Tensor math operations in TensorFlow - ML Experiment: Train & Evaluate

Experiment - Tensor math operations

Problem:You want to perform basic math operations on tensors using TensorFlow to understand how tensors work and how math operations affect them.

Current Metrics:No model or accuracy metrics yet, just tensor values before and after operations.

Issue:You are new to tensor math and want to see how addition, multiplication, and other operations change tensor values.

Your Task

Create two tensors and perform addition, subtraction, multiplication, and division. Verify the results by printing the output tensors.

Use TensorFlow 2.x API.

Use only basic math operations on tensors.

Do not convert tensors to numpy arrays for operations.

Hint 1

Hint 2

Hint 3

Solution

TensorFlow

import tensorflow as tf

# Create two tensors
tensor_a = tf.constant([[2, 4], [6, 8]], dtype=tf.float32)
tensor_b = tf.constant([[1, 3], [5, 7]], dtype=tf.float32)

# Perform math operations
add_result = tf.add(tensor_a, tensor_b)
sub_result = tf.subtract(tensor_a, tensor_b)
mul_result = tf.multiply(tensor_a, tensor_b)
div_result = tf.divide(tensor_a, tensor_b)

# Print results
print('Addition result:\n', add_result)
print('Subtraction result:\n', sub_result)
print('Multiplication result:\n', mul_result)
print('Division result:\n', div_result)

Created two 2x2 tensors with float values.

Applied addition, subtraction, multiplication, and division using TensorFlow math functions.

Printed the results directly without converting tensors to numpy arrays.

Results Interpretation

Before operations, tensors are:
tensor_a = [[2, 4], [6, 8]]
tensor_b = [[1, 3], [5, 7]]

After operations:
Addition = [[3, 7], [11, 15]]
Subtraction = [[1, 1], [1, 1]]
Multiplication = [[2, 12], [30, 56]]
Division = [[2.0, 1.3333], [1.2, 1.1429]]

TensorFlow allows easy element-wise math operations on tensors, which is fundamental for building and training machine learning models.

Bonus Experiment

Try performing matrix multiplication (dot product) on the two tensors and compare it with element-wise multiplication.

💡 Hint

Use tf.matmul for matrix multiplication and compare the results with tf.multiply.

Practice

1. What does the TensorFlow function tf.add(tensor1, tensor2) do?

easy

A. Adds two tensors element-wise

B. Multiplies two tensors element-wise

C. Performs matrix multiplication of two tensors

D. Subtracts the second tensor from the first

Solution

1. Step 1: Understand the function name and purpose

   The function tf.add is designed to add values, so it performs addition.

2. Step 2: Check the operation type

   In TensorFlow, tf.add adds two tensors element-wise, meaning it adds corresponding elements from both tensors.

3. Final Answer:

   Adds two tensors element-wise -> Option A

4. Quick Check:

   tf.add = element-wise addition [OK]

Hint: Add means element-wise sum, not matrix multiply [OK]

Common Mistakes:

• Confusing tf.add with matrix multiplication
• Thinking tf.add subtracts tensors
• Assuming tf.add multiplies tensors

2. Which of the following is the correct syntax to perform matrix multiplication of two tensors a and b in TensorFlow?

easy

A. tf.multiply(a, b)

B. tf.add(a, b)

C. tf.matmul(a, b)

D. a.dot(b)

Solution

1. Step 1: Identify the function for matrix multiplication

   TensorFlow uses tf.matmul specifically for matrix multiplication.

2. Step 2: Check other options

   tf.multiply does element-wise multiplication, tf.add adds tensors, and a.dot(b) is invalid since tf.Tensor has no .dot method.

3. Final Answer:

   tf.matmul(a, b) -> Option C

4. Quick Check:

   Matrix multiply = tf.matmul [OK]

Hint: Matrix multiply uses tf.matmul, not tf.multiply [OK]

Common Mistakes:

• Using tf.multiply for matrix multiplication
• Using a.dot(b) like in NumPy
• Confusing addition with multiplication

3. What is the output of the following TensorFlow code?

import tensorflow as tf
x = tf.constant([[1, 2], [3, 4]])
y = tf.constant([[5, 6], [7, 8]])
result = tf.add(x, y)
print(result.numpy())

medium

A. [[6 12]
[10 32]]

B. [[6 8]
[10 12]]

C. [[5 12]
[21 32]]

D. [[1 2]
[3 4]]

Solution

1. Step 1: Understand the operation

   The code uses tf.add to add two 2x2 tensors element-wise.

2. Step 2: Calculate element-wise addition

   Adding corresponding elements: [[1+5, 2+6], [3+7, 4+8]] = [[6, 8], [10, 12]]

3. Final Answer:

   [[6 8] [10 12]] -> Option B

4. Quick Check:

   Element-wise add = [[6 8] [10 12]] [OK]

Hint: Add each element pair to get the result [OK]

Common Mistakes:

• Confusing element-wise add with matrix multiply
• Adding rows or columns incorrectly
• Printing tensor object instead of numpy array

4. Identify the error in this TensorFlow code snippet and choose the fix:

import tensorflow as tf
x = tf.constant([[1, 2], [3, 4]])
y = tf.constant([5, 6])
result = tf.matmul(x, y)
print(result.numpy())

medium

A. No error, code runs fine

B. Use tf.add instead of tf.matmul

C. Change x to a 1D tensor

D. Change y to a 2x1 tensor: tf.constant([[5], [6]])

Solution

1. Step 1: Understand matrix multiplication shape rules

   x is shape (2,2), y is shape (2,). TensorFlow tf.matmul requires both inputs to be at least rank 2 tensors.

2. Step 2: Identify the error

   Passing a 1D tensor y to tf.matmul causes a shape error because tf.matmul expects rank >= 2 tensors.

3. Step 3: Fix the error

   Change y to a 2D tensor with shape (2,1): tf.constant([[5], [6]]) to make matrix multiplication valid.

4. Final Answer:

   Change y to a 2x1 tensor: tf.constant([[5], [6]]) -> Option D

5. Quick Check:

   tf.matmul requires rank 2 tensors [OK]

Hint: tf.matmul requires 2D tensors; reshape 1D vector to 2D [OK]

Common Mistakes:

• Assuming 1D tensors cause no shape errors in matmul
• Unnecessarily reshaping y to 2D
• Confusing matmul with element-wise operations

5. You have two tensors:
a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[2, 0], [1, 2]])
Which TensorFlow operation will give the element-wise product of a and b?

hard

A. tf.multiply(a, b)

B. tf.matmul(a, b)

C. tf.add(a, b)

D. tf.tensordot(a, b, axes=1)

Solution

1. Step 1: Understand element-wise product

   Element-wise product multiplies each element of a with the corresponding element of b.

2. Step 2: Identify TensorFlow function for element-wise multiplication

   tf.multiply performs element-wise multiplication, while tf.matmul does matrix multiplication.

3. Final Answer:

   tf.multiply(a, b) -> Option A

4. Quick Check:

   Element-wise multiply = tf.multiply [OK]

Hint: Use tf.multiply for element-wise product, not tf.matmul [OK]

Common Mistakes:

• Using tf.matmul instead of tf.multiply
• Confusing addition with multiplication
• Using tf.tensordot incorrectly