model1.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(1)"
   ]
  },
  {
   "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",
      "flatten (Flatten)            (None, 5408)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                54090     \n",
      "=================================================================\n",
      "Total params: 54,410\n",
      "Trainable params: 54,410\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 [==============================] - 11s 182us/sample - loss: 0.5491 - accuracy: 0.9422\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 12s 197us/sample - loss: 0.0873 - accuracy: 0.9750\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 13s 221us/sample - loss: 0.0709 - accuracy: 0.9785\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 14s 230us/sample - loss: 0.0603 - accuracy: 0.9813\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 15s 246us/sample - loss: 0.0519 - accuracy: 0.9840\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x26f9b464908>"
      ]
     },
     "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: 0.1241 - accuracy: 0.9711\n",
      "\n",
      "Test loss :  0.1241085642565391\n",
      "Test accuracy : 0.9711\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": [
      "[282, 320, 321, 340, 366, 381, 391, 417, 431, 445]\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
}