training

Training loop

xmean,xstd = 0.28, 0.35
@inplace
def transformi(b): b['image'] = [(TF.to_tensor(o)-xmean)/xstd for o in b['image']]

_dataset = load_dataset('fashion_mnist').with_transform(transformi)

_dataset = sample_dataset_dict(_dataset)

dls = DataLoaders.from_dataset_dict(_dataset, 1024, num_workers=4)

Core

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source

lsuv_init

 lsuv_init (model, trainable_module, xb, measurement_module=None,
            targ_mean=0.5, target_std=1, max_loops=100)

model = get_model_conv()
model.to(def_device)
xb = fc.first(dls.train)[0]
for module in model.modules(): lsuv_init(model, module, xb.to(def_device))