How to Build and Train a Vision Transformer From Scratch Using TensorFlow

The Transformer is a type of attention-based model that uses self-attention mechanisms to process the input data. It consists of multiple encoder and decoder layers, each of which is made up of a multi-head self-attention mechanism and a fully-connected feedforward network.

The Transformer layer takes in a sequence of input vectors and produces a sequence of output vectors. In the case of an image classification task, each input vector can represent a patch of the image, and the output vectors can be used to predict the class label for the image.

How to build a Vision Transformer from Scratch Using Tensorflow

 Building a Vision Transformer from scratch in TensorFlow can be a challenging task, but it is also a rewarding experience that can help you understand how this type of model works and how it can be used for image recognition and other computer vision tasks. Here is a step-by-step guide on how you can build a Vision Transformer in TensorFlow:

Start by installing TensorFlow and importing the necessary libraries. You will need to import the TensorFlow library, as well as other libraries such as NumPy and Matplotlib that will be used for data manipulation and visualization.

# Import libraries

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

Next, you will need to prepare your dataset. You can use a pre-existing dataset such as ImageNet or create your own dataset by collecting and labeling images. You will need to split your dataset into training and validation sets, and then create a 'tf.data.Dataset' object to represent your data.

# Load and preprocess the dataset
(x_train, y_train), (x_val, y_val) = tf.keras.datasets.mnist.load_data()
# Normalize the data
x_train = x_train / 255.0
x_val = x_val / 255.0
# Create a tf.data.Dataset object for the training data
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)
# Create a tf.data.Dataset object for the validation data
val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
val_dataset = val_dataset.batch(64)

Next, you will need to define the model architecture. In this case, you will be building a Vision Transformer, which is a type of transformer model that has been specifically designed for computer vision tasks. You can use the 'tf.keras.layers.Transformer' layer to create a Vision Transformer in TensorFlow. The Transformer layer itself is responsible for learning the relationships between the input features and the output labels.

# Define the model architecture
inputs = tf.keras.layers.Input(shape=(28, 28, 1))
# Add the transformer layer
x = tf.keras.layers.Transformer(
    num_layers=6,
    d_model=512,
    num_heads=8,
    dff=2048,
    input_vocab_size=256,
    rate=0.1
)(inputs)
# Add a dense layer for the output
outputs = tf.keras.layers.Dense(10, activation="softmax")(x)
# Create the model
model = tf.keras.Model(inputs=inputs, outputs=outputs)

Now that you have defined the model architecture, you can compile and train the model. You will need to specify the loss function and the optimizer to use, as well as any metrics that you want to track during training.

# Compile the model
def run_experiment(model):
    optimizer = tfa.optimizers.AdamW(
        learning_rate=learning_rate, weight_decay=weight_decay
    )

    model.compile(
        optimizer=optimizer,
        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        metrics=[
            keras.metrics.SparseCategoricalAccuracy(name="accuracy"),
            keras.metrics.SparseTopKCategoricalAccuracy(5, name="top-5-accuracy"),
        ],
    )

    checkpoint_filepath = "/tmp/checkpoint"
    checkpoint_callback = keras.callbacks.ModelCheckpoint(
        checkpoint_filepath,
        monitor="val_accuracy",
        save_best_only=True,
        save_weights_only=True,
    )

    history = model.fit(
        x=x_train,
        y=y_train,
        batch_size=batch_size,
        epochs=num_epochs,
        validation_split=0.1,
        callbacks=[checkpoint_callback],
    )

    model.load_weights(checkpoint_filepath)
    _, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)
    print(f"Test accuracy: {round(accuracy * 100, 2)}%")
    print(f"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%")

    return history

vit_classifier = create_vit_classifier()
history = run_experiment(vit_classifier)

The Vision Transformer model typically has a fully-connected layer at the end of it, after the Transformer layer, to classify the output vectors produced by the Transformer layer into the desired classes.

In the code snippet we provided earlier, the fully-connected layer is added using the tf.keras.layers.Dense layer. This layer takes in the output of the Transformer layer as input and applies a dense (fully-connected) transformation to it, followed by a softmax activation function to produce a probability distribution over the classes. Here is the complete code snippet again: 

# Define the model architecture
inputs = tf.keras.layers.Input(shape=(28, 28, 1))
# Add the transformer layer
x = tf.keras.layers.Transformer(
    num_layers=6,
    d_model=512,
    num_heads=8,
    dff=2048,
    input_vocab_size=256,
    rate=0.1)(inputs)

# Add a dense layer for the output
outputs = tf.keras.layers.Dense(10, activation="softmax")(x)

# Create the model
model = tf.keras.Model(inputs=inputs, outputs=outputs

Overall, building a ViT can never be easier with Tensorflow. Hope this helps. Please share it if you like it.