Best_on_Ann.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Epileptic seizure detection from EEG signals using Deep Learning\n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.utils import np_utils\n",
    "from keras.layers import Dense, Activation\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>...</th>\n",
       "      <th>X170</th>\n",
       "      <th>X171</th>\n",
       "      <th>X172</th>\n",
       "      <th>X173</th>\n",
       "      <th>X174</th>\n",
       "      <th>X175</th>\n",
       "      <th>X176</th>\n",
       "      <th>X177</th>\n",
       "      <th>X178</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X21.V1.791</td>\n",
       "      <td>135</td>\n",
       "      <td>190</td>\n",
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       "      <td>223</td>\n",
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       "      <td>-127</td>\n",
       "      <td>-116</td>\n",
       "      <td>-83</td>\n",
       "      <td>-51</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>X15.V1.924</td>\n",
       "      <td>386</td>\n",
       "      <td>382</td>\n",
       "      <td>356</td>\n",
       "      <td>331</td>\n",
       "      <td>320</td>\n",
       "      <td>315</td>\n",
       "      <td>307</td>\n",
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       "      <td>...</td>\n",
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       "      <td>146</td>\n",
       "      <td>152</td>\n",
       "      <td>157</td>\n",
       "      <td>156</td>\n",
       "      <td>154</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-12</td>\n",
       "      <td>-30</td>\n",
       "      <td>-35</td>\n",
       "      <td>-35</td>\n",
       "      <td>-36</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>X16.V1.60</td>\n",
       "      <td>-105</td>\n",
       "      <td>-101</td>\n",
       "      <td>-96</td>\n",
       "      <td>-92</td>\n",
       "      <td>-89</td>\n",
       "      <td>-95</td>\n",
       "      <td>-102</td>\n",
       "      <td>-100</td>\n",
       "      <td>-87</td>\n",
       "      <td>...</td>\n",
       "      <td>-82</td>\n",
       "      <td>-81</td>\n",
       "      <td>-80</td>\n",
       "      <td>-77</td>\n",
       "      <td>-85</td>\n",
       "      <td>-77</td>\n",
       "      <td>-72</td>\n",
       "      <td>-69</td>\n",
       "      <td>-65</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>X20.V1.54</td>\n",
       "      <td>-9</td>\n",
       "      <td>-65</td>\n",
       "      <td>-98</td>\n",
       "      <td>-102</td>\n",
       "      <td>-78</td>\n",
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       "      <td>-16</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>-41</td>\n",
       "      <td>-65</td>\n",
       "      <td>-83</td>\n",
       "      <td>-89</td>\n",
       "      <td>-73</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 180 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   X1   X2   X3   X4   X5   X6   X7   X8   X9 ...  X170  X171  \\\n",
       "0  X21.V1.791  135  190  229  223  192  125   55   -9  -33 ...   -17   -15   \n",
       "1  X15.V1.924  386  382  356  331  320  315  307  272  244 ...   164   150   \n",
       "2     X8.V1.1  -32  -39  -47  -37  -32  -36  -57  -73  -85 ...    57    64   \n",
       "3   X16.V1.60 -105 -101  -96  -92  -89  -95 -102 -100  -87 ...   -82   -81   \n",
       "4   X20.V1.54   -9  -65  -98 -102  -78  -48  -16    0  -21 ...     4     2   \n",
       "\n",
       "   X172  X173  X174  X175  X176  X177  X178  y  \n",
       "0   -31   -77  -103  -127  -116   -83   -51  4  \n",
