Pytorch學習記錄-邏輯迴歸
1. 引入必須庫&設定超參數
一樣的套路
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
# 超參數
input_size = 784
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.01
2. 獲取數據和加載數據
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=train_dataset,
batch_size=batch_size,
shuffle=False)
3. 構建邏輯迴歸模型
這裡有一個問題，為什麼使用Linear之後沒有用softmax？
答案就在損失函數，這裡的損失函數使用的是CrossEntropyLoss()，多分類用的交叉熵損失函數，用這個 loss 前面不需要加 Softmax 層。
我重新寫了一個Model類，但是使用MSELoss等損失函數都會報錯
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.linear = nn.Linear(input_size, num_classes)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
y_pred = self.sigmoid(self.linear(x))
return y_pred
model = Model()
criterion = nn.MSELoss()
# model = nn.Linear(input_size, num_classes)
# criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
4. 訓練模型
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.reshape(-1, 28 * 28)
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}] ,Loss:{:.5f}'.format(epoch + 1, num_epochs, i + 1, total_step,
loss.item()))
5. 測試模型並保存模型
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, 28 * 28)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
torch.save(model.state_dict(),'LogisticModel.ckpt')
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