TODO 2\n",
-    "    # Center the data\n",
-    "    return (data - mean) / std"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(MEAN, STD)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "VPsoMUgRCpUM"
-   },
-   "source": [
-    "Now create a numeric column. The `tf.feature_columns.numeric_column` API accepts a `normalizer_fn` argument, which will be run on each batch.\n",
-    "\n",
-    "Bind the `MEAN` and `STD` to the normalizer fn using [`functools.partial`](https://docs.python.org/3/library/functools.html#functools.partial)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Bw0I35xRS57V"
-   },
-   "outputs": [],
-   "source": [
-    "# See what you just created.\n",
-    "normalizer = functools.partial(normalize_numeric_data, mean=MEAN, std=STD)\n",
-    "\n",
-    "numeric_column = tf.feature_column.numeric_column(\n",
-    "    \"numeric\", normalizer_fn=normalizer, shape=[len(NUMERIC_FEATURES)]\n",
-    ")\n",
-    "numeric_columns = [numeric_column]\n",
-    "numeric_column"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "HZxcHXc6LCa7"
-   },
-   "source": [
-    "When you train the model, include this feature column to select and center this block of numeric data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "b61NM76Ot_kb"
-   },
-   "outputs": [],
-   "source": [
-    "example_batch[\"numeric\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "j-r_4EAJAZoI"
-   },
-   "outputs": [],
-   "source": [
-    "numeric_layer = tf.keras.layers.DenseFeatures(numeric_columns)\n",
-    "numeric_layer(example_batch).numpy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "M37oD2VcCO4R"
-   },
-   "source": [
-    "The mean based normalization used here requires knowing the means of each column ahead of time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "tSyrkSQwYHKi"
-   },
-   "source": [
-    "### Categorical data\n",
-    "\n",
-    "Some of the columns in the CSV data are categorical columns. That is, the content should be one of a limited set of options.\n",
-    "\n",
-    "Use the `tf.feature_column` API to create a collection with a `tf.feature_column.indicator_column` for each categorical column.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "mWDniduKMw-C"
-   },
-   "outputs": [],
-   "source": [
-    "CATEGORIES = {\n",
-    "    \"sex\": [\"male\", \"female\"],\n",
-    "    \"class\": [\"First\", \"Second\", \"Third\"],\n",
-    "    \"deck\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\"],\n",
-    "    \"embark_town\": [\"Cherbourg\", \"Southhampton\", \"Queenstown\"],\n",
-    "    \"alone\": [\"y\", \"n\"],\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kkxLdrsLwHPT"
-   },
-   "outputs": [],
-   "source": [
-    "categorical_columns = []\n",
-    "for feature, vocab in CATEGORIES.items():\n",
-    "    cat_col = tf.feature_column.categorical_column_with_vocabulary_list(\n",
-    "        key=feature, vocabulary_list=vocab\n",
-    "    )\n",
-    "    categorical_columns.append(tf.feature_column.indicator_column(cat_col))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "H18CxpHY_Nma"
-   },
-   "outputs": [],
-   "source": [
-    "# See what you just created.\n",
-    "categorical_columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "p7mACuOsArUH"
-   },
-   "outputs": [],
-   "source": [
-    "categorical_layer = tf.keras.layers.DenseFeatures(categorical_columns)\n",
-    "print(categorical_layer(example_batch).numpy()[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "R7-1QG99_1sN"
-   },
-   "source": [
-    "This will be become part of a data processing input later when you build the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "kPWkC4_1l3IG"
-   },
-   "source": [
-    "### Combined preprocessing layer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "R3QAjo1qD4p9"
-   },
-   "source": [
-    "Add the two feature column collections and pass them to a `tf.keras.layers.DenseFeatures` to create an input layer that will extract and preprocess both input types:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3-OYK7GnaH0r"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1\n",
-    "preprocessing_layer = tf.keras.layers.DenseFeatures(\n",
-    "    categorical_columns + numeric_columns\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "m7_U_K0UMSVS"
-   },
-   "outputs": [],
-   "source": [
-    "print(preprocessing_layer(example_batch).numpy()[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "DlF_omQqtnOP"
-   },
-   "source": [
-    "### Next Step\n",
-    "\n",
-    "A next step would be to build a build a `tf.keras.Sequential`, starting with the `preprocessing_layer`, which is beyond the scope of this lab.  We will cover the Keras Sequential API in the next Lesson."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load NumPy data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load necessary libraries \n",
-    "First, restart the Kernel.  Then, we will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load data from `.npz` file\n",
-    "\n",
-    "We use the MNIST dataset in Keras."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DATA_URL = (\n",
-    "    \"https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\"\n",
-    ")\n",
-    "\n",
-    "path = tf.keras.utils.get_file(\"mnist.npz\", DATA_URL)\n",
-    "with np.load(path) as data:\n",
-    "    # TODO 1\n",
-    "    train_examples = data[\"x_train\"]\n",
-    "    train_labels = data[\"y_train\"]\n",
-    "    test_examples = data[\"x_test\"]\n",
-    "    test_labels = data[\"y_test\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load NumPy arrays with `tf.data.Dataset`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Assuming you have an array of examples and a corresponding array of labels, pass the two arrays as a tuple into `tf.data.Dataset.from_tensor_slices` to create a `tf.data.Dataset`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO 2\n",
-    "train_dataset = tf.data.Dataset.from_tensor_slices(\n",
-    "    (train_examples, train_labels)\n",
-    ")\n",
-    "test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test_labels))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Next Step\n",
-    "\n",
-    "A next step would be to build a build a `tf.keras.Sequential`, starting with the `preprocessing_layer`, which is beyond the scope of this lab.  We will cover the Keras Sequential API in the next Lesson."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Resources \n",
