def outputs(weights, inputs):
"""Outputs normalized log-probabilities of each character, plus an
extra one at the end."""
forget_weights = parser.get(weights, 'forget')
change_weights = parser.get(weights, 'change')
ingate_weights = parser.get(weights, 'ingate')
outgate_weights = parser.get(weights, 'outgate')
predict_weights = parser.get(weights, 'predict')
num_sequences = inputs.shape[1]
hiddens = np.repeat(parser.get(weights, 'init_hiddens'), num_sequences, axis=0)
cells = np.repeat(parser.get(weights, 'init_cells'), num_sequences, axis=0)
output = [hiddens_to_output_probs(predict_weights, hiddens)]
for input in inputs: # Iterate over time steps.
hiddens, cells = update_lstm(input, hiddens, cells, forget_weights,
change_weights, ingate_weights, outgate_weights)
output.append(hiddens_to_output_probs(predict_weights, hiddens))
return output
def outputs(weights, inputs):
"""Goes from right to left, updating the state."""
num_sequences = inputs.shape[1]
hiddens = np.repeat(parser.get(weights, 'init_hiddens'), num_sequences, axis=0)
change_weights = parser.get(weights, 'change')
predict_weights = parser.get(weights, 'predict')
output = [hiddens_to_output_probs(predict_weights, hiddens)]
for input in inputs: # Iterate over time steps.
hiddens = update(input, hiddens, change_weights)
output.append(hiddens_to_output_probs(predict_weights, hiddens))
return output
