我正在使用tensorflow 2.0和keras进行针对Tensorflow的卷积自动编码器教程,可在此处找到。
使用提供的代码来构建CNN,但是在编码器和解码器上再添加一个卷积层,则会导致代码中断:
class Denoise(Model):
def __init__(self):
super(Denoise, self).__init__()
self.encoder = tf.keras.Sequential([
layers.Input(shape=(28, 28, 1)),
layers.Conv2D(16, (3,3), activation='relu', padding='same', strides=2),
layers.Conv2D(8, (3,3), activation='relu', padding='same', strides=2),
## New Layer ##
layers.Conv2D(4, (3,3), activation='relu', padding='same', strides=2)
## --------- ##
])
self.decoder = tf.keras.Sequential([
## New Layer ##
layers.Conv2DTranspose(4, kernel_size=3, strides=2, activation='relu', padding='same'),
## --------- ##
layers.Conv2DTranspose(8, kernel_size=3, strides=2, activation='relu', padding='same'),
layers.Conv2DTranspose(16, kernel_size=3, strides=2, activation='relu', padding='same'),
layers.Conv2D(1, kernel_size=(3,3), activation='sigmoid', padding='same')
])
def call(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return decoded
autoencoder = Denoise()
运行autoencoder.encoder.summary()
和autoencoder.decoder.summary()
,我可以看到这是一个形状问题:
Encoder:
Layer (type) Output Shape Param #
=================================================================
conv2d_124 (Conv2D) (None, 14, 14, 16) 160
_________________________________________________________________
conv2d_125 (Conv2D) (None, 7, 7, 8) 1160
_________________________________________________________________
conv2d_126 (Conv2D) (None, 4, 4, 4) 292
=================================================================
Total params: 1,612
Trainable params: 1,612
Non-trainable params: 0
_________________________________________________________________
Decoder:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_transpose_77 (Conv2DT (32, 8, 8, 4) 148
_________________________________________________________________
conv2d_transpose_78 (Conv2DT (32, 16, 16, 8) 296
_________________________________________________________________
conv2d_transpose_79 (Conv2DT (32, 32, 32, 16) 1168
_________________________________________________________________
conv2d_127 (Conv2D) (32, 32, 32, 1) 145
=================================================================
Total params: 1,757
Trainable params: 1,757
Non-trainable params: 0
_________________________________________________________________
为什么解码方面的领先优势32
?None, 4, 4, 4
如果输入是从编码器传递的,为什么输入层的尺寸不为?我该如何解决?
在此先感谢您的帮助!
stride=2
在最后一个编码器层中删除,然后stride=2
在最后一个解码器层中添加。
from tensorflow.keras import layers
from tensorflow.keras import Model
class Denoise(Model):
def __init__(self):
super(Denoise, self).__init__()
self.encoder = tf.keras.Sequential([
layers.Input(shape=(28, 28, 1)),
layers.Conv2D(16, (3,3), activation='relu', padding='same', strides=2),
layers.Conv2D(8, (3,3), activation='relu', padding='same', strides=2),
## New Layer ##
layers.Conv2D(4, (3,3), activation='relu', padding='same')
## --------- ##
])
self.decoder = tf.keras.Sequential([
## New Layer ##
layers.Conv2DTranspose(4, kernel_size=3, strides=2, activation='relu', padding='same'),
## --------- ##
layers.Conv2DTranspose(8, kernel_size=3, strides=2, activation='relu', padding='same'),
layers.Conv2DTranspose(16, kernel_size=3, strides=2, activation='relu', padding='same'),
layers.Conv2D(1, kernel_size=(3,3), activation='sigmoid', padding='same', strides=2)
])
def call(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return decoded
autoencoder = Denoise()
autoencoder.build(input_shape=(1, 28, 28, 1))
autoencoder.summary()
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我来说两句