image_classification_with_keras.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rX8mhOLljYeM"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BZSlp3DAjdYf"
      },
      "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": {
        "id": "3wF5wszaj97Y"
      },
      "source": [
        "# TensorFlow 2 quickstart for beginners"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUNzJc4jTj6G"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/quickstart/beginner\"><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/quickstart/beginner.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/quickstart/beginner.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/quickstart/beginner.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "04QgGZc9bF5D"
      },
      "source": [
        "This short introduction uses [Keras](https://www.tensorflow.org/guide/keras/overview) to:\n",
        "\n",
        "1. Load a prebuilt dataset.\n",
        "1. Build a neural network machine learning model that classifies images.\n",
        "2. Train this neural network.\n",
        "3. Evaluate the accuracy of the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hiH7AC-NTniF"
      },
      "source": [
        "This tutorial is a [Google Colaboratory](https://colab.research.google.com/notebooks/welcome.ipynb) notebook. Python programs are run directly in the browser—a great way to learn and use TensorFlow. To follow this tutorial, run the notebook in Google Colab by clicking the button at the top of this page.\n",
        "\n",
        "1. In Colab, connect to a Python runtime: At the top-right of the menu bar, select *CONNECT*.\n",
        "2. To run all the code in the notebook, select **Runtime** > **Run all**. To run the code cells one at a time, hover over each cell and select the **Run cell** icon.\n",
        "\n",
        "![Run cell icon](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/quickstart/images/beginner/run_cell_icon.png?raw=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nnrWf3PCEzXL"
      },
      "source": [
        "## Set up TensorFlow\n",
        "\n",
        "Import TensorFlow into your program to get started:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0trJmd6DjqBZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a2d7d0b-130f-4c05-b5b3-c69da44acb84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version: 2.17.0\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "print(\"TensorFlow version:\", tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7NAbSZiaoJ4z"
      },
      "source": [
        "If you are following along in your own development environment, rather than [Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb), see the [install guide](https://www.tensorflow.org/install) for setting up TensorFlow for development.\n",
        "\n",
        "Note: Make sure you have upgraded to the latest `pip` to install the TensorFlow 2 package if you are using your own development environment. See the [install guide](https://www.tensorflow.org/install) for details.\n",
        "\n",
        "## Load a dataset\n",
        "\n",
        "Load and prepare the MNIST dataset. The pixel values of the images range from 0 through 255. Scale these values to a range of 0 to 1 by dividing the values by `255.0`. This also converts the sample data from integers to floating-point numbers:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7FP5258xjs-v"
      },
      "outputs": [],
      "source": [
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
        "x_train = x_train.astype('float32') / 255.0\n",
        "x_test = x_test.astype('float32') / 255.0\n",
        "\n",
        "x_train = x_train.reshape((-1, 28, 28, 1))\n",
        "x_test = x_test.reshape((-1, 28, 28, 1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rotation_range=10,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    zoom_range=0.1,\n",
        "    shear_range=0.1\n",
        ")\n",
        "\n",
        "datagen.fit(x_train)"
      ],
      "metadata": {
        "id": "SJwDs-TWT8Ta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPZ68wASog_I"
      },
      "source": [
        "## Build a machine learning model\n",
        "\n",
        "Build a `tf.keras.Sequential` model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h3IKyzTCDNGo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "5ef92a30-6b92-4ccf-d46a-97d95c06dadd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_46\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_46\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_17 (\u001b[38;5;33mInputLayer\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_12 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_40 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_41 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │          \u001b[38;5;34m73,856\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_14 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_42 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_16 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_44 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │          \u001b[38;5;34m66,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_43 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_45 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m5,130\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_40 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_41 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_42 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_44 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">66,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_43 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_45 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">5,130</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m210,026\u001b[0m (820.41 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">210,026</span> (820.41 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m210,026\u001b[0m (820.41 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">210,026</span> (820.41 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.2),\n",
