lstm_crf_old.py 文件源码

def inference(self, X, reuse=None, trainMode=True):
        word_vectors = tf.nn.embedding_lookup(self.words, X)
        length = self.length(X)
        length_64 = tf.cast(length, tf.int64)
        reuse = None if trainMode else True
        if FLAGS.embedding_size_2 > 0:
            word_vectors2 = tf.nn.embedding_lookup(self.words2, X)
            word_vectors = tf.concat(2, [word_vectors, word_vectors2])
        #if trainMode:
        #  word_vectors = tf.nn.dropout(word_vectors, 0.5)
        with tf.variable_scope("rnn_fwbw", reuse=reuse) as scope:
            forward_output, _ = tf.nn.dynamic_rnn(
                tf.contrib.rnn.LSTMCell(self.numHidden,
                                        reuse=reuse),
                word_vectors,
                dtype=tf.float32,
                sequence_length=length,
                scope="RNN_forward")
            backward_output_, _ = tf.nn.dynamic_rnn(
                tf.contrib.rnn.LSTMCell(self.numHidden,
                                        reuse=reuse),
                inputs=tf.reverse_sequence(word_vectors,
                                           length_64,
                                           seq_dim=1),
                dtype=tf.float32,
                sequence_length=length,
                scope="RNN_backword")

        backward_output = tf.reverse_sequence(backward_output_,
                                              length_64,
                                              seq_dim=1)

        output = tf.concat([forward_output, backward_output], 2)
        output = tf.reshape(output, [-1, self.numHidden * 2])
        if trainMode:
            output = tf.nn.dropout(output, 0.5)

        matricized_unary_scores = tf.matmul(output, self.W) + self.b
        # matricized_unary_scores = tf.nn.log_softmax(matricized_unary_scores)
        unary_scores = tf.reshape(
            matricized_unary_scores,
            [-1, FLAGS.max_sentence_len, self.distinctTagNum])

        return unary_scores, length