.ipynb_checkpoints/built_up_from_HW-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import numpy as np\n",
    "from keras.utils.np_utils import to_categorical\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from keras.datasets import mnist\n",
    "plt.style.use('dark_background')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 2s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "train_images = train_images.astype('float32')/ 255\n",
    "\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))\n",
    "test_images = test_images.astype('float32')/255\n",
    "\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28,28,1)))\n",
    "model.add(layers.MaxPooling2D(2,2))\n",
    "model.add(layers.Conv2D(64, (3,3), activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D(2,2))\n",
    "model.add(layers.Conv2D(64, (3,3), activation = 'relu'))\n",
    "# Adding a classifier;\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(64, activation = 'relu'))\n",
    "model.add(layers.Dense(10, activation = 'softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_14 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                36928     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = 'rmsprop',\n",
    "             loss = 'categorical_crossentropy',\n",
    "             metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "938/938 [==============================] - 18s 19ms/step - loss: 0.1771 - accuracy: 0.9440\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 17s 19ms/step - loss: 0.0487 - accuracy: 0.9845\n",
      "Epoch 3/5\n",
      "938/938 [==============================] - 29s 31ms/step - loss: 0.0333 - accuracy: 0.9900\n",
      "Epoch 4/5\n",
      "938/938 [==============================] - 22s 24ms/step - loss: 0.0244 - accuracy: 0.9928\n",
      "Epoch 5/5\n",
      "938/938 [==============================] - 23s 24ms/step - loss: 0.0205 - accuracy: 0.9933\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x15ef21190>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs = 5, batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.0238 - accuracy: 0.9923\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9922999739646912"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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