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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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from . import NeuralLayer |
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from var import NeuralVariable |
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from deepy.utils import build_activation, FLOATX |
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import numpy as np |
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import theano |
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import theano.tensor as T |
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from collections import OrderedDict |
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OUTPUT_TYPES = ["sequence", "one"] |
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INPUT_TYPES = ["sequence", "one"] |
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class LSTM(NeuralLayer): |
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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, input_type="sequence", output_type="sequence", |
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inner_activation="sigmoid", outer_activation="tanh", |
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inner_init=None, outer_init=None, steps=None, |
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go_backwards=False, |
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persistent_state=False, batch_size=0, |
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reset_state_for_input=None, forget_bias=1, |
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mask=None, |
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second_input=None, second_input_size=None): |
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super(LSTM, self).__init__("lstm") |
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self._hidden_size = hidden_size |
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self._input_type = input_type |
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self._output_type = output_type |
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self._inner_activation = inner_activation |
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self._outer_activation = outer_activation |
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self._inner_init = inner_init |
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self._outer_init = outer_init |
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self._steps = steps |
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self.persistent_state = persistent_state |
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self.reset_state_for_input = reset_state_for_input |
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self.batch_size = batch_size |
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self.go_backwards = go_backwards |
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# mask |
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mask = mask.tensor if type(mask) == NeuralVariable else mask |
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self.mask = mask.dimshuffle((1,0)) if mask else None |
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self._sequence_map = OrderedDict() |
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# second input |
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if type(second_input) == NeuralVariable: |
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second_input_size = second_input.dim() |
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second_input = second_input.tensor |
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self.second_input = second_input |
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self.second_input_size = second_input_size |
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self.forget_bias = forget_bias |
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if input_type not in INPUT_TYPES: |
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raise Exception("Input type of LSTM is wrong: %s" % input_type) |
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if output_type not in OUTPUT_TYPES: |
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raise Exception("Output type of LSTM is wrong: %s" % output_type) |
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if self.persistent_state and not self.batch_size: |
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raise Exception("Batch size must be set for persistent state mode") |
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if mask and input_type == "one": |
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raise Exception("Mask only works with sequence input") |
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def _auto_reset_memories(self, x, h, m): |
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reset_matrix = T.neq(x[:, self.reset_state_for_input], 1).dimshuffle(0, 'x') |
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h = h * reset_matrix |
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m = m * reset_matrix |
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return h, m |
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def step(self, *vars): |
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# Parse sequence |
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sequence_map = dict(zip(self._sequence_map.keys(), vars[:len(self._sequence_map)])) |
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h_tm1, c_tm1 = vars[-2:] |
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# Reset state |
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if self.reset_state_for_input != None: |
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h_tm1, c_tm1 = self._auto_reset_memories(sequence_map["x"], h_tm1, c_tm1) |
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if self._input_type == "sequence": |
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xi_t, xf_t, xo_t, xc_t = map(sequence_map.get, ["xi", "xf", "xo", "xc"]) |
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else: |
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xi_t, xf_t, xo_t, xc_t = 0, 0, 0, 0 |
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# Add second input |
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if "xi2" in sequence_map: |
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xi2, xf2, xo2, xc2 = map(sequence_map.get, ["xi2", "xf2", "xo2", "xc2"]) |
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xi_t += xi2 |
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xf_t += xf2 |
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xo_t += xo2 |
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xc_t += xc2 |
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# LSTM core step |
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i_t = self._inner_act(xi_t + T.dot(h_tm1, self.U_i) + self.b_i) |
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f_t = self._inner_act(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._outer_act(xc_t + T.dot(h_tm1, self.U_c) + self.b_c) |
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o_t = self._inner_act(xo_t + T.dot(h_tm1, self.U_o) + self.b_o) |
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h_t = o_t * self._outer_act(c_t) |
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# Apply mask |
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if "mask" in sequence_map: |
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mask = sequence_map["mask"].dimshuffle(0, 'x') |
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h_t = h_t * mask + h_tm1 * (1 - mask) |
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c_t = c_t * mask + c_tm1 * (1 - mask) |
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return h_t, c_t |
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def produce_input_sequences(self, x, mask=None, second_input=None): |
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# Create sequence map |
