model3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, models\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Train images :  (60000, 28, 28)\n",
      "Shape of Train labels :  (60000,)\n",
      "\n",
      "Shape of Test images :  (10000, 28, 28)\n",
      "Shape of Test images :  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Train images : ', train_images.shape)\n",
    "print('Shape of Train labels : ', train_labels.shape)\n",
    "print('\\nShape of Test images : ', test_images.shape)\n",
    "print('Shape of Test images : ', test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train labels :  [5 0 4 ... 5 6 8]\n"
     ]
    }
   ],
   "source": [
    "print('Train labels : ',train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  51 159 253\n",
      "  159  50   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  48 238 252 252\n",
      "  252 237   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0  54 227 253 252 239\n",
      "  233 252  57   6   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  10  60 224 252 253 252 202\n",
      "   84 252 253 122   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 163 252 252 252 253 252 252\n",
      "   96 189 253 167   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  51 238 253 253 190 114 253 228\n",
      "   47  79 255 168   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  48 238 252 252 179  12  75 121  21\n",
      "    0   0 253 243  50   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  38 165 253 233 208  84   0   0   0   0\n",
      "    0   0 253 252 165   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   7 178 252 240  71  19  28   0   0   0   0\n",
      "    0   0 253 252 195   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  57 252 252  63   0   0   0   0   0   0   0\n",
      "    0   0 253 252 195   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 198 253 190   0   0   0   0   0   0   0   0\n",
      "    0   0 255 253 196   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  76 246 252 112   0   0   0   0   0   0   0   0\n",
      "    0   0 253 252 148   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 230  25   0   0   0   0   0   0   0   0\n",
      "    7 135 253 186  12   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 223   0   0   0   0   0   0   0   0   7\n",
      "  131 252 225  71   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 145   0   0   0   0   0   0   0  48 165\n",
      "  252 173   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  86 253 225   0   0   0   0   0   0 114 238 253\n",
      "  162   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 249 146  48  29  85 178 225 253 223 167\n",
      "   56   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 252 252 229 215 252 252 252 196 130   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  28 199 252 252 253 252 252 233 145   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  25 128 252 253 252 141  37   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(train_images[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Train images in MNIST dataset\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train labels match with Train label sequentialy\n",
      " [5 0 4 1 9 2 1 3 1 4]\n"
     ]
    }
   ],
   "source": [
    "print('First 10 Train images in MNIST dataset\\n')\n",
    "for i in range(10):\n",
    "    plt.subplot(1, 10, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(train_images[i])\n",
    "plt.show()\n",
    "print('\\nTrain labels match with Train label sequentialy\\n',train_labels[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = tf.reshape(train_images, [-1, 28, 28, 1])\n",
    "test_images = tf.reshape(test_images, [-1, 28, 28, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_model(model_number):\n",
    "    if model_number == 1:\n",
    "        model = keras.models.Sequential([\n",
    "            keras.layers.Conv2D(32,(3,3), activation = 'relu', input_shape = (28, 28, 1)), # Layer 1\n",
    "            keras.layers.MaxPool2D((2,2)),                                                 # Layer 2\n",
    "            keras.layers.Flatten(),\n",
    "            keras.layers.Dense(10,activation = 'softmax')])                                # Layer 3\n",
    "    if model_number == 2:\n",
    "        model = model_2 = keras.models.Sequential([\n",
    "            keras.layers.Conv2D(32,(3,3), activation = 'relu', input_shape = (28, 28, 1)), # Layer 1\n",
    "            keras.layers.MaxPool2D((2,2)),                                                 # Layer 2\n",
    "            keras.layers.Conv2D(64,(3,3), activation = 'relu'),                            # Layer 3\n",
    "            keras.layers.MaxPool2D((2,2)),                                                 # Layer 4\n",
    "            keras.layers.Flatten(),\n",
    "            keras.layers.Dense(10,activation = 'softmax')])                                # Layer 5\n",
    "    if model_number == 3:\n",
    "        model = model_3 = keras.models.Sequential([\n",
    "            keras.layers.Conv2D(32,(3,3), activation = 'relu', input_shape = (28, 28, 1)), # Layer 1\n",
