python绘图记录(模型训练)

除了用tensorboard，也可如此如此

history = model.fit(generate_data(npy_path + '/train'), 
                    steps_per_epoch=len(os.listdir(npy_path + '/train')) // 3, 
                    epochs=epochs,
                    validation_data=generate_valid_data(npy_path + '/test'), 
                    validation_steps=len(os.listdir(npy_path + '/test'))//3)

history记录该过程，可在模型训练完后进行绘制：

import matplotlib.pyplot as plt
from matplotlib.pyplot import MultipleLocator

epochs = 20
#从pyplot导入MultipleLocator类，这个类用于设置刻度间隔

#显示训练过程
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

# result = [acc,val_acc,loss,val_loss]

# np.save(save_path + 'acc.npy', acc)
# np.save(save_path + 'val_acc.npy', val_acc)
# np.save(save_path + 'loss.npy', loss)
# np.save(save_path + 'val_loss.npy', val_loss)
 
# print(history.history['acc'][-1])

plt.figure(1)
plt.title('training curve',fontsize=20)
#设置图表标题和标题字号
plt.tick_params(axis='both',which='major',labelsize=14)
x = list(range(1, epochs + 1))
plt.xlabel('epoch',fontsize=14)
plt.ylabel('acc',fontsize=14)
plt.plot(x, acc, label='Training acc')
plt.plot(x, val_acc, label='Validation acc')
x_major_locator=MultipleLocator(1)
ax=plt.gca()
#ax为两条坐标轴的实例
ax.xaxis.set_major_locator(x_major_locator)

plt.legend(loc = 'best', prop={'family':'simsun', 'size': 12})
plt.savefig(save_path + 'acc.png')
 
plt.figure(2)
 
x = list(range(1, epochs + 1))
plt.xlabel('epoch',fontsize=14)
plt.ylabel('loss',fontsize=14)
plt.plot(x, loss, label='Training loss')
plt.plot(x, val_loss, label='Validation loss')
x_major_locator=MultipleLocator(1)
ax=plt.gca()
#ax为两条坐标轴的实例
ax.xaxis.set_major_locator(x_major_locator)

plt.legend(loc = 'best', prop={'family':'simsun', 'size': 12})
plt.savefig(save_path + 'acc.png')
 
plt.show()