# 安裝PyTorch
pip install torch torchvision

# 安裝其他依賴
pip install numpy matplotlib

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# 生成訓練數(shù)據(jù)
x = torch.linspace(0, 10, 100).unsqueeze(1)
y = 2 * x + 1 + torch.randn(100, 1) * 0.5

# 定義模型
class LinearModel(nn.Module):
    def __init__(self):
        super(LinearModel, self).__init__()
        self.linear = nn.Linear(1, 1)
    
    def forward(self, x):
        return self.linear(x)

# 創(chuàng)建模型實例
model = LinearModel()

# 定義損失函數(shù)和優(yōu)化器
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 訓練模型
epochs = 100
for epoch in range(epochs):
    # 前向傳播
    outputs = model(x)
    # 計算損失
    loss = criterion(outputs, y)
    # 反向傳播
    optimizer.zero_grad()
    loss.backward()
    # 更新參數(shù)
    optimizer.step()
    
    if (epoch + 1) % 10 == 0:
        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')

# 測試模型
with torch.no_grad():
    predicted = model(x)

# 可視化結果
plt.scatter(x.numpy(), y.numpy(), label='Original data')
plt.plot(x.numpy(), predicted.numpy(), 'r-', label='Fitted line')
plt.legend()
plt.show()

print("Hello World! AI模型訓練完成")

import torch
import torch.nn as nn
import torch.optim as optim

# 生成非線性數(shù)據(jù)
x = torch.linspace(-1, 1, 100).unsqueeze(1)
y = x.pow(2) + 0.2 * torch.randn(100, 1)

# 定義神經(jīng)網(wǎng)絡模型
class NeuralNet(nn.Module):
    def __init__(self):
        super(NeuralNet, self).__init__()
        self.hidden = nn.Linear(1, 10)
        self.output = nn.Linear(10, 1)
    
    def forward(self, x):
        x = torch.relu(self.hidden(x))
        x = self.output(x)
        return x

# 創(chuàng)建模型實例
model = NeuralNet()

# 定義損失函數(shù)和優(yōu)化器
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 訓練模型
epochs = 1000
for epoch in range(epochs):
    outputs = model(x)
    loss = criterion(outputs, y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    if (epoch + 1) % 100 == 0:
        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')

# 測試模型
with torch.no_grad():
    predicted = model(x)

# 可視化結果
import matplotlib.pyplot as plt
plt.scatter(x.numpy(), y.numpy(), label='Original data')
plt.plot(x.numpy(), predicted.numpy(), 'r-', label='Neural network prediction')
plt.legend()
plt.show()

import torch
import torchvision
import torchvision.transforms as transforms

# 數(shù)據(jù)預處理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 加載MNIST數(shù)據(jù)集
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

# 查看數(shù)據(jù)
import matplotlib.pyplot as plt
import numpy as np

# 函數(shù)：顯示圖像
def imshow(img):
    img = img / 2 + 0.5  # 反歸一化
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()

# 獲取一批訓練數(shù)據(jù)
dataiter = iter(trainloader)
images, labels = next(dataiter)

# 顯示圖像
imshow(torchvision.utils.make_grid(images))
print('標簽:', ' '.join(f'{labels[j]}' for j in range(4)))

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # 卷積層
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        # 池化層
        self.pool = nn.MaxPool2d(2, 2)
        # 全連接層
        self.fc1 = nn.Linear(64 * 12 * 12, 128)
        self.fc2 = nn.Linear(128, 10)
    
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 64 * 12 * 12)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 創(chuàng)建模型實例
net = Net()
print(net)

import torch.optim as optim

# 定義損失函數(shù)和優(yōu)化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 訓練模型
epochs = 5
for epoch in range(epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # 獲取輸入
        inputs, labels = data
        
        # 清零梯度
        optimizer.zero_grad()
        
        # 前向傳播
        outputs = net(inputs)
        
        # 計算損失
        loss = criterion(outputs, labels)
        
        # 反向傳播
        loss.backward()
        
        # 更新參數(shù)
        optimizer.step()
        
        # 統(tǒng)計損失
        running_loss += loss.item()
        if i % 100 == 99:
            print(f'[{epoch + 1}, {i + 1}] loss: {running_loss / 100:.3f}')
            running_loss = 0.0

print('訓練完成')

# 測試模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'測試準確率: {100 * correct / total:.2f}%')

# 查看預測結果
dataiter = iter(testloader)
images, labels = next(dataiter)

# 顯示圖像
imshow(torchvision.utils.make_grid(images))
print('真實標簽:', ' '.join(f'{labels[j]}' for j in range(4)))

# 預測
outputs = net(images)
_, predicted = torch.max(outputs, 1)
print('預測標簽:', ' '.join(f'{predicted[j]}' for j in range(4)))