-    "1. Load text data - this link: https://www.tensorflow.org/tutorials/load_data/text\n",
-    "2. TF.text - this link:  https://www.tensorflow.org/tutorials/tensorflow_text/intro\n",
-    "3. Load image daeta - https://www.tensorflow.org/tutorials/load_data/images\n",
-    "4. Read data into a Pandas DataFrame - https://www.tensorflow.org/tutorials/load_data/pandas_dataframe\n",
-    "5. How to represent Unicode strings in TensorFlow - https://www.tensorflow.org/tutorials/load_data/unicode\n",
-    "6. TFRecord and tf.Example -  https://www.tensorflow.org/tutorials/load_data/tfrecord                  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc.\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-    "http://www.apache.org/licenses/LICENSE-2.0\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "csv.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "environment": {
-   "kernel": "python3",
-   "name": "tf2-gpu.2-12.m109",
-   "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-12:m109"
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/solutions/2c_loading_images.ipynb b/notebooks/introduction_to_tensorflow/solutions/2c_loading_images.ipynb
deleted file mode 100644
index 7abfd4e5..00000000
--- a/notebooks/introduction_to_tensorflow/solutions/2c_loading_images.ipynb
+++ /dev/null
@@ -1,608 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ucMoYase6URl"
-   },
-   "source": [
-    "# Loading Images Using tf.Data.Dataset\n",
-    "\n",
-    "**Learning Objectives**\n",
-    "\n",
-    "1. Retrieve Images using tf.keras.utils.get_file\n",
-    "2. Load Images using Keras Pre-Processing\n",
-    "3. Load Images using tf.Data.Dataset\n",
-    "4. Understand basic Methods for Training\n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "In this notebook, we load an image dataset using tf.data.  The dataset used in this example is distributed as directories of images, with one class of image per directory.\n",
-    "\n",
-    "\n",
-    "Each learning objective will correspond to a **#TODO** in the [student lab notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/load_images_tf.data.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "hoQQiZDB6URn"
-   },
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3vhAMaIOBIee"
-   },
-   "source": [
-    "## Load necessary libraries \n",
-    "We will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gIksPgtT8B6B"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import IPython.display as display\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from PIL import Image\n",
-    "\n",
-    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "KT6CcaqgQewg"
-   },
-   "outputs": [],
-   "source": [
-    "AUTOTUNE = tf.data.experimental.AUTOTUNE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wO0InzL66URu"
-   },
-   "source": [
-    "### Retrieve the images\n",
-    "\n",
-    "Before you start any training, you will need a set of images to teach the network about the new classes you want to recognize. You can use an archive of creative-commons licensed flower photos from Google.\n",
-    "\n",
-    "Note: all images are licensed CC-BY, creators are listed in the `LICENSE.txt` file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "rN-Pc6Zd6awg"
-   },
-   "outputs": [],
-   "source": [
-    "import pathlib\n",
-    "\n",
-    "data_dir = tf.keras.utils.get_file(\n",
-    "    origin=\"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\",\n",
-    "    fname=\"flower_photos\",\n",
-    "    untar=True,\n",
-    ")\n",
-    "data_dir = pathlib.Path(data_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rFkFK74oO--g"
-   },
-   "source": [
-    "After downloading (218MB), you should now have a copy of the flower photos available.\n",
-    "\n",
-    "The directory contains 5 sub-directories, one per class:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "QhewYCxhXQBX"
-   },
-   "outputs": [],
-   "source": [
-    "image_count = len(list(data_dir.glob(\"*/*.jpg\")))\n",
-    "image_count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "sJ1HKKdR4A7c"
-   },
-   "outputs": [],
-   "source": [
-    "CLASS_NAMES = np.array(\n",
-    "    [item.name for item in data_dir.glob(\"*\") if item.name != \"LICENSE.txt\"]\n",
-    ")\n",
-    "CLASS_NAMES"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IVxsk4OW61TY"
-   },
-   "source": [
-    "Each directory contains images of that type of flower. Here are some roses:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "crs7ZjEp60Ot"
-   },
-   "outputs": [],
-   "source": [
-    "roses = list(data_dir.glob(\"roses/*\"))\n",
-    "\n",
-    "for image_path in roses[:3]:\n",
-    "    display.display(Image.open(str(image_path)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6jobDTUs8Wxu"
-   },
-   "source": [
-    "## Load using `keras.preprocessing`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ehhW308g8soJ"
-   },
-   "source": [
-    "A simple way to load images is to use `tf.keras.preprocessing`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "syDdF_LWVrWE"
-   },
-   "outputs": [],
-   "source": [
-    "# The 1./255 is to convert from uint8 to float32 in range [0,1].\n",
-    "image_generator = tf.keras.preprocessing.image.ImageDataGenerator(\n",
-    "    rescale=1.0 / 255\n",
-    ")  # TODO 1a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "lAmtzsnjDNhB"
-   },
-   "source": [
-    "Define some parameters for the loader:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "1zf695or-Flq"
-   },
-   "outputs": [],
-   "source": [
-    "BATCH_SIZE = 32\n",
-    "IMG_HEIGHT = 224\n",
-    "IMG_WIDTH = 224\n",
-    "STEPS_PER_EPOCH = np.ceil(image_count / BATCH_SIZE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Pw94ajOOVrWI"
-   },
-   "outputs": [],
-   "source": [
-    "train_data_gen = image_generator.flow_from_directory(\n",
-    "    directory=str(data_dir),\n",
-    "    batch_size=BATCH_SIZE,\n",
-    "    shuffle=True,\n",
-    "    target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
-    "    classes=list(CLASS_NAMES),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2ZgIZeXaDUsF"
-   },
-   "source": [
