【深度学习实战（2）】如何使用matplotlib.pyplot模块记录自己的训练，验证损失

本文主要是介绍【深度学习实战（2）】如何使用matplotlib.pyplot模块记录自己的训练，验证损失，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

一、matplotlib库

在我们自己训练模型时，常常会使用matplotlib库来绘制oss和accuracy的曲线图，帮助我们分析模型的训练表现。
matplotlib库安装：pip install matplotlib

二、代码

import matplotlib.pyplot as plt
import torch
import torch.optim as optim  # 导入优化器模块#------------------------------------------------------------------#
#   定义损失函数
#------------------------------------------------------------------#
def loss_fn(y_true, y_pred):return torch.mean((y_true - y_pred)**2)#------------------------------------------------------------------#
#   定义模型
#------------------------------------------------------------------#
model = torch.nn.Linear(10, 1)#------------------------------------------------------------------#
#   定义训练，验证数据
#------------------------------------------------------------------#
x_train = torch.randn(1000, 10)
y_train = torch.randn(1000, 1)
x_val = torch.randn(1000, 10)
y_val = torch.randn(1000, 1)#------------------------------------------------------------------#
#   定义优化器
#------------------------------------------------------------------#
optimizer = optim.Adam(model.parameters(), lr=0.001)  # 使用 Adam 优化器，学习率为 0.001#------------------------------------------------------------------#
#   定义损失函数
#------------------------------------------------------------------#
train_loss_list = []
val_loss_list = []#------------------------------------------------------------------#
#   开始训练
#------------------------------------------------------------------#
for epoch in range(10000):# ------------------------------------------------------------------##   训练# ------------------------------------------------------------------## ------------------------------------------------------------------##   前向传播# ------------------------------------------------------------------#y_pred = model(x_train)# ------------------------------------------------------------------##   计算损失# ------------------------------------------------------------------#training_loss = loss_fn(y_train, y_pred)train_loss_list.append(training_loss.item())# ------------------------------------------------------------------##   反向传播# ------------------------------------------------------------------#training_loss.backward()# ------------------------------------------------------------------##   更新参数# ------------------------------------------------------------------#optimizer.step()# ------------------------------------------------------------------##   展示训练损失# ------------------------------------------------------------------#if epoch % 10 == 0:print(f"epoch {epoch}:training loss {training_loss.item()}")# ------------------------------------------------------------------##   验证# ------------------------------------------------------------------## ------------------------------------------------------------------##   前向传播# ------------------------------------------------------------------#y_pred = model(x_val)# ------------------------------------------------------------------##   计算损失# ------------------------------------------------------------------#val_loss = loss_fn(y_val, y_pred)val_loss_list.append(val_loss.item())# ------------------------------------------------------------------##   展示验证损失# ------------------------------------------------------------------#if epoch % 10 == 0:print(f"epoch {epoch}:validate loss {val_loss.item()}")# ------------------------------------------------------------------#
#   记录训练，验证损失
# ------------------------------------------------------------------#
plt.plot(train_loss_list,color="red",label="training_loss")
plt.plot(val_loss_list,color="blue",label="val_loss")
plt.xlabel("epoch")
plt.ylabel("loss")
plt.legend(loc='lower right')
plt.show()