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python - model.parameters() 没有在 Pytorch 的线性回归中更新

问题描述

我是 Pytorch 深度学习的新手。我在这里使用 Kaggle 的房价数据集。我尝试对前 50 行进行采样。但是在我进行训练时，model.parameters() 并没有更新。任何人都可以帮忙吗？

import torch
import numpy as np
from torch.utils.data import TensorDataset
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F

inputs = np.array(label_X_train[:50])
targets = np.array(train_y[:50])

# Tensors
inputs = torch.from_numpy(inputs)
targets = torch.from_numpy(targets)
targets = targets.view(-1, 1)
train_ds = TensorDataset(inputs, targets)
batch_size = 5
train_dl = DataLoader(train_ds, batch_size, shuffle=True)

model = nn.Linear(10, 1)
# Define Loss func
loss_fn = F.mse_loss
# Optimizer
opt = torch.optim.SGD(model.parameters(), lr = 1e-5)


num_epochs = 100
model.train()         
for epoch in range(num_epochs):
    # Train with batches of data
    for xb, yb in train_dl:

        # 1. Generate predictions
        pred = model(xb.float())

        # 2. Calculate loss
        loss = loss_fn(pred, yb.float())
    
        # 3. Compute gradients
        loss.backward()

        # 4. Update parameters using gradients
        opt.step()

        # 5. Reset the gradients to zero
        opt.zero_grad()
     
    if (epoch+1) % 10 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch +
                                                   1, num_epochs, 
                                                   loss.item()))

标签： pythonnumpydeep-learningpytorchlinear-regression

解决方案

重量确实会更新，但您没有正确捕获它。model.weight.data是一个火炬张量，但变量的名称只是一个引用，所以设置w = model.weight.data不会创建一个副本而是对对象的另一个引用。因此改变model.weight.data也会改变w。

因此，通过在循环的不同部分设置w = model.weight.data和w_new = model.weight data，意味着您将两个引用分配给同一个对象，使它们的值始终相等。

为了评估模型权重在print(model.weight.data)循环之前和之后的变化（因为你有一个包含 10 个参数的线性层，仍然可以这样做）或简单地设置w = model.weight.data.clone(). 在这种情况下，您的输出将是：

tensor([[False, False, False, False, False, False, False, False, False, False]])

这是一个示例，显示您的权重正在发生变化：

import torch
import numpy as np
from torch.utils.data import TensorDataset
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F

inputs = np.random.rand(50, 10)
targets = np.random.randint(0, 2, 50)

# Tensors
inputs = torch.from_numpy(inputs)
targets = torch.from_numpy(targets)
targets = targets.view(-1, 1)
train_ds = TensorDataset(inputs, targets.squeeze())
batch_size = 5
train_dl = DataLoader(train_ds, batch_size, shuffle=True)

model = nn.Linear(10, 1)
# Define Loss func
loss_fn = F.mse_loss
# Optimizer
opt = torch.optim.SGD(model.parameters(), lr = 1e-1)


num_epochs = 100
model.train()
w = model.weight.data.clone()         
for epoch in range(num_epochs):
    # Train with batches of data
    for xb, yb in train_dl:

        # 1. Generate predictions
        pred = model(xb.float())

        # 2. Calculate loss
        loss = loss_fn(pred, yb.float())
    
        # 3. Compute gradients
        loss.backward()

        # 4. Update parameters using gradients
        opt.step()

        # 5. Reset the gradients to zero
        opt.zero_grad()

    if (epoch+1) % 10 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch +
                                                   1, num_epochs, 
                                                   loss.item()))
print(w == model.weight.data)

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