tfa.losses.NpairsMultilabelLoss

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Class NpairsMultilabelLoss

Computes the npairs loss between multilabel data y_true and y_pred.

Aliases:

Npairs loss expects paired data where a pair is composed of samples from the same labels and each pairs in the minibatch have different labels. The loss takes each row of the pair-wise similarity matrix, y_pred, as logits and the remapped multi-class labels, y_true, as labels.

To deal with multilabel inputs, the count of label intersection is computed as follows:

L_{i,j} = | set_of_labels_for(i) \cap set_of_labels_for(j) |

Each row of the count based label matrix is further normalized so that each row sums to one.

y_true should be a binary indicator for classes. That is, if y_true[i, j] = 1, then ith sample is in jth class; if y_true[i, j] = 0, then ith sample is not in jth class.

The similarity matrix y_pred between two embedding matrices a and b with shape [batch_size, hidden_size] can be computed as follows:

# y_pred = a * b^T
y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)

See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf

Args:

  • name: (Optional) name for the loss.

__init__

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__init__(name='npairs_multilabel_loss')

Initialize self. See help(type(self)) for accurate signature.

Methods

__call__

__call__(
    y_true,
    y_pred,
    sample_weight=None
)

Invokes the Loss instance.

Args:

  • y_true: Ground truth values. shape = [batch_size, d0, .. dN]
  • y_pred: The predicted values. shape = [batch_size, d0, .. dN]
  • sample_weight: Optional sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each loss element of y_pred is scaled by the corresponding value of sample_weight. (Note ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)

Returns:

Weighted loss float Tensor. If reduction is NONE, this has shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)

Raises:

  • ValueError: If the shape of sample_weight is invalid.

from_config

from_config(
    cls,
    config
)

Instantiates a Loss from its config (output of get_config()).

Args:

  • config: Output of get_config().

Returns:

A Loss instance.

get_config

get_config()