# LayerViz
[License](LICENSE)
.png)
A Python Library for Visualizing Keras Models covering a variety of Layers.
## Table of Contents
<!-- TOC -->
* [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)
<!-- TOC -->
## 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
Raw data
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"description": "# LayerViz\r\n\r\n[License](LICENSE)\r\n\r\n.png)\r\n\r\nA Python Library for Visualizing Keras Models covering a variety of Layers.\r\n\r\n\r\n## Table of Contents\r\n\r\n<!-- TOC -->\r\n\r\n* [LayerViz](#LayerViz)\r\n * [Table of Contents](#table-of-contents)\r\n * [Installation](#installation)\r\n * [Install](#install)\r\n * [Upgrade](#upgrade)\r\n * [Usage](#usage)\r\n * [Parameters](#parameters)\r\n * [Settings](#settings)\r\n * [Sample Usage](#sample-usage)\r\n * [Supported layers](#supported-layers)\r\n\r\n<!-- TOC -->\r\n\r\n## Installation\r\n\r\n## Install\r\n\r\nUse python package manager (pip) to install LayerViz.\r\n\r\n```bash\r\npip install LayerViz\r\n```\r\n\r\n### Upgrade\r\n\r\nUse python package manager (pip) to upgrade LayerViz.\r\n\r\n```bash\r\npip install LayerViz --upgrade\r\n```\r\n\r\n## Usage\r\n\r\n```python\r\n\r\nfrom libname imoprt layerviz\r\n# create your model here\r\n# model = ...\r\n\r\nlayerviz(model, file_format='png')\r\n```\r\n\r\n## Parameters\r\n\r\n```python\r\nlayerviz(model, file_name='graph', file_format=None, view=False, settings=None)\r\n```\r\n\r\n- `model` : a Keras model instance.\r\n- `file_name` : where to save the visualization.\r\n- `file_format` : file format to save 'pdf', 'png'.\r\n- `view` : open file after process if True.\r\n- `settings` : a dictionary of available settings.\r\n\r\n> **Note :**\r\n> - set `file_format='png'` or `file_format='pdf'` to save visualization file.\r\n> - use `view=True` to open visualization file.\r\n> - use [settings](#settings) to customize output image.\r\n\r\n## Settings\r\n\r\nyou can customize settings for your output image. here is the default settings dictionary:\r\n\r\n```python\r\n recurrent_layers = ['LSTM', 'GRU']\r\n main_settings = {\r\n # ALL LAYERS\r\n 'MAX_NEURONS': 10,\r\n 'ARROW_COLOR': '#707070',\r\n # INPUT LAYERS\r\n 'INPUT_DENSE_COLOR': '#2ecc71',\r\n 'INPUT_EMBEDDING_COLOR': 'black',\r\n 'INPUT_EMBEDDING_FONT': 'white',\r\n 'INPUT_GRAYSCALE_COLOR': 'black:white',\r\n 'INPUT_GRAYSCALE_FONT': 'white',\r\n 'INPUT_RGB_COLOR': '#e74c3c:#3498db',\r\n 'INPUT_RGB_FONT': 'white',\r\n 'INPUT_LAYER_COLOR': 'black',\r\n 'INPUT_LAYER_FONT': 'white',\r\n # HIDDEN LAYERS\r\n 'HIDDEN_DENSE_COLOR': '#3498db',\r\n 'HIDDEN_CONV_COLOR': '#5faad0',\r\n 'HIDDEN_CONV_FONT': 'black',\r\n 'HIDDEN_POOLING_COLOR': '#8e44ad',\r\n 'HIDDEN_POOLING_FONT': 'white',\r\n 'HIDDEN_FLATTEN_COLOR': '#2c3e50',\r\n 'HIDDEN_FLATTEN_FONT': 'white',\r\n 'HIDDEN_DROPOUT_COLOR': '#f39c12',\r\n 'HIDDEN_DROPOUT_FONT': 'black',\r\n 'HIDDEN_ACTIVATION_COLOR': '#00b894',\r\n 'HIDDEN_ACTIVATION_FONT': 'black',\r\n 'HIDDEN_LAYER_COLOR': 'black',\r\n 'HIDDEN_LAYER_FONT': 'white',\r\n # RECURRENT LAYERS\r\n 'RECURRENT_LAYER_COLOR': '#9b59b6',\r\n 'RECURRENT_LAYER_FONT': 'white',\r\n # OUTPUT LAYER\r\n 'OUTPUT_DENSE_COLOR': '#e74c3c',\r\n 'OUTPUT_LAYER_COLOR': 'black',\r\n 'OUTPUT_LAYER_FONT': 'white',\r\n }\r\n\r\n\r\n for layer_type in recurrent_layers:\r\n main_settings[layer_type + '_COLOR'] = '#9b59b6'\r\n settings = {**main_settings, **settings} if settings is not None else {**main_settings}\r\n max_neurons = settings['MAX_NEURONS']\r\n```\r\n\r\n**Note**:\r\n\r\n* set `'MAX_NEURONS': None` to disable max neurons constraint.