Prerequisites: We assume that you have successfully downloaded the MNIST data by completing the tutorial titled CNTK_103A_MNIST_DataLoader.ipynb.
Generative models have gained a lot of attention in deep learning community which has traditionally leveraged discriminative models for (semi-supervised) and unsupervised learning.
In the previous tutorial we introduce the original GAN implementation by Goodfellow et al at NIPS 2014. This pioneering work has since then been extended and many techniques have been published amongst which the Deep Convolutional Generative Adversarial Network a.k.a. DCGAN has become the recommended launch pad in the community.
In this tutorial, we introduce an implementation of the DCGAN with some well tested architectural constraints that improve stability in the GAN training:
In [1]:
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import os
import cntk as C
from cntk import Trainer
from cntk.layers import default_options
from cntk.device import set_default_device, gpu, cpu
from cntk.initializer import normal
from cntk.io import (MinibatchSource, CTFDeserializer, StreamDef, StreamDefs,
INFINITELY_REPEAT)
from cntk.layers import Dense, Convolution2D, ConvolutionTranspose2D, BatchNormalization
from cntk.learners import (adam, UnitType, learning_rate_schedule,
momentum_as_time_constant_schedule, momentum_schedule)
from cntk.logging import ProgressPrinter
%matplotlib inline
In [2]:
# Select the right target device when this notebook is being tested:
if 'TEST_DEVICE' in os.environ:
import cntk
if os.environ['TEST_DEVICE'] == 'cpu':
C.device.set_default_device(C.device.cpu())
else:
C.device.set_default_device(C.device.gpu(0))
C.device.set_default_device(C.device.gpu(0))
Out[2]:
There are two run modes:
Fast mode: isFast
is set to True
. This is the default mode for the notebooks, which means we train for fewer iterations or train / test on limited data. This ensures functional correctness of the notebook though the models produced are far from what a completed training would produce.
Slow mode: We recommend the user to set this flag to False
once the user has gained familiarity with the notebook content and wants to gain insight from running the notebooks for a longer period with different parameters for training.
Note
If the isFlag
is set to False
the notebook will take a few hours on a GPU enabled machine. You can try fewer iterations by setting the num_minibatches
to a smaller number say 20,000
which comes at the expense of quality of the generated images.
In [3]:
isFast = True
The input to the GAN will be a vector of random numbers. At the end of the traning, the GAN "learns" to generate images of hand written digits drawn from the MNIST database. We will be using the same MNIST data generated in tutorial 103A. A more in-depth discussion of the data format and reading methods can be seen in previous tutorials. For our purposes, just know that the following function returns an object that will be used to generate images from the MNIST dataset. Since we are building an unsupervised model, we only need to read in features
and ignore the labels
.
In [4]:
# Ensure the training data is generated and available for this tutorial
# We search in two locations in the toolkit for the cached MNIST data set.
data_found = False
for data_dir in [os.path.join("..", "Examples", "Image", "DataSets", "MNIST"),
os.path.join("data", "MNIST")]:
train_file = os.path.join(data_dir, "Train-28x28_cntk_text.txt")
if os.path.isfile(train_file):
data_found = True
break
if not data_found:
raise ValueError("Please generate the data by completing CNTK 103 Part A")
print("Data directory is {0}".format(data_dir))
In [5]:
def create_reader(path, is_training, input_dim, label_dim):
deserializer = CTFDeserializer(
filename = path,
streams = StreamDefs(
labels_unused = StreamDef(field = 'labels', shape = label_dim, is_sparse = False),
features = StreamDef(field = 'features', shape = input_dim, is_sparse = False
)
)
)
return MinibatchSource(
deserializers = deserializer,
randomize = is_training,
max_sweeps = INFINITELY_REPEAT if is_training else 1
)
The random noise we will use to train the GAN is provided by the noise_sample
function to generate random noise samples from a uniform distribution within the interval [-1, 1].
