

In [5]:

    
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import datasets
from torchvision import transforms
from torchvision.utils import save_image

GAN 초간단 정리

Discriminator
- 784, 256
- ReLU
- 256, 256
- ReLU
- 256, 1
- Sigmoid
Generator
- 64, 256
- ReLU
- 256, 256
- 256, 784
- Tanh



In [4]:

    
D = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Linear(256, 256),
    nn.ReLU(),
    nn.Linear(256, 1),
    nn.Sigmoid())

G = nn.Sequential(
    nn.Linear(64, 256),
    nn.ReLU(),
    nn.Linear(256, 256),
    nn.ReLU(),
    nn.Linear(256, 784),
    nn.Tanh())



In [7]:

    
transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize(mean=(0.5, 0.5, 0.5), 
                                     std=(0.5, 0.5, 0.5))])



In [8]:

    
mnist = datasets.MNIST(root='./data/',
                       train=True,
                       transform=transform,
                       download=True)



In [37]:

    
data_loader = torch.utils.data.DataLoader(dataset=mnist,
                                          batch_size=100,
                                          shuffle=True)

torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=<function default_collate at 0x1093ef048>, pin_memory=False, drop_last=False)

Docstring:

Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.

Arguments:

dataset (Dataset): dataset from which to load the data.
batch_size (int, optional): how many samples per batch to load(default: 1).
shuffle (bool, optional): set to True to have the data reshuffled at every epoch (default: False).
sampler (Sampler, optional): defines the strategy to draw samples from the dataset. If specified, shuffle must be False.
batch_sampler (Sampler, optional): like sampler, but returns a batch of indices at a time. Mutually exclusive with batch_size, shuffle, sampler, and drop_last.
num_workers (int, optional): how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process(default: 0)
collate_fn (callable, optional): merges a list of samples to form a mini-batch.
pin_memory (bool, optional): If True, the data loader will copy tensors into CUDA pinned memory before returning them.
drop_last (bool, optional): set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False)



In [10]:

    
if torch.cuda.is_available():
    D.cuda()
    G.cuda()



In [29]:

    
num_epochs = 200



In [ ]:

    
criterion = nn.BCELoss()
d_optimizer = torch.optim.Adam(D.parameters(), lr=0.001)
g_optimizer = torch.optim.Adam(G.parameters(), lr=0.001)

for epoch in range(100):
    for i, (images, _) in enumerate(data_loader):
        batch_size = images.size(0)
        images = Variable(images.view(batch_size, -1))
        
        real_labels = Variable(torch.ones(batch_size))
        fake_labels = Variable(torch.zeros(batch_size))
        # GPU를 사용할 땐 x = x.cuda()를 하고 Variable(x)를 return
        
        # Train discriminator
        outputs = D(images)
        d_loss_real = criterion(outputs, real_labels)
        real_scroe = outputs
        
        z = Variable(torch.randn(batch_size, 64))
        fake_images = G(z)
        outputs = D(fake_images)
        d_loss_fake = criterion(outputs, fake_labels)
        fake_scroe = outputs
        
        d_loss = d_loss_real + d_loss_fake
        D.zero_grad()
        d_loss.backward()
        d_optimizer.step()
        
        # Train Generator
        z = Variable(torch.randn(batch_size, 64))
        fake_images = G(z)
        outputs = D(fake_images)
        
        g_loss = criterion(outputs, real_labels)
        
        D.zero_grad()
        G.zero_grad()
        g_loss.backward()
        g_optimizer.step()
        
        if (epoch+1) % 5 == 0:
            print ('Epoch [{}/{}], Discriminator Loss: {:4f}, Generator Loss: {:4f}'.format(
                epoch+1, num_epochs, d_loss.data[0], g_loss.data[0]))
        
torch.save(G.state_dict(), './generator.pkl')
torch.save(D.state_dict(), './discriminator.pkl')









    



/usr/local/lib/python3.6/site-packages/torch/nn/functional.py:767: UserWarning: Using a target size (torch.Size([100])) that is different to the input size (torch.Size([100, 1])) is deprecated. Please ensure they have the same size.
  "Please ensure they have the same size.".format(target.size(), input.size()))






    



Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.312897
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.399529
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.485228
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.569799
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.593847
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.619501
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.648684
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.681005
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.716059
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.753416
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.792627
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.833277
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.875092
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.917604
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 19.960199
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 20.003054
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 20.046034
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 20.088905
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 20.131559
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 20.173735
Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 20.215425
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Epoch [5/200], Discriminator Loss: 0.000000, Generator Loss: 20.505421
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Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.655251
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.652092
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.649763
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.648216
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.647381
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.647194
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.647484
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.648338
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.646482
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.645342
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.643288
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.641945
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.641277
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.641258
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.641760
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.642799
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.644207
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.634871
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.627037
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.620501
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.615154
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.610945
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.607689
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.605330
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.593269
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.583006
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.574318
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.567116
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.561174
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.555672
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.551296
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.547993
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.545628
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.544044
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.543240
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.543188
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.543694
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.544764
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.543455
Epoch [10/200], Discriminator Loss: 0.000000, Generator Loss: 23.542866



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