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- import time
-
- import torch as t
- from torch import nn, optim
- from torch.autograd import Variable
- from torch.utils.data import DataLoader
- from torchvision import transforms
- from torchvision import datasets
-
- """
- Use pytorch nn.Module to implement logistic regression
-
- FIXME: too complex, remove complete tips
-
- """
-
-
- # define hyper parameters
- batch_size = 32
- learning_rate = 1e-3
- num_epoches = 100
-
- # download/load MNIST dataset
- dataset_path = "../data/mnist"
-
- train_dataset = datasets.MNIST(
- root=dataset_path, train=True, transform=transforms.ToTensor(), download=True)
-
- test_dataset = datasets.MNIST(
- root=dataset_path, train=False, transform=transforms.ToTensor())
-
- train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
- test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
-
-
- # define Logistic Regression model
- class Logstic_Regression(nn.Module):
- def __init__(self, in_dim, n_class):
- super(Logstic_Regression, self).__init__()
- self.logstic = nn.Linear(in_dim, n_class)
-
- def forward(self, x):
- out = t.sigmoid(self.logstic(x))
- return out
-
-
- model = Logstic_Regression(28 * 28, 10) # model's input/output node size
- use_gpu = t.cuda.is_available() # GPU use or not
- if use_gpu: model = model.cuda()
-
- # define loss & optimizer
- criterion = nn.CrossEntropyLoss()
- optimizer = optim.SGD(model.parameters(), lr=learning_rate)
-
- # training
- for epoch in range(num_epoches):
- print('-' * 40)
- print('epoch {}'.format(epoch + 1))
-
- since = time.time()
- running_loss = 0.0
- running_acc = 0.0
- for i, data in enumerate(train_loader, 1):
- img, label = data
- img = img.view(img.size(0), -1) # convert input image to dimensions of (n, 28x28)
- if use_gpu:
- img = Variable(img).cuda()
- label = Variable(label).cuda()
- else:
- img = Variable(img)
- label = Variable(label)
-
- # forward calculation
- out = model(img)
- loss = criterion(out, label)
- running_loss += loss.data[0] * label.size(0)
- _, pred = t.max(out, 1)
- num_correct = (pred == label).sum()
- running_acc += float(num_correct.data[0])
-
- # bp
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
-
- if i % 300 == 0:
- print('[{}/{}] Loss: {:.6f}, Acc: {:.6f}'.format(
- epoch + 1, num_epoches, running_loss / (batch_size * i),
- running_acc / (batch_size * i)))
-
- print('Finish {} epoch, Loss: {:.6f}, Acc: {:.6f}'.format(
- epoch + 1, running_loss / (len(train_dataset)), running_acc / (len(
- train_dataset))))
-
- model.eval()
- eval_loss = 0.
- eval_acc = 0.
-
- for data in test_loader:
- img, label = data
- img = img.view(img.size(0), -1)
- if use_gpu:
- img = Variable(img, volatile=True).cuda()
- label = Variable(label, volatile=True).cuda()
- else:
- img = Variable(img, volatile=True)
- label = Variable(label, volatile=True)
- out = model(img)
- loss = criterion(out, label)
- eval_loss += loss.data[0] * label.size(0)
- _, pred = t.max(out, 1)
- num_correct = (pred == label).sum()
- eval_acc += float(num_correct.data[0])
-
- print('Test Loss: {:.6f}, Acc: {:.6f}'.format(
- eval_loss / (len(test_dataset)),
- eval_acc / (len(test_dataset))))
- print('Time:{:.1f} s'.format(time.time() - since))
- print()
-
- # save model's parameters
- #t.save(model.state_dict(), './model_LogsticRegression.pth')
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