Artificial Neural Network's functions for accelerating of Simd::Neural. More...

Functions
SIMD_API void	SimdNeuralConvert (const uint8_t src, size_t srcStride, size_t width, size_t height, float dst, size_t dstStride, int inversion)
	Converts a 8-bit gray image to the 32-bit float array. More...

SIMD_API void	SimdNeuralDerivativeSigmoid (const float src, size_t size, const float slope, float *dst)
	Multiplies output 32-bit float array by derivative of sigmoid from input 32-bit float array. More...

SIMD_API void	SimdNeuralDerivativeTanh (const float src, size_t size, const float slope, float *dst)
	Multiplies output 32-bit float array by derivative of hyperbolic tangent from input 32-bit float array. More...

SIMD_API void	SimdNeuralDerivativeRelu (const float src, size_t size, const float slope, float *dst)
	Multiplies output 32-bit float array by derivative of Relu (rectified linear unit) from input 32-bit float array. More...

SIMD_API void	SimdNeuralPow (const float src, size_t size, const float exponent, float *dst)
	Calculates Pow function for 32-bit float array. More...

SIMD_API void	SimdNeuralProductSum (const float a, const float b, size_t size, float *sum)
	Calculates sum of products for two 32-bit float arrays. More...

SIMD_API void	SimdNeuralAddVectorMultipliedByValue (const float src, size_t size, const float value, float *dst)
	Adds the product of a vector and a scalar to given vector. More...

SIMD_API void	SimdNeuralAddVector (const float src, size_t size, float dst)
	Adds a vector to given vector. More...

SIMD_API void	SimdNeuralAddValue (const float value, float dst, size_t size)
	Adds a value to each elements of given vector. More...

SIMD_API void	SimdNeuralUpdateWeights (const float x, size_t size, const float a, const float b, float d, float *w)
	Updates ANN weights. More...

SIMD_API void	SimdNeuralAdaptiveGradientUpdate (const float delta, size_t size, size_t batch, const float alpha, const float epsilon, float gradient, float *weight)
	Updates neural network weights with using of adaptive gradients method. More...

SIMD_API void	SimdNeuralAddConvolution2x2Forward (const float src, size_t srcStride, size_t width, size_t height, const float weights, float *dst, size_t dstStride)
	Adds 2x2 convolution of 32-bit float image. More...

SIMD_API void	SimdNeuralAddConvolution3x3Forward (const float src, size_t srcStride, size_t width, size_t height, const float weights, float *dst, size_t dstStride)
	Adds 3x3 convolution of 32-bit float image. More...

SIMD_API void	SimdNeuralAddConvolution4x4Forward (const float src, size_t srcStride, size_t width, size_t height, const float weights, float *dst, size_t dstStride)
	Adds 4x4 convolution of 32-bit float image. More...

SIMD_API void	SimdNeuralAddConvolution5x5Forward (const float src, size_t srcStride, size_t width, size_t height, const float weights, float *dst, size_t dstStride)
	Adds 5x5 convolution of 32-bit float image (forward propagation). More...

SIMD_API void	SimdNeuralAddConvolution2x2Backward (const float src, size_t srcStride, size_t width, size_t height, const float weights, float *dst, size_t dstStride)
	Adds 2x2 convolution of 32-bit float image (backward propagation). More...

SIMD_API void	SimdNeuralAddConvolution3x3Backward (const float src, size_t srcStride, size_t width, size_t height, const float weights, float *dst, size_t dstStride)
	Adds 3x3 convolution of 32-bit float image (backward propagation). More...

SIMD_API void	SimdNeuralAddConvolution4x4Backward (const float src, size_t srcStride, size_t width, size_t height, const float weights, float *dst, size_t dstStride)
	Adds 4x4 convolution of 32-bit float image (backward propagation). More...

SIMD_API void	SimdNeuralAddConvolution5x5Backward (const float src, size_t srcStride, size_t width, size_t height, const float weights, float *dst, size_t dstStride)
	Adds 5x5 convolution of 32-bit float image (backward propagation). More...

SIMD_API void	SimdNeuralAddConvolution2x2Sum (const float src, size_t srcStride, const float dst, size_t dstStride, size_t width, size_t height, float *sums)
	Accumulates changes of weights for 2x2 convolution of 32-bit float image during backward propagation. More...

