Implementing the Skip-Gram Architecture with TensorFlow
Explore how to implement the skip-gram architecture for word embeddings in TensorFlow using Keras's functional API. Learn to define hyperparameters, create embedding layers, process dual inputs, compute dot products, and compile the model for effective NLP feature learning.
We'll cover the following...
We'll cover the following...
We’ll now walk through an implementation of the skip-gram algorithm that uses the TensorFlow library.
Defining hyperparameters
First, let’s define the hyperparameters of the model. We’re free to change these hyperparameters to see how they affect final performance (for example, batch_size = 1024 or batch_size = 2048). However, since this is a simpler problem than the more complex real-world problems, we might not see any significant ...