dynamic_brnn.py 文件源码

def build_multi_dynamic_brnn(args,
                             maxTimeSteps,
                             inputX,
                             cell_fn,
                             seqLengths,
                             time_major=True):
    hid_input = inputX
    for i in range(args.num_layer):
        scope = 'DBRNN_' + str(i + 1)
        forward_cell = cell_fn(args.num_hidden, activation=args.activation)
        backward_cell = cell_fn(args.num_hidden, activation=args.activation)
        # tensor of shape: [max_time, batch_size, input_size]
        outputs, output_states = bidirectional_dynamic_rnn(forward_cell, backward_cell,
                                                           inputs=hid_input,
                                                           dtype=tf.float32,
                                                           sequence_length=seqLengths,
                                                           time_major=True,
                                                           scope=scope)
        # forward output, backward ouput
        # tensor of shape: [max_time, batch_size, input_size]
        output_fw, output_bw = outputs
        # forward states, backward states
        output_state_fw, output_state_bw = output_states
        # output_fb = tf.concat(2, [output_fw, output_bw])
        output_fb = tf.concat([output_fw, output_bw], 2)
        shape = output_fb.get_shape().as_list()
        output_fb = tf.reshape(output_fb, [shape[0], shape[1], 2, int(shape[2] / 2)])
        hidden = tf.reduce_sum(output_fb, 2)
        hidden = dropout(hidden, args.keep_prob, (args.mode == 'train'))

        if i != args.num_layer - 1:
            hid_input = hidden
        else:
            outputXrs = tf.reshape(hidden, [-1, args.num_hidden])
            # output_list = tf.split(0, maxTimeSteps, outputXrs)
            output_list = tf.split(outputXrs, maxTimeSteps, 0)
            fbHrs = [tf.reshape(t, [args.batch_size, args.num_hidden]) for t in output_list]
    return fbHrs