GRU

gru.py

from keras.models import Sequential
from keras.layers import GRU, Dense

n_features = 4
model_gru = Sequential()
model_gru.add(TimeDistributed(Dense(128), input_shape=(None, n_features)))
model_gru.add(GRU(64, activation='tanh', input_shape=(None, n_features), return_sequences=True))
model_gru.add(BatchNormalization())

model_gru.add(GRU(32 , activation = 'tanh'))
model_gru.add(BatchNormalization())
model_gru.add(Dropout(0.2))
model_gru.add(Dense(n_features, activation='softmax'))
model_gru.compile(optimizer='adam', loss='categorical_crossentropy')

model_gru.fit_generator(generator, epochs=500, validation_data=validation_generator)