https://github.com/mahyar-amiri/keras-visualizer
A Keras Model Visualizer
https://github.com/mahyar-amiri/keras-visualizer
keras keras-visualization keras-visualizer neural-network-visualizations python tensorflow visualization
Last synced: 6 days ago
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A Keras Model Visualizer
- Host: GitHub
- URL: https://github.com/mahyar-amiri/keras-visualizer
- Owner: mahyar-amiri
- License: mit
- Created: 2020-08-08T17:51:09.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-05-09T21:22:59.000Z (about 2 years ago)
- Last Synced: 2025-08-20T23:54:30.290Z (10 months ago)
- Topics: keras, keras-visualization, keras-visualizer, neural-network-visualizations, python, tensorflow, visualization
- Language: Python
- Homepage:
- Size: 1.78 MB
- Stars: 30
- Watchers: 1
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Keras Visualizer
[](https://pypi.org/project/keras-visualizer)
[](https://pypistats.org/packages/keras-visualizer)
[](LICENSE)
[](https://vrgl.ir/5KSoN)
[](https://colab.research.google.com/github/mahyar-amiri/keras-visualizer/)
A Python Library for Visualizing Keras Models.
## Table of Contents
* [Keras Visualizer](#keras-visualizer)
* [Table of Contents](#table-of-contents)
* [Installation](#installation)
* [Install](#install)
* [Upgrade](#upgrade)
* [Usage](#usage)
* [Parameters](#parameters)
* [Settings](#settings)
* [Examples](#examples)
* [Example 1](#example-1)
* [Example 2](#example-2)
* [Example 3](#example-3)
* [Supported layers](#supported-layers)
## Installation
### Install
Use python package manager (pip) to install Keras Visualizer.
```bash
pip install keras-visualizer
```
### Upgrade
Use python package manager (pip) to upgrade Keras Visualizer.
```bash
pip install keras-visualizer --upgrade
```
## Usage
```python
from keras_visualizer import visualizer
# create your model here
# model = ...
visualizer(model, file_format='png')
```
## Parameters
```python
visualizer(model, file_name='graph', file_format=None, view=False, settings=None)
```
- `model` : a Keras model instance.
- `file_name` : where to save the visualization.
- `file_format` : file format to save 'pdf', 'png'.
- `view` : open file after process if True.
- `settings` : a dictionary of available settings.
> **Note :**
> - set `file_format='png'` or `file_format='pdf'` to save visualization file.
> - use `view=True` to open visualization file.
> - use [settings](#settings) to customize output image.
## Settings
you can customize settings for your output image. here is the default settings dictionary:
```python
settings = {
# ALL LAYERS
'MAX_NEURONS': 10,
'ARROW_COLOR': '#707070',
# INPUT LAYERS
'INPUT_DENSE_COLOR': '#2ecc71',
'INPUT_EMBEDDING_COLOR': 'black',
'INPUT_EMBEDDING_FONT': 'white',
'INPUT_GRAYSCALE_COLOR': 'black:white',
'INPUT_GRAYSCALE_FONT': 'white',
'INPUT_RGB_COLOR': '#e74c3c:#3498db',
'INPUT_RGB_FONT': 'white',
'INPUT_LAYER_COLOR': 'black',
'INPUT_LAYER_FONT': 'white',
# HIDDEN LAYERS
'HIDDEN_DENSE_COLOR': '#3498db',
'HIDDEN_CONV_COLOR': '#5faad0',
'HIDDEN_CONV_FONT': 'black',
'HIDDEN_POOLING_COLOR': '#8e44ad',
'HIDDEN_POOLING_FONT': 'white',
'HIDDEN_FLATTEN_COLOR': '#2c3e50',
'HIDDEN_FLATTEN_FONT': 'white',
'HIDDEN_DROPOUT_COLOR': '#f39c12',
'HIDDEN_DROPOUT_FONT': 'black',
'HIDDEN_ACTIVATION_COLOR': '#00b894',
'HIDDEN_ACTIVATION_FONT': 'black',
'HIDDEN_LAYER_COLOR': 'black',
'HIDDEN_LAYER_FONT': 'white',
# OUTPUT LAYER
'OUTPUT_DENSE_COLOR': '#e74c3c',
'OUTPUT_LAYER_COLOR': 'black',
'OUTPUT_LAYER_FONT': 'white',
}
```
**Note**:
* set `'MAX_NEURONS': None` to disable max neurons constraint.
