Tensor creation (constant, variable, zeros, ones) in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output shape of this tensor?

Given the following TensorFlow code, what is the shape of the tensor t?

TensorFlow

import tensorflow as tf
t = tf.constant([[1, 2, 3], [4, 5, 6]])
shape = t.shape

A (6,)

B (3, 2)

C (2, 3)

D (1, 6)

💡 Hint

Think about how many rows and columns the list has.

The tensor is created from a list of two lists, each with three elements, so the shape is (2, 3).

❓ Model Choice

intermediate

Which tensor creation method creates a mutable tensor?

In TensorFlow, which method creates a tensor whose values can be changed after creation?

A tf.Variable()

B tf.zeros()

C tf.constant()

D tf.ones()

💡 Hint

Mutable means you can change the values after creation.

tf.Variable creates a tensor that can be updated, unlike tf.constant, tf.zeros, or tf.ones which create immutable tensors.

❓ Predict Output

advanced

What is the output of this code?

What does the following TensorFlow code print?

TensorFlow

import tensorflow as tf
x = tf.zeros(shape=(2, 3), dtype=tf.int32)
print(x.numpy())

A

[[1 1 1]
 [1 1 1]]

B

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

C

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

D

[[0 0 0]
 [0 0 0]]

💡 Hint

Check the dtype and the function used to create the tensor.

tf.zeros creates a tensor filled with zeros. dtype=tf.int32 means integers, so zeros are printed without decimals.

❓ Hyperparameter

advanced

Which argument changes the data type of a tensor?

When creating a tensor with tf.ones(), which argument sets the data type of the tensor elements?

A dtype

B size

C value

D shape

💡 Hint

This argument controls if the tensor holds integers, floats, or other types.

The dtype argument specifies the data type of the tensor elements.

🔧 Debug

expert

Why does this code raise an error?

Examine the code below. Why does it raise an error?

TensorFlow

import tensorflow as tf
v = tf.Variable(tf.constant([1, 2, 3]))
v.assign([4, 5])

A tf.Variable cannot be created from tf.constant.

B The assign method requires the new value to have the same shape as the variable.

C The list passed to assign must be a tensor, not a Python list.

D The variable v is immutable and cannot be assigned new values.

💡 Hint

Check the shapes of the original variable and the new value.

The assign method requires the new value to have the same shape as the variable. Here, the original variable has shape (3,), but the new value has shape (2,), causing a shape mismatch error.

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