From f27053ac65abcc6216e627f78d879fc91ecbcbdf Mon Sep 17 00:00:00 2001
From: nbwzlyd <420907013@qq.com>
Date: Sun, 11 Sep 2022 09:58:51 +0000
Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=20'torch-train-study2'?=
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---
 torch-train-study2 | 100 ---------------------------------------------
 1 file changed, 100 deletions(-)
 delete mode 100644 torch-train-study2

diff --git a/torch-train-study2 b/torch-train-study2
deleted file mode 100644
index 7623afc..0000000
--- a/torch-train-study2
+++ /dev/null
@@ -1,100 +0,0 @@
-import torch as t
-import torchvision as tv
-import torchvision.transforms as transforms
-import torch.nn as nn
-import torch.nn.functional as F
- 
-#错误的还未学习
-########## 超参数设置 ##########
-epochs = 200
-learning_rate = 0.001
-batch_size = 256
-gpu_ids = [0, 1, 2]
- 
- 
-########## 一、数据加载与预处理 ##########
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
-])
-trainset = tv.datasets.CIFAR10(root='./data/', train=True, download=True, transform=transform)
-testset = tv.datasets.CIFAR10(root='./data/', train=False, download=True, transform=transform)
-trainloader = t.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=4)
-testloader = t.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=4)
- 
- 
-########## 二、定义神经网络 ##########
-class LeNet(nn.Module):
-    def __init__(self):
-        super(LeNet, self).__init__()
-        # (in_channels, out_channels, kernel_size, stride, padding)
-        self.conv1 = nn.Conv2d(3, 6, 5, 1, 0)
-        self.conv2 = nn.Conv2d(6, 16, 5, 1, 0)
-        self.fc1 = nn.Linear(16 * 5 * 5, 120)
-        self.fc2 = nn.Linear(120, 84)
-        self.fc3 = nn.Linear(84, 10)
- 
-    def forward(self, x):
-        x = F.max_pool2d(F.relu(self.conv1(x)), 2, 2)
-        x = F.max_pool2d(F.relu(self.conv2(x)), 2, 2)
-        x = x.view(x.size()[0], -1)
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
- 
- 
-########## 三、训练神经网络 ##########
-# 神经网络实例化
-net = LeNet()
-if (len(gpu_ids) == 0) or (not t.cuda.is_available()):
-    # gpu_ids列表为空, 或者没GPU可用，则设置为GPU模式。
-    device = t.device("cpu")
-    print("Train Mode : CPU")
-elif t.cuda.is_available() and len(gpu_ids) > 1:
-    # gpu_ids列表大于1，表明想用多个GPU训练
-    device = t.device("cuda:0")
-    net = nn.DataParallel(net, device_ids=gpu_ids)
-    print("Train Mode : Multi GPU;", gpu_ids)
-else:
-    # gpu_ids列表等于1，表明想用一个GPU训练
-    device = t.device("cuda:" + str(gpu_ids[0]) if t.cuda.is_available() else "cpu")
-    print("Train Mode : One GPU;", device)
-net = net.to(device)
-print("\n", "##" * 10, "  NetWork  ", "##" * 10, "\n", net, "\n", "##" * 26, "\n")
-# 定义损失函数和优化器
-criterion = nn.CrossEntropyLoss()
-optimizer = t.optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
-# 开始训练
-for epoch in range(epochs):
-    running_loss = 0.0
-    for i, (inputs, labels) in enumerate(trainloader):
-        inputs, labels = inputs.to(device), labels.to(device)
-        optimizer.zero_grad()
-        outputs = net(inputs)
-        loss = criterion(outputs, labels)
-        loss.backward()
-        optimizer.step()
-        running_loss += loss.item()
-    print("Epoch%03d: Training_loss = %.5f" % (epoch + 1, running_loss))
- 
-    # 保存模型并进行验证
-    if (epoch + 1) % 10 == 0:
-        if len(gpu_ids) > 1:
-            t.save(net.module.state_dict(), "Cifar10_LeNet_Epoch" + str(epoch + 1) + ".pth")
-        else:
-            t.save(net.state_dict(), "Cifar10_LeNet_Epoch" + str(epoch + 1) + ".pth")
-        correct = 0
-        all = 0
-        with t.no_grad():
-            for (inputs, labels) in testloader:
-                inputs, labels = inputs.to(device), labels.to(device)
-                outputs = net(inputs)
-                _, predicted = t.max(outputs, 1)
-                all += inputs.size()[0]
-                correct += (predicted == labels).sum().item()
-        print("###" * 15)
-        print("Epoch%03d: TestSet_Accuracy = %.3f" % (epoch + 1, correct / all))
-        print("###" * 15)
- 
-print("Train Done!")
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