CNN on MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 551
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1970705,
     "status": "ok",
     "timestamp": 1524577821923,
     "user": {
      "displayName": "Applied AI Course",
      "photoUrl": "//lh3.googleusercontent.com/-EsJzSyawCkQ/AAAAAAAAAAI/AAAAAAAAAWk/jhKHALKaHag/s50-c-k-no/photo.jpg",
      "userId": "116292885805316472049"
     },
     "user_tz": -330
    },
    "id": "H9EU0e8yzFOm",
    "outputId": "b9a7331d-3a52-4991-f4a2-eaa64e288eb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 24, 24, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               1179776   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,199,882\n",
      "Trainable params: 1,199,882\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Credits: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=['accuracy'])\n",
    "model.summary()\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def plt_dynamic(x, vy, ty, fig, ax, colors=['b']):\n",
    "    \n",
    "def plotCurve(model,history):\n",
    "    # list of epoch numbers\n",
    "    x = list(range(1,epochs+1))\n",
    "    vy = history.history['val_loss']\n",
    "    ty = history.history['loss']\n",
    "    #plt_dynamic(x, vy, ty, fig, ax)\n",
    "    fig,ax = plt.subplots(1,1)\n",
    "    ax.set_xlabel('epoch') ; ax.set_ylabel('Categorical Crossentropy Loss')\n",
    "    ax.plot(x, vy, 'b', label=\"Validation Loss\")\n",
    "    ax.plot(x, ty, 'r', label=\"Train Loss\")\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitCalculate(model):\n",
    "    history = model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          verbose=0,\n",
    "          validation_data=(x_test, y_test))\n",
    "    score = model.evaluate(x_test, y_test, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.03665054132399339\n",
      "Test accuracy: 0.993\n"
     ]
    },
    {
     "data": {
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Aqq4UkTg/iavqNGBann3Dcj3fC/TO57y3gbfz2f8L0NrPtYPhpptg7Vr480+3bd/uHtesyXm+b1/haZQv7z/4HDwIzz7bhsWLXQAaNMiNwGrSpFSKC7hZTV9+2X0wP/105IyIyWvnTrcmxEknwejR4c5NaFSr5j6w+/SBPXvgs8/czASTJ7svGK1aQe/e7oO9dWv3ulq10OerYUP45ht44AEYMwa++87dhHfyyaG/9hNPuP+p//u/0F8rUMce62YIvuoq9zd6Xwzfxu0nkMwVkVeAd7zX1wJzQ5elyHHbbUUfs3dvTlDJfizq+apVOc937jwyvbp1KzF6tBvKmpAQmnIVpUcP94346addQEmOwLme77nHBfTZs0vnwzPcjjnG/S569nRfOMB9oIZLxYruw/Gcc9zfatu28Npr7q7+UFmxwg3tvusuqFcvdNcJpl693O9s2DC4/HJo1CjcOQoNP4HkFlxfxp3e66+BsSHLUZSpXNltJZ0OIysLduxwQWX3bvj99zl07do5qHksiWefdRPR3XAD/Phj6TVd+PHJJ/Dqq/Dgg3DWWeHOTekLZwDJ67LLXBNadlPX7be7Jp1Q/L089pj7X4um6XxEYOxYaNYMBg+GL78svdmJS1ORLe6quk9VR6nqFd42mnyanUzJVKjgqsANG0Lz5pHzIVGzpuuXWbw4fJPh5WfTJtfk16aN+2Ax4ZeUBF9/7SYtfOEFF9xXrw7uNRYvdnPD3XWXu4k2mtSr5272nTXLfQGKRSXtuj0jqLkwEemSS1yNZPhwVysJN1UXRHbscJ3PkTwQoKypWBH++U/4+GMXRJKT3cSZwfLoo64Jc8iQ4KVZmgYNgk6dXP5XrCj6+Ghj08GbQo0e7abFvuEG1x8UTq++6qZ9GT7c1d5M5OnZE376CZo2dQMB7rij6AEpRZk3zw0w+Nvfonea/uwZgsENinj4YbeOSqwoMJCISHIBWzvA16gtE/1q1HDDLZctC+8IrtWr4e673eJFd95Z9PEmfJKS3CCI7KauM88MrKnrkUdcALn77qBlMSxOPdXNr9anjxvI0rSpq7UVMblIVCisRvLPAraRwM+hz5qJFN26uXs1nnnGjYcvbVlZbhRZhQpuFbpQ30tjApfd1PXJJ+7ej5I2dX37LUyf7oYaV68e/HyWthNOcCPPvv7aBcfevd3EqsuWhTtngSnwX1JVuxS2lWYmTfj985+u0zAcTVzPPONWeHzxRbdEsIkePXoc2dR1++3F+/t55BE3ItLPUPxocvbZMHeuG9E1b567D2jIENf/F43