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Generative Adversarial Networks
# Create a wall of generated MNIST images
def saveGeneratedImages(epoch, examples=100, dim=(10, 10),
figsize=(10, 10)):
noise = np.random.normal(0, 1, size=[examples, randomDim])
generatedImages = generator.predict(noise)
generatedImages = generatedImages.reshape(examples, 28, 28)
plt.figure(figsize=figsize)
for i in range(generatedImages.shape[0]):
plt.subplot(dim[0], dim[1], i+1)
plt.imshow(generatedImages[i], interpolation='nearest',
cmap='gray_r')
plt.axis('off')
plt.tight_layout()
plt.savefig('images/gan_generated_image_epoch_%d.png' % epoch)
The complete code for this can be found in the notebook VanillaGAN.ipynb at
the GitHub repo for this chapter. In the coming sections we will cover some recent
GAN architectures and implement them in TensorFlow.
Deep convolutional GAN (DCGAN)
Proposed in 2016, DCGANs have become one of the most popular and successful
GAN architectures. The main idea in the design was using convolutional layers
without the use of pooling layers or the end classifier layers. The convolutional
strides and transposed convolutions are employed for the downsampling and
upsampling of images.
Before going into the details of the DCGAN architecture and its capabilities,
let us point out the major changes that were introduced in the paper:
• The network consisted of all convolutional layers. The pooling layers were
replaced by strided convolutions in the discriminator and transposed
convolutions in the generator.
• The fully connected classifying layers after the convolutions are removed.
• To help with the gradient flow, batch normalization is done after every
convolutional layer.
The basic idea of DCGANs is same as the vanilla GAN: we have a generator that
takes in noise of 100 dimensions; the noise is projected and reshaped, and then is
passed through convolutional layers. The following diagram shows the generator
architecture:
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