用滴滴云Notebook快速上手PyTorch-MINIST手写体

导读

在本教程中，您将学习如何快速使用PyTorch训练一个神经网络学习如何识别手写数字。

本文使用滴滴云Notebook作为开发环境，滴滴云Notebook服务集成了CUDA、CuDNN、Python、TensorFlow、Pytorch、MxNet、Keras等深度学习框架，无需用户自己安装。

Part.1

购买Notebook服务

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注册滴滴云并实名认证后可购买Notebook服务

进入控制台Notebook页面单击创建Notebook实例按钮

选择基础配置：

• 选择 付费方式:当前仅支持按时长。

• 选择 可用区:选择靠近您客户的地域，包括广州1、2区。

• 选择 配置规格:根据需要的CPU、GPU、显卡和内存，选择相关配置。

• 选择 镜像:提供了Jupyter Notebook镜像和Jupyter Lab镜像，这里选择>jupyter-lab-v1。

• 设置 系统盘:根据需求选择系统盘的大小，设置范围为80GB - 500GB。 

名称和标签

• 输入 Notebook名称。
  

• 输入 标签键以及键值，单击添加按钮，可添加多个标签。

访问Notebook

• 进入我的Notebook页面，在操作列单击打开Notebook。
  

• 进入Notebook详情页面，单击打开Notebook。

Part.2

构建MNIST手写体数字识别程序

import matplotlib.pyplot as plt import numpy as npimport torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torchvision

from torchvision import datasets, transforms

下载经典的MNIST数据集

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 训练集Dataloadertrain_loader = torch.utils.data.DataLoader( datasets.MNIST(root='.', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=64, shuffle=True, num_workers=4)# 测试集Dataloadertest_loader = torch.utils.data.DataLoader( datasets.MNIST(root='.', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=64, shuffle=True, num_workers=4)

这里我们使用一个4层CNN（卷积神经网络），网络结构：Conv-Conv-FC-FC

class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10)

def forward(self, x): # Perform the usual forward pass x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2(x), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = self.fc2(x) return F.log_softmax(x, dim=1)

model = Net().to(device)

现在我们使用SGD(随机梯度下降）算法来训练模型，以有监督的方式学习分类任务

optimizer = optim.SGD(model.parameters(), lr=0.01)

def train(epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % 1 == 0: print('\rTrain Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item()), end='')

def test(): with torch.no_grad(): model.eval() test_loss = 0 correct = 0 for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target).item() pred = output.max(1, keepdim=True)[1] correct += pred.eq(target.view_as(pred)).sum().item()

test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n' .format(test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset)))

开始训练，每训练一个epoch测试一次模型，在20个epoch内，模型准确率可以达到98.7%

epochs = 20
for epoch in range(1, epochs + 1):
    train(epoch)
    test()
    
Train Epoch: 1 [29984/60000 (100%)]  Loss: 0.130790
Test set: Average loss: 0.0033, Accuracy: 9370/10000 (94%)

Train Epoch: 2 [29984/60000 (100%)]  Loss: 0.212607
Test set: Average loss: 0.0020, Accuracy: 9594/10000 (96%)

Train Epoch: 3 [29984/60000 (100%)]  Loss: 0.054339
Test set: Average loss: 0.0016, Accuracy: 9673/10000 (97%)

Train Epoch: 4 [29984/60000 (100%)]  Loss: 0.085429
Test set: Average loss: 0.0012, Accuracy: 9766/10000 (98%)

Train Epoch: 5 [29984/60000 (100%)]  Loss: 0.084620
Test set: Average loss: 0.0010, Accuracy: 9800/10000 (98%)

Train Epoch: 6 [29984/60000 (100%)]  Loss: 0.053965
Test set: Average loss: 0.0009, Accuracy: 9826/10000 (98%)

Train Epoch: 7 [29984/60000 (100%)]  Loss: 0.098088
Test set: Average loss: 0.0008, Accuracy: 9826/10000 (98%)

Train Epoch: 8 [29184/60000 (49%)]  Loss: 0.008589

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