Jan 23, 2023
Advent of Code 2022 in pure TensorFlow - Day 9
In this article, we'll show two different solutions to the Advent of Code 2022 day 9 problem. Both of them are purely TensorFlow solutions. The first one, more traditional, just implement a solution algorithm using only TensorFlow's primitive operations - of course, due to some TensorFlow limitations this solution will contain some details worth reading (e.g. using a pairing function for being able to use n-dimensional tf.Tensor as keys for a mutable hashmap). The second one, instead, demonstrates how a different interpretation of the problem paves the way to completely different solutions. In particular, this solution is Keras based and uses a multi-layer convolutional model for modeling the rope movements.
Jan 14, 2023
Advent of Code 2022 in pure TensorFlow - Day 8
Solving problem 8 of the AoC 2022 in pure TensorFlow is straightforward. After all, this problem requires working on a bi-dimensional grid and evaluating conditions by rows or columns. TensorFlow is perfectly suited for this kind of task thanks to its native support for reduction operators (tf.reduce) which are the natural choice for solving problems of this type.
Dec 29, 2022
Advent of Code 2022 in pure TensorFlow - Day 7
Solving problem 7 of the AoC 2022 in pure TensorFlow allows us to understand certain limitations of the framework. This problem requires a lot of string manipulation, and TensorFlow (especially in graph mode) is not only not easy to use when working with this data type, but also it has a set of limitations I'll present in the article. Additionally, the strings to work with in problem 7 are (Unix) paths. TensorFlow has zero support for working with paths, and thus for simplifying a part of the solution, I resorted to the pathlib Python module, thus not designing a completely pure TensorFlow solution.
Dec 27, 2022
Advent of Code 2022 in pure TensorFlow - Day 6
Solving problem 6 of the AoC 2022 in pure TensorFlow allows us to understand how powerful this framework can be. In particular, problem 6 can be solved with a highly efficient and parallel solution, using just a single feature of tf.data.Dataset: interleave.