su53xJSHB9VH8/LMbE19a5s93Ha19+kDfvqV3XRM8J53kmrr+9jf3wel3VNfMmW6k00MPuRt0Y02FCm41z5Ur3Re0UaPgtNPcQJJoa+6yQGJ8O/98NxZ+5Eh3N3Oo7dnjpvrIXjMlFsfflxUVK7q/mylTcpq6/v3vgo9XdR3S9eu7v7lYdtxxriP+++9djfu669wIr4ULw50z/yyQmGIZOdLN3dS/v/ugD6UHH3Sdk2+8Eb2jdcyRund3TV3NmrmpQ267Lf+mrunTXXPmI4+4mxDLgtNPd8HklVdczT852Y16yz2XWqQqMpCIyEcicomIWNAxVKvmmrhWrnTfGEPliy/c3fV33OFqQiZ2ZDd1DRniappnnglpaTnvZ9dGTj7ZfWEpS8qVcwNbVq50zV4vvuhGe736qltPJlL5CQ4vAtcAq0RkuIicFuI8mQjXtav7Ix892k3eF2zbtrk24yZN3D0jJvbExblZg//zHzcxanKym/gR3D0jP/3k+sbiyuiNBjVrugkf5893/wcDB7qltCPhxuD8+Jki5QtVvRZIBtYAX4jIdyLS3+8swCb2jBjh7hfo3z/4N1bddpubCuWdd6BKleCmbSLLpZe6oNGihRtQceutbjDHaafBtdeGO3fh17q1q729/TasWwft27u75LduLfrc0uSruUpEagE3AAOBn4BncYHl85DlzES0+Hg322tamhtVEyzvv++2xx6DdoWueGNixYknwldfuWnWX3oJli51N79Gyrxz4SbiBp2sWOFu8nzjDdfc9eKLOTNBh5ufPpLJuBl/qwDdVbWHqn6gqncAwV+A20SNzp1dH8Zzz7kPgkCtW+e+kZ5xhrsBzZQdcXHufqGpU90Xk95HrVJkqld3g10WLnRNgbfdBikp7qbNcPNTI3lOVZup6j9UdWPuN9TPovAmpv3jH25VvhtvhMzMkqdz6JBrJjtwwN35G8uryZmCXXyxmz7EZi8oWLNm8Pnnbvh0Roa7ufGvf4WNG4s+N1T8/Lr+JyL3eqO3JonIPSJSRgbkmaJUrQqvv+7uDQhknYjnn3drNYweHbuL/xgTLCJu/Zfly10N7oMPXL/SqFHuy1hp8xNI3gKaA88DLwDNsPVITC4dO7p1IsaOdXciF9eyZa4p69JL3egUY4w/Vau6GtySJa5m8re/uQ76mTNLNx9+AkkLVR2gqrO8bRAusBhz2NNPQ+PGrokr7/LBhdm/33UkVqvmbsSyu9eNKb7GjV3/0pQp7gbPrl3dKLjNm0tnaVM/gWS+iHTIfiEi7Skja7Yb/6pUcU1ca9fC/ff7P+/xx93wz/HjS75csTHGfQnr3t3V8J94wgWV668/nfT00F/bTyBpB3wnImtEZA3wP+AvIrJYRBaFNHcmqpx1lhue+K9/uTvTi/Ltt+6GwxtvdGt/G2MCV7mym1pm+XK48cZfqV8/9Nf0MzamW8hzYWLGk0/Cp5+6aR4WLy54DYmdO91Ik5NOgjFjSjePxpQFSUnQu3c6EPrRK37ubF8L1AC6e1sNVV2bvYU6gya6HHOMu2EqPb3w9bXvvdeN9HrrLdc/YoyJXn5uSLwLeBeo423viMgdfhIXkW4iskJE0kTkqMGhIlJJRD7w3p8jIkne/tNFZIG3LRSRy/2macKvQwcXRMaPhxkzjn5/yhTXsf7AA26kiTEmuvnpIxkAtFfVYao6DOgADCrqJBEpD4wFLsINGe4rIs3ySXubqjYCRgMjvP1LgBRVbYNrWntZRCr4TNNEgMcfd6viDRwI27fn7N+82e1r3Tq8a8AbY4LHTyARIPeMLge9fUU5HUhT1V9UdT8wAeiZ55iewJve84lAVxERVd2tqlne/spA9nphftI0EaByZXjzTXe37b33un2qbsK5HTvchIwVK4Y3j8aY4PDT2f46MMebcwvgMuBVH+fVA9blep0OtC/oGFXNEpHtQC1gqzfM+DXgJOA6730/aQIgIoOBwQCJiYmkpqb6yHL4ZWZmRk1e/ejTpyGvvXYSjRsvYsOGmkyZArfemsbWrenEUDFj7veWVyyXz8oWuCIDiaqOEpFUILs1u7+q/hTSXLnrzgGai0hT4E0RmV7M88cB4wBSUlK0c+fOwc9kCKSmphItefXjjDPcJHPPPdeKbdsO0qULPP98I8qVi615UGLt95ZXLJfPyha4QgOJ1yexVFWbAPOLmfZ6oEGu1/W9ffkdky4iFYAEICP3Aaq6XEQygRY+0zQRpFIl18TVvj1Urqy88YZNyGdMrCn0X1pVDwIrROTEEqT9I9BYRBqKSEXgamBKnmOmANd7z3sBM1VVvXMqAIjISUAT3KJaftI0EaZdO/j4YxgxYhEnluQvyRgT0fz0kdQElorID8DhtfBUtUdhJ3l9GrcDM4DywGuqulREngDmquoUXF/L2yKSBvyBCwzgmtGGisgB4BBwq6puBcgvTf/FNeFy6aWQmroj3NkwxoSAn0DySEkTV9VpwLQ8+4bler4XOGoJG1V9mwJmGM4vTWOMMeHjJ5BcrKpHrFcnIiOAIKyJZ4wxJtr56fY8P599FwU7I8YYY6JTgTUSEbkFuBU4Oc8sv9WA70KdMWOMMdGhsKat94DpwD+A3HNa7VTVP0KaK2OMMVGjwECiqtuB7bj5rMoDid7x8SISr6q/lVIejTHGRLAiO9u94baPAZtwQ3HBzX3VKnTZMsYYEy38jNq6GzhNVTOKPNIYY0yZ42fU1jpcE5cxxhhzFD81kl+AVBGZCuzL3qmqo0KWK2OMMVHDTyD5zdsqepsxxhhzmJ9p5B8HEJEqqro79FkyxhgTTfyM2joDN7liPHCiiLQGblLVW0OdOWNM9Dhw4ADp6ens3bs33FkploSEBJYvXx7ubISE37JVrlyZ+vXrExcXV6Lr+GnaGgNciDddu6ouFJFOJbqaMSZmpaenU61aNZKSkhDxsxp3ZNi5cyfVqlULdzZCwk/ZVJWMjAzS09Np2LBhia7ja4khVV2XZ9fBfA80xpRZe/fupVatWlEVRAyICLVq1QqoJumnRrJORM4EVETigLuA2KwHGmMCYkEkOgX6e/NTI7kZuA2oh1vWto332hhjIkaXLl2YMWPGEfvGjBnDLbfcUuh5J5xwAgAbNmygV69e+R7TuXNn5s6dW2g6Y8aMYffunPFIF198MX/++aefrBfqscceY+TIkQGnE0pFBhJV3aqq16pqoqrWUdV+dpe7MSbS9O3blwkTJhyxb8KECfTt29fX+XXr1mXixIklvn7eQDJt2jRq1KhR4vSiSZGBRESeEZHqIhInIl+KyBYR6VcamTPGGL969erF1KlT2b9/PwBr1qxhw4YNdOzYkczMTLp27UpycjItW7bkk08+Oer8NWvW0KJFCwD27NnD1VdfTdOmTbn88svZs2fP4eNuueUWUlJSaN68OY8++igAzz33HBs2bKBLly506dIFgKSkJLZu3QrAqFGjaNGiBS1atGDMmDGHr9e0aVMGDRpE8+bNueCCC464TlHyS3PXrl1ccskltG7dmhYtWjBp0iQAhg4dSrNmzWjVqhVDhgwp1s/VDz99JBeo6v0icjmwBrgCmA28E/TcGGNiwt13w4IFwU2zTRvwPi/zdeyxx3L66aczffp0evbsyYQJE7jqqqsQESpXrszkyZOpXr06W7dupUOHDvTo0aPAvoGXXnqJKlWqsHz5chYtWkRycvLh955++mmOPfZYDh48SNeuXVm0aBF33nkno0aNYtasWdSuXfuItObNm8frr7/OnDlzUFXat2/POeecQ82aNVm1ahXvv/8+48eP56qrrmLSpEn061f09/SC0vzll1+oW7cuU6dOBdxIuoyMDCZPnszPP/+MiASluS0vP30k2cHmEuDf3vTyxhgTcXI3b+Vu1lJVHnroIVq1asV5553H+vXr2bRpU4HpzJ49+/AHeqtWrWjVKmey8w8//JDk5GTatm3L0qVLWbZsWaF5+uabb7j88supWrUq8fHxXHHFFXz99dcANGzYkDZt2gDQrl071qxZ46ucBaXZsmVLPv/8cx544AG+/vprEhISSEhIoHLlygwYMICPPvqIKlWq+LpGcfipkXwqIj8De4BbROQ4ILruODLGlKrCag6h1LNnT+655x7mz5/P7t27adeuHQDvvvsuW7ZsYd68ecTFxZGUlFSi4a6//vorI0eO5Mcff6RmzZrccMMNAQ2brVSp0uHn5cuXL1bTVn5OPfVU5s+fz7Rp03j44Yc5++yzefrpp/nhhx/48ssvmThxIi+88AIzZ84M6Dp5+elsHwqcCaSo6gFgF9AzqLkwxpggiI+Pp0uXLtx4441HdLJv376dOnXqEBcXx6xZs1i7dm2h6XTq1In33nsPgCVLlrBokVttfMeOHVStWpWEhAQ2bdrE9OnTD59TrVo1du7ceVRaHTt25OOPP2b37t3s2rWLyZMn07Fjx4DKWVCaGzZsoEqVKvTr14/77ruPhQsXkpmZyfbt27n44osZPXo0CxcuDOja+fEzRUpv4L+qelBEHgaSgaeA34OeG2OMCVDfvn25/PLLjxjBde2119K9e3datmxJSkoKTZo0KTSNW265hf79+9O0aVOaNm16uGbTunVr2rZtS5MmTWjQoAFnnXXW4XMGDx5Mt27dqFu3LrNmzTq8Pzk5mRtuuIHTTz8dgIEDB9K2bVvfzVgATz311OEOdXB9H/mlOWPGDO677z7KlStHXFwcI0eOZOfOnfTs2ZO9e/eiqowaFYKJ21W10A1Y5D2eDaTi+krmFHWed043YAWQBgzN5/1KwAfe+3OAJG//+cA8YLH3eG6uc1KCYaxDAAAdn0lEQVS9NBd4W52i8tGuXTuNFrNmzQp3FkLGyha9/JRv2bJloc9ICOzYsSPcWQiZ4pQtv98fMFd9fNb76WzPng7lEmCcqk7Fx3Ty3jrvY4GLgGa4td+b5TlsALBNVRsBo4ER3v6tQHdVbQlcD7yd57xrVbWNt232UQZjjDEh4ieQrBeRl4E+wDQRqeTzvNOBNFX9RVX3AxM4um+lJ/Cm93wi0FVERFV/UtUN3v6lwDHedY0xxkQYPwHhKmAGcKGq/gkcC9zn47x6uGV6s6V7+/I9RlWzcEv61spzzJXAfFXdl2vf6yKyQEQeEZvcxxhjwsrPwla7RWQ1cKGIXAh8raqfhT5rICLNcc1dF+Tafa2qrheRasAk4DrgrXzOHQwMBkhMTCQ1NTX0GQ6CzMzMqMlrcVnZopef8iUkJOQ7ainSHTx4MCrz7UdxyrZ3794S/w37GbV1FzAI+Mjb9Y6IjFPV54s4dT3QINfr+t6+/I5JF5EKQAKQ4V23PjAZ+Kuqrs4+QVXXe487ReQ9XBPaUYFEVccB4wBSUlK0c+fORRU1IqSmphIteS0uK1v08lO+5cuXR+W6HmV9PZJslStXpm3btiW6jp+mrQFAe1UdpqrDgA64wFKUH4HGItJQRCoCV+MtjpXLFFxnOkAvYKaqqojUAKbiRnp9m32wiFQQkdre8zjgUmCJj7wYY4wJET+BRDhyIauD3r5CeX0et+P6V5YDH6rqUhF5QkR6eIe9CtQSkTTgXmCot/92oBEwzOsLWSAidXDDhWeIyCLc0N/1wHgfZTDGxLiMjAzatGlDmzZtOP7446lXr97h19kTORalf//+rFixwvc1X3nlFe6+++6SZjlm+Jki5XVgjohM9l5fhgsARVLVacC0PPuG5Xq+F+idz3lP4W56zE87P9c2xpQttWrVYoE3U+Rjjz1GfHz8UTPdHr7voVz+36Fff/31kOczFvmZImUU0B/4w9v6q2qYZtIxxpjiSUtLo1mzZlx77bU0b96cjRs3Mnjw4MNTwQ8fPvzwsWeffTYLFiwgKyuLGjVqMHToUFq3bs0ZZ5zB5s3+b1l75513aNmyJS1atOChhx4CICsri+uuu+7w/ueeew6A0aNHH57i3c/Mv5Go0BqJd1PhUlVtAswvnSwZY6JeOOaRL8TPP//MW2+9RUpKCgDDhw/n2GOPJSsri06dOrFs2TKaNTvyfunt27dzzjnnMHz4cO69915ee+01hg4dml/yR0hPT+fhhx9m7ty5JCQkcN555/Hpp59y3HHHsXXrVhYvXgxweDr3Z555hrVr11KxYsWQTPFeGgqtkajqQWCFiJxYSvkxxpigO+WUUw4HEYD333+f5ORkkpOTWbFiRb5TwR9zzDFcdNFFQPGmeJ8zZw7nnnsutWvXJi4ujmuuuYbZs2fTqFEjVqxYwZ133smMGTNISEgAoHnz5vTr1493332XuLi4wAsbBn76SGoCS0XkB9zMvwCoao+CTzHGlGnhmke+AFWrVj38fNWqVTz77LP88MMP1KhRgz59+uQ7FXzFijkzQZUvX56srKyA8lCrVi0WLVrE9OnTGTt2LJMmTWLcuHHMmDGDr776iilTpvD3v/+dRYsWUb58+YCuVdr8BJJHQp4LY4wpJTt27KBatWpUr16djRs38uWXX9K9e/egpd++fXuGDBlCRkYGCQkJTJgwgSFDhrBlyxYqV65M7969ady4MQMHDuTgwYOkp6dz7rnncvbZZ9OgQQN2794ddfe1FBhIRKQRkKiqX+XZfzawMdQZM8aYUEhOTqZZs2Y0adKEk046iQ4dOgSU3quvvsrEiRMPv547dy5PPvkknTt3RlXp3r07l1xyCfPnz2fAgAGoKiLCiBEjyMrK4pprrmHnzp0cOnSIIUOGRF0QAQqeRh74FGiZz/6WwH/8TC0cKZtNIx8ZrGzRy6aRj06RMI18oqouzifwLAaSgh/SjDHGRKPCAkmNQt47JtgZMcYYE50KCyRzReSoObVEZCBu1UJjjDGm0FFbdwOTReRacgJHCm51xMtDnTFjTPRRryPZRBfXHVJyBQYSVd0EnCkiXYAW3u6pqjozoCsaY2JS5cqVycjIoFatWhZMooiqkpGRQeXKlUuchp+FrWYBs0p8BWNMmVC/fn3S09PZsmVLuLNSLHv37g3oQzSS+S1b5cqVqV+/fomv4+eGRGOMKVJcXBwNGzYMdzaKLTU1tcQLOkW60iqbn/VIjDHGmAJZIDHGGBOQwqZI2Qnk15UvgKpq9ZDlyhhjTNQobNRWFE74YowxprT57mz31kw/3P2vqr+FJEfGGGOiSpF9JCLSQ0RWAb8CXwFrgOkhzpcxxpgo4aez/UmgA7BSVRsCXYHvQ5orY4wxUcNPIDmgqhlAOREp592gmFLUScYYY8oGP30kf4pIPDAbeFdENpNryV1jjDFlm58aSU9gN3AP8F9gNRC8dSmNMcZENT+BpA5QUVWzVPVNYDzga2iwiHQTkRUikiYiQ/N5v5KIfOC9P0dEkrz954vIPBFZ7D2em+ucdt7+NBF5Tmx2OGOMCSs/geTfwKFcrw96+wolIuWBscBFQDOgr4g0y3PYAGCbqjYCRgMjvP1bge6q2hK4Hng71zkvAYOAxt7WzUcZjDHGhIifQFJBVfdnv/CeV/Rx3ulAmqr+4p0zAddMlltP4E3v+USgq4iIqv6kqhu8/UuBY7zaywlAdVX93ltP+C3gMh95McYYEyJ+AskWEemR/UJEeuJqDEWpB6zL9Trd25fvMaqaBWwHauU55kpgvqru845PLyJNY4wxpcjPqK2bcaO1XsDNs7UO+GtIc+URkea45q4LSnDuYGAwQGJiIqmpqcHNXIhkZmZGTV6Ly8oWvWK5fFa2wPlZ2Go10MEbAoyqZvpMez3QINfr+t6+/I5JF5EKQAKQASAi9YHJwF+9PGQfn3v1lfzSzM73OGAcQEpKinbu3NlntsMrNTWVaMlrcVnZolcsl8/KFrjCZv/tp6rviMi9efYDoKqjikj7R6CxiDTEfdhfDVyT55gpuM70/wG9gJmqqiJSA5gKDFXVb7MPVtWNIrJDRDoAc3A1o+eLLqYxxphQKayPpKr3WK2ArVBen8ftwAxgOfChqi4VkSdy9bm8CtQSkTTgXiB7iPDtQCNgmIgs8LY63nu3Aq8Aabh7WmzeL2OMCaPCppF/2RvCu0NVR5ckcVWdBkzLs29Yrud7gd75nPcU8FQBac4FWpQkP8YYY4Kv0FFbqnoQ6FtKeTHGGBOF/Iza+tYbsfUBuebYUtX5IcuVMcaYqOEnkLTxHp/ItU+Bc/M51hhjTBnjZ/hvl9LIiDHGmOjkZ4XEBBEZJSJzve2fIpJQGpkzxhgT+fxMkfIasBO4ytt2AK+HMlPGGGOih58+klNU9cpcrx8XkQWhypAxxpjo4qdGskdEzs5+ISJnAXtClyVjjDHRxE+N5BbgTa9fRIA/gBtCmSljjDHRw8+orQVAaxGp7r3eEfJcGWOMiRpFBpICJm3cDszzgowxxpgyzE8fSQpuTZJ63nYTbnnb8SJyfwjzZowxJgr46SOpDyRnr0MiIo/ipnjvBMwDngld9owxxkQ6PzWSOsC+XK8PAImquifPfmOMMWWQnxrJu8AcEfnEe90deE9EqgLLQpYzY4wxUcHPqK0nRWQ6cJa362ZvTRCAa0OWM2OMMVHBT9MWQGXcAlfPAmu95XONMcYYX5M2Pgo8ADzo7YoD3gllpowxxkQPPzWSy4EeeItaqeoGfKzZbowxpmzwE0j2q6riFrPC62Q3xhhjAH+B5EMReRmoISKDgC+AV0KbLWOMMdHCz6itkSJyPm4dktOAYar6echzZowxJir4mWtrhKo+AHyezz5jjDFlnJ+mrfPz2XdRsDNijDEmOhUYSETkFhFZDJwmIotybb8Ci/wkLiLdRGSFiKSJyNB83q8kIh94788RkSRvfy0RmSUimSLyQp5zUr00F3hbneIU2BhjTHAV1rT1HjAd+AeQOwjsVNU/ikpYRMoDY3E1mnTgRxGZoqq5p1UZAGxT1UYicjUwAugD7AUeAVp4W17X5rq73hhjTBgVWCNR1e2qukZV+6rqWtzyugrEi8iJPtI+HUhT1V9UdT8wAeiZ55iewJve84lAVxERVd2lqt/gAooxxpgI5ufO9u4isgr4FfgKWIOrqRSlHrAu1+t0b1++x6hqFm7BrFo+0n7da9Z6RLyVtowxxoSHn9l/nwI6AF+oalsR6QL0C222CnWtqq4XkWrAJOA64K28B4nIYGAwQGJiIqmpqaWayZLKzMyMmrwWl5UtesVy+axsgfMTSA6oaoaIlBORcqo6S0TG+DhvPdAg1+v63r78jkkXkQpAApBRWKKqut573Cki7+Ga0I4KJKo6DhgHkJKSop07d/aR5fBLTU0lWvJaXFa26BXL5bOyBc7P8N8/RSQemA28KyLP4s27VYQfgcYi0lBEKgJXA1PyHDMFuN573guY6U3Hki8RqSAitb3nccClwBIfeTHGGBMifmokPXEd7ffg1h9JAJ4o6iRVzRKR24EZQHngNVVdKiJPAHNVdQrwKvC2iKQBf+CCDQAisgaoDlQUkcuAC4C1wAwviJTHTdcy3mdZjTHGhECBgUREGuGW1P3W23UIeFNEzgZqUEQTFICqTgOm5dk3LNfzvUDvAs5NKiDZdkVd1xhjTOkprGlrDG5+rby2e+8ZY4wxhQaSRFVdnHenty8pZDkyxhgTVQoLJDUKee+YYGfEGGNMdCoskMz11h85gogMBOaFLkvGGGOiSWGjtu4GJovIteQEjhSgIm75XWOMMabgQKKqm4AzvTvZsydOnKqqM0slZ8YYY6KCnxUSZwGzSiEvxhhjopCfO9uNMcaYAlkgMcYYExALJMYYYwJigcQYY0xA/EzaaIzJKysL/vc/mDoVpk2j46pVcOaZ0LEjdOoEHTpAlSrhzqUxpcICSVmUmQk//eQezzsP4uLCnaPosHUr/Pe/LnjMmAHbtkGFCtCxI783bEi9336DJ54AVbc/JcUFlU6d4KyzoEZhk0UYE70skMS6zExYsADmzYO5c93jzz+7DzuAE06AwYPdVrduePMaaVTdz27qVLfNmeP21akDPXvCJZfA+edDQgKrUlOp17kz/PknfPcdzJ4NX38No0fDM8+ACLRqlVNj6dgRjj8+3CU0JigskMSSXbvyDxqHDrn3TzjBfUvu0wfatXP7//UvePxxePppuPxyuO0290EnEt6yhMvOnfDll4ebrNiwwe3/y1/g0Ufh4ovdz65cAd2LNWq4Yy6+2L3evRt++MEFltmz4bXX4IUX3HuNG+fUWDp2hKSksvtzN1HNAkm02r376KCxfHlO0Dj+eBc0evd2j+3auUCSV48ekJYGL73kPuT+/W9o0QJuvRWuuw7i40u3XOGwalVOrWP2bNi/H6pXhwsucLWOiy6CxMSSpV2lCnTu7DaAAwdg/nxXW5k9GyZNgldfde/Vr58TVDp1gqZNLbCYqGCBJBrs3g0LF+YEjHnzYNmynKCRmOiCxZVX5gSN4jRTNWoE//wnPPkkvP8+jB3rAskDD8D117taSpMmoSlbOOzb5z7Es2sdq1a5/U2bwp13uuBx1lmh6TuKi4P27d02ZIj7HS5dmlNjmTkT3nvPHVu7Npx9dk6tpXVr1/diTISxv8oIU27fPvj++6ODxsGD7oA6dVywuPzyI4NGML65VqkCAwbAjTe6PIwdC+PGuaaYrl1dcOnRIzo/zDZscEFj6lT44gvXd1SpEpx7Ltx1l2uKatiw9PNVrhy0bOm2225zfTCrV+f0scyeDR9/7I6Nj3cBrmNHaN7cBZrsrWZNKF++9PNvDBZICjdzphuZc+CA27Kyive8uOfs3k3HlStzahrHHeeCRc+eOUGjXr3QN3eIwBlnuG3UKHjlFdeXcuWVrvnlpptg0KCSN/eUhoMHXd9Edq3jp5/c/gYNoF8/V+s499zIG6Ir4mqIjRq5gA6wfn1OUPn6a3j44fzPq1nzyOCSvdWqlf/+GjUK7usxphgskBTmjjtcbcCvuDj3bT0uLmfL/Tq/55UqQdWqh5+v/ctfSLriChc06tcPfxt5nTrw0ENw//3w6aeulvLII26Ya+/e7lv0GWeEN5/797tBBQsXun6jhQtdP8S2be5b+plnwvDhLng0bx7+n2lx1asHV1/tNoA//oA1a9xw5K1bISMj53n2tmaNq81u2eJ+PvkpVw6OPdZX4In74w9XW4q2n50pFRZICjNxoqsp+AkK5csH5Z9sTWoqSdkds5GkQgW47DK3rVgBL74Ib7zh2vPbtHEB5ZprQv8NPyPDBYrc29Kl7vcEULmyGyxw5ZWuOe7CC9039Vhy7LFu80PVjebLHWTyCzwZGa5Jbc4c9zr75+k5C1zTWnZtqVEjN+os+/kJJ1iQKcMskBSmadNw5yAynXYaPPusGzL87ruuljJoENx3H/TvD7fc4j5kAnHokPtgy65hZD+mp+ccc/zxrgP6wgvdY+vWcOqp0dmHEyoiLgDEx7vhxX6oumHQ2QFn0yZWffYZjUXcCL9Fi1y/TVZWzjlVqsApp+QfZOrVi5wmtIMHXU01I8Ntf/xBwq+/uqbjsjBCMUTsP86UXHy86y8ZPBi++cYFlOefdzfhXXihq6VcfHHRncCZmbB4cU4NY8EC93rXLvd++fJu1Ng55+QEjNatI7uPJpqJuOHP1asfHoCwPj6exrlryllZ8NtvLrDk3n7+2fVL5W5Oq1Sp4CDToEHJBgnkDnZeQMj3ed7Xf/55VFJtwQ24aNLENSknJ7vHtm2hWrXi5y1SqLomyVJggcQETsSNJOrYETZuhPHj4eWX3QivpCS4+WY3GkwV1q07MmAsXOg+gLLvtE9IcEFiwICcgNG8uWuyMpGjQgU4+WS3XXDBke8dPOhqjnmDTFoafPYZ7N2bc2xcnEsjd5NZYqL7wC8oGGS/zl0jyishwfX11KrlmgEbNTryda7ni7/4gpYHDrg+pVmz4J13XBoiroabO7gkJ7sAG0l274aVK12T888/u8cVK2DlSs7cvdu9X6lSSLMQ0kAiIt2AZ4HywCuqOjzP+5WAt4B2QAbQR1XXiEgtYCLwF+ANVb091zntgDeAY4BpwF2q2Z9CJuxOOAGGDYMHH4RPPnFDh4cOhUcf5axKlWDHjpxjTz7Z9a/06+cCRps2cOKJ1tYe7cqXh5NOclvXrke+d+iQG4qdX5BJTc2phWY75pgjP/ibNz/ydd7AUKuW6xMrRvNmxq5dOTeMAmzalDP0ft48N1ou+94ecDWqdu1ytrZtQz+P2qFD7ktYdpDIva1bl3OciPsfOu00OOssVolwavatAyEUskAiIuWBscD5QDrwo4hMUdXcw6AGANtUtZGIXA2MAPoAe4FHcGvFtzgyZV4CBgFzcIGkGzA9VOUwJRQXB716uW3pUhg/ni1padS96CIXMFq2jLxvdib0ypVzoxHr1z/ywxtcrXTTJjfSrGZNFxSOOab085iYeOQ0NwCbN7uRgNnB5bvvYMKEnPdPOeXI4JKcXLJBHjt35h8sVq6EPXtyjqte3QWLc85xj9lb48ZH/Mw2pKZyaikMcQ9ljeR0IE1VfwEQkQlATyB3IOkJPOY9nwi8ICKiqruAb0SkUe4EReQEoLqqfu+9fgu4DAskka15cxgzhpWpqdSNxBFpJjKIuAEUkTiZZZ060K2b27Jt3eqCSnaA+eEH+PDDnPcbNjw6uNSq5Zr+1q7NCRK5m6M2bsw5v1w5l8Zpp7l7nnIHjOOPj6iau4SqVUhEegHdVHWg9/o6oH2eZqol3jHp3uvV3jFbvdc3ACnZ54hICjBcVc/zXncEHlDVS/O5/mBgMEBiYmK7Cbm/PUSwzMxM4mN09IiVLXrFcvmCWbYK27dTbdUqqq1cSfzKlVRbtYpjsif+BPbVrk3c9u2UyzW8+kD16uxu0IDdDRqwx3vc3aABe+rWRStWDCg/gZatS5cu81Q1pajjYrazXVXHAeMAUlJStHOUfBNOTU0lWvJaXFa26BXL5Qt52bZtO1xrqbR0qWs6y65ZNGlCXO3aJAAJIbh0af3eQhlI1gMNcr2u7+3L75h0EamA+1lmFJFm/SLSNMaYyFGzpht0kHfgQQwJ5V1CPwKNRaShiFQErgam5DlmCnC997wXMLOwEViquhHYISIdRESAvwKfBD/rxhhj/ApZjURVs0TkdmAGbvjva6q6VESeAOaq6hTgVeBtEUkD/sAFGwBEZA1QHagoIpcBF3gjvm4lZ/jvdKyj3RhjwiqkfSSqOg03RDf3vmG5nu