mnist_mlp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gnDCv5VBGaDD"
   },
   "outputs": [],
   "source": [
    "from keras.utils import np_utils \n",
    "from keras.datasets import mnist \n",
    "import seaborn as sns\n",
    "from keras.initializers import RandomNormal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rak6cEq-Gg_p"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import time\n",
    "def plt_dynamic(x, vy, ty, ax, colors=['b']):\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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VoI_KSNy5HUW"
   },
   "outputs": [],
   "source": [
    "(X_train,y_train),(X_test,y_test)=mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3350,
     "status": "ok",
     "timestamp": 1568827984070,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "dzBuiTIz5LAf",
    "outputId": "de30b5bc-5951-40bc-bea0-ccdc456632e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (60000, 28, 28)\n",
      "Shape of y_train: (60000,)\n",
      "Shape of X_test: (10000, 28, 28)\n",
      "Shape of y_test: (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of X_train:\",X_train.shape)\n",
    "print(\"Shape of y_train:\",y_train.shape)\n",
    "print(\"Shape of X_test:\",X_test.shape)\n",
    "print(\"Shape of y_test:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OS5JZntI5OOA"
   },
   "outputs": [],
   "source": [
    "#Converting 28*28 to 784\n",
    "X_train=X_train.reshape(X_train.shape[0],X_train.shape[1]*X_train.shape[2])\n",
    "X_test=X_test.reshape(X_test.shape[0],X_test.shape[1]*X_test.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2390,
     "status": "ok",
     "timestamp": 1568827991294,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "UqDYXyGD5RAI",
    "outputId": "70e9e14c-81a0-40cb-8d29-c9672fc10b20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Reshaping\n",
      "Shape of X_train: (60000, 784)\n",
      "Shape of y_train: (60000,)\n",
      "Shape of X_test: (10000, 784)\n",
      "Shape of y_test: (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"After Reshaping\")\n",
    "print(\"Shape of X_train:\",X_train.shape)\n",
    "print(\"Shape of y_train:\",y_train.shape)\n",
    "print(\"Shape of X_test:\",X_test.shape)\n",
    "print(\"Shape of y_test:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 756
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2380,
     "status": "ok",
     "timestamp": 1568827995101,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "faKmkXtw5V1H",
    "outputId": "406d9969-f77b-48db-9752-9340ef8018c3"
   },
   "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   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   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   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   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   3  18  18  18 126 136 175  26 166 255\n",
      " 247 127   0   0   0   0   0   0   0   0   0   0   0   0  30  36  94 154\n",
      " 170 253 253 253 253 253 225 172 253 242 195  64   0   0   0   0   0   0\n",
      "   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251  93  82\n",
      "  82  56  39   0   0   0   0   0   0   0   0   0   0   0   0  18 219 253\n",
      " 253 253 253 253 198 182 247 241   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0  14   1 154 253  90   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   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  11 190 253  70   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  35 241\n",
      " 225 160 108   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0  81 240 253 253 119  25   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  45 186 253 253 150  27   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252 253 187\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   0 249 253 249  64   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
      " 253 207   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0  39 148 229 253 253 253 250 182   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253\n",
      " 253 201  78   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0  23  66 213 253 253 253 253 198  81   2   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0  18 171 219 253 253 253 253 195\n",
      "  80   9   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "  55 172 226 253 253 253 253 244 133  11   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0 136 253 253 253 212 135 132  16\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   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   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(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZG6UTRGZ5YHh"
   },
   "outputs": [],
   "source": [
    "#normalizing \n",
    "#Values are from  0 to 255 \n",
    "#max=255,min=0\n",
    "#X => (X - Xmin)/(Xmax-Xmin) = X/255\n",
    "X_train=X_train/255\n",
    "X_test=X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2909,
     "status": "ok",
     "timestamp": 1568828004042,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "eXXNXGSk5cG6",
    "outputId": "a3f307e7-f51f-4ddd-bb04-76ad677d77ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Normalizing\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.01176471 0.07058824 0.07058824 0.07058824\n",
      " 0.49411765 0.53333333 0.68627451 0.10196078 0.65098039 1.\n",
      " 0.96862745 0.49803922 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.11764706 0.14117647 0.36862745 0.60392157\n",
      " 0.66666667 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686\n",
      " 0.88235294 0.6745098  0.99215686 0.94901961 0.76470588 0.25098039\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.19215686\n",
      " 0.93333333 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686\n",
      " 0.99215686 0.99215686 0.99215686 0.98431373 0.36470588 0.32156863\n",
      " 0.32156863 0.21960784 0.15294118 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.07058824 0.85882353 0.99215686\n",
      " 0.99215686 0.99215686 0.99215686 0.99215686 0.77647059 0.71372549\n",
      " 0.96862745 0.94509804 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.31372549 0.61176471 0.41960784 0.99215686\n",
      " 0.99215686 0.80392157 0.04313725 0.         0.16862745 0.60392157\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.05490196 0.00392157 0.60392157 0.99215686 0.35294118\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.54509804 0.99215686 0.74509804 0.00784314 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.04313725\n",
      " 0.74509804 0.99215686 0.2745098  0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.1372549  0.94509804\n",
      " 0.88235294 0.62745098 0.42352941 0.00392157 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.31764706 0.94117647 0.99215686\n",
      " 0.99215686 0.46666667 0.09803922 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.17647059 0.72941176 0.99215686 0.99215686\n",
      " 0.58823529 0.10588235 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.0627451  0.36470588 0.98823529 0.99215686 0.73333333\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.97647059 0.99215686 0.97647059 0.25098039 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.18039216 0.50980392 0.71764706 0.99215686\n",
      " 0.99215686 0.81176471 0.00784314 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.15294118 0.58039216\n",
      " 0.89803922 0.99215686 0.99215686 0.99215686 0.98039216 0.71372549\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.09411765 0.44705882 0.86666667 0.99215686 0.99215686 0.99215686\n",
      " 0.99215686 0.78823529 0.30588235 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.09019608 0.25882353 0.83529412 0.99215686\n",
      " 0.99215686 0.99215686 0.99215686 0.77647059 0.31764706 0.00784314\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.07058824 0.67058824\n",
