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焦点要闻：DL 基础：PyTorch 常用代码存档

来源：博客园


(资料图片)

1 pandas 读 csv

import torchfrom torch import nnimport numpy as npimport pandas as pdfrom copy import deepcopydevice = "cuda" if torch.cuda.is_available() else "cpu"# 读 csvdata_all = pd.read_csv("./CFD_data/record_data0.csv")# 提取某一列colume = np.array(data_all[["colume_name"]], dtype=np.float32).reshape(-1, 1)# 提取某一个值value = data[data["食物种类"]=="主食"]["卡路里"].item()# 数据操作c = np.concatenate([a[1:], b[:-1]], axis=1)c = torch.cat([a, b], axis=1)# 存 csvc.to_csv("./CFD_data/flow_rate.csv", index=False)

2 NN 的搭建、训练与评估

搭建：使用 nn.Sequential。

# modelNN_model = nn.Sequential(    nn.Linear(6, 256),     nn.ReLU(),    nn.Linear(256, 256),    nn.ReLU(),    nn.Linear(256, 256),    nn.ReLU(),    nn.Linear(256, 1),)# 优化器optimizer = torch.optim.Adam(NN_model.parameters(), lr=0.001)

训练：

def NN_train(train_x, train_y, model, loss_fn, optimizer, epoches, batch_size, save_path):    """    训练网络    输入:        train_x, train_y:   训练集        model:              网络模型        loss_fn:            损失函数        optimizer:          优化器        epoches:            epoches 个数        batch_size:         mini batch 大小        save_path:          模型保存路径    """    # 切换到train模式    model.train()    losses = []    for epoch in range(epoches):        batch_loss = []        for start in range(0, len(train_x), batch_size): # mini batch            end = start + batch_size if start + batch_size < len(train_x) else len(train_x)            xx = torch.tensor(train_x[start:end], dtype=torch.float, requires_grad=True)            yy = torch.tensor(train_y[start:end], dtype=torch.float, requires_grad=True)            xx, yy = xx.to(device), yy.to(device) # 加载到 device            pred = model(xx) # 输入数据到模型里得到输出            loss = loss_fn(pred, yy) # 计算输出和标签的 loss                       optimizer.zero_grad() # 清零            loss.backward() # 反向推导            optimizer.step() # 步进优化器            batch_loss.append(loss.data.numpy())        if epoch % max(1, epoches//8) == 0:            print(f"Training Error in epoch {epoch}: {np.mean(batch_loss):>8f}")    torch.save(model.state_dict(), save_path) # 保存模型

测试：

def NN_test(test_x, test_y, model, save_path, loss_fn):    """    测试网络    输入:        test_x, test_y:     测试集        model:              网络模型        loss_fn:            损失函数        save_path:          模型保存路径    """    model.load_state_dict(torch.load(save_path)) # 加载模型      model.eval() # 切换到测试模型    MSE_loss_fn = nn.MSELoss() # MSE loss function    test_loss, MSE = 0, 0 # 记录 loss 和 MSE    # 梯度截断    with torch.no_grad():        test_x, test_y = torch.tensor(test_x).to(device), torch.tensor(test_y).to(device) # 加载到 device        pred = model(test_x) # 输入数据到模型里得到输出        test_loss = loss_fn(pred, test_y).item() # 计算输出和标签的 loss        MSE = MSE_loss_fn(pred, test_y).item() # MSE    print(f"Test Error: \n  Avg loss: {test_loss:>8f}, MSE: {MSE:>8f}\n")    print(f"Test Result: \n  Prediction: {pred[:5]}, \n  Y: {test_y[:5]}, \n  diff: {test_y[:5]-pred[:5]}\n")

测试 ensemble model（平均值）：

def NN_test_ensemble(test_x, test_y, loaded_model_list, loss_fn):    for model in loaded_model_list:        model.eval() # 切换到测试模型    MSE_loss_fn = nn.MSELoss() # MSE loss function    test_loss, MSE = 0, 0 # 记录 loss 和 MSE    # 梯度截断    with torch.no_grad():        test_x, test_y = torch.tensor(test_x).to(device), torch.tensor(test_y).to(device) # 加载到 device        pred = torch.zeros(test_y.shape)        for model in loaded_model_list:            pred += model(test_x) # 输入数据到模型里得到输出        pred /= len(loaded_model_list)        test_loss = loss_fn(pred, test_y).item() # 计算输出和标签的 loss        MSE = MSE_loss_fn(pred, test_y).item() # MSE    print(f"Test Error: \n  Avg loss: {test_loss:>8f}, MSE: {MSE:>8f}\n")    print(f"Test Result: \n  Prediction: {pred[:5]}, \n  Y: {test_y[:5]}, \n  diff: {test_y[:5]-pred[:5]}\n")

关键词： 输入数据 保存路径 损失函数