【深度学习实战（30）】训练框架之使用tensorboard记录loss

本文主要是介绍【深度学习实战（30）】训练框架之使用tensorboard记录loss，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、安装Tensorboard库

pip install tensorflow 
pip install tensorboardx

二、LossHistory类实现过程

1. init构造函数
传入参数log保存路径，模型，模型输入尺寸

def __init__(self, log_dir, model, input_shape):

实例化SummaryWriter对象

self.writer     = SummaryWriter(self.log_dir)

tensorboard.SummaryWriter.add_graph记录model

 try:dummy_input     = torch.randn(2, 3, input_shape[0], input_shape[1])self.writer.add_graph(model, dummy_input)except:pass

训练结束后查看保存的模型
在这里插入图片描述

记录loss

self.losses.append(loss)
self.val_loss.append(val_loss)

txt文档记录loss

with open(os.path.join(self.log_dir, "epoch_loss.txt"), 'a') as f:f.write(str(loss))f.write("\n")
with open(os.path.join(self.log_dir, "epoch_val_loss.txt"), 'a') as f:f.write(str(val_loss))f.write("\n")

训练完后，查看epoch_loss.txt和epoch_val_loss.txt
在这里插入图片描述

tensorboard.SummaryWriter.add_scalar记录loss

self.writer.add_scalar('loss', loss, epoch)
self.writer.add_scalar('val_loss', val_loss, epoch)

训练结束后查看保存的loss
在这里插入图片描述

pyplot绘制loss曲线图

def loss_plot(self):iters = range(len(self.losses))plt.figure()plt.plot(iters, self.losses, 'red', linewidth = 2, label='train loss')plt.plot(iters, self.val_loss, 'coral', linewidth = 2, label='val loss')try:if len(self.losses) < 25:num = 5else:num = 15plt.plot(iters, scipy.signal.savgol_filter(self.losses, num, 3), 'green', linestyle = '--', linewidth = 2, label='smooth train loss')plt.plot(iters, scipy.signal.savgol_filter(self.val_loss, num, 3), '#8B4513', linestyle = '--', linewidth = 2, label='smooth val loss')except:passplt.grid(True)plt.xlabel('Epoch')plt.ylabel('Loss')plt.legend(loc="upper right")plt.savefig(os.path.join(self.log_dir, "epoch_loss.png"))plt.cla()plt.close("all")

查看loss曲线图
在这里插入图片描述

三、LossHistory类完整代码

import os
import torch
from torch.utils.tensorboard import SummaryWriter
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import scipy.signalclass LossHistory():def __init__(self, log_dir, model, input_shape):self.log_dir    = log_dirself.losses     = []self.val_loss   = []os.makedirs(self.log_dir)self.writer     = SummaryWriter(self.log_dir)try:# --------- 1. tensorboard.SummaryWriter.add_graph记录model -------------#dummy_input     = torch.randn(2, 3, input_shape[0], input_shape[1],use_strict_trace=False)self.writer.add_graph(model, dummy_input)except:passdef append_loss(self, epoch, loss, val_loss):if not os.path.exists(self.log_dir):os.makedirs(self.log_dir)# --------- 2. 保存loss -------------#self.losses.append(loss)self.val_loss.append(val_loss)# --------- 3. txt记录loss -------------#with open(os.path.join(self.log_dir, "epoch_loss.txt"), 'a') as f:f.write(str(loss))f.write("\n")with open(os.path.join(self.log_dir, "epoch_val_loss.txt"), 'a') as f:f.write(str(val_loss))f.write("\n")# --------- 4. tensorboard.SummaryWriter.add_scalar记录loss -------------#self.writer.add_scalar('loss', loss, epoch)self.writer.add_scalar('val_loss', val_loss, epoch)self.loss_plot()# --------- 5. pyplot绘制loss曲线图 -------------#def loss_plot(self):iters = range(len(self.losses))plt.figure()plt.plot(iters, self.losses, 'red', linewidth = 2, label='train loss')plt.plot(iters, self.val_loss, 'coral', linewidth = 2, label='val loss')try:if len(self.losses) < 25:num = 5else:num = 15plt.plot(iters, scipy.signal.savgol_filter(self.losses, num, 3), 'green', linestyle = '--', linewidth = 2, label='smooth train loss')plt.plot(iters, scipy.signal.savgol_filter(self.val_loss, num, 3), '#8B4513', linestyle = '--', linewidth = 2, label='smooth val loss')except:passplt.grid(True)plt.xlabel('Epoch')plt.ylabel('Loss')plt.legend(loc="upper right")plt.savefig(os.path.join(self.log_dir, "epoch_loss.png"))plt.cla()plt.close("all")

四、LossHistory类使用框架

import LossHistory# 构造loss_history类
loss_history = LossHistory(log_dir, model, (input_W, input_H))# 训练一轮，将训练，验证损失传进loss_history类
loss_history.append_loss(epoch + 1, train_loss / epoch_step, val_loss / epoch_step_val)# 根据loss_history类中保存的loss来保存最佳模型
if len(loss_history_.val_loss) <= 1 or (val_loss / epoch_step_val) <= min(loss_history_.val_loss):best_ckpt = {'epoch': epoch, 'model': save_state_dict, 'optimizer': optimizer.state_dict(), 'loss':val_loss}torch.save(best_ckpt, os.path.join(save_dir, name_best_weights))# 训练一轮结束后，关闭Tensorboard.SummryWriter
loss_history.writer.close()