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)+" ") fobj.write("epoch: "+ str(epochs) +" ") fobj.write("x_train shape: " + str(X_train.shape) + " ") fobj.write("x_test shape: " + str(X_test.shape)+" ") fobj.write("training accuracy: " + str(history.history["acc"][-1]) + " ") fobj.write("model evaluation results: " + str(score[0]) + " " +str(score[-1])+" ") fobj.write("--------------------------------------------------------------------------- ") fobj.write(" ") 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()
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