      " 0.85882353 0.99215686 0.99215686 0.99215686 0.99215686 0.76470588\n",
      " 0.31372549 0.03529412 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.21568627 0.6745098  0.88627451 0.99215686 0.99215686 0.99215686\n",
      " 0.99215686 0.95686275 0.52156863 0.04313725 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.53333333 0.99215686\n",
      " 0.99215686 0.99215686 0.83137255 0.52941176 0.51764706 0.0627451\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(\"After Normalizing\")\n",
    "print(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1339,
     "status": "ok",
     "timestamp": 1568828013476,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "bv-tqRuM5eqe",
    "outputId": "ffd0a29c-0e82-410c-8305-924798074a42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y label of first image is 5\n"
     ]
    }
   ],
   "source": [
    "print(\"Y label of first image is\",y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1374,
     "status": "ok",
     "timestamp": 1568828017121,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "lXpZZ2ym5hSL",
    "outputId": "b0932bbb-0966-4b08-d231-2a69d0af5c05"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After one hot encoding\n",
      "Y label of first image : [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "#one hot encoding\n",
    "y_train=np_utils.to_categorical(y_train,num_classes=10)\n",
    "y_test=np_utils.to_categorical(y_test,num_classes=10)\n",
    "print(\"After one hot encoding\")\n",
    "print(\"Y label of first image :\",y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3659,
     "status": "ok",
     "timestamp": 1568828024062,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "MeJVyLyb5lDj",
    "outputId": "ae37d98f-cf1f-485a-f281-dafa2a021b24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of y_train (60000, 10)\n",
      "Shape of y_test (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of y_train\",y_train.shape)\n",
    "print(\"Shape of y_test\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3giNc5an5o2W"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6h8-XBCo5rnS"
   },
   "outputs": [],
   "source": [
    "output_dim=y_train.shape[1]\n",
    "input_dim=X_train.shape[1]\n",
    "batch_size=128\n",
    "n_epoch=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 726
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 44917,
     "status": "ok",
     "timestamp": 1568828075376,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "2zNSvv1O5uUm",
    "outputId": "7643ed6f-bf9b-4ce8-9aa4-3a80c299be2b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 1.2635 - acc: 0.7109 - val_loss: 0.8046 - val_acc: 0.8385\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.7122 - acc: 0.8423 - val_loss: 0.6053 - val_acc: 0.8621\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.5854 - acc: 0.8590 - val_loss: 0.5249 - val_acc: 0.8744\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.5244 - acc: 0.8691 - val_loss: 0.4795 - val_acc: 0.8824\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.4871 - acc: 0.8753 - val_loss: 0.4498 - val_acc: 0.8879\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.4614 - acc: 0.8802 - val_loss: 0.4286 - val_acc: 0.8906\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.4423 - acc: 0.8839 - val_loss: 0.4126 - val_acc: 0.8931\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.4274 - acc: 0.8863 - val_loss: 0.3999 - val_acc: 0.8960\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.4155 - acc: 0.8887 - val_loss: 0.3894 - val_acc: 0.8980\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.4055 - acc: 0.8904 - val_loss: 0.3810 - val_acc: 0.8990\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.3971 - acc: 0.8923 - val_loss: 0.3733 - val_acc: 0.9004\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.3899 - acc: 0.8935 - val_loss: 0.3671 - val_acc: 0.9023\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.3835 - acc: 0.8960 - val_loss: 0.3614 - val_acc: 0.9035\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.3779 - acc: 0.8971 - val_loss: 0.3563 - val_acc: 0.9041\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.3729 - acc: 0.8982 - val_loss: 0.3521 - val_acc: 0.9045\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.3683 - acc: 0.8993 - val_loss: 0.3481 - val_acc: 0.9064\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 2s 37us/step - loss: 0.3642 - acc: 0.9003 - val_loss: 0.3446 - val_acc: 0.9071\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 2s 36us/step - loss: 0.3605 - acc: 0.9011 - val_loss: 0.3414 - val_acc: 0.9077\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 0.3571 - acc: 0.9015 - val_loss: 0.3384 - val_acc: 0.9080\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 2s 34us/step - loss: 0.3539 - acc: 0.9024 - val_loss: 0.3356 - val_acc: 0.9096\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(output_dim, input_dim=input_dim, activation='softmax'))\n",
    "model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "history = model.fit(X_train, y_train, batch_size=batch_size, epochs=n_epoch, verbose=1, validation_data=(X_test, y_test)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3377,
     "status": "ok",
     "timestamp": 1568828082506,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "2GFJmsWN5xHL",
    "outputId": "dd27e9ce-d2ab-402d-d950-a4bf6057d15b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.335577474963665\n",
      "Test accuracy: 0.9096\n"
     ]
    },
    {
     "data": {
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v4LpqRR8nhxwCw4e7VkgHDlR5uNOtm5u2c8oUmDUrwOiMMab2+GmSOqyS5RQf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\nMcaYUpYUjDHGlLKkYIwxptT/B8gCeG+AtT5QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score=model.evaluate(X_test,y_test,verbose=0)\n",
    "x = list(range(1,n_epoch+1))\n",
    "print('Test score:', score[0]) \n",
    "print('Test accuracy:', score[1])\n",
    "fig,ax = plt.subplots(1,1)\n",
    "ax.set_xlabel('epoch') ; ax.set_ylabel('Categorical Crossentropy Loss')\n",
    "vy = history.history['val_loss']\n",
    "ty = history.history['loss']\n",
    "plt_dynamic(x, vy, ty, ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vzW6uZIGMXD9"
   },
   "source": [
    "MLP + Sigmoid activation + SGDOptimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2909,
     "status": "ok",
     "timestamp": 1568828091192,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "TyX-fYJ1M4Qy",
    "outputId": "07f71985-301a-4f5b-91f5-e227d7de175a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_17\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_43 (Dense)             (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_44 (Dense)             (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_45 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 468,874\n",
      "Trainable params: 468,874\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Multilayer perceptron\n",
    "\n",
    "model_sigmoid = Sequential()\n",
    "model_sigmoid.add(Dense(512, activation='sigmoid', input_shape=(input_dim,)))\n",
    "model_sigmoid.add(Dense(128, activation='sigmoid'))\n",
    "model_sigmoid.add(Dense(output_dim, activation='softmax'))\n",
    "\n",
    "model_sigmoid.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 135201,
     "status": "ok",
     "timestamp": 1568828230414,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "hyBIlzB-M-3h",