       "1   146   152   157   156   154   143   129  1  \n",
       "2    48    19   -12   -30   -35   -35   -36  5  \n",
       "3   -80   -77   -85   -77   -72   -69   -65  5  \n",
       "4   -12   -32   -41   -65   -83   -89   -73  5  \n",
       "\n",
       "[5 rows x 180 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=X[:,1:-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "y=np.array(df['y'])\n",
    "Y=np_utils.to_categorical(y)\n",
    "Y.shape\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.20, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11500, 178) (11500,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intuition of the Epileptical Signal In the Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(X[1,:],label='1')\n",
    "plt.plot(X[7,:],label='2')\n",
    "plt.plot(X[12,:],label='3')\n",
    "plt.plot(X[0,:],label='4')\n",
    "plt.plot(X[2,:],label='5')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Artifitial Neural Net Model For the Classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 256)               11776     \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 5)                 45        \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 5)                 0         \n",
      "=================================================================\n",
      "Total params: 74,613\n",
      "Trainable params: 74,613\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "\n",
    "\n",
    "model.add(Dense(256,input_shape=(45,)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(128))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(128))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(64))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(32))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(32))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(32))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(16))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(16))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(8))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(5))\n",
    "model.add(Activation('softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9200 samples, validate on 2300 samples\n",
      "Epoch 1/100\n",
      " - 3s - loss: 0.3875 - acc: 0.8335 - val_loss: 0.3735 - val_acc: 0.8323\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.3299 - acc: 0.8489 - val_loss: 0.3124 - val_acc: 0.8479\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.2606 - acc: 0.8709 - val_loss: 0.2627 - val_acc: 0.8627\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.2306 - acc: 0.8812 - val_loss: 0.2530 - val_acc: 0.8630\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.2168 - acc: 0.8876 - val_loss: 0.2731 - val_acc: 0.8650\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.2080 - acc: 0.8916 - val_loss: 0.2851 - val_acc: 0.8590\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.1951 - acc: 0.8980 - val_loss: 0.2661 - val_acc: 0.8690\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.1829 - acc: 0.9039 - val_loss: 0.2904 - val_acc: 0.8700\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.1746 - acc: 0.9083 - val_loss: 0.3244 - val_acc: 0.8618\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.1667 - acc: 0.9118 - val_loss: 0.2947 - val_acc: 0.8710\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.1598 - acc: 0.9148 - val_loss: 0.2985 - val_acc: 0.8739\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.1506 - acc: 0.9209 - val_loss: 0.3013 - val_acc: 0.8757\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.1489 - acc: 0.9215 - val_loss: 0.3144 - val_acc: 0.8683\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.1365 - acc: 0.9277 - val_loss: 0.3396 - val_acc: 0.8780\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.1352 - acc: 0.9324 - val_loss: 0.3179 - val_acc: 0.8791\n",
