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tensorflow-haskell/docs/haddock/tensorflow-nn-0.1.0.0/tensorflow-nn.txt

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2016-10-31 22:22:48 +01:00
-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/
-- | Friendly layer around TensorFlow bindings.
--
-- Please see README.md
@package tensorflow-nn
@version 0.1.0.0
module TensorFlow.NN
-- | Computes sigmoid cross entropy given <tt>logits</tt>.
--
-- 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 = targets`. The logistic loss is
--
-- z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x)) = z * -log(1 <i>
-- (1 + exp(-x))) + (1 - z) * -log(exp(-x) </i> (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 &lt; 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)))
--
-- <tt>logits</tt> and <tt>targets</tt> must have the same type and
-- shape.
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sigmoidCrossEntropyWithLogits :: (MonadBuild m, OneOf '[Float, Double] a, TensorType a, Num a) => Tensor Value a -> Tensor Value a -> m (Tensor Value a)