torch_linear_regression 朴灿烈づ我的快乐病毒、 2022-09-10 10:18 167阅读 0赞 ### torch\_linear\_regression ### * 手打了一波linear\_regression，再次体验了一下torch的流程 import torch import torch.nn as nn import numpy as np import matplotlib.pyplot as plt input_size = 1 output_size = 1 num_epochs = 60 learning_rate = 0.001 # Toy dataset x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], [9.779], [6.182], [7.59], [2.167], [7.042], [10.791], [5.313], [7.997], [3.1]], dtype=np.float32) y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], [3.366], [2.596], [2.53], [1.221], [2.827], [3.465], [1.65], [2.904], [1.3]], dtype=np.float32) # Linear regression model model = nn.Linear(input_size, output_size) # Loss and optimizer criterion = nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) # Train the model for epoch in range(num_epochs): # Convert numpy arrays to torch tensors # 训练资料 inputs = torch.from_numpy(x_train) # 就是GT targets = torch.from_numpy(y_train) # Forward pass # Forward的话就是把数据扔到model中去过一遍，如果说是那种class xxx(nn.model): ....的自定义model类的话，数据会进入重写的forward里面去 outputs = model(inputs) # loss的计算就是把predict和gt计算，这里选的是MSELoss loss = criterion(outputs, targets) # Backward and optimize # 相当于初始化，这里的optimizer选的SGD，还有Adam这种 optimizer.zero_grad() # 反向传播 loss.backward() # 用优化器去更新值 optimizer.step() # 每5个epoch print一次 if (epoch+1) % 5 == 0: print('Epoch [{}/{}], Loss:{:.4f}'.format(epoch+1, num_epochs, loss.item())) # Plot the graph predicted = model(torch.from_numpy(x_train)).detach().numpy() # 这里是画的散点图 plt.plot(x_train, y_train, 'ro', label='Original data') # 这里画的是一条直线 plt.plot(x_train, predicted, label='Fitted line') plt.legend() plt.show() # Save the model checkpoint torch.save(model.state_dict(), 'model.ckpt')
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