example/mnist.py [12:53]:
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def get_iterator(mode):
    ds = MNIST(root='./', download=True, train=mode)
    data = getattr(ds, 'train_data' if mode else 'test_data')
    labels = getattr(ds, 'train_labels' if mode else 'test_labels')
    tds = tnt.dataset.TensorDataset([data, labels])
    return tds.parallel(batch_size=128, num_workers=4, shuffle=mode)


def conv_init(ni, no, k):
    return kaiming_normal(torch.Tensor(no, ni, k, k))


def linear_init(ni, no):
    return kaiming_normal(torch.Tensor(no, ni))


def f(params, inputs, mode):
    o = inputs.view(inputs.size(0), 1, 28, 28)
    o = F.conv2d(o, params['conv0.weight'], params['conv0.bias'], stride=2)
    o = F.relu(o)
    o = F.conv2d(o, params['conv1.weight'], params['conv1.bias'], stride=2)
    o = F.relu(o)
    o = o.view(o.size(0), -1)
    o = F.linear(o, params['linear2.weight'], params['linear2.bias'])
    o = F.relu(o)
    o = F.linear(o, params['linear3.weight'], params['linear3.bias'])
    return o


def main():
    params = {
        'conv0.weight': conv_init(1, 50, 5), 'conv0.bias': torch.zeros(50),
        'conv1.weight': conv_init(50, 50, 5), 'conv1.bias': torch.zeros(50),
        'linear2.weight': linear_init(800, 512), 'linear2.bias': torch.zeros(512),
        'linear3.weight': linear_init(512, 10), 'linear3.bias': torch.zeros(10),
    }
    params = {k: Variable(v, requires_grad=True) for k, v in params.items()}

    optimizer = torch.optim.SGD(
        params.values(), lr=0.01, momentum=0.9, weight_decay=0.0005)

    engine = Engine()
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example/mnist_with_meterlogger.py [22:63]:
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def get_iterator(mode):
    ds = MNIST(root='./', download=True, train=mode)
    data = getattr(ds, 'train_data' if mode else 'test_data')
    labels = getattr(ds, 'train_labels' if mode else 'test_labels')
    tds = tnt.dataset.TensorDataset([data, labels])
    return tds.parallel(batch_size=128, num_workers=4, shuffle=mode)


def conv_init(ni, no, k):
    return kaiming_normal(torch.Tensor(no, ni, k, k))


def linear_init(ni, no):
    return kaiming_normal(torch.Tensor(no, ni))


def f(params, inputs, mode):
    o = inputs.view(inputs.size(0), 1, 28, 28)
    o = F.conv2d(o, params['conv0.weight'], params['conv0.bias'], stride=2)
    o = F.relu(o)
    o = F.conv2d(o, params['conv1.weight'], params['conv1.bias'], stride=2)
    o = F.relu(o)
    o = o.view(o.size(0), -1)
    o = F.linear(o, params['linear2.weight'], params['linear2.bias'])
    o = F.relu(o)
    o = F.linear(o, params['linear3.weight'], params['linear3.bias'])
    return o


def main():
    params = {
        'conv0.weight': conv_init(1, 50, 5), 'conv0.bias': torch.zeros(50),
        'conv1.weight': conv_init(50, 50, 5), 'conv1.bias': torch.zeros(50),
        'linear2.weight': linear_init(800, 512), 'linear2.bias': torch.zeros(512),
        'linear3.weight': linear_init(512, 10), 'linear3.bias': torch.zeros(10),
    }
    params = {k: Variable(v, requires_grad=True) for k, v in params.items()}

    optimizer = torch.optim.SGD(
        params.values(), lr=0.01, momentum=0.9, weight_decay=0.0005)

    engine = Engine()
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