I am new to theano, so maybe my question does not make any sense.
In the example here, in the create_prediction function,
if greedy:
outputs_info = [dict(initial=self.priming_word, taps=[-1])] + [initial_state_with_taps(layer) for layer in self.model.layers[1:-1]]
result, _ = theano.scan(fn=step,
n_steps=200,
outputs_info=outputs_info)
else:
outputs_info = [initial_state_with_taps(layer, num_examples) for layer in self.model.layers[1:]]
result, _ = theano.scan(fn=step,
sequences=[inputs.T], # slice over each time step, so step fn. gets a sentence
outputs_info=outputs_info)
why did you choose n_steps to be 200 for greedy? from what I understand it should be the same as the length of the longest sentence(because you padded things in the input matrix)
I am new to theano, so maybe my question does not make any sense. In the example here, in the
create_prediction
function,why did you choose n_steps to be 200 for greedy? from what I understand it should be the same as the length of the longest sentence(because you padded things in the input matrix)