ValueError: Error when checking target: expected model_2 to have shape (None, 252, 252, 1) but got array with shape (300, 128, 128, 3)

Question

嗨，我正在为一个class classification 构建一个图像 classifier，其中我使用了自动编码器，而运行这个模型我得到了这个此行出错 (autoencoder_model.fit) (ValueError: 检查目标时出错：预期 model_2 具有形状 (None, 252, 252, 1) 但得到形状为 (300, 128) 的数组, 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

你用错了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')])

看看.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).

所以x应该是数据，y应该是数据的标签。希望有所帮助

Answer 2

这是解码器输出形状与训练数据形状之间的简单不兼容。（目标表示输出）。

我看到您有 2 个 MaxPoolings（将图像大小除以 4）和三个上采样（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以使输出形状与您的训练数据相匹配。

ValueError: Error when checking target: expected model_2 to have shape (None, 252, 252, 1) but got array with shape (300, 128, 128, 3)

ValueError: Error when checking target: expected model_2 to have shape (None, 252, 252, 1) but got array with shape (300, 128, 128, 3)

python

machine-learning

image-processing

autoencoder

keras