      "Epoch 16/100\n",
      " - 2s - loss: 0.1302 - acc: 0.9354 - val_loss: 0.3204 - val_acc: 0.8783\n",
      "Epoch 17/100\n",
      " - 2s - loss: 0.1256 - acc: 0.9400 - val_loss: 0.3200 - val_acc: 0.8852\n",
      "Epoch 18/100\n",
      " - 2s - loss: 0.1168 - acc: 0.9458 - val_loss: 0.3572 - val_acc: 0.8842\n",
      "Epoch 19/100\n",
      " - 2s - loss: 0.1112 - acc: 0.9510 - val_loss: 0.3759 - val_acc: 0.8844\n",
      "Epoch 20/100\n",
      " - 2s - loss: 0.1045 - acc: 0.9536 - val_loss: 0.3709 - val_acc: 0.8795\n",
      "Epoch 21/100\n",
      " - 2s - loss: 0.1005 - acc: 0.9568 - val_loss: 0.4078 - val_acc: 0.8811\n",
      "Epoch 22/100\n",
      " - 2s - loss: 0.0928 - acc: 0.9613 - val_loss: 0.3339 - val_acc: 0.8823\n",
      "Epoch 23/100\n",
      " - 2s - loss: 0.0851 - acc: 0.9651 - val_loss: 0.3883 - val_acc: 0.8864\n",
      "Epoch 24/100\n",
      " - 2s - loss: 0.0805 - acc: 0.9688 - val_loss: 0.3972 - val_acc: 0.8880\n",
      "Epoch 25/100\n",
      " - 2s - loss: 0.0714 - acc: 0.9717 - val_loss: 0.3806 - val_acc: 0.8919\n",
      "Epoch 26/100\n",
      " - 2s - loss: 0.0760 - acc: 0.9709 - val_loss: 0.4271 - val_acc: 0.8783\n",
      "Epoch 27/100\n",
      " - 2s - loss: 0.0624 - acc: 0.9762 - val_loss: 0.4352 - val_acc: 0.8881\n",
      "Epoch 28/100\n",
      " - 2s - loss: 0.0578 - acc: 0.9768 - val_loss: 0.4296 - val_acc: 0.8871\n",
      "Epoch 29/100\n",
      " - 2s - loss: 0.0622 - acc: 0.9763 - val_loss: 0.3884 - val_acc: 0.8874\n",
      "Epoch 30/100\n",
      " - 2s - loss: 0.0529 - acc: 0.9799 - val_loss: 0.4520 - val_acc: 0.8896\n",
      "Epoch 31/100\n",
      " - 2s - loss: 0.0608 - acc: 0.9791 - val_loss: 0.4126 - val_acc: 0.8906\n",
      "Epoch 32/100\n",
      " - 2s - loss: 0.0524 - acc: 0.9817 - val_loss: 0.4390 - val_acc: 0.8873\n",
      "Epoch 33/100\n",
      " - 2s - loss: 0.0469 - acc: 0.9834 - val_loss: 0.4449 - val_acc: 0.8881\n",
      "Epoch 34/100\n",
      " - 2s - loss: 0.0438 - acc: 0.9851 - val_loss: 0.4458 - val_acc: 0.8892\n",
      "Epoch 35/100\n",
      " - 2s - loss: 0.0398 - acc: 0.9863 - val_loss: 0.4552 - val_acc: 0.8875\n",
      "Epoch 36/100\n",
      " - 2s - loss: 0.0486 - acc: 0.9830 - val_loss: 0.4358 - val_acc: 0.8869\n",
      "Epoch 37/100\n",
      " - 2s - loss: 0.0414 - acc: 0.9858 - val_loss: 0.3994 - val_acc: 0.8870\n",
      "Epoch 38/100\n",
      " - 2s - loss: 0.0356 - acc: 0.9884 - val_loss: 0.4836 - val_acc: 0.8885\n",
      "Epoch 39/100\n",
      " - 2s - loss: 0.0405 - acc: 0.9860 - val_loss: 0.4080 - val_acc: 0.8893\n",
      "Epoch 40/100\n",
      " - 2s - loss: 0.0318 - acc: 0.9892 - val_loss: 0.5195 - val_acc: 0.8918\n",
      "Epoch 41/100\n",
      " - 2s - loss: 0.0403 - acc: 0.9862 - val_loss: 0.4485 - val_acc: 0.8890\n",
      "Epoch 42/100\n",
      " - 2s - loss: 0.0300 - acc: 0.9899 - val_loss: 0.4879 - val_acc: 0.8860\n",
      "Epoch 43/100\n",
      " - 2s - loss: 0.0286 - acc: 0.9907 - val_loss: 0.4912 - val_acc: 0.8892\n",
      "Epoch 44/100\n",
      " - 2s - loss: 0.0266 - acc: 0.9913 - val_loss: 0.4913 - val_acc: 0.8893\n",
      "Epoch 45/100\n",
      " - 2s - loss: 0.0322 - acc: 0.9895 - val_loss: 0.4841 - val_acc: 0.8885\n",
      "Epoch 46/100\n",
      " - 2s - loss: 0.0287 - acc: 0.9902 - val_loss: 0.4668 - val_acc: 0.8927\n",
      "Epoch 47/100\n",
      " - 2s - loss: 0.0289 - acc: 0.9905 - val_loss: 0.4555 - val_acc: 0.8836\n",
      "Epoch 48/100\n",