8FehdwblIB++dy9JBgY4wxYRIhE+AYY4yJVhZIjDHGBMQCiTHGmIBYIDHGGBMQCyTGGGMCErIpUiKJiGwB1oY7Hz7VBraGOxMhYmWLXrFcPitbwU5S1eOKOqhMBJJoIiJz/cxtE42sbNErlstnZQucNW0ZY4wJiAUSY4wxAbFAEnnGhTsDIWRli16xXD4rW4Csj8QYY0xArEZijDEmIBZIIoCINBCRWSKyTESWishd4c5TsIlIeRH5SUQ+DXdegk1EaojIRBH5WUSWi8gZ4c5TsIjIPd7f5BIReV9EKoc7T4EQkddEZLO3Omv2vmNF5HMRWeU9lmCx9fAroGz/z/u7XCQik0WkRiiubYEkMmQBf1PVZkAH4DYRaRbmPAXbXcDycGciRJ4F/quqTYDWxEg5RaQecCduuesWuOUgri78rIj3BtAtz76hwJeq2hj40nsdjd7g6LJ9DrRQ1VbASuDBUFzYAkkEUNWNqjrfe74T90FUL7y5Ch4RqQ9cArwS7rwEm4gkAJ1wa+ugqvtV9c/w5iqoKgDHeCuYVgE2FHF8RFPV2bi1j3LrCbzpPX8TuKxUMxUk+ZVNVT9T1Szv5fccucJs0FggiTAikgS0BeaENydBNQa4HzgU7oyEQENgC/C613T3iohUDXemgkFV1wMjgd+AjcB2Vf0svLkKiURv9VWA34HEcGYmhG4kRAsBWiCJICISD0wC7lbVHeHOTzCIyKXAZlWdF+68hEgFIBl4SVXbAruI3qaRI3h9BT1xwbIuUFVE+oU3V6HlLfUdc0NZReT/cE3o74YifQskEUJE4nBB5F1V/Sjc+Qmis4Ae3tLJE4BzReSd8GYpqNKBdFXNrkFOxAWWWHAe8KuqblHVA8BHwJlhzlMobBKREwC8x81hzk9QicgNwKXAtRqi+z0skEQAERFcG/tyVR0V7vwEk6o+qKr1vaWTrwZmqmrMfKtV1d+BdSJymrerK7AsjFkKpt+ADiJSxfsb7UqMDCTIYwpwvff8euCTMOYlqESkG65ZuYeq7g7VdSyQRIazgOtw39YXeNvF4c6U8e0O4F0RWQS0Af4e5vwEhVfLmgjMBxbjPi+i+i5wEXkf+B9wmoiki8gAYDhwvoiswtXChoczjyVVQNleAKoBn3ufK/8KybXtznZjjDGBsBqJMcaYgFggMcYYExALJMYYYwJigcQYY0xALJAYY4wJiAUSYyKYiHSOxRmTTWyxQGKMMSYgFkiMCQIR6SciP3g3fb3srb+SKSKjvfU8vhSR47xj24jI97nWiKjp7W8kIl+IyEIRmS8ip3jJx+da7+Rd7y5zYyKGBRJjAiQiTYE+wFmq2gY4CFwLVAXmqmpz4CvgUe+Ut4AHvDUiFufa/y4wVlVb4+a0yp6Rti1wN9AMOBk3E4IxEaNCuDNgTAzoCrQDfvQqC8fgJv47BHzgHfMO8JG3fkkNVf3K2/8m8G8RqQbUU9XJAKq6F8BL7wdVTfdeLwCSgG9CXyxj/LFAYkzgBHhTVY9YfU5EHslzXEnnI9qX6/lB7P/WRBhr2jImcF8CvUSkDhxeA/wk3P9XL++Ya4BvVHU7sE1EOnr7rwO+8lbGTBeRy7w0KolIlVIthTElZN9sjAmQqi4TkYeBz0SkHHAAuA23yNXp3nubcf0o4KYq/5cXKH4B+nv7rwNeFpEnvDR6l2IxjCkxm/3XmBARkUxVjQ93PowJNWvaMsYYExCrkRhjjAmI1UiMMcYExAKJMcaYgFggMcYYExALJMYYYwJigcQYY0xALJAYY4wJyP8HjM8sq4a+G/IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = fitCalculate(model)\n",
    "plotCurve(model,history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "note for me: \n",
    "\n",
    "conv2d_1 outshape = 28 - 3 +1 = 26\n",
    "\n",
    "conv2d_2 outshape = 26 - 3 +1 = 24\n",
    "\n",
    "max_pooling2d_1 outshape = ((24 - 2(max pool) + 2 * 0(padding)) / 2(stride)) + 1 = 12\n",
    "\n",
    "flatten_1 outshape = 12 \\* 12 \\* 64 = 9216\n",
    "\n",
    "number of parameters in conv2d_1 = (shape of width of the filter) \\* shape of height of the filter \\* number of feature maps +1) \\* number of filters = (3 \\* 3 \\* 1 + 1) \\* 32 = 320\n",
    "\n",
    "number of parameters in conv2d_2 = (3 \\* 3 \\* 32 + 1) \\* 64 = 18496\n",
    "\n",
    "*more details <a href=\"https://stackoverflow.com/questions/42786717/how-to-calculate-the-number-of-parameters-for-convolutional-neural-network\">here</a>*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 20, 20, 64)        51264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 10, 10, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 10, 10, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 6400)              0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 128)               819328    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 872,714\n",
      "Trainable params: 872,714\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model2 = Sequential()\n",
