Python Keras卷积自动编码器：层形状

我有一个大约70000张训练图像的列表，每个形状（颜色通道的数量，高度宽度）=（3，30，30）和大约20000张测试图像。我的卷积自动编码器定义为：

 # Same as the code above, but with some params changed
# Now let's define the model. 

# Set input dimensions:
input_img = Input(shape=(3, 30, 30))

# Encoder: define a chain of Conv2D and MaxPooling2D layers
x = Convolution2D(128, 3, 3, 
                  activation='relu', 
                  border_mode='same')(input_img)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(64, 3, 3, 
                  activation='relu', 
                  border_mode='same')(x)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(64, 3, 3, 
                  activation='relu', 
                  border_mode='same')(x)
encoded = MaxPooling2D((2, 2), border_mode='same')(x)

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

# Decoder: a stack of Conv2D and UpSampling2D layers
x = Convolution2D(64, 3, 3, 
                  activation='relu', 
                  border_mode='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(64, 3, 3, 
                  activation='relu', 
                  border_mode='same')(x)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(128, 3, 3, 
                  activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Convolution2D(1, 3, 3, 
                        activation='sigmoid', 
                        border_mode='same')(x)

autoencoder2 = Model(input_img, decoded)
autoencoder2.compile(optimizer='adadelta', loss='mse')

这是来自的自动编码器

它抛出一个错误：

Error when checking model target: expected convolution2d_14 to have shape (None, 1, 28, 28) but got array with shape (76960, 3, 30, 30)

---

这很奇怪，因为我已经将指定的输入形状更改为（3,30,30）。我缺少一些实现技术吗？

您忘记在解码器的最后一个convnet层中添加border_mode='same'

在中，他们忘了添加

'border\u mode='same'

例如，在第二个最后一个卷积层中

---

x=卷积2d（128,3,3，activation='relu'）（x）

您应该将最后一个卷积层的形状从（1,3,3）更改为（3,3,3），如下所示：

decoded = Convolution2D(3, 3, 3, 
                    activation='sigmoid', 
                    border_mode='same')(x)

试试这个答案