Part 11 - プライバシーに配慮したディープラーニングで分類問題を解く

In [ ]:

    

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms


    

In [ ]:

    

import syft as sy
hook = sy.TorchHook(torch) 
client = sy.VirtualWorker(hook, id="client")
bob = sy.VirtualWorker(hook, id="bob")
alice = sy.VirtualWorker(hook, id="alice")
crypto_provider = sy.VirtualWorker(hook, id="crypto_provider")


    

In [ ]:

    

class Arguments():
    def __init__(self):
        self.batch_size = 64
        self.test_batch_size = 50
        self.epochs = epochs
        self.lr = 0.001
        self.log_interval = 100

args = Arguments()


    

In [ ]:

    

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)


    

In [ ]:

    

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.test_batch_size, shuffle=True)

private_test_loader = []
for data, target in test_loader:
    private_test_loader.append((
        data.fix_precision().share(alice, bob, crypto_provider=crypto_provider),
        target.fix_precision().share(alice, bob, crypto_provider=crypto_provider)
    ))


    

In [ ]:

    

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        return x


    

In [ ]:

    

def train(args, model, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        output = F.log_softmax(output, dim=1)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * args.batch_size, len(train_loader) * args.batch_size,
                100. * batch_idx / len(train_loader), loss.item()))


    

In [ ]:

    

model = Net()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)

for epoch in range(1, args.epochs + 1):
    train(args, model, train_loader, optimizer, epoch)


    

In [ ]:

    

def test(args, model, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            output = F.log_softmax(output, dim=1)
            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
            pred = output.argmax(1, keepdim=True) # get the index of the max log-probability 
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


    

In [ ]:

    

test(args, model, test_loader)


    

In [ ]:

    

model.fix_precision().share(alice, bob, crypto_provider=crypto_provider)


    

In [ ]:

    

def test(args, model, test_loader):
    model.eval()
    n_correct_priv = 0
    n_total = 0
    with torch.no_grad():
        for data, target in test_loader[:n_test_batches]:
            output = model(data)
            pred = output.argmax(dim=1) 
            n_correct_priv += pred.eq(target.view_as(pred)).sum()
            n_total += args.test_batch_size
            # このテスト関数は暗号化されたデータでの評価を行えます。モデルのウェイト（パラメータ）、入力データ、推論結果、そ
            # して正解ラベルと全てが暗号されています。
            
            # しかしながら、みなさんお気づきの通り、ごくごく一般的なPyTorchのテストスクリプトとほとんど同じです。
            
            # 唯一複合化しているのは、200アイテムのバッチ事に計算している精度確認のためのすこだけです。
            # この数字を見ることで学習されたモデルの性能が良いのか悪いのか評価できます。
            
            n_correct = n_correct_priv.copy().get().float_precision().long().item()
    
            print('Test set: Accuracy: {}/{} ({:.0f}%)'.format(
                n_correct, n_total,
                100. * n_correct / n_total))


    

In [ ]:

    

test(args, model, private_test_loader)


    

In [ ]:

    




    

In [ ]: