keras-coordconv

by titu1994

titu1994 /keras-coordconv

Keras implementation of CoordConv for all Convolution layers

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CoordConv for Keras

Keras implementation of CoordConv from the paper An intriguing failing of convolutional neural networks and the CoordConv solution.

Extends the

CoordinateChannel
concatenation from only 2D rank (images) to 1D (text / time series) and 3D tensors (video / voxels).

Usage

Import

coord.py
and call it before any convolution layer in order to attach the coordinate channels to the input.

There are 3 different versions of CoordinateChannel - 1D, 2D and 3D for each of

Conv1D
,
Conv2D
and
Conv3D
.
from coord import CoordinateChannel2D

prior to first conv

ip = Input(shape=(64, 64, 2)) x = CoordinateChannel2D()(ip) x = Conv2D(...)(x) # This defines the CoordConv from the paper. ... x = CoordinateChannel2D(use_radius=True)(x) x = Conv2D(...)(x) # This adds the 3rd channel for the radius.

Experiments

The experiments folder contains the

Classification
of a 64x64 grid using the coordinate index as input as in the paper for both
Uniform
and
Quadrant
datasets.

Creating the datasets

First, edit the

make_dataset.py
file to change the
type
parameter - to either
uniform
or
quadrant
. This will generate 2 folders for the datasets and several numpy files.

Uniform Dataset

The uniform dataset model can be trained and evaluated in less than 10 epochs using

train_uniform_classifier.py
.

|Train | Test | Predictions | |:---: | :---: | :-----------: | | | | |

Quadrant Dataset

The quadrant dataset model can be trained and evaluated in less than 25 epochs using

train_quadrant_classifier.py

|Train | Test | Predictions | |:---: | :---: | :-----------: | | | | |

Checks

To see if the implementation of CoordConv index concatenation is correct, please refer to the numpy implementations in the

checks
directory, for the implementation of all 3 versions.

Difference from paper

This implementation of the coordinate channels creation differs slightly from the original paper.

The major difference is that for 2/3D Convolutions, it may not be the case that the height and width are the same for all layers. The original implementation would throw an error due to shape mismatch during the concatenation.

To over come this, the

np.ones()
operation which occurs at the first of every channel is modified and a few transpose operations are added to account for this change.

This modification along with some transpose operations allows for height and width to be different and still work.

Theano Support

Theano is partially supported with the

coord_theano.py
script and using passing a static batch size to the Input layer.

Requirements

  • Keras 2.2.0+
  • Either Tensorflow or CNTK backend.
  • Matplotlib (to plot images only)

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