SIMD_API void	SimdNeuralAddConvolution3x3Sum (const float src, size_t srcStride, const float dst, size_t dstStride, size_t width, size_t height, float *sums)
	Accumulates changes of weights for 3x3 convolution of 32-bit float image during backward propagation. More...

SIMD_API void	SimdNeuralAddConvolution4x4Sum (const float src, size_t srcStride, const float dst, size_t dstStride, size_t width, size_t height, float *sums)
	Accumulates changes of weights for 4x4 convolution of 32-bit float image during backward propagation. More...

SIMD_API void	SimdNeuralAddConvolution5x5Sum (const float src, size_t srcStride, const float dst, size_t dstStride, size_t width, size_t height, float *sums)
	Accumulates changes of weights for 5x5 convolution of 32-bit float image during backward propagation. More...

SIMD_API void	SimdNeuralPooling1x1Max3x3 (const float src, size_t srcStride, size_t width, size_t height, float dst, size_t dstStride)
	Takes maximum value in 3x3 window of input 32-bit float image and copies to the output image. More...

SIMD_API void	SimdNeuralPooling2x2Max2x2 (const float src, size_t srcStride, size_t width, size_t height, float dst, size_t dstStride)
	Reduces input 32-bit float image in two times (takes maximum value in 2x2 window and copies to the output image). More...

SIMD_API void	SimdNeuralPooling2x2Max3x3 (const float src, size_t srcStride, size_t width, size_t height, float dst, size_t dstStride)
	Reduces input 32-bit float image in two times (takes maximum value in 3x3 window and copies to the output image). More...

SIMD_API void	SimdNeuralConvolutionForward (const float src, size_t srcWidth, size_t srcHeight, size_t srcDepth, const float weight, size_t kernelX, size_t kernelY, size_t padX, size_t padY, size_t strideX, size_t strideY, size_t dilationX, size_t dilationY, void buffer, size_t size, float *dst, size_t dstWidth, size_t dstHeight, size_t dstDepth, int add)
	Adds convolution of the input multichannel 32-bit float image to the output multichannel 32-bit float image. More...

template<template< class > class A>
SIMD_INLINE void	NeuralConvert (const View< A > &src, float *dst, size_t stride, bool inversion)
	Converts a 8-bit gray image to the 32-bit float array. More...

Detailed Description

Artificial Neural Network's functions for accelerating of Simd::Neural.

Function Documentation

◆ SimdNeuralConvert()

void SimdNeuralConvert	(	const uint8_t *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		float *	dst,
		size_t	dstStride,
		int	inversion
	)

Converts a 8-bit gray image to the 32-bit float array.

The length of output array must be equal to the area of input image.

For every point:

dst[i] = inversion ? (255 - src[col]) / 255 : src[i]/255;

Note: This function has a C++ wrapper Simd::NeuralConvert(const View<A>& src, float * dst, bool inversion).

Parameters

[in]	src	- a pointer to pixels data of input image.
[in]	srcStride	- a row size (in bytes) of the image.
[in]	width	- an image width.
[in]	height	- an image height.
[out]	dst	- a pointer to output array.
[in]	dstStride	- a row size of the output array.
[in]	inversion	- a flag of color inversion.

◆ SimdNeuralDerivativeSigmoid()

void SimdNeuralDerivativeSigmoid	(	const float *	src,
		size_t	size,
		const float *	slope,
		float *	dst
	)

Multiplies output 32-bit float array by derivative of sigmoid from input 32-bit float array.

All arrays must have the same size.

For every element:

dst[i] *= slope*(1 - src[i])*src[i];

Note: This function is used in Simd::Neural::Function.

Parameters

[in]	src	- a pointer to the input array.
[in]	size	- a size of arrays.
[in]	slope	- a pointer to the slope parameter.
[in,out]	dst	- a pointer to output array.

◆ SimdNeuralDerivativeTanh()

void SimdNeuralDerivativeTanh	(	const float *	src,
		size_t	size,
		const float *	slope,
		float *	dst
	)

Multiplies output 32-bit float array by derivative of hyperbolic tangent from input 32-bit float array.

All arrays must have the same size.

For every element:

dst[i] *= slope*(1 - src[i]*src[i]);

Note: This function is used in Simd::Neural::Function.

Parameters

[in]	src	- a pointer to the input array.
[in]	size	- a size of arrays.
[in]	slope	- a pointer to the slope parameter.
[in,out]	dst	- a pointer to output array.

