PyTorch无法将直线拟合到两个数据点 [英] PyTorch will not fit straight line to two data points

问题描述

在使用pytorch拟合带有2个数据点的简单y = 4x1线时，我遇到了问题.在运行推理代码时，模型似乎向任何奇怪的输入输出相同的值.请找到随附的代码以及我使用的数据文件.感谢您的帮助.

I'm facing issues in fitting a simple y= 4x1 line with 2 data points using pytorch. While running the inference code, the model seems to output same value to any input which is strange. Pls find the code attached along with the data files used by me. Appreciate any help here.

import torch
import numpy as np
import pandas as pd

df = pd.read_csv('data.csv')
test_data = pd.read_csv('test_data.csv')

inputs = df[['x1']]
target = df['y']
inputs = torch.tensor(inputs.values).float()
target = torch.tensor(target.values).float()

test_data = torch.tensor(test_data.values).float()
#Defining Network Architecture
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):

  def __init__(self):
    super(Net,self).__init__()

    hidden1 = 3
#     hidden2 = 5 

    self.fc1 = nn.Linear(1,hidden1)
    self.fc3 = nn.Linear(hidden1,1)


  def forward(self,x):
    x = F.relu(self.fc1(x))
    x = self.fc3(x)
    return x

#instantiate the model

model = Net()
print(model)

criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)

model.train()

#epochs
epochs = 100


for x in range(epochs):
  #initialize the training loss to 0
  train_loss = 0
  #clear out gradients
  optimizer.zero_grad() 

  #calculate the output
  output = model(inputs)

  #calculate loss
  loss = criterion(output,target)

  #backpropagate
  loss.backward() 

  #update parameters
  optimizer.step()

  if ((x%5)==0):
    print('Training Loss after epoch {:2d} is {:2.6f}'.format(x,loss))

#set the model in evaluation mode
model.eval()

#Test the model on unseen data

test_output = model(test_data)

print(test_output)

下面是模型输出

#model output
tensor([[56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579],
        [56.7579]], grad_fn=<AddmmBackward>)

推荐答案

您的模型正在崩溃.您可能会在prints的基础上看到它.您可能希望使用较低的学习率(1e-5、1e-6等).如果您没有经验并且希望微调这些hparams的麻烦，那么从SGD(...)切换到Adam(...)可能会更容易.另外，也许100个时代还不够.由于您没有共享 MCVE ，因此我无法确定是什么.这是 MCVE 的线拟合，使用的是您使用的相同的Net:

Your model is collapsing. You can probably see that based on the prints. You may want to use a lower learning rate (1e-5, 1e-6, etc.). Switching from SGD(...)to Adam(...) may be easier if you do not have experience and want less trouble fine-tuning these hparams. Also, maybe 100 epochs is not enough. As you did not share an MCVE, I cannot tell you for sure what it is. Here is an MCVE of linefitting using the same Net you used:

import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F

epochs = 1000
max_range = 40
interval = 4

# DATA
x_train = torch.arange(0, max_range, interval).view(-1, 1).float()
x_train += torch.rand(x_train.size(0), 1) - 0.5  # small noise
y_train = (4 * x_train) 
y_train += torch.rand(x_train.size(0), 1) - 0.5  # small noise

x_test  = torch.arange(interval // 2, max_range, interval).view(-1, 1).float()
y_test  = 4 * x_test

class Net(nn.Module):
  def __init__(self):
    super(Net, self).__init__()
    hidden1 = 3
    self.fc1 = nn.Linear(1, hidden1)
    self.fc3 = nn.Linear(hidden1, 1)

  def forward(self, x):
    x = F.relu(self.fc1(x))
    x = self.fc3(x)
    return x

model = Net()
print(model)

criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-5)

# TRAIN
model.train()
for epoch in range(epochs):
  optimizer.zero_grad()
  y_pred = model(x_train)
  loss = criterion(y_pred, y_train)
  loss.backward()
  optimizer.step()

  if epoch % 10 == 0:
    print('Training Loss after epoch {:2d} is {:2.6f}'.format(epoch, loss))

# TEST
model.eval()
y_pred = model(x_test)
print(torch.cat((x_test, y_pred, y_test), dim=-1))

这是数据的样子:

这就是训练的样子:

Training Loss after epoch  0 is 7416.805664
Training Loss after epoch 10 is 6645.655273
Training Loss after epoch 20 is 5792.936523
Training Loss after epoch 30 is 4700.106445
Training Loss after epoch 40 is 3245.384277
Training Loss after epoch 50 is 1779.370728
Training Loss after epoch 60 is 747.418579
Training Loss after epoch 70 is 246.781311
Training Loss after epoch 80 is 68.635155
Training Loss after epoch 90 is 17.332235
Training Loss after epoch 100 is 4.280161
Training Loss after epoch 110 is 1.170808
Training Loss after epoch 120 is 0.453974
...
Training Loss after epoch 970 is 0.232296
Training Loss after epoch 980 is 0.232090
Training Loss after epoch 990 is 0.231888

这是输出的样子:

|  x_test |  y_pred  |  y_test  |
|:-------:|:--------:|:--------:|
|  2.0000 |   8.6135 |   8.0000 |
|  6.0000 |  24.5276 |  24.0000 |
| 10.0000 |  40.4418 |  40.0000 |
| 14.0000 |  56.3303 |  56.0000 |
| 18.0000 |  72.1884 |  72.0000 |
| 22.0000 |  88.0465 |  88.0000 |
| 26.0000 | 103.9047 | 104.0000 |
| 30.0000 | 119.7628 | 120.0000 |
| 34.0000 | 135.6210 | 136.0000 |
| 38.0000 | 151.4791 | 152.0000 |

这篇关于PyTorch无法将直线拟合到两个数据点的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！