tf.nn.sigmoid_cross_entropy_with_logits( _sentinel=None, labels=None, logits=None, name=None )
Defined in tensorflow/python/ops/nn_impl.py
.
See the guide: Neural Network > Classification
Computes sigmoid cross entropy given logits
.
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.
_sentinel
: Used to prevent positional parameters. Internal, do not use.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).A Tensor
of the same shape as logits
with the componentwise logistic losses.
ValueError
: If logits
and labels
do not have the same shape.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits