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Generative Adversarial Networks
We will use a simple multi-layered perceptron (MLP) and we will feed it an image
as a flat vector of size 784, so we reshape the training data:
X_train = X_train.reshape(60000, 784)
Now we will need to build a generator and discriminator. The purpose of the
generator is to take in a noisy input and generate an image similar to the training
dataset. The size of the noisy input is decided by the variable randomDim; you
can initialize it to any integral value. Conventionally people set it to 100. For our
implementation we tried a value of 10. This input is fed to a Dense layer with
256 neurons with LeakyReLU activation. We next add another Dense layer with
512 hidden neurons, followed by the third hidden layer with 1024 neurons and
finally the output layer with 784 neurons. You can change the number of neurons
in the hidden layers and see how the performance changes; however, the number
of neurons in the output unit has to match the number of pixels in the training
images. The corresponding generator is then:
generator = Sequential()
generator.add(Dense(256, input_dim=randomDim))
generator.add(LeakyReLU(0.2))
generator.add(Dense(512))
generator.add(LeakyReLU(0.2))
generator.add(Dense(1024))
generator.add(LeakyReLU(0.2))
generator.add(Dense(784, activation='tanh'))
Similarly, we build a discriminator. Notice now that the discriminator takes in the
images, either from the training set or images generated by generator, thus its input
size is 784. The output of the discriminator however is a single bit, with 0 signifying
a fake image (generated by generator) and 1 signifying that the image is from the
training dataset:
discriminator = Sequential()
discriminator.add(Dense(1024, input_dim=784) )
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(512))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(256))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(1, activation='sigmoid'))
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