Tensor creation (constant, variable, zeros, ones) in TensorFlow - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a constant tensor in TensorFlow?

A constant tensor is a fixed value tensor that cannot be changed after creation. It holds data that stays the same during the program run.

beginner

How does a variable tensor differ from a constant tensor?

A variable tensor can change its values during program execution, useful for model parameters that update during training.

beginner

What does tf.zeros(shape) do?

It creates a tensor filled with zeros of the specified shape. For example, tf.zeros([2,3]) creates a 2x3 tensor filled with zeros.

beginner

What is the use of tf.ones(shape)?

It creates a tensor filled with ones of the given shape. Useful for initializing tensors where all values start as one.

intermediate

How do you create a variable tensor initialized with ones in TensorFlow?

Use tf.Variable(tf.ones(shape)) to create a variable tensor filled with ones that can be updated later.

Which TensorFlow function creates a tensor that cannot be changed after creation?

A tf.zeros()

B tf.Variable()

C tf.constant()

D tf.ones()

tf.constant() creates a tensor with fixed values that cannot be modified.

What does tf.zeros([3,2]) produce?

A A 3x2 tensor filled with ones

B A 3x2 tensor filled with zeros

C A 2x3 tensor filled with zeros

D A variable tensor with zeros

tf.zeros([3,2]) creates a tensor with 3 rows and 2 columns filled with zeros.

Which tensor type allows changing its values during training?

A Constant tensor

B Ones tensor

C Zeros tensor

D Variable tensor

Variable tensors can be updated, which is essential for training models.

How do you create a tensor filled with ones in TensorFlow?

A tf.ones(shape)

B tf.zeros(shape)

C tf.constant(shape)

D tf.Variable(shape)

tf.ones(shape) creates a tensor filled with ones of the specified shape.

Which code creates a variable tensor initialized with zeros?

A tf.Variable(tf.zeros(shape))

B tf.zeros(shape)

C tf.constant(tf.zeros(shape))

D tf.ones(shape)

tf.Variable(tf.zeros(shape)) creates a variable tensor initialized with zeros.

Explain the difference between tf.constant and tf.Variable tensors and when to use each.

Think about whether the tensor values need to change during training.

Describe how to create tensors filled with zeros and ones and why these might be useful.

Consider initialization in neural networks.

Practice

1. Which TensorFlow function creates a tensor with arbitrary fixed values that cannot be changed later?

easy

A. tf.ones

B. tf.Variable

C. tf.zeros

D. tf.constant

Solution

1. Step 1: Understand tensor mutability

   tf.constant creates tensors with fixed values that cannot be changed after creation.

2. Step 2: Compare with other functions

   tf.Variable creates tensors that can be changed, while tf.zeros and tf.ones create tensors filled with zeros or ones but are also constants by default.

3. Final Answer:

   tf.constant -> Option D

4. Quick Check:

   Fixed tensor = tf.constant [OK]

Hint: Fixed tensors use tf.constant, variables use tf.Variable [OK]

Common Mistakes:

• Confusing tf.constant with tf.Variable
• Thinking tf.zeros creates changeable tensors
• Assuming tf.ones creates variables

2. Which of the following is the correct syntax to create a TensorFlow variable with initial value 5?

easy

A. tf.zeros(5)

B. tf.Variable(5)

C. tf.constant(5)

D. tf.ones(5)

Solution

1. Step 1: Identify variable creation syntax

   tf.Variable(5) creates a variable tensor with initial value 5.

2. Step 2: Check other options

   tf.constant(5) creates a constant, not a variable. tf.zeros(5) and tf.ones(5) create tensors of shape 5, not a single value 5.

3. Final Answer:

   tf.Variable(5) -> Option B

4. Quick Check:

   Variable init = tf.Variable(value) [OK]

Hint: Variables use tf.Variable(value), constants use tf.constant(value) [OK]

Common Mistakes:

• Using tf.constant instead of tf.Variable for changeable tensors
• Using tf.zeros or tf.ones with a single number instead of shape tuple
• Confusing value and shape in function arguments

3. What is the output of this code?

import tensorflow as tf
x = tf.zeros((2, 3))
print(x.numpy())

medium

A. [[1 1 1]
[1 1 1]]

B. [5 5 5 5 5 5]

C. [[0. 0. 0.]
[0. 0. 0.]]

D. Error: shape must be a single integer

Solution

1. Step 1: Understand tf.zeros with shape (2, 3)

   This creates a 2-row, 3-column tensor filled with zeros.

2. Step 2: Print tensor as numpy array

   Calling .numpy() converts tensor to numpy array, showing zeros in 2x3 shape.

3. Final Answer:

   [[0. 0. 0.] [0. 0. 0.]] -> Option C

4. Quick Check:

   tf.zeros((2,3)) = 2x3 zeros [OK]

Hint: tf.zeros(shape) creates zeros tensor of given shape [OK]

Common Mistakes:

• Confusing tf.zeros with tf.ones output
• Misunderstanding shape argument as single integer
• Expecting a flat list instead of 2D array

4. The following code throws an error. What is the mistake?

import tensorflow as tf
x = tf.ones(3, 4)
print(x)

medium

A. tf.ones expects a single shape tuple, not separate integers

B. tf.ones cannot create tensors with more than 2 dimensions

C. tf.ones requires dtype argument

D. tf.ones only creates scalar tensors

Solution

1. Step 1: Check tf.ones argument format

   tf.ones expects a single shape argument as a tuple, e.g., (3, 4), not two separate integers.

2. Step 2: Identify error cause

   Passing two integers separately causes a TypeError because the function signature expects one shape argument.

3. Final Answer:

   tf.ones expects a single shape tuple, not separate integers -> Option A

4. Quick Check:

   Shape must be tuple for tf.ones [OK]

Hint: Pass shape as tuple like (3,4) to tf.ones [OK]

Common Mistakes:

• Passing shape as separate arguments instead of tuple
• Assuming dtype is mandatory
• Thinking tf.ones only creates scalars

5. You want to create a TensorFlow variable initialized with a 3x3 identity matrix (ones on diagonal, zeros elsewhere). Which code correctly does this?

hard

A. tf.Variable(tf.eye(3))

B. tf.Variable(tf.ones((3,3)))

C. tf.Variable(tf.zeros((3,3)))

D. tf.Variable(tf.constant(3))

Solution

1. Step 1: Identify identity matrix creation

   tf.eye(3) creates a 3x3 identity matrix with ones on the diagonal and zeros elsewhere.

2. Step 2: Wrap identity matrix in variable

   Using tf.Variable makes this tensor changeable during training or updates.

3. Step 3: Check other options

   tf.ones and tf.zeros create all ones or zeros, not identity. tf.constant(3) creates scalar 3, not matrix.

4. Final Answer:

   tf.Variable(tf.eye(3)) -> Option A

5. Quick Check:

   Identity matrix = tf.eye + tf.Variable [OK]

Hint: Use tf.eye(shape) inside tf.Variable for identity matrix [OK]

Common Mistakes:

• Using tf.ones or tf.zeros instead of tf.eye for identity
• Passing scalar to tf.Variable instead of matrix
• Forgetting to wrap tensor in tf.Variable for mutability