project_lstm_keras_tsamp50.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, GRU, Dense, Dropout, TimeDistributed, BatchNormalization, Flatten, Activation\n",
    "from keras import regularizers\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.models import load_model\n",
    "import numpy as np\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "from data_utils import get_data\n",
    "from sklearn import preprocessing\n",
    "from functools import reduce\n",
    "from scipy.fftpack import dct, idct\n",
    "from scipy import signal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (2458, 1000, 22) \n",
      "y_train: (2458,) \n",
      "X_val: (0, 1000, 22) \n",
      "y_val: (0,) \n",
      "X_test: (100, 1000, 22) \n",
      "y_test: (100,) \n"
     ]
    }
   ],
   "source": [
    "# Load data from all .mat files, combine them, eliminate EOG signals, shuffle and \n",
    "# seperate training data, validation data and testing data.\n",
    "# Also do mean subtraction on x.\n",
    "\n",
    "data = get_data('project_datasets',num_validation=0, num_test=100, subtract_mean=False, subtract_axis=1,\n",
    "                transpose=True)\n",
    "for k in data.keys():\n",
    "    print('{}: {} '.format(k, data[k].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# substract data from list\n",
    "X_train = data.get('X_train')\n",
    "y_train = data.get('y_train')\n",
    "#X_val = data.get('X_val')\n",
    "#y_val = data.get('y_val')\n",
    "X_test = data.get('X_test')\n",
    "y_test = data.get('y_test')\n",
    "\n",
    "# get data dimension\n",
    "N_train, T_train, C_train = data.get('X_train').shape\n",
    "N_val, T_val, C_val = data.get('X_val').shape\n",
    "N_test, T_test, C_test = data.get('X_test').shape\n",
    "\n",
    "# add dummy zeros for y classification\n",
    "lb = preprocessing.LabelBinarizer()\n",
    "lb.fit([0, 1, 2, 3,])\n",
    "lb.classes_\n",
    "y_train = lb.transform(y_train)\n",
    "if (N_val > 0): y_val = lb.transform(y_val)\n",
    "y_test = lb.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering through FFT(discrete cosine transform)\n",
    "\n",
    "def filter(x, low=0, high=1, plot=False):\n",
    "    N = x.shape[0]\n",
    "    t = np.linspace(0,N,N)\n",
    "    y = dct(x, norm='ortho')\n",
    "    window = np.zeros(N)\n",
    "    window[int(low*N):int(high*N)] = 1\n",
    "    yr = idct(y*window, norm='ortho')\n",
    "    sum(abs(x-yr)**2) / sum(abs(x)**2)\n",
    "    if plot:\n",
    "        plt.plot(t, x, '-b')\n",
    "        plt.plot(t, yr, 'r')\n",
    "    return x\n",
    "\n",
    "# Filter band\n",
    "low_freq = 0.02\n",
    "high_freq = 0.4\n",
    "\n",
    "for i in np.arange(N_train):\n",
    "    for j in np.arange(C_train):\n",
    "       X_train[i,:,j]= filter(X_train[i,:,j],low_freq,high_freq)\n",
    "for i in np.arange(N_val):\n",
    "    for j in arange(C_val):\n",
    "       X_val[i,:,j]= filter(X_val[i,:,j],low_freq,high_freq)\n",
    "    \n",
    "for i in np.arange(N_test):\n",
    "    for j in np.arange(C_test):\n",
    "       X_test[i,:,j]= filter(X_test[i,:,j],low_freq,high_freq)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdebbf7c208>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdebbf7c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Downsampling in time through FFT\n",
    "\n",
    "t_sample = 50\n",
    "X_train_sub = signal.resample(X_train,t_sample,axis=1)\n",
    "if (N_val > 0): X_val_sub = signal.resample(X_val,t_sample,axis=1)\n",
    "X_test_sub = signal.resample(X_test,t_sample,axis=1)\n",
    "\n",
    "# show a downsampling example\n",
    "plt.plot(np.linspace(0,1,T_train),X_train[5,:,5],'-b')\n",
