Hands-On Artificial Intelligence for Beginners

Basic building blocks

As you may have guessed from the name, TensorFlow relies on the algebraic concept of tensors that we learned about in the previous chapter. Everything, from input data to parameters, is stored in a tensor in TensorFlow. As such, TensorFlow has its own functions for many of the basic operations normally handled by NumPy. 

When writing tensors in TensorFlow, we're really writing everything in an array structure. Remember how an array can be a rank 1 tensor? That is exactly what we are passing in the preceding example. If we wanted to pass a rank 3 tensor, we'd simply write x = tf.constant([1,2,3,4],[5,6,7,8],[9,10,11,12]). You'll notice that we defined constants in the following code; these are just one of three types ...