How to use model.reset_states() in Keras?

Question

I have sequential data and I declared a LSTM model which predicts y with x in Keras. So if I call model.predict(x1) and model.predict(x2), Is it correct to call model.reset_states between the two predict() explicitly? Does model.reset_states clear history of inputs, not weights, right?

# data1
x1 = [2,4,2,1,4]
y1 = [1,2,3,2,1]

# dat2
x2 = [5,3,2,4,5]
y2 = [5,3,2,3,2]

And in my actual code, I use model.evaluate(). In evaluate(), is reset_states called implicitly for each data sample?

model.evaluate(dataX, dataY)

Answer 1

reset_states clears only the hidden states of your network. It's worth to mention that depending on if the option stateful=True was set in your network - the behaviour of this function might be different. If it's not set - all states are automatically reset after every batch computations in your network (so e.g. after calling fit, predict and evaluate also). If not - you should call reset_states every time, when you want to make consecutive model calls independent.

Answer 2

If you use explicitly either of:

model.reset_states() 

to reset the states of all layers in the model, or

layer.reset_states() 

to reset the states of a specific stateful RNN layer (also LSTM layer), implemented here:

def reset_states(self, states=None):
  if not self.stateful:
     raise AttributeError('Layer must be stateful.')

this means your layer(s) must be stateful.

In LSTM you need to:

• explicitly specify the batch size you are using, by passing a batch_size argument to the first layer in your model or batch_input_shape argument

• set stateful=True.

• specify shuffle=False when calling fit().

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The benefits of using stateful models are probable best explained here.