# X = X.reshape(X.shape[0], X.shape[1], X.shape[2], X.shape[3], 1)
# CNN.ConvLSTM(X, y, epochs=epochs, name='ConvLSTM', num_GPU=num_GPU,
# optimizer='adam', batch_size=64, loss='categorical_crossentropy',
# metrics=['accuracy'], test_split_size=0.1, verbose=1)
print("LSTM")
LSTM
X = np.abs(np.load('stft-1D-100.npy'))
X = np.transpose(X,(0,2,1))
print(X.shape)
# CV.LSTMNN(X, y, epochs=20, name='lstm-stft', folds=3, test_size=0.1, verbose=1,
# batch_size=32, optimizer='adam', loss='categorical_crossentropy',
# metrics=['accuracy'], shuffle=True)
CNN.LSTMNN(X, y, epochs=20, name='LSTM-stft', test_split_size=0.2, verbose=1,
num_GPU=4, batch_size=32, optimizer='adam',
loss='categorical_crossentropy', metrics=['accuracy'],
shuffle=True)
exit()
X = np.load('EEG.npy')
###3
X = np.load('./person/P1X.npy')
y = np.load('./person/P1y.npy')
####
X = reshape_1D_conv(X)
print(X.shape)
print("start")
CV.CNN1D(X, y, epochs=50, verbose=2, name="TimeAllCH", folds=5, batch_size = 20)
