from dataloader import SampleLoader
from torch.utils.data import Dataset, DataLoader
from nets import InferenceAttack

def train_loop():
    batch_size=4
    samples_dataset = SampleLoader(file_name='cache/true_samples.pt')
    dataloader = DataLoader(samples_dataset, batch_size=batch_size,
                            shuffle=True, num_workers=0)
    net=InferenceAttack(100)

    for batch in (dataloader):
        ids=batch['id']
        grad = batch['grad']
        loss = batch['loss']
        prediction = batch['prediction']
        true_label = batch['true_label']
        weights = batch['weights']

        outopus=net(grad.cuda(), loss.cuda(), prediction[0].cuda(),true_label.cuda())


        print (batch)

    print ('dof')
3dc5b208de347d8c5d24aa9a4d833bd991ace5f2
<mask token>

<mask token>


def train_loop():
    batch_size = 4
    samples_dataset = SampleLoader(file_name='cache/true_samples.pt')
    dataloader = DataLoader(samples_dataset, batch_size=batch_size, shuffle
        =True, num_workers=0)
    net = InferenceAttack(100)
    for batch in dataloader:
        ids = batch['id']
        grad = batch['grad']
        loss = batch['loss']
        prediction = batch['prediction']
        true_label = batch['true_label']
        weights = batch['weights']
        outopus = net(grad.cuda(), loss.cuda(), prediction[0].cuda(),
            true_label.cuda())
        print(batch)
    print('dof')

from dataloader import SampleLoader
from torch.utils.data import Dataset, DataLoader
from nets import InferenceAttack


def train_loop():
    batch_size = 4
    samples_dataset = SampleLoader(file_name='cache/true_samples.pt')
    dataloader = DataLoader(samples_dataset, batch_size=batch_size, shuffle
        =True, num_workers=0)
    net = InferenceAttack(100)
    for batch in dataloader:
        ids = batch['id']
        grad = batch['grad']
        loss = batch['loss']
        prediction = batch['prediction']
        true_label = batch['true_label']
        weights = batch['weights']
        outopus = net(grad.cuda(), loss.cuda(), prediction[0].cuda(),
            true_label.cuda())
        print(batch)
    print('dof')

from dataloader import SampleLoader
from torch.utils.data import Dataset, DataLoader
from nets import InferenceAttack

def train_loop():
    batch_size=4
    samples_dataset = SampleLoader(file_name='cache/true_samples.pt')
    dataloader = DataLoader(samples_dataset, batch_size=batch_size,
                            shuffle=True, num_workers=0)
    net=InferenceAttack(100)

    for batch in (dataloader):
        ids=batch['id']
        grad = batch['grad']
        loss = batch['loss']
        prediction = batch['prediction']
        true_label = batch['true_label']
        weights = batch['weights']

        outopus=net(grad.cuda(), loss.cuda(), prediction[0].cuda(),true_label.cuda())


        print (batch)

    print ('dof')