torch_logistic_regression

torch_logistic_regression

  • 手打了一波logistic_regression
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms


# Hyper-parameters
input_size = 28 * 28
num_classes = 10
num_epochs = 20
batch_size = 100
learning_rate = 0.001


# Mnist dataset
train_dataset = torchvision.datasets.MNIST(root='../../data',
                                           train=True,
                                           transform=transforms.ToTensor(),
                                           download=True)
test_dataset = torchvision.datasets.MNIST(root='../../data',
                                          train=False,
                                          transform=transforms.ToTensor())


train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)


model = nn.Linear(input_size, num_classes)

# Loss and optimizer
# nn.CrossEntropyLoss() computes softmax internally
criterion = nn.CrossEntropyLoss()
# 这里优化器试了下Adam,效果还不错,在这个人物下比SGD好点
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i ,(images,lables) in enumerate(train_loader):
        # Reshape images to (batch_size,input_size)
        images = images.reshape(-1,input_size)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, lables)

        # backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if (i + 1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))


with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.reshape(-1, input_size)
        outputs = model(images)
        # 拿到最大的那个当作预测结果
        _, predicted = torch.max(outputs.data,1)
        total += labels.size(0)
        correct += (predicted == labels).sum()
    # 计算精确度
    print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))



# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')

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