ケミカルの深さモデルの訓練過程と結果の記録方法について話します。

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()#     
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()