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')