深度自动编解码结构/形状误差中的Keras NoneTypeError

Question

我的神经网络训练很困难。我把神经网络定义如下：

shared = embedding_layer

inputA = keras.Input(shape=(40, ), name="anchor")  # Variable-length sequence of ints
inputP = keras.Input(shape=(40, ), name="positive")  # Variable-length sequence of ints
inputN = keras.Input(shape=(40, ), name="negative")  # Binary vectors of size num_tags

concatenated = layers.concatenate([inputA, inputP, inputN])

embedded_A = shared(concatenated)

encoded = Dense(900, activation = "relu")(embedded_A)
encoded = Dense(600, activation = "relu")(encoded)
encoded = Dense(300, activation = "relu")(encoded)
encoded = Dense(100, activation = "relu")(encoded)

decoded = Dense(100, activation = "relu")(encoded)
decoded = Dense(300, activation = "relu")(decoded)
decoded = Dense(600, activation = "relu")(decoded)
decoded = Dense(900, activation = "relu")(decoded)


predictionsA = Dense(40, activation="sigmoid", name ='outA')(decoded)
predictionsP = Dense(40, activation="sigmoid", name ='outB')(decoded)
predictionsN = Dense(40, activation="sigmoid", name ='outC')(decoded)

ml_model = keras.Model(
    inputs=[inputA, inputP, inputN],
    outputs=[predictionsA, predictionsP, predictionsN]
)


ml_model.compile(
    optimizer='adam',
    loss='mse'
)

ml_model.fit(
    {"anchor": anchor, "positive": positive, "negative": negative},
    {"outA": anchor, "outB": positive, 'outC': negative},
    epochs=2)

从原理上看就像

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嵌入层的定义如下：

embedding_m = model.syn0
embedding_layer = Embedding(len(vocab),
                            300,
                            weights=[embedding_m],
                            input_length=40,
                            trainable=True)

What I feed into the network is three numpy arrays of shape (120000, 40) which look like this:


array([[   2334,   23764,    7590, ..., 3000001, 3000001, 3000001],
       [3000000,    1245,    1124, ..., 3000001, 3000001, 3000001],
       [    481,     491,    5202, ..., 3000001, 3000001, 3000001],
       ...,
       [3000000,     125,   20755, ..., 3000001, 3000001, 3000001],
       [1217971,  168575,     239, ...,    9383,    1039,   87315],
       [  12990,      91,  258231, ..., 3000001, 3000001, 3000001]])

输入和输出是一样的，因为我正在制作一个自动编码器解码器。

我得到的错误是：

尺寸必须相等，但对于输入形状为: 32, 120 ,40，32的{节点均方_平方差}}=SquaredDifferenceT=DT_FLOAT，则为120和32。

但我似乎找不出原因，也找不出解决办法.有什么想法吗？如果需要，我可以提供更多的例子。我怀疑存在某种尺寸错误，因为理想情况下，我希望输出的形状(120000,40)与我的输入完全相同。

Answer 1

修正了有问题的编码器-解码器的版本：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from keras.layers import Dense

#shared = embedding_layer
#Simulate that...
shared=Dense(1,activation="relu")

inputA = keras.Input(shape=(40, ), name="anchor")  # Variable-length sequence of ints
inputP = keras.Input(shape=(40, ), name="positive")  # Variable-length sequence of ints
inputN = keras.Input(shape=(40, ), name="negative")  # Binary vectors of size num_tags

concatenated = layers.concatenate([inputA, inputP, inputN])

embedded_A = shared(concatenated)

encoded = Dense(900, activation = "relu")(embedded_A)
encoded = Dense(600, activation = "relu")(encoded)
encoded = Dense(300, activation = "relu")(encoded)
encoded = Dense(100, activation = "relu")(encoded)

#decoded = Dense(100, activation = "relu")(encoded)
decoded = Dense(300, activation = "relu")(encoded)
decoded = Dense(600, activation = "relu")(decoded)
decoded = Dense(900, activation = "relu")(decoded)


predictionsA = Dense(40, activation="sigmoid", name ='outA')(decoded)
predictionsP = Dense(40, activation="sigmoid", name ='outB')(decoded)
predictionsN = Dense(40, activation="sigmoid", name ='outC')(decoded)

ml_model = keras.Model(
    inputs=[inputA, inputP, inputN],
    outputs=[predictionsA, predictionsP, predictionsN]
)


ml_model.compile(
    optimizer='adam',
    loss='mse'
)

#Simulate...
anchor=tf.random.uniform((100,40))
positive=tf.random.uniform((100,40))
negative=tf.random.uniform((100,40))

ml_model.fit(
    {"anchor": anchor, "positive": positive, "negative": negative},
    {"outA": anchor, "outB": positive, 'outC': negative},
    epochs=2)

Answer 2

删除“解码”行以修复网络结构：

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