![]() ![]() Default: None, uses the same device as model. ![]() “cuda:X”, where is the ordinal).Īlternatively, can be an object representing the device on which theĬomputation will take place. Optional ordinal for the device type (e.g. Optimizer ( Optimizer) – wrapped optimizer.Ĭriterion ( Module) – wrapped loss function.ĭevice ( Union) – device on which to test. Modified from: davidtvs/pytorch-lr-finder.Ĭyclical Learning Rates for Training Neural Networks: _init_ ( model, optimizer, criterion, device=None, memory_cache=True, cache_dir=None, amp=False, pickle_module=, pickle_protocol=2, verbose=True ) # > lr_finder.range_test(train_loader, val_loader, image_extractor, label_extractor) Returns something other than this, pass a callable function to extract it, e.g.: ![]() > acc_lr_finder.range_test(data_loader, end_lr=10, num_iter=100, accumulation_steps=accumulation_steps)īy default, image will be extracted from data loader with x and x, depending on whetherīatch data is a dictionary or not (and similar behaviour for extracting the label). > acc_lr_finder = LearningRateFinder(net, optimizer, criterion) > data_loader = (train_data, batch_size=real_bs, shuffle=True) > accumulation_steps = desired_bs // real_bs # required steps for accumulation > desired_bs, real_bs = 32, 4 # batch size Gradient accumulation is supported example: > lr_finder.range_test(train_loader, val_loader=val_loader, end_lr=1, num_iter=100, step_mode=”linear”) > lr_ot() # to inspect the loss-learning rate graph > lr_finder.range_test(data_loader, end_lr=100, num_iter=100) ![]() > lr_finder = LearningRateFinder(net, optimizer, criterion) Information on how well the network can be trained over a range of learning rates The learning rate range test increases the learning rate in a pre-training runīetween two boundaries in a linear or exponential manner. LearningRateFinder ( model, optimizer, criterion, device=None, memory_cache=True, cache_dir=None, amp=False, pickle_module=, pickle_protocol=2, verbose=True ) # Optimizers # LearningRateFinder # class monai.optimizers. ![]()
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