Pytorch笔记之分类

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前言

使用Pytorch进行MNIST分类，使用TensorDataset与DataLoader封装、加载本地数据集。

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一、导入库

import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import TensorDataset, DataLoader # 数据集工具
from load_mnist import load_mnist # 本地数据集

二、数据处理

1、导入本地数据集，将标签值设置为int类型，构建张量
2、使用TensorDataset与DataLoader封装训练集与测试集

# 构建数据
x_train, y_train, x_test, y_test = \load_mnist(normalize=True, flatten=False, one_hot_label=False)
# 数据处理
x_train = torch.from_numpy(x_train.astype(np.float32))
y_train = torch.from_numpy(y_train.astype(np.int64))
x_test = torch.from_numpy(x_test.astype(np.float32))
y_test = torch.from_numpy(y_test.astype(np.int64))
# 数据集封装
train_dataset = TensorDataset(x_train, y_train)
test_dataset = TensorDataset(x_test, y_test)
batch_size = 64
train_loader = DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)
test_loader = DataLoader(dataset=test_dataset,batch_size=batch_size,shuffle=True)

三、构建模型

输入到全连接层之前需要把(batch_size,28,28)展平为(batch_size,784)
交叉熵损失函数整合了Softmax，在模型中可以不添加Softmax

# 继承模型
class FC(nn.Module):def __init__(self):super().__init__()self.fc1 = nn.Linear(784, 10)self.softmax = nn.Softmax(dim=1)def forward(self, x):y = self.fc1(x.view(x.shape[0],-1))y = self.softmax(y)return y
# 定义模型
model = FC()
loss_function = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)

四、迭代训练

从DataLoader中取出x和y，进行前向和反向的计算

for epoch in range(10):print('Epoch:', epoch)for i,data in enumerate(train_loader):x, y = datay_pred = model.forward(x)loss = loss_function(y_pred, y)optimizer.zero_grad()loss.backward()optimizer.step()

五、模型评估

在测试集中进行验证
使用.item()获得tensor的取值

	correct = 0for i,data in enumerate(test_loader):x, y = datay_pred = model.forward(x)_, y_pred = torch.max(y_pred, 1)correct += (y_pred == y).sum().item()acc = correct / len(test_dataset)print('Accuracy:{:.2%}'.format(acc))

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总结

记录了TensorDataset与DataLoader的使用方法，模型的构建与训练和上一篇Pytorch笔记之回归相似。