Metadata-Version: 2.1
Name: xml2pytorch
Version: 0.0.10
Summary: Using xml to define pytorch neural networks
Home-page: https://github.com/yfzhoucs/xml2pytorch
Author: Yifan Zhou
Author-email: yfzhou.cs@gmail.com
License: UNKNOWN
Description: # xml2pytorch
        Using xml to define pytorch neural networks
        ## What can it Do
        With xml2pytorch, you can easily define neural networks in xml, and then declare them in pytorch.
        
        RNN and LSTM are not supported currently.
        ## Installation
        ### Environment
        OS independent. Python3. (Not tested on Python2, but it should work.)
        ### Install requirements
        torch>=0.4.1
        numpy>=1.15.1
        ### Installing by pip3
        pip3 install xml2pytorch
        ## Quick Start
        ### How to declare the CNN defined by a xml file
        ```
        import torch
        import xml2pytorch as xm
        
        # declare the net defined in .xml
        net = xm.convertXML(xml_filename)    
        
        # input a random tensor
        x = torch.randn(1, 3, 32, 32)
        y = net(x)
        print(y)
        ```
        ### How to define a simple CNN in xml
        ```
        <graph>
        	<net>
        		<layer>
        			<net_style>Conv2d</net_style>
        			<in_channels>3</in_channels>
        			<out_channels>6</out_channels>
        			<kernel_size>5</kernel_size>
        		</layer>	
        		<layer>
        			<net_style>ELU</net_style>
        		</layer>	
        		<layer>
        			<net_style>MaxPool2d</net_style>
        			<kernel_size>2</kernel_size>
        			<stride>2</stride>
        			<activation>logsigmoid</activation>
        		</layer>
        		<layer>
        			<net_style>Conv2d</net_style>
        			<in_channels>6</in_channels>
        			<out_channels>16</out_channels>
        			<kernel_size>5</kernel_size>
        			<activation>relu</activation>
        		</layer>	
        		<layer>
        			<net_style>MaxPool2d</net_style>
        			<kernel_size>2</kernel_size>
        			<stride>2</stride>
        			<activation>relu</activation>
        		</layer>
        		<layer>
        			<net_style>reshape</net_style>
        			<dimensions>[-1, 16*5*5]</dimensions>
        		</layer>
        		<layer>
        			<net_style>Linear</net_style>
        			<in_features>400</in_features> 
        			<out_features>120</out_features>
        			<activation>tanh</activation>
        		</layer>
        		<layer>
        			<net_style>Linear</net_style>
        			<in_features>120</in_features> 
        			<out_features>84</out_features>
        			<activation>sigmoid</activation>
        		</layer>
        		<layer>
        			<net_style>Linear</net_style>
        			<in_features>84</in_features>
        			<out_features>10</out_features>
        			<activation>softmax</activation>
        		</layer>
        	</net>
        </graph>
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
