在编码器和解码器 keras 上拆分自动编码器 [英] Split autoencoder on encoder and decoder keras

问题描述

我正在尝试为以下对象创建自动编码器:

I am trying to create an autoencoder for:

1. 训练模型
2. 分离编码器和解码器
3. 可视化压缩数据(编码器)
4. 使用任意压缩数据获取输出(解码器)

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
from keras.datasets import mnist
import numpy as np

(x_train, _), (x_test, _) = mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_train = x_train[:100,:,:,]
x_test = x_test.astype('float32') / 255.
x_test = x_train
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format
 input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format

x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

# at this point the representation is (7, 7, 32)

decoder = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(decoder)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

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

autoencoder.fit(x_train, x_train,
                epochs=10,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test, x_test),
                #callbacks=[TensorBoard(log_dir='/tmp/tb', histogram_freq=0, write_graph=False)]
               )

如何拆分训练并用训练好的权重拆分?

How to split train it and split with the trained weights?

推荐答案

制作编码器:

input_img = Input(shape=(28, 28, 1))

x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

encoder = Model(input_img, encoded)

制作解码器:

decoder_input= Input(shape_equal_to_encoder_output_shape)

decoder = Conv2D(32, (3, 3), activation='relu', padding='same')(decoder_input)
x = UpSampling2D((2, 2))(decoder)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

decoder = Model(decoder_input, decoded)

制作自动编码器:

auto_input = Input(shape=(28,28,1))
encoded = encoder(auto_input)
decoded = decoder(encoded)

auto_encoder = Model(auto_input, decoded)

现在您可以随心所欲地使用其中的任何一种.

Now you can use any of them any way you want to.

1. 训练自编码器
2. 使用编码器和解码器

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