◆ SimdNeuralDerivativeRelu()

void SimdNeuralDerivativeRelu	(	const float *	src,
		size_t	size,
		const float *	slope,
		float *	dst
	)

Multiplies output 32-bit float array by derivative of Relu (rectified linear unit) from input 32-bit float array.

All arrays must have the same size.

For every element:

dst[i] *=  src[i] > 0 ? 1 : slope;

Note: This function is used in Simd::Neural::Function.

Parameters

[in]	src	- a pointer to the input array.
[in]	size	- a size of arrays.
[in]	slope	- a pointer to the slope parameter.
[in,out]	dst	- a pointer to output array.

◆ SimdNeuralPow()

void SimdNeuralPow	(	const float *	src,
		size_t	size,
		const float *	exponent,
		float *	dst
	)

Calculates Pow function for 32-bit float array.

All arrays must have the same size.

For every element:

dst[i] =  Pow(src[i], exponent[0]);

Note: This function is used in Simd::Neural::Function.

Parameters

[in]	src	- a pointer to the input array.
[in]	size	- a size of arrays.
[in]	exponent	- a pointer to exponent parameter.
[out]	dst	- a pointer to output array.

◆ SimdNeuralProductSum()

void SimdNeuralProductSum	(	const float *	a,
		const float *	b,
		size_t	size,
		float *	sum
	)

Calculates sum of products for two 32-bit float arrays.

All arrays must have the same size.

For every element:

sum += a[i]*b[i];

Note: This function is used in Simd::Neural.

Parameters

[in]	a	- a pointer to the first 32-bit float array.
[in]	b	- a pointer to the second 32-bit float array.
[in]	size	- a size of arrays.
[out]	sum	- a pointer to 32-bit float sum of products.

◆ SimdNeuralAddVectorMultipliedByValue()

void SimdNeuralAddVectorMultipliedByValue	(	const float *	src,
		size_t	size,
		const float *	value,
		float *	dst
	)

Adds the product of a vector and a scalar to given vector.

All arrays must have the same size.

For every element:

dst[i] += src[i]*value[0];

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float array.
[in]	size	- a size of arrays.
[in]	value	- a pointer to the scalar 32-bit float value.
[in,out]	dst	- a pointer to cumulative 32-bit float array.

◆ SimdNeuralAddVector()

void SimdNeuralAddVector	(	const float *	src,
		size_t	size,
		float *	dst
	)

Adds a vector to given vector.

All arrays must have the same size.

For every element:

dst[i] += src[i];

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float array.
[in]	size	- a size of the arrays.
[in,out]	dst	- a pointer to cumulative 32-bit float array.

◆ SimdNeuralAddValue()

void SimdNeuralAddValue	(	const float *	value,
		float *	dst,
		size_t	size
	)

Adds a value to each elements of given vector.

For every element:

dst[i] += value;

Note: This function is used in Simd::Neural.

Parameters

[in]	value	- a pointer to the scalar 32-bit float value.
[in,out]	dst	- a pointer to cumulative 32-bit float array.
[in]	size	- a size of the array.

◆ SimdNeuralUpdateWeights()

void SimdNeuralUpdateWeights	(	const float *	x,
		size_t	size,
		const float *	a,
		const float *	b,
		float *	d,
		float *	w
	)

Updates ANN weights.

All arrays must have the same size.

The algorithm performs:

for (size_t k = 0; k < size; ++k)
{
    d[k] = a[0]*d[k] + b[0]*x[k];
    w[k] += d[k];
}

Parameters

[in]	x	- a pointer to the X array.
[in]	size	- a size of arrays.
[in]	a	- a pointer to the first parameter.
[in]	b	- a pointer to the second parameter.
[in,out]	d	- a pointer to the D array.
[in,out]	w	- a pointer to the W array.

◆ SimdNeuralAdaptiveGradientUpdate()

void SimdNeuralAdaptiveGradientUpdate	(	const float *	delta,
		size_t	size,
		size_t	batch,
		const float *	alpha,
		const float *	epsilon,
		float *	gradient,
		float *	weight
	)

Updates neural network weights with using of adaptive gradients method.

Adaptive gradients method. J Duchi, E Hazan and Y Singer, "Adaptive subgradient methods for online learning and stochastic optimization" The Journal of Machine Learning Research, pages 2121-2159, 2011.