    "outputId": "f0686c93-9227-4c11-f4d6-a8c3806a3263"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 4s 62us/step - loss: 2.2703 - acc: 0.2248 - val_loss: 2.2202 - val_acc: 0.4124\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 2.1762 - acc: 0.4666 - val_loss: 2.1183 - val_acc: 0.5586\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 3s 42us/step - loss: 2.0570 - acc: 0.5829 - val_loss: 1.9720 - val_acc: 0.6160\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 1.8854 - acc: 0.6353 - val_loss: 1.7685 - val_acc: 0.6438\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 1.6629 - acc: 0.6716 - val_loss: 1.5271 - val_acc: 0.6961\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 1.4282 - acc: 0.7001 - val_loss: 1.3018 - val_acc: 0.7131\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 1.2276 - acc: 0.7244 - val_loss: 1.1251 - val_acc: 0.7466\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 3s 42us/step - loss: 1.0742 - acc: 0.7504 - val_loss: 0.9931 - val_acc: 0.7701\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.9591 - acc: 0.7711 - val_loss: 0.8926 - val_acc: 0.7878\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 3s 42us/step - loss: 0.8698 - acc: 0.7873 - val_loss: 0.8142 - val_acc: 0.8001\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.7985 - acc: 0.8026 - val_loss: 0.7505 - val_acc: 0.8104\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.7398 - acc: 0.8149 - val_loss: 0.6977 - val_acc: 0.8267\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.6909 - acc: 0.8254 - val_loss: 0.6523 - val_acc: 0.8366\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.6500 - acc: 0.8337 - val_loss: 0.6144 - val_acc: 0.8427\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.6151 - acc: 0.8414 - val_loss: 0.5826 - val_acc: 0.8494\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.5852 - acc: 0.8476 - val_loss: 0.5553 - val_acc: 0.8546\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.5595 - acc: 0.8535 - val_loss: 0.5311 - val_acc: 0.8601\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.5370 - acc: 0.8587 - val_loss: 0.5100 - val_acc: 0.8635\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.5172 - acc: 0.8627 - val_loss: 0.4914 - val_acc: 0.8693\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.4997 - acc: 0.8670 - val_loss: 0.4751 - val_acc: 0.8732\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.4841 - acc: 0.8710 - val_loss: 0.4609 - val_acc: 0.8769\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.4702 - acc: 0.8737 - val_loss: 0.4479 - val_acc: 0.8800\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.4575 - acc: 0.8764 - val_loss: 0.4359 - val_acc: 0.8844\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.4462 - acc: 0.8795 - val_loss: 0.4253 - val_acc: 0.8850\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.4359 - acc: 0.8821 - val_loss: 0.4159 - val_acc: 0.8874\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.4266 - acc: 0.8843 - val_loss: 0.4065 - val_acc: 0.8885\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.4180 - acc: 0.8863 - val_loss: 0.3987 - val_acc: 0.8911\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.4103 - acc: 0.8878 - val_loss: 0.3914 - val_acc: 0.8922\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.4033 - acc: 0.8891 - val_loss: 0.3851 - val_acc: 0.8937\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3966 - acc: 0.8903 - val_loss: 0.3796 - val_acc: 0.8948\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3907 - acc: 0.8921 - val_loss: 0.3735 - val_acc: 0.8970\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3852 - acc: 0.8931 - val_loss: 0.3685 - val_acc: 0.8982\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3802 - acc: 0.8944 - val_loss: 0.3638 - val_acc: 0.8988\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3753 - acc: 0.8959 - val_loss: 0.3589 - val_acc: 0.8994\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3709 - acc: 0.8962 - val_loss: 0.3548 - val_acc: 0.9010\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3668 - acc: 0.8971 - val_loss: 0.3514 - val_acc: 0.9009\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3628 - acc: 0.8982 - val_loss: 0.3484 - val_acc: 0.9005\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3592 - acc: 0.8987 - val_loss: 0.3441 - val_acc: 0.9029\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3558 - acc: 0.8998 - val_loss: 0.3411 - val_acc: 0.9033\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3525 - acc: 0.9005 - val_loss: 0.3381 - val_acc: 0.9049\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3495 - acc: 0.9011 - val_loss: 0.3353 - val_acc: 0.9065\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3466 - acc: 0.9018 - val_loss: 0.3326 - val_acc: 0.9068\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3438 - acc: 0.9026 - val_loss: 0.3304 - val_acc: 0.9067\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3411 - acc: 0.9027 - val_loss: 0.3275 - val_acc: 0.9077\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3387 - acc: 0.9036 - val_loss: 0.3256 - val_acc: 0.9079\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3362 - acc: 0.9040 - val_loss: 0.3232 - val_acc: 0.9073\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3339 - acc: 0.9051 - val_loss: 0.3209 - val_acc: 0.9084\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3317 - acc: 0.9052 - val_loss: 0.3190 - val_acc: 0.9089\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3295 - acc: 0.9059 - val_loss: 0.3167 - val_acc: 0.9100\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3274 - acc: 0.9061 - val_loss: 0.3156 - val_acc: 0.9094\n"
     ]
    }
   ],
   "source": [
    "model_sigmoid.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "history = model_sigmoid.fit(X_train, y_train, batch_size=batch_size, epochs=50, verbose=1, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2254,
     "status": "ok",
     "timestamp": 1568828238284,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "X2VKRcGONN0h",
    "outputId": "456ea31d-a607-46eb-8194-a3025962594e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.31564796745181084\n",
      "Test accuracy: 0.9094\n"
     ]
    },
    {
     "data": {
      "image/png": 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P8DLeL/ZgVHWTqs73vmcCy4H6hQ4bBLzmPRl9C1QXkXpAP2Cqqu5Q1Z3AVOAc37UKkZEj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\nitgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score = model_sigmoid.evaluate(X_test, y_test, verbose=0) \n",
    "print('Test score:', score[0]) \n",
    "print('Test accuracy:', score[1])\n",
    "fig,ax = plt.subplots(1,1)\n",
    "ax.set_xlabel('epoch') ; ax.set_ylabel('Categorical Crossentropy Loss')\n",
    "# list of epoch numbers\n",
    "x = list(range(1,51))\n",
    "vy = history.history['val_loss']\n",
    "ty = history.history['loss']\n",
    "plt_dynamic(x, vy, ty, ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4162,
     "status": "ok",
     "timestamp": 1568828252442,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "Qsyc24YfORZx",
    "outputId": "41daea2d-197e-4e70-da67-98fb0af73acd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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je/fu5ZFH5hER4btzLO0RG9vI9OkFVFQUM2fObJ/tVBjKpRuwWq289957PP/8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\n/j9x50QVJ/K7EQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w_after = model_sigmoid.get_weights()\n",
    "\n",
    "h1_w = w_after[0].flatten().reshape(-1,1)\n",
    "h2_w = w_after[2].flatten().reshape(-1,1)\n",
    "out_w = w_after[4].flatten().reshape(-1,1)\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.title(\"Weight matrices after model trained\")\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.title(\"Trained model Weights\")\n",
    "ax = sns.violinplot(y=h1_w,color='b')\n",
    "plt.xlabel('Hidden Layer 1')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.title(\"Trained model Weights\")\n",
    "ax = sns.violinplot(y=h2_w, color='r')\n",
    "plt.xlabel('Hidden Layer 2 ')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.title(\"Trained model Weights\")\n",
    "ax = sns.violinplot(y=out_w,color='y')\n",
    "plt.xlabel('Output Layer ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HZHEdNZnQb_e"
