Having seen how to implement the scaled dot-product attention and integrate it within the multi-head attention of the Transformer model, let’s progress one step further toward implementing a complete Transformer model by applying its encoder. Our end goal remains to apply the complete model to Natural Language Processing (NLP). In this tutorial, you will discover how to implement the Transformer encoder from scratch in TensorFlow and Keras.  After completing this tutorial, you will know: The layers that form part of the […]

There are many similarities between the Transformer encoder and decoder, such as their implementation of multi-head attention, layer normalization, and a fully connected feed-forward network as their final sub-layer. Having implemented the Transformer encoder, we will now go ahead and apply our knowledge in implementing the Transformer decoder as a further step toward implementing the complete Transformer model. Your end goal remains to apply the complete model to Natural Language Processing (NLP). In this tutorial, you will discover how to […]

We have arrived at a point where we have implemented and tested the Transformer encoder and decoder separately, and we may now join the two together into a complete model. We will also see how to create padding and look-ahead masks by which we will suppress the input values that will not be considered in the encoder or decoder computations. Our end goal remains to apply the complete model to Natural Language Processing (NLP). In this tutorial, you will discover […]

We have put together the complete Transformer model, and now we are ready to train it for neural machine translation. We shall use a training dataset for this purpose, which contains short English and German sentence pairs. We will also revisit the role of masking in computing the accuracy and loss metrics during the training process.  In this tutorial, you will discover how to train the Transformer model for neural machine translation.  After completing this tutorial, you will know: How […]

from tensorflow.keras.optimizers import Adam from tensorflow.keras.optimizers.schedules import LearningRateSchedule from tensorflow.keras.metrics import Mean from tensorflow import data, train, math, reduce_sum, cast, equal, argmax, float32, GradientTape, function from keras.losses import sparse_categorical_crossentropy from model import TransformerModel from prepare_dataset import PrepareDataset from

We have seen how to train the Transformer model on a dataset of English and German sentence pairs and how to plot the training and validation loss curves to diagnose the model’s learning performance and decide at which epoch to run inference on the trained model. We are now ready to run inference on the trained Transformer model to translate an input sentence. In this tutorial, you will discover how to run inference on the trained Transformer model for neural […]

As we learned what a Transformer is and how we might train the Transformer model, we notice that it is a great tool to make a computer understand human language. However, the Transformer was originally designed as a model to translate one language to another. If we repurpose it for a different task, we would likely need to retrain the whole model from scratch. Given the time it takes to train a Transformer model is enormous, we would like to […]