TF 2.0 @tf.function example

Question

In the tensorflow documentation at the autograph section we have the following code snippet @tf.function def train(model, optimizer): train_ds = mnist_dataset() step = 0 loss = 0.0 accuracy = 0.0 for x, y in train_ds: step += 1 loss = train_one_step(model, optimizer, x, y) if tf.equal(step % 10, 0): tf.print('Step', step, ': loss', loss, '; accuracy', compute_accuracy.result()) return step, loss, accuracy step, loss, accuracy = train(model, optimizer) print('Final step', step, ': loss', loss, '; accuracy', compute_accuracy.result()) I have a small question concerning the step variable, it's an integer and not a tensor, autograph supports built-in python type such as integer. Therefore the tf.equal(step%10,0) could be changed to simply step%10 == 0 right ? JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

Answer 1

Yes, you're right. The integer variable step remains a Python variable even when converted to its graph representation. You can see the conversion result by calling tf.autograph.to_code(train.python_function). Without reporting all the code but only the step variable related part, you'll see that def loop_body(loop_vars, loss_1, step_1): with ag__.function_scope('loop_body'): x, y = loop_vars step_1 += 1 Is still a python operation (otherwise it will be step_1.assign_add(1) if step 1 was a tf.Tensor). For more information about autograph and tf.function I suggest reading the article https://pgaleone.eu/tensorflow/tf.function/2019/03/21/dissecting-tf-function-part-1/ that explains easily what happens when a function is converted.