EEGnet_CNN_1D_RECONST_NEW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "\n",
    "# load https://cs1.neuroimaging.group/user/nightman/notebooks/home/NEW_DL/EEGnet_CNN_Dense_Binary_Classifier_TD_1D.ipynb#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",
    "\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_0303T_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_0603T_TD.mat')\n",
    "# mat_contents = sio.loadmat('/home/amplifier/home/DATASETS/BCI_0703T_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/BCI_FD.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",
    "# 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->75-134Hz,Env=1_TD.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",
    "# mat_contents = sio.loadmat(\n",
    "#             '/home/amplifier/home/DATASETS/Merged123, DS2=80Hz, FIR=1-9Hz, centnorm=1, Env=1, TD, 143-202.mat')\n",
    "# mat_contents = sio.loadmat(\n",
    "#     '/home/amplifier/home/DATASETS/DAS_CH.mat, DS2=250Hz, FIR=1-30Hz, centnorm=1, Env=1, TD, 1-124.mat')\n",
    "\n",
    "# mat_contents = sio.loadmat(\n",
    "#     '/home/amplifier/home/DATASETS/DAS_CH_AVG_500Hz_2.mat, DS2=100Hz, FIR=1-9Hz, centnorm=1, Env=1, TD, 1-124.mat')\n",
    "mat_contents = sio.loadmat(\n",
    "    '/home/amplifier/home/DATASETS/DAS_CH_AVG_500Hz_2.mat, DS2=100Hz, FIR=1-40Hz, centnorm=1, Env=1, TD, 1-124.mat')\n",
    "\n",
    "SHUFFLE = True\n",
    "BATCH_SIZE = 20\n",
    "TEST_TRAIN = 0.2\n",
    "\n",
    "KERNEL_SIZE = (50, 50, 50, 50, 2) # (100, 50, 50, 100, 1) #\n",
    "N_KERNELS = (30,15,15,30,1)\n",
    "POOL_SIZE = (2,2)\n",
    "\n",
    "\n",
    "DROPOUT = (0.2, 0.5, 0.5, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data shape: (1200, 200, 63)\n",
      "Audio shape: (1200, 200)\n",
      "Original labels shape: (1200, 1)\n",
      "Original data type: float32\n",
      "test input shape (240, 200, 63) Nomralized MEAN: 0.49683 min 0.0 max 1.0\n",
      "train input shape (960, 200, 63) Nomralized MEAN: 0.49634 min 0.0 max 1.0\n",
      "test audio shape (240, 200, 1) Nomralized MEAN: 0.27832 min 0.0 max 1.0\n",
      "train audio shape (960, 200, 1) Nomralized MEAN: 0.28223 min 0.0 max 1.0\n",
      "test labels shape (240, 2) Nomralized MEAN: 0.5 min 0.0 max 1.0\n",
      "train labels shape (960, 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['Y']\n",
    "Z = mat_contents['Z']\n",
    "\n",
    "if X.shape[1]<X.shape[2]:\n",
    "    X = np.transpose(X,[0,2,1])\n",
    "\n",
    "# if Y.shape[1] > Y.shape[0]:\n",
    "#     Y = Y.T\n",
    "    \n",
    "print('Original data shape:', X.shape)\n",
    "print('Audio shape:', Y.shape)\n",
    "print('Original labels shape:', Z.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, z_train, z_test, q_train, q_test = train_test_split(X, Y, Z, Q, 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",
    "z_train = z_train.astype('float64')\n",
    "x_test = x_test.astype('float64')\n",
    "y_test = y_test.astype('float64')\n",
    "z_test = z_test.astype('float64')\n",
    "\n",
    "# normalize to unit variance and zero mean:\n",
    "for i in range(x_train.shape[0]):\n",
    "    x_train[i,:,:] = preprocessing.scale(x_train[i,:,:], axis=1)\n",
    "    x_train[i,:,:] = preprocessing.minmax_scale(x_train[i,:,:], axis=1)\n",
    "    y_train[i,:] = preprocessing.scale(y_train[i,:], axis=0)\n",
    "    y_train[i,:] = preprocessing.minmax_scale(y_train[i,:], axis=0)\n",
    "\n",
    "for i in range(x_test.shape[0]):\n",
    "    x_test[i,:,:] = preprocessing.scale(x_test[i,:,:], axis=1)\n",
    "    x_test[i,:,:] = preprocessing.minmax_scale(x_test[i,:,:], axis=1)\n",
    "    y_test[i,:] = preprocessing.scale(y_test[i,:], axis=0)\n",
    "    y_test[i,:] = preprocessing.minmax_scale(y_test[i,:], axis=0)\n",
    "\n",
    "# one hot encode the labels:\n",
    "onehot_encoder = preprocessing.OneHotEncoder(sparse=False)\n",
    "z_train = onehot_encoder.fit_transform(z_train)\n",
    "z_test = onehot_encoder.fit_transform(z_test)\n",
    "\n",
    "# convert to float16 to save space:\n",
    "x_train = x_train.astype('float16')\n",
    "y_train = y_train.astype('float16')\n",
    "z_train = z_train.astype('float16')\n",
    "x_test = x_test.astype('float16')\n",
    "y_test = y_test.astype('float16')\n",
    "z_test = z_test.astype('float16')\n",
    "\n",
    "leng = X.shape[1] # if you work in the FD, this is the height of the sample time-frequency image, othewise EEG channels\n",
    "chan = 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",
    "y_test = np.expand_dims(y_test,-1)\n",