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self._sequence_map.clear() |
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if self._input_type == "sequence": |
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# Input vars |
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xi = T.dot(x, self.W_i) |
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xf = T.dot(x, self.W_f) |
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xc = T.dot(x, self.W_c) |
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xo = T.dot(x, self.W_o) |
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self._sequence_map.update([("xi", xi), ("xf", xf), ("xc", xc), ("xo", xo)]) |
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# Reset state |
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if self.reset_state_for_input != None: |
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self._sequence_map["x"] = x |
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# Add mask |
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if mask: |
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self._sequence_map["mask"] = mask |
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elif self.mask: |
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self._sequence_map["mask"] = self.mask |
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# Add second input |
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if self.second_input and not second_input: |
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second_input = self.second_input |
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if second_input: |
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xi2 = T.dot(second_input, self.W_i2) |
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xf2 = T.dot(second_input, self.W_f2) |
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xc2 = T.dot(second_input, self.W_c2) |
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xo2 = T.dot(second_input, self.W_o2) |
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self._sequence_map.update([("xi2", xi2), ("xf2", xf2), ("xc2", xc2), ("xo2", xo2)]) |
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return self._sequence_map.values() |
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def produce_initial_states(self, x): |
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if self.persistent_state: |
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return self.state_h, self.state_m |
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else: |
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h0 = T.alloc(np.cast[FLOATX](0.), x.shape[0], self._hidden_size) |
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m0 = h0 |
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return h0, m0 |
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def compute_tensor(self, x): |
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h0, m0 = self.produce_initial_states(x) |
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if self._input_type == "sequence": |
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# Move middle dimension to left-most position |
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# (sequence, batch, value) |
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x = x.dimshuffle((1,0,2)) |
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sequences = self.produce_input_sequences(x) |
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else: |
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h0 = x |
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sequences = self.produce_input_sequences(None) |
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[hiddens, memories], _ = theano.scan( |
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self.step, |
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sequences=sequences, |
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outputs_info=[h0, m0], |
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n_steps=self._steps, |
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go_backwards=self.go_backwards |
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) |
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# Save persistent state |
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if self.persistent_state: |
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self.register_updates((self.state_h, hiddens[-1])) |
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self.register_updates((self.state_m, memories[-1])) |
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if self._output_type == "one": |
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return hiddens[-1] |
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elif self._output_type == "sequence": |
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return hiddens.dimshuffle((1,0,2)) |
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def prepare(self): |
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self._setup_params() |
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self._setup_functions() |
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def _setup_functions(self): |
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self._inner_act = build_activation(self._inner_activation) |
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self._outer_act = build_activation(self._outer_activation) |
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def _setup_params(self): |
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self.output_dim = self._hidden_size |
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self.W_i = self.create_weight(self.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(self.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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if self.forget_bias > 0: |
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self.b_f.set_value(np.ones((self._hidden_size,), dtype=FLOATX)) |
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self.W_c = self.create_weight(self.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(self.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 self._input_type == "sequence": |
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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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# Second input |
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if self.second_input_size: |
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self.W_i2 = self.create_weight(self.second_input_size, self._hidden_size, "wi2", initializer=self._outer_init) |
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self.W_f2 = self.create_weight(self.second_input_size, self._hidden_size, "wf2", initializer=self._outer_init) |
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self.W_c2 = self.create_weight(self.second_input_size, self._hidden_size, "wc2", initializer=self._outer_init) |
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self.W_o2 = self.create_weight(self.second_input_size, self._hidden_size, "wo2", initializer=self._outer_init) |
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self.register_parameters(self.W_i2, self.W_f2, self.W_c2, self.W_o2) |
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# Create persistent state |
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if self.persistent_state: |
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self.state_h = self.create_matrix(self.batch_size, self._hidden_size, "lstm_state_h") |
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self.state_m = self.create_matrix(self.batch_size, self._hidden_size, "lstm_state_m") |
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self.register_free_parameters(self.state_h, self.state_m) |
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else: |
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self.state_h = None |
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self.state_m = None |
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zomux /
deepy