        "  tf.keras.layers.Dense(10)\n",
        "])\n",
        "\n",
        "modified_model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(128, activation='elu'),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Dropout(0.3),\n",
        "    tf.keras.layers.Dense(256, activation='elu'),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Dropout(0.3),\n",
        "    tf.keras.layers.Dense(128, activation='elu'),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Dropout(0.3),\n",
        "    tf.keras.layers.Dense(10)\n",
        "])\n",
        "\n",
        "#The Functional API\n",
        "inputs = tf.keras.layers.Input(shape=(28, 28, 1))\n",
        "\n",
        "# First Convolutional Block\n",
        "x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(inputs)\n",
        "x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)\n",
        "x = tf.keras.layers.MaxPooling2D((2, 2))(x)\n",
        "x = tf.keras.layers.Dropout(0.25)(x)\n",
        "\n",
        "# Second Convolutional Block\n",
        "x = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)\n",
        "x = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)\n",
        "x = tf.keras.layers.MaxPooling2D((2, 2))(x)\n",
        "x = tf.keras.layers.Dropout(0.25)(x)\n",
        "\n",
        "# Third Convolutional Block\n",
        "x = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)\n",
        "x = tf.keras.layers.MaxPooling2D((2, 2))(x)\n",
        "x = tf.keras.layers.Dropout(0.25)(x)\n",
        "\n",
        "# Dense Layers\n",
        "x = tf.keras.layers.Flatten()(x)\n",
        "x = tf.keras.layers.Dense(512, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)\n",
        "x = tf.keras.layers.Dropout(0.5)(x)\n",
        "outputs = tf.keras.layers.Dense(10)(x)\n",
        "\n",
        "# Build the model\n",
        "swish_model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "# Print the model summary\n",
        "swish_model.summary()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2hiez2eIUz8"
      },
      "source": [
        "- `tf.keras.models.Sequential([])`: Це модель послідовного типу, де шари додаються один за одним. Цей підхід підходить для простих архітектур, де дані проходять через кожен шар тільки один раз, послідовно.\n",
        "\n",
        "- `tf.keras.layers.Flatten(input_shape=(28, 28))`: Перший шар перетворює вхідні двовимірні дані розміром 28x28 (наприклад, зображення) у вектор. Це потрібно для подальшої обробки нейронами у наступних шарах.\n",
        "\n",
        "- `tf.keras.layers.Dense(256, activation='relu')`: Це повнозв'язний (Dense) шар з 256 нейронів, який використовує функцію активації ReLU (Rectified Linear Unit). ReLU допомагає моделі вчитися нелінійним залежностям.\n",
        "\n",
        "- `tf.keras.layers.BatchNormalization()`: Шар нормалізації пакетів. Він допомагає прискорити навчання та стабілізувати його, зменшуючи коливання значень у мережі після кожного шару.\n",
        "\n",
        "- `tf.keras.layers.Dropout(0.2)`: Шар Dropout, який випадково вимикає 20% нейронів під час навчання, щоб уникнути перенавчання (overfitting) моделі.\n",
        "\n",
        "- `tf.keras.layers.Dense(128, activation='relu')`: Ще один повнозв'язний шар, але цього разу з 128 нейронами. Він також використовує ReLU для нелінійності.\n",
        "\n",
        "- `tf.keras.layers.BatchNormalization()`: Нормалізація пакетів для стабілізації та прискорення навчання.\n",
        "\n",
        "- `tf.keras.layers.Dropout(0.2)`: Dropout ще раз вимикає 20% нейронів для запобігання перенавчанню.\n",
        "\n",
        "- `tf.keras.layers.Dense(10)`: Останній повнозв'язний шар з 10 нейронами. Це вихідний шар, де кожен нейрон відповідає за одну з 10 можливих категорій (класів) для класифікації (наприклад, розпізнавання цифр 0-9). На цьому шарі немає активаційної функції, оскільки на етапі навчання використовується softmax для отримання ймовірностей для кожного класу.\n",
        "\n",
        "- `swish`: більш плавна функція активації, яка не обнуляє від'ємні значення, а модифікує їх, роблячи перетворення більш м'яким. Це дозволяє нейронам залишатися активними навіть для негативних значень, що сприяє більш глибокому навчанню."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OeOrNdnkEEcR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aefede5f-66cf-4d8e-8fb9-1b548021fa0b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.8903446 ,  0.3918934 ,  0.09620495, -1.7725949 , -0.8969641 ,\n",
              "        -0.17694032, -0.51628304, -0.7315246 ,  0.5523123 ,  0.2568404 ]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "predictions = model(x_train[:1]).numpy()\n",
        "predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgjhDQGcIniO"
      },
      "source": [
        "The `tf.nn.softmax` function converts these logits to *probabilities* for each class\n",
        "\n",
        "`tf.nn.softmax(predictions)`: Softmax перетворює необроблені логіти на ймовірності, де кожне значення відображає ймовірність того, що дане зображення належить до відповідного класу (цифри 0-9)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zWSRnQ0WI5eq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22b1d716-10c7-4209-8461-9988bee0120b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.04821174, 0.17378876, 0.12930219, 0.01995245, 0.04789365,\n",
              "        0.09839666, 0.07008186, 0.05651028, 0.20402846, 0.15183397]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "tf.nn.softmax(predictions).numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "he5u_okAYS4a"
      },
      "source": [
