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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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import numpy as np |
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import theano.tensor as T |
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from recurrent import RecurrentLayer |
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from deepy.utils import neural_computation, FLOATX |
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class LSTM(RecurrentLayer): |
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""" |
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Long short-term memory layer. |
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""" |
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def __init__(self, hidden_size, init_forget_bias=1, **kwargs): |
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kwargs["hidden_size"] = hidden_size |
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super(LSTM, self).__init__("LSTM", ["state", "lstm_cell"], **kwargs) |
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self._init_forget_bias = 1 |
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@neural_computation |
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def compute_new_state(self, step_inputs): |
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xi_t, xf_t, xo_t, xc_t, h_tm1, c_tm1 = map(step_inputs.get, ["xi", "xf", "xc", "xo", "state", "lstm_cell"]) |
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if not xi_t: |
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xi_t, xf_t, xo_t, xc_t = 0, 0, 0, 0 |
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# LSTM core step |
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i_t = self.gate_activate(xi_t + T.dot(h_tm1, self.U_i) + self.b_i) |
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f_t = self.gate_activate(xf_t + T.dot(h_tm1, self.U_f) + self.b_f) |
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c_t = f_t * c_tm1 + i_t * self.activate(xc_t + T.dot(h_tm1, self.U_c) + self.b_c) |
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o_t = self.gate_activate(xo_t + T.dot(h_tm1, self.U_o) + self.b_o) |
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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 |
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def merge_inputs(self, input_var, additional_inputs=None): |
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if not additional_inputs: |
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additional_inputs = [] |
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all_inputs = filter(bool, [input_var] + additional_inputs) |
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if not all_inputs: |
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return {} |
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last_dim_id = input_var.ndim - 1 |
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merged_input = T.concatenate(all_inputs, axis=last_dim_id) |
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merged_inputs = { |
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"xi": T.dot(merged_input, self.W_i), |
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"xf": T.dot(merged_input, self.W_f), |
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"xc": T.dot(merged_input, self.W_c), |
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"xo": T.dot(merged_input, self.W_o), |
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} |
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return merged_inputs |
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def prepare(self): |
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if self._input_type == "sequence": |
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all_input_dims = [self.input_dim] + self.additional_input_dims |
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else: |
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all_input_dims = self.additional_input_dims |
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summed_input_dim = sum(all_input_dims, 0) |
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self.output_dim = self.hidden_size |
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self.W_i = self.create_weight(summed_input_dim, self.hidden_size, "wi", initializer=self.outer_init) |
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self.U_i = self.create_weight(self.hidden_size, self.hidden_size, "ui", initializer=self.inner_init) |
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self.b_i = self.create_bias(self.hidden_size, "i") |
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self.W_f = self.create_weight(summed_input_dim, self.hidden_size, "wf", initializer=self.outer_init) |
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self.U_f = self.create_weight(self.hidden_size, self.hidden_size, "uf", initializer=self.inner_init) |
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self.b_f = self.create_bias(self.hidden_size, "f") |
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self.b_f.set_value(np.ones((self.hidden_size,) * self._init_forget_bias, dtype=FLOATX)) |
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self.W_c = self.create_weight(summed_input_dim, self.hidden_size, "wc", initializer=self.outer_init) |
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self.U_c = self.create_weight(self.hidden_size, self.hidden_size, "uc", initializer=self.inner_init) |
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self.b_c = self.create_bias(self.hidden_size, "c") |
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self.W_o = self.create_weight(summed_input_dim, self.hidden_size, "wo", initializer=self.outer_init) |
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self.U_o = self.create_weight(self.hidden_size, self.hidden_size, "uo", initializer=self.inner_init) |
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self.b_o = self.create_bias(self.hidden_size, suffix="o") |
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if summed_input_dim > 0: |
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self.register_parameters(self.W_i, self.U_i, self.b_i, |
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self.W_c, self.U_c, self.b_c, |
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self.W_f, self.U_f, self.b_f, |
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self.W_o, self.U_o, self.b_o) |
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else: |
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self.register_parameters(self.U_i, self.b_i, |
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self.U_c, self.b_c, |
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self.U_f, self.b_f, |
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self.U_o, self.b_o) |
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zomux /
deepy
