train_loss = sum_loss/N
train_data.append(float(train_loss))
if epoch % show_interval == 0:
print '\tepoch :'+str(epoch)
print '\ttrain mean loss='+str(train_loss)+" next save :"+str(min_loss)
print '\ttime sum : {}[sec] ave : {}[sec]'.format(sum_time,sum_time/time_count)
#%%
key = cv2.waitKey(1)
if key in [myl.KEY_ESC, ord('q')]:
break
if min_loss > train_loss and not train_loss == 0 :
network.saveModel('model/_model_pred'+str(epoch))
min_loss = train_loss*save_rate
if epoch > n_epoch:
print '\n!test clear! : '+str(train_loss)
break
#%%
print '\n---Save model\n'
network.saveModel('model/_model_pred'+str(epoch))
print '\n---Save graph\n'
g_size = (8,6)
g_legend = ["train_loss"]
g_title = "Loss of digit recognition. lasl train loss :"+str(train_loss)
g_file_name = 'graph_train_loss'+str(epoch)+'.png'
myl.showGraphData(train_data,g_size,g_legend,g_title,g_file_name)
cv2.destroyAllWindows()
