What is tf.GradientTape in TensorFlow?

The tf.GradientTape class in TensorFlow is a Python tool used for calculating the gradients of a computation concerning certain inputs, typically tf.Variables. TensorFlow keeps track of relevant operations executed within the scope of a tf.GradientTape instance, recording them onto a “tape”. Upon calling the gradient() method on the tape, TensorFlow calculates the gradients of the recorded operations with respect to the specified inputs.

tf.GradientTape is a context manager that records the operations performed on tensors. Tensors are the basic data structures in TensorFlow, similar to arrays or matrices. A tensor can have any number of dimensions, shape, and data type. tf.GradientTape can track any tensor that is watched, either explicitly or implicitly.

Variables:

By default, tf.GradientTape will automatically watch any trainable variables accessed while the tape is active. Trainable variables are the variables that can be modified by the optimizer during training. They are usually created by tf.Variable with the argument trainable=True. Non-trainable variables, such as constants, are not watched by default. To watch a non-trainable tensor, we can use the tape.watch() method. To stop watching a tensor, we can use the tape.stop_recording() method.

To compute the gradient of a function with respect to a tensor, we can use the tape.gradient() method. The tape.gradient() method takes two arguments: the output tensor and the input tensor. It returns the gradient tensor, which has the same shape as the input tensor.

The tape.gradient() method can only be called once on a non-persistent tape. A non-persistent tape will release its resources after the gradient is computed. To compute multiple gradients or higher-order derivatives, we need to create a persistent tape. A persistent tape will keep its resources until it is explicitly deleted. To create a persistent tape, we can pass the argument persistent=True to the tf.GradientTape constructor.

To delete a persistent tape, we can use the tape.delete() method

Important Terminologies

Let’s understand some of the terminologies related to tf.GradientTape.

• Tape – A tape is a data structure that records the operations executed inside the context of a tf.GradientTape onto a “tape”.
• Gradient – A gradient is a vector that represents the direction and magnitude of the steepest ascent of a function.
• Jacobian – A jacobian is a matrix that represents the partial derivatives of a vector-valued function with respect to its inputs.
• Persistent Tape – A persistent tape is a tape that can be used multiple times to compute multiple gradients. By default, a tape is not persistent and can only be used once.
• Tensor – A watched variable or tensor is a variable or tensor that the tape will track its gradient. By default, the tape will automatically watch any trainable variables accessed while the tape is active, but you can also manually watch any variable or tensor using the tape.watch() method.

TensorFlow is an open-source library for data science and machine learning. It provides various tools and APIs for building, training, and deploying models. One of the core features of TensorFlow is automatic differentiation (autodiff). Autodiff is the process of computing the gradients of a function with respect to its inputs. Gradients are the slopes or rates of change of a function. They are useful for optimizing the parameters of a model, such as weights and biases. TensorFlow provides the tf.GradientTape API for autodiff.