        "Note: It is possible to bake the `tf.nn.softmax` function into 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 provide an exact and numerically stable loss calculation for all models when using a softmax output."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQyugpgRIyrA"
      },
      "source": [
        "Define a loss function for training using `losses.SparseCategoricalCrossentropy`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RSkzdv8MD0tT"
      },
      "outputs": [],
      "source": [
        "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfR4MsSDU880"
      },
      "source": [
        "The loss function takes a vector of ground truth values and a vector of logits and returns a scalar loss for each example. This loss is equal to the negative log probability of the true class: The loss 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.math.log(1/10) ~= 2.3`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NJWqEVrrJ7ZB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9c5589f-4f98-4d74-bb73-b94a55164c61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8849678"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "loss_fn(y_train[:1], predictions).numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ada44eb947d4"
      },
      "source": [
        "Before you start training, configure and compile the model using Keras `Model.compile`. Set the [`optimizer`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) class to `adam`, set the `loss` to the `loss_fn` function you defined earlier, and specify a metric to be evaluated for the model by setting the `metrics` parameter to `accuracy`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9foNKHzTD2Vo"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.9),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "modified_model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "swish_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`optimizer=tf.keras.optimizers.Adam(learning_rate=0.001)`:\n",
        "\n",
        "- Adam (Adaptive Moment Estimation) — один із найпопулярніших оптимізаторів для навчання нейронних мереж. Він поєднує переваги двох інших методів оптимізації: AdaGrad і RMSProp.\n",
        "- `learning_rate=0.001`: Це швидкість навчання, яка контролює, як швидко модель оновлює свої ваги під час градієнтного спуску. Значення 0.001 є стандартним вибором і добре підходить для багатьох задач.\n",
        "\n",
        "`loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)`:\n",
        "\n",
        "- SparseCategoricalCrossentropy: Це функція втрат для задач класифікації, де вихідні дані є категоріями, закодованими цілими числами. Наприклад, для класифікації зображень MNIST клас може бути представлений числом від 0 до 9.\n",
        "- `from_logits=True`: Оскільки модель повертає логіти (необроблені виходи), а не ймовірності (які можна отримати через softmax), параметр from_logits=True вказує, що на вході до функції втрат використовуються саме логіти.\n"
      ],
      "metadata": {
        "id": "SQ8B78Z8jrZM"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ix4mEL65on-w"
      },
      "source": [
        "## Train and evaluate your model\n",
        "\n",
        "Use the `Model.fit` method to adjust your model parameters and minimize the loss\n",
        "\n",
        "`x_train`: Набір тренувальних даних. У цьому випадку це зображення з MNIST (розміром 28x28), які використовуються як вхідні дані для моделі.\n",
        "\n",
        "`y_train`: Відповідні мітки класів (цифри від 0 до 9 для MNIST). Це цільові значення, з якими порівнюються передбачення моделі під час навчання.\n",
        "\n",
        "`epochs=20`: Кількість епох (циклів) навчання. Модель пройде через весь набір тренувальних даних 20 разів, кожного разу коригуючи свої ваги на основі результатів. Чим більше епох, тим більше шансів, що модель краще навчатиметься, але це також може призвести до перенавчання, якщо кількість епох занадто велика."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint_1 = [\n",
        "    tf.keras.callbacks.ModelCheckpoint(\n",
        "        filepath='model_checkpoint_1.keras',  # шлях для першої моделі\n",
        "        save_weights_only=False,  # зберігати всю модель (структуру + ваги)\n",
        "        save_best_only=True,  # зберігати тільки найкращу модель\n",
        "        monitor='val_loss',  # стежити за валідаційною втратою\n",
        "        mode='min',  # зберігати, якщо втрата зменшується\n",
        "        verbose=1\n",
        "    ),\n",
        "    tf.keras.callbacks.EarlyStopping(\n",
        "        monitor='val_accuracy',\n",
        "        patience=10,\n",
        "        restore_best_weights=True\n",
        "    ),\n",
        "    tf.keras.callbacks.ReduceLROnPlateau(\n",
        "        monitor='val_loss',\n",
        "        factor=0.5,\n",
        "        patience=3,\n",
        "        min_lr=0.00001\n",
        "    )\n",
        "]\n",
        "\n",
        "\n",
        "checkpoint_2 = [\n",
        "    tf.keras.callbacks.ModelCheckpoint(\n",
        "        filepath='model_checkpoint_2.keras',\n",
        "        save_weights_only=False,\n",
        "        save_best_only=True,\n",
        "        monitor='val_loss',\n",
        "        mode='min',\n",
        "        verbose=1\n",
        "    ),\n",
        "    tf.keras.callbacks.EarlyStopping(\n",
        "        monitor='val_accuracy',\n",
        "        patience=10,\n",
        "        restore_best_weights=True\n",
        "    ),\n",
        "    tf.keras.callbacks.ReduceLROnPlateau(\n",
        "        monitor='val_loss',\n",
        "        factor=0.5,\n",
        "        patience=3,\n",
        "        min_lr=0.00001\n",
        "    )\n",
        "]\n",
        "\n",
        "checkpoint_3 = [\n",
        "    tf.keras.callbacks.ModelCheckpoint(\n",
        "        filepath='model_checkpoint_3.keras',\n",
        "        save_weights_only=False,\n",
        "        save_best_only=True,\n",