    "plt.plot(np.linspace(0,1,t_sample),X_train_sub[5,:,5],'-r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2458, 50, 22)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_sub.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # normalize data across the time sequence dimension only\n",
    "# scaler = preprocessing.MinMaxScaler()\n",
    "# for i in range(N_train):\n",
    "#     scaler.fit(X_train[i,:,:])\n",
    "#     X_train[i,:,:] = scaler.transform(X_train[i,:,:])\n",
    "    \n",
    "# for i in range(N_val):\n",
    "#     scaler.fit(X_val[i,:,:])\n",
    "#     X_val[i,:,:] = scaler.transform(X_val[i,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def downsample(data, mul=10, enableTrunc = False):\n",
    "#     assert data.shape[0] % mul == 0 or  enableTrunc, \"Can't downsample without data truncation.\\\n",
    "#                                                 \\n Set another value of mul or set enableTrunc = True\"\n",
    "#     outdata = []\n",
    "#     for i in range(data.shape[0]//mul):\n",
    "#         outdata.append(np.mean(data[i*mul:(i+1)*mul,...], axis=0, keepdims=True))\n",
    "#     outdata = reduce(lambda a,b: np.append(a,b,0), outdata)\n",
    "    \n",
    "#     return outdata\n",
    "# span = 2\n",
    "# X_train_sub = np.expand_dims(downsample(X_train[0,:,:], mul=span),axis=0)\n",
    "# for n in np.arange(1,X_train.shape[0]):\n",
    "#     X_train_sub = np.append(X_train_sub,\n",
    "#                             np.expand_dims(downsample(X_train[n,:,:], mul=span),axis=0),axis=0)\n",
    "\n",
    "# X_val_sub = np.expand_dims(downsample(X_val[0,:,:], mul=span),axis=0)\n",
    "# for n in np.arange(1,X_val.shape[0]):\n",
    "#     X_val_sub = np.append(X_val_sub,\n",
    "#                             np.expand_dims(downsample(X_val[n,:,:], mul=span),axis=0),axis=0)\n",
    "\n",
    "# print('X_train_sub: ', X_train_sub.shape)\n",
    "# print('X_val_sub: ', X_val_sub.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expected input shape: ([batch_size], timesteps, data_dim)\n",
    "# Note that we have to provide the full batch_input_shape if the network is stateful.\n",
    "# the sample of index i in batch k is the follow-up for the sample i in batch k-1.\n",
    "\n",
    "# perhaps should try masking layer\n",
    "\n",
    "data_dim = C_train\n",
    "seq_split = 1 # Set to one when using FFT to down sample\n",
    "seq_len = int(X_train_sub.shape[1]*seq_split)\n",
    "timesteps = seq_len\n",
    "num_classes = 4\n",
    "batch_size = 200\n",
    "num_epoch = 100\n",
    "\n",
    "model = Sequential()\n",
    "#1\n",
    "model.add(LSTM(200, return_sequences=True, stateful=False,\n",
    "          recurrent_dropout=0.6, dropout = 0.6, input_shape=(timesteps, data_dim)))\n",
    "\n",
    "#2\n",
    "model.add(LSTM(100, return_sequences=True, stateful=False,\n",
    "          recurrent_dropout=0.5, dropout = 0.5))\n",
    "\n",
    "#3\n",
    "model.add(LSTM(50, return_sequences=True, stateful=False,\n",
    "          recurrent_dropout=0.4, dropout = 0.4))\n",
    "model.add(Flatten())\n",
    "\n",
    "#4\n",
    "model.add(Dense(100))\n",
    "model.add(BatchNormalization(axis=-1))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "#5\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# define early stopping callback\n",
    "earlystop = EarlyStopping(monitor='val_loss', min_delta=0.001, patience=30, mode='auto')\n",
    "\n",
    "# saves the model weights after each epoch if the validation loss decreased\n",
    "checkpointer = ModelCheckpoint(filepath='LSTM_model.hdf5', monitor='val_acc', verbose=1, save_best_only=True)\n",
    "\n",
    "callbacks_list = [earlystop, checkpointer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2089 samples, validate on 369 samples\n",
      "Epoch 1/100\n",
