Pytorch_Task_7

本文档介绍如何使用Pytorch构建并测试一个CNN网络,以识别手写数字。通过定义网络结构,进行训练和测试,最终展示识别结果。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

用Pytorch实现手写数字识别

1. 定义一个CNN网络

from torch import nn
 
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 25, kernel_size=3),
            nn.BatchNorm2d(25),
            nn.ReLU(inplace=True)
        )
 
        self.layer2 = nn.Sequential(
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
 
        self.layer3 = nn.Sequential(
            nn.Conv2d(25, 50, kernel_size=3),
            nn.BatchNorm2d(50),
            nn.ReLU(inplace=True)
        )
 
        self.layer4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
 
        self.fc = nn.Sequential(
            nn.Linear(50 * 5 * 5, 1024),
            nn.ReLU(inplace=True),
            nn.Linear(1024, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 10)
        )
 
    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

2.测试

import torch
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import datasets, transforms


# 定义一些超参数
batch_size = 64
learning_rate = 0.02
num_epoches = 20

# 数据预处理。transforms.ToTensor()将图片转换成PyTorch中处理的对象Tensor,并且进行标准化(数据在0~1之间)
# transforms.Normalize()做归一化。它进行了减均值,再除以标准差。两个参数分别是均值和标准差
# transforms.Compose()函数则是将各种预处理的操作组合到了一起
data_tf = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize([0.5], [0.5])])

# 数据集的下载器
train_dataset = datasets.MNIST(
    root='./data', train=True, transform=data_tf, download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=data_tf)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 选择模型
model = CNN()
# model = net.Activation_Net(28 * 28, 300, 100, 10)
# model = net.Batch_Net(28 * 28, 300, 100, 10)
if torch.cuda.is_available():
    model = model.cuda()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

# 训练模型
epoch = 0
for data in train_loader:
    img, label = data
    # img = img.view(img.size(0), -1)
    img = Variable(img)
    if torch.cuda.is_available():
        img = img.cuda()
        label = label.cuda()
    else:
        img = Variable(img)
        label = Variable(label)
    out = model(img)
    loss = criterion(out, label)
    print_loss = loss.data.item()

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    epoch+=1
    if epoch%50 == 0:
        print('epoch: {}, loss: {:.4}'.format(epoch, loss.data.item()))

# 模型评估
model.eval()
eval_loss = 0
eval_acc = 0
for data in test_loader:
    img, label = data
    # img = img.view(img.size(0), -1)
    img = Variable(img)
    if torch.cuda.is_available():
        img = img.cuda()
        label = label.cuda()

    out = model(img)
    loss = criterion(out, label)
    eval_loss += loss.data.item()*label.size(0)
    _, pred = torch.max(out, 1)
    num_correct = (pred == label).sum()
    eval_acc += num_correct.item()
print('Test Loss: {:.6f}, Acc: {:.6f}'.format(
    eval_loss / (len(test_dataset)),
    eval_acc / (len(test_dataset))
))

3.结果

epoch: 50, loss: 1.974
epoch: 100, loss: 1.243
epoch: 150, loss: 0.6076
epoch: 200, loss: 0.4551
epoch: 250, loss: 0.3107
epoch: 300, loss: 0.3195
epoch: 350, loss: 0.1471
epoch: 400, loss: 0.3002
epoch: 450, loss: 0.08808
epoch: 500, loss: 0.1151
epoch: 550, loss: 0.1539
epoch: 600, loss: 0.1201
epoch: 650, loss: 0.1032
epoch: 700, loss: 0.05643
epoch: 750, loss: 0.09671
epoch: 800, loss: 0.05831
epoch: 850, loss: 0.08721
epoch: 900, loss: 0.1273
Test Loss: 0.098649, Acc: 0.971200
[Finished in 60.1s]
### PyTorch Tabular 使用教程 #### 安装依赖库 为了能够顺利运行 PyTorch Tabular,需要先安装必要的依赖项。推荐按照官方文档中的说明来操作[^2]: ```bash pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu pip install pytorch-tabular ``` #### 数据准备 PyTorch Tabular 支持多种格式的数据输入,最常用的是 Pandas DataFrame。对于分类特征和连续特征的支持尤为出色。 ```python import pandas as pd data = { 'age': [25, 30], 'income': [50000, 70000], 'education': ['Bachelor', 'Master'] } df = pd.DataFrame(data) ``` #### 构建模型配置 创建 `DataConfig` 和 `ModelConfig` 对象用于定义数据处理方式以及所使用的具体模型架构参数设置[^1]。 ```python from pytorch_tabular.config import DataConfig, ModelConfig from pytorch_tabular.models.tab_transformer.config import TabTransformerConfig data_config = DataConfig( target=['target_column'], continuous_cols=["age", "income"], categorical_cols=["education"] ) model_config = TabTransformerConfig(task="regression", ...) ``` #### 训练过程 完成上述准备工作之后就可以构建训练器对象并调用 fit 方法来进行实际的训练工作了[^3]。 ```python from pytorch_tabular import TabularModel tabular_model = TabularModel(data_config=data_config, model_config=model_config) tabular_model.fit(train=df_train, validation=df_val) predictions = tabular_model.predict(df_test) ``` #### 应用实例 TabTransformer 是一种专门为表格型数据设计的基于注意力机制的神经网络,在许多场景下表现出色。例如预测客户流失率、信用评分等金融领域应用中具有很大潜力[^4]。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值