Copyright	(c) Sam Stites 2017
License	BSD3
Maintainer	sam@stites.io
Stability	experimental
Portability	non-portable
Safe Haskell	None
Language	Haskell2010

Torch.Indef.Static.NN.Math

Description

Synopsis

abs_updateOutput :: Tensor d -> IO (Tensor d)
abs_updateGradInput :: Product d ~ Product d' => Tensor d -> Tensor d' -> IO (Tensor d)
_sqrt_updateOutput :: Tensor d -> Tensor d -> Double -> IO ()
_sqrt_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Tensor d -> IO ()
_square_updateOutput :: Tensor d -> Tensor d -> IO ()
_square_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> IO ()
_logSigmoid_updateOutput :: Tensor d -> Tensor d -> Tensor d -> IO ()
_logSigmoid_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Tensor d -> IO ()
_sigmoid_updateOutput :: Tensor d -> Tensor d -> IO ()
_sigmoid_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> IO ()
softmax :: KnownDim n => Reifies s W => BVar s (Tensor '[n]) -> BVar s (Tensor '[n])
softmaxBatch :: KnownDim b => KnownDim n => Reifies s W => BVar s (Tensor '[b, n]) -> BVar s (Tensor '[b, n])
softmaxN :: forall s i d. Reifies s W => (i < Length d) ~ True => Dimensions d => Dim i -> BVar s (Tensor d) -> BVar s (Tensor d)
logSoftMax :: KnownDim n => Reifies s W => BVar s (Tensor '[n]) -> BVar s (Tensor '[n])
logSoftMaxBatch :: KnownDim n => KnownDim b => Reifies s W => BVar s (Tensor '[b, n]) -> BVar s (Tensor '[b, n])
logSoftMaxN :: forall s i d. Reifies s W => (i < Length d) ~ True => Dimensions d => Dim i -> BVar s (Tensor d) -> BVar s (Tensor d)
_softPlus_updateOutput :: Tensor d -> Tensor d -> Double -> Double -> IO ()
_softPlus_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Tensor d -> Double -> Double -> IO ()
_softShrink_updateOutput :: Tensor d -> Tensor d -> Double -> IO ()
_softShrink_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Double -> IO ()
_tanh_updateOutput :: Tensor d -> Tensor d -> IO ()
_tanh_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> IO ()
_hardTanh_updateOutput :: Tensor d -> Tensor d -> Double -> Double -> Bool -> IO ()
_hardTanh_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Double -> Double -> Bool -> IO ()

Documentation

abs_updateOutput :: Tensor d -> IO (Tensor d) Source #

abs forward pass (updates the output tensor)

abs_updateGradInput Source #

Arguments

:: Product d ~ Product d'
=> Tensor d	input
-> Tensor d'	gradOutput
-> IO (Tensor d)	gradInput

abs backward-update (updates the layer and bias tensors)

_sqrt_updateOutput :: Tensor d -> Tensor d -> Double -> IO () Source #

sqrt forward pass (updates the output tensor)

_sqrt_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Tensor d -> IO () Source #

sqrt backward-update (updates the layer and bias tensors)

_square_updateOutput :: Tensor d -> Tensor d -> IO () Source #

square forward pass (updates the output tensor)

_square_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> IO () Source #

square backward-update (updates the layer and bias tensors)

_logSigmoid_updateOutput :: Tensor d -> Tensor d -> Tensor d -> IO () Source #

logSigmoid forward pass (updates the output tensor)

_logSigmoid_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Tensor d -> IO () Source #

logSigmoid backward-update (updates the layer and bias tensors)

_sigmoid_updateOutput :: Tensor d -> Tensor d -> IO () Source #

sigmoid forward pass (updates the output tensor)

_sigmoid_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> IO () Source #

sigmoid backward-update (updates the layer and bias tensors)

softmax Source #

Arguments

:: KnownDim n
=> Reifies s W
=> BVar s (Tensor '[n])	input
-> BVar s (Tensor '[n])	output

one dimensional version of softmaxN

softmaxBatch Source #

Arguments

:: KnownDim b
=> KnownDim n
=> Reifies s W
=> BVar s (Tensor '[b, n])	input
-> BVar s (Tensor '[b, n])	output

softmaxN along the mini-batch dimension.

softmaxN Source #

Arguments

:: Reifies s W
=> (i < Length d) ~ True
=> Dimensions d
=> Dim i	dimension to softmax over
-> BVar s (Tensor d)	input
-> BVar s (Tensor d)	output

run a threshold function againts two BVar variables

logSoftMax Source #

Arguments

:: KnownDim n
=> Reifies s W
=> BVar s (Tensor '[n])	input
-> BVar s (Tensor '[n])	output

run a threshold function againts two BVar variables

logSoftMaxBatch Source #

Arguments

:: KnownDim n
=> KnownDim b
=> Reifies s W
=> BVar s (Tensor '[b, n])	input
-> BVar s (Tensor '[b, n])	output

run a threshold function againts two BVar variables

logSoftMaxN Source #

Arguments

:: Reifies s W
=> (i < Length d) ~ True
=> Dimensions d
=> Dim i	dimension to logSoftMax over
-> BVar s (Tensor d)	input
-> BVar s (Tensor d)	output

run a threshold function againts two BVar variables

_softPlus_updateOutput :: Tensor d -> Tensor d -> Double -> Double -> IO () Source #

softPlus forward pass (updates the output tensor)

_softPlus_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Tensor d -> Double -> Double -> IO () Source #

softPlus backward-update (updates the layer and bias tensors)

_softShrink_updateOutput :: Tensor d -> Tensor d -> Double -> IO () Source #

softShrink forward pass (updates the output tensor)

_softShrink_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Double -> IO () Source #

softShrink backward-update (updates the layer and bias tensors)

_tanh_updateOutput :: Tensor d -> Tensor d -> IO () Source #

tanh forward pass (updates the output tensor)

_tanh_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> IO () Source #

tanh backward-update (updates the layer and bias tensors)

_hardTanh_updateOutput :: Tensor d -> Tensor d -> Double -> Double -> Bool -> IO () Source #

hardTanh forward pass (updates the output tensor)

_hardTanh_updateGradInput :: Tensor d -> Tensor d -> Tensor d -> Double -> Double -> Bool -> IO () Source #

hardTanh backward-update (updates the layer and bias tensors)