    "y_train = np.expand_dims(y_train,-1)\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 audio shape', y_test.shape, \"Nomralized MEAN:\", np.mean(y_test), \"min\", np.min(y_test),\"max\", np.max(y_test))\n",
    "print('train audio shape', y_train.shape, \"Nomralized MEAN:\", np.mean(y_train), \"min\", np.min(y_train),\"max\", np.max(y_train))\n",
    "\n",
    "print('test labels shape', z_test.shape, \"Nomralized MEAN:\", np.mean(z_test), \"min\", np.min(z_test),\"max\", np.max(z_test))\n",
    "print('train labels shape', z_train.shape, \"Nomralized MEAN:\", np.mean(z_train), \"min\", np.min(z_train),\"max\", np.max(z_train))\n",
    "\n",
    "# print('Window length', winsize)\n",
    "# print('Step size:', stepsize)\n",
    "# print('Trial length:', trial_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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, Conv1D, UpSampling1D, Reshape, Lambda\n",
    "from keras.layers import MaxPooling1D, Dropout, BatchNormalization, Flatten, ELU, Activation\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 activation():\n",
    "    return Activation('relu') #ELU(alpha=1.0) # \n",
    "\n",
    "def correlation_coefficient_loss(y_true, y_pred):\n",
    "    x = y_true\n",
    "    y = y_pred\n",
    "    mx = K.mean(x)\n",
    "    my = K.mean(y)\n",
    "    xm, ym = x-mx, y-my\n",
    "    r_num = K.sum(tf.multiply(xm,ym))\n",
    "    r_den = K.sqrt(tf.multiply(K.sum(K.square(xm)), K.sum(K.square(ym))))\n",
    "    r = r_num / r_den\n",
    "\n",
    "    r = K.maximum(K.minimum(r, 1.0), -1.0)\n",
    "    return 1 - K.square(r)\n",
    "\n",
    "def Squeeze(input_img):\n",
    "    return K.squeeze(input_img, axis=2)\n",
    "\n",
    "def conv_net(leng):\n",
    "    input_img = Input(shape=(leng, chan))  # adapt this if using `channels_first` image\n",
    "    input_norm = BatchNormalization()(input_img)\n",
    "    \n",
    "    e2 = Conv1D(N_KERNELS[0], KERNEL_SIZE[0], padding='same',\n",
    "                                    kernel_initializer = orth(gain=1.0, seed=None))(input_norm)\n",
    "    e2act = activation()(e2)\n",
    "    e2p = MaxPooling1D(pool_size=POOL_SIZE[0], strides=None, padding='same')(e2act)\n",
    "    e2b = BatchNormalization()(e2p)\n",
    "    e2a = Dropout(DROPOUT[0])(e2b)\n",
    "    \n",
    "    \n",
    "    e3 = Conv1D(N_KERNELS[1], KERNEL_SIZE[1], padding='same',\n",
    "                                    kernel_initializer = orth(gain=1.0, seed=None))(e2a)\n",
    "    e3act = activation()(e3)\n",
    "    e3p = MaxPooling1D(pool_size=POOL_SIZE[1], strides=None, padding='same')(e3act)\n",
    "    e3b = BatchNormalization()(e3p)\n",
    "    e3a = Dropout(DROPOUT[1])(e3b)\n",
    "    \n",
    "    \n",
    "    e4 = Conv1D(N_KERNELS[2], KERNEL_SIZE[2], padding='same',\n",
    "                                    kernel_initializer = orth(gain=1.0, seed=None))(e3a)\n",
    "    e4act = activation()(e4)\n",
    "    e4u = UpSampling1D(size=2)(e4act)\n",
    "    e4b = BatchNormalization()(e4u)\n",
    "    e4a = Dropout(DROPOUT[2])(e4b)\n",
    "    \n",
    "    \n",
    "    e5 = Conv1D(N_KERNELS[3], KERNEL_SIZE[3], padding='same',\n",
    "                                    kernel_initializer = orth(gain=1.0, seed=None))(e4a)\n",
    "    e5act = activation()(e5)\n",
    "    e5u = UpSampling1D(size=2)(e5act)\n",
    "    e5b = BatchNormalization()(e5u)\n",
    "    e5a = Dropout(DROPOUT[3])(e5b)\n",
    "    \n",
    "    \n",
    "    e6 = Conv1D(N_KERNELS[4], KERNEL_SIZE[4], padding='same',\n",
    "                                    kernel_initializer = orth(gain=1.0, seed=None))(e5a)\n",
    "    e6act = activation()(e6)\n",
    "    e6b = BatchNormalization()(e6act)\n",
    "#     e6r = Lambda(Squeeze)(e6b)\n",
    "    \n",
    "    return Model(input_img, e6b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_8 (InputLayer)         (None, 200, 63)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_19 (Batc (None, 200, 63)           252       \n",
      "_________________________________________________________________\n",
      "conv1d_16 (Conv1D)           (None, 200, 30)           94530     \n",
      "_________________________________________________________________\n",
      "activation_16 (Activation)   (None, 200, 30)           0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_7 (MaxPooling1 (None, 100, 30)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_20 (Batc (None, 100, 30)           120       \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 100, 30)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_17 (Conv1D)           (None, 100, 15)           22515     \n",
      "_________________________________________________________________\n",
