在net.py里面构造网络，网络的结构为输入为28*28,第一层隐藏层的输出为300, 第二层输出的输出为100, 最后一层的输出层为10,
net.py
import torch from torch import nn class Batch_Net(nn.Module): def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim): super(Batch_Net, self).__init__() self.layer_1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1), nn.BatchNorm1d(n_hidden_1), nn.ReLU(True)) self.layer_2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2), nn.BatchNorm1d(n_hidden_2), nn.ReLU(True)) self.output = nn.Sequential(nn.Linear(n_hidden_2, out_dim)) def forward(self, x): x = self.layer_1(x) x = self.layer_2(x) x = self.output(x) return x
main.py 进行网络的训练
import torch from torch import nn, optim from torch.autograd import Variable from torch.utils.data import DataLoader from torchvision import datasets, transforms import net batch_size = 128 # 每一个batch_size的大小 learning_rate = 1e-2 # 学习率的大小 num_epoches = 20 # 迭代的epoch值 # 表示data将数据变成0, 1之间，0.5, 0.5表示减去均值处以标准差 data_tf = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]) # 表示均值和标准差 # 获得训练集的数据 train_dataset = datasets.MNIST(root='./data', train=True, transform=data_tf, download=True) # 获得测试集的数据 test_dataset = datasets.MNIST(root='./data', train=False, transform=data_tf, download=True) # 获得训练集的可迭代队列 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) # 获得测试集的可迭代队列 test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) # 构造模型的网络 model = net.Batch_Net(28*28, 300, 100, 10) if torch.cuda.is_available(): # 如果有cuda就将模型放在GPU上 model.cuda() criterion = nn.CrossEntropyLoss() # 构造交叉损失函数 optimizer = optim.SGD(model.parameters(), lr=learning_rate) # 构造模型的优化器 for epoch in range(num_epoches): # 迭代的epoch train_loss = 0 # 训练的损失值 test_loss = 0 # 测试的损失值 eval_acc = 0 # 测试集的准确率 for data in train_loader: # 获得一个batch的样本 img, label = data # 获得图片和标签 img = img.view(img.size(0), -1) # 将图片进行img的转换 if torch.cuda.is_available(): # 如果存在torch img = Variable(img).cuda() # 将图片放在torch上 label = Variable(label).cuda() # 将标签放在torch上 else: img = Variable(img) # 构造img的变量 label = Variable(label) optimizer.zero_grad() # 消除optimizer的梯度 out = model.forward(img) # 进行前向传播 loss = criterion(out, label) # 计算损失值 loss.backward() # 进行损失值的后向传播 optimizer.step() # 进行优化器的优化 train_loss += loss.data # for data in test_loader: img, label = data img = img.view(img.size(0), -1) if torch.cuda.is_available(): img = Variable(img, volatile=True).cuda() label = Variable(label, volatile=True).cuda() else: img = Variable(img, volatile=True) label = Variable(label, volatile=True) out = model.forward(img) loss = criterion(out, label) test_loss += loss.data top_p, top_class = out.topk(1, dim=1) # 获得输出的每一个样本的最大损失 equals = top_class == label.view(*top_class.shape) # 判断两组样本的标签是否相等 accuracy = torch.mean(equals.type(torch.FloatTensor)) # 计算准确率 eval_acc += accuracy print('train_loss{:.6f}, test_loss{:.6f}, Acc:{:.6f}'.format(train_loss / len(train_loader), test_loss / len(test_loader), eval_acc / len(test_loader)))
