ValueError:检查目标时的错误:预期的Model_2具有形状(无，252、252、1)，但具有形状的数组(300

Question

嗨，我正在构建用于单级分类的图像分类器，其中我在运行此模型时使用了自动编码器，我会通过此行（autoencoder_model.fit）在此错误中获得此错误检查目标时的错误：预期的model_2具有形状（无，252、252、1），但具有形状的数组（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].shape

X_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-dimensional
encoded_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')])

Answer 1

这是解码器的输出形状与训练数据形状之间的简单不相容性。（目标是指输出）。

我看到您有2个MaxPoolings（将图像大小除以4个），以及三个UPS采样（将解码器的输入乘以8）。

自动编码器的最终输出太大，与您的数据不匹配。您必须简单地在模型中工作即可使输出形状与训练数据相匹配。

Answer 2

您正在使用错误的API

autoencoder_model.fit(X_train, X_train,  <--- This one is wrong
        epochs=50,
        batch_size=32,
        validation_data=(X_test, X_test),
        callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])

查看.fit方法源代码来自https://github.com/keras-team/keras/blob/master/keras/models.py

def fit(self,
        x=None,
        y=None,
        batch_size=None,
        epochs=1,
        verbose=1,
        callbacks=None,
        validation_split=0.,
        validation_data=None,
        shuffle=True,
        class_weight=None,
        sample_weight=None,
        initial_epoch=0,
        steps_per_epoch=None,
        validation_steps=None,
        **kwargs):
    """Trains the model for a fixed number of epochs (iterations on a dataset).
    # Arguments
        x: Numpy array of training data.
            If the input layer in the model is named, you can also pass a
            dictionary mapping the input name to a Numpy array.
            `x` can be `None` (default) if feeding from
            framework-native tensors (e.g. TensorFlow data tensors).
        y: Numpy array of target (label) data.
            If the output layer in the model is named, you can also pass a
            dictionary mapping the output name to a Numpy array.
            `y` can be `None` (default) if feeding from
            framework-native tensors (e.g. TensorFlow data tensors).

因此X应该是数据，Y应该是数据的标签。希望有帮助