# Generative Adversarial Networks¶

Throughout most of this book, we’ve talked about how to make predictions. In some form or another, we used deep neural networks learned mappings from data points to labels. This kind of learning is called discriminative learning, as in, we’d like to be able to discriminate between photos cats and photos of dogs. Classifiers and regressors are both examples of discriminative learning. And neural networks trained by backpropagation have upended everything we thought we knew about discriminative learning on large complicated datasets. Classification accuracies on high-res images has gone from useless to human-level (with some caveats) in just 5-6 years. We’ll spare you another spiel about all the other discriminative tasks where deep neural networks do astoundingly well.

But there’s more to machine learning than just solving discriminative
tasks. For example, given a large dataset, without any labels, we might
want to learn a model that concisely captures the characteristics of
this data. Given such a model, we could sample synthetic data points
that resemble the distribution of the training data. For example, given
a large corpus of photographs of faces, we might want to be able to
generate a *new* photorealistic image that looks like it might plausibly
have come from the same dataset. This kind of learning is called
*generative modeling*.

Until recently, we had no method that could synthesize novel photorealistic images. But the success of deep neural networks for discriminative learning opened up new possiblities. One big trend over the last three years has been the application of discriminative deep nets to overcome challenges in problems that we don’t generally think of as supervised learning problems. The recurrent neural network language models are one example of using a discriminative network (trained to predict the next character) that once trained can act as a generative model.

In 2014, a young researcher named Ian Goodfellow introduced Generative Adversarial Networks (GANs) a clever new way to leverage the power of discriminative models to get good generative models. GANs made quite a splash so it’s quite likely you’ve seen the images before. For instance, using a GAN you can create fake images of bedrooms, as done by Radford et al. in 2015 and depicted below.

At their heart, GANs rely on the idea that a data generator is good if we cannot tell fake data apart from real data. In statistics, this is called a two-sample test - a test to answer the question whether datasets \(X = \{x_1, \ldots x_n\}\) and \(X' = \{x_1', \ldots x_n'\}\) were drawn from the same distribution. The main difference between most statistics papers and GANs is that the latter use this idea in a constructive way. In other words, rather than just training a model to say ‘hey, these two datasets don’t look like they came from the same distribution’, they use the two-sample test to provide training signal to a generative model. This allows us to improve the data generator until it generates something that resembles the real data. At the very least, it needs to fool the classifier. And if our classifier is a state of the art deep neural network.

As you can see, there are two pieces to GANs - first off, we need a
device (say, a deep network but it really could be anything, such as a
game rendering engine) that might potentially be able to generate data
that looks just like the real thing. If we are dealing with images, this
needs to generate images. If we’re dealing with speech, it needs to
generate audio sequences, and so on. We call this the *generator
network*. The second component is the *discriminator network*. It
attempts to distinguish fake and real data from each other. Both
networks are in competition with each other. The generator network
attempts to fool the discriminator network. At that point, the
discriminator network adapts to the new fake data. This information, in
turn is used to improve the generator network, and so on.

**Generator** * Draw some parameter \(z\) from a source of
randomness, e.g. a normal distribution \(z \sim \mathcal{N}(0,1)\).
* Apply a function \(f\) such that we get \(x' = G(u,w)\) *
Compute the gradient with respect to \(w\) to minimize
\(\log p(y = \mathrm{fake}|x')\)

**Discriminator** * Improve the accuracy of a binary classifier
\(f\), i.e. maximize \(\log p(y=\mathrm{fake}|x')\) and
\(\log p(y=\mathrm{true}|x)\) for fake and real data respectively.

In short, there are two optimization problems running simultaneously, and the optimization terminates if a stalemate has been reached. There are lots of further tricks and details on how to modify this basic setting. For instance, we could try solving this problem in the presence of side information. This leads to cGAN, i.e. conditional Generative Adversarial Networks. We can change the way how we detect whether real and fake data look the same. This leads to wGAN (Wasserstein GAN), kernel-inspired GANs and lots of other settings, or we could change how closely we look at the objects. E.g. fake images might look real at the texture level but not so at the larger level, or vice versa.

Many of the applications are in the context of images. Since this takes too much time to solve in a Jupyter notebook on a laptop, we’re going to content ourselves with fitting a much simpler distribution. We will illustrate what happens if we use GANs to build the world’s most inefficient estimator of parameters for a Gaussian. Let’s get started.

```
In [1]:
```

```
from __future__ import print_function
import matplotlib as mpl
from matplotlib import pyplot as plt
import mxnet as mx
from mxnet import gluon, autograd, nd
from mxnet.gluon import nn
import numpy as np
ctx = mx.cpu()
```

## Generate some ‘real’ data¶

Since this is going to be the world’s lamest example, we simply generate data drawn from a Gaussian. And let’s also set a context where we’ll do most of the computation.

```
In [2]:
```

```
X = nd.random_normal(shape=(1000, 2))
A = nd.array([[1, 2], [-0.1, 0.5]])
b = nd.array([1, 2])
X = nd.dot(X, A) + b
Y = nd.ones(shape=(1000, 1))
# and stick them into an iterator
batch_size = 4
train_data = mx.io.NDArrayIter(X, Y, batch_size, shuffle=True)
```

Let’s see what we got. This should be a Gaussian shifted in some rather arbitrary way with mean \(b\) and covariance matrix \(A^\top A\).

```
In [3]:
```

```
plt.scatter(X[:,0].asnumpy(), X[:,1].asnumpy())
plt.show()
print("The covariance matrix is")
print(nd.dot(A.T, A))
```

```
The covariance matrix is
[[ 1.00999999 1.95000005]
[ 1.95000005 4.25 ]]
<NDArray 2x2 @cpu(0)>
```

## Defining the networks¶

Next we need to define how to fake data. Our generator network will be the simplest network possible - a single layer linear model. This is since we’ll be driving that linear network with a Gaussian data generator. Hence, it literally only needs to learn the parameters to fake things perfectly. For the discriminator we will be a bit more discriminating: we will use an MLP with 3 layers to make things a bit more interesting.