      "2089/2089 [==============================] - 11s 5ms/step - loss: 1.8606 - acc: 0.2633 - val_loss: 1.3946 - val_acc: 0.3360\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.33604, saving model to LSTM_model.hdf5\n",
      "Epoch 2/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.7562 - acc: 0.2614 - val_loss: 1.3884 - val_acc: 0.3279\n",
      "\n",
      "Epoch 00002: val_acc did not improve\n",
      "Epoch 3/100\n",
      "2089/2089 [==============================] - 7s 3ms/step - loss: 1.6712 - acc: 0.2738 - val_loss: 1.4122 - val_acc: 0.3062\n",
      "\n",
      "Epoch 00003: val_acc did not improve\n",
      "Epoch 4/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.6357 - acc: 0.2915 - val_loss: 1.3794 - val_acc: 0.3198\n",
      "\n",
      "Epoch 00004: val_acc did not improve\n",
      "Epoch 5/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.5849 - acc: 0.2915 - val_loss: 1.4079 - val_acc: 0.3279\n",
      "\n",
      "Epoch 00005: val_acc did not improve\n",
      "Epoch 6/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.5587 - acc: 0.2939 - val_loss: 1.3400 - val_acc: 0.3930\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.33604 to 0.39295, saving model to LSTM_model.hdf5\n",
      "Epoch 7/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.5718 - acc: 0.2906 - val_loss: 1.3856 - val_acc: 0.3659\n",
      "\n",
      "Epoch 00007: val_acc did not improve\n",
      "Epoch 8/100\n",
      "2089/2089 [==============================] - 9s 4ms/step - loss: 1.5200 - acc: 0.3025 - val_loss: 1.3467 - val_acc: 0.3686\n",
      "\n",
      "Epoch 00008: val_acc did not improve\n",
      "Epoch 9/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.4941 - acc: 0.3140 - val_loss: 1.3743 - val_acc: 0.3767\n",
      "\n",
      "Epoch 00009: val_acc did not improve\n",
      "Epoch 10/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.4946 - acc: 0.2920 - val_loss: 1.4237 - val_acc: 0.3523\n",
      "\n",
      "Epoch 00010: val_acc did not improve\n",
      "Epoch 11/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.4746 - acc: 0.2997 - val_loss: 1.4094 - val_acc: 0.3550\n",
      "\n",
      "Epoch 00011: val_acc did not improve\n",
      "Epoch 12/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.4425 - acc: 0.3260 - val_loss: 1.4016 - val_acc: 0.3848\n",
      "\n",
      "Epoch 00012: val_acc did not improve\n",
      "Epoch 13/100\n",
      "2089/2089 [==============================] - 7s 3ms/step - loss: 1.4678 - acc: 0.3088 - val_loss: 1.3857 - val_acc: 0.3740\n",
      "\n",
      "Epoch 00013: val_acc did not improve\n",
      "Epoch 14/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.4321 - acc: 0.3193 - val_loss: 1.4338 - val_acc: 0.3469\n",
      "\n",
      "Epoch 00014: val_acc did not improve\n",
      "Epoch 15/100\n",
      "2089/2089 [==============================] - 10s 5ms/step - loss: 1.4131 - acc: 0.3375 - val_loss: 1.4382 - val_acc: 0.3713\n",
      "\n",
      "Epoch 00015: val_acc did not improve\n",
      "Epoch 16/100\n",
      "2089/2089 [==============================] - 9s 4ms/step - loss: 1.3964 - acc: 0.3293 - val_loss: 1.4914 - val_acc: 0.2981\n",
      "\n",
      "Epoch 00016: val_acc did not improve\n",
      "Epoch 17/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3778 - acc: 0.3518 - val_loss: 1.3518 - val_acc: 0.4065\n",
      "\n",
      "Epoch 00017: val_acc improved from 0.39295 to 0.40650, saving model to LSTM_model.hdf5\n",
      "Epoch 18/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3740 - acc: 0.3375 - val_loss: 1.3502 - val_acc: 0.4065\n",
      "\n",
      "Epoch 00018: val_acc did not improve\n",
      "Epoch 19/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3841 - acc: 0.3576 - val_loss: 1.3164 - val_acc: 0.4146\n",
      "\n",
      "Epoch 00019: val_acc improved from 0.40650 to 0.41463, saving model to LSTM_model.hdf5\n",
