finger_network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing useful libraries\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import  scipy.misc\n",
    "import glob\n",
    "import os\n",
    "from sklearn import preprocessing\n",
    "import numpy as np\n",
    "from keras import layers\n",
    "from keras.layers import Input, Dense, Activation, ZeroPadding1D, BatchNormalization, Flatten, Conv1D\n",
    "from keras.layers import AveragePooling1D, MaxPooling1D, Dropout, GlobalMaxPooling1D, GlobalAveragePooling1D \n",
    "from keras.models import Model, Sequential\n",
    "from keras.preprocessing import image\n",
    "from keras.utils import layer_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "from keras.utils.np_utils import to_categorical\n",
    " \n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import plot_model\n",
    "import keras.backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "/Users/siddhantbansal/Desktop/Non_Diabetes/L25.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L250.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L251.png\n",
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      "/Users/siddhantbansal/Desktop/Non_Diabetes/L253.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L254.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L255.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L256.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L257.png\n",
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      "/Users/siddhantbansal/Desktop/Non_Diabetes/L26.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L27.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L28.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L29.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L3.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L30.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L31.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L32.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L33.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L34.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L35.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L36.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L37.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L38.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L39.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L4.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L40.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L41.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L42.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L43.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L44.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L45.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L46.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L47.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L48.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L49.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L5.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L50.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L51.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L52.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L53.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L54.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L55.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L56.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L57.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L58.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L59.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L6.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L60.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L61.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L62.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L63.png\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L64.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L65.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L66.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L67.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L68.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L69.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L7.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L70.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L71.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L72.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L73.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L74.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L75.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L76.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L77.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L78.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L79.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L8.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L80.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L81.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L82.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L83.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L84.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L85.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L86.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L87.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L88.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L89.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L9.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L90.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L91.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L92.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L93.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L94.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L95.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L96.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L97.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L98.png\n",
      "/Users/siddhantbansal/Desktop/Non_Diabetes/L99.png\n"
     ]
    }
   ],
   "source": [
    "# Loading the data\n",
    "train=np.empty(shape=(394,200,250))\n",
    "y=[]\n",
    "for filename in glob.iglob('/Users/siddhantbansal/Desktop/Diabetes/*.*'):\n",
    "    print(filename)\n",
    "    img=scipy.misc.imread(filename,mode='L')\n",
    "    img=scipy.misc.imresize(img,(200,250))\n",
    "    train[counter]=img\n",
    "    y.append(0)\n",
    "for filename in glob.iglob('/Users/siddhantbansal/Desktop/Non_Diabetes/*.*'):\n",
    "    print(filename)\n",
    "    img=scipy.misc.imread(filename,mode='L')\n",
    "    img=scipy.misc.imresize(img,(200,250))\n",
    "    train[counter]=img\n",
    "    y.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  ...\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]]\n",
      "\n",
      " [[1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  ...\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]\n",
      "  [1.         1.         1.         ... 1.         1.         1.        ]]\n",
      "\n",
      " [[0.95294118 0.95294118 0.95294118 ... 0.95294118 0.95294118 0.95294118]\n",
      "  [0.95294118 0.95294118 0.95294118 ... 0.95686275 0.96078431 0.96078431]\n",
      "  [0.94901961 0.95294118 0.95294118 ... 0.95686275 0.95686275 0.95686275]\n",
      "  ...\n",
      "  [0.93333333 0.92941176 0.92941176 ... 0.89411765 0.88627451 0.87843137]\n",
      "  [0.9372549  0.93333333 0.93333333 ... 0.90588235 0.90196078 0.89803922]\n",
      "  [0.9372549  0.94117647 0.94117647 ... 0.90196078 0.90588235 0.90980392]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.91372549 0.91372549 0.91372549 ... 0.91764706 0.91764706 0.91372549]\n",
      "  [0.91372549 0.91372549 0.91372549 ... 0.91764706 0.91764706 0.91372549]\n",
      "  [0.91372549 0.90980392 0.90980392 ... 0.91764706 0.91764706 0.91764706]\n",
      "  ...\n",