In [6]:
np.random.seed(123)
def noise_sample(num_samples):
return np.random.uniform(
low = -1.0,
high = 1.0,
size = [num_samples, g_input_dim]
).astype(np.float32)
First we provide a brief recap of the basics of GAN. You may skip this block if you are familiar with CNTK 206A.
A GAN network is composed of two sub-networks, one called the Generator ($G$) and the other Discriminator ($D$).
In each training iteration, the Generator produces more realistic fake images (in other words minimizes the difference between the real and generated counterpart) and the Discriminator maximizes the probability of assigning the correct label (real vs. fake) to both real examples (from training set) and the generated fake ones. The two conflicting objectives between the sub-networks ($G$ and $D$) leads to the GAN network (when trained) converge to an equilibrium, where the Generator produces realistic looking fake MNIST images and the Discriminator can at best randomly guess whether images are real or fake. The resulting Generator model once trained produces realistic MNIST image with the input being a random number.
First, we establish some of the architectural and training hyper-parameters for our model.
In [7]:
# architectural parameters
img_h, img_w = 28, 28
kernel_h, kernel_w = 5, 5
stride_h, stride_w = 2, 2
# Input / Output parameter of Generator and Discriminator
g_input_dim = 100
g_output_dim = d_input_dim = img_h * img_w
# We expect the kernel shapes to be square in this tutorial and
# the strides to be of the same length along each data dimension
if kernel_h == kernel_w:
gkernel = dkernel = kernel_h
else:
raise ValueError('This tutorial needs square shaped kernel')
if stride_h == stride_w:
gstride = dstride = stride_h
else:
raise ValueError('This tutorial needs same stride in all dims')
In [8]:
# Helper functions
def bn_with_relu(x, activation=C.relu):
h = BatchNormalization(map_rank=1)(x)
return C.relu(h)
# We use param-relu function to use a leak=0.2 since CNTK implementation
# of Leaky ReLU is fixed to 0.01
def bn_with_leaky_relu(x, leak=0.2):
h = BatchNormalization(map_rank=1)(x)
r = C.param_relu(C.constant((np.ones(h.shape)*leak).astype(np.float32)), h)
return r
Generator
The generator takes a 100-dimensional random vector (for starters) as input ($z$) and the outputs a 784 dimensional vector, corresponding to a flattened version of a 28 x 28 fake (synthetic) image ($x^*$). In this tutorial, we use fractionally strided convolutions (a.k.a ConvolutionTranspose) with ReLU activations except for the last layer. We use a tanh activation on the last layer to make sure that the output of the generator function is confined to the interval [-1, 1]. The use of ReLU and tanh activation functions are key in addition to using the fractionally strided convolutions.
In [9]:
def convolutional_generator(z):
with default_options(init=C.normal(scale=0.02)):
print('Generator input shape: ', z.shape)
s_h2, s_w2 = img_h//2, img_w//2 #Input shape (14,14)
s_h4, s_w4 = img_h//4, img_w//4 # Input shape (7,7)
gfc_dim = 1024
gf_dim = 64
h0 = Dense(gfc_dim, activation=None)(z)
h0 = bn_with_relu(h0)
print('h0 shape', h0.shape)
h1 = Dense([gf_dim * 2, s_h4, s_w4], activation=None)(h0)
h1 = bn_with_relu(h1)
print('h1 shape', h1.shape)
h2 = ConvolutionTranspose2D(gkernel,
num_filters=gf_dim*2,
strides=gstride,
pad=True,
output_shape=(s_h2, s_w2),
activation=None)(h1)
h2 = bn_with_relu(h2)
print('h2 shape', h2.shape)
h3 = ConvolutionTranspose2D(gkernel,
num_filters=1,
strides=gstride,
pad=True,
output_shape=(img_h, img_w),
activation=C.sigmoid)(h2)
print('h3 shape :', h3.shape)
return C.reshape(h3, img_h * img_w)
Discriminator
The discriminator takes as input ($x^*$) the 784 dimensional output of the generator or a real MNIST image, re-shapes the input to a 28 x 28 image and outputs the estimated probability that the input image is a real MNIST image. The network is modeled using strided convolution with Leaky ReLU activation except for the last layer. We use a sigmoid activation on the last layer to ensure the discriminator output lies in the inteval of [0,1].