      "Epoch 20/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3745 - acc: 0.3490 - val_loss: 1.2913 - val_acc: 0.4173\n",
      "\n",
      "Epoch 00020: val_acc improved from 0.41463 to 0.41734, saving model to LSTM_model.hdf5\n",
      "Epoch 21/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.3642 - acc: 0.3447 - val_loss: 1.3165 - val_acc: 0.3902\n",
      "\n",
      "Epoch 00021: val_acc did not improve\n",
      "Epoch 22/100\n",
      "2089/2089 [==============================] - 9s 4ms/step - loss: 1.3754 - acc: 0.3351 - val_loss: 1.3765 - val_acc: 0.3767\n",
      "\n",
      "Epoch 00022: val_acc did not improve\n",
      "Epoch 23/100\n",
      "2089/2089 [==============================] - 10s 5ms/step - loss: 1.3393 - acc: 0.3715 - val_loss: 1.3790 - val_acc: 0.3740\n",
      "\n",
      "Epoch 00023: val_acc did not improve\n",
      "Epoch 24/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3540 - acc: 0.3657 - val_loss: 1.4278 - val_acc: 0.3496\n",
      "\n",
      "Epoch 00024: val_acc did not improve\n",
      "Epoch 25/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3359 - acc: 0.3633 - val_loss: 1.3331 - val_acc: 0.3930\n",
      "\n",
      "Epoch 00025: val_acc did not improve\n",
      "Epoch 26/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3074 - acc: 0.3901 - val_loss: 1.3522 - val_acc: 0.3848\n",
      "\n",
      "Epoch 00026: val_acc did not improve\n",
      "Epoch 27/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3131 - acc: 0.3854 - val_loss: 1.3078 - val_acc: 0.4146\n",
      "\n",
      "Epoch 00027: val_acc did not improve\n",
      "Epoch 28/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3286 - acc: 0.3782 - val_loss: 1.3097 - val_acc: 0.4173\n",
      "\n",
      "Epoch 00028: val_acc did not improve\n",
      "Epoch 29/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.3134 - acc: 0.3940 - val_loss: 1.3165 - val_acc: 0.3984\n",
      "\n",
      "Epoch 00029: val_acc did not improve\n",
      "Epoch 30/100\n",
      "2089/2089 [==============================] - 9s 4ms/step - loss: 1.3011 - acc: 0.3911 - val_loss: 1.2949 - val_acc: 0.4173\n",
      "\n",
      "Epoch 00030: val_acc did not improve\n",
      "Epoch 31/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2998 - acc: 0.3983 - val_loss: 1.3148 - val_acc: 0.4011\n",
      "\n",
      "Epoch 00031: val_acc did not improve\n",
      "Epoch 32/100\n",
      "2089/2089 [==============================] - 7s 3ms/step - loss: 1.3026 - acc: 0.3863 - val_loss: 1.2920 - val_acc: 0.4309\n",
      "\n",
      "Epoch 00032: val_acc improved from 0.41734 to 0.43089, saving model to LSTM_model.hdf5\n",
      "Epoch 33/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2717 - acc: 0.4232 - val_loss: 1.3101 - val_acc: 0.3631\n",
      "\n",
      "Epoch 00033: val_acc did not improve\n",
      "Epoch 34/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.2775 - acc: 0.4083 - val_loss: 1.3145 - val_acc: 0.3848\n",
      "\n",
      "Epoch 00034: val_acc did not improve\n",
      "Epoch 35/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2828 - acc: 0.4059 - val_loss: 1.2868 - val_acc: 0.4065\n",
      "\n",
      "Epoch 00035: val_acc did not improve\n",
      "Epoch 36/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2826 - acc: 0.4174 - val_loss: 1.2789 - val_acc: 0.4173\n",
      "\n",
      "Epoch 00036: val_acc did not improve\n",
      "Epoch 37/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2672 - acc: 0.4337 - val_loss: 1.2451 - val_acc: 0.4417\n",
      "\n",
      "Epoch 00037: val_acc improved from 0.43089 to 0.44173, saving model to LSTM_model.hdf5\n",
      "Epoch 38/100\n",
      "2089/2089 [==============================] - 9s 4ms/step - loss: 1.2618 - acc: 0.4155 - val_loss: 1.2354 - val_acc: 0.4282\n",
