浅谈keras的深度模型训练过程及结果记录方式

记录训练过程

history=model.fit(X_train, Y_train, epochs=epochs,batch_size=batch_size,validation_split=0.1)

将训练过程记录在history中

利用时间记录模型

import time
model_id = np.int64(time.strftime('％Y％m％d％H％M', time.localtime(time.time())))
model.save('./VGG16'+str(model_id)+'.h5')

保存模型及结构图

from keras.utils import plot_model
model.save('/opt/Data1/lixiang/letter_recognition/models/VGG16'+str(model_id)+'.h5')
plot_model(model, to_file='/opt/Data1/lixiang/letter_recognition/models/VGG16'+str(model_id)+'.png')

绘制训练过程曲线

import matplotlib.pyplot as plt
fig = plt.figure()#新建一张图
plt.plot(history.history['acc'],label='training acc')
plt.plot(history.history['val_acc'],label='val acc')
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(loc='lower right')
fig.savefig('VGG16'+str(model_id)+'acc.png')
fig = plt.figure()
plt.plot(history.history['loss'],label='training loss')
plt.plot(history.history['val_loss'], label='val loss')
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(loc='upper right')
fig.savefig('VGG16'+str(model_id)+'loss.png')

文件记录最终训练结果

logFilePath = './log.txt'
fobj = open(logFilePath, 'a')
fobj.write('model id: ' + str(model_id)+'\n')
fobj.write('epoch: '+ str(epochs) +'\n')
fobj.write('x_train shape: ' + str(X_train.shape) + '\n')
fobj.write('x_test shape: ' + str(X_test.shape)+'\n')
fobj.write('training accuracy: ' + str(history.history['acc'][-1]) + '\n')
fobj.write('model evaluation results: ' + str(score[0]) + ' ' +str(score[-1])+'\n')
fobj.write('---------------------------------------------------------------------------\n')
fobj.write('\n')
fobj.close()

以字典格式保存训练中间过程

import pickle
file = open('./models/history.pkl', 'wb')
pickle.dump(history.history, file)
file.close()

来源：https://blog.csdn.net/sjtuxx_lee/article/details/80502775