27_pytorch全连接层，使用MNIST的分类案例（学习笔记）

# -*- coding: UTF-8 -*-import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transformsbatch_size=200 learning_rate=0.01 epochs=10# torchvision.transforms.Compose()类。这个类的主要作用是串联多个图片变换的操作。 # (1) transforms.Compose就是将transforms组合在一起 # (2) transforms.Normalize使用如下公式进行归一化 # (3) torchvision.transforms.ToTensor() 起到的作用是把PIL.Image或者numpy.narray数据类型转变为torch.FloatTensor类型，shape是C*H*W，数值范围缩小为[0.0, 1.0] # (4) 如果想把数值范围调整为[-1.0, 1.0],则可加torchvision.transforms.Normalize([mean_channel1,mean_channel2,mean_channel3],[std_channel1,std_channel2,std_channel3]) train_loader = torch.utils.data.DataLoader(datasets.MNIST('../data', train=True, download=True,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])),batch_size=batch_size, shuffle=True )test_loader = torch.utils.data.DataLoader(datasets.MNIST('../data', train=False,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])),batch_size=batch_size, shuffle=True )# 继承自nn.Module class MLP(nn.Module):def __init__(self):# 初始化层在 __init__中super(MLP,self).__init__()self.model = nn.Sequential(nn.Linear(784, 200),# 使用ReLU激活函数，关于22个激活函数的，可以参考：https://blog.csdn.net/tototuzuoquan/article/details/113791252?spm=1001.2014.3001.5501nn.ReLU(inplace=True),nn.Linear(200, 200),nn.ReLU(inplace=True),nn.Linear(200, 10),nn.ReLU(inplace=True),)# 要实现forward()def forward(self, x):x = self.model(x)return xnet = MLP() # PyTorch的十个优化器：https://blog.csdn.net/tototuzuoquan/article/details/113779970?spm=1001.2014.3001.5501 # 另外一篇关于优化器的文章（这里面有优化器的特征、数学公式等）：https://blog.csdn.net/tototuzuoquan/article/details/112724028?spm=1001.2014.3001.5501 optimizer = optim.SGD(net.parameters(), lr=learning_rate) # 关于PyTorch的十九个损失函数的文章有：https://blog.csdn.net/tototuzuoquan/article/details/113777788?spm=1001.2014.3001.5501 criteon = nn.CrossEntropyLoss()# enumerate() 函数用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列，同时列出数据和数据下标，一般用在 for 循环当中。 # enumerate() 方法的语法: # 参数： # sequence -- 一个序列、迭代器或其他支持迭代对象 # start -- 下标起始位置for epoch in range(epochs):for batch_idx, (data, target) in enumerate(train_loader):data = data.view(-1, 28*28)logits = net(data)loss = criteon(logits, target)optimizer.zero_grad()loss.backward()# print(w1.grad.norm(), w2.grad.norm())optimizer.step()if batch_idx % 100 == 0:print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, batch_idx * len(data), len(train_loader.dataset),100. * batch_idx / len(train_loader), loss.item()))test_loss = 0correct = 0for data, target in test_loader:data = data.view(-1, 28 * 28)logits = net(data)test_loss += criteon(logits, target).item()pred = logits.data.max(1)[1]correct += pred.eq(target.data).sum()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)))

输出结果：

runfile('E:/workspace/pytorch-learn/27_MLP网络层/main.py', wdir='E:/workspace/pytorch-learn/27_MLP网络层') Train Epoch: 0 [0/60000 (0%)] Loss: 2.311366 Train Epoch: 0 [20000/60000 (33%)] Loss: 2.019119 Train Epoch: 0 [40000/60000 (67%)] Loss: 1.321077 Test set: Average loss: 0.0040, Accuracy: 8394/10000 (84%) Train Epoch: 1 [0/60000 (0%)] Loss: 0.845444 Train Epoch: 1 [20000/60000 (33%)] Loss: 0.593395 Train Epoch: 1 [40000/60000 (67%)] Loss: 0.391533 Test set: Average loss: 0.0020, Accuracy: 8918/10000 (89%) Train Epoch: 2 [0/60000 (0%)] Loss: 0.489720 Train Epoch: 2 [20000/60000 (33%)] Loss: 0.439235 Train Epoch: 2 [40000/60000 (67%)] Loss: 0.424977 Test set: Average loss: 0.0017, Accuracy: 9050/10000 (90%) Train Epoch: 3 [0/60000 (0%)] Loss: 0.354985 Train Epoch: 3 [20000/60000 (33%)] Loss: 0.367654 Train Epoch: 3 [40000/60000 (67%)] Loss: 0.279793 Test set: Average loss: 0.0015, Accuracy: 9133/10000 (91%) Train Epoch: 4 [0/60000 (0%)] Loss: 0.241317 Train Epoch: 4 [20000/60000 (33%)] Loss: 0.336772 Train Epoch: 4 [40000/60000 (67%)] Loss: 0.314246 Test set: Average loss: 0.0014, Accuracy: 9195/10000 (92%) Train Epoch: 5 [0/60000 (0%)] Loss: 0.380214 Train Epoch: 5 [20000/60000 (33%)] Loss: 0.184812 Train Epoch: 5 [40000/60000 (67%)] Loss: 0.316628 Test set: Average loss: 0.0013, Accuracy: 9237/10000 (92%) Train Epoch: 6 [0/60000 (0%)] Loss: 0.331256 Train Epoch: 6 [20000/60000 (33%)] Loss: 0.288215 Train Epoch: 6 [40000/60000 (67%)] Loss: 0.228315 Test set: Average loss: 0.0012, Accuracy: 9277/10000 (93%) Train Epoch: 7 [0/60000 (0%)] Loss: 0.312616 Train Epoch: 7 [20000/60000 (33%)] Loss: 0.219588 Train Epoch: 7 [40000/60000 (67%)] Loss: 0.294207 Test set: Average loss: 0.0012, Accuracy: 9298/10000 (93%) Train Epoch: 8 [0/60000 (0%)] Loss: 0.302778 Train Epoch: 8 [20000/60000 (33%)] Loss: 0.227025 Train Epoch: 8 [40000/60000 (67%)] Loss: 0.212254 Test set: Average loss: 0.0011, Accuracy: 9333/10000 (93%) Train Epoch: 9 [0/60000 (0%)] Loss: 0.178860 Train Epoch: 9 [20000/60000 (33%)] Loss: 0.232927 Train Epoch: 9 [40000/60000 (67%)] Loss: 0.224159 Test set: Average loss: 0.0011, Accuracy: 9384/10000 (94%)

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