【实验目的】
理解神经网络原理,掌握神经网络前向推理和后向传播方法;
掌握使用pytorch框架训练和推理全连接神经网络模型的编程实现方法。
【实验内容】
1.使用pytorch框架,设计一个全连接神经网络,实现Mnist手写数字字符集的训练与识别。
【实验报告要求】
修改神经网络结构,改变层数观察层数对训练和检测时间,准确度等参数的影响;
修改神经网络的学习率,观察对训练和检测效果的影响;
修改神经网络结构,增强或减少神经元的数量,观察对训练的检测效果的影响。
import torch from torchvision import transforms from torchvision import datasets from torch.utils.data import DataLoader import torch.nn.functional as F import torch.optim as optim # 准备数据集 batch_size = 64 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform) train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size) test_dataset = datasets.MNIST(root='../dataset/mnist', train=False, download=True, transform=transform) test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size) # 设计模型 class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.l1 = torch.nn.Linear(784, 512) self.l2 = torch.nn.Linear(512, 256) self.l3 = torch.nn.Linear(256, 128) self.l4 = torch.nn.Linear(128, 64) self.l5 = torch.nn.Linear(64, 10) def forward(self, x): x = x.view(-1, 784) x = F.relu(self.l1(x)) x = F.relu(self.l2(x)) x = F.relu(self.l3(x)) x = F.relu(self.l4(x)) return self.l5(x) model = Net() # 构建损失函数和优化器 criterion = torch.nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5) # 定义训练函数 def train(epoch): running_loss = 0.0 for batch_idx, data in enumerate(train_loader, 0): inputs, target = data optimizer.zero_grad() # 前馈+反馈+更新 outputs = model(inputs) loss = criterion(outputs, target) loss.backward() optimizer.step() running_loss += loss.item() if batch_idx % 300 == 299: print('[%d,%5d] loss:%.3f' % (epoch + 1, batch_idx + 1, running_loss / 300)) running_loss = 0.0 # 定义测试函数 def test(): correct = 0 total = 0 with torch.no_grad(): for data in test_loader: images, labels = data outputs = model(images) _, predicted = torch.max(outputs.data, dim=1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Accuracy on test set:%d %%' % (100 * correct / total)) # 实例化训练和测试 if __name__ == '__main__': for epoch in range(10): train(epoch) test()
【结果】:
标签:loss,nn,self,torch,batch,神经网络,train,实验,手写 From: https://www.cnblogs.com/duyidan/p/16927784.html