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https://github.com/stared/keras-sequential-ascii
ASCII summary for simple sequential models in Keras
https://github.com/stared/keras-sequential-ascii
ascii convnet deep-learning keras keras-visualization sequential-models
Last synced: 10 days ago
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ASCII summary for simple sequential models in Keras
- Host: GitHub
- URL: https://github.com/stared/keras-sequential-ascii
- Owner: stared
- Created: 2017-01-12T16:02:50.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2019-01-28T15:19:03.000Z (almost 6 years ago)
- Last Synced: 2024-10-18T21:36:18.634Z (3 months ago)
- Topics: ascii, convnet, deep-learning, keras, keras-visualization, sequential-models
- Language: Jupyter Notebook
- Size: 15.6 KB
- Stars: 126
- Watchers: 9
- Forks: 18
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-keras - keras-sequential-ascii - An ASCII summary for simple sequential models in Keras. (Network Visualisation)
README
# Sequential model in Keras -> ASCII
by [Piotr Migdał](http://p.migdal.pl/)
A library for [Keras](https://keras.io/) for investigating architectures and parameters of sequential models.
**(discontinuted)** For more general approaches, see: [Simple diagrams of convoluted neural networks](https://medium.com/inbrowserai/simple-diagrams-of-convoluted-neural-networks-39c097d2925b)
Both `model.summary()` and graph export were not enough - I wanted array dimensions, numbers of parameters and activation functions in one place.
I use it for didactic purpose.
* TODO
* Add ASCII art for more layers.
* Go beyond simple sequential models (e.g. to allow *merge* layers); any ideas how?
* Consider PRing to the main Keras repo, see [#3873](https://github.com/fchollet/keras/issues/3873).
See this library in the wild, for example:
* [Starting deep learning hands-on: image classification on CIFAR-10](https://blog.deepsense.ai/deep-learning-hands-on-image-classification/) - my post at deepsense.ai
* [Cifar-10 Classification using Keras Tutorial](https://blog.plon.io/tutorials/cifar-10-classification-using-keras-tutorial/) at Plon.io
## Installation
From PyPI:
```
pip install keras_sequential_ascii
```
Or from this repo:
```
pip install git+git://github.com/stared/keras-sequential-ascii.git
```
## Usage
```
from keras_sequential_ascii import keras2ascii
keras2ascii(model)
```
## Examples
### Proof of principle
```
OPERATION DATA DIMENSIONS WEIGHTS(N) WEIGHTS(%)
Input ##### 3 32 32
BatchNormalization μ|σ ------------------- 64 0.1%
##### 3 32 32
Convolution2D \|/ ------------------- 448 0.8%
relu ##### 16 30 30
Convolution2D \|/ ------------------- 2320 4.3%
relu ##### 16 28 28
MaxPooling2D Y max ------------------- 0 0.0%
##### 16 14 14
Convolution2D \|/ ------------------- 272 0.5%
tanh ##### 16 14 14
Flatten ||||| ------------------- 0 0.0%
##### 3136
Dense XXXXX ------------------- 50192 94.1%
##### 16
Dropout | || ------------------- 0 0.0%
##### 16
Dense XXXXX ------------------- 51 0.1%
softmax ##### 3
```
### VGG16
```
OPERATION DATA DIMENSIONS WEIGHTS(N) WEIGHTS(%)
Input ##### 3 224 224
InputLayer | ------------------- 0 0.0%
##### 3 224 224
Convolution2D \|/ ------------------- 1792 0.0%
relu ##### 64 224 224
Convolution2D \|/ ------------------- 36928 0.0%
relu ##### 64 224 224
MaxPooling2D Y max ------------------- 0 0.0%
##### 64 112 112
Convolution2D \|/ ------------------- 73856 0.1%
relu ##### 128 112 112
Convolution2D \|/ ------------------- 147584 0.1%
relu ##### 128 112 112
MaxPooling2D Y max ------------------- 0 0.0%
##### 128 56 56
Convolution2D \|/ ------------------- 295168 0.2%
relu ##### 256 56 56
Convolution2D \|/ ------------------- 590080 0.4%
relu ##### 256 56 56
Convolution2D \|/ ------------------- 590080 0.4%
relu ##### 256 56 56
MaxPooling2D Y max ------------------- 0 0.0%
##### 256 28 28
Convolution2D \|/ ------------------- 1180160 0.9%
relu ##### 512 28 28
Convolution2D \|/ ------------------- 2359808 1.7%
relu ##### 512 28 28
Convolution2D \|/ ------------------- 2359808 1.7%
relu ##### 512 28 28
MaxPooling2D Y max ------------------- 0 0.0%
##### 512 14 14
Convolution2D \|/ ------------------- 2359808 1.7%
relu ##### 512 14 14
Convolution2D \|/ ------------------- 2359808 1.7%
relu ##### 512 14 14
Convolution2D \|/ ------------------- 2359808 1.7%
relu ##### 512 14 14
MaxPooling2D Y max ------------------- 0 0.0%
##### 512 7 7
Flatten ||||| ------------------- 0 0.0%
##### 25088
Dense XXXXX ------------------- 102764544 74.3%
relu ##### 4096
Dense XXXXX ------------------- 16781312 12.1%
relu ##### 4096
Dense XXXXX ------------------- 4097000 3.0%
softmax ##### 1000
```