keras - Layer conv2d_3 was called with an input that isn't a symbolic tensor

Question

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')])

Answer 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)