Torch.Indef.Static.NN.Math

Documentation

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

abs forward pass (updates the output tensor)

abs_updateGradInput

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 ()

sqrt forward pass (updates the output tensor)

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

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

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

square forward pass (updates the output tensor)

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

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

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

logSigmoid forward pass (updates the output tensor)

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

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

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

sigmoid forward pass (updates the output tensor)

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

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

softmax

Arguments

:: KnownDim n

=> Reifies s W

=> BVar s (Tensor '[n])

input

-> BVar s (Tensor '[n])

output

one dimensional version of softmaxN

softmaxBatch

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

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

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

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

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 ()

softPlus forward pass (updates the output tensor)

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

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

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

softShrink forward pass (updates the output tensor)

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

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

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

tanh forward pass (updates the output tensor)

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

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

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

hardTanh forward pass (updates the output tensor)

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

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