EEGnet_CNN_Dense_Binary_Classifier_TD,FD_2D.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "\n",
    "# load data from the .mat file:\n",
    "# mat_contents = sio.loadmat('EEG_big.mat') # (EEG_data.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/EEG_big4CNN.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/EEG_big4CNN_1subj.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/KOS_100Hz_noICA->0.5-38Hz,Env=1.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/Merged123->0.5-38Hz,Env=1.mat')\n",
    "\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/KOS_100Hz_ICA->0.5-38Hz,Env=1.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/Merged123->1-48Hz,Env=1.mat')\n",
    "\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/BCI_TD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/BCI_0101T_TD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/BCI_0202T_TD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/BCI_0403T_TD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/BCI_0503T_TD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/BCI_0801T_TD.mat')\n",
    "\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/ks.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/ks_FD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/ks_2TD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/ksenia2_long_chunked.mat') \n",
    "\n",
    "\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/BCI_FD_fullspectrum.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/BCI_FD_lowspectrum.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/KOS_100Hz_ICA->1-38Hz,Env=1_FD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/Merged123->1-100Hz,Env=1_FD')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/Merged123->1-47Hz,Env=1_FD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/Merged123->15-47Hz,Env=1_TD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/Merged123->15-47Hz,Env=1_TD5_64_.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/Merged123->2-30Hz,Env=1_TD5_64_.mat')\n",
    "\n",
    "mat_contents = sio.loadmat(\n",
    "    '/home/amplifier/home/DATASETS/Merged456->2-30Hz,Env=1_TD1_94_.mat')\n",
    "# mat_contents = sio.loadmat(\n",
    "#     '/home/amplifier/home/DATASETS/Merged456->2-30Hz,Env=1_TD101_192_.mat')\n",
    "# mat_contents = sio.loadmat(\n",
    "#     '/home/amplifier/home/DATASETS/Merged456->2-30Hz,Env=1_TD197_289_.mat')\n",
    "\n",
    "\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/Merged456->2-30Hz,Env=1_TD1_289_.mat')\n",
    "\n",
    "\n",
    "BATCH_SIZE = 20\n",
    "SHUFFLE = True\n",
    "TEST_TRAIN = 0.2\n",
    "\n",
    "KERNEL_SIZE = ((1,100), (5,5), (4,4), (3,3), (2,2))\n",
    "\n",
    "# N_KERNELS = (10,30,50,80,160)\n",
    "N_KERNELS = (10,20,40,80,160)\n",
    "POOL_SIZE = ((2,2),(2,2),(2,2),(2,2),(2,2))\n",
    "\n",
    "DROPOUT = (0.1, 0.2, 0.3, 0.4, 0.8, 0.8)\n",
    "DENSE_LEN = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data shape: (450, 60, 500)\n",
      "Original labels shape: (450, 1)\n",
      "Original data type: float32\n",
      "Normalized data type: float16\n",
      "test input shape (90, 60, 500, 1) Nomralized MEAN: 0.011047 min -1113.0 max 855.0\n",
      "train input shape (360, 60, 500, 1) Nomralized MEAN: -0.026642 min -12984.0 max 13112.0\n",
      "test labels shape (90, 2) Nomralized MEAN: 0.5 min 0.0 max 1.0\n",
      "train labels shape (360, 2) Nomralized MEAN: 0.5 min 0.0 max 1.0\n"
     ]
    }
   ],
   "source": [
    "# get the Dataset:\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "X = mat_contents['X']\n",
    "Y = mat_contents['Z']\n",
    "    \n",
    "\n",
    "if Y.shape[1] > Y.shape[0]:\n",
    "    Y = Y.T\n",
    "    \n",
    "print('Original data shape:', X.shape)\n",
    "print('Original labels shape:', Y.shape)\n",
    "\n",
    "# verify that the model REALLY finds a mapping between the input and the labels. If we get\n",
    "# our accuracy by chance, then we should get the same accuracy on a permuted dataset:\n",
    "# Y = np.random.permutation(Y)\n",
    "\n",
    "\n",
    "# winsize = mat_contents['winsize']\n",
    "# stepsize = mat_contents['stepsize']\n",
    "# trial_len = mat_contents['trial_len']\n",
    "# low_cutoff = mat_contents['low_cutoff']\n",
    "# high_cutoff = mat_contents['high_cutoff']\n",
    "# source = mat_contents['filein']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=TEST_TRAIN, shuffle=SHUFFLE)\n",
    "print('Original data type:', x_train.dtype)\n",
    "\n",
    "# convert to float64 for numerical stability:\n",
    "x_train = x_train.astype('float64')\n",
    "y_train = y_train.astype('float64')\n",
    "x_test = x_test.astype('float64')\n",
    "y_test = y_test.astype('float64')\n",
    "\n",
    "# normalize to unit variance and zero mean:\n",
    "# for i in range(x_train.shape[0]):\n",
    "#     for j in range(x_train.shape[1]):\n",
    "#         x_train[i,j,:] = preprocessing.scale(x_train[i,j,:])\n",
    "# #         x_train[i,j,:] = preprocessing.MaxAbsScaler(x_train[i,j,:])\n",
    "\n",
    "# for i in range(x_test.shape[0]):\n",
    "#     for j in range(x_test.shape[1]):\n",
    "#         x_test[i,j,:] = preprocessing.scale(x_test[i,j,:])\n",
    "# #         x_test[i,j,:] = preprocessing.minmax_scale(x_test[i,j,:])\n",
    "\n",
    "# ensure the tensors are sized right:\n",
    "if len(X.shape)==3:\n",
    "    x_train = np.expand_dims(x_train,3)\n",
    "    x_test = np.expand_dims(x_test,3)\n",
    "    \n",
    "\n",
    "# one hot encode the labels:\n",
    "onehot_encoder = preprocessing.OneHotEncoder(sparse=False)\n",
    "y_train = onehot_encoder.fit_transform(y_train)\n",
    "y_test = onehot_encoder.fit_transform(y_test)\n",
    "\n",
    "# convert to float16 to save space:\n",
    "x_train = x_train.astype('float16')\n",
    "y_train = y_train.astype('float16')\n",
    "x_test = x_test.astype('float16')\n",
    "y_test = y_test.astype('float16')\n",
    "print('Normalized data type:', x_train.dtype)\n",
    "\n",
    "chan = X.shape[1] # if you work in the FD, this is the height of the sample time-frequency image, othewise EEG channels\n",
    "leng = X.shape[2] # if you work in the FD, this is the width of the sample time-frequency image, othewise time samples of EEG signal\n",
    "\n",
    "if len(X.shape)==3:\n",
    "    streams = 1 # this is EEG channels if you work with frequency domain, in the TD streams = 1\n",
    "if len(X.shape)==4:\n",
