grid-1D_my_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "SHUFFLE = True\n",
    "BATCH_SIZE = 20\n",
    "TEST_TRAIN = 0.2\n",
    "\n",
    "KERNEL_SIZE = (100, 50, 25, 2, 2)\n",
    "N_KERNELS = (10,20,40,80,160)\n",
    "POOL_SIZE = (5,2,5,2)\n",
    "\n",
    "\n",
    "DROPOUT = (0.1, 0.2, 0.25, 0.25, 0.8, 0.8)\n",
    "DENSE_LEN = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\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",
    "from keras.layers import Input, Dense, Conv1D, MaxPooling1D, UpSampling2D, Dropout, BatchNormalization, Flatten, ELU\n",
    "from keras.models import Model\n",
    "from keras.models import load_model\n",
    "from keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint\n",
    "from keras import optimizers\n",
    "from keras.initializers import Orthogonal as orth\n",
    "from keras.callbacks import TensorBoard, EarlyStopping\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# paths = ['/home/amplifier/home/DATASETS/BCI_0101T_TD.mat',\n",
    "#          '/home/amplifier/home/DATASETS/BCI_0202T_TD.mat',\n",
    "#         '/home/amplifier/home/DATASETS/BCI_0303T_TD.mat',\n",
    "#         '/home/amplifier/home/DATASETS/BCI_0403T_TD.mat',\n",
    "#         '/home/amplifier/home/DATASETS/BCI_0503T_TD.mat',\n",
    "#         '/home/amplifier/home/DATASETS/BCI_0603T_TD.mat',\n",
    "#         '/home/amplifier/home/DATASETS/BCI_0703T_TD.mat',\n",
    "#         '/home/amplifier/home/DATASETS/BCI_0801T_TD.mat']\n",
    "\n",
    "paths = ['/home/amplifier/home/DATASETS/Merged123->2-30Hz,Env=1_TD5_64_.mat',\n",
    "         '/home/amplifier/home/DATASETS/Merged123->2-30Hz,Env=1_TD75_134_.mat',\n",
    "         '/home/amplifier/home/DATASETS/Merged123->2-30Hz,Env=1_TD143_202_.mat',\n",
    "         '/home/amplifier/home/DATASETS/Merged456->2-30Hz,Env=1_TD1_94_.mat',\n",
    "         '/home/amplifier/home/DATASETS/Merged456->2-30Hz,Env=1_TD101_192_.mat',\n",
    "         '/home/amplifier/home/DATASETS/Merged456->2-30Hz,Env=1_TD197_289_.mat']  \n",
    "\n",
    "def load_file(file):\n",
    "    print(file)\n",
    "    mat_contents = sio.loadmat(file)\n",
    "    X = mat_contents['X']\n",
    "    Y = 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",
    "    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=TEST_TRAIN, shuffle=SHUFFLE)\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",
    "\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",
    "\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",
    "    return x_train, x_test, y_train, y_test, leng, chan, streams\n",
    "\n",
    "\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 = ELU(alpha=1.0)(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 = ELU(alpha=0.1)(e3)\n",
    "    e4 = MaxPooling1D(pool_size=POOL_SIZE[1], strides=None, padding='same')(e3act)\n",
    "    e4b = BatchNormalization()(e4)\n",
    "    e4a = Dropout(DROPOUT[1])(e4b)\n",
    "    \n",
    "    \n",
    "    e5 = Conv1D(N_KERNELS[2], KERNEL_SIZE[2], padding='same',\n",
    "                                    kernel_initializer = orth(gain=1.0, seed=None))(e4a)\n",
    "    e5act = ELU(alpha=1.0)(e5)\n",
    "    e6 = MaxPooling1D(pool_size=POOL_SIZE[2], strides=None, padding='same')(e5act)\n",
    "    e6b = BatchNormalization()(e6)\n",
    "    e6a = Dropout(DROPOUT[2])(e6b)\n",
    "    \n",
    "    \n",
    "    e7 = Conv1D(N_KERNELS[3], KERNEL_SIZE[3], padding='same',\n",
    "                                    kernel_initializer = orth(gain=1.0, seed=None))(e6a)\n",
    "    e7act = ELU(alpha=1.0)(e7)\n",
    "    e8 = MaxPooling1D(pool_size=POOL_SIZE[3], strides=None, padding='same')(e7act)\n",
    "    e8b = BatchNormalization()(e8)\n",
    "    e9a = Dropout(DROPOUT[3])(e8b)\n",
    "    \n",
    "    \n",
    "    e10 = Conv1D(N_KERNELS[4], KERNEL_SIZE[4], padding='same',\n",
    "                                    kernel_initializer = orth(gain=1.0, seed=None))(e9a)\n",
    "    e10act = ELU(alpha=1.0)(e10)\n",
    "    e10b = BatchNormalization()(e10act)\n",
    "    e10a = Dropout(DROPOUT[4])(e10b)\n",
    "    \n",
    "    \n",
    "    e11 = Flatten()(e10a)\n",
    "    \n",
    "    e12 = Dense(DENSE_LEN, activation='sigmoid',\n",
    "                                     kernel_initializer = orth(gain=1.0, seed=None))(e11)\n",
    "    e12b = BatchNormalization()(e12)\n",
    "    e12a = Dropout(DROPOUT[5])(e12b)\n",
    "\n",
    "    e13 = Dense(2, activation='softmax')(e12a)\n",
    "\n",
    "    return Model(input_img, e13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "VAL_ACC = np.zeros([len(paths),5])\n",
    "\n",
    "for file in range(len(paths)):\n",
    "    for iter in range(5):\n",
    "        x_train, x_test, y_train, y_test, leng, chan, streams = load_file(paths[file])\n",
    "        print(paths[file], 'trial', iter)\n",
    "        \n",
    "        x = Input(shape=(chan, leng, streams))\n",
    "        model = conv_net(leng)\n",
    "        model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n",
    "\n",
    "        early_stopping = EarlyStopping(monitor='val_loss', patience=100, mode='min')\n",
    "        checkpointer = ModelCheckpoint(filepath='/home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_mydat.h5',\n",
    "                               verbose=1,\n",
    "                               monitor='val_acc',\n",
    "                               save_best_only=True)\n",
