mnist_digit_classification_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train),(X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a5afecd4c0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train / 255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2764 - accuracy: 0.9197\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1273 - accuracy: 0.9624\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0895 - accuracy: 0.9730\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0679 - accuracy: 0.9799\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0533 - accuracy: 0.9838\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0424 - accuracy: 0.9874\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0355 - accuracy: 0.9894\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0292 - accuracy: 0.9909\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0233 - accuracy: 0.9931\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0205 - accuracy: 0.9934\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a5ad4bd7f0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(100, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 928us/step - loss: 0.0920 - accuracy: 0.9739\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.09202373027801514, 0.9739000201225281]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train.reshape(-1,28,28,1)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test.reshape(-1,28,28,1)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    layers.Conv2D(30, (3,3), activation='relu', input_shape=(28, 28, 1)),\n",
    "    layers.MaxPooling2D((2,2)),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(100, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1587 - accuracy: 0.9539\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0563 - accuracy: 0.9828\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0362 - accuracy: 0.9889\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 16s 8ms/step - loss: 0.0238 - accuracy: 0.9925\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0165 - accuracy: 0.9948\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a5afbad700>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, 1, 9], dtype=uint8)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.0513 - accuracy: 0.9834\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.05130781978368759, 0.9833999872207642]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4500448f4f772448be54c6ea4948e02bb88657f6bb8feaa33dc7ddba8513fe33"
  },
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit ('venv_deeplearning': venv)",
   "language": "python",
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