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