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dcgan-mnist-4.2.1.py
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dcgan-mnist-4.2.1.py
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'''Trains DCGAN on MNIST using Keras
DCGAN is a Generative Adversarial Network (GAN) using CNN.
The generator tries to fool the discriminator by generating fake images.
The discriminator learns to discriminate real from fake images.
The generator discriminator form an adversarial network.
DCGAN trains the discriminator and adversarial networks alternately.
During training, not only the discriminator learns to distinguish real from
fake images, it also coaches the generator part of the adversarial on how
to improve its ability to generate fake images.
[1] Radford, Alec, Luke Metz, and Soumith Chintala.
"Unsupervised representation learning with deep convolutional
generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.keras.layers import Activation, Dense, Input
from tensorflow.keras.layers import Conv2D, Flatten
from tensorflow.keras.layers import Reshape, Conv2DTranspose
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.models import Model
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import load_model
import numpy as np
import math
import matplotlib.pyplot as plt
import os
import argparse
def build_generator(inputs, image_size):
"""Build a Generator Model
Stack of BN-ReLU-Conv2DTranpose to generate fake images
Output activation is sigmoid instead of tanh in [1].
Sigmoid converges easily.
Arguments:
inputs (Layer): Input layer of the generator
the z-vector)
image_size (tensor): Target size of one side
(assuming square image)
Returns:
generator (Model): Generator Model
"""
image_resize = image_size // 4
# network parameters
kernel_size = 5
layer_filters = [128, 64, 32, 1]
x = Dense(image_resize * image_resize * layer_filters[0])(inputs)
x = Reshape((image_resize, image_resize, layer_filters[0]))(x)
for filters in layer_filters:
# first two convolution layers use strides = 2
# the last two use strides = 1
if filters > layer_filters[-2]:
strides = 2
else:
strides = 1
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same')(x)
x = Activation('sigmoid')(x)
generator = Model(inputs, x, name='generator')
return generator
def build_discriminator(inputs):
"""Build a Discriminator Model
Stack of LeakyReLU-Conv2D to discriminate real from fake.
The network does not converge with BN so it is not used here
unlike in [1] or original paper.
Arguments:
inputs (Layer): Input layer of the discriminator (the image)
Returns:
discriminator (Model): Discriminator Model
"""
kernel_size = 5
layer_filters = [32, 64, 128, 256]
x = inputs
for filters in layer_filters:
# first 3 convolution layers use strides = 2
# last one uses strides = 1
if filters == layer_filters[-1]:
strides = 1
else:
strides = 2
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same')(x)
x = Flatten()(x)
x = Dense(1)(x)
x = Activation('sigmoid')(x)
discriminator = Model(inputs, x, name='discriminator')
return discriminator
def train(models, x_train, params):
"""Train the Discriminator and Adversarial Networks
Alternately train Discriminator and Adversarial networks by batch.
Discriminator is trained first with properly real and fake images.
Adversarial is trained next with fake images pretending to be real
Generate sample images per save_interval.
Arguments:
models (list): Generator, Discriminator, Adversarial models
x_train (tensor): Train images
params (list) : Networks parameters
"""
# the GAN component models
generator, discriminator, adversarial = models
# network parameters
batch_size, latent_size, train_steps, model_name = params
# the generator image is saved every 500 steps
save_interval = 500
# noise vector to see how the generator output evolves during training
noise_input = np.random.uniform(-1.0, 1.0, size=[16, latent_size])
# number of elements in train dataset
train_size = x_train.shape[0]
for i in range(train_steps):
# train the discriminator for 1 batch
# 1 batch of real (label=1.0) and fake images (label=0.0)
# randomly pick real images from dataset
rand_indexes = np.random.randint(0, train_size, size=batch_size)
real_images = x_train[rand_indexes]
# generate fake images from noise using generator
# generate noise using uniform distribution
noise = np.random.uniform(-1.0,
1.0,
size=[batch_size, latent_size])
# generate fake images
fake_images = generator.predict(noise)
# real fake images = 1 batch of train data
x = np.concatenate((real_images, fake_images))
# label real and fake images
# real images label is 1.0
y = np.ones([2 * batch_size, 1])
# fake images label is 0.0
y[batch_size:, :] = 0.0
# train discriminator network, log the loss and accuracy
loss, acc = discriminator.train_on_batch(x, y)
log = "%d: [discriminator loss: %f, acc: %f]" % (i, loss, acc)
# train the adversarial network for 1 batch
# 1 batch of fake images with label=1.0
# since the discriminator weights
# are frozen in adversarial network
# only the generator is trained
# generate noise using uniform distribution
noise = np.random.uniform(-1.0,
1.0,
size=[batch_size, latent_size])
# label fake images as real or 1.0
y = np.ones([batch_size, 1])
# train the adversarial network
# note that unlike in discriminator training,
# we do not save the fake images in a variable
# the fake images go to the discriminator input of the adversarial
# for classification
# log the loss and accuracy
loss, acc = adversarial.train_on_batch(noise, y)
log = "%s [adversarial loss: %f, acc: %f]" % (log, loss, acc)
print(log)
if (i 1) % save_interval == 0:
# plot generator images on a periodic basis
plot_images(generator,
noise_input=noise_input,
show=False,
step=(i 1),
model_name=model_name)
# save the model after training the generator
# the trained generator can be reloaded for
# future MNIST digit generation
generator.save(model_name ".h5")
def plot_images(generator,
noise_input,
show=False,
step=0,
model_name="gan"):
"""Generate fake images and plot them
For visualization purposes, generate fake images
then plot them in a square grid
Arguments:
generator (Model): The Generator Model for
fake images generation
noise_input (ndarray): Array of z-vectors
show (bool): Whether to show plot or not
step (int): Appended to filename of the save images
model_name (string): Model name
"""
os.makedirs(model_name, exist_ok=True)
filename = os.path.join(model_name, "d.png" % step)
images = generator.predict(noise_input)
plt.figure(figsize=(2.2, 2.2))
num_images = images.shape[0]
image_size = images.shape[1]
rows = int(math.sqrt(noise_input.shape[0]))
for i in range(num_images):
plt.subplot(rows, rows, i 1)
image = np.reshape(images[i], [image_size, image_size])
plt.imshow(image, cmap='gray')
plt.axis('off')
plt.savefig(filename)
if show:
plt.show()
else:
plt.close('all')
def build_and_train_models():
# load MNIST dataset
(x_train, _), (_, _) = mnist.load_data()
# reshape data for CNN as (28, 28, 1) and normalize
image_size = x_train.shape[1]
x_train = np.reshape(x_train, [-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
model_name = "dcgan_mnist"
# network parameters
# the latent or z vector is 100-dim
latent_size = 100
batch_size = 64
train_steps = 40000
lr = 2e-4
decay = 6e-8
input_shape = (image_size, image_size, 1)
# build discriminator model
inputs = Input(shape=input_shape, name='discriminator_input')
discriminator = build_discriminator(inputs)
# [1] or original paper uses Adam,
# but discriminator converges easily with RMSprop
optimizer = RMSprop(lr=lr, decay=decay)
discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
discriminator.summary()
# build generator model
input_shape = (latent_size, )
inputs = Input(shape=input_shape, name='z_input')
generator = build_generator(inputs, image_size)
generator.summary()
# build adversarial model
optimizer = RMSprop(lr=lr * 0.5, decay=decay * 0.5)
# freeze the weights of discriminator during adversarial training
discriminator.trainable = False
# adversarial = generator discriminator
adversarial = Model(inputs,
discriminator(generator(inputs)),
name=model_name)
adversarial.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
adversarial.summary()
# train discriminator and adversarial networks
models = (generator, discriminator, adversarial)
params = (batch_size, latent_size, train_steps, model_name)
train(models, x_train, params)
def test_generator(generator):
noise_input = np.random.uniform(-1.0, 1.0, size=[16, 100])
plot_images(generator,
noise_input=noise_input,
show=True,
model_name="test_outputs")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
help_ = "Load generator h5 model with trained weights"
parser.add_argument("-g", "--generator", help=help_)
args = parser.parse_args()
if args.generator:
generator = load_model(args.generator)
test_generator(generator)
else:
build_and_train_models()