image-processing - keras 在拟合方法 : expected model_2 to have shape (None, 252, 252, 1) 中出错，但得到了形状为 (300, 128, 128, 3) 的数组

Question

我正在构建一个用于一类分类的图像分类器，其中我使用了自动编码器。

运行此模型时，我收到以下错误 autoencoder_model.fit:

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

当自动编码器尝试重新创建原始图像时，您似乎正在重建尺寸与原始图像不同的图像，因为事实上只有两个 MaxPool2D编码器中的 层和解码器中的三个 UpSampling2D 层。

当自动编码器尝试评估重建的损失时，由于维度不匹配，它会遇到错误。

将此用于您的编码器，并让我们知道它是否有效:

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)