    "    streams = X.shape[3] # this is EEG channels if you work with frequency domain, in the TD streams = 1\n",
    "\n",
    "\n",
    "print('test input shape', x_test.shape, \"Nomralized MEAN:\", np.mean(x_test), \"min\", np.min(x_test),\"max\", np.max(x_test))\n",
    "print('train input shape', x_train.shape, \"Nomralized MEAN:\", np.mean(x_train), \"min\", np.min(x_train),\"max\", np.max(x_train))\n",
    "\n",
    "print('test labels shape', y_test.shape, \"Nomralized MEAN:\", np.mean(y_test), \"min\", np.min(y_test),\"max\", np.max(y_test))\n",
    "print('train labels shape', y_train.shape, \"Nomralized MEAN:\", np.mean(y_train), \"min\", np.min(y_train),\"max\", np.max(y_train))\n",
    "\n",
    "# print('Window length', winsize)\n",
    "# print('Step size:', stepsize)\n",
    "# print('Trial length:', trial_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we first define the autoencoder's architecture:\n",
    "\n",
    "# make sure you don't hog all the video memory\n",
    "import tensorflow as tf\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = tf.Session(config=config)\n",
    "from keras import backend as K\n",
    "K.set_session(sess)\n",
    "###################################\n",
    "\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, Dropout, BatchNormalization, Flatten, ELU, Permute\n",
    "from keras.models import Model\n",
    "from keras.models import load_model\n",
    "from keras.callbacks import TensorBoard, EarlyStopping\n",
    "from keras import optimizers\n",
    "from keras.initializers import Orthogonal as orth\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def conv_net(leng):\n",
    "    \n",
    "    # input layer\n",
    "    input_img = Input(shape=(chan, leng, streams))  # adapt this if using `channels_first` image\n",
    "    \n",
    "    # Layer 1\n",
    "    input_norm = BatchNormalization()(input_img)\n",
    "    e1c = Conv2D(N_KERNELS[0], KERNEL_SIZE[0], padding='same',\n",
    "                kernel_initializer = orth(gain=1.0, seed=None))(input_norm)\n",
    "    e1t = Permute((3,2,1))(e1c)\n",
    "    e1act = ELU(alpha=1.0)(e1t)\n",
    "    e1p = MaxPooling2D(POOL_SIZE[0], padding='same')(e1act)\n",
    "\n",
    "\n",
    "    # Layer 2\n",
    "    e2b = BatchNormalization()(e1p)\n",
    "    e2d = Dropout(DROPOUT[0])(e2b)\n",
    "    e2c = Conv2D(N_KERNELS[1], KERNEL_SIZE[1], padding='same',\n",
    "                kernel_initializer = orth(gain=1.0, seed=None))(e2d)\n",
    "    e2act = ELU(alpha=1.0)(e2c)\n",
    "    e2p = MaxPooling2D(POOL_SIZE[1], padding='same')(e2act)\n",
    "    \n",
    "    # Layer 3\n",
    "    e3b = BatchNormalization()(e2p)\n",
    "    e3d = Dropout(DROPOUT[1])(e2b)\n",
    "    e3c = Conv2D(N_KERNELS[2], KERNEL_SIZE[2], padding='same',\n",
    "                kernel_initializer = orth(gain=1.0, seed=None))(e3d)\n",
    "    e3act = ELU(alpha=1.0)(e3c)\n",
    "    e3p = MaxPooling2D(POOL_SIZE[2], padding='same')(e3act)\n",
    "    \n",
    "    # Layer 4\n",
    "    e4b = BatchNormalization()(e3p)\n",
    "    e4d = Dropout(DROPOUT[2])(e4b)\n",
    "    e4c = Conv2D(N_KERNELS[3], KERNEL_SIZE[3], padding='same',\n",
    "                kernel_initializer = orth(gain=1.0, seed=None))(e4d)\n",
    "    e4act = ELU(alpha=1.0)(e4c)\n",
    "    e4p = MaxPooling2D(POOL_SIZE[3], padding='same')(e4act)\n",
    "    \n",
    "    # Layer 5\n",
    "    e5b = BatchNormalization()(e4p)\n",
    "    e5d = Dropout(DROPOUT[3])(e4b)\n",
    "    e5c = Conv2D(N_KERNELS[4], KERNEL_SIZE[4], padding='same',\n",
    "                 kernel_initializer = orth(gain=1.0, seed=None))(e5d)\n",
    "    e5act = ELU(alpha=1.0)(e5c)\n",
    "    e5p = MaxPooling2D(POOL_SIZE[4], padding='same')(e5act)\n",
    "    \n",
    "    # Layer 6\n",
    "    e6 = Flatten()(e4p)\n",
    "    \n",
    "    # Layer 7\n",
    "    e7b = BatchNormalization()(e6)\n",
    "    e7d = Dropout(DROPOUT[4])(e7b)\n",
    "    e7c = Dense(DENSE_LEN, activation='sigmoid',\n",
    "                kernel_initializer = orth(gain=1.0, seed=None))(e7d)\n",
    "\n",
    "    # Layer 8\n",
    "    e8b = BatchNormalization()(e7c)\n",
    "    e8d = Dropout(DROPOUT[5])(e8b)\n",
    "    e8c = Dense(2, activation='softmax')(e8d)\n",
    "\n",
    "    return Model(input_img, e8c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_32 (InputLayer)        (None, 60, 500, 1)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_106 (Bat (None, 60, 500, 1)        4         \n",
      "_________________________________________________________________\n",
      "conv2d_76 (Conv2D)           (None, 60, 500, 10)       1010      \n",
      "_________________________________________________________________\n",
      "permute_16 (Permute)         (None, 10, 500, 60)       0         \n",
      "_________________________________________________________________\n",
      "elu_76 (ELU)                 (None, 10, 500, 60)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_76 (MaxPooling (None, 5, 250, 60)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_107 (Bat (None, 5, 250, 60)        240       \n",
      "_________________________________________________________________\n",
      "dropout_92 (Dropout)         (None, 5, 250, 60)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_78 (Conv2D)           (None, 5, 250, 40)        38440     \n",
      "_________________________________________________________________\n",
      "elu_78 (ELU)                 (None, 5, 250, 40)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_78 (MaxPooling (None, 3, 125, 40)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_109 (Bat (None, 3, 125, 40)        160       \n",
      "_________________________________________________________________\n",
      "dropout_93 (Dropout)         (None, 3, 125, 40)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_79 (Conv2D)           (None, 3, 125, 80)        28880     \n",
      "_________________________________________________________________\n",
      "elu_79 (ELU)                 (None, 3, 125, 80)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_79 (MaxPooling (None, 2, 63, 80)         0         \n",
      "_________________________________________________________________\n",
      "flatten_16 (Flatten)         (None, 10080)             0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_111 (Bat (None, 10080)             40320     \n",