    "model2.add(Conv2D(32, kernel_size=(5, 5),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "model2.add(Conv2D(64, (5, 5), activation='relu'))\n",
    "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model2.add(Dropout(0.25))\n",
    "model2.add(Flatten())\n",
    "model2.add(Dense(128, activation='relu'))\n",
    "model2.add(Dropout(0.5))\n",
    "model2.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model2.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=\"adam\",\n",
    "              metrics=['accuracy'])\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.025976000575942362\n",
      "Test accuracy: 0.9926\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history_2 = fitCalculate(model2)\n",
    "plotCurve(model2,history_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_9 (Conv2D)            (None, 22, 22, 32)        1600      \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 18, 18, 64)        51264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 9, 9, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 9, 9, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 5184)              0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 128)               663680    \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 717,834\n",
      "Trainable params: 717,834\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model3 = Sequential()\n",
    "model3.add(Conv2D(32, kernel_size=(7, 7),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "model3.add(Conv2D(64, (5, 5), activation='relu'))\n",
    "model3.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model3.add(Dropout(0.25))\n",
    "model3.add(Flatten())\n",
    "model3.add(Dense(128, activation='relu'))\n",
    "model3.add(Dropout(0.5))\n",
    "model3.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model3.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=\"adam\",\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.022470235473414004\n",
      "Test accuracy: 0.9937\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history_3 = fitCalculate(model3)\n",
    "plotCurve(model3,history_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_53 (Conv2D)           (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_48 (MaxPooling (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_67 (Dropout)         (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_54 (Conv2D)           (None, 8, 8, 16)          12816     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_49 (MaxPooling (None, 4, 4, 16)          0         \n",
      "_________________________________________________________________\n",
      "dropout_68 (Dropout)         (None, 4, 4, 16)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_55 (Conv2D)           (None, 2, 2, 16)          2320      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_50 (MaxPooling (None, 2, 2, 16)          0         \n",
      "_________________________________________________________________\n",
      "dropout_69 (Dropout)         (None, 2, 2, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten_20 (Flatten)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 50)                3250      \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 10)                510       \n",
