我正在构建一个用于单类分类的图像分类器，其中使用了自编码器。

运行此模型时，我在 autoencoder_model.fit 这一行遇到了以下错误：

ValueError: 在检查目标时出错：期望 model_2 的形状为 (None, 252, 252, 1)，但得到的数组形状为 (300, 128, 128, 3)

num_of_samples = img_data.shape[0]labels = np.ones((num_of_samples,),dtype='int64')labels[0:376]=0 names = ['cats']input_shape=img_data[0].shapeX_train, X_test = train_test_split(img_data, test_size=0.2, random_state=2)inputTensor = Input(input_shape)x = Conv2D(16, (3, 3), activation='relu', padding='same')(inputTensor)x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)x = MaxPooling2D((2, 2), padding='same')(x)x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)encoded_data = MaxPooling2D((2, 2), padding='same')(x)encoder_model = Model(inputTensor,encoded_data)# at this point the representation is (4, 4, 8) i.e. 128-dimensionalencoded_input = Input((4,4,8))x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded_input)x = UpSampling2D((2, 2))(x)x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)x = UpSampling2D((2, 2))(x)x = Conv2D(16, (3, 3), activation='relu',padding='same')(x)x = UpSampling2D((2, 2))(x)decoded_data = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)decoder_model = Model(encoded_input,decoded_data)autoencoder_input = Input(input_shape)encoded = encoder_model(autoencoder_input)decoded = decoder_model(encoded)autoencoder_model = Model(autoencoder_input, decoded)autoencoder_model.compile(optimizer='adadelta', enter code here`loss='binary_crossentropy')autoencoder_model.fit(X_train, X_train,        epochs=50,        batch_size=32,        validation_data=(X_test, X_test),        callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])

回答：

由于自编码器试图重建原始图像，似乎您重建的图像尺寸与原始图像不同，这是因为编码器中只有 两个 MaxPool2D 层，而解码器中有 三个 UpSampling2D 层。

当自编码器试图评估重建的损失时，由于维度不匹配而导致错误。

请使用以下代码作为您的编码器，并告诉我们是否有效：

inputTensor = Input(input_shape)x = Conv2D(16, (3, 3), activation='relu', padding='same')(inputTensor)x = MaxPooling2D((2, 2), padding='same')(x)x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)x = MaxPooling2D((2, 2), padding='same')(x)x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)encoded_data = MaxPooling2D((2, 2), padding='same')(x)encoder_model = Model(inputTensor,encoded_data)