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
- Add Convolutional layers, Maxpooling, Flatten layers
- Add Sequence model layers
- Directly from the pickle files
- Specific colors for activation functions
- 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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file neuralnet_visualize-0.2.3.tar.gz
.
File metadata
- Download URL: neuralnet_visualize-0.2.3.tar.gz
- Upload date:
- Size: 7.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.41.0 CPython/3.6.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 51c8b62a02c255226b330e989820931924157ecca368c833660a8df250b1fe9d |
|
MD5 | 95f2e7b02dfe85b5a4759428378737ff |
|
BLAKE2b-256 | f8a5aa14fc5fb4986cdf30bfc929fcceefdc8345dfe04e53cd7db1962d04ccc1 |
File details
Details for the file neuralnet_visualize-0.2.3-py3-none-any.whl
.
File metadata
- Download URL: neuralnet_visualize-0.2.3-py3-none-any.whl
- Upload date:
- Size: 12.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.41.0 CPython/3.6.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 08309efa6a885599c1f0b2fae766f6af12b88595b70ea33387a2bfffef4a1b4d |
|
MD5 | 91cf27184ed50d29ce79c96977f8a801 |
|
BLAKE2b-256 | e3ce3dbdea4b7dcad23fd58337d679b9f269b605c68dfe9d5c8a5d51c9f79dcf |