Deconv1D

Warning

Documentation for the module is under development.

Description

Info

Parent class: DeconvND

Derived classes: -

This module performs the operation of 1-dimensional transposed convolution. For more detailed theoretical information about the transposed convolution operation, see DeconvND.

Initializing

def __init__(self, inmaps, outmaps, size, stride=1, pad=0, dilation=1, wscale=1.0, useBias=True, name=None,
                 initscheme=None, empty=False, groups=1):

Parameters

Parameter	Allowed types	Description	Default
inmaps	int	Number of maps in the input tensor	-
outmaps	int	Number of maps in the output tensor	-
size	int	Convolution kernel size	-
stride	int	Convolution stride	1
pad	int	Map padding	0
dilation	int	Convolution window dilation	1
wscale	float	Random layer weights variance	1.0
useBias	bool	Whether to use the bias vector	True
initscheme	Union[tuple, str]	Specifies the layer weights initialization scheme (see createTensorWithScheme)	None -> ("xavier_uniform", "in")
name	str	Layer name	None
empty	bool	Whether to initialize the matrix of weights and biases	False
groups	int	Number of groups the maps are split into for separate processing	1

Explanations

-

Examples

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Basic deconvolution example

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Necessary imports

import numpy as np
from PuzzleLib.Backend import gpuarray
from PuzzleLib.Modules import Deconv1D

Info

gpuarray is required to properly place the tensor in the GPU.

batchsize, inmaps, l = 1, 2, 5
outsize = 2

Synthetic tensor:

data = gpuarray.to_gpu(np.arange(batchsize * inmaps * l).reshape((batchsize, inmaps, l)).astype(np.float32))
deconv = Deconv1D(inmaps=inmaps, outmaps=outsize, size=2, useBias=False)
print(deconv(data))

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Size parameter

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deconv = Deconv1D(inmaps=inmaps, outmaps=outsize, size=3, useBias=False)
print(deconv(data))

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Pad parameter

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deconv = Deconv1D(inmaps=inmaps, outmaps=outsize, size=3, pad=1, useBias=False)
print(deconv(data))

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Stride parameter

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deconv = Deconv1D(inmaps=inmaps, outmaps=outsize, size=2, stride=2, useBias=False)
print(deconv(data))

deconv = Deconv1D(inmaps=inmaps, outmaps=outsize, size=2, stride=2, pad=3, useBias=False)
print(deconv(data))

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Dilation parameter

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deconv = Deconv1D(inmaps=inmaps, outmaps=outsize, size=2, stride=1, pad=0, dilation=2, useBias=False)
print(deconv(data))

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Groups parameter

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deconv = Deconv1D(inmaps=inmaps, outmaps=outsize, size=2, groups=2, useBias=False)
print(deconv(data))