python|（七）PyTorch深度学习（全连接层网络）深度学习|pytorch|python|机器学习

1、Softmax（层）函数：将输出值转换成概率值（全部值加起来等于一）
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2、交叉熵损失流程框图： 【python|（七）PyTorch深度学习（全连接层网络）】
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3、交叉熵损失函数（CrossEntropyLoss）：
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4、模型设计
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代码：

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 optimbatch_size = 64 transform = transforms.Compose([ transforms.ToTensor(),# 将PIL格式图像转换成Tensor矩阵向量（维度28x28转换成1x28x28，1：为RGB通道）【 [0...255]--->[0,1] 】 transforms.Normalize((0.1307, ), (0.3081, ))# 均一化处理（均值、标准差） ]) # 训练集数据 train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform) # 加载训练集数据 train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size) # 测试集数据集 test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True, transform=transform) # 加载测试集数据集 test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.linear1 = torch.nn.Linear(784, 512)# 将PIL格式图像转换成Tensor矩阵向量（维度28x28转换成1x28x28，1：为RGB通道）28x28=784 self.linear2 = torch.nn.Linear(512, 256) self.linear3 = torch.nn.Linear(256, 128) self.linear4 = torch.nn.Linear(128, 64) self.linear5 = torch.nn.Linear(64, 10)def forward(self, x): x = x.view(-1, 784)# -1：自动检测矩阵有有多少行，列指定为784 x = F.relu(self.linear1(x)) x = F.relu(self.linear2(x)) x = F.relu(self.linear3(x)) x = F.relu(self.linear4(x)) return self.linear5(x)model = Net() ###################3 构建损失函数、优化器############################### criterion = torch.nn.CrossEntropyLoss()# 交叉熵损失 optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)# 参数优化#####################4 循环训练 ######################### def train(epoch): running_loss = 0.0 for batch_idx, data in enumerate(train_loader, 0): # 准备数据（input：输入，target：实际值） inputs, target = data # 梯度清0 optimizer.zero_grad() # 前向传播 outputs = model(inputs) # 交叉熵损失函数计算 loss = criterion(outputs, target) # 反向传播 loss.backward() # 参数优化 optimizer.step() # 累计loss running_loss += loss.item() # 数据集一共有batch_idx个数据，每隔300个打印一次平均损失函数值 if batch_idx % 300 ==299: print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300)) running_loss = 0.0def 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))if __name__ == '__main__': for epoch in range(10): train(epoch) test()

运行结果：