新增深度學習
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KynixInHK
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.gitignore
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.venv
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cifar-10/**/*
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cifar-10
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data
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.idea/.gitignore
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# Default ignored files
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/shelf/
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/workspace.xml
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# Editor-based HTTP Client requests
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/httpRequests/
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# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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<component name="InspectionProjectProfileManager">
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.idea/learn-pytorch.iml
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.idea/learn-pytorch.iml
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$">
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<excludeFolder url="file://$MODULE_DIR$/.venv" />
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</content>
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<orderEntry type="inheritedJdk" />
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<orderEntry type="sourceFolder" forTests="false" />
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.idea/misc.xml
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.idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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.idea/other.xml
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.idea/other.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="PySciProjectComponent">
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.idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$" vcs="Git" />
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</component>
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</project>
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130
classification.py
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classification.py
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import torch
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import torchvision
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import torchvision.transforms as transforms
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from torch import optim
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from torch.utils.data import Dataset
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import torch.nn as nn
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import torch.nn.functional as F
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data_path = "data/"
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device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.mps.is_available() else "cpu")
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transforms = transforms.Compose([
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transforms.ToTensor(), # 將圖像轉換為 Tensor
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transforms.Normalize((0.4915, 0.4823, 0.4468), (0.2470, 0.2435, 0.2616))
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# 歸一化，第一個 tuple 代表 CIFAR-10 這個資料集 RGB 三個通道的平均值，第二個 tuple 代表標準差
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])
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train_dataset = torchvision.datasets.CIFAR10(
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root=data_path,
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train=True,
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download=True,
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transform=transforms
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)
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test_dataset = torchvision.datasets.CIFAR10(
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root=data_path,
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train=False,
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download=True,
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transform=transforms
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)
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label_map = {
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0: 0, # 飛機
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2: 1, # 小鳥
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}
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class_names = ["airplane", "bird"]
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train_dataset = [(img, label_map[label]) for img, label in train_dataset if label in [0, 2]]
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test_dataset = [(img, label_map[label]) for img, label in test_dataset if label in [0, 2]]
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class ModelDataset(Dataset):
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def __init__(self, dataset):
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self.dataset = dataset
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def __getitem__(self, index):
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img, label = self.dataset[index]
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return img, label
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def __len__(self):
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return len(self.dataset)
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train_dataset = ModelDataset(train_dataset)
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test_dataset = ModelDataset(test_dataset)
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train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
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test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True)
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# 宣告模型
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class Net(nn.Module):
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def __init__(self, n_chansl=16):
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super().__init__()
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self.n_chansl = n_chansl
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self.conv1 = nn.Conv2d(3, n_chansl, kernel_size=3, padding=1)
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self.conv1_batchnorm = nn.BatchNorm2d(n_chansl) # 歸一化器，輸入為通道數，輸出為相同的通道數
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self.conv2 = nn.Conv2d(n_chansl, n_chansl // 2, kernel_size=3, padding=1)
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self.conv2_batchnorm = nn.BatchNorm2d(n_chansl // 2)
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self.fc1 = nn.Linear(8 * 8 * n_chansl // 2, 32)
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self.fc2 = nn.Linear(32, 2)
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def forward(self, x):
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out = F.max_pool2d(self.conv1_batchnorm(torch.relu(self.conv1(x))), kernel_size=2)
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out = F.max_pool2d(self.conv2_batchnorm(torch.relu(self.conv2(out))), kernel_size=2)
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out = out.view(-1, 8 * 8 * self.n_chansl // 2)
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out = torch.relu(self.fc1(out))
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out = self.fc2(out)
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return out
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model = Net().to(device)
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optimizer = optim.SGD(model.parameters(), lr=1e-2)
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loss_fn = nn.CrossEntropyLoss()
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def train(epoch):
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global loss
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for epoch in range(epoch):
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for (image, label) in train_loader:
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image = image.to(device)
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label = label.to(device)
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outputs = model(image)
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loss = loss_fn(outputs, label)
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l2_lambda = 0.001
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l2_norm = sum(p.pow(2.0).sum() for p in model.parameters())
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loss = loss + l2_lambda * l2_norm
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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print(f"Epoch {epoch}, Loss {loss}")
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train(100)
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def test():
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correct = 0
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total = 0
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with torch.no_grad():
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for (image, label) in test_loader:
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image = image.to(device)
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label = label.to(device)
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outputs = model(image)
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_, predicted = torch.max(outputs, dim=1)
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total += label.size(0)
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correct += (predicted == label).sum().item()
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print(f"Accuracy: {correct / total}")
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test()
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# torch.save(model.state_dict(), "model/model.pt")
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linear.py
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linear.py
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# 1. 收集數據
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import torch
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t_c = [0.5, 14.0, 15.0, 28.0, 11.0, 8.0, 3.0, -4.0, 6.0, 13.0, 21.0]
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t_u = [35.7, 55.9, 58.2, 81.9, 56.3, 48.9, 33.9, 21.8, 48.4, 60.4, 68.4]
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t_c = torch.tensor(t_c).unsqueeze(1) # 升維，作用是將資料轉化成單個樣本
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t_u = torch.tensor(t_u).unsqueeze(1)
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n_samples = t_u.shape[0] # 樣本量
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n_test = int(n_samples * 0.2) # 測試集數量
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shuffled_samples = torch.randperm(n_samples) # 隨機化樣本
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train_indics = shuffled_samples[:-n_test] # 訓練集索引
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test_indics = shuffled_samples[-n_test:] # 測試集索引
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# 訓練集
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t_u_train = t_u[train_indics]
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t_c_train = t_c[train_indics]
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# 測試集
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t_u_test = t_u[test_indics]
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t_c_test = t_c[test_indics]
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print(t_u_train)
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print(t_c_train)
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print(t_u_test)
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print(t_c_test)
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# 歸一化
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t_u_mean = t_u_train.mean()
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t_u_std = t_u_train.std()
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t_u_train_norm = (t_u_train - t_u_mean) / t_u_std
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t_u_test_norm = (t_u_test - t_u_mean) / t_u_std
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t_c_mean = t_c_train.mean()
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t_c_std = t_c_train.std()
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t_c_train_norm = (t_c_train - t_c_mean) / t_c_std
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t_c_test_norm = (t_c_test - t_c_mean) / t_c_std
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# 2. 搭建模型
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# import torch.nn as nn
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#
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# linear_model = nn.Linear(in_features = 1, out_features = 1) # in_features 表示輸入神經元的個數，out_features 表示輸出神經元的個數
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import torch.nn as nn
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from collections import OrderedDict
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# 構建一個多層神經網路，隱藏層有 13 個神經元，輸出層有 1 個神經元
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neural_network = nn.Sequential(OrderedDict([
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('hidden', nn.Linear(1, 13)), # 隱藏層
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('hidden_activation', nn.Tanh()), # 隱藏層激勵函數
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('output', nn.Linear(13, 1)) # 輸出層
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]))
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# 3. 宣告優化器和損失函數
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optimizer = torch.optim.SGD(
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neural_network.parameters(),
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lr=1e-2
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)
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def loss_fn(t_p, t_c):
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return ((t_p - t_c) ** 2).mean()
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# 4. 宣告 train loop
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def train_loop(n_epochs, optimizer, model, loss_fun, t_u_train, t_u_test, t_c_train, t_c_test):
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for epoch in range(1, n_epochs + 1):
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t_p_train = model(t_u_train)
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loss_train = loss_fun(t_p_train, t_c_train)
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t_p_test = model(t_u_test)
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loss_test = loss_fun(t_p_test, t_c_test)
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optimizer.zero_grad()
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loss_train.backward()
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optimizer.step()
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if epoch == 1 or epoch % 10 == 0:
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print(f'Epoch {epoch}: Training Loss: {loss_train:.4f}')
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print(f'Test Loss: {loss_test:.4f}')
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# 5. 開始訓練
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train_loop(
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n_epochs = 500,
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optimizer = optimizer,
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model = neural_network,
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loss_fun = loss_fn,
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t_u_train = t_u_train_norm,
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t_u_test = t_u_test_norm,
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t_c_train = t_c_train_norm,
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t_c_test = t_c_test_norm
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)
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print('output', neural_network(t_u_test_norm) * t_c_std + t_c_mean)
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print('val', t_c_test)
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