layers.py 文件源码

def bidirectional_GRU(inputs, inputs_len, cell = None, cell_fn = tf.contrib.rnn.GRUCell, units = Params.attn_size, layers = 1, scope = "Bidirectional_GRU", output = 0, is_training = True, reuse = None):
    '''
    Bidirectional recurrent neural network with GRU cells.

    Args:
        inputs:     rnn input of shape (batch_size, timestep, dim)
        inputs_len: rnn input_len of shape (batch_size, )
        cell:       rnn cell of type RNN_Cell.
        output:     if 0, output returns rnn output for every timestep,
                    if 1, output returns concatenated state of backward and
                    forward rnn.
    '''
    with tf.variable_scope(scope, reuse = reuse):
        if cell is not None:
            (cell_fw, cell_bw) = cell
        else:
            shapes = inputs.get_shape().as_list()
            if len(shapes) > 3:
                inputs = tf.reshape(inputs,(shapes[0]*shapes[1],shapes[2],-1))
                inputs_len = tf.reshape(inputs_len,(shapes[0]*shapes[1],))

            # if no cells are provided, use standard GRU cell implementation
            if layers > 1:
                cell_fw = MultiRNNCell([apply_dropout(cell_fn(units), size = inputs.shape[-1] if i == 0 else units, is_training = is_training) for i in range(layers)])
                cell_bw = MultiRNNCell([apply_dropout(cell_fn(units), size = inputs.shape[-1] if i == 0 else units, is_training = is_training) for i in range(layers)])
            else:
                cell_fw, cell_bw = [apply_dropout(cell_fn(units), size = inputs.shape[-1], is_training = is_training) for _ in range(2)]

        outputs, states = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs,
                                                        sequence_length = inputs_len,
                                                        dtype=tf.float32)
        if output == 0:
            return tf.concat(outputs, 2)
        elif output == 1:
            return tf.reshape(tf.concat(states,1),(Params.batch_size, shapes[1], 2*units))