-    "Inspect a batch:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nLp0XVG_Vgi2"
-   },
-   "outputs": [],
-   "source": [
-    "def show_batch(image_batch, label_batch):\n",
-    "    plt.figure(figsize=(10, 10))\n",
-    "    for n in range(25):\n",
-    "        ax = plt.subplot(5, 5, n + 1)  # TODO 1b\n",
-    "        plt.imshow(image_batch[n])  # TODO 1b\n",
-    "        plt.title(CLASS_NAMES[label_batch[n] == 1][0].title())\n",
-    "        plt.axis(\"off\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "suh6Sjv68rY3"
-   },
-   "outputs": [],
-   "source": [
-    "image_batch, label_batch = next(train_data_gen)\n",
-    "show_batch(image_batch, label_batch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "AxS1cLzM8mEp"
-   },
-   "source": [
-    "## Load using `tf.data`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Ylj9fgkamgWZ"
-   },
-   "source": [
-    "The above `keras.preprocessing` method is convienient, but has three downsides: \n",
-    "\n",
-    "1. It's slow. See the performance section below.\n",
-    "1. It lacks fine-grained control.\n",
-    "1. It is not well integrated with the rest of TensorFlow."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "IIG5CPaULegg"
-   },
-   "source": [
-    "To load the files as a `tf.data.Dataset` first create a dataset of the file paths:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "lAkQp5uxoINu"
-   },
-   "outputs": [],
-   "source": [
-    "list_ds = tf.data.Dataset.list_files(str(data_dir / \"*/*\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "coORvEH-NGwc"
-   },
-   "outputs": [],
-   "source": [
-    "for f in list_ds.take(5):\n",
-    "    print(f.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "91CPfUUJ_8SZ"
-   },
-   "source": [
-    "Write a short pure-tensorflow function that converts a file path to an `(img, label)` pair:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "arSQzIey-4D4"
-   },
-   "outputs": [],
-   "source": [
-    "def get_label(file_path):\n",
-    "    # convert the path to a list of path components\n",
-    "    parts = tf.strings.split(file_path, os.path.sep)  # TODO 2a\n",
-    "    # The second to last is the class-directory\n",
-    "    return parts[-2] == CLASS_NAMES  # TODO 2a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "MGlq4IP4Aktb"
-   },
-   "outputs": [],
-   "source": [
-    "def decode_img(img):\n",
-    "    # convert the compressed string to a 3D uint8 tensor\n",
-    "    img = tf.image.decode_jpeg(img, channels=3)  # TODO 2b\n",
-    "    # Use `convert_image_dtype` to convert to floats in the [0,1] range.\n",
-    "    img = tf.image.convert_image_dtype(img, tf.float32)  # TODO 2b\n",
-    "    # resize the image to the desired size.\n",
-    "    return tf.image.resize(img, [IMG_WIDTH, IMG_HEIGHT])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "-xhBRgvNqRRe"
-   },
-   "outputs": [],
-   "source": [
-    "def process_path(file_path):\n",
-    "    label = get_label(file_path)\n",
-    "    # load the raw data from the file as a string\n",
-    "    img = tf.io.read_file(file_path)  # TODO 2c\n",
-    "    img = decode_img(img)\n",
-    "    return img, label"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "S9a5GpsUOBx8"
-   },
-   "source": [
-    "Use `Dataset.map` to create a dataset of `image, label` pairs:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3SDhbo8lOBQv"
-   },
-   "outputs": [],
-   "source": [
-    "# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\n",
-    "labeled_ds = list_ds.map(process_path, num_parallel_calls=AUTOTUNE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kxrl0lGdnpRz"
-   },
-   "outputs": [],
-   "source": [
-    "for image, label in labeled_ds.take(1):\n",
-    "    print(\"Image shape: \", image.numpy().shape)\n",
-    "    print(\"Label: \", label.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "vYGCgJuR_9Qp"
-   },
-   "source": [
-    "### Next Steps:  Basic methods for training"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "wwZavzgsIytz"
-   },
-   "source": [
-    "To train a model with this dataset you will want the data:\n",
-    "\n",
-    "* To be well shuffled.\n",
-    "* To be batched.\n",
-    "* Batches to be available as soon as possible.\n",
-    "\n",
-    "These features can be easily added using the `tf.data` api."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "uZmZJx8ePw_5"
-   },
-   "outputs": [],
-   "source": [
-    "def prepare_for_training(ds, cache=True, shuffle_buffer_size=1000):\n",
-    "    # This is a small dataset, only load it once, and keep it in memory.\n",
-    "    # use `.cache(filename)` to cache preprocessing work for datasets that don't\n",
-    "    # fit in memory.\n",
-    "    if cache:\n",
-    "        if isinstance(cache, str):\n",
-    "            ds = ds.cache(cache)\n",
-    "        else:\n",
-    "            ds = ds.cache()\n",
-    "\n",
-    "    ds = ds.shuffle(buffer_size=shuffle_buffer_size)  # TODO 3a\n",
-    "\n",
-    "    # Repeat forever\n",
-    "    ds = ds.repeat()\n",
-    "\n",
-    "    ds = ds.batch(BATCH_SIZE)\n",
-    "\n",
-    "    # `prefetch` lets the dataset fetch batches in the background while the model\n",
-    "    # is training.\n",
-    "    ds = ds.prefetch(buffer_size=AUTOTUNE)\n",
-    "\n",
-    "    return ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "-YKnrfAeZV10"
-   },
-   "outputs": [],
-   "source": [
-    "train_ds = prepare_for_training(labeled_ds)\n",
-    "\n",
-    "image_batch, label_batch = next(iter(train_ds))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "UN_Dnl72YNIj"
-   },
-   "outputs": [],
-   "source": [
-    "show_batch(image_batch.numpy(), label_batch.numpy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc.\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-    "http://www.apache.org/licenses/LICENSE-2.0\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "images.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "environment": {
-   "kernel": "python3",
-   "name": "tf2-gpu.2-12.m109",
-   "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-12:m109"
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/introduction_to_tensorflow/solutions/2d_loading_tfrecords.ipynb b/notebooks/introduction_to_tensorflow/solutions/2d_loading_tfrecords.ipynb
deleted file mode 100644
index 91ad2125..00000000