      "_________________________________________________________________\n",
      "dropout_95 (Dropout)         (None, 10080)             0         \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 10)                100810    \n",
      "_________________________________________________________________\n",
      "batch_normalization_112 (Bat (None, 10)                40        \n",
      "_________________________________________________________________\n",
      "dropout_96 (Dropout)         (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 2)                 22        \n",
      "=================================================================\n",
      "Total params: 209,926\n",
      "Trainable params: 189,544\n",
      "Non-trainable params: 20,382\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define input to the model:\n",
    "x = Input(shape=(chan, leng, streams))\n",
    "\n",
    "# make the model:\n",
    "model = conv_net(leng)\n",
    "\n",
    "# compile the model:\n",
    "sgd = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.95, nesterov=True)\n",
    "# model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
    "# model.compile(optimizer='rmsprop', loss=correlation_coefficient_loss)\n",
    "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n",
    "\n",
    "\n",
    "# SEE INSIDE THE MODEL:\n",
    "# conv_net().summary() #or\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 360 samples, validate on 90 samples\n",
      "Epoch 1/1000\n",
      " - 3s - loss: 1.4536 - acc: 0.5472 - val_loss: 0.7144 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.54444, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5\n",
      "Epoch 2/1000\n",
      " - 1s - loss: 1.4190 - acc: 0.5083 - val_loss: 0.7375 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00002: val_acc did not improve\n",
      "Epoch 3/1000\n",
      " - 1s - loss: 1.3522 - acc: 0.5528 - val_loss: 1.0102 - val_acc: 0.5000\n",
      "\n",
      "Epoch 00003: val_acc did not improve\n",
      "Epoch 4/1000\n",
      " - 1s - loss: 1.3996 - acc: 0.5333 - val_loss: 0.9026 - val_acc: 0.5000\n",
      "\n",
      "Epoch 00004: val_acc did not improve\n",
      "Epoch 5/1000\n",
      " - 1s - loss: 1.1776 - acc: 0.5667 - val_loss: 1.0176 - val_acc: 0.4889\n",
      "\n",
      "Epoch 00005: val_acc did not improve\n",
      "Epoch 6/1000\n",
      " - 1s - loss: 1.0936 - acc: 0.5444 - val_loss: 0.8030 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00006: val_acc did not improve\n",
      "Epoch 7/1000\n",
      " - 1s - loss: 1.1673 - acc: 0.5611 - val_loss: 0.7859 - val_acc: 0.5000\n",
      "\n",
      "Epoch 00007: val_acc did not improve\n",
      "Epoch 8/1000\n",
      " - 1s - loss: 0.8704 - acc: 0.6472 - val_loss: 0.8844 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00008: val_acc did not improve\n",
      "Epoch 9/1000\n",
      " - 1s - loss: 0.9349 - acc: 0.5944 - val_loss: 1.2361 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00009: val_acc did not improve\n",
      "Epoch 10/1000\n",
      " - 1s - loss: 0.8123 - acc: 0.6306 - val_loss: 0.9330 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00010: val_acc did not improve\n",
      "Epoch 11/1000\n",
      " - 1s - loss: 0.8337 - acc: 0.5917 - val_loss: 0.7748 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00011: val_acc did not improve\n",
      "Epoch 12/1000\n",
      " - 1s - loss: 0.8314 - acc: 0.6250 - val_loss: 1.0679 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00012: val_acc did not improve\n",
      "Epoch 13/1000\n",
      " - 1s - loss: 0.7498 - acc: 0.6389 - val_loss: 0.9568 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00013: val_acc did not improve\n",
      "Epoch 14/1000\n",
      " - 1s - loss: 0.6894 - acc: 0.6694 - val_loss: 0.8154 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.54444 to 0.55556, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5\n",
      "Epoch 15/1000\n",
      " - 1s - loss: 0.7265 - acc: 0.6528 - val_loss: 0.8880 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00015: val_acc did not improve\n",
      "Epoch 16/1000\n",
      " - 1s - loss: 0.6750 - acc: 0.6583 - val_loss: 0.6911 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00016: val_acc improved from 0.55556 to 0.57778, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5\n",
      "Epoch 17/1000\n",
      " - 1s - loss: 0.6151 - acc: 0.7111 - val_loss: 1.1573 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00017: val_acc did not improve\n",
      "Epoch 18/1000\n",
      " - 1s - loss: 0.6313 - acc: 0.6694 - val_loss: 0.6270 - val_acc: 0.6333\n",
      "\n",
      "Epoch 00018: val_acc improved from 0.57778 to 0.63333, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5\n",
      "Epoch 19/1000\n",
      " - 1s - loss: 0.6466 - acc: 0.6750 - val_loss: 0.9008 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00019: val_acc did not improve\n",
      "Epoch 20/1000\n",
      " - 1s - loss: 0.7097 - acc: 0.6722 - val_loss: 0.9692 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00020: val_acc did not improve\n",
      "Epoch 21/1000\n",
      " - 1s - loss: 0.6613 - acc: 0.6750 - val_loss: 1.0235 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00021: val_acc did not improve\n",
      "Epoch 22/1000\n",
      " - 1s - loss: 0.6523 - acc: 0.6694 - val_loss: 0.7590 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00022: val_acc did not improve\n",
      "Epoch 23/1000\n",
      " - 1s - loss: 0.5589 - acc: 0.7167 - val_loss: 0.9821 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00023: val_acc did not improve\n",
      "Epoch 24/1000\n",
      " - 1s - loss: 0.5552 - acc: 0.7639 - val_loss: 0.7957 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00024: val_acc did not improve\n",
      "Epoch 25/1000\n",
      " - 1s - loss: 0.5115 - acc: 0.7583 - val_loss: 0.7079 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00025: val_acc did not improve\n",
      "Epoch 26/1000\n",
      " - 1s - loss: 0.5467 - acc: 0.7278 - val_loss: 1.0636 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00026: val_acc did not improve\n",
      "Epoch 27/1000\n",
      " - 1s - loss: 0.5945 - acc: 0.6972 - val_loss: 0.6303 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00027: val_acc improved from 0.63333 to 0.65556, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5\n",
      "Epoch 28/1000\n",
