Feature: ASCII prints for sequential models
stared opened this issue · comments
I wanted to be able to show sequential networks in a clean and minimalistic way for didactic purpose. Both model.summary() and graph export were not enough - I wanted dimensions, numbers of parameters and activation functions in one place, at the same time without unnecessary overhead.
Bear in mind that I purposefully make no distinction between adding activation function as a keyword argument or as a separate layer (vide Activations - Keras documentation), unlike in model.summary() or SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg')).
Code: https://gist.github.com/stared/8411d4e7e457b0f14f39d700afc8511c
Should I clean and generalise it, so that it can be a part of keras/utils?
Any comments, remarks and (sub)feature requests ale welcomed! :)
Examples
Proof of principle
OPERATION DATA DIMENSIONS WEIGHTS(N) WEIGHTS(%)
Input ##### (1, 28, 28)
Convolution2D \|/ ------------------- 100 0.6%
relu ##### (10, 26, 26)
MaxPooling2D YYYYY ------------------- 0 0.0%
##### (10, 13, 13)
Flatten ||||| ------------------- 0 0.0%
##### (1690,)
Dense XXXXX ------------------- 16910 98.8%
##### (10,)
Dropout | || ------------------- 0 0.0%
relu ##### (10,)
Dense XXXXX ------------------- 110 0.6%
softmax ##### (10,)
VGG16
OPERATION DATA DIMENSIONS WEIGHTS(N) WEIGHTS(%)
Input ##### (3, 224, 224)
Convolution2D \|/ ------------------- 1792 0.0%
relu ##### (64, 224, 224)
Convolution2D \|/ ------------------- 36928 0.0%
relu ##### (64, 224, 224)
MaxPooling2D YYYYY ------------------- 0 0.0%
##### (64, 112, 112)
Convolution2D \|/ ------------------- 73856 0.1%
relu ##### (128, 112, 112)
Convolution2D \|/ ------------------- 147584 0.1%
relu ##### (128, 112, 112)
MaxPooling2D YYYYY ------------------- 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 YYYYY ------------------- 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 YYYYY ------------------- 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 YYYYY ------------------- 0 0.0%
##### (512, 7, 7)
Flatten ||||| ------------------- 0 0.0%
##### (25088,)
Dense XXXXX ------------------- 102764544 74.3%
relu ##### (4096,)
Dropout | || ------------------- 0 0.0%
##### (4096,)
Dense XXXXX ------------------- 16781312 12.1%
relu ##### (4096,)
Dropout | || ------------------- 0 0.0%
##### (4096,)
Dense XXXXX ------------------- 4097000 3.0%
##### (1000,)
||||| ------------------- 0 0.0%
softmax ##### (1000,)
Just in case, I started repo with this project: https://github.com/stared/keras-sequential-ascii