cv-net-library 0.0.3

A library of complex-valued neural networks

cv_net_library

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The idea of this library is just to implement Complex layers. so that everything else stays the same as any PyTorch code.

Installation

Using [PIP]

Only use complex-valued neural networks library: pip install cv-net-library==0.0.2

Using GitHub

Useful if you want to modify the source code and view the relevant tests: address

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To Use

Import the cv_net_library base class. then Import related classes or functions from the corresponding module.

The functions in cv_net_library has the same function as the corresponding function in Pytorch, and the prefix Complex is added before the original function. as the following modules. If you have any inquiries regarding the functionality or parameters of any function, please visit GitHub to examine the original source code.

layer

• ComplexLayers

  ComplexConv2d ComplexConv1d ComplexConv3d ComplexFlatten ComplexConvTransposed2d ComplexLinear

• ComplexDropout

  ComplexDropout2D ComplexDropout ComplexDropoutRespectively

• ComplexPooling

  ComplexAvgPool1D ComplexAvgPool2D ComplexAvgPool3D ComplexPolarAvgPooling2D ComplexMaxPool2D ComplexUnPooling2D

• ComplexUpSampooling

  ComplexUpSampling ComplexUpSamplingBilinear2d ComplexUpSamplingNearest2d

function

• ComplexBatchNorm

  ComplexBatchNorm ComplexBatchNorm1d ComplexBatchNorm2d

activation

• ComplexActivations

  complex_relu complex_elu complex_exponential complex_sigmoid complex_tanh complex_hard_sigmoid complex_leaky_relu complex_selu complex_softplus complex_softsign complex_softmax modrelu zrelu complex_cardioid sigmoid_real softmax_real_with_abs softmax_real_with_avg softmax_real_with_mult softmax_of_softmax_real_with_mult softmax_of_softmax_real_with_avg softmax_real_by_parameter softmax_real_with_polar georgiou_cdbp complex_signum mvn_activation apply_pol pol_tanh pol_sigmoid pol_selu

loss

• ComplexLoss

  ComplexAverageCrossEntropy ComplexAverageCrossEntropyAbs ComplexMeanSquareError ComplexAverageCrossEntropyIgnoreUnlabeled ComplexWeightedAverageCrossEntropy ComplexWeightedAverageCrossEntropyIgnoreUnlabeled

Example

# Make a A-ConvNets 
import torch.nn as nn
import cv_net_library
from cv_net_library.activation.ComplexActivation import complex_relu, complex_softmax
from cv_net_library.layer.ComplexLayers import ComplexLinear, ComplexConv2d
from cv_net_library.layer.ComplexDropout import ComplexDropout2D
from cv_net_library.layer.ComplexPooling import ComplexMaxPool2D
from cv_net_library.layer.ComplexUpSampling import ComplexUpSamplingBilinear2d
from cv_net_library.layer.ComplexLayers import ComplexLinear, ComplexConv2d
from cv_net_library.function.ComplexBatchNorm import ComplexBatchNorm2d, ComplexBatchNorm1d
from cv_net_library.loss.ComplexLoss import ComplexAverageCrossEntropy, ComplexAverageCrossEntropyAbs

class ComplexNet(nn.Module):

    def __init__(self):
        super(ComplexNet, self).__init__()
        self.conv1 = ComplexConv2d(1, 16, 13, 1)
        self.bn2d1 = ComplexBatchNorm2d(16, track_running_stats=False)
        self.maxpool1 = ComplexMaxPool2D(2, 2)
        self.conv2 = ComplexConv2d(16, 32, 13, 1)
        self.bn2d2 = ComplexBatchNorm2d(32, track_running_stats=False)
        self.maxpool2 = ComplexMaxPool2D(2, 2)
        self.conv3 = ComplexConv2d(32, 64, 12, 1)
        self.bn2d3 = ComplexBatchNorm2d(64, track_running_stats=False)
        self.maxpool3 = ComplexMaxPool2D(2, 2)
        self.dropout1 = ComplexDropout2D(p=0.5)
        self.conv4 = ComplexConv2d(64, 128, 10, 1)
        self.bn2d4 = ComplexBatchNorm2d(128, track_running_stats=False)
        self.conv5 = ComplexConv2d(128, 7, 6, 1)
        self.bn2d5 = ComplexBatchNorm2d(7, track_running_stats=False)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn2d1(x)
        x = complex_relu(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.bn2d2(x)
        x = complex_relu(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.bn2d3(x)
        x = complex_relu(x)
        x = self.maxpool3(x)
        x = self.dropout1(x)
        x = self.conv4(x)
        x = self.bn2d4(x)
        x = complex_relu(x)
        x = self.conv5(x)
        x = self.bn2d5(x)
        x = x.view(x.shape[0], -1)
        x = complex_softmax(x, 1)
        return x

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Update Log

0.0.3 The README file for version 0.0.2 was not updated successfully

0.0.2 fix bug, Added pip installation mode, verified the example.

0.0.1 : The original upload includes a variety of complex-valued neural networks modules.

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