Step-By-Step Guide To Setting up and Training GANs for Image Generation
Learn to set up and train Generative Adversarial Networks (GANs) using TensorFlow and PyTorch for image generation and manipulation.
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Join For FreeHere is a basic tutorial on setting up and training image generation models using Generative Adversarial Networks (GANs) with TensorFlow and PyTorch. This guide assumes a fundamental understanding of Python and basic machine learning concepts.
1. Setting up Your Environment
Install Necessary Libraries
Ensure you have Python installed. You will also need to install TensorFlow or PyTorch along with some other essential libraries.
For TensorFlow:
Shell
pip install tensorflow numpy matplotlibFor PyTorch:
Shell
pip install torch torchvision numpy matplotlibImport Libraries
Python
import numpy as np
import matplotlib.pyplot as plt
# For TensorFlow
import tensorflow as tf
from tensorflow.keras.layers import Dense, Reshape, Flatten, Conv2D, Conv2DTranspose, LeakyReLU, Dropout
from tensorflow.keras.models import Sequential
# For PyTorch
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
2. Load and Prepare the Dataset
Using the MNIST dataset as an example.
For TensorFlow:
Python
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5 # Normalize to [-1, 1]
BUFFER_SIZE = 60000
BATCH_SIZE = 256
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
For PyTorch:
Python
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
3. Define the GAN Architecture
Generator Model
For TensorFlow:
Python
def make_generator_model():
model = Sequential()
model.add(Dense(7*7*256, use_bias=False, input_shape=(100,)))
model.add(LeakyReLU())
model.add(Reshape((7, 7, 256)))
model.add(Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
model.add(LeakyReLU())
model.add(Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
model.add(LeakyReLU())
model.add(Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
return model
For PyTorch:
Python
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(100, 256, 7, 1, 0, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.ConvTranspose2d(128, 64, 4, 2, 1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.ConvTranspose2d(64, 1, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, input):
return self.main(input)
Discriminator Model
For TensorFlow:
Python
def make_discriminator_model():
model = Sequential()
model.add(Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=[28, 28, 1]))
model.add(LeakyReLU())
model.add(Dropout(0.3))
model.add(Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
model.add(LeakyReLU())
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(1))
return model
For PyTorch:
Python
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(1, 64, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 128, 4, 2, 1, bias=False),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 256, 4, 2, 1, bias=False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(256, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
return self.main(input)
4. Define the Loss and Optimizers
For TensorFlow:
Python
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def discriminator_loss(real_output, fake_output):
real_loss = cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
total_loss = real_loss + fake_loss
return total_loss
def generator_loss(fake_output):
return cross_entropy(tf.ones_like(fake_output), fake_output)
generator = make_generator_model()
discriminator = make_discriminator_model()
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
For PyTorch:
Python
criterion = nn.BCELoss()
fixed_noise = torch.randn(64, 100, 1, 1, device=device)
real_label = 1.
fake_label = 0.
optimizerD = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizerG = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
5. Training the GAN
For TensorFlow:
Python
EPOCHS = 50
noise_dim = 100
num_examples_to_generate = 16
seed = tf.random.normal([num_examples_to_generate, noise_dim])
@tf.function
def train_step(images):
noise = tf.random.normal([BATCH_SIZE, noise_dim])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_output = discriminator(images, training=True)
fake_output = discriminator(generated_images, training=True)
gen_loss = generator_loss(fake_output)
disc_loss = discriminator_loss(real_output, fake_output)
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
def train(dataset, epochs):
for epoch in range(epochs):
for image_batch in dataset:
train_step(image_batch)
display.clear_output(wait=True)
generate_and_save_images(generator, epoch + 1, seed)
print ('Epoch {} completed'.format(epoch+1))
def generate_and_save_images(model, epoch, test_input):
predictions = model(test_input, training=False)
fig = plt.figure(figsize=(4, 4))
for i in range(predictions.shape[0]):
plt.subplot(4, 4, i+1)
plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
plt.axis('off')
plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
plt.show()
train(train_dataset, EPOCHS)
For PyTorch:
Python
num_epochs = 5
for epoch in range(num_epochs):
for i, data in enumerate(train_loader, 0):
# Update Discriminator: maximize log(D(x)) + log(1 - D(G(z)))
discriminator.zero_grad()
real_cpu = data[0].to(device)
b_size = real_cpu.size(0)
label = torch.full((b_size,), real_label, dtype=torch.float, device=device)
output = discriminator(real_cpu).view(-1)
errD_real = criterion(output, label)
errD_real.backward()
D_x = output.mean().item()
noise = torch.randn(b_size, 100, 1, 1, device=device)
fake = generator(noise)
label.fill_(fake_label)
output = discriminator(fake.detach()).view(-1)
errD_fake = criterion(output, label)
errD_fake.backward()
D_G_z1 = output.mean().item()
errD = errD_real + errD_fake
optimizerD.step()
# Update Generator: maximize log(D(G(z)))
generator.zero_grad()
label.fill_(real_label)
output = discriminator(fake).view(-1)
errG = criterion(output, label)
errG.backward()
D_G_z2 = output.mean().item()
optimizerG.step()
if i % 100 == 0:
print(f'[{epoch}/{num_epochs}][{i}/{len(train_loader)}] '
f'Loss_D: {errD.item():.4f} Loss_G: {errG.item():.4f} '
f'D(x): {D_x:.4f} D(G(z)): {D_G_z1:.4f} / {D_G_z2:.4f}')
with torch.no_grad():
fake = generator(fixed_noise).detach().cpu()
plt.figure(figsize=(10,10))
plt.axis("off")
plt.title("Fake Images")
plt.imshow(np.transpose(vutils.make_grid(fake, padding=2, normalize=True).cpu(),(1,2,0)))
plt.show()
These tutorials provide a starting point for setting up and training basic GAN models in both TensorFlow and PyTorch. Adjusting parameters and exploring more complex architectures can further enhance the understanding and results.
Generative adversarial network
PyTorch
TensorFlow
Python (language)
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