      "activation_17 (Activation)   (None, 100, 15)           0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_8 (MaxPooling1 (None, 50, 15)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_21 (Batc (None, 50, 15)            60        \n",
      "_________________________________________________________________\n",
      "dropout_14 (Dropout)         (None, 50, 15)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_18 (Conv1D)           (None, 50, 15)            11265     \n",
      "_________________________________________________________________\n",
      "activation_18 (Activation)   (None, 50, 15)            0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_7 (UpSampling1 (None, 100, 15)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_22 (Batc (None, 100, 15)           60        \n",
      "_________________________________________________________________\n",
      "dropout_15 (Dropout)         (None, 100, 15)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_19 (Conv1D)           (None, 100, 30)           22530     \n",
      "_________________________________________________________________\n",
      "activation_19 (Activation)   (None, 100, 30)           0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_8 (UpSampling1 (None, 200, 30)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_23 (Batc (None, 200, 30)           120       \n",
      "_________________________________________________________________\n",
      "dropout_16 (Dropout)         (None, 200, 30)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_20 (Conv1D)           (None, 200, 1)            61        \n",
      "_________________________________________________________________\n",
      "activation_20 (Activation)   (None, 200, 1)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_24 (Batc (None, 200, 1)            4         \n",
      "=================================================================\n",
      "Total params: 151,517\n",
      "Trainable params: 151,209\n",
      "Non-trainable params: 308\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define input to the model:\n",
    "x = Input(shape=(leng, chan))\n",
    "\n",
    "# make the model:\n",
    "model = conv_net(leng)\n",
    "\n",
    "# compile the model:\n",
    "# sgd = optimizers.SGD(lr=0.01, decay=1e-4, 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(optimizer='adadelta', loss=correlation_coefficient_loss)\n",
    "model.compile(optimizer='adadelta', loss='mean_squared_error')\n",
    "\n",
    "\n",
    "# SEE INSIDE THE MODEL:\n",
    "# conv_net().summary() #or\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 960 samples, validate on 240 samples\n",
      "Epoch 1/1000\n",
      " - 4s - loss: 1.0617 - val_loss: 1.8664\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.86639, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 2/1000\n",
      " - 1s - loss: 0.8894 - val_loss: 2.2427\n",
      "\n",
      "Epoch 00002: val_loss did not improve\n",
      "Epoch 3/1000\n",
      " - 1s - loss: 0.7504 - val_loss: 1.3728\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.86639 to 1.37280, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 4/1000\n",
      " - 1s - loss: 0.6271 - val_loss: 0.7073\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.37280 to 0.70731, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 5/1000\n",
      " - 1s - loss: 0.5202 - val_loss: 0.5125\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.70731 to 0.51246, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 6/1000\n",
      " - 1s - loss: 0.4287 - val_loss: 0.5208\n",
      "\n",
      "Epoch 00006: val_loss did not improve\n",
      "Epoch 7/1000\n",
      " - 1s - loss: 0.3562 - val_loss: 0.3906\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.51246 to 0.39060, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 8/1000\n",
      " - 1s - loss: 0.2887 - val_loss: 0.5245\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      "Epoch 9/1000\n",
      " - 1s - loss: 0.2353 - val_loss: 0.3121\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.39060 to 0.31208, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 10/1000\n",
      " - 1s - loss: 0.1896 - val_loss: 0.2301\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.31208 to 0.23013, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 11/1000\n",
      " - 1s - loss: 0.1532 - val_loss: 0.2349\n",
      "\n",
      "Epoch 00011: val_loss did not improve\n",
      "Epoch 12/1000\n",
      " - 1s - loss: 0.1245 - val_loss: 0.1509\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.23013 to 0.15086, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 13/1000\n",