    "\n",
    "        train_history = model.fit(x_train, y_train,\n",
    "                        epochs=1000,\n",
    "                        batch_size=BATCH_SIZE,\n",
    "                        verbose=0,\n",
    "                        shuffle=True,\n",
    "                        validation_data=(x_test, y_test),\n",
    "                        callbacks=[checkpointer, early_stopping])\n",
    "        \n",
    "        model.load_weights('/home/amplifier/home/NEW_DL/weights/EEGnet_weights_1D_mydat.h5')\n",
    "        decoded_train = model.predict(x_train)\n",
    "        decoded_test = model.predict(x_test)\n",
    "\n",
    "        \n",
    "\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]) and (y_train[sample_no,0] > y_train[sample_no,1]))):\n",
    "                cor_train.append(1)\n",
    "            else:\n",
    "                cor_train.append(0)\n",
    "\n",
    "        print('Accuracy on the test data:', np.mean(cor_test))\n",
    "        print('Accuracy on the training data:', np.mean(cor_train))\n",
    "        VAL_ACC[file,iter] = np.mean(cor_test)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea6054f470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "SE = stats.sem(VAL_ACC[:,:], axis=1)\n",
    "M = np.mean(VAL_ACC[:,:], axis=1)\n",
    "\n",
    "x = list(range(1,VAL_ACC.shape[0]+1))\n",
    "plt.plot(x, M, 'k-')\n",
    "plt.fill_between(x, M-SE, M+SE)\n",
    "plt.xticks(x)\n",
    "plt.xlabel('DATASETS (Subjects)')\n",
    "plt.ylabel('VAL_ACC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_64 (InputLayer)        (None, 500, 60)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_218 (Bat (None, 500, 60)           240       \n",
      "_________________________________________________________________\n",
      "conv1d_156 (Conv1D)          (None, 500, 10)           60010     \n",
      "_________________________________________________________________\n",
      "elu_156 (ELU)                (None, 500, 10)           0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_125 (MaxPoolin (None, 100, 10)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_219 (Bat (None, 100, 10)           40        \n",
      "_________________________________________________________________\n",
      "dropout_187 (Dropout)        (None, 100, 10)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_157 (Conv1D)          (None, 100, 20)           10020     \n",
      "_________________________________________________________________\n",
      "elu_157 (ELU)                (None, 100, 20)           0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_126 (MaxPoolin (None, 50, 20)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_220 (Bat (None, 50, 20)            80        \n",
      "_________________________________________________________________\n",
      "dropout_188 (Dropout)        (None, 50, 20)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_158 (Conv1D)          (None, 50, 40)            20040     \n",
      "_________________________________________________________________\n",
      "elu_158 (ELU)                (None, 50, 40)            0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_127 (MaxPoolin (None, 10, 40)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_221 (Bat (None, 10, 40)            160       \n",
      "_________________________________________________________________\n",
      "dropout_189 (Dropout)        (None, 10, 40)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_159 (Conv1D)          (None, 10, 80)            6480      \n",
      "_________________________________________________________________\n",
      "elu_159 (ELU)                (None, 10, 80)            0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_128 (MaxPoolin (None, 5, 80)             0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_222 (Bat (None, 5, 80)             320       \n",
      "_________________________________________________________________\n",
      "dropout_190 (Dropout)        (None, 5, 80)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_160 (Conv1D)          (None, 5, 160)            25760     \n",
      "_________________________________________________________________\n",
      "elu_160 (ELU)                (None, 5, 160)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_223 (Bat (None, 5, 160)            640       \n",
      "_________________________________________________________________\n",
      "dropout_191 (Dropout)        (None, 5, 160)            0         \n",
      "_________________________________________________________________\n",
      "flatten_32 (Flatten)         (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "dense_63 (Dense)             (None, 20)                16020     \n",
      "_________________________________________________________________\n",
      "batch_normalization_224 (Bat (None, 20)                80        \n",
      "_________________________________________________________________\n",
      "dropout_192 (Dropout)        (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_64 (Dense)             (None, 2)                 42        \n",
      "=================================================================\n",
      "Total params: 139,932\n",
      "Trainable params: 139,152\n",
      "Non-trainable params: 780\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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