EEGnet_CNN+Dense_Binary_TESTER.ipynb

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    {
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     "text": [
      "Original data type: float32\n",
      "Normalized data type: float16\n",
      "test input shape (468, 400, 60) Nomralized MEAN: 0.51758 min 0.0 max 1.0\n",
      "train input shape (1872, 400, 60) Nomralized MEAN: 0.5166 min 0.0 max 1.0\n",
      "test labels shape (468, 2) Nomralized MEAN: 0.5 min 0.0 max 1.0\n",
      "train labels shape (1872, 2) Nomralized MEAN: 0.5 min 0.0 max 1.0\n",
      "Window length [[5]]\n",
      "Step size: [[1]]\n",
      "Length of trial during experiment [[30]]\n",
      "Low cutoff freq. [[ 0.5]]\n",
      "High cutoff freq. [[38]]\n",
      "Source data file: ['Merged123']\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",
    "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_ICA->0.5-38Hz,Env=1.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/Merged123->0.5-38Hz,Env=1.mat')\n",
    "\n",
    "X = mat_contents['X']\n",
    "Y = mat_contents['Z'].T\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",
    "# 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",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20)\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",
    "    x_train[i,:,:] = preprocessing.scale(x_train[i,:,:], axis=1)\n",
    "    x_train[i,:,:] = preprocessing.minmax_scale(x_train[i,:,:], axis=1)\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",
    "\n",
    "# ensure the tensors are sized right:\n",
    "x_train = np.transpose(x_train,(0,2,1))\n",
    "x_test = np.transpose(x_test,(0,2,1))\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",
    "leng = X.shape[2]\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('Length of trial during experiment', trial_len)\n",
    "print('Low cutoff freq.', low_cutoff)\n",
    "print('High cutoff freq.', high_cutoff)\n",
    "print('Source data file:', source)\n",
    "\n",
    "leng = x_test.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 400, 60)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 400, 60)           1600      \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 400, 30)           5430      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 400, 30)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 400, 30)           120       \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 200, 30)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 200, 60)           5460      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 200, 60)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 200, 60)           240       \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 100, 60)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 100, 120)          21720     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 100, 120)          0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 100, 120)          480       \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 50, 120)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6000)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                60010     \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 10)                40        \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2)                 22        \n",
      "=================================================================\n",
      "Total params: 95,122\n",
      "Trainable params: 93,882\n",
      "Non-trainable params: 1,240\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# http://cs231n.github.io/convolutional-networks/#case\n",
    "# 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, UpSampling2D, Dropout, BatchNormalization, Flatten, Conv1D, MaxPooling1D\n",
    "from keras.models import Model\n",
    "from keras.models import load_model\n",
    "from keras.callbacks import TensorBoard, EarlyStopping, Callback\n",
    "from keras import optimizers\n",
    "from keras.initializers import Orthogonal as orth\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "enc = load_model('/home/amplifier/home/NEW_DL/models/EEGnet.h5')\n",
    "\n",
    "enc.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1872, 2)\n",
      "(468, 2)\n"
     ]
    }
   ],
   "source": [
    "# let's predict a TRAINING sammple:\n",
    "decoded_train = enc.predict(x_train)\n",
    "decoded_test = enc.predict(x_test)\n",
    "print(decoded_train.shape)\n",
    "print(decoded_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the test data: 0.967948717949\n",
      "Accuracy on the training data: 0.967948717949\n"
     ]
    }
   ],
   "source": [
    "# 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 = enc.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 = enc.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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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