        "        monitor='val_loss',\n",
        "        mode='min',\n",
        "        verbose=1\n",
        "    ),\n",
        "    tf.keras.callbacks.EarlyStopping(\n",
        "        monitor='val_accuracy',\n",
        "        patience=10,\n",
        "        restore_best_weights=True\n",
        "    ),\n",
        "    tf.keras.callbacks.ReduceLROnPlateau(\n",
        "        monitor='val_loss',\n",
        "        factor=0.5,\n",
        "        patience=3,\n",
        "        min_lr=0.00001\n",
        "    )\n",
        "]"
      ],
      "metadata": {
        "id": "ZVHgWVR-jLO0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y7suUbJXVLqP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e63aa7e7-3a4c-4f34-9b61-17ffbfbb91c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7110 - loss: 0.8352\n",
            "Epoch 1: val_loss improved from inf to 0.47652, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.7111 - loss: 0.8349 - val_accuracy: 0.8281 - val_loss: 0.4765 - learning_rate: 0.0100\n",
            "Epoch 2/10\n",
            "\u001b[1m909/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8355 - loss: 0.4594\n",
            "Epoch 2: val_loss improved from 0.47652 to 0.42415, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8356 - loss: 0.4590 - val_accuracy: 0.8471 - val_loss: 0.4241 - learning_rate: 0.0100\n",
            "Epoch 3/10\n",
            "\u001b[1m927/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8493 - loss: 0.4154\n",
            "Epoch 3: val_loss improved from 0.42415 to 0.41048, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8494 - loss: 0.4153 - val_accuracy: 0.8551 - val_loss: 0.4105 - learning_rate: 0.0100\n",
            "Epoch 4/10\n",
            "\u001b[1m920/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8636 - loss: 0.3859\n",
            "Epoch 4: val_loss did not improve from 0.41048\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8635 - loss: 0.3859 - val_accuracy: 0.8534 - val_loss: 0.4158 - learning_rate: 0.0100\n",
            "Epoch 5/10\n",
            "\u001b[1m933/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8678 - loss: 0.3683\n",
            "Epoch 5: val_loss improved from 0.41048 to 0.38155, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8678 - loss: 0.3683 - val_accuracy: 0.8636 - val_loss: 0.3815 - learning_rate: 0.0100\n",
            "Epoch 6/10\n",
            "\u001b[1m905/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8712 - loss: 0.3545\n",
            "Epoch 6: val_loss improved from 0.38155 to 0.37887, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8712 - loss: 0.3545 - val_accuracy: 0.8639 - val_loss: 0.3789 - learning_rate: 0.0100\n",
            "Epoch 7/10\n",
            "\u001b[1m931/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8764 - loss: 0.3431\n",
            "Epoch 7: val_loss improved from 0.37887 to 0.36461, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8763 - loss: 0.3431 - val_accuracy: 0.8686 - val_loss: 0.3646 - learning_rate: 0.0100\n",
            "Epoch 8/10\n",
            "\u001b[1m920/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8774 - loss: 0.3378\n",
            "Epoch 8: val_loss improved from 0.36461 to 0.35733, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8775 - loss: 0.3377 - val_accuracy: 0.8721 - val_loss: 0.3573 - learning_rate: 0.0100\n",
            "Epoch 9/10\n",
            "\u001b[1m921/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8809 - loss: 0.3278\n",
            "Epoch 9: val_loss did not improve from 0.35733\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8809 - loss: 0.3277 - val_accuracy: 0.8691 - val_loss: 0.3578 - learning_rate: 0.0100\n",
            "Epoch 10/10\n",
            "\u001b[1m916/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8836 - loss: 0.3159\n",
            "Epoch 10: val_loss did not improve from 0.35733\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8836 - loss: 0.3160 - val_accuracy: 0.8702 - val_loss: 0.3615 - learning_rate: 0.0100\n",
            "Epoch 1/10\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6175 - loss: 1.1450\n",
            "Epoch 1: val_loss improved from inf to 0.50793, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.6176 - loss: 1.1446 - val_accuracy: 0.8193 - val_loss: 0.5079 - learning_rate: 1.0000e-04\n",
            "Epoch 2/10\n",
            "\u001b[1m926/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7882 - loss: 0.6078\n",
            "Epoch 2: val_loss improved from 0.50793 to 0.48093, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.7883 - loss: 0.6075 - val_accuracy: 0.8284 - val_loss: 0.4809 - learning_rate: 1.0000e-04\n",
            "Epoch 3/10\n",
            "\u001b[1m921/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8151 - loss: 0.5321\n",
            "Epoch 3: val_loss improved from 0.48093 to 0.44554, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8151 - loss: 0.5321 - val_accuracy: 0.8436 - val_loss: 0.4455 - learning_rate: 1.0000e-04\n",
            "Epoch 4/10\n",
            "\u001b[1m921/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8280 - loss: 0.4919\n",
            "Epoch 4: val_loss improved from 0.44554 to 0.43617, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8280 - loss: 0.4919 - val_accuracy: 0.8472 - val_loss: 0.4362 - learning_rate: 1.0000e-04\n",
            "Epoch 5/10\n",
            "\u001b[1m916/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8301 - loss: 0.4825\n",
            "Epoch 5: val_loss improved from 0.43617 to 0.41952, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8302 - loss: 0.4823 - val_accuracy: 0.8468 - val_loss: 0.4195 - learning_rate: 1.0000e-04\n",
            "Epoch 6/10\n",
            "\u001b[1m916/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8361 - loss: 0.4589\n",