   },
   "source": [
    "MLP + Sigmoid activation + ADAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 47392,
     "status": "ok",
     "timestamp": 1568828303378,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "Cz4b75lWQdUC",
    "outputId": "81b67d2f-169e-47ba-f70c-4e3b9f91cf4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_18\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_46 (Dense)             (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_47 (Dense)             (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_48 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 468,874\n",
      "Trainable params: 468,874\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/25\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.7313 - acc: 0.8135 - val_loss: 0.3043 - val_acc: 0.9176\n",
      "Epoch 2/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.2715 - acc: 0.9226 - val_loss: 0.2322 - val_acc: 0.9318\n",
      "Epoch 3/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.2123 - acc: 0.9382 - val_loss: 0.1901 - val_acc: 0.9415\n",
      "Epoch 4/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1714 - acc: 0.9492 - val_loss: 0.1556 - val_acc: 0.9522\n",
      "Epoch 5/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1431 - acc: 0.9577 - val_loss: 0.1383 - val_acc: 0.9577\n",
      "Epoch 6/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1180 - acc: 0.9654 - val_loss: 0.1230 - val_acc: 0.9630\n",
      "Epoch 7/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1005 - acc: 0.9704 - val_loss: 0.1047 - val_acc: 0.9676\n",
      "Epoch 8/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.0847 - acc: 0.9754 - val_loss: 0.0940 - val_acc: 0.9710\n",
      "Epoch 9/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0717 - acc: 0.9795 - val_loss: 0.0869 - val_acc: 0.9734\n",
      "Epoch 10/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0620 - acc: 0.9821 - val_loss: 0.0853 - val_acc: 0.9738\n",
      "Epoch 11/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.0531 - acc: 0.9845 - val_loss: 0.0807 - val_acc: 0.9759\n",
      "Epoch 12/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.0451 - acc: 0.9875 - val_loss: 0.0760 - val_acc: 0.9759\n",
      "Epoch 13/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0393 - acc: 0.9889 - val_loss: 0.0697 - val_acc: 0.9781\n",
      "Epoch 14/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0327 - acc: 0.9914 - val_loss: 0.0652 - val_acc: 0.9794\n",
      "Epoch 15/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.0276 - acc: 0.9927 - val_loss: 0.0641 - val_acc: 0.9804\n",
      "Epoch 16/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0237 - acc: 0.9943 - val_loss: 0.0643 - val_acc: 0.9801\n",
      "Epoch 17/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0205 - acc: 0.9949 - val_loss: 0.0628 - val_acc: 0.9811\n",
      "Epoch 18/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.0167 - acc: 0.9966 - val_loss: 0.0621 - val_acc: 0.9800\n",
      "Epoch 19/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.0139 - acc: 0.9972 - val_loss: 0.0643 - val_acc: 0.9810\n",
      "Epoch 20/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0115 - acc: 0.9981 - val_loss: 0.0633 - val_acc: 0.9808\n",
      "Epoch 21/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0098 - acc: 0.9983 - val_loss: 0.0635 - val_acc: 0.9810\n",
      "Epoch 22/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0081 - acc: 0.9989 - val_loss: 0.0633 - val_acc: 0.9812\n",
      "Epoch 23/25\n",
      "60000/60000 [==============================] - 2s 29us/step - loss: 0.0067 - acc: 0.9990 - val_loss: 0.0647 - val_acc: 0.9817\n",
      "Epoch 24/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0063 - acc: 0.9990 - val_loss: 0.0602 - val_acc: 0.9824\n",
      "Epoch 25/25\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0046 - acc: 0.9995 - val_loss: 0.0623 - val_acc: 0.9831\n"
     ]
    }
   ],
   "source": [
    "model_sigmoid=Sequential()\n",
    "model_sigmoid.add(Dense(512, activation='sigmoid', input_shape=(input_dim,)))\n",
    "model_sigmoid.add(Dense(128, activation='sigmoid'))\n",
    "model_sigmoid.add(Dense(output_dim, activation='softmax'))\n",
    "model_sigmoid.summary()\n",
    "model_sigmoid.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "history = model_sigmoid.fit(X_train, y_train, batch_size=256, epochs=25, verbose=1, validation_data=(X_test, y_test))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2484,
     "status": "ok",
     "timestamp": 1568828312735,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "vTOwnnVJRBEI",
    "outputId": "22634661-22a9-442c-d5fb-573faa0b09d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.0622804889400315\n",
      "Test accuracy: 0.9831\n"
     ]
    },
    {
     "data": {
      "image/png": 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imKeAK0O2PwJOKOu8VZ4lNeh3v1Pt1avyr/dZLM8UGst1V43t+sdy3VWrVn+qcZbUBFVd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\nQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score = model_sigmoid.evaluate(X_test, y_test, verbose=0) \n",
    "print('Test score:', score[0]) \n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "fig,ax = plt.subplots(1,1)\n",
    "ax.set_xlabel('epoch') ; ax.set_ylabel('Categorical Crossentropy Loss')\n",
    "\n",
    "# list of epoch numbers\n",
    "x = list(range(1,26))\n",
    "\n",
    "\n",
    "vy = history.history['val_loss']\n",
    "ty = history.history['loss']\n",
    "plt_dynamic(x, vy, ty, ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4338,
     "status": "ok",
     "timestamp": 1568828322146,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "CyGkgut6USG1",
    "outputId": "9bdc9ef9-fcd4-4db8-851b-05aa9d6b2132"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dhw6xfPlyDh06VF/RC/wnhAgCngH6A1cCzwghAq1nvW1RGkuOzbp169i5cxf9++fj7X3+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\niHPOuYR5EnHOOZew/wdKErn4cA6MUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w_after = model_sigmoid.get_weights()\n",
    "\n",
    "h1_w = w_after[0].flatten().reshape(-1,1)\n",
    "h2_w = w_after[2].flatten().reshape(-1,1)\n",
    "out_w = w_after[4].flatten().reshape(-1,1)\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.title(\"Weight matrices after model trained\")\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.title(\"Trained model Weights\")\n",
    "ax = sns.violinplot(y=h1_w,color='b')\n",
    "plt.xlabel('Hidden Layer 1')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.title(\"Trained model Weights\")\n",
    "ax = sns.violinplot(y=h2_w, color='r')\n",
    "plt.xlabel('Hidden Layer 2 ')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.title(\"Trained model Weights\")\n",
    "ax = sns.violinplot(y=out_w,color='y')\n",
    "plt.xlabel('Output Layer ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SFKO21inVejB"
   },
   "source": [
    "MLP + ReLU +SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1509,
     "status": "ok",
     "timestamp": 1568828326761,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "APPWOCSPVkIY",
    "outputId": "dd5f9148-c385-42a6-c876-29a5d1bbdeb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_19\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_49 (Dense)             (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_50 (Dense)             (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_51 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 468,874\n",
      "Trainable params: 468,874\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# for relu layers\n",
    "# If we sample weights from a normal distribution N(0,σ) we satisfy this condition with σ=√(2/(in). \n",
    "# h1 =>  σ=√(2/(fan_in) = 0.062  => N(0,σ) = N(0,0.062)\n",
    "# h2 =>  σ=√(2/(fan_in) = 0.125  => N(0,σ) = N(0,0.125)\n",