      " - 1s - loss: 0.5658 - acc: 0.7333 - val_loss: 1.1105 - val_acc: 0.5000\n",
      "\n",
      "Epoch 00028: val_acc did not improve\n",
      "Epoch 29/1000\n",
      " - 1s - loss: 0.4831 - acc: 0.7917 - val_loss: 0.8948 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00029: val_acc did not improve\n",
      "Epoch 30/1000\n",
      " - 1s - loss: 0.4782 - acc: 0.7611 - val_loss: 0.9331 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00030: val_acc did not improve\n",
      "Epoch 31/1000\n",
      " - 1s - loss: 0.4947 - acc: 0.7750 - val_loss: 0.6998 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00031: val_acc did not improve\n",
      "Epoch 32/1000\n",
      " - 1s - loss: 0.5073 - acc: 0.7556 - val_loss: 0.8744 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00032: val_acc did not improve\n",
      "Epoch 33/1000\n",
      " - 1s - loss: 0.4770 - acc: 0.7694 - val_loss: 1.1386 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00033: val_acc did not improve\n",
      "Epoch 34/1000\n",
      " - 1s - loss: 0.5185 - acc: 0.7361 - val_loss: 0.6785 - val_acc: 0.6444\n",
      "\n",
      "Epoch 00034: val_acc did not improve\n",
      "Epoch 35/1000\n",
      " - 1s - loss: 0.4930 - acc: 0.7667 - val_loss: 0.8557 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00035: val_acc did not improve\n",
      "Epoch 36/1000\n",
      " - 1s - loss: 0.4943 - acc: 0.7500 - val_loss: 0.7245 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00036: val_acc did not improve\n",
      "Epoch 37/1000\n",
      " - 1s - loss: 0.4973 - acc: 0.7667 - val_loss: 0.8830 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00037: val_acc did not improve\n",
      "Epoch 38/1000\n",
      " - 1s - loss: 0.4517 - acc: 0.7972 - val_loss: 1.0798 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00038: val_acc did not improve\n",
      "Epoch 39/1000\n",
      " - 1s - loss: 0.4547 - acc: 0.7806 - val_loss: 0.9373 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00039: val_acc did not improve\n",
      "Epoch 40/1000\n",
      " - 1s - loss: 0.4750 - acc: 0.7778 - val_loss: 0.9531 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00040: val_acc did not improve\n",
      "Epoch 41/1000\n",
      " - 1s - loss: 0.4242 - acc: 0.8250 - val_loss: 1.1003 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00041: val_acc did not improve\n",
      "Epoch 42/1000\n",
      " - 1s - loss: 0.4306 - acc: 0.7889 - val_loss: 1.0071 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00042: val_acc did not improve\n",
      "Epoch 43/1000\n",
      " - 1s - loss: 0.4424 - acc: 0.7972 - val_loss: 1.0357 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00043: val_acc did not improve\n",
      "Epoch 44/1000\n",
      " - 1s - loss: 0.4182 - acc: 0.7889 - val_loss: 0.6197 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00044: val_acc improved from 0.65556 to 0.70000, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5\n",
      "Epoch 45/1000\n",
      " - 1s - loss: 0.4346 - acc: 0.7917 - val_loss: 1.1300 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00045: val_acc did not improve\n",
      "Epoch 46/1000\n",
      " - 1s - loss: 0.4418 - acc: 0.7778 - val_loss: 1.0689 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00046: val_acc did not improve\n",
      "Epoch 47/1000\n",
      " - 1s - loss: 0.4147 - acc: 0.8167 - val_loss: 1.1886 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00047: val_acc did not improve\n",
      "Epoch 48/1000\n",
      " - 1s - loss: 0.3946 - acc: 0.8194 - val_loss: 1.0852 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00048: val_acc did not improve\n",
      "Epoch 49/1000\n",
      " - 1s - loss: 0.4485 - acc: 0.8083 - val_loss: 1.0990 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00049: val_acc did not improve\n",
      "Epoch 50/1000\n",
      " - 1s - loss: 0.4367 - acc: 0.7833 - val_loss: 1.1242 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00050: val_acc did not improve\n",
      "Epoch 51/1000\n",
      " - 1s - loss: 0.3841 - acc: 0.8250 - val_loss: 0.8684 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00051: val_acc did not improve\n",
      "Epoch 52/1000\n",
      " - 1s - loss: 0.3900 - acc: 0.8167 - val_loss: 1.1707 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00052: val_acc did not improve\n",
      "Epoch 53/1000\n",
      " - 1s - loss: 0.4511 - acc: 0.8111 - val_loss: 0.9846 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00053: val_acc did not improve\n",
      "Epoch 54/1000\n",
      " - 1s - loss: 0.4118 - acc: 0.8083 - val_loss: 1.2003 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00054: val_acc did not improve\n",
      "Epoch 55/1000\n",
      " - 1s - loss: 0.4595 - acc: 0.7722 - val_loss: 0.8286 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00055: val_acc did not improve\n",
      "Epoch 56/1000\n",
      " - 1s - loss: 0.4501 - acc: 0.8000 - val_loss: 1.1917 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00056: val_acc did not improve\n",
      "Epoch 57/1000\n",
      " - 1s - loss: 0.4399 - acc: 0.8056 - val_loss: 1.1875 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00057: val_acc did not improve\n",
      "Epoch 58/1000\n",
      " - 1s - loss: 0.4218 - acc: 0.7917 - val_loss: 0.9683 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00058: val_acc did not improve\n",
      "Epoch 59/1000\n",
      " - 1s - loss: 0.3658 - acc: 0.8278 - val_loss: 1.1790 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00059: val_acc did not improve\n",
      "Epoch 60/1000\n",
      " - 1s - loss: 0.4415 - acc: 0.8000 - val_loss: 0.5530 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00060: val_acc improved from 0.70000 to 0.71111, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5\n",
      "Epoch 61/1000\n",
      " - 1s - loss: 0.3677 - acc: 0.8250 - val_loss: 1.2649 - val_acc: 0.5111\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00061: val_acc did not improve\n",
      "Epoch 62/1000\n",
      " - 1s - loss: 0.3946 - acc: 0.8278 - val_loss: 0.9796 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00062: val_acc did not improve\n",
      "Epoch 63/1000\n",
      " - 1s - loss: 0.3192 - acc: 0.8500 - val_loss: 0.8510 - val_acc: 0.6000\n",
      "\n",
      "Epoch 00063: val_acc did not improve\n",
      "Epoch 64/1000\n",
      " - 1s - loss: 0.3872 - acc: 0.8333 - val_loss: 1.2001 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00064: val_acc did not improve\n",