            "Epoch 6: val_loss improved from 0.41952 to 0.41447, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8361 - loss: 0.4589 - val_accuracy: 0.8540 - val_loss: 0.4145 - learning_rate: 1.0000e-04\n",
            "Epoch 7/10\n",
            "\u001b[1m932/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8382 - loss: 0.4473\n",
            "Epoch 7: val_loss improved from 0.41447 to 0.40731, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8382 - loss: 0.4473 - val_accuracy: 0.8520 - val_loss: 0.4073 - learning_rate: 1.0000e-04\n",
            "Epoch 8/10\n",
            "\u001b[1m920/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8471 - loss: 0.4269\n",
            "Epoch 8: val_loss improved from 0.40731 to 0.40278, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8470 - loss: 0.4270 - val_accuracy: 0.8549 - val_loss: 0.4028 - learning_rate: 1.0000e-04\n",
            "Epoch 9/10\n",
            "\u001b[1m924/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8510 - loss: 0.4188\n",
            "Epoch 9: val_loss improved from 0.40278 to 0.39666, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8510 - loss: 0.4189 - val_accuracy: 0.8561 - val_loss: 0.3967 - learning_rate: 1.0000e-04\n",
            "Epoch 10/10\n",
            "\u001b[1m927/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8480 - loss: 0.4190\n",
            "Epoch 10: val_loss improved from 0.39666 to 0.39057, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8480 - loss: 0.4189 - val_accuracy: 0.8598 - val_loss: 0.3906 - learning_rate: 1.0000e-04\n",
            "Epoch 1/10\n",
            "\u001b[1m931/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.5141 - loss: 1.5609\n",
            "Epoch 1: val_loss improved from inf to 0.80085, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 30ms/step - accuracy: 0.5152 - loss: 1.5578 - val_accuracy: 0.7702 - val_loss: 0.8009 - learning_rate: 5.0000e-04\n",
            "Epoch 2/10\n",
            "\u001b[1m936/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.7451 - loss: 0.8777\n",
            "Epoch 2: val_loss improved from 0.80085 to 0.71830, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.7451 - loss: 0.8776 - val_accuracy: 0.7966 - val_loss: 0.7183 - learning_rate: 5.0000e-04\n",
            "Epoch 3/10\n",
            "\u001b[1m932/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.7754 - loss: 0.7604\n",
            "Epoch 3: val_loss improved from 0.71830 to 0.61550, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 25ms/step - accuracy: 0.7754 - loss: 0.7602 - val_accuracy: 0.8278 - val_loss: 0.6155 - learning_rate: 5.0000e-04\n",
            "Epoch 4/10\n",
            "\u001b[1m936/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.7968 - loss: 0.6922\n",
            "Epoch 4: val_loss improved from 0.61550 to 0.56944, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 25ms/step - accuracy: 0.7968 - loss: 0.6921 - val_accuracy: 0.8437 - val_loss: 0.5694 - learning_rate: 5.0000e-04\n",
            "Epoch 5/10\n",
            "\u001b[1m931/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.8105 - loss: 0.6478\n",
            "Epoch 5: val_loss improved from 0.56944 to 0.52852, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 26ms/step - accuracy: 0.8106 - loss: 0.6477 - val_accuracy: 0.8526 - val_loss: 0.5285 - learning_rate: 5.0000e-04\n",
            "Epoch 6/10\n",
            "\u001b[1m931/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.8202 - loss: 0.6165\n",
            "Epoch 6: val_loss improved from 0.52852 to 0.50399, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 26ms/step - accuracy: 0.8203 - loss: 0.6165 - val_accuracy: 0.8610 - val_loss: 0.5040 - learning_rate: 5.0000e-04\n",
            "Epoch 7/10\n",
            "\u001b[1m931/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.8257 - loss: 0.6028\n",
            "Epoch 7: val_loss did not improve from 0.50399\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.8258 - loss: 0.6027 - val_accuracy: 0.8586 - val_loss: 0.5158 - learning_rate: 5.0000e-04\n",
            "Epoch 8/10\n",
            "\u001b[1m936/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.8315 - loss: 0.5870\n",
            "Epoch 8: val_loss improved from 0.50399 to 0.46892, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 27ms/step - accuracy: 0.8315 - loss: 0.5870 - val_accuracy: 0.8750 - val_loss: 0.4689 - learning_rate: 5.0000e-04\n",
            "Epoch 9/10\n",
            "\u001b[1m931/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.8383 - loss: 0.5648\n",
            "Epoch 9: val_loss improved from 0.46892 to 0.46595, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 26ms/step - accuracy: 0.8383 - loss: 0.5648 - val_accuracy: 0.8726 - val_loss: 0.4659 - learning_rate: 5.0000e-04\n",
            "Epoch 10/10\n",
            "\u001b[1m931/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.8421 - loss: 0.5637\n",
            "Epoch 10: val_loss improved from 0.46595 to 0.45796, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 25ms/step - accuracy: 0.8421 - loss: 0.5637 - val_accuracy: 0.8810 - val_loss: 0.4580 - learning_rate: 5.0000e-04\n"
          ]
        }
      ],
      "source": [
        "history_1 = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test), callbacks=[checkpoint_1], verbose=1, batch_size=64, validation_split=0.2)\n",
        "\n",
        "history_2 = modified_model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test), callbacks=[checkpoint_2], verbose=1, batch_size=64, validation_split=0.2)\n",
        "\n",
        "history_3 = swish_model.fit(datagen.flow(x_train, y_train, batch_size=64), epochs=10, validation_data=(x_test, y_test), callbacks=[checkpoint_3], verbose=1, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_comparison(histories, key='accuracy'):\n",
        "    plt.figure(figsize=(10, 6))\n",
        "\n",
        "    colors = ['royalblue', 'green', 'orange']\n",
        "    for i, history in enumerate(histories):\n",
