Implementing Encoder Layers in Transformer using Functional API

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This is gonna be fun, I promise.

First things first: let’s start with some code snippets that will make your eyes bleed and your brain hurt. But don’t worry, we’ll explain what each line does as we go along. Here’s a basic encoder layer using the functional API in TensorFlow 2.0:

# This is a basic encoder layer using the functional API in TensorFlow 2.0

# Import necessary libraries
from tensorflow import keras
import numpy as np

# Define the EncoderLayer class
class EncoderLayer(keras.layers.Layer):
    # Initialize the class with d_model and num_heads parameters
    def __init__(self, d_model, num_heads):
        # Call the parent class constructor
        super().__init__()
        # Set the d_model and num_heads attributes
        self.d_model = d_model
        self.num_heads = num_heads
        
        # Define the attention mechanism weights and biases
        # Create a dense layer for query, key and value matrices
        self.query_kernel = keras.layers.Dense(d_model)
        self.key_kernel = keras.layers.Dense(d_model)
        self.value_kernel = keras.layers.Dense(d_model)
        
        # Define the feed-forward neural network weights and biases
        # Create two dense layers for the feed-forward network
        self.ffn1_kernel = keras.layers.Dense(4 * d_model, activation='relu')
        self.ffn2_kernel = keras.layers.Dense(d_model)
        
    # Define the call method to perform the operations of the encoder layer
    def call(self, inputs):
        # Split the input sequence into query, key and value matrices
        # Use the split function from TensorFlow to split the input into 3 parts along the second axis
        queries, keys, values = tf.split(inputs, 3, axis=1)
        
        # Calculate attention scores using dot product between query and key matrices
        # Use the dot function from Keras backend to perform dot product between queries and keys
        # Transpose the keys matrix to match the dimensions for dot product
        # Divide by square root of d_model to scale the scores
        attention_scores = keras.backend.dot(queries, self.query_kernel(keys), transpose_b=True) / np.sqrt(self.d_model)
        
        # Apply softmax to get the weights for each value matrix
        # Use the softmax function from TensorFlow to calculate the weights along the last axis
        attention_weights = tf.nn.softmax(attention_scores, axis=-1)
        
        # Calculate context vector by weighted sum of value matrices
        # Use the sum function from Keras backend to perform weighted sum of values and weights
        # Sum along the second axis to get the context vector
        context_vector = keras.backend.sum(values * attention_weights, axis=1)
        
        # Apply feed-forward neural network to the context vector
        # Pass the context vector through the first dense layer with relu activation
        # Pass the output through the second dense layer
        x = self.ffn1_kernel(context_vector)
        x = self.ffn2_kernel(x)
        
        # Return the output of the feed-forward network
        return x

Wow, that’s a lot of code! Let’s break it down:

– We define the `EncoderLayer` class which inherits from keras.layers.Layer and takes two arguments (d_model and num_heads). These are hyperparameters for our transformer model.
– Inside the constructor, we initialize some weights and biases for the attention mechanism and feed-forward neural network using keras.layers.Dense(). We also define the activation function for the feed-forward neural network as ‘relu’.
– In the `call` method, we split the input sequence into query, key and value matrices using tf.split(). This is a common operation in transformers to handle multi-head attention.
– Next, we calculate the attention scores between the queries and keys using dot product (keras.backend.dot()) and apply softmax to get the weights for each value matrix.
– Finally, we calculate the context vector by weighted sum of value matrices and pass it through a feed-forward neural network before returning the output.

And that’s it! You now have an encoder layer using functional API in TensorFlow 2.0. It may not be pretty or elegant, but it works.

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