https://github.com/rajsoni03/neuralplot
Neural Plot is a python library for visualizing Neural Networks. It helps to plot Keras/Tensorflow model with matplotlib backend.
https://github.com/rajsoni03/neuralplot
3d deep-neural-networks graph keras library matpolotlib neural-network neuralplot tensorflow visualization
Last synced: 3 months ago
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Neural Plot is a python library for visualizing Neural Networks. It helps to plot Keras/Tensorflow model with matplotlib backend.
- Host: GitHub
- URL: https://github.com/rajsoni03/neuralplot
- Owner: Rajsoni03
- License: mit
- Created: 2020-09-29T13:57:46.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-01-16T11:58:25.000Z (over 3 years ago)
- Last Synced: 2024-10-11T04:41:25.620Z (7 months ago)
- Topics: 3d, deep-neural-networks, graph, keras, library, matpolotlib, neural-network, neuralplot, tensorflow, visualization
- Language: Jupyter Notebook
- Homepage: https://pypi.org/project/neuralplot/
- Size: 1.88 MB
- Stars: 8
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural Plot
[](https://pypi.org/project/neuralplot/)
[](https://www.python.org/)
[](https://github.com/Rajsoni03/neuralplot/blob/master/LICENSE)
[](https://colab.research.google.com/github/Rajsoni03/neuralplot/blob/master/others/Example%20Notebook.ipynb)Neural Plot is a python library for visualizing Neural Networks.
It helps to plot Keras/Tensorflow model with matplotlib backend.[](https://youtu.be/CsWnmWdk5-g "NeuralPlot")
https://youtu.be/CsWnmWdk5-g## Installation
Run the following to install:
```sh
$ pip install neuralplot
```## Example
```python
# Importing Libraries
from neuralplot import ModelPlot
import tensorflow as tf
import numpy as np
```
```python
# Uncomment while using Colab.
# %matplotlib inline# Uncomment while using jupyter notebook. This feature is not working in colab.
%matplotlib notebook
```
```python
#Creating Model
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()
```
[]()```python
modelplot = ModelPlot(model=model, grid=False, connection=True, linewidth=0.1)
modelplot.show()
```
[]()License
----
MIT