      "  [0.92941176 0.92156863 0.92156863 ... 0.93333333 0.93333333 0.93333333]\n",
      "  [0.92941176 0.92156863 0.92156863 ... 0.93333333 0.93333333 0.93333333]\n",
      "  [0.9254902  0.92156863 0.92156863 ... 0.93333333 0.93333333 0.93333333]]\n",
      "\n",
      " [[0.90980392 0.91372549 0.90980392 ... 0.92156863 0.92156863 0.92156863]\n",
      "  [0.90980392 0.90980392 0.90980392 ... 0.92156863 0.92156863 0.92156863]\n",
      "  [0.90980392 0.90588235 0.90588235 ... 0.92156863 0.92156863 0.92156863]\n",
      "  ...\n",
      "  [0.91372549 0.91372549 0.91764706 ... 0.9254902  0.9254902  0.92941176]\n",
      "  [0.91372549 0.91372549 0.91764706 ... 0.9254902  0.9254902  0.92941176]\n",
      "  [0.91764706 0.92156863 0.92156863 ... 0.9254902  0.9254902  0.9254902 ]]\n",
      "\n",
      " [[0.91764706 0.92156863 0.92156863 ... 0.92156863 0.92156863 0.92156863]\n",
      "  [0.92156863 0.92156863 0.9254902  ... 0.92156863 0.92156863 0.92156863]\n",
      "  [0.9254902  0.92941176 0.92941176 ... 0.91764706 0.91372549 0.92156863]\n",
      "  ...\n",
      "  [0.92156863 0.92156863 0.92156863 ... 0.92941176 0.92941176 0.9254902 ]\n",
      "  [0.91764706 0.91764706 0.91764706 ... 0.92941176 0.92941176 0.92941176]\n",
      "  [0.91372549 0.90588235 0.90588235 ... 0.92941176 0.92941176 0.92941176]]]\n",
      "394\n"
     ]
    }
   ],
   "source": [
    "train = train/255.0\n",
    "print(train)\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(394, 200, 250)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "394"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(train)\n",
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.array(y)\n",
    "y=y.reshape(394,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Perfect, now having train and test we also need to shuffle the data as a good practice for evaluation and fitting \n",
    "from random import shuffle\n",
    "N=train.shape[0]\n",
    "ind_list = [i for i in range(N)]\n",
    "shuffle(ind_list)\n",
    "\n",
    "train_new  = train[ind_list, :,:]\n",
    "target_new = y[ind_list,]\n",
    "train=train_new\n",
    "y=target_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []\n",
    "\n",
    "for i in range(50):\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.2, random_state=i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "def DocumentModel(input_shape):\n",
    "    \"\"\"\n",
    "    Implementation of the DocumentModel.\n",
    "    \n",
    "    Arguments:\n",
    "    input_shape -- shape of the images of the dataset\n",
    "    Returns:\n",
    "    model -- a Model() instance in Keras\n",
    "    \"\"\"\n",
    "    \n",
    " \n",
    "   \n",
    "    # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!\n",
    "    X_input = Input(input_shape)\n",
    "    X=Sequential()\n",
    "\n",
    "   \n",
    "   \n",
    " \n",
    "    # CONV -> BN -> RELU Block applied to X\n",
    "    X = Conv1D(32, 7, strides = 1, name = 'conv1')(X_input)\n",
    "    X = BatchNormalization(axis = 2, name = 'bn1')(X)\n",
    "    X = Activation('relu')(X)\n",
    "    X = MaxPooling1D(2, name='max_pool1')(X)\n",
    "    \n",
    " \n",
    "   \n",
    "    # CONV -> BN -> RELU Block applied to X\n",
    "    X = Conv1D(64, 5, strides = 1, name = 'conv2')(X)\n",
    "    X = BatchNormalization(axis = 2, name = 'bn2')(X)\n",
    "    X = Activation('relu')(X)\n",
    "    X = MaxPooling1D(2, name='max_pool2')(X)\n",
    "    \n",
    " \n",
    " \n",
    "    # CONV -> BN -> RELU Block applied to X\n",
    "    X = Conv1D(128, 3, strides = 1, name = 'conv3')(X)\n",
    "    X = BatchNormalization(axis = 2, name = 'bn3')(X)\n",
    "    X = Activation('relu')(X)\n",
    "    X = MaxPooling1D(2, name='max_pool3')(X)\n",
    "    \n",
    " \n",
    "    # CONV -> BN -> RELU Block applied to X\n",
    "    X = Conv1D(64, 1, strides = 1, name = 'conv4')(X)\n",
    "    X = BatchNormalization(axis = 2, name = 'bn4')(X)\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "    #layer group5 4*4*32\n",
    "  \n",
    " \n",
    "    X = Conv1D(32, 3, strides = 1, name = 'conv5')(X)\n",
    "    X = BatchNormalization(axis = 2, name = 'bn5')(X)\n",
    "    X = Activation('relu')(X)\n",
    "    X = MaxPooling1D(2, name='max_pool5')(X)\n",
    "    \n",
    "    # FLATTEN X (means convert it to a vector) + FULLYCONNECTED\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(128, activation='sigmoid', name='fc1')(X)\n",
    "    X = Dense(32, activation='sigmoid', name='fc2')(X)\n",
    "    X = Dense(2, activation='sigmoid', name='fc3')(X)\n",
    "    \n",
    "    # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.\n",
    "    model = Model(inputs = X_input, outputs = X, name='DocumentModel')\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return model\n",
    "\n",
    "happyModel = DocumentModel(((200,250)))\n",
    "happyModel.compile(optimizer = \"adam\", loss = \"categorical_crossentropy\", metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "315/315 [==============================] - 7s 21ms/step - loss: 0.5890 - acc: 0.6476\n",
      "Epoch 2/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.4174 - acc: 0.7746\n",
      "Epoch 3/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.2995 - acc: 0.9270\n",
      "Epoch 4/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.2212 - acc: 0.9619\n",
      "Epoch 5/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.1767 - acc: 0.9746\n",
      "Epoch 6/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.1581 - acc: 0.9683\n",
      "Epoch 7/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.1210 - acc: 0.9810\n",
      "Epoch 8/20\n",
      "315/315 [==============================] - 2s 6ms/step - loss: 0.1049 - acc: 0.9810\n",
      "Epoch 9/20\n",
      "315/315 [==============================] - 2s 6ms/step - loss: 0.0968 - acc: 0.9778\n",
      "Epoch 10/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.0932 - acc: 0.9746\n",
      "Epoch 11/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.1062 - acc: 0.9683\n",
      "Epoch 12/20\n",
      "315/315 [==============================] - 2s 6ms/step - loss: 0.1367 - acc: 0.9587\n",
      "Epoch 13/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.1012 - acc: 0.9746\n",
      "Epoch 14/20\n",
      "315/315 [==============================] - 2s 6ms/step - loss: 0.0905 - acc: 0.9778\n",
      "Epoch 15/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.0789 - acc: 0.9841\n",
      "Epoch 16/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.0820 - acc: 0.9778\n",
      "Epoch 17/20\n",
      "315/315 [==============================] - 2s 6ms/step - loss: 0.0594 - acc: 0.9873\n",
      "Epoch 18/20\n",
      "315/315 [==============================] - 2s 5ms/step - loss: 0.0422 - acc: 0.9968\n",
      "Epoch 19/20\n",
      "315/315 [==============================] - 2s 6ms/step - loss: 0.0413 - acc: 0.9968\n",
      "Epoch 20/20\n",
      "315/315 [==============================] - 2s 6ms/step - loss: 0.0359 - acc: 0.9968\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x133e54278>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_labels = to_categorical(y_train, num_classes=None)\n",
    "\n",
    "happyModel.fit(x = X_train, y = categorical_labels, epochs = 20, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy  0.7848101265822784\n"
     ]
    }
   ],
   "source": [
    "pred=happyModel.predict(X_test)\n",
    " \n",
    "y_classes = pred.argmax(axis=-1)\n",
    "from sklearn.metrics import   accuracy_score\n",
