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

这是一个显示 LSTM 输出形状的示例，test_rnn_output_v1默认设置 - 仅返回最后一步的test_rnn_output_v2输出，返回所有步骤的输出，即我需要类似test_rnn_output_v2但具有输出形状(None, variable_seq_length, rnn_dim)或至少(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()
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1 回答 1

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根据定义，RNN 层不能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 层和中间的一个RepeatVector层来实现不相等但固定的输出长度。这是一个最小示例模型，它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()

以下是模型摘要：

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
_________________________________________________________________

这种结构可以用在序列到序列模型中，其中输入序列的长度可能不一定与输出序列相同。

于 2020-04-21T20:17:53.213 回答