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simplegan.py
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simplegan.py
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import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd.variable import Variable
from torchvision import transforms
from torchvision.datasets import MNIST
from torchvision.utils import make_grid
from torch.utils.data import DataLoader
import imageio
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,),(0.5,))
])
to_image = transforms.ToPILImage()
trainset = MNIST(root='./data/', train=True, download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=100, shuffle=True)
device = 'cuda'
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.n_features = 128
self.n_out = 784
self.fc0 = nn.Sequential(
nn.Linear(self.n_features, 256),
nn.LeakyReLU(0.2)
)
self.fc1 = nn.Sequential(
nn.Linear(256, 512),
nn.LeakyReLU(0.2)
)
self.fc2 = nn.Sequential(
nn.Linear(512, 1024),
nn.LeakyReLU(0.2)
)
self.fc3 = nn.Sequential(
nn.Linear(1024, self.n_out),
nn.Tanh()
)
def forward(self, x):
x = self.fc0(x)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
x = x.view(-1, 1, 28, 28)
return x
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.n_in = 784
self.n_out = 1
self.fc0 = nn.Sequential(
nn.Linear(self.n_in, 1024),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.fc1 = nn.Sequential(
nn.Linear(1024, 512),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.fc2 = nn.Sequential(
nn.Linear(512, 256),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.fc3 = nn.Sequential(
nn.Linear(256, self.n_out),
nn.Sigmoid()
)
def forward(self, x):
x = x.view(-1, 784)
x = self.fc0(x)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
generator = Generator()
discriminator = Discriminator()
generator.to(device)
discriminator.to(device)
g_optim = optim.Adam(generator.parameters(), lr=2e-4)
d_optim = optim.Adam(discriminator.parameters(), lr=2e-4)
g_losses = []
d_losses = []
images = []
criterion = nn.BCELoss()
def noise(n, n_features=128):
return Variable(torch.randn(n, n_features)).to(device)
def make_ones(size):
data = Variable(torch.ones(size, 1))
return data.to(device)
def make_zeros(size):
data = Variable(torch.zeros(size, 1))
return data.to(device)
def train_discriminator(optimizer, real_data, fake_data):
n = real_data.size(0)
optimizer.zero_grad()
prediction_real = discriminator(real_data)
error_real = criterion(prediction_real, make_ones(n))
error_real.backward()
prediction_fake = discriminator(fake_data)
error_fake = criterion(prediction_fake, make_zeros(n))
error_fake.backward()
optimizer.step()
return error_real + error_fake
def train_generator(optimizer, fake_data):
n = fake_data.size(0)
optimizer.zero_grad()
prediction = discriminator(fake_data)
error = criterion(prediction, make_ones(n))
error.backward()
optimizer.step()
return error
num_epochs = 250
k = 1
test_noise = noise(64)
generator.train()
discriminator.train()
for epoch in range(num_epochs):
g_error = 0.0
d_error = 0.0
for i, data in enumerate(trainloader):
imgs, _ = data
n = len(imgs)
for j in range(k):
fake_data = generator(noise(n)).detach()
real_data = imgs.to(device)
d_error += train_discriminator(d_optim, real_data, fake_data)
fake_data = generator(noise(n))
g_error += train_generator(g_optim, fake_data)
img = generator(test_noise).cpu().detach()
img = make_grid(img)
images.append(img)
g_losses.append(g_error/i)
d_losses.append(d_error/i)
print('Epoch {}: g_loss: {:.8f} d_loss: {:.8f}\r'.format(epoch, g_error/i, d_error/i))
print('Training Finished')
torch.save(generator.state_dict(), 'mnist_generator.pth')
import numpy as np
from matplotlib import pyplot as plt
imgs = [np.array(to_image(i)) for i in images]
imageio.mimsave('progress.gif', imgs)
plt.plot(g_losses, label='Generator_Losses')
plt.plot(d_losses, label='Discriminator Losses')
plt.legend()
plt.savefig('loss.png')