    "print('accuracy ' , accuracy_score(y_classes,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "160/160 [==============================] - 4s 25ms/step - loss: 0.6720 - acc: 0.6375\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 2s 10ms/step - loss: 0.5810 - acc: 0.9375\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 9ms/step - loss: 0.4604 - acc: 0.9750\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 9ms/step - loss: 0.3405 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.2541 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1943 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1562 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1306 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 9ms/step - loss: 0.1215 - acc: 0.9938\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 9ms/step - loss: 0.1059 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0923 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0817 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0741 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0712 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0633 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0585 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0870 - acc: 0.9813\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0765 - acc: 0.9875\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1070 - acc: 0.9750\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0869 - acc: 0.9813\n",
      "accuracy  0.725\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 3s 18ms/step - loss: 0.6935 - acc: 0.5000\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.5733 - acc: 0.9313\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.4240 - acc: 0.9875\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 9ms/step - loss: 0.3038 - acc: 0.9875\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1885 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1455 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1073 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 9ms/step - loss: 0.0833 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0940 - acc: 0.9875\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0623 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0587 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0563 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0506 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0455 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0418 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0384 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0361 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0359 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0327 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0311 - acc: 1.0000\n",
      "accuracy  0.6\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 3s 19ms/step - loss: 0.6530 - acc: 0.7000\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.5045 - acc: 0.9562\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.3685 - acc: 0.9500\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.2475 - acc: 0.9938\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1819 - acc: 0.9875\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1245 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0966 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0795 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0668 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0591 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0545 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0488 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0450 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0404 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0373 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0356 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0332 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0325 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0292 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0342 - acc: 0.9938\n",
      "accuracy  0.875\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 3s 20ms/step - loss: 0.6915 - acc: 0.5000\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.5815 - acc: 0.9000\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.4575 - acc: 0.9938\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.3305 - acc: 0.9875\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.2280 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1686 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1241 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0986 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0828 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0751 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0629 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0605 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0525 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0502 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0447 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0435 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0601 - acc: 0.9938\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0371 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0371 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0343 - acc: 1.0000\n",
      "accuracy  0.55\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 3s 21ms/step - loss: 0.7192 - acc: 0.4937\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.5839 - acc: 0.7625\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.4648 - acc: 0.9938\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.3553 - acc: 0.9750\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.2603 - acc: 1.0000\n",
      "Epoch 6/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1973 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1567 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1319 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1136 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1003 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0910 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0854 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0851 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0775 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0724 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0733 - acc: 0.9938\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0634 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0606 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0544 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0507 - acc: 1.0000\n",
      "accuracy  0.55\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 4s 24ms/step - loss: 0.7673 - acc: 0.4937\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.5893 - acc: 0.6750\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.4733 - acc: 0.9688\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.3813 - acc: 0.9562\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.2658 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1962 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1527 - acc: 0.9938\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1161 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0928 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0831 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0685 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0628 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0567 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0521 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0479 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0461 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0426 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0403 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0390 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0357 - acc: 1.0000\n",
      "accuracy  0.75\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 4s 23ms/step - loss: 0.6594 - acc: 0.5750\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.5142 - acc: 0.9688\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.3551 - acc: 0.9875\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.2430 - acc: 0.9938\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1640 - acc: 0.9938\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1193 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0923 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0762 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0638 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0569 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0484 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0442 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0409 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0379 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0353 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0325 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0307 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0292 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0273 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0262 - acc: 1.0000\n",
      "accuracy  0.775\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 4s 25ms/step - loss: 0.6579 - acc: 0.6312\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.5199 - acc: 0.9750\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.3740 - acc: 0.9875\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.2504 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1804 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1359 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1117 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0930 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0804 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0714 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0639 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0583 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0537 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0493 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0460 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0431 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0404 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0384 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0362 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0340 - acc: 1.0000\n",
      "accuracy  0.825\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 4s 26ms/step - loss: 0.6981 - acc: 0.5812\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.5160 - acc: 0.9688\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.3852 - acc: 0.9688\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.2687 - acc: 0.9875\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1915 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1515 - acc: 0.9875\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1113 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0847 - acc: 1.0000\n",
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     ]
    },
    {
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     "text": [
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0668 - acc: 1.0000\n",
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      "accuracy  0.65\n"
     ]
    },
    {
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     "text": [
      "Epoch 1/20\n",
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      "accuracy  0.525\n",
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      "accuracy  0.875\n",
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      "accuracy  0.725\n",
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      "accuracy  0.575\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1606 - acc: 1.0000\n",
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      "accuracy  0.675\n",
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      "accuracy  0.75\n",
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      "accuracy  0.975\n",
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      "accuracy  0.75\n",
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      "accuracy  0.725\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0172 - acc: 1.0000\n",
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      "accuracy  0.85\n",
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      "accuracy  0.7\n",
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      "accuracy  0.525\n",
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      "accuracy  0.925\n",
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      "accuracy  0.9\n"
     ]
    },
    {
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     "text": [
      "Epoch 1/20\n",
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      "accuracy  0.875\n",
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      "accuracy  0.9\n",
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      "accuracy  0.6\n",
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      "accuracy  0.525\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1525 - acc: 1.0000\n",
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      "accuracy  0.875\n",
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      "160/160 [==============================] - 1s 8ms/step - loss: 0.0472 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0420 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0393 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0383 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0345 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0326 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0307 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0294 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0276 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0263 - acc: 1.0000\n",
      "accuracy  0.9\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 15s 91ms/step - loss: 0.6764 - acc: 0.5187\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.5980 - acc: 0.8625\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.4973 - acc: 0.9562\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.3887 - acc: 0.9875\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.2981 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.2400 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.2021 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1720 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1480 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1366 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1212 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1102 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1026 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0943 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0894 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0836 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0779 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0732 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0689 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0649 - acc: 1.0000\n",
      "accuracy  0.575\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 15s 97ms/step - loss: 0.6943 - acc: 0.4937\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.5879 - acc: 0.9062\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.4654 - acc: 0.9813\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.3398 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.2513 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1861 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1659 - acc: 0.9938\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1241 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1127 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0952 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0834 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0746 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0718 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0617 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0571 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0534 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0501 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0471 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0443 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0420 - acc: 1.0000\n",
