我试图为灰度图像做一个模型。它看起来像是有一个问题的输出形状，我试图添加一个填充到conv2d，把它给我一个输入形状的错误在测试中。the model
实施：

model=keras.Sequential()

model.add(Conv2D(64, kernel_size=(48, 48), activation='relu', input_shape=(105,105,1)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))

model.add(Conv2D(128, kernel_size=(24, 24), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2DTranspose(128, (24,24), strides = (2,2), activation = 'relu', padding='same', kernel_initializer='uniform'))
model.add(UpSampling2D(size=(2, 2)))

model.add(Conv2DTranspose(64, (12,12), strides = (2,2), activation = 'relu', padding='same', kernel_initializer='uniform'))
model.add(UpSampling2D(size=(2, 2)))

model.add(Conv2D(256, kernel_size=(12, 12), activation='relu'))

model.add(Conv2D(256, kernel_size=(12, 12), activation='relu'))

model.add(Conv2D(256, kernel_size=(12, 12), activation='relu'))

model.add(Flatten())

model.add(Dense(4096, activation='relu'))

model.add(Dropout(0.5))

model.add(

Dense(4096,activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(2383,activation='relu'))

model.add(Dense(5, activation='softmax'))

错误：

ValueError: One of the dimensions in the output is <= 0 due to downsampling in conv2d_9. Consider increasing the input size. Received input shape [None, 105, 105, 1] which would produce output shape with a zero or negative value in a dimension.