提高Keras自动编码器的收敛速度

时间：2018-09-16 19:46:26

标签： python tensorflow machine-learning keras autoencoder

我想在（相当嘈杂的）图像上训练卷积自动编码器。为此，我构建了一个training_generator和一个validation_generator，它们均产生120张图像的批处理。我每个时期使用111个批次进行训练，每个时期使用47个批次进行验证。图像具有三种颜色，每通道（颜色）的值在0到255之间，但是生成器使用images = images.astype('float32') / 255.0将它们转换为0到1之间的值。架构是

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model

input_img = Input(shape=(152, 360, 3))  # adapt this if using `channels_first` image data format

x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)    #1
x = MaxPooling2D((2, 2), padding='same')(x)                             #2
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)             #3
x = MaxPooling2D((2, 2), padding='same')(x)                             #4
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)             #5
encoded = MaxPooling2D((2, 2), padding='same')(x)                       #6

# at this point the representation is autoencoder.layers[6].output_shape = (None, 19, 45, 8)

x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)       #7
x = UpSampling2D((2, 2))(x)                                             #8
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)             #9
x = UpSampling2D((2, 2))(x)                                             #10
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)            #11
x = UpSampling2D((2, 2))(x)                                             #12
decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)    #13

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

autoencoder.fit_generator(
    generator=training_generator,
    validation_data=validation_generator,
    use_multiprocessing=True,
    workers=2,
    epochs=2)

我根据https://blog.keras.io/building-autoencoders-in-keras.html定位自己，但将损失从"binary_crossentropy"更改为"mean_squared_error“，因为图像的值不只是0或1，对于MNIST数据集，但介于0和1之间（在提到的归一化之后），在这里，我可以看到我训练了两个时期，但我仍然期望损失的快速改善，但是这种改善并不令人印象深刻。如何在训练过程中提高优化器的收敛速度？

要问几个更具体的问题：

输入后是否应该使用AveragePooling层，以降低噪声水平？
我是否应该使用其他一些标准化方法？
是否应该对所有图层使用Sigmoid函数而不是relu？

附录：损失的发展

（我用...标记，没有发生任何损失变化。）

foroptimizer ='adadelta'

Epoch 1/2
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111/111 [==============================] - 1864s 17s/step - loss: 0.0214 - val_loss: 0.0201
Epoch 2/2
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111/111 [==============================] - 1976s 18s/step - loss: 0.0202 - val_loss: 0.0214

对于optimizer = keras.optimizers.Adam（lr = 1e-2）

Epoch 1/2
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Epoch 2/2
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0 个答案:

没有答案