        "        plt.plot(history.history[key], color=colors[i], label=f'Model {i+1} - Train {key}', linestyle='-')\n",
        "        plt.plot(history.history['val_' + key], color=colors[i], label=f'Model {i+1} - Validation {key}', linestyle='--')\n",
        "\n",
        "    plt.title(f'Model comparison: {key}')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel(key.capitalize())\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "plot_comparison([history_1, history_2, history_3], key='accuracy')\n",
        "\n",
        "plot_comparison([history_1, history_2, history_3], key='loss')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0SoUgdZMlg39",
        "outputId": "19e63f43-c92a-4b86-8a2e-9f0c3803065c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_1 = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test), callbacks=[checkpoint_1], verbose=1)\n",
        "\n",
        "history_2 = modified_model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test), callbacks=[checkpoint_2], verbose=1)\n",
        "\n",
        "history_3 = swish_model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test), callbacks=[checkpoint_3], verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qi7nGk2HxYQb",
        "outputId": "e23e2a62-c224-47e4-adb5-53b6e433d6bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8754 - loss: 0.3445\n",
            "Epoch 1: val_loss did not improve from 0.35733\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8754 - loss: 0.3445 - val_accuracy: 0.8645 - val_loss: 0.3692 - learning_rate: 0.0100\n",
            "Epoch 2/10\n",
            "\u001b[1m1870/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8725 - loss: 0.3436\n",
            "Epoch 2: val_loss did not improve from 0.35733\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8725 - loss: 0.3436 - val_accuracy: 0.8693 - val_loss: 0.3773 - learning_rate: 0.0100\n",
            "Epoch 3/10\n",
            "\u001b[1m1857/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8812 - loss: 0.3274\n",
            "Epoch 3: val_loss improved from 0.35733 to 0.35728, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8812 - loss: 0.3274 - val_accuracy: 0.8702 - val_loss: 0.3573 - learning_rate: 0.0100\n",
            "Epoch 4/10\n",
            "\u001b[1m1850/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8838 - loss: 0.3226\n",
            "Epoch 4: val_loss improved from 0.35728 to 0.35695, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8838 - loss: 0.3226 - val_accuracy: 0.8727 - val_loss: 0.3570 - learning_rate: 0.0100\n",
            "Epoch 5/10\n",
            "\u001b[1m1864/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8848 - loss: 0.3146\n",
            "Epoch 5: val_loss improved from 0.35695 to 0.34999, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8848 - loss: 0.3146 - val_accuracy: 0.8755 - val_loss: 0.3500 - learning_rate: 0.0100\n",
            "Epoch 6/10\n",
            "\u001b[1m1867/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8892 - loss: 0.3011\n",
            "Epoch 6: val_loss did not improve from 0.34999\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8892 - loss: 0.3011 - val_accuracy: 0.8728 - val_loss: 0.3542 - learning_rate: 0.0100\n",
            "Epoch 7/10\n",
            "\u001b[1m1872/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8919 - loss: 0.2956\n",
            "Epoch 7: val_loss improved from 0.34999 to 0.33600, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8919 - loss: 0.2956 - val_accuracy: 0.8804 - val_loss: 0.3360 - learning_rate: 0.0100\n",
            "Epoch 8/10\n",
            "\u001b[1m1865/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8930 - loss: 0.2878\n",
            "Epoch 8: val_loss did not improve from 0.33600\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8930 - loss: 0.2878 - val_accuracy: 0.8807 - val_loss: 0.3368 - learning_rate: 0.0100\n",
            "Epoch 9/10\n",
            "\u001b[1m1851/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8969 - loss: 0.2794\n",
            "Epoch 9: val_loss did not improve from 0.33600\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8969 - loss: 0.2795 - val_accuracy: 0.8813 - val_loss: 0.3362 - learning_rate: 0.0100\n",
            "Epoch 10/10\n",
            "\u001b[1m1864/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8961 - loss: 0.2816\n",
            "Epoch 10: val_loss improved from 0.33600 to 0.32989, saving model to model_checkpoint_1.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8961 - loss: 0.2816 - val_accuracy: 0.8858 - val_loss: 0.3299 - learning_rate: 0.0100\n",
            "Epoch 1/10\n",
            "\u001b[1m1856/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8475 - loss: 0.4295\n",
            "Epoch 1: val_loss improved from 0.39057 to 0.38776, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8474 - loss: 0.4295 - val_accuracy: 0.8599 - val_loss: 0.3878 - learning_rate: 1.0000e-04\n",
            "Epoch 2/10\n",
            "\u001b[1m1855/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8471 - loss: 0.4302\n",
            "Epoch 2: val_loss improved from 0.38776 to 0.37972, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8471 - loss: 0.4301 - val_accuracy: 0.8641 - val_loss: 0.3797 - learning_rate: 1.0000e-04\n",
            "Epoch 3/10\n",
            "\u001b[1m1856/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8512 - loss: 0.4174\n",
            "Epoch 3: val_loss improved from 0.37972 to 0.37329, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8512 - loss: 0.4173 - val_accuracy: 0.8668 - val_loss: 0.3733 - learning_rate: 1.0000e-04\n",
            "Epoch 4/10\n",
            "\u001b[1m1866/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8536 - loss: 0.4117\n",
            "Epoch 4: val_loss did not improve from 0.37329\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8536 - loss: 0.4117 - val_accuracy: 0.8646 - val_loss: 0.3796 - learning_rate: 1.0000e-04\n",