      "accuracy  0.55\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 15s 96ms/step - loss: 0.6382 - acc: 0.7875\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.4791 - acc: 0.9625\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.3229 - acc: 0.9813\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.2025 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1371 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0990 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0754 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0665 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0537 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0557 - acc: 0.9938\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0425 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0405 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0421 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0351 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0329 - acc: 1.0000\n",
      "Epoch 16/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0286 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0258 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0337 - acc: 0.9938\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0277 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.0261 - acc: 1.0000\n",
      "accuracy  0.525\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 17s 107ms/step - loss: 0.6566 - acc: 0.7438\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.5173 - acc: 0.9500\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.3762 - acc: 0.9813\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 2s 9ms/step - loss: 0.2602 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1856 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1422 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 7ms/step - loss: 0.1115 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1082 - acc: 0.9938\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0868 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0773 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0753 - acc: 0.9938\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0601 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0646 - acc: 0.9938\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0525 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0506 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0527 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0637 - acc: 0.9875\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0424 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0428 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0376 - acc: 1.0000\n",
      "accuracy  0.525\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 16s 101ms/step - loss: 0.6370 - acc: 0.7188\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.4789 - acc: 0.9437\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.3253 - acc: 0.9875\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.2149 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1486 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1057 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0904 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0756 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0596 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0543 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0536 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0758 - acc: 0.9813\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0474 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0944 - acc: 0.9688\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0713 - acc: 0.9875\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0567 - acc: 0.9875\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0342 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0334 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0433 - acc: 0.9938\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0299 - acc: 1.0000\n",
      "accuracy  0.525\n",
      "Epoch 1/20\n",
      "160/160 [==============================] - 17s 106ms/step - loss: 0.6524 - acc: 0.6937\n",
      "Epoch 2/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.5229 - acc: 0.9500\n",
      "Epoch 3/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.4028 - acc: 0.9625\n",
      "Epoch 4/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.2777 - acc: 0.9938\n",
      "Epoch 5/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1912 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.1375 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0976 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0772 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0638 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0551 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0489 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "160/160 [==============================] - 1s 9ms/step - loss: 0.0454 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0397 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0382 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0381 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0344 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0312 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0295 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0271 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "160/160 [==============================] - 1s 8ms/step - loss: 0.0259 - acc: 1.0000\n",
      "accuracy  0.575\n"
     ]
    }
   ],
   "source": [
    "scores=[]\n",
    "for i in range(50):\n",
    "    \n",
    "    from sklearn.model_selection import train_test_split\n",
    "    X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.2, random_state=42)\n",
    "    happyModel = DocumentModel(((200,250)))\n",
    "    happyModel.compile(optimizer = \"adam\", loss = \"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
    "    from keras.utils.np_utils import to_categorical\n",
    "    categorical_labels = to_categorical(y_train, num_classes=None)\n",
    "    happyModel.fit(x = X_train, y = categorical_labels, epochs = 20, batch_size = 32)\n",
    "    pred=happyModel.predict(X_test)\n",
    "    y_classes = pred.argmax(axis=-1)\n",
    "    from sklearn.metrics import   accuracy_score\n",
    "    scores.append(accuracy_score(y_classes,y_test))\n",
    "    print('accuracy ' , accuracy_score(y_classes,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.725,\n",
       " 0.6,\n",
       " 0.875,\n",
       " 0.55,\n",
       " 0.55,\n",
       " 0.75,\n",
       " 0.775,\n",
       " 0.825,\n",
       " 0.85,\n",
       " 0.85,\n",
       " 0.525,\n",
       " 0.525,\n",
       " 0.725,\n",
       " 0.675,\n",
       " 0.7,\n",
       " 0.525,\n",
       " 0.65,\n",
       " 0.525,\n",
       " 0.875,\n",
       " 0.725,\n",
       " 0.575,\n",
       " 0.675,\n",
       " 0.75,\n",
       " 0.825,\n",
       " 0.7,\n",
       " 0.525,\n",
       " 0.975,\n",
       " 0.75,\n",
       " 0.725,\n",
       " 0.85,\n",
       " 0.7,\n",
       " 0.525,\n",
       " 0.925,\n",
       " 0.9,\n",
       " 0.875,\n",
       " 0.9,\n",
       " 0.6,\n",
       " 0.525,\n",
       " 0.7,\n",
       " 0.875,\n",
       " 0.625,\n",
       " 0.525,\n",
       " 0.525,\n",
       " 0.9,\n",
       " 0.575,\n",
       " 0.55,\n",
       " 0.525,\n",
       " 0.525,\n",
       " 0.525,\n",
       " 0.575]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  }
 ],
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