--- a/notebooks/introduction_to_tensorflow/solutions/2d_loading_tfrecords.ipynb
+++ /dev/null
@@ -1,1259 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3pkUd_9IZCFO"
-   },
-   "source": [
-    "# TFRecord and tf.Example\n",
-    "\n",
-    "**Learning Objectives**\n",
-    "\n",
-    "1. Understand the TFRecord format for storing data\n",
-    "2. Understand the tf.Example message type\n",
-    "3. Read and Write a TFRecord file\n",
-    "\n",
-    "\n",
-    "## Introduction \n",
-    "\n",
-    "In this notebook, you create, parse, and use the `tf.Example` message, and then serialize, write, and read `tf.Example` messages to and from `.tfrecord` files.  To read data efficiently it can be helpful to serialize your data and store it in a set of files (100-200MB each) that can each be read linearly. This is especially true if the data is being streamed over a network. This can also be useful for caching any data-preprocessing.\n",
-    "\n",
-    "\n",
-    "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](../labs/tfrecord-tf.example.ipynb) -- try to complete that notebook first before reviewing this solution notebook. \n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Ac83J0QxjhFt"
-   },
-   "source": [
-    "### The TFRecord format \n",
-    "\n",
-    "The TFRecord format is a simple format for storing a sequence of binary records.  [Protocol buffers](https://developers.google.com/protocol-buffers/) are a cross-platform, cross-language library for efficient serialization of structured data.  Protocol messages are defined by `.proto` files, these are often the easiest way to understand a message type.\n",
-    "\n",
-    "The `tf.Example` message (or protobuf) is a flexible message type that represents a `{\"string\": value}` mapping. It is designed for use with TensorFlow and is used throughout the higher-level APIs such as [TFX](https://www.tensorflow.org/tfx/).\n",
-    "Note: While useful, these structures are optional. There is no need to convert existing code to use TFRecords, unless you are using [`tf.data`](https://www.tensorflow.org/guide/datasets) and reading data is still the bottleneck to training. See [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets) for dataset performance tips."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "WkRreBf1eDVc"
-   },
-   "source": [
-    "## Load necessary libraries \n",
-    "We will start by importing the necessary libraries for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Ja7sezsmnXph"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import IPython.display as display\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
-    "\n",
-    "print(\"TensorFlow version: \", tf.version.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "e5Kq88ccUWQV"
-   },
-   "source": [
-    "## `tf.Example`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "VrdQHgvNijTi"
-   },
-   "source": [
-    "### Data types for `tf.Example`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "lZw57Qrn4CTE"
-   },
-   "source": [
-    "Fundamentally, a `tf.Example` is a `{\"string\": tf.train.Feature}` mapping.\n",
-    "\n",
-    "The `tf.train.Feature` message type can accept one of the following three types (See the [`.proto` file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto) for reference). Most other generic types can be coerced into one of these:\n",
-    "\n",
-    "1. `tf.train.BytesList` (the following types can be coerced)\n",
-    "\n",
-    "  - `string`\n",
-    "  - `byte`\n",
-    "\n",
-    "1. `tf.train.FloatList` (the following types can be coerced)\n",
-    "\n",
-    "  - `float` (`float32`)\n",
-    "  - `double` (`float64`)\n",
-    "\n",
-    "1. `tf.train.Int64List` (the following types can be coerced)\n",
-    "\n",
-    "  - `bool`\n",
-    "  - `enum`\n",
-    "  - `int32`\n",
-    "  - `uint32`\n",
-    "  - `int64`\n",
-    "  - `uint64`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "_e3g9ExathXP"
-   },
-   "source": [
-    "In order to convert a standard TensorFlow type to a `tf.Example`-compatible `tf.train.Feature`, you can use the shortcut functions below. Note that each function takes a scalar input value and returns a `tf.train.Feature` containing one of the three `list` types above:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "mbsPOUpVtYxA"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1a\n",
-    "# The following functions can be used to convert a value to a type compatible\n",
-    "# with tf.Example.\n",
-    "\n",
-    "\n",
-    "def _bytes_feature(value):\n",
-    "    \"\"\"Returns a bytes_list from a string / byte.\"\"\"\n",
-    "    if isinstance(value, type(tf.constant(0))):\n",
-    "        value = (\n",
-    "            value.numpy()\n",
-    "        )  # BytesList won't unpack a string from an EagerTensor.\n",
-    "    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n",
-    "\n",
-    "\n",
-    "def _float_feature(value):\n",
-    "    \"\"\"Returns a float_list from a float / double.\"\"\"\n",
-    "    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n",
-    "\n",
-    "\n",
-    "def _int64_feature(value):\n",
-    "    \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n",
-    "    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Wst0v9O8hgzy"
-   },
-   "source": [
-    "Note: To stay simple, this example only uses scalar inputs. The simplest way to handle non-scalar features is to use `tf.serialize_tensor` to convert tensors to binary-strings. Strings are scalars in tensorflow. Use `tf.parse_tensor` to convert the binary-string back to a tensor."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "vsMbkkC8xxtB"
-   },
-   "source": [
-    "Below are some examples of how these functions work. Note the varying input types and the standardized output types. If the input type for a function does not match one of the coercible types stated above, the function will raise an exception (e.g. `_int64_feature(1.0)` will error out, since `1.0` is a float, so should be used with the `_float_feature` function instead):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "hZzyLGr0u73y"
-   },
-   "outputs": [],
-   "source": [
-    "print(_bytes_feature(b\"test_string\"))\n",
-    "\n",
-    "print(_float_feature(np.exp(1)))\n",
-    "\n",
-    "print(_int64_feature(True))\n",