            "Epoch 5/10\n",
            "\u001b[1m1866/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8583 - loss: 0.4027\n",
            "Epoch 5: val_loss did not improve from 0.37329\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8583 - loss: 0.4027 - val_accuracy: 0.8674 - val_loss: 0.3751 - learning_rate: 1.0000e-04\n",
            "Epoch 6/10\n",
            "\u001b[1m1866/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8623 - loss: 0.3858\n",
            "Epoch 6: val_loss improved from 0.37329 to 0.36647, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8623 - loss: 0.3859 - val_accuracy: 0.8699 - val_loss: 0.3665 - learning_rate: 1.0000e-04\n",
            "Epoch 7/10\n",
            "\u001b[1m1847/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8635 - loss: 0.3889\n",
            "Epoch 7: val_loss improved from 0.36647 to 0.36571, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8634 - loss: 0.3890 - val_accuracy: 0.8697 - val_loss: 0.3657 - learning_rate: 1.0000e-04\n",
            "Epoch 8/10\n",
            "\u001b[1m1862/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8629 - loss: 0.3833\n",
            "Epoch 8: val_loss did not improve from 0.36571\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8629 - loss: 0.3833 - val_accuracy: 0.8675 - val_loss: 0.3750 - learning_rate: 1.0000e-04\n",
            "Epoch 9/10\n",
            "\u001b[1m1872/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8639 - loss: 0.3841\n",
            "Epoch 9: val_loss improved from 0.36571 to 0.36130, saving model to model_checkpoint_2.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8639 - loss: 0.3841 - val_accuracy: 0.8704 - val_loss: 0.3613 - learning_rate: 1.0000e-04\n",
            "Epoch 10/10\n",
            "\u001b[1m1863/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8618 - loss: 0.3846\n",
            "Epoch 10: val_loss did not improve from 0.36130\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8618 - loss: 0.3846 - val_accuracy: 0.8709 - val_loss: 0.3628 - learning_rate: 1.0000e-04\n",
            "Epoch 1/10\n",
            "\u001b[1m1859/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8613 - loss: 0.5106\n",
            "Epoch 1: val_loss improved from 0.45796 to 0.44058, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8614 - loss: 0.5104 - val_accuracy: 0.8890 - val_loss: 0.4406 - learning_rate: 5.0000e-04\n",
            "Epoch 2/10\n",
            "\u001b[1m1866/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8816 - loss: 0.4651\n",
            "Epoch 2: val_loss improved from 0.44058 to 0.42138, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8816 - loss: 0.4650 - val_accuracy: 0.8970 - val_loss: 0.4214 - learning_rate: 5.0000e-04\n",
            "Epoch 3/10\n",
            "\u001b[1m1858/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8823 - loss: 0.4591\n",
            "Epoch 3: val_loss did not improve from 0.42138\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8823 - loss: 0.4591 - val_accuracy: 0.8930 - val_loss: 0.4232 - learning_rate: 5.0000e-04\n",
            "Epoch 4/10\n",
            "\u001b[1m1865/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8862 - loss: 0.4449\n",
            "Epoch 4: val_loss did not improve from 0.42138\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8862 - loss: 0.4449 - val_accuracy: 0.8971 - val_loss: 0.4220 - learning_rate: 5.0000e-04\n",
            "Epoch 5/10\n",
            "\u001b[1m1869/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8896 - loss: 0.4429\n",
            "Epoch 5: val_loss improved from 0.42138 to 0.41755, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8896 - loss: 0.4429 - val_accuracy: 0.8976 - val_loss: 0.4175 - learning_rate: 5.0000e-04\n",
            "Epoch 6/10\n",
            "\u001b[1m1862/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8924 - loss: 0.4311\n",
            "Epoch 6: val_loss improved from 0.41755 to 0.41250, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8924 - loss: 0.4311 - val_accuracy: 0.8975 - val_loss: 0.4125 - learning_rate: 5.0000e-04\n",
            "Epoch 7/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8901 - loss: 0.4323\n",
            "Epoch 7: val_loss improved from 0.41250 to 0.40069, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - accuracy: 0.8901 - loss: 0.4323 - val_accuracy: 0.9003 - val_loss: 0.4007 - learning_rate: 5.0000e-04\n",
            "Epoch 8/10\n",
            "\u001b[1m1865/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8910 - loss: 0.4322\n",
            "Epoch 8: val_loss did not improve from 0.40069\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8910 - loss: 0.4322 - val_accuracy: 0.8992 - val_loss: 0.4046 - learning_rate: 5.0000e-04\n",
            "Epoch 9/10\n",
            "\u001b[1m1865/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8966 - loss: 0.4202\n",
            "Epoch 9: val_loss improved from 0.40069 to 0.38861, saving model to model_checkpoint_3.keras\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8966 - loss: 0.4203 - val_accuracy: 0.9044 - val_loss: 0.3886 - learning_rate: 5.0000e-04\n",
            "Epoch 10/10\n",
            "\u001b[1m1873/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8969 - loss: 0.4172\n",
            "Epoch 10: val_loss did not improve from 0.38861\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - accuracy: 0.8969 - loss: 0.4172 - val_accuracy: 0.9045 - val_loss: 0.3916 - learning_rate: 5.0000e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_comparison([history_1, history_2, history_3], key='accuracy')\n",
        "\n",
        "plot_comparison([history_1, history_2, history_3], key='loss')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_vMrROkLxZBZ",
        "outputId": "744bcfb6-587f-455f-fee3-d4ed4d8836a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4mDAAPFqVVgn"
