(self, dataset, num_replicas=None, rank=None, shuffle=True)

(self, params, stats=None, on_step:Callable=None)

(self, learn, beta=2, eps=1e-15, thresh=0.3, sigmoid=True, average="micro")

(self, ds:Collection=None)

(self, ds:ItemList)

(self, items:Iterator, classes:Collection=None, **kwargs)

(self, items:Iterator, classes:Collection=None, label_delim:str=None, **kwargs)

(self, ds:ItemList, one_hot:bool=False)

(self, items:Iterator, classes:Collection=None, label_delim:str=None, one_hot:bool=False, **kwargs)

(self, items:Iterator, log:bool=False, classes:Collection=None, **kwargs)

(self, path:PathOrStr, train:ItemList, valid:ItemList)

(self, x:ItemList, y:ItemList, tfms:TfmList=None, tfm_y:bool=False, **kwargs)

(self, procs:Collection[Union[PreProcessor, Collection[PreProcessor]]])

(self, items)

(self, item_lists, path:PathOrStr=None, label_cls:Callable=None, inner_df:Any=None, x:'ItemLi

(self, emb_szs:ListSizes, layers:Collection[int]=None, ps:Collection[float]=None, emb_drop:fl

(self, n_factors:int, n_users:int, n_items:int, y_range:Tuple[float,float]=None)

(self, silent=False, ctx=None, on_exit_report=True)

(self,learn:Learner)

(self, learn:Learner, phases:Collection[TrainingPhase], start_epoch:int=None)

(self, learn:Learner, base_dir:Path, name:str, loss_iters:int=25, hist_iters:int=500, stats_iters:int=100)

(self, learn:GANLearner, base_dir:Path, name:str, loss_iters:int=25, hist_iters:int=500, stat

(self, learn:Learner, base_dir:Path, name:str, loss_iters:int=25, hist_iters:int=500, stats_iters:int=100,

(self, tbwriter: SummaryWriter, iteration:int)

(self, orig:Image, real:Image, gen:Image)

(self, model:nn.Module, iteration:int, tbwriter:SummaryWriter, name:str)

(self)

(self, model:nn.Module, iteration:int, tbwriter:SummaryWriter, name:str)

(self, learn:Learner, batch:Tuple, iteration:int, tbwriter:SummaryWriter, ds_type:DatasetType)

(self)

(self, model:nn.Module, tbwriter:SummaryWriter, input_to_model:torch.Tensor)

(self, learn:Learner, loss_scale:float=None, max_noskip:int=1000, dynamic:bool=True, clip:float=None,

(self, n_batches:int=2)

(self, learn:Learner, start_lr:float=1e-7, end_lr:float=10, num_it:int=100, stop_div:bool=True)

(self, ms:Collection[nn.Module], hook_func:HookFunc, is_forward:bool=True, detach:bool=True)

(self, learn:Learner, modules:Sequence[nn.Module]=None, do_remove:bool=True)

(self, learn:Learner, alpha:float=0.4, stack_x:bool=False, stack_y:bool=True)

(self, learn:Learner, filename: str = 'history', append: bool = False)

(self)

(self, learn:Learner, monitor:str='valid_loss', mode:str='auto', min_delta:int=0, patience:int=0)

(self, learn:Learner, monitor:str='valid_loss', mode:str='auto', every:str='improvement', name:str='bestmodel'

(self, learn:Learner, monitor:str='valid_loss', mode:str='auto', patience:int=0, factor:float=0.2,

(self, learn:Learner, exp_name: str, params: dict, nb_path: str, uri: str = "http://localhost:5000")

(self, learn:Learner, alpha:float=0., beta:float=0.)

(self, learn:Learner)

(self,learn:Learner,weights:torch.Tensor=None)

(self, learn:Learner, lr_max:float, moms:Floats=(0.95,0.85), div_factor:float=25., pct_start:float=0.3,

(self, flow:FlowField, scale:bool=True, y_first:bool=True)

(self, flow:FlowField, scale:bool=True, y_first:bool=True, labels:Collection=None, classes:di

(self, generator:nn.Module=None, critic:nn.Module=None, gen_mode:bool=False)

(self, learn:Learner, switch_eval:bool=False, clip:float=None, beta:float=0.98, gen_first:bool=False,

(self, learn:Learner, n_crit:Union[int,Callable]=1, n_gen:Union[int,Callable]=1)

(self, learn:Learner, gen_thresh:float=None, critic_thresh:float=None)

(self, data:DataBunch, generator:nn.Module, critic:nn.Module, gen_loss_func:LossFunction, cri

(self, noise_sz)

(self, items, noise_sz:int=100, **kwargs)

(self, learn:Learner, mult_lr:float = 5.)

(self, crit)

(self, dim:int, pad_mode:str='reflection', norm_layer:nn.Module=None, dropout:float=0., bias:bool=True)

(self, ch_in:int, ch_out:int, n_features:int=64, disc_layers:int=3, gen_blocks:int=6, lsgan:bool=True,

(self, crit)

(self, cgan:nn.Module, lambda_A:float=10., lambda_B:float=10, lambda_idt:float=0.5, lsgan:bool=True)

(self, learn:Learner, preds:Tensor, y_true:Tensor, losses:Tensor, ds_type:DatasetType=DatasetType.Valid)

(self, ds:ItemList, pad_idx:int=0)

(self, ds:ItemList)

(self, items:Iterator, classes:Collection=None, **kwargs)

(self, ds:ItemList)

(self, items:Iterator, **kwargs)

(self,ni,nf=None,act=True)

(self, encoder:nn.Module, n_classes:int, img_size:Tuple[int,int]=(256,256), blur:bool=False, blur_final=True,

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(self, inplanes, planes, stride=1, downsample=None)

(self, expansion, ni, nh, stride=1)

(self, expansion, layers, c_in=3, c_out=1000)

(self, ni, nf, stride, drop_p=0.0)

(self, num_groups:int, N:int, num_classes:int, k:int=1, drop_p:float=0.0, start_nf:int=16, n_in_channels:int=3

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(self, ni, nf, stride=1, downsample=None)

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(self, bptt:int, max_len:int, module:nn.Module, pad_idx:int=1)

(self, lang:str)

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(self, tok_func:Callable=SpacyTokenizer, lang:str='en', pre_rules:ListRules=None, post_rules:

(self, itos:Collection[str])

(self, learn: Learner, preds: Tensor, y_true: Tensor, losses: Tensor, ds_type: DatasetType = DatasetType.Valid

(self, length:int, forward:bool)

(self, dataset:LabelList, lengths:Collection[int]=None, bs:int=32, bptt:int=70, backwards:bool=False,

(self, data_source:NPArrayList, key:KeyFunc)

(self, data_source:NPArrayList, key:KeyFunc, bs:int)