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Smooth_f ( float) – the loss smoothing factor within the [0, 1[ interval. Step_mode ( str) – schedule for increasing learning rate: ( linear or exp). Num_iter ( int) – the max number of iterations for test. The default is the optimizer’s learning rate.Įnd_lr ( int) – the maximum learning rate to test. Start_lr ( Optional) – the starting learning rate for the range test. Label_extractor ( Callable) – callable function to get the label from a batch of data. Image_extractor ( Callable) – callable function to get the image from a batch of data.ĭefault: x if isinstance(x, dict) else x. Val_loader ( Optional) – validation data loader (if desired). Train_loader ( DataLoader) – training set data loader. range_test ( train_loader, val_loader=None, image_extractor=, label_extractor=, start_lr=None, end_lr=10, num_iter=100, step_mode='exp', smooth_f=0.05, diverge_th=5, accumulation_steps=1, non_blocking_transfer=True, auto_reset=True ) # Steepest_lr ( bool) – plot the learning rate which had the steepest gradient. If None, then the figure and axes object areĬreated in this method and the figure is shown. Scale otherwise, plotted in a linear scale.Īx – the plot is created in the specified matplotlib axes object and theįigure is not be shown. Log_lr ( bool) – True to plot the learning rate in a logarithmic Skip_end ( int) – number of batches to trim from the start. Learning rate which has steepest gradient and its corresponding loss plot ( skip_start = 0, skip_end = 0, log_lr = True, ax = None, steepest_lr = True ) # Get learning rate which has steepest gradient and its corresponding loss Parameters Tuple get_steepest_gradient ( skip_start = 0, skip_end = 0 ) # Skip_end ( int) – number of batches to trim from the end. Skip_start ( int) – number of batches to trim from the start. Get learning rates and their corresponding losses Parameters None get_lrs_and_losses ( skip_start = 0, skip_end = 0 ) # Pickle_protocol ( int) – can be specified to override the default protocol, default to 2. This arg is used by torch.save, for more details, please check: Pickle_module – module used for pickling metadata and objects, default to pickle. Parameter will be ignored if memory_cache is True.Īmp ( bool) – use Automatic Mixed Precision Specified, system-wide temporary directory is used. Otherwise, they will be savedĬache_dir ( Optional) – path for storing temporary files. Model and optimizer will be cached in memory. Memory_cache ( bool) – if this flag is set to True, state_dict of 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.
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Optimizer ( Optimizer) – wrapped optimizer.Ĭriterion ( Module) – wrapped loss function.ĭevice ( Union) – device on which to test.
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> lr_finder.range_test(train_loader, val_loader, image_extractor, label_extractor)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 ) # 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.