main.py

from __future__ import print_function
import os
import argparse
import pickle
import torch
from torchvision import datasets, transforms

from model import SoftDecisionTree

# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                    help='input batch size for training (default: 64)')
parser.add_argument('--input-dim', type=int, default=28*28, metavar='N',
                    help='input dimension size(default: 28 * 28)')
parser.add_argument('--output-dim', type=int, default=10, metavar='N',
                    help='output dimension size(default: 10)')
parser.add_argument('--max-depth', type=int, default=8, metavar='N',
                    help='maximum depth of tree(default: 8)')
parser.add_argument('--epochs', type=int, default=40, metavar='N',
                    help='number of epochs to train (default: 40)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                    help='learning rate (default: 0.01)')
parser.add_argument('--lmbda', type=float, default=0.1, metavar='LR',
                    help='temperature rate (default: 0.1)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                    help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
                    help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
                    help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='how many batches to wait before logging training status')

args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

torch.manual_seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)

try:
    os.makedirs('./data')
except:
    print('directory ./data already exists')

kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('./data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('./data', train=False, transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])),
    batch_size=args.batch_size, shuffle=True, **kwargs)

def save_result(acc):
    try:
        os.makedirs('./result')
    except:
        print('directory ./result already exists')
    filename = os.path.join('./result/', 'bp.pickle')
    f = open(filename,'wb')
    pickle.dump(acc, f)
    f.close()

model = SoftDecisionTree(args)

if args.cuda:
    model.cuda()

for epoch in range(1, args.epochs + 1):
    model.train_(train_loader, epoch)
    model.test_(test_loader, epoch)

save_result(model)