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#!/usr/bin/env python | ||
# -*- coding: utf-8 -*- | ||
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import numpy as np | ||
import theano.tensor as T | ||
from recurrent import RecurrentLayer | ||
from deepy.utils import neural_computation, FLOATX | ||
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class PeepholeLSTM(RecurrentLayer): | ||
""" | ||
Long short-term memory layer with peepholes. | ||
""" | ||
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def __init__(self, hidden_size, init_forget_bias=1, **kwargs): | ||
kwargs["hidden_size"] = hidden_size | ||
super(PeepholeLSTM, self).__init__("PLSTM", ["state", "lstm_cell"], **kwargs) | ||
self._init_forget_bias = 1 | ||
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@neural_computation | ||
def compute_new_state(self, step_inputs): | ||
xi_t, xf_t, xo_t, xc_t, h_tm1, c_tm1 = map(step_inputs.get, ["xi", "xf", "xc", "xo", "state", "lstm_cell"]) | ||
if not xi_t: | ||
xi_t, xf_t, xo_t, xc_t = 0, 0, 0, 0 | ||
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# LSTM core step | ||
hs = self.hidden_size | ||
dot_h = T.dot(h_tm1, self.U) | ||
dot_c = T.dot(h_tm1, self.C) | ||
i_t = self.gate_activate(xi_t + dot_h[:, :hs] + self.b_i + dot_c[:, :hs]) | ||
f_t = self.gate_activate(xf_t + dot_h[:, hs:hs*2] + self.b_f + dot_c[:, hs:hs*2]) | ||
c_t = f_t * c_tm1 + i_t * self.activate(xc_t + dot_h[:, hs*2:hs*3] + self.b_c) | ||
o_t = self.gate_activate(xo_t + dot_h[:, hs*3:hs*4] + dot_c[:, hs*2:hs*3] + self.b_o) | ||
h_t = o_t * self.activate(c_t) | ||
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return {"state": h_t, "lstm_cell": c_t} | ||
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@neural_computation | ||
def merge_inputs(self, input_var, additional_inputs=None): | ||
if not additional_inputs: | ||
additional_inputs = [] | ||
all_inputs = filter(bool, [input_var] + additional_inputs) | ||
if not all_inputs: | ||
return {} | ||
last_dim_id = all_inputs[0].ndim - 1 | ||
merged_input = T.concatenate(all_inputs, axis=last_dim_id) | ||
dot_input = T.dot(merged_input, self.W) | ||
merged_inputs = { | ||
"xi": dot_input[:, :, :self.hidden_size], | ||
"xf": dot_input[:, :, self.hidden_size:self.hidden_size*2], | ||
"xc": dot_input[:, :, self.hidden_size*2:self.hidden_size*3], | ||
"xo": dot_input[:, :, self.hidden_size*3:self.hidden_size*4], | ||
} | ||
return merged_inputs | ||
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def prepare(self): | ||
if self._input_type == "sequence": | ||
all_input_dims = [self.input_dim] + self.additional_input_dims | ||
else: | ||
all_input_dims = self.additional_input_dims | ||
summed_input_dim = sum(all_input_dims, 0) | ||
self.output_dim = self.hidden_size | ||
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self.W = self.create_weight(summed_input_dim, self.hidden_size * 4, "W", initializer=self.outer_init) | ||
self.U = self.create_weight(self.hidden_size, self.hidden_size * 4, "U", initializer=self.inner_init) | ||
self.C = self.create_weight(self.hidden_size, self.hidden_size * 3, "C", initializer=self.inner_init) | ||
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self.b_i = self.create_bias(self.hidden_size, "bi") | ||
self.b_f = self.create_bias(self.hidden_size, "bf") | ||
self.b_f.set_value(np.ones((self.hidden_size,) * self._init_forget_bias, dtype=FLOATX)) | ||
self.b_c = self.create_bias(self.hidden_size, "bc") | ||
self.b_o = self.create_bias(self.hidden_size, "bo") | ||
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if summed_input_dim > 0: | ||
self.register_parameters(self.W, self.U, self.C, | ||
self.b_i, self.b_f, self.b_c, self.b_o) | ||
else: | ||
self.register_parameters(self.U, self.C, | ||
self.b_i, self.b_f, self.b_c, self.b_o) |