-    "print(_int64_feature(1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "nj1qpfQU5qmi"
-   },
-   "source": [
-    "All proto messages can be serialized to a binary-string using the `.SerializeToString` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "5afZkORT5pjm"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1b\n",
-    "feature = _float_feature(np.exp(1))\n",
-    "\n",
-    "feature.SerializeToString()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "laKnw9F3hL-W"
-   },
-   "source": [
-    "### Creating a `tf.Example` message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "b_MEnhxchQPC"
-   },
-   "source": [
-    "Suppose you want to create a `tf.Example` message from existing data. In practice, the dataset may come from anywhere, but the procedure of creating the `tf.Example` message from a single observation will be the same:\n",
-    "\n",
-    "1. Within each observation, each value needs to be converted to a `tf.train.Feature` containing one of the 3 compatible types, using one of the functions above.\n",
-    "\n",
-    "1. You create a map (dictionary) from the feature name string to the encoded feature value produced in #1.\n",
-    "\n",
-    "1. The map produced in step 2 is converted to a [`Features` message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "4EgFQ2uHtchc"
-   },
-   "source": [
-    "In this notebook, you will create a dataset using NumPy.\n",
-    "\n",
-    "This dataset will have 4 features:\n",
-    "\n",
-    "* a boolean feature, `False` or `True` with equal probability\n",
-    "* an integer feature uniformly randomly chosen from `[0, 5]`\n",
-    "* a string feature generated from a string table by using the integer feature as an index\n",
-    "* a float feature from a standard normal distribution\n",
-    "\n",
-    "Consider a sample consisting of 10,000 independently and identically distributed observations from each of the above distributions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "CnrguFAy3YQv"
-   },
-   "outputs": [],
-   "source": [
-    "# The number of observations in the dataset.\n",
-    "n_observations = int(1e4)\n",
-    "\n",
-    "# Boolean feature, encoded as False or True.\n",
-    "feature0 = np.random.choice([False, True], n_observations)\n",
-    "\n",
-    "# Integer feature, random from 0 to 4.\n",
-    "feature1 = np.random.randint(0, 5, n_observations)\n",
-    "\n",
-    "# String feature\n",
-    "strings = np.array([b\"cat\", b\"dog\", b\"chicken\", b\"horse\", b\"goat\"])\n",
-    "feature2 = strings[feature1]\n",
-    "\n",
-    "# Float feature, from a standard normal distribution\n",
-    "feature3 = np.random.randn(n_observations)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "aGrscehJr7Jd"
-   },
-   "source": [
-    "Each of these features can be coerced into a `tf.Example`-compatible type using one of `_bytes_feature`, `_float_feature`, `_int64_feature`. You can then create a `tf.Example` message from these encoded features:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "RTCS49Ij_kUw"
-   },
-   "outputs": [],
-   "source": [
-    "def serialize_example(feature0, feature1, feature2, feature3):\n",
-    "    \"\"\"\n",
-    "    Creates a tf.Example message ready to be written to a file.\n",
-    "    \"\"\"\n",
-    "    # Create a dictionary mapping the feature name to the tf.Example-compatible\n",
-    "    # data type.\n",
-    "    feature = {\n",
-    "        \"feature0\": _int64_feature(feature0),\n",
-    "        \"feature1\": _int64_feature(feature1),\n",
-    "        \"feature2\": _bytes_feature(feature2),\n",
-    "        \"feature3\": _float_feature(feature3),\n",
-    "    }\n",
-    "\n",
-    "    # Create a Features message using tf.train.Example.\n",
-    "\n",
-    "    example_proto = tf.train.Example(\n",
-    "        features=tf.train.Features(feature=feature)\n",
-    "    )\n",
-    "    return example_proto.SerializeToString()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "XftzX9CN_uGT"
-   },
-   "source": [
-    "For example, suppose you have a single observation from the dataset, `[False, 4, bytes('goat'), 0.9876]`. You can create and print the `tf.Example` message for this observation using `create_message()`. Each single observation will be written as a `Features` message as per the above. Note that the `tf.Example` [message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88) is just a wrapper around the `Features` message:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "N8BtSx2RjYcb"
-   },
-   "outputs": [],
-   "source": [
-    "# This is an example observation from the dataset.\n",
-    "\n",
-    "example_observation = []\n",
-    "\n",
-    "serialized_example = serialize_example(False, 4, b\"goat\", 0.9876)\n",
-    "serialized_example"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "_pbGATlG6u-4"
-   },
-   "source": [
-    "To decode the message use the `tf.train.Example.FromString` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "dGim-mEm6vit"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 1c\n",
-    "example_proto = tf.train.Example.FromString(serialized_example)\n",
-    "example_proto"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "o6qxofy89obI"
-   },
-   "source": [
-    "## TFRecords format details\n",
-    "\n",
-    "A TFRecord file contains a sequence of records. The file can only be read sequentially.\n",
-    "\n",
-    "Each record contains a byte-string, for the data-payload, plus the data-length, and  CRC32C (32-bit CRC using the Castagnoli polynomial) hashes for integrity checking.\n",
-    "\n",
-    "Each record is stored in the following formats:\n",
-    "\n",
-    "    uint64 length\n",
-    "    uint32 masked_crc32_of_length\n",
-    "    byte   data[length]\n",
-    "    uint32 masked_crc32_of_data\n",
-    "\n",
-    "The records are concatenated together to produce the file. CRCs are\n",
-    "[described here](https://en.wikipedia.org/wiki/Cyclic_redundancy_check), and\n",
-    "the mask of a CRC is:\n",
-    "\n",
-    "    masked_crc = ((crc >> 15) | (crc << 17)) + 0xa282ead8ul\n",
-    "\n",
-    "Note: There is no requirement to use `tf.Example` in TFRecord files. `tf.Example` is just a method of serializing dictionaries to byte-strings. Lines of text, encoded  image data, or serialized tensors (using `tf.io.serialize_tensor`, and\n",
