问题:

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 (ValueError: Layer conv2d_3 was called with an input that isn't a symbolic tensor. Received type: . Full input: [(128, 128, 3)]. All inputs to the layer should be tensors.)

num_of_samples = img_data.shape[0] labels = np.ones((num_of_samples,),dtype='int64')    labels[0:376]=0  names = ['cat']  Y = np_utils.to_categorical(labels, num_class) input_shape=img_data[0].shape  x,y = shuffle(img_data,Y, random_state=2)  X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)  x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_shape) 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 = MaxPooling2D((2, 2), padding='same')(x)  # at this point the representation is (4, 4, 8) i.e. 128-dimensional  x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded) 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')(x) x = UpSampling2D((2, 2))(x) decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)  autoencoder = Model(input_shape, decoded) autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')   autoencoder.fit(X_train, X_train,             epochs=50,             batch_size=32,             shuffle=True,             validation_data=(X_test, X_test),             callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])

回答1:

Here:

x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_shape)

A shape is not a tensor.

Do this:

from keras.layers import * inputTensor = Input(input_shape) x = Conv2D(16, (3, 3), activation='relu', padding='same')(inputTensor)

Hint about autoencoders

You should separate the encoder and decoder as individual models. Later you will probably want to work with only one of them.

Encoder:

inputTensor = Input(input_shape) x = .... encodedData = MaxPooling2D((2, 2), padding='same')(x)  encoderModel = Model(inputTensor,encodedData)

Decoder:

encodedInput = Input((4,4,8)) x = .... decodedData = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)  decoderModel = Model(encodedInput,decodedData)

Autoencoder:

autoencoderInput = Input(input_shape) encoded = encoderModel(autoencoderInput) decoded = decoderModel(encoded)  autoencoderModel = Model(autoencoderInput,decoded)

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