Keras Implementation of Structured data learning with TabTransformer

This repo contains the trained model of Structured data learning with TabTransformer. The full credit goes to: Khalid Salama

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Model summary:

• The trained model uses self-attention based Transformers structure following by multiple feed forward layers in order to serve supervised and semi-supervised learning.
• The model's inputs can contain both numerical and categorical features.
• All the categorical features will be encoded into embedding vector with the same number of embedding dimensions, before adding (point-wise) with each other and feeding into a stack of Transformer blocks.
• The contextual embeddings of the categorical features after the final Transformer layer, are concatenated with the input numerical features, and fed into a final MLP block.
• A SoftMax function is applied at the end of the model.

Intended uses & limitations:

• This model can be used for both supervised and semi-supervised tasks on tabular data.

Training and evaluation data:

• This model was trained using the United States Census Income Dataset provided by the UC Irvine Machine Learning Repository. The task of the dataset is to predict whether a person is likely to be making over USD 50,000 a year (binary classification).
• The dataset consists of 14 input features: 5 numerical features and 9 categorical features.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: 'AdamW'
• learning_rate: 0.001
• weight decay: 1e-04
• loss: 'sparse_categorical_crossentropy'
• beta_1: 0.9
• beta_2: 0.999
• epsilon: 1e-07
• epochs: 50
• batch_size: 16
• training_precision: float32

Training Metrics

Model history needed

Model Plot

<details> <summary>View Model Plot</summary>

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