如错误跟踪所示，您在尝试使用此自动编码器时遇到的问题是，模型期望输入形状为(?, 800, 20, 1)，但输出形状为(?, 796, 20, 1)。
(800, 20, 1). （检查模型摘要的输入和输出形状!）

我的建议-

1.我已经修正了padding = 'same'，并重新调整了形状，使输入Tensor形状和输出相同。检查我修改过的内核大小，以获得所需的输出形状。
1.最重要的是，对于一个堆叠的conv编码器-解码器架构（如自动编码器），建议您将空间信息转换为后续层的特征Map/过滤器/通道。现在您从8个过滤器开始，然后在编码器中移动到4个。它应该更像4->8->16。检查此图以参考直觉。

根据上述建议，我在此进行了修改。

x = Input(shape=(800, 20,1)) 

# Encoder
conv1_1 = Conv2D(4, (3, 3), activation='relu', padding='same')(x)
pool1 = MaxPooling2D((2, 2), padding='same')(conv1_1)
conv1_2 = Conv2D(8, (3, 3), activation='relu', padding='same')(pool1)
pool2 = MaxPooling2D((2, 2), padding='same')(conv1_2)
conv1_3 = Conv2D(16, (3, 3), activation='relu', padding='same')(pool2)
h = MaxPooling2D((2, 1), padding='same')(conv1_3) #<------

# # Decoder

conv2_1 = Conv2D(16, (3, 3), activation='relu', padding='same')(h)
up1 = UpSampling2D((2, 2))(conv2_1)
conv2_2 = Conv2D(8, (3, 3), activation='relu', padding='same')(up1)
up2 = UpSampling2D((2, 2))(conv2_2)
conv2_3 = Conv2D(4, (3, 3), activation='sigmoid', padding='same')(up2) #<--- ADD PADDING HERE
up3 = UpSampling2D((2, 1))(conv2_3)
r = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(up3)

model = Model(inputs=x, outputs=r)
model.compile(optimizer='adadelta', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()

Model: "model_13"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_16 (InputLayer)        [(None, 800, 20, 1)]      0         
_________________________________________________________________
conv2d_101 (Conv2D)          (None, 800, 20, 4)        40        
_________________________________________________________________
max_pooling2d_45 (MaxPooling (None, 400, 10, 4)        0         
_________________________________________________________________
conv2d_102 (Conv2D)          (None, 400, 10, 8)        296       
_________________________________________________________________
max_pooling2d_46 (MaxPooling (None, 200, 5, 8)         0         
_________________________________________________________________
conv2d_103 (Conv2D)          (None, 200, 5, 16)        1168      
_________________________________________________________________
max_pooling2d_47 (MaxPooling (None, 100, 5, 16)        0         
_________________________________________________________________
conv2d_104 (Conv2D)          (None, 100, 5, 16)        2320      
_________________________________________________________________
up_sampling2d_43 (UpSampling (None, 200, 10, 16)       0         
_________________________________________________________________
conv2d_105 (Conv2D)          (None, 200, 10, 8)        1160      
_________________________________________________________________
up_sampling2d_44 (UpSampling (None, 400, 20, 8)        0         
_________________________________________________________________
conv2d_106 (Conv2D)          (None, 400, 20, 4)        292       
_________________________________________________________________
up_sampling2d_45 (UpSampling (None, 800, 20, 4)        0         
_________________________________________________________________
conv2d_107 (Conv2D)          (None, 800, 20, 1)        37        
=================================================================
Total params: 5,313
Trainable params: 5,313
Non-trainable params: 0
_________________________________________________________________

为了显示模型现在可以运行了，让我创建一个随机数据集，它与您正在查看的数据集相同。
一个二个一个一个