gMLP: What it is and how to use it in practice with Tensorflow and Keras?

It demonstrates near state-of-the-art results on NLP and computer vision tasks but using a lot less trainable parameters than corresponding Transformer models. The most important component of state-of-the art Transformer architectures is the attention mechanism. It is used to find what relationships between data items are important for the neural network. To spot the innovation of the gMLP, let's first understand what the already mentioned terms static parameterization and spatial projections mean. As described above, attention mechanisms change dynamically depending on the inputs.