      "\n",
      "Epoch 00038: val_acc did not improve\n",
      "Epoch 39/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2628 - acc: 0.4208 - val_loss: 1.2431 - val_acc: 0.4472\n",
      "\n",
      "Epoch 00039: val_acc improved from 0.44173 to 0.44715, saving model to LSTM_model.hdf5\n",
      "Epoch 40/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2598 - acc: 0.4414 - val_loss: 1.2537 - val_acc: 0.4553\n",
      "\n",
      "Epoch 00040: val_acc improved from 0.44715 to 0.45528, saving model to LSTM_model.hdf5\n",
      "Epoch 41/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2621 - acc: 0.4241 - val_loss: 1.2496 - val_acc: 0.4499\n",
      "\n",
      "Epoch 00041: val_acc did not improve\n",
      "Epoch 42/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.2521 - acc: 0.4236 - val_loss: 1.2641 - val_acc: 0.4282\n",
      "\n",
      "Epoch 00042: val_acc did not improve\n",
      "Epoch 43/100\n",
      "2089/2089 [==============================] - 7s 3ms/step - loss: 1.2441 - acc: 0.4371 - val_loss: 1.2411 - val_acc: 0.4444\n",
      "\n",
      "Epoch 00043: val_acc did not improve\n",
      "Epoch 44/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.2322 - acc: 0.4423 - val_loss: 1.2360 - val_acc: 0.4526\n",
      "\n",
      "Epoch 00044: val_acc did not improve\n",
      "Epoch 45/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2089/2089 [==============================] - 9s 4ms/step - loss: 1.2419 - acc: 0.4327 - val_loss: 1.2290 - val_acc: 0.4390\n",
      "\n",
      "Epoch 00045: val_acc did not improve\n",
      "Epoch 46/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2271 - acc: 0.4404 - val_loss: 1.2094 - val_acc: 0.4499\n",
      "\n",
      "Epoch 00046: val_acc did not improve\n",
      "Epoch 47/100\n",
      "2089/2089 [==============================] - 7s 3ms/step - loss: 1.2171 - acc: 0.4548 - val_loss: 1.2167 - val_acc: 0.4797\n",
      "\n",
      "Epoch 00047: val_acc improved from 0.45528 to 0.47967, saving model to LSTM_model.hdf5\n",
      "Epoch 48/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2252 - acc: 0.4461 - val_loss: 1.2321 - val_acc: 0.4688\n",
      "\n",
      "Epoch 00048: val_acc did not improve\n",
      "Epoch 49/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.2231 - acc: 0.4509 - val_loss: 1.2359 - val_acc: 0.4499\n",
      "\n",
      "Epoch 00049: val_acc did not improve\n",
      "Epoch 50/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.2318 - acc: 0.4423 - val_loss: 1.2283 - val_acc: 0.4634\n",
      "\n",
      "Epoch 00050: val_acc did not improve\n",
      "Epoch 51/100\n",
      "2089/2089 [==============================] - 7s 3ms/step - loss: 1.2241 - acc: 0.4572 - val_loss: 1.2307 - val_acc: 0.4390\n",
      "\n",
      "Epoch 00051: val_acc did not improve\n",
      "Epoch 52/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2162 - acc: 0.4629 - val_loss: 1.2108 - val_acc: 0.4553\n",
      "\n",
      "Epoch 00052: val_acc did not improve\n",
      "Epoch 53/100\n",
      "2089/2089 [==============================] - 9s 4ms/step - loss: 1.2144 - acc: 0.4591 - val_loss: 1.2068 - val_acc: 0.4580\n",
      "\n",
      "Epoch 00053: val_acc did not improve\n",
      "Epoch 54/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.2091 - acc: 0.4653 - val_loss: 1.2193 - val_acc: 0.4553\n",
      "\n",
      "Epoch 00054: val_acc did not improve\n",
      "Epoch 55/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.2178 - acc: 0.4562 - val_loss: 1.2125 - val_acc: 0.4607\n",
      "\n",
      "Epoch 00055: val_acc did not improve\n",
      "Epoch 56/100\n",
      "2089/2089 [==============================] - 7s 3ms/step - loss: 1.1911 - acc: 0.4830 - val_loss: 1.2097 - val_acc: 0.4770\n",