-    "`tf.io.parse_tensor` when loading). See the `tf.io` module for more options."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "y-Hjmee-fbLH"
-   },
-   "source": [
-    "## TFRecord files using `tf.data`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "GmehkCCT81Ez"
-   },
-   "source": [
-    "The `tf.data` module also provides tools for reading and writing data in TensorFlow."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "1FISEuz8ubu3"
-   },
-   "source": [
-    "### Writing a TFRecord file\n",
-    "\n",
-    "The easiest way to get the data into a dataset is to use the `from_tensor_slices` method.\n",
-    "\n",
-    "Applied to an array, it returns a dataset of scalars:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "mXeaukvwu5_-"
-   },
-   "outputs": [],
-   "source": [
-    "tf.data.Dataset.from_tensor_slices(feature1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "f-q0VKyZvcad"
-   },
-   "source": [
-    "Applied to a tuple of arrays, it returns a dataset of tuples:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "H5sWyu1kxnvg"
-   },
-   "outputs": [],
-   "source": [
-    "features_dataset = tf.data.Dataset.from_tensor_slices(\n",
-    "    (feature0, feature1, feature2, feature3)\n",
-    ")\n",
-    "features_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "m1C-t71Nywze"
-   },
-   "outputs": [],
-   "source": [
-    "# Use `take(1)` to only pull one example from the dataset.\n",
-    "for f0, f1, f2, f3 in features_dataset.take(1):\n",
-    "    print(f0)\n",
-    "    print(f1)\n",
-    "    print(f2)\n",
-    "    print(f3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "mhIe63awyZYd"
-   },
-   "source": [
-    "Use the `tf.data.Dataset.map` method to apply a function to each element of a `Dataset`.\n",
-    "\n",
-    "The mapped function must operate in TensorFlow graph mode—it must operate on and return `tf.Tensors`. A non-tensor function, like `serialize_example`, can be wrapped with `tf.py_function` to make it compatible.\n",
-    "\n",
-    "Using `tf.py_function` requires to specify the shape and type information that is otherwise unavailable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "apB5KYrJzjPI"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 2a\n",
-    "def tf_serialize_example(f0, f1, f2, f3):\n",
-    "    tf_string = tf.py_function(\n",
-    "        serialize_example,\n",
-    "        (f0, f1, f2, f3),  # pass these args to the above function.\n",
-    "        tf.string,\n",
-    "    )  # the return type is `tf.string`.\n",
-    "    return tf.reshape(tf_string, ())  # The result is a scalar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "lHFjW4u4Npz9"
-   },
-   "outputs": [],
-   "source": [
-    "tf_serialize_example(f0, f1, f2, f3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "CrFZ9avE3HUF"
-   },
-   "source": [
-    "Apply this function to each element in the dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "VDeqYVbW3ww9"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 2b\n",
-    "serialized_features_dataset = features_dataset.map(tf_serialize_example)\n",
-    "serialized_features_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "DlDfuh46bRf6"
-   },
-   "outputs": [],
-   "source": [
-    "def generator():\n",
-    "    for features in features_dataset:\n",
-    "        yield serialize_example(*features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "iv9oXKrcbhvX"
-   },
-   "outputs": [],
-   "source": [
-    "serialized_features_dataset = tf.data.Dataset.from_generator(\n",
-    "    generator, output_types=tf.string, output_shapes=()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Dqz8C4D5cIj9"
-   },
-   "outputs": [],
-   "source": [
-    "serialized_features_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "p6lw5VYpjZZC"
-   },
-   "source": [
-    "And write them to a TFRecord file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "vP1VgTO44UIE"
-   },
-   "outputs": [],
-   "source": [
-    "filename = \"test.tfrecord\"\n",
-    "writer = tf.data.experimental.TFRecordWriter(filename)\n",
-    "writer.write(serialized_features_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6aV0GQhV8tmp"
-   },
-   "source": [
-    "### Reading a TFRecord file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "o3J5D4gcSy8N"
-   },
-   "source": [
-    "You can also read the TFRecord file using the `tf.data.TFRecordDataset` class.\n",
-    "\n",
-    "More information on consuming TFRecord files using `tf.data` can be found [here](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data).\n",
-    "\n",
-    "Using `TFRecordDataset`s can be useful for standardizing input data and optimizing performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "6OjX6UZl-bHC"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO 2c\n",
-    "filenames = [filename]\n",
-    "raw_dataset = tf.data.TFRecordDataset(filenames)\n",
-    "raw_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6_EQ9i2E_-Fz"
-   },
-   "source": [
-    "At this point the dataset contains serialized `tf.train.Example` messages. When iterated over it returns these as scalar string tensors.\n",
-    "\n",
-    "Use the `.take` method to only show the first 10 records.\n",
-    "\n",
-    "Note: iterating over a `tf.data.Dataset` only works with eager execution enabled."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "hxVXpLz_AJlm"
-   },
-   "outputs": [],
-   "source": [
-    "for raw_record in raw_dataset.take(10):\n",
-    "    print(repr(raw_record))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "W-6oNzM4luFQ"
-   },
-   "source": [
-    "These tensors can be parsed using the function below. Note that the `feature_description` is necessary here because datasets use graph-execution, and need this description to build their shape and type signature:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "zQjbIR1nleiy"
-   },
-   "outputs": [],
-   "source": [
-    "# Create a description of the features.\n",
-    "feature_description = {\n",
-    "    \"feature0\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
-    "    \"feature1\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
-    "    \"feature2\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n",
-    "    \"feature3\": tf.io.FixedLenFeature([], tf.float32, default_value=0.0),\n",
-    "}\n",
