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我不知道这是一种错误还是错误。我也在这里报告了这个问题。

我想做的事情是我想让我的自定义 LSTM 有状态。所以这段代码运行without良好return_state=True。一旦我将它添加到代码中，它就会引发这个错误：The two structures don't have the same nested structure。

这是一个可重现的代码：

from keras.layers import Lambda
import keras
import numpy as np
import tensorflow as tf
SEQUENCE_LEN = 45
LATENT_SIZE = 20
EMBED_SIZE = 50
VOCAB_SIZE = 100
BATCH_SIZE = 10
def rev_entropy(x):
        def row_entropy(row):
            _, _, count = tf.unique_with_counts(row)
            count = tf.cast(count,tf.float32)
            prob = count / tf.reduce_sum(count)
            prob = tf.cast(prob,tf.float32)
            rev = -tf.reduce_sum(prob * tf.log(prob))
            return rev

        nw = tf.reduce_sum(x,axis=1)
        rev = tf.map_fn(row_entropy, x)
        rev = tf.where(tf.is_nan(rev), tf.zeros_like(rev), rev)
        rev = tf.cast(rev, tf.float32)
        max_entropy = tf.log(tf.clip_by_value(nw,2,LATENT_SIZE))
        concentration = (max_entropy/(1+rev))
        new_x = x * (tf.reshape(concentration, [BATCH_SIZE, 1]))
        return new_x

inputs = keras.layers.Input(shape=(SEQUENCE_LEN,), name="input")

embedding = keras.layers.Embedding(output_dim=EMBED_SIZE, input_dim=VOCAB_SIZE, input_length=SEQUENCE_LEN, trainable=True)(inputs)
encoded = keras.layers.Bidirectional(keras.layers.LSTM(LATENT_SIZE,return_state=True), merge_mode="sum", name="encoder_lstm")(embedding)

encoded = Lambda(rev_entropy)(encoded)
decoded = keras.layers.RepeatVector(SEQUENCE_LEN, name="repeater")(encoded)
decoded = keras.layers.Bidirectional(keras.layers.LSTM(EMBED_SIZE, return_sequences=True,return_state=True), merge_mode="sum", name="decoder_lstm")(decoded)
autoencoder = keras.models.Model(inputs, decoded)
autoencoder.compile(optimizer="sgd", loss='mse')
autoencoder.summary()

x = np.random.randint(0, 90, size=(10, 45))
print(x.shape)

y = np.random.normal(size=(10, 45, 50))
print(y.shape)
history = autoencoder.fit(x, y, epochs=1)

更新1

在应用了评论的想法后tf.map_fn(row_entropy, encoded,dtype=tf.float32)，我收到了一个新的错误：

ValueError: Layer repeater expects 1 inputs, but it received 5 input tensors. Input received: [<tf.Tensor 'encoder_lstm/add_16:0' shape=(?, 20) dtype=float32>, <tf.Tensor 'encoder_lstm/while/Exit_3:0' shape=(?, 20) dtype=float32>, <tf.Tensor 'encoder_lstm/while/Exit_4:0' shape=(?, 20) dtype=float32>, <tf.Tensor 'encoder_lstm/while_1/Exit_3:0' shape=(?, 20) dtype=float32>, <tf.Tensor 'encoder_lstm/while_1/Exit_4:0' shape=(?, 20) dtype=float32>]

另外，考虑到即使没有那个 lambda 层也会引发这个错误，所以似乎还有其他问题。如果我尝试encoded.shape，它会说encoded是一个列表，length 5但是它必须是一个张量(batch_size, latent size)！！！

一切都很好，没有添加return_state=True 任何帮助表示赞赏！

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