如何拆分卷积自动编码器?

问题描述

我已经编译了一个自动编码器(下面是完整的代码)，在对其进行了训练之后，我想将其分为两个单独的模型:编码器(e1 ... encoded层)和解码器(所有其他层)在其中馈送由解码器编码的手动修改的图像.我已成功使用以下方法将编码器创建为单独的模型:

I have compiled an autoencoder (full code is below), and after training it I would like to split it into two separate models: encoder (layers e1...encoded) and decoder (all other layers) in which to feed manually modified images that had been encoded by the decoder. I have succeeded in creating an encoder as a separate model with:

encoder = Model(input_img, autoencoder.layers[6].output)

但是当我尝试制作解码器时，相同的方法会失败:

But the same approach fails when I try to make a decoder:

encoded_input = Input(shape=(4,4,8))
decoder = Model(input_img, decoded)

这是我的完整代码:

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
from keras.models import load_model

input_img = Input(shape=(28, 28, 1))  # adapt this if using channels_first` image data format

e1 = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
e2 = MaxPooling2D((2, 2), padding='same')(e1)
e3 = Conv2D(8, (3, 3), activation='relu', padding='same')(e2)
e4 = MaxPooling2D((2, 2), padding='same')(e3)
e5 = Conv2D(8, (3, 3), activation='relu', padding='same')(e4)
encoded = MaxPooling2D((2, 2), padding='same')(e5)

# at this point the representation is (4, 4, 8) i.e. 128-dimensional

d1 = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
d2 = UpSampling2D((2, 2))(d1)
d3 = Conv2D(8, (3, 3), activation='relu', padding='same')(d2)
d4 = UpSampling2D((2, 2))(d3)
d5 = Conv2D(16, (3, 3), activation='relu')(d4)
d6 = UpSampling2D((2, 2))(d5)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(d6)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

请帮助.

编辑顺便说一下，我能够使用由密集层组成的自动编码器来做到这一点:

EDITBy the way, I was able to do this with an autoencoder consisting of dense layers:

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

# this is the size of our encoded representations
encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming     the input is 784 floats

# this is our input placeholder
input_img = Input(shape=(784,))

# "encoded" is the encoded representation of the input
encoded = Dense(encoding_dim, activation='relu')(input_img)

# "decoded" is the lossy reconstruction of the input
decoded = Dense(784, activation='sigmoid')(encoded)

# this model maps an input to its reconstruction
autoencoder = Model(input_img, decoded)

# this model maps an input to its encoded representation
encoder = Model(input_img, 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(encoded_input, decoder_layer(encoded_input))

推荐答案

好，几个小时后我发现了这一点.对我有用的是:1.为编码器创建一个单独的模型2.为解码器创建一个单独的模型3.为自动编码器创建一个通用模型:

Ok, I figured this out after a few hours. What worked for me was to:1. Create a separate model for the encoder2. Create a separate model for teh decoder3. Create a general model for the autoencoder:

autoencoder = Model(input, Decoder()(Encoder(input))

完整的工作代码如下:

def Encoder():
    input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format
    e1 = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
    e2 = MaxPooling2D((2, 2), padding='same')(e1)
    e3 = Conv2D(8, (3, 3), activation='relu', padding='same')(e2)
    e4 = MaxPooling2D((2, 2), padding='same')(e3)
    e5 = Conv2D(8, (3, 3), activation='relu', padding='same')(e4)
    e6 = MaxPooling2D((2, 2), padding='same')(e5)
    return Model(input_img, e6)


def Decoder():
    input_img = Input(shape=(4, 4, 8))  # adapt this if using `channels_first` image data format
    d1 = Conv2D(8, (3, 3), activation='relu', padding='same')(input_img)
    d2 = UpSampling2D((2, 2))(d1)
    d3 = Conv2D(8, (3, 3), activation='relu', padding='same')(d2)
    d4 = UpSampling2D((2, 2))(d3)
    d5 = Conv2D(16, (3, 3), activation='relu')(d4)
    d6 = UpSampling2D((2, 2))(d5)
    d7 = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(d6)
    return Model(input_img, d7)


# define input to the model:
x = Input(shape=(28, 28, 1))

# make the model:
autoencoder = Model(x, Decoder()(Encoder()(x)))

# compile the model:
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

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