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Experimenting with GANs in Tensorflow/Keras

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#### GAN Lab
This repository is a lab to learn about and train variants of generative adversarial networks or GANs. There are also examples of training GANs of Cloud ML Engine (a cheap option to train models).

#### Why learn GANs?
GANs are interesting because they are capable of learning even complex data distributions and generate (fake) samples from that distribution. For example:

Fake MNIST images generated by [DCGAN](https://arxiv.org/abs/1511.06434) after 4000 training iterations.

#### How to train GANs

The basic framework consists of 2 neural networks:
- G or Generator and
- D or Discriminator

G takes as input a noise vector and outputs a sample with same shape as real samples (we call these fake samples). D takes as input a sample (real or fake) and outputs the probability that the input sample is real. Mathematically, G and D are playing a min-max game for a value function V:

Above equation implies that D tries to maximize `V(D, G)` and G tries to minimize `V(D, G)`. Also the `log` in the above equation can be modified to things like mean-squared error as in LSGAN. The above formulation is for the binary-crossentropy loss.

However, this objective function has small gradient near 0 for G, so training the generator is difficult in initial phase.
So, we modify the objective function as:

and

In practice, we first construct G, D and AM or Adversarial Model (D stacked on top of G) and then at each training step, we teach D to differentiate between fake and real samples and simultaneously we teach AM to generate fake samples which are able to fool D. Also, D should be set to non-trainable in the 2nd part.

The idea is that with enough training, G will be able to fool D (which means we can construct real-looking samples using G.)

The pattern for the above looks like this:

```
"""Part 1 - Training discriminator(D)""
# Generate a noise of appropriate shape
noise = np.random.normal(0, 1, size=[batch_size, noise_dim])

# Generate a fake sample from this random noise
fake = G.predict(noise)

# Take a sample from real data
real = data[np.random.randint(0, data.shape[0], batch_size), :, :, :]

# train_x is the concatenation of real and fake samples
train_x = np.concatenate((real, fake))

# and train_y is the matrix containing 1's for real and 0's for fake samples
train_y = np.concatenate((np.ones((batch_size, 1)), np.zeros((batch_size, 1))))

# Train D on this batch of train_x and train_y
d_loss = D.train_on_batch(train_x, train_y)
# Actually, in this repo this step has been done with 2 separate train_on_batch calls for real and fake samples

"""Part 2 - Training adversarial model(G+D)"""
# Generate a noisy sample of appropriate shape
noise = np.random.normal(0, 1, size=[batch_size, noise_dim])

# Here's the trick: forcing the Adversarial model to have output class 1 for fake samples
Y = np.ones((batch_size, 1))

# Train AM (G+D) on this batch of noise and Y
a_loss = AM.train_on_batch(noise, Y)
```
Above steps are repeated for a pre-specified number of steps and after enough steps, G will be able to generate samples visually indistinguishable from actual images.

<!-- #### The GAN Class
I have created a `GAN` class in `gan.py` which takes in its constructor, a generator and a discriminator as arguments.
Both of these need to be Keras' `Model` objects
It contains methods `makeGAN` and `train` which can be called on a `GAN` object to create GAN from G and D and then train it.
The API for this class is not direct though and it must be defined by inheritance.
For example you would use this class in this way:
```
from gan import GAN
class demoGAN(GAN):
 def __init__(self, kwargs):
 self.G = self.Generator()
 self.D = self.Discriminator()
 super().__init__(kwargs)
 def Generator(self):
 # Generator model goes here
 pass
 def Discriminator(self):
 # Discriminator model goes here
 pass

demo_gan = demoGAN()
demo_gan.makeGAN()
demo_gan.train()
```
I have included a complete example in `example.py` to use this class.
My hope is that this class will make experimentation much faster because now new `GAN` objects can inherit all the boilerplate methods for making and training GANs from the `GAN` class.
Contributions and feedbacks of all kind are more than welcome; they are encouraged. -->

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