!pip install torch torchvision
Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.1.0) Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.3.0) Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch) (1.16.4) Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.12.0) Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (4.3.0) Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.1.1->torchvision) (0.46)
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.init as init
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
batch_size = 256
learning_rate = 0.0002
num_epoch = 10
mnist_train = dset.MNIST("./", train=True, transform=transforms.ToTensor(), target_transform=None, download=True)
mnist_test = dset.MNIST("./", train=False, transform=transforms.ToTensor(), target_transform=None, download=True)
print(mnist_train.__getitem__(0)[0].size(), mnist_train.__len__())
mnist_test.__getitem__(0)[0].size(), mnist_test.__len__()
torch.Size([1, 28, 28]) 60000
(torch.Size([1, 28, 28]), 10000)
train_loader = torch.utils.data.DataLoader(mnist_train,batch_size=batch_size, shuffle=True,num_workers=2,drop_last=True)
test_loader = torch.utils.data.DataLoader(mnist_test,batch_size=batch_size, shuffle=False,num_workers=2,drop_last=True)
# ÀÔ·Â µ¥ÀÌÅ͸¦ Á¤±ÔÈÇϴ°Íó·³ ¿¬»êÀ» Åë°úÇÑ °á°ú°ªÀ» Á¤±ÔÈÇÒ ¼ö ÀÖ½À´Ï´Ù.
# ±× ´Ù¾çÇÑ ¹æ¹ýÁß¿¡ ´ëÇ¥ÀûÀΰÍÀÌ ¹Ù·Î Batch NormalizationÀ̰í ÀÌ´Â ÄÁº¼·ç¼Ç ¿¬»êó·³ ¸ðµ¨¿¡ ÇÑ ÃþÀ¸·Î ±¸ÇöÇÒ ¼ö ÀÖ½À´Ï´Ù.
# https://pytorch.org/docs/stable/nn.html?highlight=batchnorm#torch.nn.BatchNorm2d
# nn.BatchNorm2d(x)¿¡¼ x´Â ÀÔ·ÂÀ¸·Î µé¾î¿À´Â ä³ÎÀÇ °³¼öÀÔ´Ï´Ù.
class CNN(nn.Module):
def __init__(self):
super(CNN,self).__init__()
self.layer = nn.Sequential(
nn.Conv2d(1,16,3,padding=1), # 28 x 28
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(16,32,3,padding=1), # 28 x 28
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(2,2), # 14 x 14
nn.Conv2d(32,64,3,padding=1), # 14 x 14
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2,2) # 7 x 7
)
self.fc_layer = nn.Sequential(
nn.Linear(64*7*7,100),
nn.BatchNorm1d(100),
nn.ReLU(),
nn.Linear(100,10)
)
def forward(self,x):
out = self.layer(x)
out = out.view(batch_size,-1)
out = self.fc_layer(out)
return out
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
model = CNN().to(device)
loss_func = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
cuda:0
for i in range(num_epoch):
for j,[image,label] in enumerate(train_loader):
x = image.to(device)
y_= label.to(device)
optimizer.zero_grad()
output = model.forward(x)
loss = loss_func(output,y_)
loss.backward()
optimizer.step()
if i % 10 == 0:
print(loss)
tensor(1.9699, device='cuda:0', grad_fn=<NllLossBackward>)
#param_list = list(model.parameters())
#print(param_list)
correct = 0
total = 0
# ¹èÄ¡Á¤±Ôȳª µå·Ó¾Æ¿ôÀº ÇнÀÇÒ¶§¿Í Å×½ºÆ® ÇÒ¶§ ´Ù¸£°Ô µ¿ÀÛÇϱ⠶§¹®¿¡ ¸ðµ¨À» evaluation ¸ðµå·Î ¹Ù²ã¼ Å×½ºÆ®ÇؾßÇÕ´Ï´Ù.
model.eval()
with torch.no_grad():
for image,label in test_loader:
x = image.to(device)
y_= label.to(device)
output = model.forward(x)
_,output_index = torch.max(output,1)
total += label.size(0)
correct += (output_index == y_).sum().float()
print("Accuracy of Test Data: {}".format(100*correct/total))
Accuracy of Test Data: 88.87220001220703