ValueError：检查目标时出错：预期model_2具有形状（无，252,252,1）但是具有形状的数组（300,128,128,3） [英] ValueError: Error when checking target: expected model_2 to have shape (None, 252, 252, 1) but got array with shape (300, 128, 128, 3)

问题描述

嗨我正在构建一个类别分类的图像分类器，其中我在运行此模型时使用了自动编码器，我通过此行获取此错误（autoencoder_model.fit）（ValueError：检查目标时出错：期望的model_2到有形状（无，252,252,1）但有阵形（300,128,128,3）。）

hi I am building a image classifier for one-class classification in which i've used autoencoder while running this model I am getting this error by this line (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')])

推荐答案

解码器的输出形状与训练数据的形状之间存在简单的不兼容性。 （目标意味着输出）。

It's a simple incompatibility between the output shape of the decoder and the shape of your training data. (Target means output).

我看到你有2个MaxPoolings（将你的图像大小除以4）和三个上采样（将解码器的输入乘以8）。

I see you've got 2 MaxPoolings (dividing your image size by 4), and three upsamplings (multiplying the decoder's input by 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才能使输出形状与您的训练数据相匹配。

The final output of the autoencoder is too big and doesn't match your data. You must simply work in the model to make the output shape match your training data.

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