Python/Keras/Theano错误的自动编码器维度

Question

我正在尝试遵循Deep Autoencoder Keras 示例.我得到了一个维度不匹配异常,但对于我的生活,我无法弄清楚为什么.当我只使用一个编码维度时它可以工作,但是当我堆叠它们时却不行.

例外:输入0与图层dense_18不兼容:
expected shape =(None,128),found shape =(None,32)*

错误就行了 decoder = Model(input=encoded_input, output=decoder_layer(encoded_input))

from keras.layers import Dense,Input
from keras.models import Model

import numpy as np

# this is the size of the encoded representations
encoding_dim = 32

#NPUT LAYER
input_img = Input(shape=(784,))

#ENCODE LAYER
# "encoded" is the encoded representation of the input
encoded = Dense(encoding_dim*4, activation='relu')(input_img)
encoded = Dense(encoding_dim*2, activation='relu')(encoded)
encoded = Dense(encoding_dim, activation='relu')(encoded)

#DECODED LAYER
# "decoded" is the lossy reconstruction of the input
decoded = Dense(encoding_dim*2, activation='relu')(encoded)
decoded = Dense(encoding_dim*4, activation='relu')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded)

#MODEL
autoencoder = Model(input=input_img, output=decoded)


#SEPERATE ENCODER MODEL
encoder = Model(input=input_img, output=encoded)

# create a placeholder for an encoded (32-dimensional) input
encoded_input = Input(shape=(encoding_dim,))

# retrieve the last layer of the autoencoder model
decoder_layer = autoencoder.layers[-1]

# create the decoder model
decoder = Model(input=encoded_input, output=decoder_layer(encoded_input))

#COMPILER
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

Answer 1

感谢Marcin的暗示.原来需要展开所有解码器层以使其工作.

# retrieve the last layer of the autoencoder model
decoder_layer1 = autoencoder.layers[-3]
decoder_layer2 = autoencoder.layers[-2]
decoder_layer3 = autoencoder.layers[-1]

# create the decoder model
decoder = Model(input=encoded_input, output=decoder_layer3(decoder_layer2(decoder_layer1(encoded_input))))