      " - 1s - loss: 0.1038 - val_loss: 0.1230\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.15086 to 0.12300, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 14/1000\n",
      " - 1s - loss: 0.0875 - val_loss: 0.1128\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.12300 to 0.11276, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 15/1000\n",
      " - 1s - loss: 0.0756 - val_loss: 0.1028\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.11276 to 0.10284, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 16/1000\n",
      " - 1s - loss: 0.0684 - val_loss: 0.0886\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.10284 to 0.08857, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 17/1000\n",
      " - 1s - loss: 0.0635 - val_loss: 0.0774\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.08857 to 0.07744, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 18/1000\n",
      " - 1s - loss: 0.0612 - val_loss: 0.0732\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.07744 to 0.07318, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 19/1000\n",
      " - 1s - loss: 0.0600 - val_loss: 0.0691\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.07318 to 0.06914, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 20/1000\n",
      " - 1s - loss: 0.0596 - val_loss: 0.0699\n",
      "\n",
      "Epoch 00020: val_loss did not improve\n",
      "Epoch 21/1000\n",
      " - 2s - loss: 0.0591 - val_loss: 0.0714\n",
      "\n",
      "Epoch 00021: val_loss did not improve\n",
      "Epoch 22/1000\n",
      " - 1s - loss: 0.0590 - val_loss: 0.0687\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.06914 to 0.06871, saving model to /home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5\n",
      "Epoch 23/1000\n",
      " - 1s - loss: 0.0587 - val_loss: 0.0696\n",
      "\n",
      "Epoch 00023: val_loss did not improve\n",
      "Epoch 24/1000\n",
      " - 1s - loss: 0.0586 - val_loss: 0.0702\n",
      "\n",
      "Epoch 00024: val_loss did not improve\n",
      "Epoch 25/1000\n",
      " - 1s - loss: 0.0585 - val_loss: 0.0715\n",
      "\n",
      "Epoch 00025: val_loss did not improve\n",
      "Epoch 26/1000\n",
      " - 1s - loss: 0.0580 - val_loss: 0.0705\n",
      "\n",
      "Epoch 00026: val_loss did not improve\n",
      "Epoch 27/1000\n",
      " - 1s - loss: 0.0582 - val_loss: 0.0705\n",
      "\n",
      "Epoch 00027: val_loss did not improve\n",
      "Epoch 28/1000\n",
      " - 1s - loss: 0.0575 - val_loss: 0.0712\n",
      "\n",
      "Epoch 00028: val_loss did not improve\n",
      "Epoch 29/1000\n",
      " - 1s - loss: 0.0575 - val_loss: 0.0720\n",
      "\n",
      "Epoch 00029: val_loss did not improve\n",
      "Epoch 30/1000\n",
      " - 1s - loss: 0.0573 - val_loss: 0.0702\n",
      "\n",
      "Epoch 00030: val_loss did not improve\n",
      "Epoch 31/1000\n",
      " - 1s - loss: 0.0572 - val_loss: 0.0722\n",
      "\n",
      "Epoch 00031: val_loss did not improve\n",
      "Epoch 32/1000\n",
      " - 1s - loss: 0.0570 - val_loss: 0.0704\n",
      "\n",
      "Epoch 00032: val_loss did not improve\n",
      "Epoch 33/1000\n",
      " - 1s - loss: 0.0566 - val_loss: 0.0702\n",
      "\n",
      "Epoch 00033: val_loss did not improve\n",
      "Epoch 34/1000\n",
      " - 1s - loss: 0.0568 - val_loss: 0.0722\n",
      "\n",
      "Epoch 00034: val_loss did not improve\n",
      "Epoch 35/1000\n",
      " - 1s - loss: 0.0563 - val_loss: 0.0714\n",
      "\n",
      "Epoch 00035: val_loss did not improve\n",
      "Epoch 36/1000\n",
      " - 1s - loss: 0.0560 - val_loss: 0.0701\n",
      "\n",
      "Epoch 00036: val_loss did not improve\n",
      "Epoch 37/1000\n",
      " - 1s - loss: 0.0562 - val_loss: 0.0691\n",
      "\n",
      "Epoch 00037: val_loss did not improve\n",
      "Epoch 38/1000\n",
      " - 1s - loss: 0.0558 - val_loss: 0.0706\n",
      "\n",
      "Epoch 00038: val_loss did not improve\n",
      "Epoch 39/1000\n",
      " - 1s - loss: 0.0555 - val_loss: 0.0696\n",
      "\n",
      "Epoch 00039: val_loss did not improve\n",
      "Epoch 40/1000\n",
      " - 1s - loss: 0.0552 - val_loss: 0.0703\n",
      "\n",
      "Epoch 00040: val_loss did not improve\n",
      "Epoch 41/1000\n",
      " - 1s - loss: 0.0553 - val_loss: 0.0706\n",
      "\n",
      "Epoch 00041: val_loss did not improve\n",
      "Epoch 42/1000\n",
      " - 1s - loss: 0.0553 - val_loss: 0.0706\n",
      "\n",
      "Epoch 00042: val_loss did not improve\n",
      "Epoch 43/1000\n",
      " - 1s - loss: 0.0548 - val_loss: 0.0709\n",
      "\n",
      "Epoch 00043: val_loss did not improve\n",
      "Epoch 44/1000\n",
      " - 1s - loss: 0.0549 - val_loss: 0.0714\n",
      "\n",