* see list of color names [here](https://graphviz.org/doc/info/colors.html).
```python
from keras_visualizer import visualizer
my_settings = {
'MAX_NEURONS': None,
'INPUT_DENSE_COLOR': 'teal',
'HIDDEN_DENSE_COLOR': 'gray',
'OUTPUT_DENSE_COLOR': 'crimson'
}
# model = ...
visualizer(model, file_format='png', settings=my_settings)
```
## Examples
you can use simple examples as `.py` or `.ipynb` format in [examples directory](examples).
### Example 1
```python
from keras import models, layers
from keras_visualizer import visualizer
model = models.Sequential([
layers.Dense(64, activation='relu', input_shape=(8,)),
layers.Dense(6, activation='softmax'),
layers.Dense(32),
layers.Dense(9, activation='sigmoid')
])
visualizer(model, file_format='png', view=True)
```
---
### Example 2
```python
from keras import models, layers
from keras_visualizer import visualizer
model = models.Sequential()
model.add(layers.Conv2D(64, (3, 3), input_shape=(28, 28, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(3))
model.add(layers.Dropout(0.5))
model.add(layers.Activation('sigmoid'))
model.add(layers.Dense(1))
visualizer(model, file_format='png', view=True)
```
---
### Example 3
```python
from keras import models, layers
from keras_visualizer import visualizer
model = models.Sequential()
model.add(layers.Embedding(64, output_dim=256))
model.add(layers.LSTM(128))
model.add(layers.Dense(1, activation='sigmoid'))
visualizer(model, file_format='png', view=True)
```
## Supported layers
[Explore list of **keras layers**](https://keras.io/api/layers/)
1. Core layers
- [x] Input object
- [x] Dense layer
- [x] Activation layer
- [ ] Embedding layer
- [ ] Masking layer
- [ ] Lambda layer
2. Convolution layers
- [x] Conv1D layer
- [x] Conv2D layer
- [x] Conv3D layer
- [x] SeparableConv1D layer
- [x] SeparableConv2D layer
- [x] DepthwiseConv2D layer
- [x] Conv1DTranspose layer
- [x] Conv2DTranspose layer
- [x] Conv3DTranspose layer
3. Pooling layers
- [x] MaxPooling1D layer
- [x] MaxPooling2D layer
- [x] MaxPooling3D layer
- [x] AveragePooling1D layer
- [x] AveragePooling2D layer
- [x] AveragePooling3D layer
- [x] GlobalMaxPooling1D layer
- [x] GlobalMaxPooling2D layer
- [x] GlobalMaxPooling3D layer
- [x] GlobalAveragePooling1D layer
- [x] GlobalAveragePooling2D layer
- [x] GlobalAveragePooling3D layer
4. Reshaping layers
- [ ] Reshape layer
- [x] Flatten layer
- [ ] RepeatVector layer
- [ ] Permute layer
- [ ] Cropping1D layer
- [ ] Cropping2D layer
- [ ] Cropping3D layer
- [ ] UpSampling1D layer
- [ ] UpSampling2D layer
- [ ] UpSampling3D layer
- [ ] ZeroPadding1D layer
- [ ] ZeroPadding2D layer
- [ ] ZeroPadding3D layer
5. Regularization layers
- [x] Dropout layer
- [x] SpatialDropout1D layer
- [x] SpatialDropout2D layer
- [x] SpatialDropout3D layer
- [x] GaussianDropout layer
- [ ] GaussianNoise layer
- [ ] ActivityRegularization layer
- [x] AlphaDropout layer