使用 Pytorch 的线性回归中的损失没有减少

Question

我正在研究 Pytorch 的线性回归问题。 我使用的数据集是来自 Kaggle 的房价。 在训练 model 时，我发现损失并没有减少。 它显示出一种不稳定的模式。 这是我在 100 个 epoch 后得到的损失：

Epoch [10/100], Loss: 222273830912.0000
Epoch [20/100], Loss: 348813688832.0000
Epoch [30/100], Loss: 85658296320.0000
Epoch [40/100], Loss: 290305572864.0000
Epoch [50/100], Loss: 59399933952.0000
Epoch [60/100], Loss: 80360054784.0000
Epoch [70/100], Loss: 90352918528.0000
Epoch [80/100], Loss: 534457679872.0000
Epoch [90/100], Loss: 256064503808.0000
Epoch [100/100], Loss: 102400483328.0000

这是代码：

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 = normalized_X
targets = np.array(train_y)

# 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()
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
        yb = yb.view(yb.size(0), -1)
        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()))

Answer 1

我已经运行了您提供的代码，但出现此错误：

    p.py:38: UserWarning: Using a target size (torch.Size([50])) that is 
different to the input size (torch.Size([50, 1])). This will likely lead 
to incorrect results due to broadcasting. Please ensure they have the same size.

您的问题是由于pred和yb之间的尺寸差异造成的。

此代码显示如何解决它

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)
train_ds = TensorDataset(inputs, targets.squeeze())
batch_size = 100
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()
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
    yb = yb.view(yb.size(0), -1)
    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()))

本讨论向您详细展示 https://discuss.pytorch.org/t/target-size-torch-size-10-must-be-the-same-as-input-size-torch-size-2/ 72354/6

Answer 2

我之前的评论无效，我删除了它。 您的示例代码按预期工作。 您想从独立随机变量中预测随机变量。 没有模式，这就是它不收敛的原因。