Pytorch 實現線性迴歸
阿新 • • 發佈:2022-03-27
Pytorch 實現線性迴歸
import torch from torch.utils import data from torch import nn # 合成數據 def synthetic_data(w, b, num_examples): """y = Xw + b + zs""" X = torch.normal(0, 1, (num_examples, len(w))) y = torch.matmul(X, w) + b y += torch.normal(0, 0.01, y.shape) return X, y.reshape((-1, 1)) # 用於合成數據的模板 true_w = torch.tensor([2, -3.4, 2]) true_b = 4.2 # 合成1000個數據 features, labels = synthetic_data(true_w, true_b, 1000) # 隨機批量載入資料 def load_array(data_arrays, batch_size, is_train=True): dataset = data.TensorDataset(*data_arrays) return data.DataLoader(dataset, batch_size, shuffle=is_train) batch_size = 10 data_iter = load_array((features, labels), batch_size) # 初始化線性網路,3輸入1輸出 net = nn.Sequential(nn.Linear(3, 1)) # 均方誤差損失函式 loss = nn.MSELoss() # 優化演算法 trainer = torch.optim.SGD(net.parameters(), lr=0.03) # 開始迭代 num_epochs = 3 for epoch in range(num_epochs): for X, y in data_iter: l = loss(net(X), y) trainer.zero_grad() l.backward() trainer.step() l = loss(net(features), labels) print(f'epoch {epoch + 1}, loss {l:f}')