Project 03 - Mnist (handwritten_numbers).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bad7187b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)\n",
    "from tensorflow import keras \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce1bee13",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = keras.datasets.mnist \n",
    "(train_images , train_labels) , (test_images , test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "312597ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "66c389c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "367063c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "plt.imshow(test_images[123], cmap = 'gray' )\n",
    "print(test_labels[123])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "331f1cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images , test_images = train_images/255.0 , test_images/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33fb92f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1fdcdaf7ba8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.imshow(test_images[123], cmap = 'gray' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0fb75e04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1831 - accuracy: 0.9457\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0737 - accuracy: 0.9770\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0482 - accuracy: 0.9845\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0345 - accuracy: 0.9886\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0261 - accuracy: 0.9914\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0214 - accuracy: 0.9927\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0186 - accuracy: 0.9939\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0134 - accuracy: 0.9957\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0163 - accuracy: 0.9948\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0105 - accuracy: 0.9966\n"
     ]
    }
   ],
   "source": [
    "model1 = tf.keras.models.Sequential([tf.keras.layers.Flatten(),\n",
    "                                    tf.keras.layers.Dense(1024, activation=tf.nn.relu),\n",
    "                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])\n",
    "\n",
    "model1.compile(optimizer = 'adam',loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "history  = model1.fit(train_images, train_labels, epochs= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5344cf97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 6ms/step - loss: 0.0839 - accuracy: 0.9828\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.08393516391515732, 0.9828000068664551]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.evaluate(test_images , test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1065e0f",
   "metadata": {},
   "source": [
    "# Model 2 - writing training as an function  with call back "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e677da42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "mnist = tf.keras.datasets.mnist\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\n",
    "\n",
    "\n",
    "def train_mnist():\n",
    "    class myCallback(tf.keras.callbacks.Callback):\n",
    "        def on_epoch_end(self, epoch, logs = {}):\n",
    "            if(logs.get('accuracy') > 0.99):\n",
    "                print(\"\\nReached 99% accuracy so cancelling training!\")\n",
    "                self.model.stop_training = True\n",
    "    callbacks = myCallback()\n",
    "    model = tf.keras.models.Sequential([\n",
    "      tf.keras.layers.Flatten(input_shape = (28, 28)),\n",
    "      tf.keras.layers.Dense(1024, activation = tf.nn.relu),\n",
    "      tf.keras.layers.Dense(10, activation = tf.nn.softmax)])\n",
    "    \n",
    "    model.compile(optimizer = 'adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
    "    history = model.fit(x_train, y_train, epochs=10, callbacks = [callbacks])\n",
    "    return model , history "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99b08cb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "   1/1875 [..............................] - ETA: 8:26 - loss: 2.2869 - accuracy: 0.0938WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0000s vs `on_train_batch_end` time: 0.0053s). Check your callbacks.\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.3118 - accuracy: 0.9039 0s - loss: 0.3159 - ac\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0760 - accuracy: 0.9770\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0472 - accuracy: 0.9847\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0353 - accuracy: 0.9877\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0225 - accuracy: 0.9919\n",
      "\n",
      "Reached 99% accuracy so cancelling training!\n"
     ]
    }
   ],
   "source": [
    "model2 ,history =  train_mnist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c46f0d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.0716 - accuracy: 0.9796\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07159947603940964, 0.9796000123023987]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "72ba76dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifications = model2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4bfd794c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.87544250e-12, 1.70364611e-10, 1.02336783e-09, ...,\n",
       "        9.99999881e-01, 6.63767760e-13, 1.41231169e-08],\n",
       "       [5.42816592e-14, 4.14550030e-08, 1.00000000e+00, ...,\n",
       "        1.02427186e-17, 9.11431833e-11, 1.27098276e-18],\n",
       "       [3.45575547e-11, 9.99978304e-01, 9.54305051e-07, ...,\n",
       "        1.49839243e-05, 1.05230151e-06, 7.97642508e-10],\n",
       "       ...,\n",
       "       [2.48990761e-16, 6.35644488e-12, 4.72132311e-15, ...,\n",
       "        2.37420181e-08, 6.81353099e-11, 6.44795819e-07],\n",
       "       [2.13554380e-13, 3.91220768e-14, 8.02502816e-15, ...,\n",
       "        2.14486740e-13, 7.34424574e-08, 1.17033255e-14],\n",
       "       [2.65249017e-10, 1.80865116e-14, 2.36911592e-14, ...,\n",
       "        9.14847075e-19, 5.71124203e-13, 8.91538972e-14]], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d37ece31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.0203286e-09 6.3483640e-08 3.9613375e-09 2.4026936e-05 1.9656150e-12\n",
      " 1.1824870e-06 7.5888778e-14 9.9997270e-01 2.3512268e-09 2.0312682e-06]\n",
      "7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2a1bb5f1cf8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "print(classifications[0])  # It's the probability that this item is each of the 10 classes\n",
    "print(y_test[0])\n",
    "plt.imshow(x_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28354541",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}