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.Conv1d

Description

Temporal (1D) Convolutions

Synopsis

newtype Conv1d f o kW dW = Conv1d {
- getTensors :: (Tensor '[o, f * kW], Tensor '[o])
}
weights :: Conv1d f o kW dW -> Tensor '[o, f * kW]
bias :: Conv1d f o kW dW -> Tensor '[o]
featureSize :: forall f o kW dW. KnownDim f => Conv1d f o kW dW -> Int
kernelWidth :: forall f o kW dW. KnownDim kW => Conv1d f o kW dW -> Int
stepSize :: forall f o kW dW. KnownDim dW => Conv1d f o kW dW -> Int
conv1d :: forall s seq f kW dW o. Reifies s W => KnownDim (f * kW) => TemporalConvC seq f kW dW o => Double -> BVar s (Conv1d f o kW dW) -> BVar s (Tensor '[seq, f]) -> BVar s (Tensor '[seq, o])
conv1d_forward :: TemporalConvC s f kW dW o => Conv1d f o kW dW -> Tensor '[s, f] -> IO (Tensor '[s, o])
conv1d_backwardGradInput :: TemporalConvC seq f kW dW o => Conv1d f o kW dW -> Tensor '[seq, f] -> Tensor '[seq, o] -> IO (Tensor '[seq, f])
conv1d_updGradParams :: TemporalConvC s f kW dW o => Conv1d f o kW dW -> Tensor '[s, f] -> Tensor '[s, o] -> Double -> IO (Conv1d f o kW dW)
conv1dBatch :: forall s seq f kW dW o b. Reifies s W => KnownDim b => KnownDim (f * kW) => TemporalConvC seq f kW dW o => Double -> BVar s (Conv1d f o kW dW) -> BVar s (Tensor '[b, seq, f]) -> BVar s (Tensor '[b, seq, o])
conv1d_forwardBatch :: TemporalConvC s f kW dW o => Conv1d f o kW dW -> Tensor '[b, s, f] -> IO (Tensor '[b, s, o])
conv1d_backwardGradInputBatch :: TemporalConvC s f kW dW o => KnownDim b => Conv1d f o kW dW -> Tensor '[b, s, f] -> Tensor '[b, s, o] -> IO (Tensor '[b, s, f])
conv1d_updGradParamsBatch :: TemporalConvC s f kW dW o => KnownDim b => Conv1d f o kW dW -> Tensor '[b, s, f] -> Tensor '[b, s, o] -> Double -> IO (Conv1d f o kW dW)
_temporalRowConvolution_updateOutput :: Tensor d -> Tensor d' -> Tensor d'' -> Tensor d''' -> Tensor d -> Tensor d -> Int -> Int -> Int -> Bool -> IO ()
_temporalRowConvolution_updateGradInput :: Tensor d -> Tensor d' -> Tensor d'' -> Tensor d''' -> Tensor d -> Tensor d -> Int -> Int -> Int -> Bool -> IO ()
_temporalRowConvolution_updGradParameters :: Tensor d -> Tensor d' -> Tensor d'' -> Tensor d''' -> Tensor d -> Tensor d -> Int -> Int -> Int -> Bool -> Double -> IO ()

Documentation

newtype Conv1d f o kW dW Source #

ADT representation of a convolutional 1d layer.

FIXME: the type is a bit of a hiccup: can we remove the kernel dimensions or move pad/stride into the phantoms?

See Conv2d for ideas.

Constructors

Conv1d
Fields getTensors :: (Tensor '[o, f * kW], Tensor '[o])

Instances

(KnownDim f, KnownDim o, KnownDim kW, KnownDim dW) => Show (Conv1d f o kW dW) Source #
Instance details Defined in Torch.Indef.Static.NN.Conv1d Methods showsPrec :: Int -> Conv1d f o kW dW -> ShowS # show :: Conv1d f o kW dW -> String # showList :: [Conv1d f o kW dW] -> ShowS #
(KnownDim (f * kW), KnownDim o) => Backprop (Conv1d f o kW dW) Source #
Instance details Defined in Torch.Indef.Static.NN.Conv1d Methods zero :: Conv1d f o kW dW -> Conv1d f o kW dW add :: Conv1d f o kW dW -> Conv1d f o kW dW -> Conv1d f o kW dW one :: Conv1d f o kW dW -> Conv1d f o kW dW

weights :: Conv1d f o kW dW -> Tensor '[o, f * kW] Source #

get the weights from a Conv1d ADT

bias :: Conv1d f o kW dW -> Tensor '[o] Source #

get the bias from a Conv1d ADT

featureSize :: forall f o kW dW. KnownDim f => Conv1d f o kW dW -> Int Source #

get the featureSize from a Conv1d ADT

kernelWidth :: forall f o kW dW. KnownDim kW => Conv1d f o kW dW -> Int Source #

kW: The kernel width of the convolution

stepSize :: forall f o kW dW. KnownDim dW => Conv1d f o kW dW -> Int Source #

dW: The step of the convolution. Default is 1 in C.

