CODEWITHSUNDEEP
image-processingmachine-learningkerasautoencoder
Dexter
Dexter

Reputation: 630

keras error in fit method : expected model_2 to have shape (None, 252, 252, 1) but got array with shape (300, 128, 128, 3)

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 the 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')])

Upvotes: 1

Views: 485

Answers (1)

Oringa
Oringa

Reputation: 360

As the auto-encoder tries to re-create the original images, it seems you are reconstructing an image with different dimensions than the original, due to the fact to have only two MaxPool2D layers in your encoder and three UpSampling2D layers in your decoder.

When the auto-encoder tries to evaluate the loss of the reconstruction, it runs into an error due to a dimension miss-match.

Use this for your encoder and let us know if it works:

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)

Upvotes: 2

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