      "Epoch 00044: val_loss did not improve\n",
      "Epoch 45/1000\n",
      " - 1s - loss: 0.0548 - val_loss: 0.0697\n",
      "\n",
      "Epoch 00045: val_loss did not improve\n",
      "Epoch 46/1000\n",
      " - 1s - loss: 0.0544 - val_loss: 0.0697\n",
      "\n",
      "Epoch 00046: val_loss did not improve\n",
      "Epoch 47/1000\n",
      " - 1s - loss: 0.0546 - val_loss: 0.0727\n",
      "\n",
      "Epoch 00047: val_loss did not improve\n",
      "Epoch 48/1000\n",
      " - 1s - loss: 0.0542 - val_loss: 0.0716\n",
      "\n",
      "Epoch 00048: val_loss did not improve\n",
      "Epoch 49/1000\n",
      " - 1s - loss: 0.0536 - val_loss: 0.0711\n",
      "\n",
      "Epoch 00049: val_loss did not improve\n",
      "Epoch 50/1000\n",
      " - 1s - loss: 0.0538 - val_loss: 0.0696\n",
      "\n",
      "Epoch 00050: val_loss did not improve\n",
      "Epoch 51/1000\n",
      " - 1s - loss: 0.0538 - val_loss: 0.0715\n",
      "\n",
      "Epoch 00051: val_loss did not improve\n",
      "Epoch 52/1000\n",
      " - 1s - loss: 0.0534 - val_loss: 0.0703\n",
      "\n",
      "Epoch 00052: val_loss did not improve\n",
      "Epoch 53/1000\n",
      " - 1s - loss: 0.0531 - val_loss: 0.0702\n",
      "\n",
      "Epoch 00053: val_loss did not improve\n",
      "Epoch 54/1000\n",
      " - 1s - loss: 0.0535 - val_loss: 0.0713\n",
      "\n",
      "Epoch 00054: val_loss did not improve\n",
      "Epoch 55/1000\n",
      " - 1s - loss: 0.0529 - val_loss: 0.0711\n",
      "\n",
      "Epoch 00055: val_loss did not improve\n",
      "Epoch 56/1000\n",
      " - 1s - loss: 0.0528 - val_loss: 0.0712\n",
      "\n",
      "Epoch 00056: val_loss did not improve\n",
      "Epoch 57/1000\n",
      " - 1s - loss: 0.0527 - val_loss: 0.0716\n",
      "\n",
      "Epoch 00057: val_loss did not improve\n",
      "Epoch 58/1000\n",
      " - 1s - loss: 0.0522 - val_loss: 0.0705\n",
      "\n",
      "Epoch 00058: val_loss did not improve\n",
      "Epoch 59/1000\n",
      " - 1s - loss: 0.0521 - val_loss: 0.0713\n",
      "\n",
      "Epoch 00059: val_loss did not improve\n",
      "Epoch 60/1000\n",
      " - 1s - loss: 0.0528 - val_loss: 0.0709\n",
      "\n",
      "Epoch 00060: val_loss did not improve\n",
      "Epoch 61/1000\n",
      " - 1s - loss: 0.0523 - val_loss: 0.0714\n",
      "\n",
      "Epoch 00061: val_loss did not improve\n",
      "Epoch 62/1000\n",
      " - 1s - loss: 0.0519 - val_loss: 0.0716\n",
      "\n",
      "Epoch 00062: val_loss did not improve\n",
      "Epoch 63/1000\n",
      " - 1s - loss: 0.0519 - val_loss: 0.0722\n",
      "\n",
      "Epoch 00063: val_loss did not improve\n",
      "Epoch 64/1000\n",
      " - 1s - loss: 0.0519 - val_loss: 0.0726\n",
      "\n",
      "Epoch 00064: val_loss did not improve\n",
      "Epoch 65/1000\n",
      " - 1s - loss: 0.0515 - val_loss: 0.0711\n",
      "\n",
      "Epoch 00065: val_loss did not improve\n",
      "Epoch 66/1000\n",
      " - 1s - loss: 0.0517 - val_loss: 0.0708\n",
      "\n",
      "Epoch 00066: val_loss did not improve\n",
      "Epoch 67/1000\n",
      " - 1s - loss: 0.0514 - val_loss: 0.0714\n",
      "\n",
      "Epoch 00067: val_loss did not improve\n",
      "Epoch 68/1000\n",
      " - 1s - loss: 0.0511 - val_loss: 0.0715\n",
      "\n",
      "Epoch 00068: val_loss did not improve\n",
      "Epoch 69/1000\n",
      " - 1s - loss: 0.0514 - val_loss: 0.0704\n",
      "\n",
      "Epoch 00069: val_loss did not improve\n",
      "Epoch 70/1000\n",
      " - 1s - loss: 0.0507 - val_loss: 0.0708\n",
      "\n",
      "Epoch 00070: val_loss did not improve\n",
      "Epoch 71/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 1s - loss: 0.0510 - val_loss: 0.0742\n",
      "\n",
      "Epoch 00071: val_loss did not improve\n",
      "Epoch 72/1000\n",
      " - 1s - loss: 0.0502 - val_loss: 0.0732\n",
      "\n",
      "Epoch 00072: val_loss did not improve\n",
      "Epoch 73/1000\n",
      " - 1s - loss: 0.0503 - val_loss: 0.0714\n",
      "\n",
      "Epoch 00073: val_loss did not improve\n",
      "Epoch 74/1000\n",
      " - 1s - loss: 0.0500 - val_loss: 0.0712\n",
      "\n",
      "Epoch 00074: val_loss did not improve\n",
      "Epoch 75/1000\n",
      " - 1s - loss: 0.0502 - val_loss: 0.0706\n",
      "\n",
      "Epoch 00075: val_loss did not improve\n",
      "Epoch 76/1000\n",
      " - 1s - loss: 0.0499 - val_loss: 0.0716\n",
      "\n",
      "Epoch 00076: val_loss did not improve\n",
      "Epoch 77/1000\n",
      " - 1s - loss: 0.0503 - val_loss: 0.0704\n",
      "\n",
      "Epoch 00077: val_loss did not improve\n",
      "Epoch 78/1000\n",
      " - 1s - loss: 0.0498 - val_loss: 0.0703\n",
      "\n",
      "Epoch 00078: val_loss did not improve\n",
