# 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
<!-- TOC -->
* [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)
<!-- TOC -->
## 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
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"description": "# Keras Visualizer\r\n\r\n\r\n\r\n[](https://pypi.org/project/keras-visualizer)\r\n[](https://pypistats.org/packages/keras-visualizer)\r\n[](LICENSE)\r\n[](https://vrgl.ir/5KSoN)\r\n[](https://colab.research.google.com/github/mahyar-amiri/keras-visualizer/)\r\n\r\nA Python Library for Visualizing Keras Models.\r\n\r\n## Table of Contents\r\n\r\n<!-- TOC -->\r\n\r\n* [Keras Visualizer](#keras-visualizer)\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 * [Examples](#examples)\r\n * [Example 1](#example-1)\r\n * [Example 2](#example-2)\r\n * [Example 3](#example-3)\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 Keras Visualizer.\r\n\r\n```bash\r\npip install keras-visualizer\r\n```\r\n\r\n### Upgrade\r\n\r\nUse python package manager (pip) to upgrade Keras Visualizer.\r\n\r\n```bash\r\npip install keras-visualizer --upgrade\r\n```\r\n\r\n## Usage\r\n\r\n```python\r\nfrom keras_visualizer import visualizer\r\n\r\n# create your model here\r\n# model = ...\r\n\r\nvisualizer(model, file_format='png')\r\n```\r\n\r\n## Parameters\r\n\r\n```python\r\nvisualizer(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\nsettings = {\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 # 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**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\nfrom keras_visualizer import visualizer\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\nvisualizer(model, file_format='png', settings=my_settings)\r\n```\r\n\r\n## Examples\r\n\r\nyou can use simple examples as `.py` or `.ipynb` format in [examples directory](examples).\r\n\r\n### Example 1\r\n\r\n```python\r\nfrom keras import models, layers\r\nfrom keras_visualizer import visualizer\r\n\r\nmodel = models.Sequential([\r\n layers.Dense(64, activation='relu', input_shape=(8,)),\r\n layers.Dense(6, activation='softmax'),\r\n layers.Dense(32),\r\n layers.Dense(9, activation='sigmoid')\r\n])\r\n\r\nvisualizer(model, file_format='png', view=True)\r\n```\r\n\r\n\r\n\r\n---\r\n\r\n### Example 2\r\n\r\n```python\r\nfrom keras import models, layers\r\nfrom keras_visualizer import visualizer\r\n\r\nmodel = models.Sequential()\r\nmodel.add(layers.Conv2D(64, (3, 3), input_shape=(28, 28, 3), activation='relu'))\r\nmodel.add(layers.MaxPooling2D((2, 2)))\r\nmodel.add(layers.Flatten())\r\nmodel.add(layers.Dense(3))\r\nmodel.add(layers.Dropout(0.5))\r\nmodel.add(layers.Activation('sigmoid'))\r\nmodel.add(layers.Dense(1))\r\n\r\nvisualizer(model, file_format='png', view=True)\r\n```\r\n\r\n\r\n\r\n---\r\n\r\n### Example 3\r\n\r\n```python\r\nfrom keras import models, layers\r\nfrom keras_visualizer import visualizer\r\n\r\nmodel = models.Sequential()\r\nmodel.add(layers.Embedding(64, output_dim=256))\r\nmodel.add(layers.LSTM(128))\r\nmodel.add(layers.Dense(1, activation='sigmoid'))\r\n\r\nvisualizer(model, file_format='png', view=True)\r\n```\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 - [ ] 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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