Python绘制loss曲线和准确率曲线实例代码

for epoch in range(resume_epoch, num_epochs):   # 就是这一行
	####
	...
	loss = criterion(outputs, labels.long())              # 损失样例
	...
    epoch_acc = running_corrects.double() / trainval_sizes[phase]    # 准确率样例
    ...
    ###

train_loss = []
train_acc = []
for epoch in range(resume_epoch, num_epochs):          # 就是这一行
	###
	...
	loss = criterion(outputs, labels.long())           # 损失样例
	train_loss.append(loss.item())                     # 损失加入到列表中
	...
	epoch_acc = running_corrects.double() / trainval_sizes[phase]    # 准确率样例
	train_acc.append(epoch_acc.item())                 # 准确率加入到列表中
	... 
with open("./train_loss.txt", 'w') as train_los:
    train_los.write(str(train_loss))

with open("./train_acc.txt", 'w') as train_ac:
     train_ac.write(str(train_acc))

import numpy as np

# 读取存储为txt文件的数据
def data_read(dir_path):
    with open(dir_path, "r") as f:
        raw_data = f.read()
        data = raw_data[1:-1].split(", ")   # [-1:1]是为了去除文件中的前后中括号"[]"

    return np.asfarray(data, float)

if __name__ == "__main__":

	train_loss_path = r"E:\relate_code\Gaitpart-master\train_loss.txt"   # 存储文件路径
	
	y_train_loss = data_read(train_loss_path)        # loss值，即y轴
	x_train_loss = range(len(y_train_loss))			 # loss的数量，即x轴

	plt.figure()

    # 去除顶部和右边框框
    ax = plt.axes()
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

    plt.xlabel('iters')    # x轴标签
    plt.ylabel('loss')     # y轴标签
	
	# 以x_train_loss为横坐标，y_train_loss为纵坐标，曲线宽度为1，实线，增加标签，训练损失，
	# 默认颜色，如果想更改颜色，可以增加参数color='red',这是红色。
    plt.plot(x_train_loss, y_train_loss, linewidth=1, linestyle="solid", label="train loss")
    plt.legend()
    plt.title('Loss curve')
    plt.show()

if __name__ == "__main__":

	train_acc_path = r"E:\relate_code\Gaitpart-master\train_acc.txt"   # 存储文件路径
	
	y_train_acc = data_read(train_acc_path)       # 训练准确率值，即y轴
	x_train_acc = range(len(y_train_acc))			 # 训练阶段准确率的数量，即x轴

	plt.figure()

    # 去除顶部和右边框框
    ax = plt.axes()
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

    plt.xlabel('epochs')    # x轴标签
    plt.ylabel('accuracy')     # y轴标签
	
	# 以x_train_acc为横坐标，y_train_acc为纵坐标，曲线宽度为1，实线，增加标签，训练损失，
	# 增加参数color='red',这是红色。
    plt.plot(x_train_acc, y_train_acc, color='red',linewidth=1, linestyle="solid", label="train acc")
    plt.legend()
    plt.title('Accuracy curve')
    plt.show()

import numpy as np
import matplotlib.pyplot as plt


# 读取存储为txt文件的数据
def data_read(dir_path):
    with open(dir_path, "r") as f:
        raw_data = f.read()
        data = raw_data[1:-1].split(", ")

    return np.asfarray(data, float)


# 不同长度数据，统一为一个标准，倍乘x轴
def multiple_equal(x, y):
    x_len = len(x)
    y_len = len(y)
    times = x_len/y_len
    y_times = [i * times for i in y]
    return y_times


if __name__ == "__main__":

    train_loss_path = r"E:\relate_code\Gaitpart-master\file_txt\train_loss.txt"
    train_acc_path = r"E:\relate_code\Gaitpart-master\train_acc.txt"

    y_train_loss = data_read(train_loss_path)
    y_train_acc = data_read(train_acc_path)

    x_train_loss = range(len(y_train_loss))
    x_train_acc = multiple_equal(x_train_loss, range(len(y_train_acc)))

    plt.figure()

    # 去除顶部和右边框框
    ax = plt.axes()
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

    plt.xlabel('iters')
    plt.ylabel('accuracy')

    # plt.plot(x_train_loss, y_train_loss, linewidth=1, linestyle="solid", label="train loss")
    plt.plot(x_train_acc, y_train_acc,  color='red', linestyle="solid", label="train accuracy")
    plt.legend()

    plt.title('Accuracy curve')
    plt.show()