Updates in TensorFlow 2.0

TensorFlow is an open-source machine learning library developed by Google that is widely used for building and training deep learning models. TensorFlow 2.0 brings several important changes and improvements that make it even more user-friendly and efficient than the previous versions. In this tutorial, we will explore some of the key changes in TensorFlow 2.0 and how they can benefit machine learning practitioners.

1. Eager Execution:
   One of the most significant changes in TensorFlow 2.0 is the adoption of eager execution by default. In previous versions of TensorFlow, computations were executed in a graph mode, where users had to define the entire computation graph before running it. With eager execution, computations are executed eagerly as they are defined, making it easier to debug and prototype models. This change brings TensorFlow closer to other deep learning libraries like PyTorch, which also use eager execution.

To enable eager execution in TensorFlow 2.0, you simply need to set tf.config.experimental_run_functions_eagerly(True). Once enabled, you can define and run TensorFlow operations just like you would with NumPy arrays.

import tensorflow as tf

# Enable eager execution
tf.config.experimental_run_functions_eagerly(True)

# Define a simple computation
a = tf.constant(2)
b = tf.constant(3)
c = a + b

# Print the result
print(c.numpy())  # Output: 5

1. Keras Integration:
   Another major change in TensorFlow 2.0 is the tighter integration with the Keras API. Keras is a high-level neural networks API that was integrated into TensorFlow in version 1.10. In TensorFlow 2.0, Keras is the official high-level API for building and training deep learning models. This integration simplifies the process of building and training models, as Keras provides a more user-friendly and intuitive interface.

To create a simple neural network model using Keras in TensorFlow 2.0, you can use the tf.keras.Sequential class to define a sequence of layers. Here is an example that creates a simple two-layer neural network with Keras:

import tensorflow as tf

# Define a simple neural network model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
model.fit(x_train, y_train, epochs=10, batch_size=32)

1. Model Subclassing:
   In TensorFlow 2.0, you can also define custom models using the model subclassing API, which allows for more flexibility and control over the model architecture. This approach is useful when you need to define complex and custom architectures that cannot be easily constructed using the sequential or functional API in Keras.

To define a custom model using model subclassing in TensorFlow 2.0, you need to create a new class that inherits from tf.keras.Model and define the model architecture in the __init__ method and the forward pass in the call method. Here is an example that defines a custom model using model subclassing:

import tensorflow as tf

class CustomModel(tf.keras.Model):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.dense1 = tf.keras.layers.Dense(64, activation='relu')
        self.dense2 = tf.keras.layers.Dense(10, activation='softmax')

    def call(self, inputs):
        x = self.dense1(inputs)
        return self.dense2(x)

# Create an instance of the custom model
model = CustomModel()

# Compile and train the model as usual
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10, batch_size=32)

1. Improved Performance:
   TensorFlow 2.0 also introduces several performance improvements that make training deep learning models faster and more efficient. For example, TensorFlow now uses XLA (Accelerated Linear Algebra) by default, which optimizes the computation graph and makes use of hardware accelerators like GPUs and TPUs for faster execution.

In addition, TensorFlow 2.0 introduces tf.function, a new decorator that allows you to decorate Python functions and automatically convert them into TensorFlow graphs. This can further improve performance by reducing overhead and optimizing the computation graph.

@tf.function
def train_step(inputs, targets):
    with tf.GradientTape() as tape:
        predictions = model(inputs)
        loss_value = loss_fn(targets, predictions)
    gradients = tape.gradient(loss_value, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss_value

# Use tf.function to compile the train_step function
train_step = tf.function(train_step)

Overall, TensorFlow 2.0 brings significant changes that make it easier and more efficient to build and train deep learning models. With eager execution, tighter integration with Keras, model subclassing, and improved performance, TensorFlow 2.0 offers a more user-friendly and powerful platform for machine learning practitioners. By familiarizing yourself with these changes and best practices, you can take advantage of the new features and capabilities in TensorFlow 2.0 to build and deploy cutting-edge deep learning models.