本次分类问题使用的数据集是MNIST,每个图像的大小为\(28*28\)。
编写代码的步骤如下
- 载入数据集,分别为训练集和测试集
- 让数据集可以迭代
- 定义模型,定义损失函数,训练模型
代码
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as dsets
from torch.autograd import Variable
'''下载训练集和测试集'''
train_dataset = dsets.MNIST(root='./datasets',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = dsets.MNIST(root='./datasets',
train=False,
transform=transforms.ToTensor())
'''让数据集可以迭代'''
batch_size = 100
n_iters = 3000
num_epochs = n_iters / (len(train_dataset) / batch_size)
num_epochs = int(num_epochs)
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)
'''定义模型'''
class LogisticRegressionModel(nn.Module):
def __init__(self, input_dim, output_dim):
super(LogisticRegressionModel, self).__init__()
self.linear = nn.Linear(input_dim, output_dim)
def forward(self, x):
out = self.linear(x)
return out
'''实例化模型'''
input_dim = 28*28
output_dim = 10
model = LogisticRegressionModel(input_dim, output_dim)
'''定义损失计算方式'''
criterion = nn.CrossEntropyLoss()
learning_rate = 0.001
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
'''训练次数'''
iter = 0
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = Variable(images.view(-1, 28*28))
labels = Variable(labels)
#梯度置零
optimizer.zero_grad()
#计算输出
outputs = model(images)
#计算损失,内部会自动softmax然后进行Crossentropy
loss = criterion(outputs, labels)
#反向传播
loss.backward()
#更新参数
optimizer.step()
iter += 1
if iter % 500 == 0:
#计算准确度
correct = 0
total = 0
for images, labels in test_loader:
images = Variable(images.view(-1, 28*28))
#获得输出,输出的大小为(batch_size,10)
outputs = model(images)
#获得预测值,输出的大小为(batch_size,1)
_, predicted = torch.max(outputs.data, 1)
#labels的size是(100,)
total += labels.size(0)
#返回的是预测值和标签值相等的个数
correct += (predicted == labels).sum()
accuracy = 100 * correct / total
# Print Loss
print('Iteration: {}. Loss: {}. Accuracy: {}'.format(iter, loss.data[0], accuracy))
输出如下
原文地址:https://www.cnblogs.com/MartinLwx/p/10354889.html
时间: 2024-11-13 20:27:04