    "# out =>  σ=√(2/(fan_in+1) = 0.120  => N(0,σ) = N(0,0.120)\n",
    "model_relu = Sequential()\n",
    "model_relu.add(Dense(512, activation='relu', input_shape=(input_dim,), kernel_initializer=RandomNormal(mean=0.0, stddev=0.062, seed=None)))\n",
    "model_relu.add(Dense(128, activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.125, seed=None)) )\n",
    "model_relu.add(Dense(output_dim, activation='softmax'))\n",
    "\n",
    "model_relu.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 726
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 32320,
     "status": "ok",
     "timestamp": 1568828363136,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "8BAL-Z8vV1mb",
    "outputId": "f553b4ed-1f80-4c6c-9a73-80b97c834820"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 3s 47us/step - loss: 1.1173 - acc: 0.6885 - val_loss: 0.5812 - val_acc: 0.8474\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.4966 - acc: 0.8664 - val_loss: 0.4063 - val_acc: 0.8881\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.3910 - acc: 0.8895 - val_loss: 0.3445 - val_acc: 0.9028\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3425 - acc: 0.9023 - val_loss: 0.3093 - val_acc: 0.9113\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3117 - acc: 0.9102 - val_loss: 0.2866 - val_acc: 0.9174\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.2891 - acc: 0.9163 - val_loss: 0.2686 - val_acc: 0.9221\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.2714 - acc: 0.9215 - val_loss: 0.2529 - val_acc: 0.9264\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2567 - acc: 0.9257 - val_loss: 0.2402 - val_acc: 0.9309\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.2442 - acc: 0.9294 - val_loss: 0.2299 - val_acc: 0.9341\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.2332 - acc: 0.9328 - val_loss: 0.2219 - val_acc: 0.9366\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 2s 25us/step - loss: 0.2235 - acc: 0.9359 - val_loss: 0.2136 - val_acc: 0.9384\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2149 - acc: 0.9383 - val_loss: 0.2064 - val_acc: 0.9394\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2070 - acc: 0.9408 - val_loss: 0.2003 - val_acc: 0.9407\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.1999 - acc: 0.9431 - val_loss: 0.1940 - val_acc: 0.9422\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.1933 - acc: 0.9449 - val_loss: 0.1889 - val_acc: 0.9428\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.1873 - acc: 0.9469 - val_loss: 0.1841 - val_acc: 0.9451\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.1816 - acc: 0.9481 - val_loss: 0.1799 - val_acc: 0.9447\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.1763 - acc: 0.9503 - val_loss: 0.1754 - val_acc: 0.9470\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 1s 24us/step - loss: 0.1712 - acc: 0.9516 - val_loss: 0.1717 - val_acc: 0.9479\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.1666 - acc: 0.9528 - val_loss: 0.1668 - val_acc: 0.9493\n"
     ]
    }
   ],
   "source": [
    "model_relu.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "history = model_relu.fit(X_train, y_train, batch_size=256, epochs=n_epoch, verbose=1, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3048,
     "status": "ok",
     "timestamp": 1568828370524,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "qVEMXZWbV9CZ",
    "outputId": "5de23cf9-01f4-44de-8c95-fdc27882f6b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.16679219267740847\n",
      "Test accuracy: 0.9493\n"
     ]
    },
    {
     "data": {
      "image/png": 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iQo8ecOGF8OST4LvH6a9+BSec4Nb6tNXajDGtiJ/eRytU9XhgCHCcqg5T1ZV+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\nZEnBGGNMkCUFY4wxQf8f6U8I2JiZDtsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score = model_relu.evaluate(X_test, y_test, verbose=0) \n",
    "print('Test score:', score[0]) \n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "fig,ax = plt.subplots(1,1)\n",
    "ax.set_xlabel('epoch') ; ax.set_ylabel('Categorical Crossentropy Loss')\n",
    "\n",
    "# list of epoch numbers\n",
    "x = list(range(1,n_epoch+1))\n",
    "vy = history.history['val_loss']\n",
    "ty = history.history['loss']\n",
    "plt_dynamic(x, vy, ty, ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3879,
     "status": "ok",
     "timestamp": 1568828378833,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "NNscBBziWyGq",
    "outputId": "f20e2423-d85d-43f8-8621-316712c1f6e4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SExMBFF4QkNYa1q9fz44ffmBSZiYxTsprdlZuLn1KSlj60kvajbsBysvLuf/+\n+wgJqWb8+CNeE0TobEwmGDcum3btqpk//z7DxxvZabURUUpVA/8G1lLPIp2IDBORdOBS4EUR2d3a\nft1BXl4ed989h8zMHNLSbqSsrLvL+1QqgP37/0VpaTcWLHiCLVu2uLzPunBmQBq4JyitpWRkZPDS\niy/Sq6SEkTk5TjuvCbgqPR0qKnjyiSd8Mm+SM3j11Vc5dCidMWOOGD6NiasJDFSMGZNFZmYWL730\nkqfFaRJOWRNRSn2ulOqtlEpSSj1s2zdPKbXStr1dKdVFKRWqlOqolOrvjH5dSW5uLnfddTcZGUdJ\nS/sXpaWJbuvbYgkkLe1GSkq68thjj3t0RNIUGgtIA+Oud1VUVPDoww9jrqjgmvR0py8SRlZVccWh\nQ/y+dy+vv/66k8/u/WRnZ7N8+bv06VNsuEy8niI+voK+fYtYseIDMjMzPS1OoxhqYd0oZGRkMGvW\nnRw+fIy0tBspLe3pdhkslmDS0m6itLQ7jz++wO3z6t4QkNZaLBYLC599lgOHDvF/Bw4Q6aKAzyGF\nhZydk8PHH3/MF1984ZI+vJVly5ZRU1N13ENJY+WMM/KAGq9w+9dGpBa7d+9m5sxZHDlSwr59/6a0\nNMljslgsQaSl3URR0SksWrSIN954w21+5PaAtLS0NLAGwxgqIM0ZvPrqq3zz7bdclJVFPxdXm/xr\nVha9S0p4buFCdu7c6dK+vIWKigrWrFlNUlIxERF6qs+Rdu1q6NWriLVr11BebuwRmjYiDnz55ZfM\nmXMvBQWhJCff7pY1kMawWALZv38aubkjeP/991mwYIFbvlT2gLRx48YB9MdAAWnOYPny5Xz88ceM\nOnasVe68TcVfKaYcOEBsWRmPPvwwv/76q8v7NDrff/89xcUlhqlIaDT69i2mrKzc8NPZ2ohgndZ4\n4403ePrppykq6kFKym1UVRkpQMzM4cOXk5V1EZs3b2H27HvIceICcH1MmDCBvXv3gjUora61rjFK\nqRil1CDba1JD5zMKH3zwAcuWLWNYXh6Ts7JamXOg6QRbLNyYlkZ4SQn3z5vHnj173NSzMdm5cyd+\nftClS5mnRWHTpg4cPRrA0aMBrFgRy6ZNHTwtEnFx5QQEYPiRa5s3IjU1NTzzzDO8//775OaeSVra\nTdTUhHparDoQjh0bw4EDU0lJOcjMmXfoolYt4IMPPuD1119nSH4+V2dkuP0HEFFdzc2pqYSWlHDf\n3Llt2pDs2vUzMTHlhnDpPXo0gMpKM5WVZjIygjl6NMDTImEyQWxsGT///D9Pi9IgbdqIVFVV8dhj\nj/PVV19x5MiFHD58JUo5IxOIRar/AAAgAElEQVSM6ygqGkBq6i0cO1bGHXfM1nW9m8G777573IC4\nwhOrqbSvrmZGaiqhxcXMnTOH3bu9wuPdqSilOHjwAB06OCcmx1fp0KGS9PR0Q2c9aLNGpLS0lPvv\nn893320lM3MyR4+Op7XJFN1FeXkCqakzyM+3cOeds/ntt988LZKhUUrx2muv8dZbbzEsL4//S09v\ndUR6a4msqmJGSgoRthHJDz/84GGJ3EtRURElJWVEROgSCA0REVFFRUUlubm5nhalXtqkEcnNzeXO\nO2fz88+7SE+/ipyccz0tUrOpqOhMcvKtFBYGc889c/juu+88LZIhqamp4fnnn2fFihWclZPjkSms\n+mhfXc2MlBSiiot58IEHPJ7qxp3Y1/TCwnyjRoirCA21Xp9jbnD+aClG+T25jYMHDzJz5iz27z/M\n/v03kJ//J0+L1GKqqqJITr6N4uI4HnnkEa+o7+1OKioqeOzRR1m/fj3js7O5LDPTcF/48JoabklN\npUdxMU89+SSrVq3ytEhuwf5kHRysjUhDhIRYr48eiRiEXbt2MWvWnRw9WkFq6gyKi2vnE/Q+amra\nkZp6M4WF/fnvf//LK6+84pU1CZxNcXEx8+bO5fvvv+dvhw9zYXa2YScr7V5bAwoLWbJkCcuWLTP0\nHLgzsI9E7E/amrqxXx93eGO2lDZjRL766ivmzp1HQUE7kpNnUl7u2nolsbErCApKJygonR49FhIb\nu8JlfSkVyIEDU8nJOZuPPvqIBQsWUOmkJILeSF5eHnffdRe///Yb1x48yCgDP8XZCVCK6w8e5Mzc\nXJYvX87ixYupqfHdG6y9fHBoqA4ybAi7ETFavjlHjO2K5ASUUrz33nssW7aM0tKe7N8/FYslxOX9\nBgenYzZbgwJDQ1Nc3h+YyMy8lMrKjmze/DHHjuUwd+69REQ4s+6J8cnKyuLee+4h7+hRbti/n74u\njkR3JmbgysOHCaupYe3atRQVFTFr1iz8/f09LZrTOXToECEh1oSDmvrx81O0a6dqpyAyFD49ErFY\nLLz44ossW7aM/PxhpKXd5BYD4jmEnJzRHDx4Pb//nsIdd9xl6GGws0lJSeGO22+nKDubf6emepUB\nsSPApCNHuDgzky1btnD/ffdRUlLiabGcTlraPtq3N3Y6D6PQvn05aWn7PC1GvfisEampqWHRokV8\n9tlnHDt2Lunp16CU7z3R1UVh4ens2zedw4dzuOOOuzhy5IinRXI5P/zwA3ffdRemvDxuTUmhR5nn\no6Bbw+icHK5JT2f3rl3cdccdhvbOaS7V1dXs3buXTp28Kl+nx+jUqYKUlBQqWliu2dX4pBGxWCws\nWrSI9evXk509nqysv+ItMSDOorS0J/v2TSc7u5C77rrb0HOqrWXDhg08MH8+UUVFzExOprNBf2zN\nZXh+Pv9KS+PIwYPMmjnTZwJL9+7dS2VlFbGxvqEnVxMbW0FNjcWw2Q18zogopVi6dCkbNmwgO3s8\n2dkX0tYMiJ2ysu6kpt7MsWNFzJkzl/z8fE+L5FSUUixfvpyFCxfSq6iIGampRPhY4adTSkq4JTWV\n6pwc7rrjDp+IbrfngjJCzixvIC6uHBHj5tDyOSOyZs0aVq5cybFj55CdPcHT4nic8vIE0tL+RWZm\nNg899IjPePwopXj55ZetiRTz8/nXgQMEu8G1eUVsLOlBQaQHBbGwRw9WxMa6vM8u5eXclpxMWGEh\nc+fMYfv27S7v05Xs2LGDjh2rCQnRruhNITjYQnR0FTt2GFPvPmVE0tPTeemllyku7tsmp7Dqo7Q0\niUOHruT3339jxQrXuRq7C4vFwuLFi1m5ciXnHDvG1enp+LkpriI9OJhys5lys5mU0FDSg4Pd0m/H\nqipuS0khtqSEhx96yPDpweujurqaXbv+R1ycd9QPNwpxcaXs3r3bkK77PmNElFI8//wiKiv9SU+/\nCh/615xCQcFQ8vMH89Zbb3t19l/7etfatWsZm53N5KysNqPp0Joabt63j64lJSx43Phlk+siNTWV\nsrIK4uO1Z1ZziIsrp7Kyit9//93TopyEz/z+fvzxR379dTeZmROorm5bsRFNQ8jKugSLxczbb7/t\naWFahFKKF198kfXr1zMuO5uJBo5CdxXBFgvT09LoVlrKEwsWsG3bNk+L1Cz27bO6qkZFGe+J2shE\nR1uvl/36GQmfMCJKKV5//Q2qqzuSl/dnT4tjWKqrwzl6dBRff73JXvbWq3jvvff4/PPPOe/YMS7M\n9rpqvE4j0JYmJb60lMceecReOMwrOHjwICYTOntvMwkPr8bPz3r9jIZPGJEtW7awb18qWVkXGL4e\niKc5enQMSgWxbNkyT4vSLLZt28Zby5YxND+fi91YjdCoHK+SWFHBww8+aOgEfY4UFxcTGIghClF5\nEyIQFGS9fkbD642IUoply96hsjKW/PxhnhbH8FgsIWRnj2bbtm2kpLgjHUvryc/P5+knn6RLRQVX\nZmS0eQNiJ6ymhin791Ocn8/CZ5/1iqSNFRUVmM3Gl9OImM0WQwYcOsWIiMh4EfldRFJEZHYdxwNF\nZLnt+Pci0t0Z/YLVdzo9/QDZ2efjAzbRLeTkjESpID766CNPi9Ik3nzzTcrLyvi/AwcI8IIbpTvp\nUl7OXzIz2fnjj17h+hsWFkZFhX4MaAkVFSbatWvnaTFOotV3XRExA4uBC4B+wJUiUjvH+j+BPKVU\nT+AZ4PHW9mtn/fr1WCzhFBQMdtYpfR6LJZicnD/xzTffUlpqbFfL9PR01q9bx8hjx4g1oHujERiZ\nk0NsZSWvLl1q+DIAkZGRVFVBZaU2JM2hulooL7deP6PhjEf34UCKUmqfUqoSeBe4qFabi4DXbdsf\nAKNFxCnfogMHDlFc3M1wayEmUxlBQUFMmjSJoKAgTCZjReeWliailIXMzExPi9Ign376KWZgjA+n\nbWktZmDskSOkHz7MTz/95GlxGqR79+4A5OQEeFYQLyM315r3z379jIQzjEg84JinON22r842Sqlq\noADoWPtEIjJNRHaIyI6m5nrKzc2lqsp4Lr1mcxljx45l6tSpnH/++ZjNxjIiVVXtAWNXTCsrK2Pj\n+vWcnpdHuI9E2ruKQYWFhFssTa5uuWbNGoBT3T0F3bNnTwCOHtVGpDnYr1dSUpKHJTkZQy0iKKWW\nKKWGKqWGRkdHN+kzUVHRBAQY70ZYUxPMunXrWLJkCevXr6emxj2RzU3Ffs2ioqI8LEn9bNq0ifLK\nSs4ysKEzCv5KcUZODju2b280/X9NTQ3Tp08H2Iubp6BjY2OJjIwgKyvQGadrM2RlBREWFkpCQoKn\nRTkJZxiRDMDxP+ti21dnGxHxAyIApxS6SEzsRmhoOmCsuWCLJZjy8nJWrVpFeXk5FouxjEhQ0CFM\nJjNdunTxtCj1smH9emIrK70+rbu7ODMvD4tSfPHFFw2227Ztm31EUOnuKWgRYcCA08jKCm3tqZxK\nRYWJ4OBgLrvsMoKDg6moMNTzNZmZIQwYMBCTyVhygXOMyHagl4j0EJEA4ApgZa02K4Frbdt/A75Q\nTvJHPP300zGZCgkONl4QjnFRtG+/i1NPPdWwVfOys7PZ8/vvDM3L0y69TSS6spLuZWV8u2lTg+0y\nMjJqP9G6dQp6wIAB5OebKC01zg2xstLExIkTufXWW5k4cSKVlcaRraLCRG6umVNPPdXTotRJq6+U\n7Qv2b2At8BvwnlJqt4g8ICKTbM2WAh1FJAWYCZw0B9tShg8fjslkJiJih7NO6fMEBR3C3z+bESMa\nju5fs2YNffr0AevcuVtdt+3uqqcXFDjrlG2CQfn57Nu/3231Y1oyBd2vn3Xm7MgR40xpBQRYWLVq\nFc8++yyrVq0iIMA4MxtHjljXQ/r37+9hSerGKeZWKfW5Uqq3UipJKfWwbd88pdRK23a5UupSpVRP\npdRwpZTTEsCEhYUxatRIOnb8DrPZ98qIuoKoqI0EBgYzatSoetvY581Xr14NsBs3u27v27ePUKWI\n1m69zaKHzWW7obQ28fHxtWt2u3UKulevXgAcO2acxfXAQAtlZWW89957lJWVERhoHCNiv07262Y0\njDNmawWTJ09GpILo6HWeFsXwBAUdon37H7nwwgsIDa1/Xto+b56YmAigcLPrdvqhQ8SWlhpuKqvM\nZDrBdbvMYHPU9qqOtYzECQwbNozk5GSAAE9MQYeFhREd3ZHcXOMYESOTmxtAZGSEIWNEwEeMSPfu\n3Rk3bhxRUV8SHLzf0+IYFpFqEhLeIiKiPZdeemmDbZ05b27tu3lz51WVlQQYMHCuzGw+wXW7zGBJ\noOzXrLqBCo9+fn4sWrQIoDcemIIGiI9PoLDQmOtxRqOw0I/4eOM6wBgrQq8V/OMf/2Dbth107fom\nycl3YrEYZ77VKERHryYwMIObb76XsLAwt/atlFoCLAEYOnRoo0+0YjKhnDOocSrBNTWsW7cOpRTr\n16+no8HiVyxNvGYTJkwA+EUpNdS+Tyk1z2G7HGj4SaMVxMbGsm+fHok0heLiQPr0cX0FzZbiEyMR\ngNDQUGbNmom//1E6d37f0+IYjtDQPXTqtJ6xY8dyxhlnNNre0/PmkR06UBRgvJtMsMVyguu2O0ry\nNociP+tzYfv27T0sScO0b9+esjLjPSQYkfJyk2GnssCHjAjAwIEDufzyy4iM/J6OHTd6WhzDEBiY\nSbdurxEXF8/UqVOb9Bn7vLltgVZw87x5VFQUOf7+Bov+MT45NsPbVE8pTxEeHk5VFTQw66YBlILy\ncgyZeNGOTxkRgCuvvJIRI86ic+eP6dChYX/5tkBAQDZJSYuIjAzgvvvmEhQU1KTP2efNx40bB9Af\nN8+b9+zZk3IRsgP1tGRz2G+r+W7E9BiOBNvkrK72uVuQU6muto7Wmvq79QQ+syZix2w2M2vW7VRW\nVrJ9+/uYzWUcPToWDOfn43qCgw/Qo8cSwsPhkUceIi4urlmfnzBhAhMmTEBEfnF03bYfd+W8ed++\nfQHYExZGrAFrKBiVPe3aERcbS0SE8fLJORJgGzHV1LS932VzsBuRAANO7drxyccAPz8/7r57NqNG\njSIm5lPi45ch0rbKcYaH/0RS0kI6dQri8ccfpWvXrp4WqVnEx8fTq2dPvomK0lNaTSQrMJC9oaGc\nN2aMp0VpFHv6Dl0epmHs18dsMC9AR3zSiAD4+/tz++238/e//53IyG1067YEkbbxRBsZ+S1du75C\nr16JPPPMU15nQOxMuugisv39+V94uKdF8QrWRUXh7+fH+PHjPS1Kk9FGxPvxWSMC1mRvV155Jbfc\ncgvt2v1OYuJiTCZjF2FqLVFR64mPX87QoUN59NGHDe+l0xBnn3023RIS+DgujkoDuvsaif3BwWyP\njOSiiy82/FQW/BHHokvlNoz9+lRVGXcmxaeNiJ0xY8Zw1113ERp6kKSk5zCbC13eZ1lZF2pqgqip\nCaKkpCdlZa4OFlLExHxCbOxKzj77bObMuYdAL1+UNpvNTPvXv8j182Otwb2NPEkNsLxLFyLDwxsN\nIjUK9lrh2og0jP36VBo4/U+bMCIAI0aM4L775hEWdoxevZ7F398p4Qz1kpV1CeXlXSgv70Ja2i1k\nZV3iwt4sxMW9S3T0BsaPH8/tt9+On59v+EwMHDiQ0aNHs6FTJw4EGyudvlFYFx1NemAgN/7734SE\nhHhanCZRXFyMCAQEaCPSEH5+1ldxcbGnRamXNmNEAAYPHswjjzxEeHgZvXo9TVBQ/fmFvAWRSrp2\nfZkOHbZw2WWXcdNNNxl6Ea4lTJkyhcj27Xm9WzdKDZarytOkhISwNiaGkSNHcuaZZ3panCZTVFRE\nYCDoWcrGCQyEwkLXz560lDb3izzllFN44onHiYoKJClpIWFhv3papBZjNheRmPg84eG/MG3aNK65\n5hqclP/QUISFhXHH7NnkBgTwWteu2lvLRq6/P0u7dye2c2duuukmT4vTLHJzcwkJMVbKGKMSElJN\nXl6ep8WolzZnRAC6du3K008/QffucXTv/iKRkZs9LVKzCQg4Qq9eT9OuXQb33HM3EydO9LRILqV/\n//7ceNNN/BYWxrtxcW3ekBSZzbzQoweWkBDunTevwYzMRiQnJ4fgYOMuFhuJ4OBKjh1zT32YltAm\njQhAx44dWbDgMQYPHkx8/LvExHyM0Urs1kdoaDK9ej1NZGQVjz76iFdNY7SGcePGcdlll7G1QwdW\ndO5MW51NLzWZ+E9iIjk2A2LkEsf1kZ2dRViYznnSFEJDa7QRMSohISHMm3cvEyZMIDp6I127voTJ\nZOx63pGR39Kjx2Li4zvw9NNPcsopp3haJLdy9dVXc/HFF7OpY0eWt8ERSaHZzPNJSWSFhHDv3LkM\nGDDA0yI1G6UUOTl6OquphIXVkJubT43BMkbb8Q0XnlZgNpv517/+RUJCAkuWvERQ0NPs3z+VyspO\nnhbtBESq6dz5Azp02Mzppw/mzjvvcHs6dyMgIlx//fWYzWZWrFhBqdnMNenp+LeBqLVj/v4sTkqi\nKDiYuffey+DBgz0tUosoKSmhqqqa0FA9EmkKISE1WCwWCgoK6NChg6fFOYk2b0TAemP6y1/+Qteu\nXXnkkcfw93+KtLQbKC1N9LRoAJhMZXTr9jKhoXu55JJLuOaaa3zOA6s5iAjXXXcdERERvPLKK5T4\n+THlwAG3pGXvUlZGui0ZXpfycrqUuWfkeigoiP8mJmIJC+Oh+fO9egRqXyQODm5r48iWERxsHYHk\n5eUZ0oi06ems2gwcOJBnn32a2Nj2JCYuol27/3laJPz8CkhKWki7dqnceuutXHfddW3agDjy17/+\nldtuu43Udu14LimJAjfExlySlWU1HuXl3JKWxiVZWS7v8/fQUJ5LSiIgKorHn3jCqw0IQKmtDnxA\ngDYiTcF+nezXzWhoI1KL2NhYnnpqAb16JdKt21IiInZ4TBY/vzx69nyWdu1yuO++eYwePdpjshiV\n8847j7nz5nGsXTue6dmTowbOdtoSfgwP54UePYjp2pUnn37aa/OgOWKPVvfz8/0pSGfg72+9ThUG\nzWatjUgdRERE8MgjD9G//6kkJLxBePhOt8tgHYEsIiyslEceechr57/dwZAhQ3j40UepjIjg2Z49\nj083eTubIyN5tWtX+pxyCo8tWEDHjnWWr/c6fDGWyZXYl/tMBg20NaZUBiAoKIj77ptL37596dr1\ndbcGJZpMpSQmLiY0tJAHH5xPnz593Na3t9K7d28ef+IJ/Dt25LmkJFK9JP1HXSisGXnfjY9nyJAh\nzH/wQZ9yovD39wf+qJWhaRh7zRX7dTMarTIiItJBRNaLSLLtb52FgEVkjYjki8inrenP3QQHB3P/\n/ffRrVs3unV7haCggy7vU6SK7t1fIigom3nz7vX6+W93kpCQwIInn6RDXByLExP5xcAlRevDAnwU\nG8uq2FhGjRrFnHvvNXRVu5ZgzyxdVmacZ9jo6EoCAmoICKghPr6M6GjjJDwsLbWugRq1znprtTgb\n2KiU6gVspP7yqE8A17SyL48QEhLCAw/cT1RUOImJLxAQkO3C3mpISHiNkJAUbr99JqeddpoL+/JN\nOnXqxONPPEG3xERe6taN7V6QFt1ODfBWfDxfRkUxadIkZs6c6TOJNB3p2LEjIkJxsXH+t5Ejc4mO\nriQ6upJLLsli5MhcT4t0nOJiqxExomcWtN6IXAS8btt+Hbi4rkZKqY1AUSv78hgdOnTgoYceIDzc\nRGLiYvz88l3QiyIubjnh4T8zZcoURo4c6YI+2gYRERE8/Oij9D/1VN5MSGCTQX98jlSJ8ErXrmyL\njOSqq65iypQphp0Dby3+/v7Ex8eRk+NbThCu4tixAGJiog2bobm139IYpVSmbTsLiGnl+QxLly5d\nePDB+YSElJKY+B+nR7Z36vQZHTps5fLLL+eiiy5y6rnbIiEhIdw/fz7Dhg3j/bg4Nhp4UbpShJe6\ndePn8HCmTZvGFVdc4fOLz336nMKRIyG6smETOHo0hN69jTut3agREZENIvJLHa8T7nRKKQWtS2ck\nItNEZIeI7Dh61Hi5Ynr27MncuXMICsqmW7eXEXFOxG1k5GY6dVrL+eefz1VXXeWUc2ogICCAu++5\nh7NGjODjzp1ZFxXlaZFOolKEJd27sycsjBkzZvh8Ik07Q4cOpbhYyM015mKxUcjP9yM/38SwYcM8\nLUq9NGpElFJjlFKn1vH6BDgiIp0BbH9btWCglFqilBqqlBoabdBKdoMGDeKWW2YQGrqX2NgVrT5f\nSEgK8fHvMWjQ6dx0000+/wTqbvz8/Jh1xx2MGjmSVbGxfB4dbZjEjeUmEy/06MHesDBuve02zj//\nfE+L5DbsSUNTUrwr+7C7SU21Xh8jJ1lt7XTWSuBa2/a1wCetPJ9XcN555zF58mQ6dvyWiIjtLT6P\n2VxI9+6vERMTw+zZd/nkIqoRMJvN3DZzJqNHj2Z1TAyrYmI8bkjKTCb+06MH+0JDmTVrFuedd56H\nJXIvnTp1Ytiwofz6awRuyFbjlVgs8Msv7Rk06DTi4+M9LU69tNaIPAacLyLJwBjbe0RkqIi8bG8k\nIt8A7wOjRSRdRMa1sl+Pc80119C3bz+6dFlOQEBLpt4UCQnLCAgoZc6cu72uHoS3YTabmTFjBuPH\nj2d9dDQfx8Z6zJCUmkwsSkzkYGgos+++u806Ufztb5dSVGRizx7fiYFxJsnJoRQUmLjkkr95WpQG\naZURUUrlKKVGK6V62aa9cm37dyilpji0O1spFa2UClZKdVFKrW2t4J7Gz8+PO+6YRXCwmS5dltHc\nWiSRkZsJC/uNKVP+SY8ePVwjpOYETCYTN910E3/5y1/4IiqKDz1gSOwG5HBoKPfMmWPoaQpXM2LE\nCPr378vWrVFUVOhpXEeqqoTNm6Pp3bsX55xzjqfFaRDf9CF0E9HR0dx44w2EhOyjQ4dvm/w5P78C\n4uI+ZuDA07jgggtcKKGmNiLCtGnTmDhxIl9FRfFpJ/el/K8Q4YUePci0GZDhw4e7rW8jYjKZuO22\n2yktFb7+uqP21HLgm286UFwszJx5u+ETrmoj0krOPfdc+vcfQOfOn2MyNS3LZkzMKszmaqZPv8mQ\nsQC5ubmcf/759OrVy77Ye9K3WEQGichWEdktIj+LyOXul7RliAhTp05l3LhxrOvUiS/d4P5bLcLS\nbt04EBLCnXfd5TFvG0fdAr3qyjLhTt3269ePf/zjevbsaceuXd6XYcAV7N4dxi+/hHPNNdcwcOBA\nT4vTKMa7g3kZIsINN0xFpJTo6A2Ntg8MzCQychsXXTSJuLg4N0jYfB577DFGjx5NcnKyPXNwbB3N\nSoH/U0r1B8YDz4pIe3fK2RpEhBtvvJE/n3kmH3buzA8ujGxXwNtxcfwWFsbNN9/s0SksR91iDQCu\nK8uEW3V7/fXXc+aZf2LTpiiSk40ZUOcuUlND+OqraIYOHcK0adM8LU6T0EbECfTo0YM///lMoqK+\nxWQqb7BtVNQX+Pv7c8kll7hJuubzySefcO21Vqc729+TnlaVUnuVUsm27cNY3buN6ZddD2azmdtn\nzaJ/v34sS0gg2UURwZ926sT2yEiuvvpqj7vxOuoWyKGOLBPu1q3JZOKBBx6kf/9TWbs2hr1726aT\nSUpKCKtXx9Cnzyk8/PAjhp/GsqONiJO45JJLECkjMnJrvW38/AqJjNzO2LHnE2HgnE5Hjhyhc+fO\ngLW+Co1UwBSR4UAAkNpAG0MGkgYEBDDn3nuJjYvj5R49yHZyPZLv2rdnXadOjB07lssuu8yp524J\njroFqmgky0RjunWWXkNDQ3nmmWcZMGAga9d2Yvv2iDazRqIU7NwZzurVMfTt24+FC5+nnRclD9VG\nxEn07t2bXr36EBX1LfV5akVGbgFqmDRpkltlq4sxY8Zw6qmnnvT65JMTQ30aC360BZm+CfxDKVWv\ni5qRA0nbtWvHffPnYw4LY0mPHpQ5aZ1qX3Awy7t04bQBA9waSNpU3dqo91bdFN06U68hISE888yz\njB49mq1bO7BmTTRVVb7ttVVdLaxbF82333Zk1KhzWLjwOa9z99fRbU5k4sQLSU5+mtDQFEpKetc6\naiEqaiunnTbIEGshGzbUv34TExNDZmYmnTt3JjMzE6DO/C4iEg58BsxRSn3nEkHdRGxsLHfPmcPc\ne+/l7fh4rj90iNbcvorNZl7p0YOoTp246+673To10VTdAv7Uk2XCU7oNCgpi/vwH6N27Dy+88B+O\nHQtm7NgsYmKMk5rdWWRnB7BuXQx5eX5MmzaNa6+91iszVuiRiBM588wzCQgIqrOkbkjIPszmXMaM\nMX6J20mTJvH669bkzLa/J6UtFpEA4CPgDaXUB24V0EUMGDCAq6+5hp8iIviufcvXkRXwdnw8xX5+\nzL7nHkNNTTjqFuhIHVkmPK1bEeHqq6/mueeex8+vE++/H8+2be19JrLdYoEdOyJ4//14RKJ55pln\nue6667zSgIA2Ik4lKCiIM888g8jI/2GtDvEHERE78fcP4IwzzvCMcM1g9uzZrF+/nl69etmfajPh\npEwElwEjgetE5Cfba5CHRHYakydPZuCAAayIjyevhZXktrdvz67wcK697jqSkpKcLGHrcNQtEE7d\nWSYModshQ4bw5ptvMXr0GL77LpL33uvC0aPenT4+J8efDz6IZ8uWDowceQ7Llr3t9fFCejrLyZx1\n1ll8/fXXhIYmO+y1EBn5M0OHDiE4ONhjsjWVjh07snHjxuPvRaQGrJkIgCm27WXAMo8I6EJMJhMz\nbrmFm268kfc7d2baweZVsyw2m/koLo4+vXoZMqW/o25FZK9jlgkMqNvw8HDmz3+AUaPO4cknF7B8\nuT+DB+czfHg+fn7es/JeXQ07drTnhx8iCQtrx/33387555/vtaMPR/RIxMmcfvrpBAQEER7+v+P7\ngoMPYjIV8Oc//9mDkknGcmYAABMYSURBVGmaSkxMDFddfTW7wsPZ3cza5p916kSZnx//njHDkIGk\n3sp5553HO+8sZ9y4C9ixoz3vvJPAoUPeUTY4IyOId9/tyrZtkYwePZZ33lnO2LFjfcKAgDYiTicw\nMJBBgwYSEfHb8X3t2v2KiDB48GAPSqZpDhMnTqRzTAwfx8XVmpisn8zAQDZ37MgFEybQvXt3V4rX\nJomIiGDu3Lk8++yzBAfH89FHnVm3LorSUmPexsrLTWzYEMWKFZ0JCOjMU089xf3333+8xryvYMyr\n7+UMHjwYP78cRKoACAv7naSknoSHh3tYMk1T8ff35x///CdZAQFsa+KPflVMDMFBQVxxxRUulq5t\nM3z4cN566x2uvfZakpMjeOutruzZE2qYuBKlYO/eUJYt68qePRFcffXVvP32uz6bbFMbERfQp08f\nAEymCgBCQtLp29e45S01dfOnP/2JnomJrI2NbXQ0cigoiF3h4Vw8ebKhA0l9hcDAQG644QZee+11\nevToy7p1nVi5MpbCQs8u8xYVmfn00xjWrOlE1659ePXVV7npppsICvKOqbeWoI2IC+jWrRsmkxmT\nqQKRSqDScF46msYREa665hpy/PzY0choZF10NKHBwYYIJG1LJCUl8eKLL3Hbbbdx5Eh73n47gd27\nw9w+KlEKfvstjLff7srhw+2ZMWMGL7201O4F59NoI+IC/P396dQpFpOpEpPJOqWVkJDgYak0LWHI\nkCF0iYvj2wYy/eb7+fFzRARjx4/3umhjX8BsNnPppZfy1ltv07//IDZujObTT2MoKXFPgGdpqYnP\nP+/E+vXR9OkzgGXL3uKKK67wmtxXrUUbERfRtWs8IoqaGqtLrxGi1DXNR0S44MIL2R8cTGZgYJ1t\ntrVvjwWYMGGCe4XTnEDnzp15/vlF3HLLLaSnh/Puuwmkp7t2Gunw4UDefbcrBw6EM336dBYv/o+h\nS9m6Am1EXERsbCwmUw3l5XEEB4cR1kxXUY1xGDFiBAC76ok8/yUigqTERHuySo0HMZlMXH755bzy\nyqt06JDARx91ZscO5ydzVAp+/DGcDz+MIzy8My+/vJSrrrqqzYw+HNFGxEXExMQgUkFw8EFiYtxX\nPU/jfDp27EhSjx78Vod3XanJxP7gYIZ7QSaCtkRSUhKvvPIq5557Hlu2dGD9+ihqmuqr3QgWC3zx\nRRTffNORs84ayWuvvUHv3rVz5bUdtBFxEVFRUQCEhBwkJsZYWWs1zWfAaadxICSEqloBYvtCQlBY\n825pjEVoaCgPPvggU6dOZc+ednz6aSyVla0L8KuqEj77LIbdu9tx7bXX8uijj7b5WQZtRFyE3YjU\n3tZ4J/369aMKOFQrbU1qaChmk6lNeOF4IyLCP/7xD+6++24OHQpl1arOVFe3zJBUV8Onn8ayf38o\ns2bN4oYbbvCZqPPWoI2Ii3CMSvW1CNW2SL9+/QBIruV9lRIWRq9evXw6DsAXmDhxIvPmzePw4UBW\nr+7U7KktiwXWru3EoUNB3HPPPUyePNk1gnoh2oi4CMfodB2p7v1ERETQLSGBvQ5GpMxk4mBwMAMG\nDvSgZJqmMnbsWG6/fRZpaSFs3tyhWZ/dujWS1NRQbrnlFi688EIXSeidaCPiIgId3EHb+pyprzBo\n8GD2hYUdLwWYHBqKBWvSTY13MHnyZP72t7/x008RpKSENOkzaWnB/PBDey666CIuv/xyF0vofbTK\niIhIBxFZLyLJtr+RdbQZJCJbRWS3iPwsIm1CC45zpSEhTfuyaozNoEGDqIbj5XP3hoUR6O/PKafo\nlDbexM0330zfvqfwxRcxjSZvtCZRjKFnzyRuvfVWN0noXbR2JDIb2KiU6gVstL2vTSn8f3v3H2RV\ned9x/P3ZdRfB3ZWfpRtgFSOEHwo7YZMWjQQFnEQ7AmpiUoNAGx1r+mNiMx2mwTQ24wzOxGnSGM1Q\nG/wR2nRiE0msmQRIE1slHReDiAhsnWK1LARYTYiR5Yff/nGeA3fXc3f33ru7955zv6+ZO3vuuefe\n8+x9zr3f+zznOd+HW8xsNvAR4CuSquokQRrmEHH9mzVrFpI4Hq4FeKWhgffNmEFdkZNXufKoq6tj\n7dq7OHmyhmef7btba9u2MXR317B27V09ehfcWaUGkaVAPNfmI8Cy3huY2T4z6wjLB4jmdK6qMa9+\n8GXDqFGjuLClheM1NRjwfyNGMDOccHfpMnXqVD7xiU+ye3cjR44k/wjo6qpj164mbrjhxqq+DqQ/\npQaRiWbWGZYPAhP72ljSB4F64JU8j98mqV1S++HDh0ssWuXwlkh2XDB1KidqazkRAonPG5JeK1as\nYOTIEbS3J3eMtLefT11dHatWrRregqVMv0FE0hZJuxJuPeb+NDMD8iYXkNQMPAasNrN3krYxs/Vm\n1mZmbRMmZKexUl+f7nmh3VlTpkzhlMTIMEbUE2umV1NTE9dffyMdHQ0cO9YzXclbb9Wyb18jS5cu\nY8yYd53qdTn6Tb5vZovzPSbpkKRmM+sMQeKXebZrAv4N+LyZ/bzo0qaUB5HsaG5uBqDh1CkgSm/j\n0mvZsmVs3LiRPXt6jqDcs6eBd96B5cuXl6lk6VFqd9b3gZVheSWwqfcGkuqB7wGPmtnjJe4vlaox\nKVtWxUGj47zzaGpo8IsMU27SpEm0ts5l796eE4nt29fI7NkzvbtyAEoNIuuAJZI6gMXhPpLaJD0U\ntvk4sABYJWlHuLWWuN9UOeec8s625gZPnMLmWF0dE37HE2tmwcKFV9LVVXsmHcqxY7UcPlzHwoVX\nlblk6VBSEDGzo2a2yMymmdliM+sK69vN7NNh+VtmVmdmrTm3HYNR+LSoqfFrOrNi9OjR1IRrgMZn\n6LxdNbvssssAOH486jF49dWRPda7vvm32zDw7qzsqKmpoSGkPvF0NtkwadIkxo8fy4kT0ddhZ+e5\nnH9+k3dlDZAHkWHgLZFsiafA9alws0ESc+a0ngkiBw+O4tJL53iG3gHyb7dh4AdjtsQXj/pFpNkx\nffp0Tp8Wp0+LN9+s9YsLC+BBxCXq6upiyZIl8TwZ05LyosUkNUl6XdL9w1fC8qkNAyU83Ul2XHTR\nRQC8/XYtZmfvu/55EHGJ1q1bx6JFi+jo6AA4RnJetNiXgKeHpWCVILQsvSWSHZMnTwagu7umx33X\nPw8iLtGmTZtYuTK+BIijJORFA5A0jyjdzY+HqWhlFyVn8JZIlsQXkcZBJL7v+udBxCU6dOhQ7gfp\nJAl50STVAPcBn+vv9bKYF81zomXHiBEjqKmpwUzU19f5HEAF8KvgqtjixYs5ePDgu9bfc889SZsn\n5UW7A3jKzF7vb/CAma0H1gO0tbXlzbGWJt6dlS11defQ3X2CsWNH+2CYAngQqWJbtmzJ+9jEiRPp\n7OyMWyN1JOdFmw9cIekOoAGol/QbM+vr/ElmpDHlSVdXFzfddBP79++HMGDCzN5I2jbkvNsNPGFm\nfzqMxSyL2tpzgBM0NVXVdEcl8+4sl+i6667jkUfiqWIYR0JeNDO72cxazOxCoi6tR6slgEA6WyI+\nYCK/OD1RY6NfRFoIDyIu0Zo1a9i8eXM8xLeJ5LxoVS2N2Zl9wER+8UXBfhFpYbw7yyUaN24cW7du\nBUDSvty8aMCne29vZg8DDw9jEcsujZkIEgZMXNB7m5wBE58iSqyal6TbgNsAWlpaBrWswy1OT+QD\nJgrjQcS5jPEBE8WJ/9c0nusqJw8izmWMD5gozrhx4zhy5Egqz3WVU/ra4865ovmAifwuv/xyIJ3n\nusrJg4hzVcQHTOQXj87ya0QK491ZzlURHzCRnweP4nhLxDnnOBtE4txobmA8iDjnXA5vkRTGg4hz\nzuXwlkhhPIg45xxng0caLyItJ3+3nHMuh7dECuNBxDnncvg5kcKUFEQkjZW0WVJH+PuuebglXSDp\neUk7JL0k6fZS9umcc0PJWyKFKbUlsgbYambTgK0kp5XuBOabWSvwe8AaSe8pcb/OOecqQKlBZCkQ\n51B4hIS00mZ2wsy6w90Rg7BP58rKryfIJq/X4pT6hT7RzDrD8kES5uEGkDRF0k7gNeBeMzuQZ7vM\nzcPtsif+kvG+82zyei1Mv0FE0hZJuxJuS3O3s+iTlRjCzew1M5sDXAyslJQYbMxsvZm1mVnbhAkT\nivh3Ksu1117rcxNk0NVXXw1EKURcdsyePRuAuXPnlrkk6dJv7iwzyzspjaRDkprNrFNSM8lppXNf\n64CkXcAVwOMFlzZlbr31VlasWFHuYrhBds0117BgwQIaGxvLXRQ3iObNm8dTTz3F6NE+x3ohSu3O\n+j4Qz7W5koS00pImSxoZlscAHwL2lrjfVKitrfWpNjNIkgeQjPIAUrhSg8g6YImkDqJpNJPSSs8E\n/kvSC8DPgC+b2Ysl7tc551wFKCkVvJkdBRYlrD+TVtrMNgNzStmPc865yuTDbZ1zzhXNg4hzzrmi\neRBxzjlXNA8izjnniuZBxDnnXNFUqXliJB0GXi13OQbBeOBIuQtRogvMbNBSCGSkbr1ee/F6rRiD\nWq/9qdggkhWS2s2srdzlcIPL6zWbvF4L591ZzjnniuZBxDnnXNE8iAy99eUugBsSXq/Z5PVaID8n\n4pxzrmjeEnHOOVc0DyLOOeeK5kHEOedc0TyIOOecK5oHEeecc0XzIOKcc65omQoikn7T6/4qSfeH\n5dsl3ZLwnAsl7crzej+VVHIKBEkLJT1Z6usUue8ZkrZJ6pb0uXKUoVhen4n7vlnSTkkvSnpW0txy\nlGMoSJosaZOkDkmvSPqqpPoBPO+vS9zvQkmX5XnszDHnkmUqiPTFzL5hZo+WuxxDTVLvKY+7gD8H\nvlyG4gyZKq7P/wE+bGaXAl8iIxfHSRLwXeAJM5sGTAcagHsG8PSSggiwEEgMIsNJUm25y1CMqgki\nkr4Y/xKXNE/SC5JeAD6Ts81ISd+W9LKk7wEjcx67Ovyif17SdyQ1hPX7Jd0d1r8oaUYBZfqCpOck\n7ZK0XpH3Sno+Z5tp8f1Q7p9J2i7pR5Kaw/qfSvqKpHbgL3L3YWa/NLPngJPFvG+Vqorr81kzeyPc\n/TkwucC3rlJdBRw3sw0AZnYa+CzwR5JG9W4RSHoytCDWASMl7ZC0MbRE94TllyU9LmlUeM5+SePD\nclt4ny8Ebgc+G17jioEUVtKDktolvSTp7rDuKklP5GyzJBx3/R1v94Zj4mMlvodlkbUgEh9MOyTt\nAP42z3YbgD8zs95dAX8C/NbMZgJ/A8wDCAfeWmCxmb0faAfuzHnekbD+QaCQLqP7zewDZnYJ0Rfc\nH5jZK8CvJLWGbVYDGyTVAV8DbjSzecA36fkrrd7M2szsvgL2X+m8Pvuuzz8GflhA+SrZbGB77goz\n+zXwv8DF+Z5kZmuAt82s1cxuDqvfBzwQ6v3XwB19PH8/8A3g78Jr/McAy/v5kO13DvBhSXOAfwdm\nSIrTsK8GvjmA4+2omb3fzL49wH1XlN5N5bR728ziDyuSVgE9+sAljQZGm9nTYdVjwEfD8gLg7wHM\nbKeknWH97wOzgGckAdQD23Je9rvh73bg+gLKe6WkvwJGAWOBl4AfAA8BqyXdCdwEfJDog3EJsDmU\noRbozHmtfylgv2nh9ZmHpCuJgsiHCihftXjNzJ4Jy99iaLpzPy7pNqLv0GZgVjjGHgM+JWkDMB+4\nBfgIfR9vqf7sZi2IDBUBm83sk3ke7w5/TzPA91TSucADQJuZvSbpi8C54eF/Jfrl/BNgu5kdlfQe\n4CUzm5/nJd8ayH4dkPL6DL96HwI+amZHB1K+FNgN3Ji7QlIT0AL8N9Ev/tyek3PJr3dCwPj+qZzX\n6Ov5fZI0laiF+gEze0PSwzmvt4Hoh8Nx4DtmdkpR5OjreEv1Zzdr3Vn9MrM3gTclxb/gbs55+Gng\nDwEkXUJ04ELU93y5pIvDY+dJml5iUeKD7kjoHz3zATKz48CPiLpTNoTVe4EJkuaHMtRJml1iGVKv\n2upTUgtRS2mFme0rscyVZCswSmHEnaKTzPcBD5vZb4H9QKukGklTiFpzsZOhezDWEr+vRPX/n2F5\nP6FLE7ghZ/tjQGMBZW0i+uL/laSJnG35YmYHgANE3VdxXQ/F8VYxqi6IBKuBr4d+duWsfxBokPQy\nUf/7dgAzOwysAv45dIlsAwZ8wjVYJOn1+AbMBP4B2EX0BfNcr+03Au8APw5lOEH0xXSvohPIOxjA\niBJJvxv2dyewNuy/qcCyV7qqqU/gC8A44IFwrqi9wHJXJIvSiS8HPiapA9hH9Gs+Hnn1DNHItN1E\nXZTP5zx9PbBT0sZwfy/wmVDvY4iOA4C7ga+G9+x0zvN/ACzv48T6ql51fRT4BbAH+KdQtlwbibrU\nXg7/22AcbxXLU8FXKEUjj843s7vKXRZXOq/P4aFotNWTYXBDucpwP/ALM/vHcpVhOPk5kQoUhgW+\nl2jYo0s5r8/qIWk7UVfXX5a7LMPFWyLOOeeKVq3nRJxzzg0CDyLOOeeK5kHEOedc0TyIOOecK5oH\nEeecc0X7f7sBl3A2GIgIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w_after = model_relu.get_weights()\n",