      "Epoch 79/1000\n",
      " - 1s - loss: 0.0500 - val_loss: 0.0701\n",
      "\n",
      "Epoch 00079: val_loss did not improve\n",
      "Epoch 80/1000\n",
      " - 1s - loss: 0.0498 - val_loss: 0.0715\n",
      "\n",
      "Epoch 00080: val_loss did not improve\n",
      "Epoch 81/1000\n",
      " - 1s - loss: 0.0494 - val_loss: 0.0711\n",
      "\n",
      "Epoch 00081: val_loss did not improve\n",
      "Epoch 82/1000\n",
      " - 1s - loss: 0.0489 - val_loss: 0.0722\n",
      "\n",
      "Epoch 00082: val_loss did not improve\n",
      "Epoch 83/1000\n",
      " - 1s - loss: 0.0493 - val_loss: 0.0703\n",
      "\n",
      "Epoch 00083: val_loss did not improve\n",
      "Epoch 84/1000\n",
      " - 1s - loss: 0.0490 - val_loss: 0.0712\n",
      "\n",
      "Epoch 00084: val_loss did not improve\n",
      "Epoch 85/1000\n",
      " - 1s - loss: 0.0493 - val_loss: 0.0711\n",
      "\n",
      "Epoch 00085: val_loss did not improve\n",
      "Epoch 86/1000\n",
      " - 1s - loss: 0.0488 - val_loss: 0.0703\n",
      "\n",
      "Epoch 00086: val_loss did not improve\n",
      "Epoch 87/1000\n",
      " - 1s - loss: 0.0484 - val_loss: 0.0728\n",
      "\n",
      "Epoch 00087: val_loss did not improve\n",
      "Epoch 88/1000\n",
      " - 1s - loss: 0.0487 - val_loss: 0.0718\n",
      "\n",
      "Epoch 00088: val_loss did not improve\n",
      "Epoch 89/1000\n",
      " - 1s - loss: 0.0484 - val_loss: 0.0720\n",
      "\n",
      "Epoch 00089: val_loss did not improve\n",
      "Epoch 90/1000\n",
      " - 1s - loss: 0.0486 - val_loss: 0.0719\n",
      "\n",
      "Epoch 00090: val_loss did not improve\n",
      "Epoch 91/1000\n",
      " - 1s - loss: 0.0482 - val_loss: 0.0719\n",
      "\n",
      "Epoch 00091: val_loss did not improve\n",
      "Epoch 92/1000\n",
      " - 1s - loss: 0.0481 - val_loss: 0.0701\n",
      "\n",
      "Epoch 00092: val_loss did not improve\n",
      "Epoch 93/1000\n",
      " - 1s - loss: 0.0477 - val_loss: 0.0711\n",
      "\n",
      "Epoch 00093: val_loss did not improve\n",
      "Epoch 94/1000\n",
      " - 1s - loss: 0.0480 - val_loss: 0.0710\n",
      "\n",
      "Epoch 00094: val_loss did not improve\n",
      "Epoch 95/1000\n",
      " - 1s - loss: 0.0478 - val_loss: 0.0701\n",
      "\n",
      "Epoch 00095: val_loss did not improve\n",
      "Epoch 96/1000\n",
      " - 1s - loss: 0.0480 - val_loss: 0.0731\n",
      "\n",
      "Epoch 00096: val_loss did not improve\n",
      "Epoch 97/1000\n",
      " - 1s - loss: 0.0474 - val_loss: 0.0711\n",
      "\n",
      "Epoch 00097: val_loss did not improve\n",
      "Epoch 98/1000\n",
      " - 1s - loss: 0.0472 - val_loss: 0.0712\n",
      "\n",
      "Epoch 00098: val_loss did not improve\n",
      "Epoch 99/1000\n",
      " - 1s - loss: 0.0477 - val_loss: 0.0703\n",
      "\n",
      "Epoch 00099: val_loss did not improve\n",
      "Epoch 100/1000\n",
      " - 1s - loss: 0.0472 - val_loss: 0.0715\n",
      "\n",
      "Epoch 00100: val_loss did not improve\n",
      "Epoch 101/1000\n",
      " - 1s - loss: 0.0471 - val_loss: 0.0705\n",
      "\n",
      "Epoch 00101: val_loss did not improve\n",
      "Epoch 102/1000\n",
      " - 1s - loss: 0.0469 - val_loss: 0.0702\n",
      "\n",
      "Epoch 00102: val_loss did not improve\n",
      "Epoch 103/1000\n",
      " - 1s - loss: 0.0466 - val_loss: 0.0724\n",
      "\n",
      "Epoch 00103: val_loss did not improve\n",
      "Epoch 104/1000\n",
      " - 1s - loss: 0.0470 - val_loss: 0.0695\n",
      "\n",
      "Epoch 00104: val_loss did not improve\n",
      "Epoch 105/1000\n",
      " - 1s - loss: 0.0469 - val_loss: 0.0721\n",
      "\n",
      "Epoch 00105: val_loss did not improve\n",
      "Epoch 106/1000\n",
      " - 1s - loss: 0.0463 - val_loss: 0.0714\n",
      "\n",
      "Epoch 00106: val_loss did not improve\n",
      "Epoch 107/1000\n",
      " - 1s - loss: 0.0462 - val_loss: 0.0707\n",
      "\n",
      "Epoch 00107: val_loss did not improve\n",
      "Epoch 108/1000\n",
      " - 1s - loss: 0.0463 - val_loss: 0.0704\n",
      "\n",
      "Epoch 00108: val_loss did not improve\n",
      "Epoch 109/1000\n",
      " - 1s - loss: 0.0461 - val_loss: 0.0709\n",
      "\n",
      "Epoch 00109: val_loss did not improve\n",
      "Epoch 110/1000\n",
      " - 1s - loss: 0.0459 - val_loss: 0.0709\n",
      "\n",
      "Epoch 00110: val_loss did not improve\n",
      "Epoch 111/1000\n",
      " - 1s - loss: 0.0461 - val_loss: 0.0729\n",
      "\n",
      "Epoch 00111: val_loss did not improve\n",
      "Epoch 112/1000\n",
      " - 1s - loss: 0.0461 - val_loss: 0.0722\n",
      "\n",
      "Epoch 00112: val_loss did not improve\n",
      "Epoch 113/1000\n",
