Inspecting the attributes of a given tensor

If you need to inspect a tensor, you can use the following properties and methods:

1. Shape
2. Size of the tensor
3. Number of axes/dimensions of the tensor
4. Elements in a particular axis
5. Type of data

Let's first create a rank-4 tensor using tf.zeros() method that creates a tensor with all elements = 0. We need to pass the required shape of the tensor.

r4_tensor = tf.zeros([3,4,5,6], dtype='int32')

Here's an interesting visual from TensorFlow to understand what rank-4 tensor would look like:

Also, it is very important to understand what each axis means in a rank-4 tensor. Here's what a typical axis order looks like:

Now, to print all the information about this tensor, you can use the following attributes and methods:

##let's look at all the attributes of this tensor:
print(f"Shape of the Tensor: {r4_tensor.shape}")
print(f"Number of axes in the Tensor: {r4_tensor.ndim}")
print(f"Size/Total number of elements in the tensor(3*4*5*6): {tf.size(r4_tensor.numpy())}")
print(f"Type of each element: {r4_tensor.dtype}")
print(f"Elements in the first axis: {r4_tensor.shape[0]}")
print(f"Elements in the last axis: {r4_tensor.shape[-1]}")

Go over each line of code and try to play around with different values and tensors.

Some other methods of creating tensors that you should try out:

Creating a tensor using tf.Variable(), maintains a shared, persistent state manipulated by a program which is not possible with tf.constant():

v = tf.Variable([3,4,5,6], dtype='int32')
print(v)

Creating tensors with uniformly distributed data:

u = tf.random.uniform([2,3])
print(u)

Note: An important point here to be noted is that the base tf.Tensor class requires tensors to be "rectangular"---that is, along each axis, every element is the same size.

Output:

Apart from these methods, there is a lot more you can learn about setting seed for reproducibility and different distribution methods here.

Next Steps!

Woah! tensors do contain a lot underneath. There is a lot you can do with them and images, text, structured data, all of them have to be turned into these tensors before you feed them to a neural network. Isn't it amazing!

Whenever we talk about numbers, first thing that comes to mind is mathematical operations and yes, we can perform mathematical operations on tensors. 

Let's see how!