language_models.py 文件源码

def __init__(self, lstm_num_layers, lstm_layer_size, trainable_embeddings, **kw):
        """Initializes the Keras LSTM question processing component.

        Args:
            lstm_num_layers: Number of stacked LSTM layers.
            lstm_layer_size: Dimensionality of each LSTM unit.

        Keyword Args:
            max_sentence_length: Maximum number of words to consider in each
                                 question, necessary at train time.
            bidirectional: Whether to use bidirectional LSTM layers.
        """
        print('Loading GloVe data... ', end='', flush=True)
        self._nlp = English()
        print('Done.')
        #embedding_dims = 300
        embeddings = get_embeddings(self._nlp.vocab)
        embedding_dims = embeddings.shape[1] 

        # TODO(Bernhard): Investigate how the LSTM parameters influence the
        # overall performance.
        self._max_len = kw.get('max_sentence_length', 15)
        self._bidirectional = kw.get('bidirectional', False)

        self._model = Sequential()
        shallow = lstm_num_layers == 1  # marks a one layer LSTM

        if trainable_embeddings:
            # if embeddings are trainable we have to enforce CPU usage in order to not run out of memory.
            # this is device dependent.
            # TODO(Bernhard): preprocess questions ans vocab and try if we can get rid of enough words to make
            # this run on gpu anyway
            with tf.device("/cpu:0"):
                self._model.add(Embedding(embeddings.shape[0], embeddings.shape[1],
                                      input_length=self._max_len, trainable=True, weights=[embeddings]))
        else:
            # a non-trainable embedding layer can run on GPU without exhausting all the memory
            self._model.add(Embedding(embeddings.shape[0], embeddings.shape[1],
                                      input_length=self._max_len, trainable=False, weights=[embeddings]))

        lstm = LSTM(output_dim=lstm_layer_size,
                    return_sequences=not shallow,
                    input_shape=(self._max_len, embedding_dims))
        if self._bidirectional:
            lstm = Bidirectional(lstm)
        self._model.add(lstm)
        if not shallow:
            for i in range(lstm_num_layers-2):
                lstm = LSTM(output_dim=lstm_layer_size, return_sequences=True)
                if self._bidirectional:
                    lstm = Bidirectional(lstm)
                self._model.add(lstm)

            lstm = LSTM(output_dim=lstm_layer_size, return_sequences=False)
            if self._bidirectional:
                lstm = Bidirectional(lstm)
            self._model.add(lstm)