      "=================================================================\n",
      "Total params: 19,728\n",
      "Trainable params: 19,728\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "from keras.initializers import glorot_uniform\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model4 = Sequential()\n",
    "model4.add(Conv2D(32, kernel_size=(5, 5),activation='relu',input_shape=input_shape)) #outshape 28-5+1 = 24\n",
    "model4.add(MaxPooling2D(pool_size=(2, 2))) #outshape ((24-2)/2)+1 = 12\n",
    "model4.add(Dropout(0.25))\n",
    "model4.add(Conv2D(16, (5, 5), activation='relu')) #outshape 12-5 + 1 = 8\n",
    "model4.add(MaxPooling2D(pool_size=(2, 2))) #outshape ((8-2)/2)+1 = 4\n",
    "model4.add(Dropout(0.25))\n",
    "model4.add(Conv2D(16, (3, 3), activation='relu'))#outshape 4-3+1 = 2\n",
    "model4.add(MaxPooling2D(pool_size=(1, 1)))\n",
    "model4.add(Dropout(0.25))\n",
    "model4.add(Flatten())\n",
    "model4.add(Dense(50, activation='relu',kernel_initializer=glorot_uniform()))\n",
    "#model4.add(Dropout(0.2))\n",
    "model4.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model4.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=\"adam\",\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.018983773088160523\n",
      "Test accuracy: 0.9944\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = 100\n",
    "history_4 = fitCalculate(model4)\n",
    "plotCurve(model4,history_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+----------------------------+------------+---------------+\n",
      "|  Model  | Number Of Trainable Params | Test Score | Test Accuracy |\n",
      "+---------+----------------------------+------------+---------------+\n",
      "| Model 1 |         1,199,882          |   0.0366   |     0.993     |\n",
      "| Model 2 |          872,714           |   0.0259   |     0.9926    |\n",
      "| Model 3 |          717,834           |   0.0225   |     0.9937    |\n",
      "| Model 4 |           19,728           |  0.02646   |     0.992     |\n",
      "+---------+----------------------------+------------+---------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "\n",
    "pt = PrettyTable()\n",
    "pt.field_names = [\"Model\",\"Number Of Trainable Params\", \"Test Score\", \"Test Accuracy\"]\n",
    "\n",
    "pt.add_row([\"Model 1\", \"1,199,882\" , 0.0366, 0.993])\n",
    "pt.add_row([\"Model 2\", \"872,714\" , 0.0259, 0.9926])\n",
    "pt.add_row([\"Model 3\", \"717,834\" , 0.0225, 0.9937])\n",
    "pt.add_row([\"Model 4\", \"19,728\" , 0.0189, 0.9944])\n",
    "print(pt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Model 1 is *overfitted*, can be said after looking at the plot of train loss vs validation loss curve.\n",
    "\n",
    "2) Model 2 and Model 3 is *neither* overfitted *nor* underfitted, can be said after looking at the plot of train loss vs validation loss curve.\n",
    "\n",
    "3) The number of parameters in model 4 is very less than that of model 1 to model 3, still test acore and test accuracy are better.\n",
    "\n",
    "4) It is very important to understand how to calculate outshape of each layer and number of parameters for desigining a model architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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