3 is a big file, but I would like to reset the state after
mini_batch_size of 50.
n_epoch=10000 n_batch=50 # create and fit the LSTM network model = Sequential() model.add(LSTM(3,batch_input_shape =(n_batch,trainX.shape, trainX.shape),stateful=True)) model.add(Dense(1)) model.add(Activation("linear")) model.compile(loss="mse", optimizer="adam") model.summary() #fitting model for i in range(n_epoch): history=model.fit(trainX, trainY, epochs=1, batch_size=n_batch,verbose=2, shuffle=False) model.reset_states()
I am getting the error:
value error: In a stateful network, you should only pass inputs with a number of samples that can be divided by the batch size. Found: 63648 samples<
How can I train a LSTM in
mini_batch size of 50 (a number which is not divisible by trainX)?