keras-efficientnets 0.1.7
Keras implementation of EfficientNets of any configuration.
License: MIT
Requires Python: >=3.0.0


# EfficientNets in Keras
[![Build Status](https://travis-ci.org/titu1994/keras-efficientnets.svg?branch=master)](https://travis-ci.org/titu1994/keras-efficientnets)

Keras implementation of EfficientNets from the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946).

Contains code to build the EfficientNets B0-B7 from the paper, and includes weights for configurations B0-B3. B4-B7 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 B0-B7 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 inter-connected hyper parameters of the model - Resolution of the input, Depth of the Network and Width of the Network.

<p align="center">
<img src="https://latex.codecogs.com/png.latex?\inline&space;\dpi{300}&space;\bg_white&space;\begin{align*}&space;depth:&&space;d&space;=&space;\alpha&space;^&space;\phi&space;\\&space;width:&&space;w&space;=&space;\beta&space;^&space;\phi&space;\\&space;resolution:&&space;r&space;=&space;\gamma&space;^&space;\phi&space;\end{align*}" title="\begin{align*} depth:& d = \alpha ^ \phi \\ width:& w = \beta ^ \phi \\ resolution:& r = \gamma ^ \phi \end{align*}" height=25% width=25%/>
</p>

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:

<p align="center">
<img src="https://latex.codecogs.com/png.latex?\inline&space;\dpi{300}&space;\begin{align*}&space;\alpha&space;\cdot&space;\beta&space;^&space;2&space;\cdot&space;\gamma&space;^&space;2&space;&\approx&space;2&space;\\&space;\alpha&space;\ge&space;1,&space;\beta&space;\ge&space;&1,&space;\gamma&space;\ge&space;1&space;\end{align*}" title="\begin{align*} \alpha \cdot \beta ^ 2 \cdot \gamma ^ 2 &\approx 2 \\ \alpha \ge 1, \beta \ge &1, \gamma \ge 1 \end{align*}" height=25% width=25%/>
</p>

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.

<img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/params.png" height=100% width=49%> <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/flops.png" height=100% width=49%>

# Installation

## From PyPI:

```$ pip install keras_efficientnets```

## From Master branch:

```
pip install git+https://github.com/titu1994/keras-efficientnets.git

OR

git clone https://github.com/titu1994/keras-efficientnets.git
cd keras-efficientnets
pip install .
```

# Usage
Simply import `keras_efficientnets` and call either the model builder `EfficientNet` or the pre-built versions `EfficientNetBX` where `X` ranger from 0 to 7.

```python
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`.

```python
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.

```python
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.

```python
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=1e-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 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.
```