\r\n* see list of color names [here](https://graphviz.org/doc/info/colors.html).\r\n\r\n```python\r\n\r\n\r\nmy_settings = {\r\n 'MAX_NEURONS': None,\r\n 'INPUT_DENSE_COLOR': 'teal',\r\n 'HIDDEN_DENSE_COLOR': 'gray',\r\n 'OUTPUT_DENSE_COLOR': 'crimson'\r\n}\r\n\r\n# model = ...\r\n\r\nlayerviz(model, file_format='png', settings=my_settings)\r\n```\r\n## Sample Usage \r\n\u00f0\u0178\u201c\u2013 **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/).\r\n\r\nFor reference, the model we're using replicates TinyVGG, the computer vision architecture which fuels the [CNN explainer webpage](https://poloclub.github.io/cnn-explainer/).\r\n```python\r\nfrom keras import models, layers\r\nfrom libname import layerviz\r\nimport tensorflow as tf\r\nmodel_1 = tf.keras.models.Sequential([\r\n tf.keras.layers.Conv2D(filters=10, \r\n kernel_size=3, # can also be (3, 3)\r\n activation=\"relu\", \r\n input_shape=(224, 224, 3)), # first layer specifies input shape (height, width, colour channels)\r\n tf.keras.layers.Conv2D(10, 3, activation=\"relu\"),\r\n tf.keras.layers.MaxPool2D(pool_size=2, # pool_size can also be (2, 2)\r\n padding=\"valid\"), # padding can also be 'same'\r\n tf.keras.layers.Conv2D(10, 3, activation=\"relu\"),\r\n tf.keras.layers.Conv2D(10, 3, activation=\"relu\"), # activation='relu' == tf.keras.layers.Activations(tf.nn.relu)\r\n tf.keras.layers.MaxPool2D(2),\r\n tf.keras.layers.Flatten(),\r\n tf.keras.layers.Dense(1, activation=\"sigmoid\") # binary activation output\r\n])\r\n\r\nlayerviz(model_1, file_name='sample1', file_format='png')\r\n\r\nfrom IPython.display import Image\r\nImage('sample1.png')\r\n```\r\n\r\n\r\n## Supported layers\r\n\r\n[Explore list of **keras layers**](https://keras.io/api/layers/)\r\n\r\n1. Core layers\r\n - [x] Input object\r\n - [x] Dense layer\r\n - [x] Activation layer\r\n - [x] Embedding layer\r\n - [ ] Masking layer\r\n - [ ] Lambda layer\r\n\r\n2. Convolution layers\r\n - [x] Conv1D layer\r\n - [x] Conv2D layer\r\n - [x] Conv3D layer\r\n - [x] SeparableConv1D layer\r\n - [x] SeparableConv2D layer\r\n - [x] DepthwiseConv2D layer\r\n - [x] Conv1DTranspose layer\r\n - [x] Conv2DTranspose layer\r\n - [x] Conv3DTranspose layer\r\n\r\n3. Pooling layers\r\n - [x] MaxPooling1D layer\r\n - [x] MaxPooling2D layer\r\n - [x] MaxPooling3D layer\r\n - [x] AveragePooling1D layer\r\n - [x] AveragePooling2D layer\r\n - [x] AveragePooling3D layer\r\n - [x] GlobalMaxPooling1D layer\r\n - [x] GlobalMaxPooling2D layer\r\n - [x] GlobalMaxPooling3D layer\r\n - [x] GlobalAveragePooling1D layer\r\n - [x] GlobalAveragePooling2D layer\r\n - [x] GlobalAveragePooling3D layer\r\n\r\n4. 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