Similarly, if the target(s) are not scalar the gradient of the sum is calculated: x = tf.Variable(2.) Here is the gradient calculation again, this time passing a dictionary of variables: my_vars =, x).numpy()) The gradient with respect to each source has the shape of the source: print(w.shape) The tape is flexible about how sources are passed and will accept any nested combination of lists or dictionaries and return the gradient structured the same way (see tf.nest). To get the gradient of loss with respect to both variables, you can pass both as sources to the gradient method. With tf.GradientTape(persistent=True) as tape: The above example uses scalars, but tf.GradientTape works as easily on any tensor: w = tf.Variable(tf.random.normal((3, 2)), name='w')ī = tf.Variable(tf.zeros(2, dtype=tf.float32), name='b') Once you've recorded some operations, use adient(target, sources) to calculate the gradient of some target (often a loss) relative to some source (often the model's variables): # dy = 2x * dx Here is a simple example: x = tf.Variable(3.0) TensorFlow then uses that tape to compute the gradients of a 'recorded' computation using reverse mode differentiation. TensorFlow 'records' relevant operations executed inside the context of a tf.GradientTape onto a 'tape'. TensorFlow provides the tf.GradientTape API for automatic differentiation that is, computing the gradient of a computation with respect to some inputs, usually tf.Variables. Then, during the backward pass, TensorFlow traverses this list of operations in reverse order to compute gradients. To differentiate automatically, TensorFlow needs to remember what operations happen in what order during the forward pass. In this guide, you will explore ways to compute gradients with TensorFlow, especially in eager execution. Is useful for implementing machine learning algorithms such as

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