Keras 自动编码器输入图像大小

Question

考虑这个自动编码器：

import numpy as np

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

class ConvAutoencoder:

    def __init__(self, image_size, latent_dim):

        inp = Input(shape=(image_size[0], image_size[1], 1))

        x = Conv2D(16, (3, 3), activation='relu', padding='same')(inp)
        x = MaxPooling2D((2, 2), padding='same')(x)
        x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
        x = MaxPooling2D((2, 2), padding='same')(x)
        x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
        encoded = MaxPooling2D((2, 2), padding='same')(x)
        # at this point the representation is (4, 4, 8) i.e. 128-dimensional

        d = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
        d = UpSampling2D((2, 2))(d)
        d = Conv2D(8, (3, 3), activation='relu', padding='same')(d)
        d = UpSampling2D((2, 2))(d)
        d = Conv2D(16, (3, 3), activation='relu')(d)
        d = UpSampling2D((2, 2))(d)

        decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(d)

        self.model = Model(inp, decoded)
        self.encoder = Model(inp, encoded)
        self.model.compile(loss='mse', optimizer='Adam')

        print(self.model.summary())

我用

实例化它

ConvAutoencoder(image_size=(32,32), latent_dim=10)

打印

Model: "model_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 32, 32, 1)         0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 32, 32, 16)        160       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 16, 16, 16)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 16, 16, 8)         1160      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 8, 8, 8)           0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 8, 8, 8)           584       
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 4, 4, 8)           0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 4, 4, 8)           584       
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 8, 8, 8)           0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 8, 8, 8)           584       
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 16, 16, 8)         0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 14, 14, 16)        1168      
_________________________________________________________________
up_sampling2d_3 (UpSampling2 (None, 28, 28, 16)        0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 28, 28, 1)         145       
=================================================================
Total params: 4,385
Trainable params: 4,385
Non-trainable params: 0
_________________________________________________________________
None

如您所见，输入图像大小为 (32,32)，但输出图像大小为 (28,28)。
* 问题 1：如何更改自动编码器的架构，使输出图像大小变为 (32,32)?
* 问题 2：如您所见，class 需要一个名为 latent_dim 的参数。目前，此参数未被使用。有没有一种简单的方法可以 "forcing" 将自动编码器的潜在维度降低到一定数量？例如。在中间添加一个完全连接的层或沿着这些线添加什么？

Answer 1

Question 1

好吧，你忘记了上次上采样中的一个padding='same'。

应该是这样的

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

        d = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
        d = UpSampling2D((2, 2))(d)
        d = Conv2D(8, (3, 3), activation='relu', padding='same')(d)
        d = UpSampling2D((2, 2))(d)
        d = Conv2D(16, (3, 3), activation='relu', padding='same')(d)
        d = UpSampling2D((2, 2))(d)

        decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(d)

Question 2

你是指内核吗？那么

        x = Conv2D(latent_dim*4, (3, 3), activation='relu', padding='same')(inp)
        x = MaxPooling2D((2, 2), padding='same')(x)
        x = Conv2D(latent_dim*2, (3, 3), activation='relu', padding='same')(x)
        x = MaxPooling2D((2, 2), padding='same')(x)
        x = Conv2D(latent_dim, (3, 3), activation='relu', padding='same')(x)
        encoded = MaxPooling2D((2, 2), padding='same')(x)
        # at this point the representation is (4, 4, 8) i.e. 128-dimensional

        d = Conv2D(latent_dim, (3, 3), activation='relu', padding='same')(encoded)
        d = UpSampling2D((2, 2))(d)
        d = Conv2D(latent_dim*2, (3, 3), activation='relu', padding='same')(d)
        d = UpSampling2D((2, 2))(d)
        d = Conv2D(latent_dim*4, (3, 3), activation='relu', padding='same')(d)
        d = UpSampling2D((2, 2))(d)

但是如果你的意思是你希望中间层有一个特定的内核大小，那么你可以用这样的步幅替换 MaxPooling2D 到 Conv2D。

encoded = Conv2D(latent_dim, (3, 3), activation='relu', padding='same', strides=2)(x)

实际上，您可以删除所有 Maxpooling2D 并将 strides=2 添加到所有 Conv2D。

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