conv1d :: forall s seq f kW dW o. Reifies s W => KnownDim (f * kW) => TemporalConvC seq f kW dW o => Double -> BVar s (Conv1d f o kW dW) -> BVar s (Tensor '[seq, f]) -> BVar s (Tensor '[seq, o]) Source #

Backprop convolution function

conv1d_forward :: TemporalConvC s f kW dW o => Conv1d f o kW dW -> Tensor '[s, f] -> IO (Tensor '[s, o]) Source #

If the input sequence is a 2D tensor of dimension (nInputFrame x inputFrameSize), the output sequence will be (nOutputFrame x outputFrameSize) where

nOutputFrame = (nInputFrame - kW) / dW + 1

conv1d_backwardGradInput Source #

Arguments

:: TemporalConvC seq f kW dW o
=> Conv1d f o kW dW	conv1d state
-> Tensor '[seq, f]	input: s for 'sequence dimension', f for 'feature dimension'
-> Tensor '[seq, o]	grad output
-> IO (Tensor '[seq, f])	grad input

backward pass, computing the gradient input

conv1d_updGradParams Source #

Arguments

:: TemporalConvC s f kW dW o
=> Conv1d f o kW dW	input state of conv1d (which includes weights and bias)
-> Tensor '[s, f]	input tensor
-> Tensor '[s, o]	output gradient
-> Double	scale
-> IO (Conv1d f o kW dW)	gradient of (weights, bias)

backward pass, computing the weight and bias parameters

WARNING: this is _pure_ which may be slow for large tensors. Speeding this up will be in active development as the need arises (see issue hasktorch/hasktorch#85)

conv1dBatch :: forall s seq f kW dW o b. Reifies s W => KnownDim b => KnownDim (f * kW) => TemporalConvC seq f kW dW o => Double -> BVar s (Conv1d f o kW dW) -> BVar s (Tensor '[b, seq, f]) -> BVar s (Tensor '[b, seq, o]) Source #

Backprop convolution function with batching

conv1d_forwardBatch :: TemporalConvC s f kW dW o => Conv1d f o kW dW -> Tensor '[b, s, f] -> IO (Tensor '[b, s, o]) Source #

Applies a 1D convolution over an input sequence composed of nInputFrame frames. The input tensor in forward(input) is expected to be a 2D tensor (nInputFrame x inputFrameSize) or a 3D tensor (nBatchFrame x nInputFrame x inputFrameSize).

conv1d_backwardGradInputBatch Source #

Arguments

:: TemporalConvC s f kW dW o
=> KnownDim b
=> Conv1d f o kW dW	conv1d state
-> Tensor '[b, s, f]	input: s for 'sequence dimension', f for 'feature dimension'
-> Tensor '[b, s, o]	grad output
-> IO (Tensor '[b, s, f])	output

conv1d_backwardGradInput with a batch dimension

conv1d_updGradParamsBatch Source #

Arguments

:: TemporalConvC s f kW dW o
=> KnownDim b
=> Conv1d f o kW dW	conv1d state
-> Tensor '[b, s, f]	input: s for 'sequence dimension', f for 'feature dimension'
-> Tensor '[b, s, o]	grad output
-> Double	scale
-> IO (Conv1d f o kW dW)	output

conv1d_updGradParams with a batch dimension

_temporalRowConvolution_updateOutput :: Tensor d -> Tensor d' -> Tensor d'' -> Tensor d''' -> Tensor d -> Tensor d -> Int -> Int -> Int -> Bool -> IO () Source #

temporalRowConvolution forward pass (updates the output tensor)

_temporalRowConvolution_updateGradInput :: Tensor d -> Tensor d' -> Tensor d'' -> Tensor d''' -> Tensor d -> Tensor d -> Int -> Int -> Int -> Bool -> IO () Source #

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

_temporalRowConvolution_updGradParameters :: Tensor d -> Tensor d' -> Tensor d'' -> Tensor d''' -> Tensor d -> Tensor d -> Int -> Int -> Int -> Bool -> Double -> IO () Source #

temporalRowConvolution backward-update (updates the layer and bias tensors). Called accGradParameters in C to indicate accumulating the gradient parameters.