Python/Keras/Theano wrong dimensions for Deep Auto

问题:

I'm trying to follow the Deep Autoencoder Keras example. I'm getting a dimension mismatch exception, but for the life of me, I can't figure out why. It works when I use only one encoded dimension, but not when I stack them.

Exception: Input 0 is incompatible with layer dense_18:
expected shape=(None, 128), found shape=(None, 32)*

The error is on the line 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')

回答1:

Thanks for the hint from Marcin. Turns out all the decoder layers need to be unrolled in order to get it to work.

# 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))))

回答2:

The problem lies in:

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

In previous model - the last layer was the only decoder layer. So it input was also an input to decoder. But right now you have 3 decoding layer so you have to go back to the first one in order to obtain decoder first layer. So changing this line to:

# retrieve the last layer of the autoencoder model
decoder_layer = autoencoder.layers[-3]

Should do the work.

回答3:

You need to apply the transformation from each decoder layer to the previous. You can manually unroll and hard code these as in the accepted answer, or the following loop should take care of it:

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

# retrieve the decoder layers and apply to each prev layer
num_decoder_layers = 3
decoder_layer = encoded_input
for i in range(-num_decoder_layers, 0):
    decoder_layer = autoencoder.layers[i](decoder_layer)

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