import matplotlib.pyplot as plt
import numpy as np
def plot_loss(n):
loss = []
loss1 = []
acc = []
sum_loss = 0.0
sum_loss1 = 0.0
sum_acc = 0.0
plt.figure(figsize=(8, 7))
for i in range(0,n):
enc = np.load('./loss/loss_epoch_{}.npy'.format(i)) #文件返回数组
tempy_loss = enc.tolist()
tempy_loss = float(tempy_loss)
sum_loss = sum_loss + tempy_loss/100
loss.append(tempy_loss)
enc1 = np.load('./loss1/loss_epoch_{}.npy'.format(i)) # 文件返回数组
tempy_loss1 = enc1.tolist()
tempy_loss1 = float(tempy_loss1)
sum_loss1 = sum_loss1 + tempy_loss1
loss1.append(tempy_loss1)
enc2 = np.load('./acc/acc_epoch_{}.npy'.format(i)) # 文件返回数组
tempy_acc = enc2.tolist()
tempy_acc = float(tempy_acc)
sum_acc = sum_acc + tempy_acc * 100
acc.append(tempy_acc)
x1 = len(loss)
x2 = len(loss1)
x3 = len(acc)
average_train_loss = sum_loss / x1
average_test_loss = sum_loss1 / x2
average_acc = sum_acc / x3
print_res = "average train loss: {: .3f}; average test loss: {: .3f}; map:{}".format(average_train_loss,average_test_loss,average_acc)
plt.title(print_res)
print(print_res)
if __name__ == "__main__":
plot_loss(299)#epoch个数=300
代码记录-根据.npy文件计算平均acc\平均trainloss\平均testloss
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转载自blog.csdn.net/LZL2020LZL/article/details/127283712
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