      " - 2s - loss: 0.0329 - acc: 0.9893 - val_loss: 0.4049 - val_acc: 0.8894\n",
      "Epoch 49/100\n",
      " - 2s - loss: 0.0282 - acc: 0.9905 - val_loss: 0.4844 - val_acc: 0.8877\n",
      "Epoch 50/100\n",
      " - 2s - loss: 0.0194 - acc: 0.9940 - val_loss: 0.5461 - val_acc: 0.8906\n",
      "Epoch 51/100\n",
      " - 2s - loss: 0.0253 - acc: 0.9921 - val_loss: 0.6216 - val_acc: 0.8850\n",
      "Epoch 52/100\n",
      " - 2s - loss: 0.0392 - acc: 0.9881 - val_loss: 0.5152 - val_acc: 0.8922\n",
      "Epoch 53/100\n",
      " - 2s - loss: 0.0318 - acc: 0.9902 - val_loss: 0.4435 - val_acc: 0.8929\n",
      "Epoch 54/100\n",
      " - 2s - loss: 0.0270 - acc: 0.9915 - val_loss: 0.3915 - val_acc: 0.8914\n",
      "Epoch 55/100\n",
      " - 2s - loss: 0.0193 - acc: 0.9937 - val_loss: 0.4941 - val_acc: 0.8908\n",
      "Epoch 56/100\n",
      " - 2s - loss: 0.0176 - acc: 0.9943 - val_loss: 0.5435 - val_acc: 0.8946\n",
      "Epoch 57/100\n",
      " - 2s - loss: 0.0263 - acc: 0.9917 - val_loss: 0.4733 - val_acc: 0.8900\n",
      "Epoch 58/100\n",
      " - 2s - loss: 0.0242 - acc: 0.9914 - val_loss: 0.5015 - val_acc: 0.8928\n",
      "Epoch 59/100\n",
      " - 2s - loss: 0.0167 - acc: 0.9951 - val_loss: 0.5649 - val_acc: 0.8932\n",
      "Epoch 60/100\n",
      " - 2s - loss: 0.0350 - acc: 0.9891 - val_loss: 0.4666 - val_acc: 0.8914\n",
      "Epoch 61/100\n",
      " - 2s - loss: 0.0204 - acc: 0.9937 - val_loss: 0.6010 - val_acc: 0.8881\n",
      "Epoch 62/100\n",
      " - 2s - loss: 0.0218 - acc: 0.9928 - val_loss: 0.5429 - val_acc: 0.8901\n",
      "Epoch 63/100\n",
      " - 2s - loss: 0.0174 - acc: 0.9949 - val_loss: 0.5062 - val_acc: 0.8905\n",
      "Epoch 64/100\n",
      " - 2s - loss: 0.0195 - acc: 0.9936 - val_loss: 0.5727 - val_acc: 0.8917\n",
      "Epoch 65/100\n",
      " - 2s - loss: 0.0155 - acc: 0.9950 - val_loss: 0.5617 - val_acc: 0.8913\n",
      "Epoch 66/100\n",
      " - 2s - loss: 0.0216 - acc: 0.9931 - val_loss: 0.5122 - val_acc: 0.8884\n",
      "Epoch 67/100\n",
      " - 2s - loss: 0.0200 - acc: 0.9933 - val_loss: 0.4625 - val_acc: 0.8912\n",
      "Epoch 68/100\n",
      " - 2s - loss: 0.0215 - acc: 0.9930 - val_loss: 0.4952 - val_acc: 0.8897\n",
      "Epoch 69/100\n",
      " - 2s - loss: 0.0198 - acc: 0.9940 - val_loss: 0.5357 - val_acc: 0.8923\n",
      "Epoch 70/100\n",
      " - 2s - loss: 0.0178 - acc: 0.9942 - val_loss: 0.5536 - val_acc: 0.8885\n",
      "Epoch 71/100\n",
      " - 2s - loss: 0.0155 - acc: 0.9953 - val_loss: 0.5428 - val_acc: 0.8857\n",
      "Epoch 72/100\n",
      " - 2s - loss: 0.0234 - acc: 0.9926 - val_loss: 0.5206 - val_acc: 0.8954\n",
      "Epoch 73/100\n",
      " - 2s - loss: 0.0149 - acc: 0.9956 - val_loss: 0.6341 - val_acc: 0.8929\n",
      "Epoch 74/100\n",
      " - 2s - loss: 0.0217 - acc: 0.9929 - val_loss: 0.5183 - val_acc: 0.8928\n",
      "Epoch 75/100\n",
      " - 2s - loss: 0.0135 - acc: 0.9954 - val_loss: 0.6802 - val_acc: 0.8908\n",
      "Epoch 76/100\n",
      " - 2s - loss: 0.0132 - acc: 0.9954 - val_loss: 0.6668 - val_acc: 0.8907\n",
      "Epoch 77/100\n",
      " - 2s - loss: 0.0161 - acc: 0.9950 - val_loss: 0.5509 - val_acc: 0.8924\n",
      "Epoch 78/100\n",
      " - 2s - loss: 0.0166 - acc: 0.9950 - val_loss: 0.6150 - val_acc: 0.8927\n",
      "Epoch 79/100\n",
      " - 2s - loss: 0.0150 - acc: 0.9953 - val_loss: 0.5993 - val_acc: 0.8932\n",
      "Epoch 80/100\n",
      " - 2s - loss: 0.0169 - acc: 0.9945 - val_loss: 0.5711 - val_acc: 0.8932\n",
      "Epoch 81/100\n",
      " - 2s - loss: 0.0139 - acc: 0.9960 - val_loss: 0.6038 - val_acc: 0.8917\n",
      "Epoch 82/100\n",
      " - 2s - loss: 0.0186 - acc: 0.9948 - val_loss: 0.5279 - val_acc: 0.8853\n",
