#-----------------------------------------------------------------------------------
print("nX_train size: {}".format(X_train.shape))  #X_train size: (40, 1, 1440)
print("X_test  size: {}".format(X_test.shape))     #X_test  size: (40, 1, 1440)
#-----------------------------------------------------------------------------------
model_RNN = Sequential()
model_RNN.add(SimpleRNN(units=1440, input_shape=(X_train.shape[1], X_train.shape[2])))
model_RNN.add(Dense(960))
#model_RNN.add(BatchNormalization())
#model_RNN.add(Activation('sigmoid'))
#model_RNN.add(Activation('softmax'))
#model_RNN.add(Activation('tanh'))
model_RNN.compile(loss='mean_squared_error', optimizer='adam')
hist=model_RNN.fit(X_train, Y_train, epochs =50, batch_size =20,validation_data=(X_test,Y_test),verbose=1)

y_pred=model_RNN.predict(X_train)
train_RNN= pd.DataFrame.from_records(y_pred)

y_pred=model_RNN.predict(X_test)
test_RNN= pd.DataFrame.from_records(y_pred)