01-Keras-CNN-MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Neural Networks for Image Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualizing the Image Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_image = x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x294807c1400>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XRiYxvaPI3szsTkmrNHLWV7+kGyT9p6R7JH1Y0iuSrnD3hn/xVqa3VRp56/q7mZtHP2M3uLcLJD0q6RlJw9nizRr5fF3Ya5foa50KeN34hR8QFL/wA4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8Q1P8D6+E2hIAP97kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(single_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PreProcessing Data\n",
    "\n",
    "We first need to make sure the labels will be understandable by our CNN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm, looks like our labels are literally categories of numbers. We need to translate this to be \"one hot encoded\" so our CNN can understand, otherwise it will think this is some sort of regression problem on a continuous axis. Luckily , Keras has an easy to use function for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_example = to_categorical(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_example.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_example[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_cat_test = to_categorical(y_test,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_cat_train = to_categorical(y_train,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing X Data\n",
    "\n",
    "We should normalize the X data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_image.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_image.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train/255\n",
    "x_test = x_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_single = x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_single.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x29481827b38>"
      ]
     },
     "execution_count": 23,
     "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(scaled_single)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping the Data\n",
    "\n",
    "Right now our data is 60,000 images stored in 28 by 28 pixel array formation. \n",
    "\n",
    "This is correct for a CNN, but we need to add one more dimension to show we're dealing with 1 RGB channel (since technically the images are in black and white, only showing values from 0-255 on a single channel), an color image would have 3 dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reshape to include channel dimension (in this case, 1 channel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(60000, 28, 28, 1)"
   ]
  },
  {
   "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.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = x_test.reshape(10000,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28, 1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# CONVOLUTIONAL LAYER\n",
    "model.add(Conv2D(filters=32, kernel_size=(4,4),input_shape=(28, 28, 1), activation='relu',))\n",
    "# POOLING LAYER\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "# FLATTEN IMAGES FROM 28 by 28 to 764 BEFORE FINAL LAYER\n",
    "model.add(Flatten())\n",
    "\n",
    "# 128 NEURONS IN DENSE HIDDEN LAYER (YOU CAN CHANGE THIS NUMBER OF NEURONS)\n",
    "model.add(Dense(128, activation='relu'))\n",
    "\n",
    "# LAST LAYER IS THE CLASSIFIER, THUS 10 POSSIBLE CLASSES\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 25, 25, 32)        544       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               589952    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 591,786\n",
      "Trainable params: 591,786\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "60000/60000 [==============================] - 7s 124us/step - loss: 0.1344 - acc: 0.9593\n",
      "Epoch 2/2\n",
      "60000/60000 [==============================] - 6s 92us/step - loss: 0.0488 - acc: 0.9858\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2949bfe5b38>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# THIS WILL TAKE AWHILE ON MOST COMPUTERS!!!\n",
    "# CHANGE NUMBER OF EPOCHS IF NECESSARY\n",
    "# YOUR ACCURACY MAY ALSO BE LOWER THAN WHAT IS SHOWN HERE SINCE THIS WAS TRAINED ON GPU\n",
    "model.fit(x_train,y_cat_train,epochs=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loss', 'acc']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 39us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.043689755835279356, 0.9855]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_cat_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cat_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cat_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      1.00      0.99       980\n",
      "          1       1.00      1.00      1.00      1135\n",
      "          2       0.97      0.99      0.98      1032\n",
      "          3       0.98      0.99      0.99      1010\n",
      "          4       0.99      0.99      0.99       982\n",
      "          5       0.98      0.98      0.98       892\n",
      "          6       1.00      0.98      0.99       958\n",
      "          7       0.99      0.98      0.98      1028\n",
      "          8       0.99      0.97      0.98       974\n",
      "          9       0.98      0.98      0.98      1009\n",
      "\n",
      "avg / total       0.99      0.99      0.99     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the CNN performed quite well!"
   ]
  }
 ],
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