      "Epoch 83/100\n",
      " - 2s - loss: 0.0185 - acc: 0.9944 - val_loss: 0.6989 - val_acc: 0.8931\n",
      "Epoch 84/100\n",
      " - 2s - loss: 0.0170 - acc: 0.9953 - val_loss: 0.5401 - val_acc: 0.8935\n",
      "Epoch 85/100\n",
      " - 2s - loss: 0.0129 - acc: 0.9954 - val_loss: 0.5520 - val_acc: 0.8937\n",
      "Epoch 86/100\n",
      " - 2s - loss: 0.0089 - acc: 0.9974 - val_loss: 0.6681 - val_acc: 0.8894\n",
      "Epoch 87/100\n",
      " - 2s - loss: 0.0217 - acc: 0.9937 - val_loss: 0.4911 - val_acc: 0.8923\n",
      "Epoch 88/100\n",
      " - 2s - loss: 0.0185 - acc: 0.9941 - val_loss: 0.5638 - val_acc: 0.8873\n",
      "Epoch 89/100\n",
      " - 2s - loss: 0.0132 - acc: 0.9963 - val_loss: 0.6573 - val_acc: 0.8933\n",
      "Epoch 90/100\n",
      " - 2s - loss: 0.0135 - acc: 0.9959 - val_loss: 0.5771 - val_acc: 0.8879\n",
      "Epoch 91/100\n",
      " - 2s - loss: 0.0127 - acc: 0.9961 - val_loss: 0.5578 - val_acc: 0.8920\n",
      "Epoch 92/100\n",
      " - 2s - loss: 0.0127 - acc: 0.9965 - val_loss: 0.5963 - val_acc: 0.8934\n",
      "Epoch 93/100\n",
      " - 2s - loss: 0.0163 - acc: 0.9951 - val_loss: 0.5411 - val_acc: 0.8959\n",
      "Epoch 94/100\n",
      " - 2s - loss: 0.0160 - acc: 0.9955 - val_loss: 0.5866 - val_acc: 0.8930\n",
      "Epoch 95/100\n",
      " - 2s - loss: 0.0091 - acc: 0.9972 - val_loss: 0.7125 - val_acc: 0.8921\n",
      "Epoch 96/100\n",
      " - 2s - loss: 0.0123 - acc: 0.9963 - val_loss: 0.6279 - val_acc: 0.8910\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      " - 2s - loss: 0.0157 - acc: 0.9949 - val_loss: 0.6183 - val_acc: 0.8898\n",
      "Epoch 98/100\n",
      " - 2s - loss: 0.0157 - acc: 0.9954 - val_loss: 0.5372 - val_acc: 0.8943\n",
      "Epoch 99/100\n",
      " - 2s - loss: 0.0080 - acc: 0.9973 - val_loss: 0.6947 - val_acc: 0.8896\n",
      "Epoch 100/100\n",
      " - 2s - loss: 0.0154 - acc: 0.9957 - val_loss: 0.6004 - val_acc: 0.8914\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11cb4df28>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(((X_train[:,::4]-X_train.mean())/X_train.std()),Y_train[:,1:],10,100,verbose=2,validation_data=((X_test[:,::4]-X_test.mean())/X_test.std(),Y_test[:,1:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy on Validation Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2300, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred=model.predict((X_test[:,::4]-X_test.mean())/X_test.std())\n",
    "ypred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "yp=np.zeros((Y_test.shape[0]))\n",
    "yo=np.ones((Y_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(Y_test.shape[0]):\n",
    "    yp[i]=np.argmax(ypred[i])+1\n",
    "    yo[i]=np.argmax(Y_test[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2300,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2300,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(yo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1.], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(yp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2300,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conversion of classes\n",
    "for i in range(Y_test.shape[0]):\n",
    "    if yo[i]!=1:\n",
    "        yo[i]=0\n",
    "    if yp[i]!=1:\n",
    "        yp[i]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(yo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(yp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9760869565217392"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(yo,yp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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