How to integrate custom dynamic-time-warper with TensorFlow 2.5.0?

Question

I'm currently working on a project where I have implemented a custom dynamic time warping (DTW) algorithm in Python. Now, I want to integrate this custom DTW with TensorFlow 2.5.0, specifically within a custom layer for an RNN model. The TensorFlow documentation doesn't cover this specific scenario and I have not been able to find any resources or examples that discuss this. Can anyone provide guidance on how to do this?

Here's the Python code for my custom DTW algorithm:

import numpy as np

def custom_dtw(seq1, seq2):
    # DTW algorithm implementation here...
    pass

I'm looking to use this within a custom TensorFlow layer like so:

import tensorflow as tf

class CustomDTWLayer(tf.keras.layers.Layer):
    def __init__(self):
        super(CustomDTWLayer, self).__init__()

    def call(self, inputs):
        # Use custom_dtw here...
        pass

I'd appreciate any help or pointers in the right direction. Thank you!

Answer 1

To integrate a custom dynamic time warping algorithm with TensorFlow, you'll need to wrap your DTW function in a tf.py_function, which allows you to run arbitrary Python code within a TensorFlow graph. Here's how to do this:

import tensorflow as tf

class CustomDTWLayer(tf.keras.layers.Layer):
    def __init__(self):
        super(CustomDTWLayer, self).__init__()

    def call(self, inputs):
        result = tf.py_function(custom_dtw, [inputs], tf.float32)
        return result

In this code, custom_dtw is your DTW function, [inputs] is the list of tensor inputs to your function, and tf.float32 is the output type of your function.

Note: Because tf.py_function operates outside of the TensorFlow graph, it cannot benefit from GPU acceleration and its gradients are not automatically computed.

Reference:

TensorFlow documentation on tf.py_function: https://www.tensorflow.org/api_docs/python/tf/py_function

I hope this helps!