      "\n",
      "Epoch 00056: val_acc did not improve\n",
      "Epoch 57/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.1779 - acc: 0.4835 - val_loss: 1.2049 - val_acc: 0.4634\n",
      "\n",
      "Epoch 00057: val_acc did not improve\n",
      "Epoch 58/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.1803 - acc: 0.4634 - val_loss: 1.1947 - val_acc: 0.4743\n",
      "\n",
      "Epoch 00058: val_acc did not improve\n",
      "Epoch 59/100\n",
      "2089/2089 [==============================] - 7s 3ms/step - loss: 1.1782 - acc: 0.4768 - val_loss: 1.2064 - val_acc: 0.4715\n",
      "\n",
      "Epoch 00059: val_acc did not improve\n",
      "Epoch 60/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.1959 - acc: 0.4696 - val_loss: 1.1949 - val_acc: 0.4743\n",
      "\n",
      "Epoch 00060: val_acc did not improve\n",
      "Epoch 61/100\n",
      "2089/2089 [==============================] - 9s 4ms/step - loss: 1.1904 - acc: 0.4643 - val_loss: 1.2013 - val_acc: 0.4688\n",
      "\n",
      "Epoch 00061: val_acc did not improve\n",
      "Epoch 62/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.1969 - acc: 0.4734 - val_loss: 1.2103 - val_acc: 0.4797\n",
      "\n",
      "Epoch 00062: val_acc did not improve\n",
      "Epoch 63/100\n",
      "2089/2089 [==============================] - 7s 3ms/step - loss: 1.1794 - acc: 0.4777 - val_loss: 1.2077 - val_acc: 0.4824\n",
      "\n",
      "Epoch 00063: val_acc improved from 0.47967 to 0.48238, saving model to LSTM_model.hdf5\n",
      "Epoch 64/100\n",
      "2089/2089 [==============================] - 8s 4ms/step - loss: 1.1599 - acc: 0.4902 - val_loss: 1.2436 - val_acc: 0.4932\n",
      "\n",
      "Epoch 00064: val_acc improved from 0.48238 to 0.49322, saving model to LSTM_model.hdf5\n",
      "Epoch 65/100\n",
      "2089/2089 [==============================] - 7s 4ms/step - loss: 1.1721 - acc: 0.4883 - val_loss: 1.2200 - val_acc: 0.4444\n",
      "\n",
      "Epoch 00065: val_acc did not improve\n",
      "Epoch 66/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.1543 - acc: 0.5012 - val_loss: 1.2105 - val_acc: 0.4553\n",
      "\n",
      "Epoch 00066: val_acc did not improve\n",
      "Epoch 67/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.1685 - acc: 0.4921 - val_loss: 1.1993 - val_acc: 0.4851\n",
      "\n",
      "Epoch 00067: val_acc did not improve\n",
      "Epoch 68/100\n",
      "2089/2089 [==============================] - 3s 2ms/step - loss: 1.1493 - acc: 0.5093 - val_loss: 1.2017 - val_acc: 0.4905\n",
      "\n",
      "Epoch 00068: val_acc did not improve\n",
      "Epoch 69/100\n",
      "2089/2089 [==============================] - 3s 2ms/step - loss: 1.1461 - acc: 0.5165 - val_loss: 1.2090 - val_acc: 0.4770\n",
      "\n",
      "Epoch 00069: val_acc did not improve\n",
      "Epoch 70/100\n",
      "2089/2089 [==============================] - 3s 2ms/step - loss: 1.1594 - acc: 0.4864 - val_loss: 1.2180 - val_acc: 0.4824\n",
      "\n",
      "Epoch 00070: val_acc did not improve\n",
      "Epoch 71/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.1447 - acc: 0.4911 - val_loss: 1.2169 - val_acc: 0.4743\n",
      "\n",
      "Epoch 00071: val_acc did not improve\n",
      "Epoch 72/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.1561 - acc: 0.4873 - val_loss: 1.2016 - val_acc: 0.4743\n",
      "\n",
      "Epoch 00072: val_acc did not improve\n",
      "Epoch 73/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.1281 - acc: 0.5199 - val_loss: 1.2002 - val_acc: 0.4878\n",
      "\n",
      "Epoch 00073: val_acc did not improve\n",
      "Epoch 74/100\n",
      "2089/2089 [==============================] - 3s 2ms/step - loss: 1.1317 - acc: 0.5089 - val_loss: 1.2092 - val_acc: 0.5122\n",
      "\n",