      " - 1s - loss: 0.0455 - val_loss: 0.0726\n",
      "\n",
      "Epoch 00113: val_loss did not improve\n",
      "Epoch 114/1000\n",
      " - 1s - loss: 0.0456 - val_loss: 0.0725\n",
      "\n",
      "Epoch 00114: val_loss did not improve\n",
      "Epoch 115/1000\n",
      " - 1s - loss: 0.0456 - val_loss: 0.0704\n",
      "\n",
      "Epoch 00115: val_loss did not improve\n",
      "Epoch 116/1000\n",
      " - 1s - loss: 0.0457 - val_loss: 0.0722\n",
      "\n",
      "Epoch 00116: val_loss did not improve\n",
      "Epoch 117/1000\n",
      " - 1s - loss: 0.0457 - val_loss: 0.0715\n",
      "\n",
      "Epoch 00117: val_loss did not improve\n",
      "Epoch 118/1000\n",
      " - 1s - loss: 0.0452 - val_loss: 0.0728\n",
      "\n",
      "Epoch 00118: val_loss did not improve\n",
      "Epoch 119/1000\n",
      " - 1s - loss: 0.0452 - val_loss: 0.0709\n",
      "\n",
      "Epoch 00119: val_loss did not improve\n",
      "Epoch 120/1000\n",
      " - 1s - loss: 0.0452 - val_loss: 0.0710\n",
      "\n",
      "Epoch 00120: val_loss did not improve\n",
      "Epoch 121/1000\n",
      " - 1s - loss: 0.0448 - val_loss: 0.0703\n",
      "\n",
      "Epoch 00121: val_loss did not improve\n",
      "Epoch 122/1000\n",
      " - 1s - loss: 0.0449 - val_loss: 0.0714\n",
      "\n",
      "Epoch 00122: val_loss did not improve\n"
     ]
    }
   ],
   "source": [
    "##########################################################\n",
    "\n",
    "# Training time!\n",
    "# Try 20 epochs using batch size = 3, then switch to 4\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_1D_RECONST_NEW.h5',\n",
    "                               verbose=1,\n",
    "                               monitor='val_loss',\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=[early_stopping, checkpointer])\n",
    "\n",
    "# save the entire model and the weights after the training:\n",
    "# model.save('/home/amplifier/home/NEW_DL/models/EEGnet.h5')\n",
    "# model.save_weights('/home/amplifier/home/NEW_DL/weights/EEGnet_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f4ed24e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(960, 200, 1)\n",
      "(240, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "model.load_weights('/home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_RECONST_NEW.h5')\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": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer number: 2 Layer name: conv1d_16\n",
      "(50, 63, 30)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Convolution kernels in layer conv1d_16')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f4e9c8438>"
      ]
     },
     "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",
    "I = 10 # sh[1]\n",
    "J = 10 # sh[2]\n",
    "c = 0\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "axs = axs.ravel()\n",
    "for i in range(I):\n",
    "    for j in range(J):\n",
    "        axs[c].plot(x[0][:,i,j])\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": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson's R: [ 0.33274832] p-value [  1.47813182e-06]\n",
      "0.0838015\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e98ed7710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reconstruct_training_sample = False\n",
    "samp = 4\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "# now let's see how the model performs:\n",
    "\n",
    "if (reconstruct_training_sample==True):\n",
    "    reconst = decoded_train[samp,:]\n",
    "    orig = y_train[samp,:]\n",
    "    R,p = pearsonr(reconst, orig)\n",
    "    title = \"Reconstructed input based on TRAINING data, \" + \"Corr\"+ str(R) + \", P-value\" + str(p)\n",
    "    \n",
    "else:\n",
    "    reconst = decoded_test[samp,:]\n",
    "    orig = y_test[samp,:]\n",
    "    R,p = pearsonr(reconst, orig)\n",
    "    title = \"Reconstructed input based on TEST data, \" + \"Corr\"+ str(R) + \", P-value\" + str(p)\n",
    "\n",
    "\n",
    "print(\"Pearson's R:\", R, \"p-value\", p)\n",
    "%matplotlib inline\n",
    "plt.subplot(211)\n",
    "plt.plot(reconst)\n",
    "ax = plt.gca()\n",
    "ax.set_title(title)\n",
    "plt.subplot(212)\n",
    "plt.plot(orig)\n",
    "ax = plt.gca()\n",
    "ax.set_title(\"Original\")\n",
    "plt.tight_layout()\n",
    "\n",
    "R_test = []\n",
    "for i in range(len(decoded_test)):\n",
    "    R,p = pearsonr(decoded_test[i,:], y_test[i,:])\n",
    "    R_test.append(R)\n",
    "print(np.mean(R_test))\n",
    "# R_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e98466ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n, bins, patches = plt.hist(np.array(R_test), 10, normed=1, facecolor='green', alpha=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3125\n"
     ]
    }
   ],
   "source": [
    "TT = []\n",
    "for i in range(len(R_test)):\n",
    "#     print(z_test[i][0])\n",
    "    if (R_test[i]>0) and (z_test[i][0]==1):\n",
    "#         print('success')\n",
    "        TT.append(1)\n",
    "    else:\n",
    "        TT.append(0)\n",
    "#         print('fail')\n",
    "print(np.mean(TT))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson's R: [ 0.39919239] p-value [  4.76167905e-09]\n"
     ]
    },
    {
     "data": {
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       " array([-0.23574401], dtype=float32),\n",
       " array([-0.03864077], dtype=float32),\n",
       " array([ 0.22932237], dtype=float32),\n",
       " array([ 0.35421976], dtype=float32),\n",
       " array([ 0.24665213], dtype=float32),\n",
       " array([ 0.42972681], dtype=float32),\n",
       " array([ 0.52614015], dtype=float32),\n",
       " array([ 0.62034863], dtype=float32)]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e9172b4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reconstruct_training_sample = True\n",
    "samp = 6\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "# now let's see how the model performs:\n",
    "\n",
    "if (reconstruct_training_sample==True):\n",
    "    reconst = decoded_train[samp,:]\n",
    "    orig = y_train[samp,:]\n",
    "    R,p = pearsonr(reconst, orig)\n",
    "    title = \"Reconstructed input based on TRAINING data, \" + \"Corr\"+ str(R) + \", P-value\" + str(p)\n",
    "    \n",
    "else:\n",
    "    reconst = decoded_test[samp,:]\n",
    "    orig = y_test[samp,:]\n",
    "    R,p = pearsonr(reconst, orig)\n",
    "    title = \"Reconstructed input based on TEST data, \" + \"Corr\"+ str(R) + \", P-value\" + str(p)\n",
    "\n",
    "\n",
    "print(\"Pearson's R:\", R, \"p-value\", p)\n",
    "%matplotlib inline\n",
    "plt.subplot(211)\n",
    "plt.plot(reconst)\n",
    "ax = plt.gca()\n",
    "ax.set_title(title)\n",
    "plt.subplot(212)\n",
    "plt.plot(orig)\n",
    "ax = plt.gca()\n",
    "ax.set_title(\"Original\")\n",
    "\n",
    "R_train = []\n",
    "for i in range(len(decoded_train)):\n",
    "    R,p = pearsonr(decoded_train[i,:], y_train[i,:])\n",
    "    R_train.append(R)\n",
    "R_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log performance to file:\n",
    "# import csv   \n",
    "# fields=['1D', '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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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",
    "I = sh[1]\n",
    "J = sh[2]\n",
    "c = 0\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "axs = axs.ravel()\n",
    "for i in range(I):\n",
    "    for j in range(J):\n",
    "        axs[c].plot(x[0][:,i,j])\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": null,
   "metadata": {},
   "outputs": [],
   "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",
    "im = layer_output[-1,:,:]\n",
    "plt.imshow(im.T)\n",
    "plt.suptitle(('Output of layer ' + model.layers[2].name), fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import scipy.fftpack\n",
    "\n",
    "x = model.layers[2].get_weights()\n",
    "\n",
    "I = x[0].shape[1]\n",
    "J = x[0].shape[2]\n",
    "c = 0\n",
    "\n",
    "fs = 250\n",
    "T = 1/fs * x[0].shape[0]\n",
    "f = 50\n",
    "t = np.arange(0,T, 1/fs)\n",
    "\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "axs = axs.ravel()\n",
    "for i in range(I):\n",
    "    for j in range(J):\n",
    "        y = x[0][:,i,j].reshape(KERNEL_SIZE[0],1)\n",
    "        yf = scipy.fftpack.fft(y, axis=0)\n",
    "\n",
    "        YF = np.abs(yf)\n",
    "        xf = np.linspace(0,fs/2, YF.shape[0]/2)\n",
    "        YF_trunc = YF[0:int(len(YF)/2),-1]\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(i)+str(j))\n",
    "        c = c + 1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.suptitle('FFT of kernels in layer ' + model.layers[2].name, fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# df = pd.read_csv('/home/amplifier/home/NEW_DL/weights/document.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv   \n",
    "# fields=['algorithm', 'dataset', 'val_acc', 'acc']\n",
    "# with open(r'/home/amplifier/home/NEW_DL/weights/document.csv', 'w') as f:\n",
    "#     writer = csv.writer(f)\n",
    "#     writer.writerow(fields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df.algorithm=='1D'].acc.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
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 },
 "nbformat": 4,
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