https://github.com/swajayresources/layerviz
A Python Library for Visualizing Keras Models covering a variety of Layers.
https://github.com/swajayresources/layerviz
keras layers tensorflow visualization
Last synced: about 2 months ago
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A Python Library for Visualizing Keras Models covering a variety of Layers.
- Host: GitHub
- URL: https://github.com/swajayresources/layerviz
- Owner: swajayresources
- License: mit
- Created: 2024-06-17T22:16:26.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-17T22:19:50.000Z (about 2 years ago)
- Last Synced: 2024-06-18T00:02:44.752Z (about 2 years ago)
- Topics: keras, layers, tensorflow, visualization
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LayerViz
A Python Library for Visualizing Keras Models covering a variety of Layers.
## Table of Contents
* [LayerViz](#LayerViz)
* [Table of Contents](#table-of-contents)
* [Installation](#installation)
* [Install](#install)
* [Upgrade](#upgrade)
* [Usage](#usage)
* [Parameters](#parameters)
* [Settings](#settings)
* [Sample Usage](#sample-usage)
* [Supported layers](#supported-layers)
## Installation
## Install
Use python package manager (pip) to install LayerViz.
```bash
pip install LayerViz
```
### Upgrade
Use python package manager (pip) to upgrade LayerViz.
```bash
pip install LayerViz --upgrade
```
## Usage
```python
from libname imoprt layerviz
# create your model here
# model = ...
layerviz(model, file_format='png')
```
## Parameters
```python
layerviz(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
recurrent_layers = ['LSTM', 'GRU']
main_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',
# RECURRENT LAYERS
'RECURRENT_LAYER_COLOR': '#9b59b6',
'RECURRENT_LAYER_FONT': 'white',
# OUTPUT LAYER
'OUTPUT_DENSE_COLOR': '#e74c3c',
'OUTPUT_LAYER_COLOR': 'black',
'OUTPUT_LAYER_FONT': 'white',
}
for layer_type in recurrent_layers:
main_settings[layer_type + '_COLOR'] = '#9b59b6'
settings = {**main_settings, **settings} if settings is not None else {**main_settings}
max_neurons = settings['MAX_NEURONS']
```
**Note**:
* set `'MAX_NEURONS': None` to disable max neurons constraint.
* see list of color names [here](https://graphviz.org/doc/info/colors.html).
```python
my_settings = {
'MAX_NEURONS': None,
'INPUT_DENSE_COLOR': 'teal',
'HIDDEN_DENSE_COLOR': 'gray',
'OUTPUT_DENSE_COLOR': 'crimson'
}
# model = ...
layerviz(model, file_format='png', settings=my_settings)
```
## Sample Usage
📖 **Resource:** The architecture we're using below is a scaled-down version of [VGG-16](https://arxiv.org/abs/1505.06798), a convolutional neural network which came 2nd in the 2014 [ImageNet classification competition](http://image-net.org/).
For reference, the model we're using replicates TinyVGG, the computer vision architecture which fuels the [CNN explainer webpage](https://poloclub.github.io/cnn-explainer/).
```python
from keras import models, layers
from libname import layerviz
import tensorflow as tf
model_1 = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(filters=10,
kernel_size=3, # can also be (3, 3)
activation="relu",
input_shape=(224, 224, 3)), # first layer specifies input shape (height, width, colour channels)
tf.keras.layers.Conv2D(10, 3, activation="relu"),
tf.keras.layers.MaxPool2D(pool_size=2, # pool_size can also be (2, 2)
padding="valid"), # padding can also be 'same'
tf.keras.layers.Conv2D(10, 3, activation="relu"),
tf.keras.layers.Conv2D(10, 3, activation="relu"), # activation='relu' == tf.keras.layers.Activations(tf.nn.relu)
tf.keras.layers.MaxPool2D(2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(1, activation="sigmoid") # binary activation output
])
layerviz(model_1, file_name='sample1', file_format='png')
from IPython.display import Image
Image('sample1.png')
```
## Supported layers
[Explore list of **keras layers**](https://keras.io/api/layers/)
1. Core layers
- [x] Input object
- [x] Dense layer
- [x] Activation layer
- [x] 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
6. Recurrent Layers
- [x] LSTM
- [x] GRU