Keras implementation of EfficientNets of any configuration.
Project description
EfficientNets in Keras
Keras implementation of EfficientNets from the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
Contains code to build the EfficientNets B0B7 from the paper, and includes weights for configurations B0B3. B4B7 weights will be ported when made available from the Tensorflow repository.
Supports building any other configuration model of efficient nets as well, other than the B0B7 variants.
Efficient Nets and Compound Coefficeint Scaling
The core idea about Efficient Nets is the use of compound scaling  using a weighted scale of three interconnected hyper parameters of the model  Resolution of the input, Depth of the Network and Width of the Network.
When phi
, the compound coefficient, is initially set to 1, we get the base configuration  in this case EfficientNetB0
. We then use this configuration in a grid search to find the coefficients alpha
, beta
and gamma
which optimize the following objective under the constraint:
Once these coefficients for alpha
, beta
and gamma
are found, then simply scale phi
, the compound coeffieints by different amounts to get a family of models with more capacity and possibly better performance.
In doing so, and using Neural Architecture Search to get the base configuration as well as great coefficients for the above, the paper generates EfficientNets, which outperform much larger and much deeper models while using less resources during both training and evaluation.
Installation
From PyPI:
$ pip install keras_efficientnets
From Master branch:
pip install git+https://github.com/titu1994/kerasefficientnets.git
OR
git clone https://github.com/titu1994/kerasefficientnets.git
cd kerasefficientnets
pip install .
Usage
Simply import keras_efficientnets
and call either the model builder EfficientNet
or the prebuilt versions EfficientNetBX
where X
ranger from 0 to 7.
from keras_efficientnets import EfficientNetB0 model = EfficientNetB0(input_size, classes=1000, include_top=True, weights='imagenet')
To construct custom EfficientNets, use the EfficientNet
builder. The EfficientNet
builder code requires a list of BlockArgs
as input to define the structure of each block in model. A default set of BlockArgs
are provided in keras_efficientnets.config
.
from keras_efficientnets import EfficientNet, BlockArgs block_args_list = [ # First number is `input_channels`, second is `output_channels`. BlockArgs(32, 16, kernel_size=3, strides=(1, 1), num_repeat=1, se_ratio=0.25, expand_ratio=1), BlockArgs(16, 24, kernel_size=3, strides=(2, 2), num_repeat=2, se_ratio=0.25, expand_ratio=6), ... ] model = EfficientNet(input_shape, block_args_list, ...)
Computing Valid Compound Coefficients
In the paper, compound coefficients are obtained via simple grid search to find optimal values of alpha
,
beta
and gamma
while keeping phi
as 1.
This library provides a utility function to compute valid candidates that satisfy a user defined criterion function (the one from the paper is provided as the default cost function), and quickly computes the set of hyper parameters that closely satisfy the cost function (here, MSE between the value and max cost permissible).
An example is shown below which uses the default parameters from the paper. The user can change the number of coefficients as well as the cost function itself in order to get different values of the compound coefficients.
from keras_efficientnets.optimize import optimize_coefficients from keras_efficientnets.optimize import get_compound_coeff_func results = optimize_coefficients(phi=1., max_cost=2.0, search_per_coeff=10) cost_func = get_compound_coeff_func(phi=1.0, max_cost=2.0) print("Num unique configs = ", len(results)) for i in range(10): # print just the first 10 results out of 1000 results print(i + 1, results[i], "Cost :", cost_func(results[i]))
Increase the number of search scopes using search_per_coeff
to some larger int value. You could also combine this
with tol
to compute a vast set of coefficients, and then select only those that have a cost value lower than the
specified tolerance.
from keras_efficientnets.optimize import optimize_coefficients from keras_efficientnets.optimize import get_compound_coeff_func results = optimize_coefficients(phi=1., max_cost=2.0, search_per_coeff=10, tol=1e10) cost_func = get_compound_coeff_func(phi=1.0, max_cost=2.0) print("Num unique configs = ", len(results)) for i in range(10): # print just the first 10 results out of 125 results print(i + 1, results[i], "Cost :", cost_func(results[i]))
Requirements
 Tensorflow 1.13+ (CPU or GPU version must be installed before installation of this library)
 Keras 2.2.4+
References
[1] Mingxing Tan and Quoc V. Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML 2019. Arxiv link: https://arxiv.org/abs/1905.11946.
Project details
Release history Release notifications  RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Filename, size  File type  Python version  Upload date  Hashes 

Filename, size keras_efficientnets0.1.7py2.py3noneany.whl (15.4 kB)  File type Wheel  Python version py2.py3  Upload date  Hashes View 
Hashes for keras_efficientnets0.1.7py2.py3noneany.whl
Algorithm  Hash digest  

SHA256  44230997e89ade54adc26c647b0e2817b055a0e257052a61c9fe582e8c56339d 

MD5  879d72d41218c32686e1fab3eb855f06 

BLAKE2256  3a414dce4e88042b4934003b2b51cfb6a99fc446d375d5fc1ffb2fdf8e069d36 