      "Epoch 65/1000\n",
      " - 1s - loss: 0.4509 - acc: 0.8167 - val_loss: 0.8696 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00065: val_acc did not improve\n",
      "Epoch 66/1000\n",
      " - 1s - loss: 0.3780 - acc: 0.8250 - val_loss: 0.7359 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00066: val_acc did not improve\n",
      "Epoch 67/1000\n",
      " - 1s - loss: 0.3107 - acc: 0.8611 - val_loss: 1.2579 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00067: val_acc did not improve\n",
      "Epoch 68/1000\n",
      " - 1s - loss: 0.4344 - acc: 0.8222 - val_loss: 0.6958 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00068: val_acc did not improve\n",
      "Epoch 69/1000\n",
      " - 1s - loss: 0.3449 - acc: 0.8500 - val_loss: 0.8056 - val_acc: 0.6444\n",
      "\n",
      "Epoch 00069: val_acc did not improve\n",
      "Epoch 70/1000\n",
      " - 1s - loss: 0.4030 - acc: 0.8250 - val_loss: 1.3317 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00070: val_acc did not improve\n",
      "Epoch 71/1000\n",
      " - 1s - loss: 0.3140 - acc: 0.8528 - val_loss: 1.3568 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00071: val_acc did not improve\n",
      "Epoch 72/1000\n",
      " - 1s - loss: 0.3940 - acc: 0.7972 - val_loss: 1.1225 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00072: val_acc did not improve\n",
      "Epoch 73/1000\n",
      " - 1s - loss: 0.3258 - acc: 0.8556 - val_loss: 0.9884 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00073: val_acc did not improve\n",
      "Epoch 74/1000\n",
      " - 1s - loss: 0.3428 - acc: 0.8500 - val_loss: 0.5574 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00074: val_acc did not improve\n",
      "Epoch 75/1000\n",
      " - 1s - loss: 0.3600 - acc: 0.8361 - val_loss: 0.6158 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00075: val_acc improved from 0.71111 to 0.71111, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5\n",
      "Epoch 76/1000\n",
      " - 1s - loss: 0.3564 - acc: 0.8472 - val_loss: 0.9232 - val_acc: 0.6000\n",
      "\n",
      "Epoch 00076: val_acc did not improve\n",
      "Epoch 77/1000\n",
      " - 1s - loss: 0.3794 - acc: 0.8361 - val_loss: 1.0326 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00077: val_acc did not improve\n",
      "Epoch 78/1000\n",
      " - 1s - loss: 0.2857 - acc: 0.8889 - val_loss: 1.3312 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00078: val_acc did not improve\n",
      "Epoch 79/1000\n",
      " - 1s - loss: 0.3343 - acc: 0.8611 - val_loss: 1.3319 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00079: val_acc did not improve\n",
      "Epoch 80/1000\n",
      " - 1s - loss: 0.3144 - acc: 0.8667 - val_loss: 0.8469 - val_acc: 0.6222\n",
      "\n",
      "Epoch 00080: val_acc did not improve\n",
      "Epoch 81/1000\n",
      " - 1s - loss: 0.3199 - acc: 0.8389 - val_loss: 1.0490 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00081: val_acc did not improve\n",
      "Epoch 82/1000\n",
      " - 1s - loss: 0.3069 - acc: 0.8778 - val_loss: 1.3058 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00082: val_acc did not improve\n",
      "Epoch 83/1000\n",
      " - 1s - loss: 0.3632 - acc: 0.8222 - val_loss: 1.1864 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00083: val_acc did not improve\n",
      "Epoch 84/1000\n",
      " - 1s - loss: 0.3291 - acc: 0.8778 - val_loss: 1.2214 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00084: val_acc did not improve\n",
      "Epoch 85/1000\n",
      " - 1s - loss: 0.3248 - acc: 0.8472 - val_loss: 1.4409 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00085: val_acc did not improve\n",
      "Epoch 86/1000\n",
      " - 1s - loss: 0.2939 - acc: 0.8833 - val_loss: 1.3783 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00086: val_acc did not improve\n",
      "Epoch 87/1000\n",
      " - 1s - loss: 0.3037 - acc: 0.8528 - val_loss: 1.2542 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00087: val_acc did not improve\n",
      "Epoch 88/1000\n",
      " - 1s - loss: 0.3488 - acc: 0.8667 - val_loss: 0.7243 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00088: val_acc did not improve\n",
      "Epoch 89/1000\n",
      " - 1s - loss: 0.3479 - acc: 0.8361 - val_loss: 0.5866 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00089: val_acc improved from 0.71111 to 0.72222, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5\n",
      "Epoch 90/1000\n",
      " - 1s - loss: 0.3265 - acc: 0.8472 - val_loss: 1.4684 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00090: val_acc did not improve\n",
      "Epoch 91/1000\n",
      " - 1s - loss: 0.3273 - acc: 0.8611 - val_loss: 1.4281 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00091: val_acc did not improve\n",
      "Epoch 92/1000\n",
      " - 1s - loss: 0.3369 - acc: 0.8667 - val_loss: 1.1013 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00092: val_acc did not improve\n",
      "Epoch 93/1000\n",
      " - 1s - loss: 0.3399 - acc: 0.8472 - val_loss: 0.7284 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00093: val_acc did not improve\n",
      "Epoch 94/1000\n",
      " - 1s - loss: 0.3896 - acc: 0.8417 - val_loss: 0.6880 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00094: val_acc did not improve\n",
      "Epoch 95/1000\n",
      " - 1s - loss: 0.3013 - acc: 0.8750 - val_loss: 1.2288 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00095: val_acc did not improve\n",
      "Epoch 96/1000\n",
      " - 1s - loss: 0.3028 - acc: 0.8806 - val_loss: 0.9352 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00096: val_acc did not improve\n",
      "Epoch 97/1000\n",
      " - 1s - loss: 0.3263 - acc: 0.8500 - val_loss: 1.2571 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00097: val_acc did not improve\n",
      "Epoch 98/1000\n",
      " - 1s - loss: 0.2571 - acc: 0.8917 - val_loss: 1.5131 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00098: val_acc did not improve\n",
      "Epoch 99/1000\n",
      " - 1s - loss: 0.2828 - acc: 0.8694 - val_loss: 1.0127 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00099: val_acc did not improve\n",
      "Epoch 100/1000\n",
      " - 1s - loss: 0.2912 - acc: 0.8861 - val_loss: 1.3341 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00100: val_acc did not improve\n",
      "Epoch 101/1000\n",
