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MNIST_with_centerloss.py
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MNIST_with_centerloss.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.optim.lr_scheduler as lr_scheduler
from CenterLoss import CenterLoss
import matplotlib.pyplot as plt
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1_1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)
self.prelu1_1 = nn.PReLU()
self.conv1_2 = nn.Conv2d(32, 32, kernel_size=5, padding=2)
self.prelu1_2 = nn.PReLU()
self.conv2_1 = nn.Conv2d(32, 64, kernel_size=5, padding=2)
self.prelu2_1 = nn.PReLU()
self.conv2_2 = nn.Conv2d(64, 64, kernel_size=5, padding=2)
self.prelu2_2 = nn.PReLU()
self.conv3_1 = nn.Conv2d(64, 128, kernel_size=5, padding=2)
self.prelu3_1 = nn.PReLU()
self.conv3_2 = nn.Conv2d(128, 128, kernel_size=5, padding=2)
self.prelu3_2 = nn.PReLU()
self.preluip1 = nn.PReLU()
self.ip1 = nn.Linear(128*3*3, 2)
self.ip2 = nn.Linear(2, 10, bias=False)
def forward(self, x):
x = self.prelu1_1(self.conv1_1(x))
x = self.prelu1_2(self.conv1_2(x))
x = F.max_pool2d(x,2)
x = self.prelu2_1(self.conv2_1(x))
x = self.prelu2_2(self.conv2_2(x))
x = F.max_pool2d(x,2)
x = self.prelu3_1(self.conv3_1(x))
x = self.prelu3_2(self.conv3_2(x))
x = F.max_pool2d(x,2)
x = x.view(-1, 128*3*3)
ip1 = self.preluip1(self.ip1(x))
ip2 = self.ip2(ip1)
return ip1, F.log_softmax(ip2, dim=1)
def visualize(feat, labels, epoch):
plt.ion()
c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',
'#ff00ff', '#990000', '#999900', '#009900', '#009999']
plt.clf()
for i in range(10):
plt.plot(feat[labels == i, 0], feat[labels == i, 1], '.', c=c[i])
plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], loc = 'upper right')
plt.xlim(xmin=-8,xmax=8)
plt.ylim(ymin=-8,ymax=8)
plt.text(-7.8,7.3,"epoch=%d" % epoch)
plt.savefig('./images/epoch=%d.jpg' % epoch)
plt.draw()
plt.pause(0.001)
def train(epoch):
print "Training... Epoch = %d" % epoch
ip1_loader = []
idx_loader = []
for i,(data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
ip1, pred = model(data)
loss = nllloss(pred, target) + loss_weight * centerloss(target, ip1)
optimizer4nn.zero_grad()
optimzer4center.zero_grad()
loss.backward()
optimizer4nn.step()
optimzer4center.step()
ip1_loader.append(ip1)
idx_loader.append((target))
feat = torch.cat(ip1_loader, 0)
labels = torch.cat(idx_loader, 0)
visualize(feat.data.cpu().numpy(),labels.data.cpu().numpy(),epoch)
use_cuda = torch.cuda.is_available() and True
device = torch.device("cuda" if use_cuda else "cpu")
# Dataset
trainset = datasets.MNIST('../MNIST', download=True,train=True, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))]))
train_loader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=4)
# Model
model = Net().to(device)
# NLLLoss
nllloss = nn.NLLLoss().to(device) #CrossEntropyLoss = log_softmax + NLLLoss
# CenterLoss
loss_weight = 1
centerloss = CenterLoss(10, 2).to(device)
# optimzer4nn
optimizer4nn = optim.SGD(model.parameters(),lr=0.001,momentum=0.9, weight_decay=0.0005)
sheduler = lr_scheduler.StepLR(optimizer4nn,20,gamma=0.8)
# optimzer4center
optimzer4center = optim.SGD(centerloss.parameters(), lr =0.5)
for epoch in range(100):
sheduler.step()
# print optimizer4nn.param_groups[0]['lr']
train(epoch+1)