embedding.py 文件源码

def build(self, input_shape):
        # input shape is (batch_size, num_words, num_senses, num_hyps)
        self.num_senses = input_shape[-2]
        self.num_hyps = input_shape[-1] - 1  # -1 because the last value is a word index
        # embedding of size 1.
        if self.set_sense_priors:
            self.sense_priors = self._get_initial_sense_priors((self.word_index_size, 1), name='{}_sense_priors'.format(self.name))
        else:
            # OntoLSTM makes sense proabilities uniform if the passed sense parameters are zero.
            self.sense_priors = K.zeros((self.word_index_size, 1))  # uniform sense probs
        # Keeping aside the initial weights to not let Embedding set them. It wouldn't know what sense priors are.
        if self.initial_weights is not None:
            self.onto_aware_embedding_weights = self.initial_weights
            self.initial_weights = None
        # The following method will set self.trainable_weights
        super(OntoAwareEmbedding, self).build(input_shape)  # input_shape will not be used by Embedding's build.
        if not self.tune_embedding:
            # Move embedding to non_trainable_weights
            self._non_trainable_weights.append(self._trainable_weights.pop())

        if self.set_sense_priors:
            self._trainable_weights.append(self.sense_priors)

        if self.onto_aware_embedding_weights is not None:
            self.set_weights(self.onto_aware_embedding_weights)