The algorithm performs:

for (i = 0; i < size; ++i)
{
    d = delta[i]/batch;
    gradient[i] += d*d;
    weight[i] -= alpha * d / sqrt(gradient[i] + epsilon);
}

Note: All arrays must have the same size. This function is used in Simd::Neural.

Parameters

[in]	delta	- a pointer to the array with error (delta).
[in]	size	- a size of arrays.
[in]	batch	- a batch size.
[in]	alpha	- a pointer to alpha parameter (update speed).
[in]	epsilon	- a pointer to epsilon parameter (a small number used to avoid division by zero).
[in,out]	gradient	- a pointer to the array with gradients.
[in,out]	weight	- a pointer to the array with weights.

◆ SimdNeuralAddConvolution2x2Forward()

void SimdNeuralAddConvolution2x2Forward	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		const float *	weights,
		float *	dst,
		size_t	dstStride
	)

Adds 2x2 convolution of 32-bit float image.

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the output image (input image width must be equal to output image width + 1).
[in]	height	- a height of the output image (input image height must be equal to output image height + 1).
[in]	weights	- a pointer to the array with weights (its size must be at least 4).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralAddConvolution3x3Forward()

void SimdNeuralAddConvolution3x3Forward	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		const float *	weights,
		float *	dst,
		size_t	dstStride
	)

Adds 3x3 convolution of 32-bit float image.

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the output image (input image width must be equal to output image width + 2).
[in]	height	- a height of the output image (input image height must be equal to output image height + 2).
[in]	weights	- a pointer to the array with weights (its size must be at least 9).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralAddConvolution4x4Forward()

void SimdNeuralAddConvolution4x4Forward	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		const float *	weights,
		float *	dst,
		size_t	dstStride
	)

Adds 4x4 convolution of 32-bit float image.

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the output image (input image width must be equal to output image width + 3).
[in]	height	- a height of the output image (input image height must be equal to output image height + 3).
[in]	weights	- a pointer to the array with weights (its size must be at least 16).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralAddConvolution5x5Forward()

void SimdNeuralAddConvolution5x5Forward	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		const float *	weights,
		float *	dst,
		size_t	dstStride
	)

Adds 5x5 convolution of 32-bit float image (forward propagation).

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the output image (input image width must be equal to output image width + 4).
[in]	height	- a height of the output image (input image height must be equal to output image height + 4).
[in]	weights	- a pointer to the array with weights (its size must be at least 25).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralAddConvolution2x2Backward()

void SimdNeuralAddConvolution2x2Backward	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		const float *	weights,
		float *	dst,
		size_t	dstStride
	)

Adds 2x2 convolution of 32-bit float image (backward propagation).

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the input image (output image width must be equal to input image width + 1).
[in]	height	- a height of the input image (output image height must be equal to input image height + 1).
[in]	weights	- a pointer to the array with weights (its size must be at least 4).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralAddConvolution3x3Backward()

void SimdNeuralAddConvolution3x3Backward	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		const float *	weights,
		float *	dst,
		size_t	dstStride
	)

Adds 3x3 convolution of 32-bit float image (backward propagation).

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the input image (output image width must be equal to input image width + 2).
[in]	height	- a height of the input image (output image height must be equal to input image height + 2).
[in]	weights	- a pointer to the array with weights (its size must be at least 9).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralAddConvolution4x4Backward()

void SimdNeuralAddConvolution4x4Backward	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		const float *	weights,
		float *	dst,
		size_t	dstStride
	)

Adds 4x4 convolution of 32-bit float image (backward propagation).

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the input image (output image width must be equal to input image width + 3).
[in]	height	- a height of the input image (output image height must be equal to input image height + 3).
[in]	weights	- a pointer to the array with weights (its size must be at least 16).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralAddConvolution5x5Backward()

void SimdNeuralAddConvolution5x5Backward	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		const float *	weights,
		float *	dst,
		size_t	dstStride
	)

Adds 5x5 convolution of 32-bit float image (backward propagation).

