maximal 1.1

TensorFlow-compatible Transformer layers and models.

# maximal

See the [Official Documentation site](https://ivanbongiorni.github.io/maximal/)

Current version: 1.0

A TensorFlow-compatible Python library that provides models and layers to implement custom Transformer neural networks.

Built on TensorFlow 2.

<a href=”url”><img src=”https://github.com/IvanBongiorni/maximal/blob/main/utils/maximal_stablediffusion_00.png” align=”center”></a> <br> Logo generated by Stable Diffusion 2.1 <br>

# Installation Its installation is straightforward:

` pip install maximal `

# How to use it? maximal is commonly called as:

` import maximal from maximal.layers import TransformerLayer, GPTLayer `

and can be used in a tf.keras model as any common layer.

# Documentation An [Official Website](https://ivanbongiorni.github.io/maximal/) is now available with documentation and tutorials.

# Elements

In layers.py: - SelfAttention: keras.Layer, computes Scaled Dot-Product Attention.

• MultiHeadSelfAttention: keras.Layer, it is a concatenation of SelfAttention layers, resized back to original input shape through linear transformation.

• PositionalEmbedding: keras.Layer, implements double Embedding layers used in Transformers literature, for tokens and positions. Positional encoding is learned through a tf.keras.layers.Embedding() layer, instead of deterministic positional encoding in the original paper.

• TransformerLayer: keras.Layer single Transformer Encoder piece. It can be used inside any Sequential() model in Keras.

• GPTLayer: keras.Layer GPT block. Similar to TransformerLayer but with causal Attention mechanism. It can be used inside any Sequential() model in Keras.

In schedules.py: - OriginalTransformerSchedule: keras.Layer implements the learning rate schedule of the original Transformer paper. It is taken from this [official TensorFlow tutorial](https://www.tensorflow.org/text/tutorials/transformer).

# Requirements ` numpy tensorflow >= 2.0 `

# Author Ivan Bongiorni. [LinkedIn](https://www.linkedin.com/in/ivan-bongiorni-b8a583164/)

# License 2020 Ivan Bongiorni

This repository is licensed under the MIT license. See [LICENCE.txt]() for further details.