      " - 1s - loss: 0.3036 - acc: 0.8722 - val_loss: 1.4647 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00101: val_acc did not improve\n",
      "Epoch 102/1000\n",
      " - 1s - loss: 0.3752 - acc: 0.8222 - val_loss: 1.4955 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00102: val_acc did not improve\n",
      "Epoch 103/1000\n",
      " - 1s - loss: 0.2841 - acc: 0.8639 - val_loss: 1.0186 - val_acc: 0.6222\n",
      "\n",
      "Epoch 00103: val_acc did not improve\n",
      "Epoch 104/1000\n",
      " - 1s - loss: 0.3035 - acc: 0.8556 - val_loss: 0.9241 - val_acc: 0.6222\n",
      "\n",
      "Epoch 00104: val_acc did not improve\n",
      "Epoch 105/1000\n",
      " - 1s - loss: 0.2945 - acc: 0.8528 - val_loss: 1.5010 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00105: val_acc did not improve\n",
      "Epoch 106/1000\n",
      " - 1s - loss: 0.3353 - acc: 0.8611 - val_loss: 1.2492 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00106: val_acc did not improve\n",
      "Epoch 107/1000\n",
      " - 1s - loss: 0.2580 - acc: 0.8972 - val_loss: 1.5838 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00107: val_acc did not improve\n",
      "Epoch 108/1000\n",
      " - 1s - loss: 0.3338 - acc: 0.8639 - val_loss: 1.3623 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00108: val_acc did not improve\n",
      "Epoch 109/1000\n",
      " - 1s - loss: 0.3675 - acc: 0.8222 - val_loss: 0.7216 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00109: val_acc did not improve\n",
      "Epoch 110/1000\n",
      " - 1s - loss: 0.3225 - acc: 0.8472 - val_loss: 1.2892 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00110: val_acc did not improve\n",
      "Epoch 111/1000\n",
      " - 1s - loss: 0.2500 - acc: 0.9000 - val_loss: 1.4181 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00111: val_acc did not improve\n",
      "Epoch 112/1000\n",
      " - 1s - loss: 0.3194 - acc: 0.8528 - val_loss: 1.2128 - val_acc: 0.6000\n",
      "\n",
      "Epoch 00112: val_acc did not improve\n",
      "Epoch 113/1000\n",
      " - 1s - loss: 0.2861 - acc: 0.8833 - val_loss: 0.5981 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00113: val_acc did not improve\n",
      "Epoch 114/1000\n",
      " - 1s - loss: 0.2980 - acc: 0.8750 - val_loss: 1.5669 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00114: val_acc did not improve\n",
      "Epoch 115/1000\n",
      " - 1s - loss: 0.2424 - acc: 0.9167 - val_loss: 0.7432 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00115: val_acc did not improve\n",
      "Epoch 116/1000\n",
      " - 1s - loss: 0.3081 - acc: 0.8667 - val_loss: 1.4043 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00116: val_acc did not improve\n",
      "Epoch 117/1000\n",
      " - 1s - loss: 0.3022 - acc: 0.8639 - val_loss: 0.7016 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00117: val_acc did not improve\n",
      "Epoch 118/1000\n",
      " - 1s - loss: 0.2504 - acc: 0.9000 - val_loss: 1.3843 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00118: val_acc did not improve\n",
      "Epoch 119/1000\n",
      " - 1s - loss: 0.2439 - acc: 0.8889 - val_loss: 1.5445 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00119: val_acc did not improve\n",
      "Epoch 120/1000\n",
      " - 1s - loss: 0.3358 - acc: 0.8694 - val_loss: 1.0581 - val_acc: 0.6333\n",
      "\n",
      "Epoch 00120: val_acc did not improve\n",
      "Epoch 121/1000\n",
      " - 1s - loss: 0.2554 - acc: 0.9028 - val_loss: 1.5451 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00121: val_acc did not improve\n",
      "Epoch 122/1000\n",
      " - 1s - loss: 0.2839 - acc: 0.8833 - val_loss: 1.1221 - val_acc: 0.6333\n",
      "\n",
      "Epoch 00122: val_acc did not improve\n",
      "Epoch 123/1000\n",
      " - 1s - loss: 0.2552 - acc: 0.8806 - val_loss: 0.7345 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00123: val_acc did not improve\n",
      "Epoch 124/1000\n",
      " - 1s - loss: 0.2925 - acc: 0.8806 - val_loss: 1.0309 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00124: val_acc did not improve\n",
      "Epoch 125/1000\n",
      " - 1s - loss: 0.2867 - acc: 0.8833 - val_loss: 1.2045 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00125: val_acc did not improve\n",
      "Epoch 126/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 1s - loss: 0.2271 - acc: 0.9028 - val_loss: 1.6738 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00126: val_acc did not improve\n",
      "Epoch 127/1000\n",
      " - 1s - loss: 0.2613 - acc: 0.8944 - val_loss: 1.4439 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00127: val_acc did not improve\n",
      "Epoch 128/1000\n",
      " - 1s - loss: 0.2831 - acc: 0.8833 - val_loss: 0.7475 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00128: val_acc did not improve\n",
      "Epoch 129/1000\n",
      " - 1s - loss: 0.3072 - acc: 0.8694 - val_loss: 0.7804 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00129: val_acc did not improve\n",
      "Epoch 130/1000\n",
      " - 1s - loss: 0.3038 - acc: 0.8444 - val_loss: 1.3252 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00130: val_acc did not improve\n",
      "Epoch 131/1000\n",
      " - 1s - loss: 0.2167 - acc: 0.9028 - val_loss: 0.8589 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00131: val_acc did not improve\n",
      "Epoch 132/1000\n",
      " - 1s - loss: 0.2800 - acc: 0.8861 - val_loss: 1.5758 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00132: val_acc did not improve\n",
      "Epoch 133/1000\n",
      " - 1s - loss: 0.3413 - acc: 0.8667 - val_loss: 0.8618 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00133: val_acc did not improve\n",
      "Epoch 134/1000\n",
      " - 1s - loss: 0.2401 - acc: 0.8722 - val_loss: 1.5117 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00134: val_acc did not improve\n",
      "Epoch 135/1000\n",
      " - 1s - loss: 0.3078 - acc: 0.8722 - val_loss: 0.6819 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00135: val_acc did not improve\n",
      "Epoch 136/1000\n",
      " - 1s - loss: 0.2717 - acc: 0.8972 - val_loss: 1.5900 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00136: val_acc did not improve\n",
      "Epoch 137/1000\n",
      " - 1s - loss: 0.2539 - acc: 0.8917 - val_loss: 1.2024 - val_acc: 0.6222\n",
      "\n",
      "Epoch 00137: val_acc did not improve\n",
      "Epoch 138/1000\n",
