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mindspore.nn.SampledSoftmaxLoss

class mindspore.nn.SampledSoftmaxLoss(num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=True, seed=0, reduction='none')

Computes the sampled softmax training loss. This operator can accelerate the training of the softmax classifier over a large number of classes. It is generally an underestimate of the full softmax loss.

Parameters

• num_sampled (int) – The number of classes to randomly sample per batch.

• num_classes (int) – The number of possible classes.

• num_true (int) – The number of labels classes per training example. Default: 1 .

• sampled_values (Union[list, tuple]) – List or tuple of (sampled_candidates, true_expected_count, sampled_expected_count) returned by a *CandidateSampler function. Default to None, UniformCandidateSampler is applied. Default: None .

• remove_accidental_hits (bool) – Whether to remove "accidental hits" where a sampled class equals to one of the labels classes. Default: True .

• seed (int) – Random seed for candidate sampling. Default: 0.

• reduction (str, optional) –

  Apply specific reduction method to the output: 'none' , 'mean' , 'sum' . Default: 'none' .

  • 'none': no reduction will be applied.

  • 'mean': compute and return the mean of elements in the output.

  • 'sum': the output elements will be summed.

Inputs:

• weights (Tensor) - The weights of input. Tensor of shape $(C,dim)$.

• bias (Tensor) - Tensor of shape $(C,)$. The class biases.

• labels (Tensor) - Tensor of shape $(N,num\mathrm{\_}true)$, type int64, int32. The labels classes.

• logits (Tensor) - Tensor of shape $(N,dim)$. The forward activations of the input network.

Outputs:

Tensor or Scalar, if reduction is 'none', then output is a tensor with shape $(N,)$. Otherwise, the output is a scalar.

Raises

• TypeError – If sampled_values is not a list or tuple.

• TypeError – If dtype of labels is neither int32 nor int64.

• ValueError – If reduction is not one of 'none', 'mean', 'sum'.

• ValueError – If num_sampled or num_true is greater than num_classes.

• ValueError – If length of sampled_values is not equal to 3.

Supported Platforms:

GPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> mindspore.set_seed(1)
>>> loss = nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, num_true=1)
>>> weights = Tensor(np.random.randint(0, 9, [7, 10]), mindspore.float32)
>>> biases = Tensor(np.random.randint(0, 9, [7]), mindspore.float32)
>>> labels = Tensor([0, 1, 2])
>>> logits = Tensor(np.random.randint(0, 9, [3, 10]), mindspore.float32)
>>> output = loss(weights, biases, labels, logits)
>>> print(output)
[4.6051701e+01 1.4000047e+01 6.1989022e-06]