<reponame>adam2392/dnn-unsupervised
import sys
sys.path.append('./dnn/')

import model.ieeg_cnn_rnn
import model.train

import processing.util as util

import keras
from keras.callbacks import ModelCheckpoint
import os
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale

if __name__ == '__main__':
    traindatadir = str(sys.argv[1])
    tempdatadir = str(sys.argv[2])

    sys.stdout.write(os.environ["CUDA_VISIBLE_DEVICES"])
    ##################### PARAMETERS FOR NN ####################
    # image parameters
    imsize = 32
    numfreqs = 5

    # layer parameters
    w_init = None
    n_layers = (4, 2, 1)
    poolsize = (2, 2)
    filtersize = (3, 3)

    size_fc = 1024
    DROPOUT = False  # True

    sys.stdou.write("Completed this!!!!")
    # ieegdnn = model.ieeg_cnn_rnn.IEEGdnn(imsize=imsize, n_colors=numfreqs)

import sys
sys.path.append('./dnn/')

import model.ieeg_cnn_rnn
import model.train

import processing.util as util

import keras
from keras.callbacks import ModelCheckpoint
import os
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale

if __name__ == '__main__':
    traindatadir = str(sys.argv[1])
    tempdatadir = str(sys.argv[2])

    sys.stdout.write(os.environ["CUDA_VISIBLE_DEVICES"])
    ##################### PARAMETERS FOR NN ####################
    # image parameters
    imsize = 32
    numfreqs = 5

    # layer parameters
    w_init = None
    n_layers = (4, 2, 1)
    poolsize = (2, 2)
    filtersize = (3, 3)

    size_fc = 1024
    DROPOUT = False  # True

    sys.stdou.write("Completed this!!!!")
    # ieegdnn = model.ieeg_cnn_rnn.IEEGdnn(imsize=imsize, n_colors=numfreqs)
##################### PARAMETERS FOR NN #################### # image parameters # layer parameters # True # ieegdnn = model.ieeg_cnn_rnn.IEEGdnn(imsize=imsize, n_colors=numfreqs)