By using the created iterator we can get the elements from the dataset to feed the model. To take a closer look at what’s changed, and to learn about best practices, check out the new Effective TensorFlow 2.0 guide (published on GitHub). import tensorflow as tf tensor1 = tf.Variable (4) tensor2 = tf.Variable (6) tensor3 = tf.Variable (3) result = (tensor1 + tensor2) * tensor3 for result in tf.get_default_graph ().get_operations (): print (result.name) Here is the implementation of the following given code. TensorFlow contrib module: Contrib module containing volatile or experimental code. TensorFlow Cannot iterate over a scalar. - fixexception.com It is the standard data format used in Tensorflow. the result the operation will . fill () is used to generate a tensor having scalar value. Iterator.get_next() adds ops to the graph, and executing each op allocates resources (including threads); as a consequence, invoking it in every iteration of a training loop causes slowdown and eventual resource exhaustion. I hope this sample code will help. Use the resizing () method in method 4 to resizing images. Python – tensorflow.convert_to_tensor () Last Updated : 26 Jun, 2020. TensorFlow Data - W3Schools for TensorFlow Users Binary tensor with shape (samples, time, 1), with a zero for every element that is masked. iterating over 'tf.Tensor' is not allowed AutoGraph did not convert ...