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the input image (output image width must be equal to input image width + 4).
[in]	height	- a height of the input image (output image height must be equal to input image height + 4).
[in]	weights	- a pointer to the array with weights (its size must be at least 25).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralAddConvolution2x2Sum()

void SimdNeuralAddConvolution2x2Sum	(	const float *	src,
		size_t	srcStride,
		const float *	dst,
		size_t	dstStride,
		size_t	width,
		size_t	height,
		float *	sums
	)

Accumulates changes of weights for 2x2 convolution of 32-bit float image during backward propagation.

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).
[in]	width	- a width of the output image (input image width must be equal to output image width + 1).
[in]	height	- a height of the output image (input image height must be equal to output image height + 1).
[in,out]	sums	- a pointer to the array with changes of weights (its size must be at least 4).

◆ SimdNeuralAddConvolution3x3Sum()

void SimdNeuralAddConvolution3x3Sum	(	const float *	src,
		size_t	srcStride,
		const float *	dst,
		size_t	dstStride,
		size_t	width,
		size_t	height,
		float *	sums
	)

Accumulates changes of weights for 3x3 convolution of 32-bit float image during backward propagation.

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).
[in]	width	- a width of the output image (input image width must be equal to output image width + 2).
[in]	height	- a height of the output image (input image height must be equal to output image height + 2).
[in,out]	sums	- a pointer to the array with changes of weights (its size must be at least 9).

◆ SimdNeuralAddConvolution4x4Sum()

void SimdNeuralAddConvolution4x4Sum	(	const float *	src,
		size_t	srcStride,
		const float *	dst,
		size_t	dstStride,
		size_t	width,
		size_t	height,
		float *	sums
	)

Accumulates changes of weights for 4x4 convolution of 32-bit float image during backward propagation.

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).
[in]	width	- a width of the output image (input image width must be equal to output image width + 3).
[in]	height	- a height of the output image (input image height must be equal to output image height + 3).
[in,out]	sums	- a pointer to the array with changes of weights (its size must be at least 16).

◆ SimdNeuralAddConvolution5x5Sum()

void SimdNeuralAddConvolution5x5Sum	(	const float *	src,
		size_t	srcStride,
		const float *	dst,
		size_t	dstStride,
		size_t	width,
		size_t	height,
		float *	sums
	)

Accumulates changes of weights for 5x5 convolution of 32-bit float image during backward propagation.

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).
[in]	width	- a width of the output image (input image width must be equal to output image width + 4).
[in]	height	- a height of the output image (input image height must be equal to output image height + 4).
[in,out]	sums	- a pointer to the array with changes of weights (its size must be at least 25).

◆ SimdNeuralPooling1x1Max3x3()

void SimdNeuralPooling1x1Max3x3	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		float *	dst,
		size_t	dstStride
	)

Takes maximum value in 3x3 window of input 32-bit float image and copies to the output image.

Note: This function is used in Simd::Neural. Output image must have the same size.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the input image.
[in]	height	- a height of the input image.
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralPooling2x2Max2x2()

void SimdNeuralPooling2x2Max2x2	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		float *	dst,
		size_t	dstStride
	)

Reduces input 32-bit float image in two times (takes maximum value in 2x2 window and copies to the output image).

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the input image (output image width must have size (width + 1)/2).
[in]	height	- a height of the input image (output image height must have size (height + 1)/2).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralPooling2x2Max3x3()

void SimdNeuralPooling2x2Max3x3	(	const float *	src,
		size_t	srcStride,
		size_t	width,
		size_t	height,
		float *	dst,
		size_t	dstStride
	)

Reduces input 32-bit float image in two times (takes maximum value in 3x3 window and copies to the output image).

Note: This function is used in Simd::Neural.

Parameters

[in]	src	- a pointer to the input 32-bit float image.
[in]	srcStride	- a row size of the input image (in 32-float values).
[in]	width	- a width of the input image (output image width must have size width/2).
[in]	height	- a height of the input image (output image height must have size height/2).
[in,out]	dst	- a pointer to the output 32-bit float image.
[in]	dstStride	- a row size of the output image (in 32-float values).

◆ SimdNeuralConvolutionForward()

void SimdNeuralConvolutionForward	(	const float *	src,
		size_t	srcWidth,
		size_t	srcHeight,
		size_t	srcDepth,
		const float *	weight,
		size_t	kernelX,
		size_t	kernelY,
		size_t	padX,
		size_t	padY,
		size_t	strideX,
		size_t	strideY,
		size_t	dilationX,
		size_t	dilationY,
		void *	buffer,
		size_t *	size,
		float *	dst,
		size_t	dstWidth,
		size_t	dstHeight,
		size_t	dstDepth,
		int	add
	)

Adds convolution of the input multichannel 32-bit float image to the output multichannel 32-bit float image.

