ea09d1fd36b347bc86cbc52de8ea39ffbeb24dc1,examples/mnist/reservoir.py,,,#,105

Before Change

images *= 0.25

// Create lazily iterating Poisson-distributed data loader.
loader = zip(poisson_loader(images, time=250), iter(labels))

n_iters = 500
test_pairs = []
for i, (datum, label) in enumerate(loader):

After Change

n_iters = n_epochs
test_pairs = []
pbar = tqdm(enumerate(dataloader))
i = 0
for (step, dataPoint) in pbar:
    datum = dataPoint["encoded_image"]
    label = dataPoint["label"]
    pbar.set_description("Train progress: (%d / %d)" % (i, n_iters))

    network.run(inpts={"I": datum}, time=250, input_time_dim=1)
    test_pairs.append([spikes["O"].get("s").sum(-1), label])

    inpt_axes, inpt_ims = plot_input(
        dataPoint["image"].view(28, 28),
        datum.view(time, 784).sum(0).view(28, 28),
        label=label,
        axes=inpt_axes,

Instances