      " - 1s - loss: 0.2234 - acc: 0.8944 - val_loss: 1.0406 - val_acc: 0.6333\n",
      "\n",
      "Epoch 00138: val_acc did not improve\n",
      "Epoch 139/1000\n",
      " - 1s - loss: 0.3035 - acc: 0.8472 - val_loss: 1.3171 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00139: val_acc did not improve\n",
      "Epoch 140/1000\n",
      " - 1s - loss: 0.2690 - acc: 0.8917 - val_loss: 1.2426 - val_acc: 0.6000\n",
      "\n",
      "Epoch 00140: val_acc did not improve\n",
      "Epoch 141/1000\n",
      " - 1s - loss: 0.2424 - acc: 0.9056 - val_loss: 1.4616 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00141: val_acc did not improve\n",
      "Epoch 142/1000\n",
      " - 1s - loss: 0.3070 - acc: 0.8694 - val_loss: 1.6206 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00142: val_acc did not improve\n",
      "Epoch 143/1000\n",
      " - 1s - loss: 0.2827 - acc: 0.8444 - val_loss: 1.5797 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00143: val_acc did not improve\n",
      "Epoch 144/1000\n",
      " - 1s - loss: 0.2641 - acc: 0.8861 - val_loss: 1.5589 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00144: val_acc did not improve\n",
      "Epoch 145/1000\n",
      " - 1s - loss: 0.3431 - acc: 0.8667 - val_loss: 1.2434 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00145: val_acc did not improve\n",
      "Epoch 146/1000\n",
      " - 1s - loss: 0.2367 - acc: 0.9000 - val_loss: 1.6161 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00146: val_acc did not improve\n",
      "Epoch 147/1000\n",
      " - 1s - loss: 0.3049 - acc: 0.8833 - val_loss: 0.9746 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00147: val_acc did not improve\n",
      "Epoch 148/1000\n",
      " - 1s - loss: 0.2043 - acc: 0.9056 - val_loss: 1.6067 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00148: val_acc did not improve\n",
      "Epoch 149/1000\n",
      " - 1s - loss: 0.2264 - acc: 0.8972 - val_loss: 1.2029 - val_acc: 0.6333\n",
      "\n",
      "Epoch 00149: val_acc did not improve\n",
      "Epoch 150/1000\n",
      " - 1s - loss: 0.2528 - acc: 0.8917 - val_loss: 1.6169 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00150: val_acc did not improve\n",
      "Epoch 151/1000\n",
      " - 1s - loss: 0.2220 - acc: 0.8833 - val_loss: 1.5368 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00151: val_acc did not improve\n",
      "Epoch 152/1000\n",
      " - 1s - loss: 0.1948 - acc: 0.9333 - val_loss: 1.5049 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00152: val_acc did not improve\n",
      "Epoch 153/1000\n",
      " - 1s - loss: 0.2979 - acc: 0.8806 - val_loss: 1.4850 - val_acc: 0.6000\n",
      "\n",
      "Epoch 00153: val_acc did not improve\n",
      "Epoch 154/1000\n",
      " - 1s - loss: 0.2431 - acc: 0.8806 - val_loss: 1.7414 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00154: val_acc did not improve\n",
      "Epoch 155/1000\n",
      " - 1s - loss: 0.1992 - acc: 0.9056 - val_loss: 1.7185 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00155: val_acc did not improve\n",
      "Epoch 156/1000\n",
      " - 1s - loss: 0.2612 - acc: 0.8889 - val_loss: 0.7945 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00156: val_acc did not improve\n",
      "Epoch 157/1000\n",
      " - 1s - loss: 0.2587 - acc: 0.8722 - val_loss: 1.7161 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00157: val_acc did not improve\n",
      "Epoch 158/1000\n",
      " - 1s - loss: 0.2837 - acc: 0.8583 - val_loss: 0.8826 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00158: val_acc did not improve\n",
      "Epoch 159/1000\n",
      " - 1s - loss: 0.2710 - acc: 0.8861 - val_loss: 1.7862 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00159: val_acc did not improve\n",
      "Epoch 160/1000\n",
      " - 1s - loss: 0.3224 - acc: 0.8583 - val_loss: 1.8856 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00160: val_acc did not improve\n"
     ]
    }
   ],
   "source": [
    "# Training time!\n",
    "from keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=100, mode='min')\n",
    "checkpointer = ModelCheckpoint(filepath='/home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5',\n",
    "                               verbose=1,\n",
    "                               monitor='val_acc',\n",
    "                               save_best_only=True)\n",
    "TB = TensorBoard(log_dir='/home/amplifier/home/CNN_stim')\n",
    "\n",
    "train_history = model.fit(x_train, y_train,\n",
    "                epochs=1000,\n",
    "                batch_size=BATCH_SIZE,\n",
    "                verbose=2,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, y_test),\n",
    "                callbacks=[TB, checkpointer, early_stopping])\n",
    "\n",
    "# model.save('/home/amplifier/home/NEW_DL/models/EEGnet.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 2)\n",
      "(90, 2)\n"
     ]
    }
   ],
   "source": [
    "# load the best model:\n",
    "model.load_weights('/home/amplifier/home/NEW_DL/weights/EEGnet_weights_2D.h5')\n",
    "\n",
    "# let's predict a TRAINING sammple:\n",
    "decoded_train = model.predict(x_train)\n",
    "decoded_test = model.predict(x_test)\n",
    "print(decoded_train.shape)\n",
    "print(decoded_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[31 13]\n",
      " [12 34]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e4eaada58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "W = []\n",
    "GT = []\n",
    "\n",
    "for i in range(len(decoded_test)):\n",
    "    if decoded_test[i][0]<decoded_test[i][1]:\n",
    "        W.append('right')\n",
    "    if decoded_test[i][0]>decoded_test[i][1]:\n",
    "        W.append('left')\n",
    "\n",
    "    if y_test[i][0]<y_test[i][1]:\n",
    "        GT.append('right')\n",
    "    if y_test[i][0]>y_test[i][1]:\n",
    "        GT.append('left')\n",
    "        \n",
    "from sklearn.metrics import confusion_matrix\n",
    "labels = ['left', 'right'] # 1 for right, 0 for left\n",
    "cm = confusion_matrix(GT, W, labels)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.matshow(cm)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "ax.set_xticklabels([''] + labels)\n",
    "ax.set_yticklabels([''] + labels)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d01aa3470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####################################################\n",
    "\n",
    "# print(np.array(history.losses))\n",
