ADD file via upload

main.py

@@ -0,0 +1,139 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+import torch.nn as nn
+from torch.utils.data import DataLoader, TensorDataset
+from tqdm import tqdm
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+losses = []
+
+data = pd.read_csv("./mchar_train/train.csv")
+data = np.array(data)
+labels = data[:, 0]
+data = data[:, 1:]
+data = np.resize(data, (42000, 28, 28))
+data = torch.from_numpy(data).float().to(device)
+labels = torch.from_numpy(labels).to(device)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_planes, out_planes, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_planes)
+        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_planes)
+        self.relu = nn.ReLU(inplace=False)
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion * out_planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, self.expansion * out_planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion * out_planes)
+            )
+
+    def forward(self, x):
+        out = self.relu(self.bn1(self.conv1(x)))
+        out = self.relu(self.bn2(out))
+        out = out + self.shortcut(x)
+        out = F.relu(out)
+        return out
+
+
+class ResNet(nn.Module):
+    # num_classes = 10 十分类问题
+    def __init__(self, block, num_blocks, num_classes=10):
+        super(ResNet, self).__init__()
+        self.in_planes = 64
+
+        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        # 四个残差结构
+        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
+
+        self.linear = nn.Linear(512 * block.expansion, num_classes)
+        # 激活函数 relu = max(0, x)
+        self.relu = nn.ReLU(inplace=False)
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = self.bn1(self.conv1(x))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+        # 平均池化
+        out = F.avg_pool2d(out, 4)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+        return out
+
+
+model = ResNet(BasicBlock, [2, 2, 2, 2])
+model.to(device)
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+data = torch.unsqueeze(data, 1)
+dataset = TensorDataset(data, labels)
+data_loader = DataLoader(dataset, batch_size=400, shuffle=True)
+
+epochs = 10
+
+loss = 0
+for epoch in tqdm(range(epochs), desc=f'Training Progress', leave=False):
+    for inputs, labels in data_loader:
+        outputs = model(inputs)
+        # 梯度
+        loss = criterion(outputs, labels)
+        losses.append(loss.item())
+        optimizer.zero_grad()
+
+        loss.backward()
+        optimizer.step()
+
+    print(f'Epoch [{epoch + 1}/{epochs}], Loss: {float(loss.item()):.4f}')
+np.savetxt('loss.csv', losses, delimiter=',')
+y = losses
+y = np.array(y)
+x = np.arange(1, len(y) + 1)
+plt.xlabel("iteration")
+plt.ylabel("loss")
+plt.plot(x, y)
+plt.show()
+torch.save(model, 'model_name.pth')
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = torch.load('./model_name.pth', map_location=torch.device(device))
+model.eval()  # 设置为评估模式
+
+data = pd.read_csv("./mchar_train/test.csv")
+data = np.array(data)
+data = torch.from_numpy(data).float().to(device)
+data = data.resize(28000, 28, 28)
+data = torch.unsqueeze(data, 1)
+# 使用模型进行预测
+ans = np.zeros(28000)
+for i in tqdm(range(len(data)), desc='Predicting Progress', leave=False):
+    d = torch.unsqueeze(data[i], 0)
+    with torch.no_grad():
+        output = model(d)
+    predicted_class = torch.argmax(output, dim=1).item()
+    ans[i] = predicted_class
+np.savetxt('sample.csv', ans, delimiter=",")