# PyTorch tutorial codes for course EL-7143 Advanced Machine Learning, NYU, Spring 2018 # train.py: trainig convolutional or linear neural networks for MNIST classification import time, datetime from Pipeline.option import args from Pipeline.run import train start_time = datetime.datetime.now().replace(microsecond=0) print('\n---Started training at---', (start_time)) for epoch in range(1, args.epochs + 1): train(epoch) current_time = datetime.datetime.now().replace(microsecond=0) print('Time Interval:', current_time - start_time, '\n')
import time, datetime import numpy as np from Pipeline.option import args from Pipeline.run import train, test start_time = datetime.datetime.now().replace(microsecond=0) print('\n---Started training at---', (start_time)) train_acc = np.zeros([args.epochs,2]) test_acc = np.zeros([args.epochs,2]) for epoch in range(1, args.epochs + 1): tr_corr , tr_per = train(epoch) ts_corr , ts_per = test() train_acc[epoch-1,0] = tr_corr train_acc[epoch-1,1] = tr_per test_acc[epoch-1,0] = ts_corr test_acc[epoch-1,1] = ts_per current_time = datetime.datetime.now().replace(microsecond=0) print('Time Interval:', current_time - start_time, '\n') if args.aug == 0: np.save('train_acc_'+args.model+'_bs'+str(args.batch_size)+'.npy',train_acc) np.save('test_acc_'+args.model+'_bs'+str(args.batch_size)+'.npy',test_acc) else: np.save('train_acc_'+args.model+'_bs'+str(args.batch_size)+'_aug'+'.npy',train_acc) np.save('test_acc_'+args.model+'_bs'+str(args.batch_size)+'_aug'+'.npy',test_acc)