最近设计了一个深度神经网络，虽然使用 logging 模块记录 Train loss 和 Valid loss 也算方便，但作图观察 loss 的变动趋势还是要更加直观的，考虑到网上貌似没有这方面的方法，这里放一下自己的记录方式。
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| import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import time
class loss_tracer():
def __init__(self, outpath):
self.train_loss_list = []
self.test_loss_list = []
self.outpath = outpath
timestamp = time.time()
self.starttime = time.strftime('%Y_%m_%d_%H_%M', time.localtime(timestamp))
def __call__(self, trainloss, testloss, plot=False, length=6, width=3):
self.train_loss_list.append(trainloss)
self.test_loss_list.append(testloss)
if plot:
self.plot(length, width)
def plot(self, length=6, width=3):
plt.figure(figsize=(length,width))
epoch_list = range(1, len(self.train_loss_list) + 1)
plt.plot(epoch_list, self.train_loss_list, color='blue', label='Train loss')
plt.plot(epoch_list, self.test_loss_list, color='red', label='Valid loss')
plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))
plt.legend()
plt.tight_layout()
plt.savefig(f'{self.outpath}/{self.starttime}_train_loss_valid_loss.png')
plt.close()
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该方法要求已经安装 matplotlib 库，以下是一些具体的操作方式：
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lt = loss_tracer('./')
lt(train_loss, valid_loss)
lt(train_loss, valid_loss, plot=True)
lt.plot()
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需要注意的一些地方：
- 频繁的进行绘制会导致性能开销过大，如果一个 epoch 用时并不长不建议设置
plot=True 。反之，如果一个 epoch 需要的时间较长，那么在每个 epoch 后储存新的 loss 图也是很好的选择。
- 该图的名称为
年月日时分_train_loss_valid_loss.png，如果有需求可自行修改。
- 该图默认长度为
6 宽度为 3，有需求可在参数中自行修改。
简单的实战示例：
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| import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import numpy as np
import torch
import torch.nn as nn
import time
class loss_tracer():
def __init__(self, outpath):
self.train_loss_list = []
self.test_loss_list = []
self.outpath = outpath
timestamp = time.time()
self.starttime = time.strftime('%Y_%m_%d_%H_%M', time.localtime(timestamp))
def __call__(self, trainloss, testloss, plot=False, length=6, width=3):
self.train_loss_list.append(trainloss)
self.test_loss_list.append(testloss)
if plot:
self.plot(length, width)
def plot(self, length=6, width=3):
plt.figure(figsize=(length,width))
epoch_list = range(1, len(self.train_loss_list) + 1)
plt.plot(epoch_list, self.train_loss_list, color='blue', label='Train loss')
plt.plot(epoch_list, self.test_loss_list, color='red', label='Valid loss')
plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))
plt.legend()
plt.tight_layout()
plt.savefig(f'{self.outpath}/{self.starttime}_train_loss_valid_loss.png')
plt.close()
np.random.seed(114514)
a_train = np.random.randn(400)
b_train = np.random.randn(400)
c_train = a_train*0.7 + b_train*0.6 + 0.5 + np.random.rand(400)
a_valid = np.random.randn(100)
b_valid = np.random.randn(100)
c_valid = a_valid*0.7 + b_valid*0.6 + 0.5 + np.random.rand(100)
a_train = torch.from_numpy(a_train)
b_train = torch.from_numpy(b_train)
c_train = torch.from_numpy(c_train)
a_valid = torch.from_numpy(a_valid)
b_valid = torch.from_numpy(b_valid)
c_valid = torch.from_numpy(c_valid)
train_data = torch.stack((a_train, b_train), dim=1)
valid_data = torch.stack((a_valid, b_valid), dim=1)
net = nn.Sequential(nn.Linear(2, 1))
opt = torch.optim.SGD(net.parameters(), lr=0.1)
lf = nn.MSELoss()
lt = loss_tracer('./')
for epoch in range(100):
opt.zero_grad()
train_outputs = net(train_data.float())
train_loss = lf(train_outputs, c_train.float().unsqueeze(1))
train_loss.backward()
opt.step()
with torch.no_grad():
valid_outputs = net(valid_data.float())
valid_loss = lf(valid_outputs, c_valid.float().unsqueeze(1))
lt(train_loss.item(), valid_loss.item())
lt.plot()
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最后生成的图片实例：
事实证明，只要数据的模式足够简单且密集，想要过拟合其实也挺困难的。
最后提供一种上述函数的变体，可以实现每 x 个 epoch 绘图 1 次的功能：
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| import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import time
class loss_tracer():
def __init__(self, outpath, plotepoch=5):
self.train_loss_list = []
self.test_loss_list = []
self.plotepoch = plotepoch
self.outpath = outpath
timestamp = time.time()
self.starttime = time.strftime('%Y_%m_%d_%H_%M', time.localtime(timestamp))
def __call__(self, trainloss, testloss, plot=True, length=6, width=3):
self.train_loss_list.append(trainloss)
self.test_loss_list.append(testloss)
losslist_len = len(self.train_loss_list)
if losslist_len % self.plotepoch == 0 and plot:
self.plot(length, width)
def plot(self, length=6, width=3):
plt.figure(figsize=(length,width))
epoch_list = range(1, len(self.train_loss_list) + 1)
plt.plot(epoch_list, self.train_loss_list, color='blue', label='Train loss')
plt.plot(epoch_list, self.test_loss_list, color='red', label='Valid loss')
plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))
plt.legend()
plt.tight_layout()
plt.savefig(f'{self.outpath}/{self.starttime}_train_loss_valid_loss.png')
plt.close()
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操作方法和之前一致，如果在新添 loss 的时候不需要绘图，则在调取实例时指定 plot=False 即可。