Awesome
Sequential model in Keras -> ASCII
by Piotr Migdał
A library for Keras for investigating architectures and parameters of sequential models.
(discontinuted) For more general approaches, see: Simple diagrams of convoluted neural networks
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.
See this library in the wild, for example:
- Starting deep learning hands-on: image classification on CIFAR-10 - my post at deepsense.ai
- 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