Tensor shapes and reshaping in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the shape of the tensor after reshaping?

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

TensorFlow

import tensorflow as tf
original = tf.constant([[1, 2, 3], [4, 5, 6]])
reshaped = tf.reshape(original, [3, 2])
result_shape = reshaped.shape

A (3, 2)

B (2, 3)

C (6,)

D (1, 6)

💡 Hint

Remember that reshaping changes the shape but keeps the total number of elements the same.

The original tensor has shape (2, 3) with 6 elements. Reshaping to (3, 2) keeps the total elements 6 but changes the shape accordingly.

❓ Model Choice

intermediate

Choosing the correct reshape for a batch of images

You have a batch of 10 grayscale images, each 28x28 pixels, stored in a tensor of shape (10, 28, 28). Which reshape will convert this batch into a 2D tensor suitable for feeding into a fully connected layer?

A tf.reshape(tensor, [10, 784])

B tf.reshape(tensor, [10, 28, 28, 1])

C tf.reshape(tensor, [280, 28])

D tf.reshape(tensor, [28, 280])

💡 Hint

Fully connected layers expect 2D inputs: (batch_size, features).

Each 28x28 image has 784 pixels. Reshaping to (10, 784) flattens each image while keeping the batch size.

❓ Metrics

advanced

Calculating output shape after Conv2D layer

A Conv2D layer has input shape (batch_size, 64, 64, 3), kernel size (3, 3), stride 2, and 'valid' padding. What is the output shape (excluding batch size)?

A (33, 33, filters)

B (32, 32, filters)

C (31, 31, filters)

D (30, 30, filters)

💡 Hint

Output size = floor((input_size - kernel_size)/stride) + 1 for 'valid' padding.

For each spatial dimension: floor((64 - 3)/2) + 1 = floor(61/2) + 1 = 30 + 1 = 31.

🔧 Debug

advanced

Why does this reshape cause an error?

Consider this code snippet:

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

Why does this raise an error?

A The reshape function requires the new shape to have the same number of dimensions as the original.

B TensorFlow does not allow reshaping 2D tensors.

C The new shape dimensions must be equal to the original shape dimensions.

D The total number of elements does not match between original and new shape.

💡 Hint

Check how many elements are in the original tensor and the new shape.

Original tensor has 6 elements (2*3). New shape requires 8 elements (4*2). This mismatch causes an error.

🧠 Conceptual

expert

Effect of -1 in TensorFlow reshape

What does using -1 in a TensorFlow reshape shape argument do?

A It removes that dimension from the tensor.

B It automatically calculates the dimension size to keep the total number of elements constant.

C It sets that dimension size to zero.

D It causes a runtime error because -1 is invalid.

💡 Hint

Think about how to reshape without manually calculating one dimension.

Using -1 lets TensorFlow infer the correct size for that dimension to keep the total elements unchanged.

Practice

1. What does the shape of a tensor represent in TensorFlow?

easy

A. The size of the tensor in each dimension

B. The data type of the tensor elements

C. The memory address of the tensor

D. The number of operations performed on the tensor

Solution

1. Step 1: Understand tensor shape meaning

   The shape of a tensor tells us how many elements it has along each dimension, like rows and columns in a matrix.

2. Step 2: Differentiate shape from other properties

   Data type, memory address, and operations are different properties, not shape.

3. Final Answer:

   The size of the tensor in each dimension -> Option A

4. Quick Check:

   Tensor shape = size per dimension [OK]

Hint: Shape means size per dimension, not data or memory [OK]

Common Mistakes:

• Confusing shape with data type
• Thinking shape is memory location
• Mixing shape with number of operations

2. Which of the following is the correct way to reshape a tensor t to shape (2, 3) in TensorFlow?

easy

A. tf.reshape(t, (2, 3))

B. t.reshape(2, 3)

C. tf.change_shape(t, (2, 3))

D. reshape(t, (2, 3))

Solution

1. Step 1: Recall TensorFlow reshape syntax

   TensorFlow uses tf.reshape(tensor, new_shape) to reshape tensors.

2. Step 2: Check each option

   tf.reshape(t, (2, 3)) uses correct function and parameters. t.reshape(2, 3) is invalid because tensors don't have a reshape method. tf.change_shape(t, (2, 3)) uses a non-existent function. reshape(t, (2, 3)) misses the module prefix.

3. Final Answer:

   tf.reshape(t, (2, 3)) -> Option A

4. Quick Check:

   Use tf.reshape(t, shape) to reshape [OK]

Hint: Use tf.reshape(tensor, shape) to reshape tensors [OK]

Common Mistakes:

• Using tensor.reshape() method which doesn't exist
• Using wrong function name like tf.change_shape
• Omitting tf module prefix

3. What will be the output shape of the following code?

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

medium

A. (2, 3)

B. (6,)

C. (3, 2)

D. (1, 6)

Solution

1. Step 1: Check original tensor shape

   The tensor t has shape (2, 3) because it has 2 rows and 3 columns.

2. Step 2: Understand reshape operation

   Reshape changes the shape to (3, 2) without changing data count. The total elements remain 6.

3. Final Answer:

   (3, 2) -> Option C

4. Quick Check:

   Reshape to (3, 2) changes shape accordingly [OK]

Hint: Reshape keeps total elements same, just changes shape [OK]

Common Mistakes:

• Confusing original shape with reshaped shape
• Assuming reshape flattens tensor
• Mixing up rows and columns

4. Identify the error in the following TensorFlow code:

import tensorflow as tf
t = tf.constant([1, 2, 3, 4])
t_reshaped = tf.reshape(t, (3, 2))
print(t_reshaped)

medium

A. print statement syntax is incorrect

B. tf.constant cannot create 1D tensors

C. tf.reshape requires a list, not a tuple for shape

D. The reshape shape (3, 2) does not match total elements

Solution

1. Step 1: Count elements in original tensor

   The tensor t has 4 elements: [1, 2, 3, 4].

2. Step 2: Check reshape target shape

   The target shape (3, 2) requires 6 elements (3*2=6), which does not match 4 elements available.

3. Final Answer:

   The reshape shape (3, 2) does not match total elements -> Option D

4. Quick Check:

   Reshape shape must match total elements [OK]

Hint: Total elements before and after reshape must be equal [OK]

Common Mistakes:

• Ignoring mismatch in total elements
• Thinking tf.constant can't create 1D tensors
• Believing shape must be list, not tuple

5. You have a tensor t with shape (4, 3, 2). You want to reshape it to a 2D tensor where the first dimension is 6. What should the second dimension be?

hard

A. 24

B. 4

C. 12

D. 8

Solution

1. Step 1: Calculate total elements in original tensor

   Original shape is (4, 3, 2). Total elements = 4 * 3 * 2 = 24.

2. Step 2: Find second dimension for reshape

   We want first dimension = 6. So second dimension = total elements / 6 = 24 / 6 = 4.

3. Final Answer:

   4 -> Option B

4. Quick Check:

   New shape dims multiply to total elements [OK]

Hint: Divide total elements by known dimension to find missing one [OK]

Common Mistakes:

• Multiplying instead of dividing total elements
• Forgetting to multiply all original dimensions
• Choosing wrong option without calculation