浅谈keras的深度模型训练过程及结果记录方式

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

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

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")

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")

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()

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