Basics#

Let’s Start#

  • The main difference between tensors and NumPy arrays is that tensors can be used on GPUs (graphical processing units) and TPUs (tensor processing units).

  • The number of dimensions of a tensor is called its rank. A scalar has rank \(0\), a vector has rank \(1\), a matrix is rank \(2\), a tensor has rank \(n\).

  • There are \(2\) ways of creating tensors. tf.Variable() and tf.constant() the difference being tensors created with tf.constant() are immutable, tensors created with tf.Variable() are mutable. any_tensor[2].assign(7) can be used to change a value of a specific element in the tensor, same would fail for tf.constant().

  • There are other ways of creating tensors examples being tf.zeros or tf.ones. You can also convert numpy arrays into tensors.

  • Tensors can also be indexed just like Python lists.

  • You can an extra dimension by using tf.newaxis or tf.expand_dims

  • tf.reshape(),tf.transpose() allows us to reshape a tensor

  • Data type of a tesnor can be changed with tf.cast(t1, dtype=tf.float16)

  • You can squeeze a tensor to remove single-dimensions (dimensions with size 1) using tf.squeeze().

# Some common commands are as follows
import tensorflow as tf
print("Check TF version: ",tf.__version__)

t1 = tf.constant([[10., 7.],
                [3., 2.],
                [8., 9.]], dtype=tf.float16) # by default TF creates tensors with either an int32 or float32 datatype.

print("Access a specific feature of the tensor, in this case shape of t1: ",t1.shape)
print("Size of t1: ", tf.size(t1))
print("Datatype of every element:", t1.dtype)
print("Number of dimensions (rank):", t1.ndim)
print("Shape of tensor:", t1.shape)
print("Elements along axis 0 of tensor:", t1.shape[0])
print("Elements along last axis of tensor:", t1.shape[-1])
print("Total number of elements:", tf.size(t1).numpy()) # .numpy() converts to NumPy array
print("Details of the tensor: ",t1)

print("Index tensors: ", t1[:1,:])

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Input In [1], in <cell line: 2>()
      1 # Some common commands are as follows
----> 2 import tensorflow as tf
      3 print("Check TF version: ",tf.__version__)
      5 t1 = tf.constant([[10., 7.],
      6                 [3., 2.],
      7                 [8., 9.]], dtype=tf.float16) # by default TF creates tensors with either an int32 or float32 datatype.

ModuleNotFoundError: No module named 'tensorflow'

import tensorflow as tf

# Math operations

t1 = tf.constant([[10., 7.],
                [3., 2.],
                [8., 9.]], dtype=tf.float16) # by default TF creates tensors with either an int32 or float32 datatype.

print("Sum: ",t1+10)
print("Substraction: ",t1-10)
print("Multiplication: ",t1*10, tf.multiply(t1, 10))
print("Matrix Multiplication: ",t1 @ tf.transpose(t1)) # can also be done with tf.tensordot()

# Aggregation functions

print("Max: ", tf.reduce_max(t1)) # same or min, mean
print("Sum: ", tf.reduce_sum(t1))
print("Max Position: ", tf.argmax(t1)) # same or min

Sum:  tf.Tensor(
[[20. 17.]
 [13. 12.]
 [18. 19.]], shape=(3, 2), dtype=float16)
Substraction:  tf.Tensor(
[[ 0. -3.]
 [-7. -8.]
 [-2. -1.]], shape=(3, 2), dtype=float16)
Multiplication:  tf.Tensor(
[[100.  70.]
 [ 30.  20.]
 [ 80.  90.]], shape=(3, 2), dtype=float16) tf.Tensor(
[[100.  70.]
 [ 30.  20.]
 [ 80.  90.]], shape=(3, 2), dtype=float16)
Matrix Multiplication:  tf.Tensor(
[[149.  44. 143.]
 [ 44.  13.  42.]
 [143.  42. 145.]], shape=(3, 3), dtype=float16)
Max:  tf.Tensor(10.0, shape=(), dtype=float16)
Sum:  tf.Tensor(39.0, shape=(), dtype=float16)
Max Position:  tf.Tensor([0 2], shape=(2,), dtype=int64)

Random#

Randomness is often used in deep learning, be it initializing weights in a Neural Network or shuffling images while feeding data to the model.

random_1 = tf.random.Generator.from_seed(35) # setting seed ensures reproducibility
random_1 = random_1.normal(shape = (3,2))
print("Generating tensor from a normal distribution: ", random_1)
print("Shuffling the elements of the tesnor: ", tf.random.shuffle(random_1))

Generating tensor from a normal distribution:  tf.Tensor(
[[ 0.495291   -0.648484  ]
 [-1.8700892   2.7830641 ]
 [-0.645002    0.18022095]], shape=(3, 2), dtype=float32)
Shuffling the elements of the tesnor:  tf.Tensor(
[[-0.645002    0.18022095]
 [ 0.495291   -0.648484  ]
 [-1.8700892   2.7830641 ]], shape=(3, 2), dtype=float32)