-    "\n",
-    "\n",
-    "def _parse_function(example_proto):\n",
-    "    # Parse the input `tf.Example` proto using the dictionary above.\n",
-    "    return tf.io.parse_single_example(example_proto, feature_description)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "gWETjUqhEQZf"
-   },
-   "source": [
-    "Alternatively, use `tf.parse example` to parse the whole batch at once. Apply this function to each item in the dataset using the `tf.data.Dataset.map` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "6Ob7D-zmBm1w"
-   },
-   "outputs": [],
-   "source": [
-    "parsed_dataset = raw_dataset.map(_parse_function)\n",
-    "parsed_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "sNV-XclGnOvn"
-   },
-   "source": [
-    "Use eager execution to display the observations in the dataset. There are 10,000 observations in this dataset, but you will only display the first 10. The data is displayed as a dictionary of features. Each item is a `tf.Tensor`, and the `numpy` element of this tensor displays the value of the feature:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "x2LT2JCqhoD_"
-   },
-   "outputs": [],
-   "source": [
-    "for parsed_record in parsed_dataset.take(10):\n",
-    "    print(repr(parsed_record))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Cig9EodTlDmg"
-   },
-   "source": [
-    "Here, the `tf.parse_example` function unpacks the `tf.Example` fields into standard tensors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "jyg1g3gU7DNn"
-   },
-   "source": [
-    "## TFRecord files in Python"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "3FXG3miA7Kf1"
-   },
-   "source": [
-    "The `tf.io` module also contains pure-Python functions for reading and writing TFRecord files."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "CKn5uql2lAaN"
-   },
-   "source": [
-    "### Writing a TFRecord file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "LNW_FA-GQWXs"
-   },
-   "source": [
-    "Next, write the 10,000 observations to the file `test.tfrecord`. Each observation is converted to a `tf.Example` message, then written to file. You can then verify that the file `test.tfrecord` has been created:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "MKPHzoGv7q44"
-   },
-   "outputs": [],
-   "source": [
-    "# Write the `tf.Example` observations to the file.\n",
-    "with tf.io.TFRecordWriter(filename) as writer:\n",
-    "    for i in range(n_observations):\n",
-    "        example = serialize_example(\n",
-    "            feature0[i], feature1[i], feature2[i], feature3[i]\n",
-    "        )\n",
-    "        writer.write(example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "EjdFHHJMpUUo"
-   },
-   "outputs": [],
-   "source": [
-    "!du -sh {filename}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2osVRnYNni-E"
-   },
-   "source": [
-    "### Reading a TFRecord file\n",
-    "\n",
-    "These serialized tensors can be easily parsed using `tf.train.Example.ParseFromString`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "U3tnd3LerOtV"
-   },
-   "outputs": [],
-   "source": [
-    "filenames = [filename]\n",
-    "raw_dataset = tf.data.TFRecordDataset(filenames)\n",
-    "raw_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nsEAACHcnm3f"
-   },
-   "outputs": [],
-   "source": [
-    "for raw_record in raw_dataset.take(1):\n",
-    "    example = tf.train.Example()\n",
-    "    example.ParseFromString(raw_record.numpy())\n",
-    "    print(example)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "S0tFDrwdoj3q"
-   },
-   "source": [
-    "## Walkthrough: Reading and writing image data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rjN2LFxFpcR9"
-   },
-   "source": [
-    "This is an end-to-end example of how to read and write image data using TFRecords. Using an image as input data, you will write the data as a TFRecord file, then read the file back and display the image.\n",
-    "\n",
-    "This can be useful if, for example, you want to use several models on the same input dataset. Instead of storing the image data raw, it can be preprocessed into the TFRecords format, and that can be used in all further processing and modelling.\n",
-    "\n",
-    "First, let's download [this image](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg) of a cat in the snow and [this photo](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg) of the Williamsburg Bridge, NYC under construction."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "5Lk2qrKvN0yu"
-   },
-   "source": [
-    "### Fetch the images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3a0fmwg8lHdF"
-   },
-   "outputs": [],
-   "source": [
-    "cat_in_snow = tf.keras.utils.get_file(\n",
-    "    \"320px-Felis_catus-cat_on_snow.jpg\",\n",
-    "    \"https://storage.googleapis.com/download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg\",\n",
-    ")\n",
-    "williamsburg_bridge = tf.keras.utils.get_file(\n",
-    "    \"194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\",\n",
-    "    \"https://storage.googleapis.com/download.tensorflow.org/example_images/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "7aJJh7vENeE4"
-   },
-   "outputs": [],
-   "source": [
-    "display.display(display.Image(filename=cat_in_snow))\n",
-    "display.display(\n",
-    "    display.HTML(\n",
-    "        'Image cc-by: <a \"href=https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg\">Von.grzanka</a>'\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "KkW0uuhcXZqA"
-   },
-   "outputs": [],
-   "source": [
-    "display.display(display.Image(filename=williamsburg_bridge))\n",
-    "display.display(\n",
-    "    display.HTML(\n",
-    "        '<a \"href=https://commons.wikimedia.org/wiki/File:New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\">From Wikimedia</a>'\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "VSOgJSwoN5TQ"
-   },
-   "source": [
-    "### Write the TFRecord file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Azx83ryQEU6T"
-   },
-   "source": [
-    "As before, encode the features as types compatible with `tf.Example`. This stores the raw image string feature, as well as the height, width, depth, and arbitrary `label` feature. The latter is used when you write the file to distinguish between the cat image and the bridge image. Use `0` for the cat image, and `1` for the bridge image:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kC4TS1ZEONHr"