      "Epoch 00074: val_acc improved from 0.49322 to 0.51220, saving model to LSTM_model.hdf5\n",
      "Epoch 75/100\n",
      "2089/2089 [==============================] - 3s 2ms/step - loss: 1.1081 - acc: 0.5199 - val_loss: 1.1997 - val_acc: 0.4905\n",
      "\n",
      "Epoch 00075: val_acc did not improve\n",
      "Epoch 76/100\n",
      "2089/2089 [==============================] - 3s 2ms/step - loss: 1.1087 - acc: 0.5318 - val_loss: 1.2263 - val_acc: 0.4851\n",
      "\n",
      "Epoch 00076: val_acc did not improve\n",
      "Epoch 77/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.1070 - acc: 0.5098 - val_loss: 1.2219 - val_acc: 0.4743\n",
      "\n",
      "Epoch 00077: val_acc did not improve\n",
      "Epoch 78/100\n",
      "2089/2089 [==============================] - 3s 2ms/step - loss: 1.1160 - acc: 0.5237 - val_loss: 1.2383 - val_acc: 0.4797\n",
      "\n",
      "Epoch 00078: val_acc did not improve\n",
      "Epoch 79/100\n",
      "2089/2089 [==============================] - 3s 2ms/step - loss: 1.0907 - acc: 0.5404 - val_loss: 1.2400 - val_acc: 0.4797\n",
      "\n",
      "Epoch 00079: val_acc did not improve\n",
      "Epoch 80/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.1049 - acc: 0.5304 - val_loss: 1.2557 - val_acc: 0.4688\n",
      "\n",
      "Epoch 00080: val_acc did not improve\n",
      "Epoch 81/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.0951 - acc: 0.5290 - val_loss: 1.2606 - val_acc: 0.4661\n",
      "\n",
      "Epoch 00081: val_acc did not improve\n",
      "Epoch 82/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.0938 - acc: 0.5285 - val_loss: 1.2492 - val_acc: 0.4553\n",
      "\n",
      "Epoch 00082: val_acc did not improve\n",
      "Epoch 83/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.0855 - acc: 0.5294 - val_loss: 1.2544 - val_acc: 0.4770\n",
      "\n",
      "Epoch 00083: val_acc did not improve\n",
      "Epoch 84/100\n",
      "2089/2089 [==============================] - 3s 2ms/step - loss: 1.0945 - acc: 0.5261 - val_loss: 1.2539 - val_acc: 0.4580\n",
      "\n",
      "Epoch 00084: val_acc did not improve\n",
      "Epoch 85/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.0798 - acc: 0.5462 - val_loss: 1.2691 - val_acc: 0.4688\n",
      "\n",
      "Epoch 00085: val_acc did not improve\n",
      "Epoch 86/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.0748 - acc: 0.5376 - val_loss: 1.2565 - val_acc: 0.4526\n",
      "\n",
      "Epoch 00086: val_acc did not improve\n",
      "Epoch 87/100\n",
      "2089/2089 [==============================] - 4s 2ms/step - loss: 1.0621 - acc: 0.5562 - val_loss: 1.2356 - val_acc: 0.4715\n",
      "\n",
      "Epoch 00087: val_acc did not improve\n",
      "Epoch 88/100\n",
      "2089/2089 [==============================] - 5s 2ms/step - loss: 1.0619 - acc: 0.5515 - val_loss: 1.2616 - val_acc: 0.4661\n",
      "\n",
      "Epoch 00088: val_acc did not improve\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train_sub[:,:seq_len,:], y_train,\n",
    "                    batch_size=batch_size, epochs=num_epoch, shuffle=True,\n",
    "                    validation_split = 0.15, callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdea1211f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdea11d00f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'],'o')\n",
    "plt.plot(history.history['val_loss'],'o')\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "100/100 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.1831811666488647, 0.55000001192092896]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate model on test set\n",
    "model = load_model('LSTM_model.hdf5')\n",
    "model.evaluate(X_test_sub,y_test,batch_size=N_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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