      },
      "source": [
        "The `Model.evaluate` method checks the model's 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).\n",
        "\n",
        "`verbose=2` показує короткий звіт для кожної епохи з мінімальною кількістю деталей."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Збереження моделей\n",
        "model.save('model.keras')\n",
        "modified_model.save('modified_model.keras')\n",
        "swish_model.save('swish_model.keras')"
      ],
      "metadata": {
        "id": "QZquE9oT3Mm3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Завантаження моделей\n",
        "loaded_model = tf.keras.models.load_model('model.keras')\n",
        "loaded_modified_model = tf.keras.models.load_model('modified_model.keras')\n",
        "loaded_swish_model = tf.keras.models.load_model('swish_model.keras')\n"
      ],
      "metadata": {
        "id": "Ia6Yce503cBc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Оцінка завантаженої моделі\n",
        "loaded_model.evaluate(x_test, y_test, verbose=2)\n",
        "loaded_modified_model.evaluate(x_test, y_test, verbose=2)\n",
        "loaded_swish_model.evaluate(x_test, y_test, verbose=2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZP2hHKa3qST",
        "outputId": "550f177a-53da-46e6-f51c-e5e752d1aa22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - 5ms/step - accuracy: 0.8858 - loss: 0.3299\n",
            "313/313 - 1s - 4ms/step - accuracy: 0.8709 - loss: 0.3628\n",
            "313/313 - 2s - 6ms/step - accuracy: 0.9045 - loss: 0.3916\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3916170001029968, 0.9045000076293945]"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F7dTAzgHDUh7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eee9a63e-823e-4000-a86b-003e50d6a4b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 0s - 1ms/step - accuracy: 0.8858 - loss: 0.3299\n",
            "313/313 - 0s - 1ms/step - accuracy: 0.8709 - loss: 0.3628\n",
            "313/313 - 1s - 2ms/step - accuracy: 0.9045 - loss: 0.3916\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3916170299053192, 0.9045000076293945]"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "model.evaluate(x_test,  y_test, verbose=2)\n",
        "modified_model.evaluate(x_test,  y_test, verbose=2)\n",
        "swish_model.evaluate(x_test,  y_test, verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "y_pred_1 = np.argmax(model.predict(x_test), axis=1)\n",
        "y_pred_2 = np.argmax(modified_model.predict(x_test), axis=1)\n",
        "y_pred_3 = np.argmax(swish_model.predict(x_test), axis=1)\n",
        "\n",
        "conf_matrix_1 = confusion_matrix(y_test, y_pred_1)\n",
        "conf_matrix_2 = confusion_matrix(y_test, y_pred_2)\n",
        "conf_matrix_3 = confusion_matrix(y_test, y_pred_3)\n",
        "\n",
        "def plot_confusion_matrices(conf_matrices, titles, color_maps):\n",
        "    plt.figure(figsize=(20, 6))\n",
        "\n",
        "    for i, (conf_matrix, title, cmap) in enumerate(zip(conf_matrices, titles, color_maps)):\n",
        "        plt.subplot(1, 3, i + 1)  # 1 row, 3 columns\n",
        "        sns.heatmap(conf_matrix, annot=True, fmt='d', cmap=cmap,\n",
        "                    xticklabels=np.arange(10), yticklabels=np.arange(10))\n",
        "        plt.title(title)\n",
        "        plt.xlabel('Predicted Label')\n",
        "        plt.ylabel('True Label')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "plot_confusion_matrices(\n",
        "    [conf_matrix_1, conf_matrix_2, conf_matrix_3],\n",
        "    ['Confusion Matrix for Original Model',\n",
        "     'Confusion Matrix for Modified Model',\n",
        "     'Confusion Matrix for Swish Model'],\n",
        "    ['Blues', 'Greens', 'Oranges']\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "OsXj0t4TI96e",
        "outputId": "9619a18e-7fb3-4264-d383-16adb9611092"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x600 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_model = model.predict(x_test)\n",
        "predictions_modified_model = modified_model.predict(x_test)\n",
        "predictions_swish_model = swish_model.predict(x_test)\n",
        "\n",
        "def plot_predictions(x_test, y_test, predictions, model_name):\n",
        "    plt.figure(figsize=(15, 4))\n",
        "\n",
        "    # Display the first 10 test images with predictions\n",
        "    for i in range(10):\n",
        "        plt.subplot(2, 10, i + 1)\n",
        "        plt.imshow(x_test[i], cmap='gray')\n",
        "        plt.title(f'{np.argmax(predictions[i])}\\n({y_test[i]})')\n",
        "        plt.axis('off')\n",
        "\n",
        "    plt.suptitle(f'Predictions of {model_name}')\n",
        "    plt.show()\n",
        "\n",
        "plot_predictions(x_test, y_test, predictions_model, 'Original Model')\n",
        "plot_predictions(x_test, y_test, predictions_modified_model, 'Modified Model')\n",
        "plot_predictions(x_test, y_test, predictions_swish_model, 'Swish Model')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "7haZ16v8JbSI",
        "outputId": "70ad7c4d-c18d-45a5-f1b8-17e28525c6ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-47O6_GLdRuT"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "Congratulations! You have trained a machine learning model using a prebuilt dataset using the [Keras](https://www.tensorflow.org/guide/keras/overview) API.\n",
        "\n",
        "For more examples of using Keras, check out the [tutorials](https://www.tensorflow.org/tutorials/keras/). To learn more about building models with Keras, read the [guides](https://www.tensorflow.org/guide/keras). If you want learn more about loading and preparing data, see the tutorials on [image data loading](https://www.tensorflow.org/tutorials/load_data/images) or [CSV data loading](https://www.tensorflow.org/tutorials/load_data/csv).\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}