    "\n",
    "h1_w = w_after[0].flatten().reshape(-1,1)\n",
    "h2_w = w_after[2].flatten().reshape(-1,1)\n",
    "out_w = w_after[4].flatten().reshape(-1,1)\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.title(\"Weight matrices after model trained\")\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.title(\"Trained model Weights\")\n",
    "ax = sns.violinplot(y=h1_w,color='b')\n",
    "plt.xlabel('Hidden Layer 1')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.title(\"Trained model Weights\")\n",
    "ax = sns.violinplot(y=h2_w, color='r')\n",
    "plt.xlabel('Hidden Layer 2 ')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.title(\"Trained model Weights\")\n",
    "ax = sns.violinplot(y=out_w,color='y')\n",
    "plt.xlabel('Output Layer ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fMzDYyeGYAbp"
   },
   "source": [
    "MLP + ReLU + ADAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 978
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 69060,
     "status": "ok",
     "timestamp": 1568828602231,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "NUAQjLSgYBSF",
    "outputId": "b1fb9e9d-410a-42fa-de7e-dd646ba8d065"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_21\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_55 (Dense)             (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_56 (Dense)             (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_57 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 468,874\n",
      "Trainable params: 468,874\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 5s 83us/step - loss: 0.2348 - acc: 0.9308 - val_loss: 0.1139 - val_acc: 0.9659\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 3s 56us/step - loss: 0.0872 - acc: 0.9742 - val_loss: 0.0903 - val_acc: 0.9715\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0536 - acc: 0.9834 - val_loss: 0.0929 - val_acc: 0.9721\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.0350 - acc: 0.9894 - val_loss: 0.0695 - val_acc: 0.9774\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0261 - acc: 0.9915 - val_loss: 0.0939 - val_acc: 0.9721\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.0210 - acc: 0.9932 - val_loss: 0.0942 - val_acc: 0.9713\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0167 - acc: 0.9943 - val_loss: 0.0770 - val_acc: 0.9785\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.0124 - acc: 0.9958 - val_loss: 0.0707 - val_acc: 0.9796\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.0146 - acc: 0.9949 - val_loss: 0.0691 - val_acc: 0.9818\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.0136 - acc: 0.9955 - val_loss: 0.0813 - val_acc: 0.9794\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.0106 - acc: 0.9965 - val_loss: 0.0819 - val_acc: 0.9793\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0098 - acc: 0.9964 - val_loss: 0.0850 - val_acc: 0.9788\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.0091 - acc: 0.9970 - val_loss: 0.0929 - val_acc: 0.9770\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0105 - acc: 0.9966 - val_loss: 0.0802 - val_acc: 0.9819\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0085 - acc: 0.9973 - val_loss: 0.0950 - val_acc: 0.9804\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.0069 - acc: 0.9976 - val_loss: 0.0842 - val_acc: 0.9822\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.0073 - acc: 0.9976 - val_loss: 0.0844 - val_acc: 0.9813\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0062 - acc: 0.9979 - val_loss: 0.0947 - val_acc: 0.9803\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0085 - acc: 0.9973 - val_loss: 0.1131 - val_acc: 0.9781\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.0104 - acc: 0.9970 - val_loss: 0.0829 - val_acc: 0.9808\n"
     ]
    }
   ],
   "source": [
    "model_relu = Sequential()\n",
    "model_relu.add(Dense(512, activation='relu', input_shape=(input_dim,), kernel_initializer=RandomNormal(mean=0.0, stddev=0.062, seed=None)))\n",
    "model_relu.add(Dense(128, activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.125, seed=None)) )\n",
    "model_relu.add(Dense(output_dim, activation='softmax'))\n",
    "\n",
    "print(model_relu.summary())\n",
    "\n",
    "model_relu.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "history = model_relu.fit(X_train, y_train, batch_size=batch_size, epochs=n_epoch, verbose=1, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2310,
     "status": "ok",
     "timestamp": 1568828610503,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "7tdADiSvdqUC",
    "outputId": "26cdcebd-95d6-4b69-9aab-13141e526ce0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.08292084760254202\n",
      "Test accuracy: 0.9808\n"
     ]
    },
    {
     "data": {
      "image/png": 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rNmrkKoUD8R04oDp2rIsnNVX1lltUd+yIfTx+ZGZm6sGDqi+8oFq/vnu/Bg5U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\nQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score = model_relu.evaluate(X_test, y_test, verbose=0) \n",
    "print('Test score:', score[0]) \n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "fig,ax = plt.subplots(1,1)\n",
    "ax.set_xlabel('epoch') ; ax.set_ylabel('Categorical Crossentropy Loss')\n",
    "\n",
    "# list of epoch numbers\n",
    "x = list(range(1,n_epoch+1))\n",
    "\n",
    "vy = history.history['val_loss']\n",
    "ty = history.history['loss']\n",
    "plt_dynamic(x, vy, ty, ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PSjMizOqbWJF"
   },
   "source": [
    "Hyper-parameter tuning of Keras models using Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dzIEqpFYbYHr"
   },
   "outputs": [],
   "source": [
    "from keras.optimizers import Adam,RMSprop,SGD\n",
    "def best_hyperparameters(activ):\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Dense(512, activation=activ, input_shape=(input_dim,), kernel_initializer=RandomNormal(mean=0.0, stddev=0.062, seed=None)))\n",
    "    model.add(Dense(128, activation=activ, kernel_initializer=RandomNormal(mean=0.0, stddev=0.125, seed=None)) )\n",
    "    model.add(Dense(output_dim, activation='softmax'))\n",
    "\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 315132,
     "status": "ok",
     "timestamp": 1568829100240,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "STFWvPRBbbBP",
    "outputId": "1808ee65-9073-475f-a395-48be48a8ea18"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "activ = ['sigmoid','relu']\n",
    "\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "model = KerasClassifier(build_fn=best_hyperparameters, epochs=n_epoch, batch_size=batch_size, verbose=0)\n",
    "param_grid = dict(activ=activ)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid)\n",
    "grid_result = grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2089,
     "status": "ok",
     "timestamp": 1568829102340,
     "user": {
      "displayName": "Adarsh Biradar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mAm9HytxHuI173iprIcQ55s1_MvTZFZA4Z4KN-9Ag=s64",
      "userId": "17453293577466099752"
     },
     "user_tz": -330
    },
    "id": "edF3THGbbioT",
    "outputId": "cdb9f88b-6291-4f09-a82f-d44398301551"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.977750 using {'activ': 'relu'}\n",
      "0.975267 (0.001621) with: {'activ': 'sigmoid'}\n",
      "0.977750 (0.001246) with: {'activ': 'relu'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lpKj9zjMf-oE"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "MnistWithKeras.ipynb",
   "provenance": []
  },
  "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}