https://github.com/spratiher9/netplot

A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures
https://github.com/spratiher9/netplot

keras python visualization

Last synced: 17 days ago
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A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures

• Host: GitHub
• URL: https://github.com/spratiher9/netplot
• Owner: Spratiher9
• License: mit
• Created: 2021-09-20T17:34:43.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2021-10-27T05:16:53.000Z (about 3 years ago)
• Last Synced: 2024-10-09T12:33:17.053Z (28 days ago)
• Topics: keras, python, visualization
• Language: Python
• Homepage:
• Size: 10.9 MB
• Stars: 19
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
![NETPLOT](https://github.com/Spratiher9/Netplot/blob/5a7b0807114bd858deeb99e17c893b749ab95b93/Netplot.png)

## Netplot🚀   [![Downloads](https://static.pepy.tech/personalized-badge/netplot?period=total&units=international_system&left_color=black&right_color=orange&left_text=PYPI%20Downloads)](https://pepy.tech/project/netplot)

A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures.

This Library is working on Matplotlib visualization for now. In future the visualization can be moved to plotly 

for a more interactive visual of the neural network architecture. 

**Note:** *For now the rendering is working in Jupyter only Google Colab support is in works.*  

For more details visit [NetPlot](https://pypi.org/project/netplot/0.1.2/)

### How to use it

![NetPLOT DEMO Notebook](https://github.com/Spratiher9/Netplot/blob/1e16251651d4c947c7a33fd7bac2f7701d7d162b/NetPLOT_demo.gif)

### Install with Pip

```python

pip install netplot

```

### Notebook Codelets

```python

from netplot import ModelPlot

import tensorflow as tf

import numpy as np

```

```python

%matplotlib notebook

```

```python

X_input = tf.keras.layers.Input(shape=(32,32,3))

X = tf.keras.layers.Conv2D(4, 3, activation='relu')(X_input)

X = tf.keras.layers.MaxPool2D(2,2)(X)

X = tf.keras.layers.Conv2D(16, 3, activation='relu')(X)

X = tf.keras.layers.MaxPool2D(2,2)(X)

X = tf.keras.layers.Conv2D(8, 3, activation='relu')(X)

X = tf.keras.layers.MaxPool2D(2,2)(X)

X = tf.keras.layers.Flatten()(X)

X = tf.keras.layers.Dense(10, activation='relu')(X)

X = tf.keras.layers.Dense(2, activation='softmax')(X)

model = tf.keras.models.Model(inputs=X_input, outputs=X)

```

```python

modelplot = ModelPlot(model=model, grid=True, connection=True, linewidth=0.1)

modelplot.show()

```

![Keras Model Summarized](https://github.com/Spratiher9/Netplot/blob/master/screenshot%20demo/model_summary.png) 

![Keras Model Visualized](https://github.com/Spratiher9/Netplot/blob/master/screenshot%20demo/ModelPlot%203D%20with%20grid.png)