    "            keras.layers.MaxPool2D((2,2)),                                                 # Layer 2\n",
    "            keras.layers.Conv2D(64,(3,3), activation = 'relu'),                            # Layer 3\n",
    "            keras.layers.Conv2D(64,(3,3), activation = 'relu'),                            # Layer 4\n",
    "            keras.layers.MaxPool2D((2,2)),                                                 # Layer 5\n",
    "            keras.layers.Conv2D(128,(3,3), activation = 'relu'),                           # Layer 6\n",
    "            keras.layers.Flatten(),\n",
    "            keras.layers.Dense(10,activation = 'softmax')])                                # Layer 7\n",
    "    \n",
    "    return model\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = select_model(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 9, 9, 64)          36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 2, 2, 128)         73856     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 134,730\n",
      "Trainable params: 134,730\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer = 'adam',\n",
    "    loss = 'sparse_categorical_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 38s 635us/sample - loss: 0.1841 - accuracy: 0.9558\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 49s 810us/sample - loss: 0.0551 - accuracy: 0.9833\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 45s 745us/sample - loss: 0.0440 - accuracy: 0.9865\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 43s 721us/sample - loss: 0.0361 - accuracy: 0.9889\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 46s 763us/sample - loss: 0.0360 - accuracy: 0.9892\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1fe6876a788>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 - 1s - loss: 0.0422 - accuracy: 0.9867\n",
      "\n",
      "Test loss :  0.04219524208535004\n",
      "Test accuracy : 0.9867\n"
     ]
    }
   ],
   "source": [
    "test_loss, accuracy = model.evaluate(test_images, test_labels, verbose = 2)\n",
    "print('\\nTest loss : ', test_loss)\n",
    "print('Test accuracy :', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images = tf.cast(test_images, tf.float32)\n",
    "pred = model.predict(test_images)\n",
    "Number = [0,1,2,3,4,5,6,7,8,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction :  (10000, 10)\n",
      "Test labels :  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('Prediction : ', pred.shape)\n",
    "print('Test labels : ', test_labels.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "    predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    \n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "    \n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    if predicted_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "        \n",
    "    plt.xlabel(\"{} {:2.0f}% ({})\".format(Number[predicted_label],\n",
    "                                  100*np.max(predictions_array),\n",
    "                                  Number[true_label]),\n",
    "                                  color=color)\n",
    "    \n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "    predictions_array, true_label = predictions_array[i], true_label[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "    plt.ylim([0,1])\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    plt.xticks(Number)\n",
    "    \n",
    "    thisplot[predicted_label].set_color('red')\n",
    "    thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i,pred, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i,pred, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "    plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "    plot_image(i, pred, test_labels, test_images)\n",
    "    plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "    plot_value_array(i, pred, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def error_mnist(prediction_array, true_label):\n",
    "    error_index = []\n",
    "    \n",
    "    for i in range(true_label.shape[0]):\n",
    "        if np.argmax(prediction_array[i]) != true_label[i]:\n",
    "            error_index.append(i)\n",
    "    return error_index\n",
    "# change num_cols, num_rows if you want to see more result.\n",
    "def plot_error(index, prediction_array, true_label):\n",
    "    num_cols = 5\n",
    "    num_rows = 5\n",
    "    plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "    \n",
    "    assert len(index) < num_cols * num_rows\n",
    "    for i in range(len(index)):\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "        idx = index[i]\n",
    "        plt.imshow(test_images[idx])\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "        plt.bar(range(10), prediction_array[idx])\n",
    "        plt.xticks(Number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[43, 115, 158, 320, 321, 445, 447, 449, 450, 495]\n"
     ]
    }
   ],
   "source": [
    "index = error_mnist(pred, test_labels)\n",
    "index_slice = index[:10]\n",
    "print(index[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_error(index_slice, pred, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DONE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}