How do I use the "group_by_window" function in TensorFlow

Question

In TensorFlow's new set of input pipeline functions, there is an ability to group sets of records together using the "group_by_window" function. It is described in the documentation here:

https://www.tensorflow.org/api_docs/python/tf/contrib/data/Dataset#group_by_window

I don't fully understand the explanation here used to describe the function, and I tend to learn best by example. I can't find any example code anywhere on the internet for this function. Could someone please whip up a barebones and runnable example of this function to show how it works, and what to give this function?

Answer 1

For tensorflow version 1.9.0 Here is a quick example I could come up with:

import tensorflow as tf
import numpy as np
components = np.arange(100).astype(np.int64)
dataset = tf.data.Dataset.from_tensor_slices(components)
dataset = dataset.apply(tf.contrib.data.group_by_window(key_func=lambda x: x%2, reduce_func=lambda _, els: els.batch(10), window_size=100)
iterator = dataset.make_one_shot_iterator()
features = iterator.get_next()
sess = tf.Session()
sess.run(features) # array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18], dtype=int64)

The first argument key_func maps every element in the dataset to a key.

The window_size defines the bucket size that is given to the reduce_fund.

In the reduce_func you receive a block of window_size elements. You can shuffle, batch or pad however you want.

EDIT for dynamic padding and bucketing using the group_by_window fucntion more here :

If you have a tf.contrib.dataset which holds (sequence, sequence_length, label) and sequence is a tensor of tf.int64:

def bucketing_fn(sequence_length, buckets):
    """Given a sequence_length returns a bucket id"""
    t = tf.clip_by_value(buckets, 0, sequence_length)
    return tf.argmax(t)

def reduc_fn(key, elements, window_size):
    """Receives `window_size` elements"""
    return elements.shuffle(window_size, seed=0)
# Create buckets from 0 to 500 with an increment of 15 -> [0, 15, 30, ... , 500]
buckets = [tf.constant(num, dtype=tf.int64) for num in range(0, 500, 15)
window_size = 1000
# Bucketing
dataset = dataset.group_by_window(
        lambda x, y, z: bucketing_fn(x, buckets), 
        lambda key, x: reduc_fn(key, x, window_size), window_size)
# You could pad it in the reduc_func, but I'll do it here for clarity
# The last element of the dataset is the dynamic sentences. By giving it tf.Dimension(None) it will pad the sencentences (with 0) according to the longest sentence.
dataset = dataset.padded_batch(batch_size, padded_shapes=(
        tf.TensorShape([]), tf.TensorShape([]), tf.Dimension(None)))
dataset = dataset.repeat(num_epochs)
iterator = dataset.make_one_shot_iterator()
features = iterator.get_next()