Question

是否可以从RNN获取可变的输出长度，即input_seq_length！= output_seq_length？

这里是一个显示LSTM输出形状的示例，test_rnn_output_v1默认设置-仅返回最后一步的输出，test_rnn_output_v2返回所有步骤的输出，即我需要类似test_rnn_output_v2的东西输出形状为(None, variable_seq_length, rnn_dim)或至少为(None, max_output_seq_length, rnn_dim)。

from keras.layers import Input
from keras.layers import LSTM
from keras.models import Model


def test_rnn_output_v1():
    max_seq_length = 10
    n_features = 4
    rnn_dim = 64

    input = Input(shape=(max_seq_length, n_features))
    out = LSTM(rnn_dim)(input)

    model = Model(inputs=[input], outputs=out)

    print(model.summary())

    # (None, max_seq_length, n_features)
    # (None, rnn_dim)


def test_rnn_output_v2():
    max_seq_length = 10
    n_features = 4
    rnn_dim = 64

    input = Input(shape=(max_seq_length, n_features))
    out = LSTM(rnn_dim, return_sequences=True)(input)

    model = Model(inputs=[input], outputs=out)

    print(model.summary())

    # (None, max_seq_length, n_features)
    # (None, max_seq_length, rnn_dim)


test_rnn_output_v1()
test_rnn_output_v2()

Answer 1

根据定义，RNN层不能具有不相等的输入和输出长度。但是，有一个技巧可以使用两个RNN层和一个介于两者之间的RepeatVector层来实现不相等但固定的输出长度。这是一个最小的示例模型，该模型接受可变长度的输入序列并产生具有固定和任意长度的输出序列：

import tensorflow as tf

max_output_length = 35

inp = tf.keras.layers.Input(shape=(None, 10))
x = tf.keras.layers.LSTM(20)(inp)
x = tf.keras.layers.RepeatVector(max_output_length)(x)
out = tf.keras.layers.LSTM(30, return_sequences=True)(x)

model = tf.keras.Model(inp, out)
model.summary()

这是模型摘要：

Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, None, 10)]        0         
_________________________________________________________________
lstm (LSTM)                  (None, 20)                2480      
_________________________________________________________________
repeat_vector (RepeatVector) (None, 35, 20)            0         
_________________________________________________________________
lstm_1 (LSTM)                (None, 35, 30)            6120      
=================================================================
Total params: 8,600
Trainable params: 8,600
Non-trainable params: 0
_________________________________________________________________

此结构可用于序列到序列模型，其中输入序列的长度不一定与输出序列相同。

在TF / Keras中具有不相等的输入和输出长度的RNN层

1 个答案: