pytorch自定义loss损失函数

目录

• 步骤1：添加自定义的类
• 步骤2：修改使用的loss函数

自定义loss的方法有很多，但是在博主查资料的时候发现有挺多写法会有问题，靠谱一点的方法是把loss作为一个pytorch的模块，

比如：

class CustomLoss(nn.Module): # 注意继承 nn.Module
  def __init__(self):
    super(CustomLoss, self).__init__()

  def forward(self, x, y):
    # .....这里写x与y的处理逻辑，即loss的计算方法
    return loss # 注意最后只能返回Tensor值，且带梯度，即 loss.requires_grad == True

示例代码：

以一个pytorch求解线性回归的代码为例：

import torch
import torch.nn as nn
import numpy as np
import os

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


def get_x_y():
  np.random.seed(0)
  x = np.random.randint(0, 50, 300)
  y_values = 2 * x + 21
  x = np.array(x, dtype=np.float32)
  y = np.array(y_values, dtype=np.float32)
  x = x.reshape(-1, 1)
  y = y.reshape(-1, 1)
  return x, y


class LinearRegressionModel(nn.Module):
  def __init__(self, input_dim, output_dim):
    super(LinearRegressionModel, self).__init__()
    self.linear = nn.Linear(input_dim, output_dim) # 输入的个数，输出的个数

  def forward(self, x):
    out = self.linear(x)
    return out


if __name__ == '__main__':
  input_dim = 1
  output_dim = 1
  x_train, y_train = get_x_y()

  model = LinearRegressionModel(input_dim, output_dim)
  epochs = 1000 # 迭代次数
  optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
  model_loss = nn.MSELoss() # 使用MSE作为loss
  # 开始训练模型
  for epoch in range(epochs):
    epoch += 1
    # 注意转行成tensor
    inputs = torch.from_numpy(x_train)
    labels = torch.from_numpy(y_train)
    # 梯度要清零每一次迭代
    optimizer.zero_grad()
    # 前向传播
    outputs: torch.Tensor = model(inputs)
    # 计算损失
    loss = model_loss(outputs, labels)
    # 返向传播
    loss.backward()
    # 更新权重参数
    optimizer.step()
    if epoch % 50 == 0:
      print('epoch {}, loss {}'.format(epoch, loss.item()))

步骤1：添加自定义的类

我们就用自定义的写法来写与MSE相同的效果，MSE计算公式如下：

添加一个类：

class CustomLoss(nn.Module):
  def __init__(self):
    super(CustomLoss, self).__init__()
    self.mse_loss = nn.MSELoss()

  def forward(self, x, y):
    mse_loss = torch.mean(torch.pow((x - y), 2)) # x与y相减后平方，求均值即为MSE
    return http://www.cppcns.commse_loss

步骤2：修改使用的loss函数

只需要把原始代码中的：

model_loss = nn.MSELoss() # 使用MSE作为loss

改为：

model_loss = CustomLoss() # 自定义loss

即可

完整代码：

import torch
import torch.nn as nn
import numpy as np
import os

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


def get_x_y():
  np.random.seed(0)
  x = np.random.randint(0, 50, 300)
  y_values = 2 * x + 21
  x = np.array(x, dtype=np.float32)
  y = np.array(y_values, dtype=np.float32)
  x = x.reshape(-1, 1)
  y = y.reshape(-1, 1)
  return x, y


class LinearRegressionModel(nn.Module):
  def __init__(self, input_dim, output_dim):
    super(LinearRegressionModel, self).__init__()
 www.cppcns.com   self.linear = nn.Linear(input_dim, output_dim) # 输入的个数，输出的个数

  def forward(self, x):
    out = self.linear(x)
    return out


class CustomLoss(nn.Module):
  def __init__(self):
    super(CustomLoss, self).__init__()
    self.mse_loss = nn.MSELoss()

  def forward(sehttp://www.cppcns.comlf, x, y):
    mse_loss = torch.mean(torch.pow((x - y), 2))
    return mse_loss


if __name__ == '__main__':
  input_dim = 1
  output_dim = 1
  x_train, y_train = get_x_y()

  model = LinearRegressionModel(input_dim, output_dim)
  epochs = 1000 # 迭代次数
  optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
  # model_loss = nn.MSELoss() # 使用MSE作qDIrlPbs为loss
  model_loss = CustomLoss() # 自定义loss
  # 开始训练模型
  for epoch in range(epochs):
    epoch += 1
    # 注意转行成tensor
    inputs = torch.from_numpy(x_train)
    labels = torch.from_numpy(y_train)
    # 梯度要清零每一次迭代
    optimizer.zero_grad()
    # 前向传播
    outputs: torch.Tensor = m编程客栈odel(inputs)
    # 计算损失
    loss = model_loss(outputs, labels)
    # 返向传播
    loss.backward()
    # 更新权重参数
    optimizer.step()
    if epoch % 50 == 0:
      print('epoch {}, loss {}'.format(epoch, loss.item()))

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