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Natural Language Processing with TensorFlow

Implementing and Training the Model with TensorFlow

Learn to implement and train the transformer model with TensorFlow.

We'll cover the following...

We’ll now implement the model we just studied. First, let’s import a few things: 

import tensorflow_hub as hub
import tensorflow as tf
import tensorflow.keras.backend as K

Implementing the ViT model

Next, we’re going to download the pretrained ViT model from TensorFlow Hub. We’ll be using a model submitted by Sayak Paul. You can see other ViT models here.

image_encoder = hub.KerasLayer("https://tfhub.dev/sayakpaul/vit_s16_fe/1", trainable=False)

We then define an input layer to input images and pass that to the image_encoder to get the final feature vector for that image: 

image_input = tf.keras.layers.Input(shape=(224, 224, 3))
image_features = image_encoder(image_input)

We can look at the size of the final image representation by running: 

print(f"Final representation shape: {image_features.shape}")

This will output: 

Final representation shape: (None, 384)

Next, we’ll look at the details of how to implement the text-based transformer model, which will take in the image representation to generate the image caption.

Implementing the text-based decoder

Here, we’ll implement a transformer decoder model from the ground up. This is different from how we used transformer models before, where we downloaded a pretrained model and used them.

Before we implement the model itself, we’re going to implement two custom Keras layers: one for the self-attention mechanism and the other one to capture the functionality of a single layer in the transformer model. Let’s start with the self-attention layer.

Defining the self-attention layer

Here, we define the self-attention layer using the Keras subclassing API: 

class SelfAttentionLayer(tf.keras.layers.Layer):
    """ Defines the computations in the self-attention layer """
    def __init__(self, d): 
        super(SelfAttentionLayer, self).__init__() 
        # Feature dimensionality of the output 
        self.d = d
    def build(self, input_shape): 
        # Query weight matrix 
        self.Wq = self.add_weight(
            shape=(input_shape[-1], self.d),
            initializer='glorot_uniform',
            trainable=True, dtype='float32'
        )
        # Key weight matrix
        self.Wk = self.add_weight(
            shape=(input_shape[-1], self.d),
            initializer='glorot_uniform', 
            trainable=True, dtype='float32'
        )
        # Value weight matrix
        self.Wv = self.add_weight(
            shape=(input_shape[-1], self.d),
            initializer='glorot_uniform',
            trainable=True, dtype='float32'
        )
    
    def call(self, q_x, k_x, v_x, mask=None):
        q = tf.matmul(q_x,self.Wq) #[None, t, d] 
        k = tf.matmul(k_x,self.Wk) #[None, t, d] 
        v = tf.matmul(v_x,self.Wv) #[None, t, d]
        
        # Computing the final output
        h = tf.keras.layers.Attention(causal=True)([ 
            q, #q
            v, #v
            k, #k
        ], mask=[None, mask])
        # [None, t, t] . [None, t, d] => [None, t, d]
        
        return h
Implementing the self-attention layer

Here, we have to populate the logic for three functions:

• __init__() and __build__(): Define various hyperparameters and layer initialization-specific logic.

• call(): Computations that need to happen when the layer is called.

We define the dimensionality of the attention output, d, as an argument to the __init__() method. Next, in the __build__() method, we define three weight matrices: Wq, Wk, and Wv. These represent the weights of the query, key, and value, respectively.

Finally, in the call() method, we have the logic. It takes four inputs: query, key, value inputs, and an optional mask for values. We then compute the latent q, k, and v by multiplying with the corresponding weight matrices Wq, Wk, and Wv. To compute attention, we’ll be using the out-of-the-box layer tf.keras.layers.Attention. The tf.keras.layers.Attention() layer has several arguments. One that we care about here is setting causal=True.

By doing this, we’re instructing the layer to mask the tokens ...

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