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Generate a neural network architecture Image

Project description

Neural Network Visualizer

General Description

A module which creates a neural network image with the given architecture. It's a handy tool to see how your network is built as compared to a model summary.

Installation

Before installation

Before installing the module, run the below command at your prompt to install the graphviz

$ sudo apt install graphviz

Normal installation

$ sudo pip3 install neuralnet-visualize

Development installation

$ git clone https://github.com/AnuragAnalog/nn_visualize.git
$ cd nn_visualize

After installation

After installing the module, if you want to upgrade the module, run the below command.

sudo pip3 install neuralnet-visualize --upgrade

Future Works

  • <input type="checkbox" checked="" disabled="" /> Add Convolutional layers, Maxpooling, Flatten layers
  • <input type="checkbox" disabled="" /> Add Sequence model layers
  • <input type="checkbox" disabled="" /> Directly from the pickle files
  • <input type="checkbox" disabled="" /> Specific colors for activation functions
  • <input type="checkbox" disabled="" /> Directly convert from pytorch models

Neural Network Visualizer Version History

0.2.3

  • Added from_pytorch method
  • Fixed a bug of multiple titles in an image
  • Changed the logic of from_tensorflow method
  • Fixed a bug, unique layer names for non trainable layers

0.2.2

  • Added more docstring
  • Can now be downloaded in py2

0.2.1

  • Added title to the image
  • Optimized some code

0.2.0

  • Made it independent of tensorflow
  • Fixed some bugs

0.1.4

  • Fixed some bugs
  • Added more layers maxpooling, avgpooling, flatten
  • changed the shape of conv2d layer

0.1.3

  • Added class docstrings with examples
  • Added some more parameters for conv layer
  • Disabled custom addition of layers after from_tensorflow call

0.1.2

  • Added Conv layer, with kernel size parameter
  • Refactored add_layer function
  • Added Conv layer to from_tensorflow

0.1.1

  • Added some possible exceptions
  • Restructred basic functions in while initial import

0.1.0

  • Added documentation

0.0.4

  • Refactored summarize function
  • Added a code part which colors output layer to red
  • Added from_tensorflow method

0.0.3

  • Added more display orientations
  • Refactored the build network and add layer code
  • Added some more file extensions to which image can be saved
  • Added summarize function

0.0.2

  • Added colors to type of layers
  • Added Dense layers

0.0.1

Intial Version

  • Starting code

Project details


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