The cool thing here is that we have *two* different networks, each of
them with their own gradients, optimizers, losses, etc. that we can
optimize as we please.

```
In [4]:
```

```
# build the generator
netG = nn.Sequential()
with netG.name_scope():
netG.add(nn.Dense(2))
# build the discriminator (with 5 and 3 hidden units respectively)
netD = nn.Sequential()
with netD.name_scope():
netD.add(nn.Dense(5, activation='tanh'))
netD.add(nn.Dense(3, activation='tanh'))
netD.add(nn.Dense(2))
# loss
loss = gluon.loss.SoftmaxCrossEntropyLoss()
# initialize the generator and the discriminator
netG.initialize(mx.init.Normal(0.02), ctx=ctx)
netD.initialize(mx.init.Normal(0.02), ctx=ctx)
# trainer for the generator and the discriminator
trainerG = gluon.Trainer(netG.collect_params(), 'adam', {'learning_rate': 0.01})
trainerD = gluon.Trainer(netD.collect_params(), 'adam', {'learning_rate': 0.05})
```

## Setting up the training loop¶

We are going to iterate over the data a few times. To make life simpler we need a few variables

```
In [5]:
```

```
real_label = mx.nd.ones((batch_size,), ctx=ctx)
fake_label = mx.nd.zeros((batch_size,), ctx=ctx)
metric = mx.metric.Accuracy()
# set up logging
from datetime import datetime
import os
import time
```

## Training loop¶

```
In [6]:
```

```
stamp = datetime.now().strftime('%Y_%m_%d-%H_%M')
for epoch in range(10):
tic = time.time()
train_data.reset()
for i, batch in enumerate(train_data):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
# train with real_t
data = batch.data[0].as_in_context(ctx)
noise = nd.random_normal(shape=(batch_size, 2), ctx=ctx)
with autograd.record():
real_output = netD(data)
errD_real = loss(real_output, real_label)
fake = netG(noise)
fake_output = netD(fake.detach())
errD_fake = loss(fake_output, fake_label)
errD = errD_real + errD_fake
errD.backward()
trainerD.step(batch_size)
metric.update([real_label,], [real_output,])
metric.update([fake_label,], [fake_output,])
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
with autograd.record():
output = netD(fake)
errG = loss(output, real_label)
errG.backward()
trainerG.step(batch_size)
name, acc = metric.get()
metric.reset()
print('\nbinary training acc at epoch %d: %s=%f' % (epoch, name, acc))
print('time: %f' % (time.time() - tic))
noise = nd.random_normal(shape=(100, 2), ctx=ctx)
fake = netG(noise)
plt.scatter(X[:,0].asnumpy(), X[:,1].asnumpy())
plt.scatter(fake[:,0].asnumpy(), fake[:,1].asnumpy())
plt.show()
```

```
binary training acc at epoch 0: accuracy=0.764500
time: 5.838877
```

```
binary training acc at epoch 1: accuracy=0.639000
time: 6.052228
```

```
binary training acc at epoch 2: accuracy=0.551000
time: 5.773329
```

```
binary training acc at epoch 3: accuracy=0.522000
time: 5.613472
```

```
binary training acc at epoch 4: accuracy=0.498000
time: 6.069607
```

```
binary training acc at epoch 5: accuracy=0.496500
time: 5.800509
```

```
binary training acc at epoch 6: accuracy=0.498500
time: 5.982538
```

```
binary training acc at epoch 7: accuracy=0.515500
time: 6.017519
```

```
binary training acc at epoch 8: accuracy=0.500000
time: 6.143714
```

```
binary training acc at epoch 9: accuracy=0.499000
time: 6.123487
```

## Checking the outcome¶

Let’s now generate some fake data and check whether it looks real.

```
In [7]:
```

```
noise = mx.nd.random_normal(shape=(100, 2), ctx=ctx)
fake = netG(noise)
plt.scatter(X[:,0].asnumpy(), X[:,1].asnumpy())
plt.scatter(fake[:,0].asnumpy(), fake[:,1].asnumpy())
plt.show()
```

## Conclusion¶

A word of caution here - to get this to converge properly, we needed to
adjust the learning rates *very carefully*. And for Gaussians, the
result is rather mediocre - a simple mean and covariance estimator would
have worked *much better*. However, whenever we don’t have a really good
idea of what the distribution should be, this is a very good way of
faking it to the best of our abilities. Note that a lot depends on the
power of the discriminating network. If it is weak, the fake can be very
different from the truth. E.g. in our case it had trouble picking up
anything along the axis of reduced variance. In summary, this isn’t
exactly easy to set and forget. One nice resource for dirty practioner’s
knowledge is Soumith Chintala’s handy list of
tricks for how to babysit GANs.

For whinges or inquiries, open an issue on GitHub.