From 8ce42bb1b21d5bdc9c38bcf880276c64d1e4252e Mon Sep 17 00:00:00 2001
From: ptjlmfa8p <2698062595@qq.com>
Date: Fri, 28 May 2021 23:40:31 +0800
Subject: [PATCH] =?UTF-8?q?=E4=BB=A3=E7=A0=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 机器学习.py | 102 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 机器学习.py

diff --git a/机器学习.py b/机器学习.py
new file mode 100644
index 0000000..47d6b9b
--- /dev/null
+++ b/机器学习.py
@@ -0,0 +1,102 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+from torchvision import transforms
+
+
+class Rnn(nn.Module):
+    def __init__(self, in_dim, hidden_dim, n_layer, n_classes):  # （输入层维度，隐藏层维度，循环网络个数，分类个数）
+        super(Rnn, self).__init__()
+        # 存入self
+        self.n_layer = n_layer
+        self.hidden_dim = hidden_dim
+        self.lstm = nn.LSTM(in_dim, hidden_dim, n_layer, batch_first=True)  # LSTM（输入层，隐藏层，网络个数，批数量置于第一位）
+        self.classifier = nn.Linear(hidden_dim, n_classes)  # （输入大小，输出大小）
+
+    def forward(self, x):
+        out, (h_n, c_n) = self.lstm(x)
+        # 取得最后一层隐藏层h_t
+        x = h_n[-1, :, :]
+        # 进行分类，并返回结果
+        x = self.classifier(x)
+        return x
+
+
+# 数据预处理模块
+transform = transforms.Compose([
+    transforms.ToTensor(),  # 将numpy转换为tensor
+    transforms.Normalize([0.5], [0.5]),  # 将tensor正则化
+])
+
+trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)  # 设置数据集下载地址并下载训练集
+trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True)  # 载入训练集，设置批大小为128
+
+testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)  # 下载测试集
+testloader = torch.utils.data.DataLoader(testset, batch_size=512, shuffle=False)  # 设置测试集批大小为512
+
+# 对用户是否能够使用cuda进行判断
+if torch.cuda.is_available():
+    device = torch.device('cuda:0')
+else:
+    device = torch.device('cpu')
+
+net = Rnn(28, 10, 2, 10)  # 构造网络
+
+net = net.to(device)  # 将网络载入设备
+criterion = nn.CrossEntropyLoss()  # 使用CEL作为此次训练的损失函数
+optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9)  # 构造SGD随机梯度下降优化器
+
+
+# Training
+def train(epoch):
+    print('\nEpoch: %d' % epoch)
+    net.train()  # 设置为train模式
+    train_loss = 0
+    correct = 0
+    total = 0
+
+    for batch_idx, (inputs, targets) in enumerate(trainloader):
+        inputs, targets = inputs.to(device), targets.to(device)  # 将数据集取出并载入设备
+        optimizer.zero_grad()  # 将优化器导数清零
+
+        outputs = net(torch.squeeze(inputs, 1))  # 将数据集改造成网络输入要求形式
+
+        loss = criterion(outputs, targets)  # 使用损失函数计算loss
+        loss.backward()  # 反向传播
+        optimizer.step()  # 优化
+
+        train_loss += loss.item()  # 累加loss数值
+        _, predicted = outputs.max(1)  # 取出当前网络在第0维度输出的最高值
+        total += targets.size(0)  # 获取当前所有训练数据的label
+        correct += predicted.eq(targets).sum().item()  # 计算错误
+
+        print(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
+              % (train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
+
+
+def test(epoch):
+    global best_acc
+    net.eval()
+    test_loss = 0
+    correct = 0
+    total = 0
+    with torch.no_grad():
+        for batch_idx, (inputs, targets) in enumerate(testloader):
+            inputs, targets = inputs.to(device), targets.to(device)
+            outputs = net(torch.squeeze(inputs, 1))
+            loss = criterion(outputs, targets)
+
+            test_loss += loss.item()
+            _, predicted = outputs.max(1)
+            total += targets.size(0)
+            correct += predicted.eq(targets).sum().item()
+
+            print(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
+                  % (test_loss / (batch_idx + 1), 100. * correct / total, correct, total))
+
+
+print(net)
+for epoch in range(100):
+    train(epoch)
+    test(epoch)