I'm simply trying to do what the title says. Here's my code:
def ConvAutoEncoder(train_data,test_data,n_epochs = 50,batchSize = 128,data_shape=(IMAGE_SIZE,IMAGE_SIZE,3)):
print('Training Neural Network')
input_img = Input(shape=data_shape)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
print(x.shape)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
print(x.shape)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
print(x.shape)
x = Conv2D(4, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
print(encoded.shape)
# at this point the representation is (6, 6, 4 i.e. 128-dimensional
encoded = Flatten()(encoded)
encoded = Dense( 6*6*4,activation='relu')(encoded)
print(encoded.shape)
endoded = Reshape((6,6,4))(encoded)
print(encoded.shape)
x = Conv2D(4, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
print(x.shape)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
print(x.shape)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
print(x.shape)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
print(x.shape)
decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.fit(train_data, train_data,
epochs=n_epochs,
batch_size=batchSize,
shuffle=True,
verbose=2,
validation_data=(test_data, test_data),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
return autoencoder
However when I run it the Reshape layer doesn't do anything at all, the shape of the output before the reshape is (?,144) and the shape after is also (?,144). Am I using reshape wrong or is there some other way to connect a dense layer to a conv2D layer?