In [10]:
def convolutional_discriminator(x):
with default_options(init=C.normal(scale=0.02)):
dfc_dim = 1024
df_dim = 64
print('Discriminator convolution input shape', x.shape)
x = C.reshape(x, (1, img_h, img_w))
h0 = Convolution2D(dkernel, 1, strides=dstride)(x)
h0 = bn_with_leaky_relu(h0, leak=0.2)
print('h0 shape :', h0.shape)
h1 = Convolution2D(dkernel, df_dim, strides=dstride)(h0)
h1 = bn_with_leaky_relu(h1, leak=0.2)
print('h1 shape :', h1.shape)
h2 = Dense(dfc_dim, activation=None)(h1)
h2 = bn_with_leaky_relu(h2, leak=0.2)
print('h2 shape :', h2.shape)
h3 = Dense(1, activation=C.sigmoid)(h2)
print('h3 shape :', h3.shape)
return h3
We use a minibatch size of 128 and a fixed learning rate of 0.0002 for training. In the fast mode (isFast = True
) we verify only functional correctness with 5000 iterations.
Note: In the slow mode, the results look a lot better but it requires in the order of 10 minutes depending on your hardware. In general, the more number of minibatches one trains, the better is the fidelity of the generated images.
In [11]:
# training config
minibatch_size = 128
num_minibatches = 5000 if isFast else 10000
lr = 0.0002
momentum = 0.5 #equivalent to beta1
The rest of the computational graph is mostly responsible for coordinating the training algorithms and parameter updates, which is particularly tricky with GANs for couple reasons. The GANs are sensitive to the choice of learner and the parameters. Many of the parameters chosen here are based on many hard learnt lessons from the community. You may directly go to the code if you have read the basic GAN tutorial.
method=share
in the clone
function ensures that both paths through the discriminator model use the same set of parameters.Function
in the graph with the parameters
attribute. However, when updating the model parameters, update only the parameters of the respective models while keeping the other parameters unchanged. In other words, when updating the generator we will update only the parameters of the $G$ function while keeping the parameters of the $D$ function fixed and vice versa.The code for training the GAN very closely follows the algorithm as presented in the original NIPS 2014 paper. In this implementation, we train $D$ to maximize the probability of assigning the correct label (fake vs. real) to both training examples and the samples from $G$. In other words, $D$ and $G$ play the following two-player minimax game with the value function $V(G,D)$:
$$ \min_G \max_D V(D,G)= \mathbb{E}_{x}[ log D(x) ] + \mathbb{E}_{z}[ log(1 - D(G(z))) ] $$At the optimal point of this game the generator will produce realistic looking data while the discriminator will predict that the generated image is indeed fake with a probability of 0.5. The algorithm referred below is implemented in this tutorial.