Note

There is a restriction to the size of output image:

dstWidth = (srcWidth + 2 * padX - (dilationX * (kernelX - 1) + 1)) / strideX + 1.
dstHeight = (srcHeight + 2 * padY - (dilationY * (kernelY - 1) + 1)) / strideY + 1.

Parameters

[in]	src	- a pointer to the input multichannel 32-bit float image. Total size of the input image is equal to srcWidthsrcHeightsrcDepth.
[in]	srcWidth	- a width of the input image.
[in]	srcHeight	- a height of the input image.
[in]	srcDepth	- a number of channels in the input image.
[in]	weight	- a pointer to the convolution weights. Total size of the weights is equal to `kernelXkernelYsrcDepth*dstDepth`.
[in]	kernelX	- a width of the convolution kernel.
[in]	kernelY	- a height of the convolution kernel.
[in]	padX	- a pad to the x-coordinate of the input image.
[in]	padY	- a pad to the y-coordinate of the input image.
[in]	strideX	- a x-stride of the convolution.
[in]	strideY	- a y-stride of the convolution.
[in]	dilationX	- a x-stride of the convolution.
[in]	dilationY	- a y-stride of the convolution.
[in,out]	buffer	- a pointer to the external temporal buffer used by the algorithm. Can be NULL (the algorithm uses internal buffer).
[in,out]	size	- a pointer to the size of the external temporal buffer. If the size is too small it will contain required value. Required size is approximately equal to `dstWidthdstHeightsrcDepthkernelXkernelY*sizeof(float)`. Can be NULL.
[in,out]	dst	- a pointer to the output multichannel 32-bit float image. Total size of the output image is equal to `dstWidthdstHeightdstDepth`.
[in]	dstWidth	- a width of the output image.
[in]	dstHeight	- a height of the output image.
[in]	dstDepth	- a number of channels in the output image.
[in]	add	- a flag which signalizes that we want add or assign value of convolution to the output image.

◆ NeuralConvert()

void NeuralConvert	(	const View< A > &	src,
		float *	dst,
		size_t	stride,
		bool	inversion
	)

Converts a 8-bit gray image to the 32-bit float array.

The length of output array must be equal to the area of input image.

For every point:

dst[i] = inversion ? (255 - src[col]) / 255 : src[i]/255;

Note: This function is a C++ wrapper for function SimdNeuralConvert.

Parameters

[in]	src	- an input image.
[out]	dst	- a pointer to output array.
[in]	stride	- a row size of the output array.
[in]	inversion	- a flag of color inversion.

Simd Library Documentation.

Functions

Detailed Description

Function Documentation

◆ SimdNeuralConvert()

◆ SimdNeuralDerivativeSigmoid()

◆ SimdNeuralDerivativeTanh()

◆ SimdNeuralDerivativeRelu()

◆ SimdNeuralPow()

◆ SimdNeuralProductSum()

◆ SimdNeuralAddVectorMultipliedByValue()

◆ SimdNeuralAddVector()

◆ SimdNeuralAddValue()

◆ SimdNeuralUpdateWeights()

◆ SimdNeuralAdaptiveGradientUpdate()

◆ SimdNeuralAddConvolution2x2Forward()

◆ SimdNeuralAddConvolution3x3Forward()

◆ SimdNeuralAddConvolution4x4Forward()

◆ SimdNeuralAddConvolution5x5Forward()

◆ SimdNeuralAddConvolution2x2Backward()

◆ SimdNeuralAddConvolution3x3Backward()

◆ SimdNeuralAddConvolution4x4Backward()

◆ SimdNeuralAddConvolution5x5Backward()

◆ SimdNeuralAddConvolution2x2Sum()

◆ SimdNeuralAddConvolution3x3Sum()

◆ SimdNeuralAddConvolution4x4Sum()

◆ SimdNeuralAddConvolution5x5Sum()

◆ SimdNeuralPooling1x1Max3x3()

◆ SimdNeuralPooling2x2Max2x2()

◆ SimdNeuralPooling2x2Max3x3()

◆ SimdNeuralConvolutionForward()

◆ NeuralConvert()