class torch.nn.AdaptiveLogSoftmaxWithLoss(in_features, n_classes, cutoffs, div_value=4.0, head_bias=False) [source]
 
Efficient softmax approximation as described in Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou. Adaptive softmax is an approximate strategy for training models with large output spaces. It is most effective when the label distribution is highly imbalanced, for example in natural language modelling, where the word frequency distribution approximately follows the Zipf’s law. Adaptive softmax partitions the labels into several clusters, according to their frequency. These clusters may contain different number of targets each. Additionally, clusters containing less frequent labels assign lower dimensional embeddings to those labels, which speeds up the computation. For each minibatch, only clusters for which at least one target is present are evaluated. The idea is that the clusters which are accessed frequently (like the first one, containing most frequent labels), should also be cheap to compute – that is, contain a small number of assigned labels. We highly recommend taking a look at the original paper for more details.  
cutoffs should be an ordered Sequence of integers sorted in the increasing order. It controls number of clusters and the partitioning of targets into clusters. For example setting cutoffs = [10, 100, 1000] means that first 10 targets will be assigned to the ‘head’ of the adaptive softmax, targets 11, 12, …, 100 will be assigned to the first cluster, and targets 101, 102, …, 1000 will be assigned to the second cluster, while targets 1001, 1002, …, n_classes - 1 will be assigned to the last, third cluster. 
div_value is used to compute the size of each additional cluster, which is given as ⌊in_featuresdiv_valueidx⌋\left\lfloor\frac{\texttt{in\_features}}{\texttt{div\_value}^{idx}}\right\rfloor , where idxidx  is the cluster index (with clusters for less frequent words having larger indices, and indices starting from 11 ). 
head_bias if set to True, adds a bias term to the ‘head’ of the adaptive softmax. See paper for details. Set to False in the official implementation.   Warning Labels passed as inputs to this module should be sorted according to their frequency. This means that the most frequent label should be represented by the index 0, and the least frequent label should be represented by the index n_classes - 1.   Note This module returns a NamedTuple with output and loss fields. See further documentation for details.   Note To compute log-probabilities for all classes, the log_prob method can be used.   Parameters 
 
in_features (int) – Number of features in the input tensor 
n_classes (int) – Number of classes in the dataset 
cutoffs (Sequence) – Cutoffs used to assign targets to their buckets 
div_value (float, optional) – value used as an exponent to compute sizes of the clusters. Default: 4.0 
head_bias (bool, optional) – If True, adds a bias term to the ‘head’ of the adaptive softmax. Default: False
   Returns 
 
output is a Tensor of size N containing computed target log probabilities for each example 
loss is a Scalar representing the computed negative log likelihood loss   Return type 
NamedTuple with output and loss fields    Shape:

 input: (N,in_features)(N, \texttt{in\_features}) 
 target: (N)(N)  where each value satisfies 0<=target[i]<=n_classes0 <= \texttt{target[i]} <= \texttt{n\_classes} 
 output1: (N)(N) 
 output2: Scalar
     
log_prob(input) [source]
 
Computes log probabilities for all n_classes\texttt{n\_classes}   Parameters 
input (Tensor) – a minibatch of examples  Returns 
log-probabilities of for each class cc  in range 0<=c<=n_classes0 <= c <= \texttt{n\_classes} , where n_classes\texttt{n\_classes}  is a parameter passed to AdaptiveLogSoftmaxWithLoss constructor.    Shape:

 Input: (N,in_features)(N, \texttt{in\_features}) 
 Output: (N,n_classes)(N, \texttt{n\_classes}) 
    
  
predict(input) [source]
 
This is equivalent to self.log_pob(input).argmax(dim=1), but is more efficient in some cases.  Parameters 
input (Tensor) – a minibatch of examples  Returns 
a class with the highest probability for each example  Return type 
output (Tensor)    Shape:

 Input: (N,in_features)(N, \texttt{in\_features}) 
 Output: (N)(N)