Gradients: The Building Blocks of Backpropagation in TensorFlow

In a neural network, backpropagation is essential for error minimization. It involves calculating the partial derivatives, or gradients, of the loss function with respect to trainable parameters. Manually computing and implementing these derivatives in Python can be complex and require mathematical knowledge. Fortunately, TensorFlow simplifies this with tf.GradientTape.

What is tf.GradientTape?

tf.GradientTape in TensorFlow is used to calculate the gradients of computations involving tensors.

How Does It Calculate Gradients?

TensorFlow records operations applied to tensors, especially tf. Variable objects, within the scope of tf.GradientTape. When the gradient method is called on the tape, it calculates the gradient with respect to the specified inputs.

Example Code:

import tensorflow as tf
w = tf.Variable(initial_value=5.)

with tf.GradientTape() as tape:

    loss = tf.square(w)

gradient = tape.gradient(loss, w)

print(gradient.numpy())  # Output: 10.0

Explanation:

• w is initialized as a TensorFlow variable with a value of 5.

• tf.GradientTape records operations to compute gradients.

• loss is defined as the square of w.

• The gradient computes the derivative of loss with respect to w.

• The gradient of w^2 is 2w. So for w = 5, the gradient will be 2×5 = 10.

Note: tf.GradientTape records operations on tf.Variable. To compute gradients for constants, explicitly tell the tape to watch them using tape.watch(const_var).