我设置了卷积层和池化层，然后进行反卷积和反池化，输入形状为 256 * 256 * 3 的图像，但最终出现了形状错误：

def build_auto_encode_model(shape=(256,256,3)):    input_img = Input(shape=shape)      x = Convolution2D(16, (3, 3), activation='relu', padding='same')(input_img)      x = MaxPooling2D((2, 2), padding='same')(x)      x = Convolution2D(8, (3, 3), activation='relu', padding='same')(x)      x = MaxPooling2D((2, 2), padding='same')(x)      x = Convolution2D(8, (3, 3), activation='relu', padding='same')(x)      encoded = MaxPooling2D((2, 2), padding='same')(x)      x = Convolution2D(8, (3, 3), activation='relu', padding='same')(encoded)      x = UpSampling2D((2, 2))(x)      x = Convolution2D(8, (3, 3), activation='relu', padding='same')(x)      x = UpSampling2D((2, 2))(x)      x = Convolution2D(16, (3, 3), activation='relu')(x)      x = UpSampling2D((2, 2))(x)      decoded = Convolution2D(3, (3, 3), activation='sigmoid', padding='same')(x)      encoder = Model(inputs=input_img, outputs=encoded)    autoencoder = Model(inputs=input_img, outputs=decoded)      autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')     return encoder, autoencoderdef train_auto_encode_model(encoder_model_path="./data/encoder.h5"):    X = np.load("data/train.npy")    X_train = X[int(round(X.shape[0] * 0.2)):, :]    X_test = X[0:int(round(X.shape[0] * 0.2)), :]    encoder, autoencoder = build_auto_encode_model()    autoencoder.fit(X_train, X_train, epochs=10, batch_size=64, shuffle=True, validation_data=(X_test, X_test))    encoder.save(encoder_model_path)

这是我得到的错误信息：

Error when checking target: expected conv2d_7 to have shape (252, 252, 3) but got array with shape (256, 256, 3)

错误追溯：

回答：

通过使用 autoencoder.summary()，你会看到最后一个 Conv2D 层的输出形状为 (None, 252, 252, 3)；因此，形状为 (256,256,3) 的标签是不兼容的。这个问题的根源是你忘记设置前一个 Conv2D 层的 padding 参数。将其设置为 'same' 可以解决这个问题：

x = Convolution2D(16, (3, 3), activation='relu', padding='same')(x)