In [12]:
def build_graph(noise_shape, image_shape, generator, discriminator):
input_dynamic_axes = [C.Axis.default_batch_axis()]
Z = C.input(noise_shape, dynamic_axes=input_dynamic_axes)
X_real = C.input(image_shape, dynamic_axes=input_dynamic_axes)
X_real_scaled = X_real / 255.0
# Create the model function for the generator and discriminator models
X_fake = generator(Z)
D_real = discriminator(X_real_scaled)
D_fake = D_real.clone(
method = 'share',
substitutions = {X_real_scaled.output: X_fake.output}
)
# Create loss functions and configure optimazation algorithms
G_loss = 1.0 - C.log(D_fake)
D_loss = -(C.log(D_real) + C.log(1.0 - D_fake))
G_learner = adam(
parameters = X_fake.parameters,
lr = learning_rate_schedule(lr, UnitType.sample),
momentum = momentum_schedule(0.5)
)
D_learner = adam(
parameters = D_real.parameters,
lr = learning_rate_schedule(lr, UnitType.sample),
momentum = momentum_schedule(0.5)
)
# Instantiate the trainers
G_trainer = Trainer(
X_fake,
(G_loss, None),
G_learner
)
D_trainer = Trainer(
D_real,
(D_loss, None),
D_learner
)
return X_real, X_fake, Z, G_trainer, D_trainer
With the value functions defined we proceed to interatively train the GAN model. The training of the model can take significnantly long depending on the hardware especiallly if isFast
flag is turned off.
In [13]:
def train(reader_train, generator, discriminator):
X_real, X_fake, Z, G_trainer, D_trainer = \
build_graph(g_input_dim, d_input_dim, generator, discriminator)
# print out loss for each model for upto 25 times
print_frequency_mbsize = num_minibatches // 25
print("First row is Generator loss, second row is Discriminator loss")
pp_G = ProgressPrinter(print_frequency_mbsize)
pp_D = ProgressPrinter(print_frequency_mbsize)
k = 2
input_map = {X_real: reader_train.streams.features}
for train_step in range(num_minibatches):
# train the discriminator model for k steps
for gen_train_step in range(k):
Z_data = noise_sample(minibatch_size)
X_data = reader_train.next_minibatch(minibatch_size, input_map)
if X_data[X_real].num_samples == Z_data.shape[0]:
batch_inputs = {X_real: X_data[X_real].data, Z: Z_data}
D_trainer.train_minibatch(batch_inputs)
# train the generator model for a single step
Z_data = noise_sample(minibatch_size)
batch_inputs = {Z: Z_data}
G_trainer.train_minibatch(batch_inputs)
G_trainer.train_minibatch(batch_inputs)
pp_G.update_with_trainer(G_trainer)
pp_D.update_with_trainer(D_trainer)
G_trainer_loss = G_trainer.previous_minibatch_loss_average
return Z, X_fake, G_trainer_loss
In [14]:
reader_train = create_reader(train_file, True, d_input_dim, label_dim=10)
# G_input, G_output, G_trainer_loss = train(reader_train, dense_generator, dense_discriminator)
G_input, G_output, G_trainer_loss = train(reader_train,
convolutional_generator,
convolutional_discriminator)
In [15]:
# Print the generator loss
print("Training loss of the generator is: {0:.2f}".format(G_trainer_loss))
In [16]:
def plot_images(images, subplot_shape):
plt.style.use('ggplot')
fig, axes = plt.subplots(*subplot_shape)
for image, ax in zip(images, axes.flatten()):
ax.imshow(image.reshape(28, 28), vmin=0, vmax=1.0, cmap='gray')
ax.axis('off')
plt.show()
noise = noise_sample(36)
images = G_output.eval({G_input: noise})
plot_images(images, subplot_shape=[6, 6])
Larger number of iterations should generate more realistic looking MNIST images. A sampling of such generated images are shown below.
Note: It takes a large number of iterations to capture a representation of the real world signal. Even simple dense networks can be quite effective in modelling data albeit MNIST is a relatively simple dataset as well.
Suggested Task
Please refer to several hacks presented in this article by Soumith Chintala, Facebook Research. While some of the hacks have been incorporated in this notebook, there are several others I would suggest that you try out.
Performance is a key aspect to deep neural networks training. Study how the changing the minibatch sizes impact the performance both with regards to quality of the generated images and the time it takes to train a model.
Try generating fake images using the CIFAR-10 data set as the training data. How does the network above performs? There are other variation in GAN, such as conditional GAN where the network is additionally conditioned on the input label. Try implementing the labels.