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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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import theano |
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import theano.tensor as T |
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from deepy.layers import NeuralLayer |
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class WordEmbedding(NeuralLayer): |
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""" |
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Word embedding layer. |
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The word embeddings are randomly initialized, and are learned over the time. |
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""" |
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def __init__(self, size, vocab_size, zero_index=None, mask=None, load_values=None, init=None): |
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from deepy.core.neural_var import NeuralVariable |
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super(WordEmbedding, self).__init__("word_embed") |
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self.size = size |
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self.vocab_size = vocab_size |
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self.output_dim = size |
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self.zero_index = zero_index |
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self._mask = mask.tensor if type(mask) == NeuralVariable else mask |
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self._init = init |
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self._load_values = load_values |
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self.init(1) |
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def prepare(self): |
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if self._load_values is not None: |
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self.embed_matrix = theano.shared(self._load_values, name="embeddings") |
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else: |
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self.embed_matrix = self.create_weight(self.vocab_size, self.size, "embeddings", initializer=self._init) |
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self.register_parameters(self.embed_matrix) |
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def compute_tensor(self, x, mask=None): |
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mask = mask if mask else self._mask |
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if self.zero_index is not None: |
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mask = T.neq(x, self.zero_index) |
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# To avoid negative index |
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x = T.cast(x * mask, "int32") |
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if x.ndim == 1: |
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ret_tensor = self.embed_matrix[x] |
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else: |
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ret_tensor = self.embed_matrix[x.flatten()].reshape(list(x.shape) + [self.size]) |
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if mask: |
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if x.ndim == 2: |
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ret_tensor *= mask[:, :, None] |
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elif x.ndim == 1: |
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ret_tensor *= mask[:, None] |
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return ret_tensor |
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zomux /
deepy