-   },
-   "outputs": [],
-   "source": [
-    "image_labels = {\n",
-    "    cat_in_snow: 0,\n",
-    "    williamsburg_bridge: 1,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "c5njMSYNEhNZ"
-   },
-   "outputs": [],
-   "source": [
-    "# This is an example, just using the cat image.\n",
-    "image_string = open(cat_in_snow, \"rb\").read()\n",
-    "\n",
-    "label = image_labels[cat_in_snow]\n",
-    "\n",
-    "\n",
-    "# Create a dictionary with features that may be relevant.\n",
-    "def image_example(image_string, label):\n",
-    "    image_shape = tf.image.decode_jpeg(image_string).shape\n",
-    "\n",
-    "    feature = {\n",
-    "        \"height\": _int64_feature(image_shape[0]),\n",
-    "        \"width\": _int64_feature(image_shape[1]),\n",
-    "        \"depth\": _int64_feature(image_shape[2]),\n",
-    "        \"label\": _int64_feature(label),\n",
-    "        \"image_raw\": _bytes_feature(image_string),\n",
-    "    }\n",
-    "\n",
-    "    return tf.train.Example(features=tf.train.Features(feature=feature))\n",
-    "\n",
-    "\n",
-    "for line in str(image_example(image_string, label)).split(\"\\n\")[:15]:\n",
-    "    print(line)\n",
-    "print(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2G_o3O9MN0Qx"
-   },
-   "source": [
-    "Notice that all of the features are now stored in the `tf.Example` message. Next, functionalize the code above and write the example messages to a file named `images.tfrecords`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "qcw06lQCOCZU"
-   },
-   "outputs": [],
-   "source": [
-    "# Write the raw image files to `images.tfrecords`.\n",
-    "# First, process the two images into `tf.Example` messages.\n",
-    "# Then, write to a `.tfrecords` file.\n",
-    "record_file = \"images.tfrecords\"\n",
-    "with tf.io.TFRecordWriter(record_file) as writer:\n",
-    "    for filename, label in image_labels.items():\n",
-    "        image_string = open(filename, \"rb\").read()\n",
-    "        tf_example = image_example(image_string, label)\n",
-    "        writer.write(tf_example.SerializeToString())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "yJrTe6tHPCfs"
-   },
-   "outputs": [],
-   "source": [
-    "!du -sh {record_file}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "jJSsCkZLPH6K"
-   },
-   "source": [
-    "### Read the TFRecord file\n",
-    "\n",
-    "You now have the file—`images.tfrecords`—and can now iterate over the records in it to read back what you wrote. Given that in this example you will only reproduce the image, the only feature you will need is the raw image string. Extract it using the getters described above, namely `example.features.feature['image_raw'].bytes_list.value[0]`. You can also use the labels to determine which record is the cat and which one is the bridge:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "M6Cnfd3cTKHN"
-   },
-   "outputs": [],
-   "source": [
-    "raw_image_dataset = tf.data.TFRecordDataset(\"images.tfrecords\")\n",
-    "\n",
-    "# Create a dictionary describing the features.\n",
-    "image_feature_description = {\n",
-    "    \"height\": tf.io.FixedLenFeature([], tf.int64),\n",
-    "    \"width\": tf.io.FixedLenFeature([], tf.int64),\n",
-    "    \"depth\": tf.io.FixedLenFeature([], tf.int64),\n",
-    "    \"label\": tf.io.FixedLenFeature([], tf.int64),\n",
-    "    \"image_raw\": tf.io.FixedLenFeature([], tf.string),\n",
-    "}\n",
-    "\n",
-    "\n",
-    "def _parse_image_function(example_proto):\n",
-    "    # Parse the input tf.Example proto using the dictionary above.\n",
-    "    return tf.io.parse_single_example(example_proto, image_feature_description)\n",
-    "\n",
-    "\n",
-    "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n",
-    "parsed_image_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "0PEEFPk4NEg1"
-   },
-   "source": [
-    "Recover the images from the TFRecord file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "yZf8jOyEIjSF"
-   },
-   "outputs": [],
-   "source": [
-    "for image_features in parsed_image_dataset:\n",
-    "    image_raw = image_features[\"image_raw\"].numpy()\n",
-    "    display.display(display.Image(data=image_raw))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright 2020 Google Inc.\n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
-    "http://www.apache.org/licenses/LICENSE-2.0\n",
-    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [
-    "pL--_KGdYoBz"
-   ],
-   "name": "tfrecord.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "environment": {
-   "kernel": "python3",
-   "name": "tf2-gpu.2-12.m109",
-   "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-12:m109"
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 9a03703b602c509ab94d00db9409be50d1ea3c58 Mon Sep 17 00:00:00 2001
From: BenoitDherin <dherin@google.com>
Date: Thu, 18 Jan 2024 20:25:35 +0000
Subject: [PATCH 2/2] pre-commit

---
 notebooks/text_models/labs/load_text.ipynb      | 2 +-
 notebooks/text_models/solutions/load_text.ipynb | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/notebooks/text_models/labs/load_text.ipynb b/notebooks/text_models/labs/load_text.ipynb
index d172581a..955b5514 100644
--- a/notebooks/text_models/labs/load_text.ipynb
+++ b/notebooks/text_models/labs/load_text.ipynb
@@ -1103,7 +1103,7 @@
    "source": [
     "tokenized_ds = configure_dataset(tokenized_ds)\n",
     "\n",
-    "vocab_dict = collections.defaultdict(lambda: 0)\n",
+    "vocab_dict = collections.defaultdict(int)\n",
     "for toks in tokenized_ds.as_numpy_iterator():\n",
     "    for tok in toks:\n",
     "        vocab_dict[tok] += 1\n",
diff --git a/notebooks/text_models/solutions/load_text.ipynb b/notebooks/text_models/solutions/load_text.ipynb
index 414c918c..64aaa0e9 100644
--- a/notebooks/text_models/solutions/load_text.ipynb
+++ b/notebooks/text_models/solutions/load_text.ipynb
@@ -1105,7 +1105,7 @@
    "source": [
     "tokenized_ds = configure_dataset(tokenized_ds)\n",
     "\n",
-    "vocab_dict = collections.defaultdict(lambda: 0)\n",
+    "vocab_dict = collections.defaultdict(int)\n",
     "for toks in tokenized_ds.as_numpy_iterator():\n",
     "    for tok in toks:\n",
     "        vocab_dict[tok] += 1\n",