    "loss = train_history.history['loss']\n",
    "val_loss = train_history.history['val_loss']\n",
    "plt.plot(loss)\n",
    "plt.plot(val_loss)\n",
    "plt.legend(['loss', 'val_loss'])\n",
    "plt.title('AdaDelta')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the test data: 0.722222222222\n",
      "Accuracy on the training data: 0.944444444444\n"
     ]
    }
   ],
   "source": [
    "##########################################################\n",
    "\n",
    "# now test sample by sample\n",
    "cor_test = []\n",
    "L = x_test.shape\n",
    "for sample_no in range(x_test.shape[0]):\n",
    "    pred = model.predict(np.expand_dims(x_test[sample_no],0))\n",
    "    if ((pred[0][0]<pred[0][1]) and (y_test[sample_no,0] < y_test[sample_no,1]) or\n",
    "       ((pred[0][0]>pred[0][1]) & (y_test[sample_no,0] > y_test[sample_no,1]))):\n",
    "        cor_test.append(1)\n",
    "    else:\n",
    "        cor_test.append(0)\n",
    "\n",
    "cor_train = []\n",
    "L = x_train.shape\n",
    "for sample_no in range(x_train.shape[0]):\n",
    "    pred = model.predict(np.expand_dims(x_train[sample_no],0))\n",
    "    if ((pred[0][0]<pred[0][1]) and (y_train[sample_no,0] < y_train[sample_no,1]) or\n",
    "       ((pred[0][0]>pred[0][1]) & (y_train[sample_no,0] > y_train[sample_no,1]))):\n",
    "        cor_train.append(1)\n",
    "    else:\n",
    "        cor_train.append(0)\n",
    "\n",
    "# Report accuracies\n",
    "print('Accuracy on the test data:', np.mean(cor_test))\n",
    "print('Accuracy on the training data:', np.mean(cor_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv   \n",
    "fields=['2D', 'MY04', np.mean(cor_test), np.mean(cor_train)]\n",
    "with open(r'/home/amplifier/home/NEW_DL/weights/document.csv', 'a') as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow(fields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.display import SVG\n",
    "# from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "# SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "\n",
    "def factors(n):    \n",
    "    return set(reduce(list.__add__, \n",
    "                ([i, n//i] for i in range(1, int(pow(n, 0.5) + 1)) if n % i == 0)))\n",
    "\n",
    "def sqfac(input):\n",
    "    xx = list(factors(input))\n",
    "    xx.sort()\n",
    "    ind = []\n",
    "    ind1 = int(len(xx)/2)-1\n",
    "    ind.append(xx[ind1])\n",
    "    ind.append(xx[ind1+1])\n",
    "    ind.sort(reverse=True)\n",
    "    return ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 10\n"
     ]
    }
   ],
   "source": [
    "print(I, J)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e4c1393c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_1st_conv_layer_output = K.function([model.layers[0].input],\n",
    "                                  [model.layers[3].output])\n",
    "layer_output = get_1st_conv_layer_output([np.expand_dims(x_train[3],0)])[0]\n",
    "\n",
    "count = layer_output.shape[-1]\n",
    "ind = sqfac(count)\n",
    "I = ind[0]\n",
    "J = ind[1]\n",
    "\n",
    "fig, axs = plt.subplots(I, J)\n",
    "fig.set_size_inches(18, 18)\n",
    "axs = axs.ravel()\n",
    "\n",
    "for chan in range(count):\n",
    "    im = layer_output[-1,:,:,chan]\n",
    "    axs[chan].imshow(im)\n",
    "#     axs[c].get_xaxis().set_visible(False)\n",
    "#     axs[c].get_yaxis().set_visible(False) \n",
    "    axs[chan].set_title('Chan' + str(c+1) + ' ' ) # + str(ch_name[chan])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer number: 2 Layer name: conv2d_76\n",
      "(1, 100, 1, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Convolution kernels in layer conv2d_76')"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e575119b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize a few kernels from the first convolutional layer:\n",
    "# enc.layers[2].get_config()\n",
    "# dir(enc.layers[2])\n",
    "x = model.layers[2].get_weights()\n",
    "print ('Layer number:', 2, 'Layer name:', model.layers[2].name)\n",
    "sh = x[0].shape\n",
    "print(sh) # kernel length x input channels x output channels\n",
    "\n",
    "ind = sqfac(sh[-1])\n",
    "I = ind[0]\n",
    "J = ind[1]\n",
    "\n",
    "c = 0\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 4)\n",
    "axs = axs.ravel()\n",
    "for i in range(I):\n",
    "    for j in range(J):\n",
    "        axs[c].plot(x[0][:,:,:,j].reshape(100,1))\n",
    "        axs[c].get_xaxis().set_visible(False)\n",
    "        axs[c].get_yaxis().set_visible(False)        \n",
    "        axs[c].set_title(str(i)+str(j))\n",
    "        c = c + 1\n",
    "plt.suptitle(('Convolution kernels in layer ' + model.layers[2].name), fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 1, 10)\n",
      "(100, 10)\n",
      "(100, 10)\n",
      "(100, 10)\n",
      "(50,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:26: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'FFT of kernels in layer conv2d_76')"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d000ee048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.fftpack\n",
    "\n",
    "I = x[0].shape[2]\n",
    "J = x[0].shape[3]\n",
    "\n",
    "x = model.layers[2].get_weights()\n",
    "print(x[0].shape)\n",
    "\n",
    "y = x[0].reshape(x[0].shape[1], x[0].shape[3])\n",
    "fs = 250\n",
    "# T = 1/fs * filt.shape[0]\n",
    "# f = 50\n",
    "# t = np.arange(0,T, 1/fs)\n",
    "\n",
    "# add diagnostic 50-Hz hum:\n",
    "# for i in range(y.shape[1]):\n",
    "#     y[:,i] = y[:,i] + 0.1 * np.sin(2.0*np.pi*f*t)\n",
    "\n",
    "print(y.shape)\n",
    "\n",
    "yf = scipy.fftpack.fft(y, axis=0)\n",
    "print(yf.shape)\n",
    "\n",
    "YF = np.abs(yf)\n",
    "print(YF.shape)\n",
    "xf = np.linspace(0,fs/2, YF.shape[0]/2)\n",
    "print(xf.shape)\n",
    "\n",
    "c = 0\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18, 3)\n",
    "axs = axs.ravel()\n",
    "for i in range(I):\n",
    "    for j in range(J):\n",
    "        YF_trunc = YF[0:int(len(YF)/2),j]\n",
    "        axs[c].plot(xf, YF_trunc)\n",
    "        axs[c].get_xaxis().set_visible(True)\n",
    "        axs[c].get_yaxis().set_visible(False)        \n",
    "        axs[c].set_title(str(c))\n",
    "        c = c + 1\n",
    "plt.suptitle('FFT of kernels in layer ' + model.layers[2].name, fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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