Computes sigmoid cross entropy given `logits`

.

tf.compat.v2.nn.sigmoid_cross_entropy_with_logits( labels=None, logits=None, name=None )

Measures the probability error in discrete classification tasks in which each class is independent and not mutually exclusive. For instance, one could perform multilabel classification where a picture can contain both an elephant and a dog at the same time.

For brevity, let `x = logits`

, `z = labels`

. The logistic loss is

z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x)) = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x))) = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x))) = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x)) = (1 - z) * x + log(1 + exp(-x)) = x - x * z + log(1 + exp(-x))

For x < 0, to avoid overflow in exp(-x), we reformulate the above

x - x * z + log(1 + exp(-x)) = log(exp(x)) - x * z + log(1 + exp(-x)) = - x * z + log(1 + exp(x))

Hence, to ensure stability and avoid overflow, the implementation uses this equivalent formulation

max(x, 0) - x * z + log(1 + exp(-abs(x)))

`logits`

and `labels`

must have the same type and shape.

Args | |
---|---|

`labels` | A `Tensor` of the same type and shape as `logits` . |

`logits` | A `Tensor` of type `float32` or `float64` . |

`name` | A name for the operation (optional). |

Returns | |
---|---|

A `Tensor` of the same shape as `logits` with the componentwise logistic losses. |

Raises | |
---|---|

`ValueError` | If `logits` and `labels` do not have the same shape. |

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/compat/v2/nn/sigmoid_cross_entropy_with_logits