Indexing and slicing tensors in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the first row of the tensor.

TensorFlow

import tensorflow as tf

tensor = tf.constant([[1, 2], [3, 4], [5, 6]])
first_row = tensor[1]

Drag options to blanks, or click blank then click option'

A[0]

B[:, 0]

C[0, :]

D[1]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [0] alone returns a 1D tensor but may not be explicit about columns.

Using [:, 0] selects the first column, not the first row.

The correct way to select the first row of a 2D tensor is using [0, :], which means row 0 and all columns.

2fill in blank

medium

Complete the code to slice the tensor to get the last two rows.

TensorFlow

import tensorflow as tf

tensor = tf.constant([[10, 20], [30, 40], [50, 60], [70, 80]])
last_two_rows = tensor[1]

Drag options to blanks, or click blank then click option'

A[1:3]

B[:2]

C[-3:-1]

D[-2:]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [:2] gets the first two rows, not the last two.

Using [1:3] gets the middle rows, not the last two.

Using [-2:] slices the tensor to get the last two rows.

3fill in blank

hard

Fix the error in the code to select the second column of the tensor.

TensorFlow

import tensorflow as tf

tensor = tf.constant([[5, 10, 15], [20, 25, 30], [35, 40, 45]])
second_column = tensor[1]

Drag options to blanks, or click blank then click option'

A[:, 1]

B[1, :]

C[1]

D[:, 2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [1, :] selects the second row, not the column.

Using [:, 2] selects the third column, not the second.

To select the second column, use [:, 1] which means all rows and column index 1.

4fill in blank

hard

Fill both blanks to create a tensor slice with the first two rows and last two columns.

TensorFlow

import tensorflow as tf

tensor = tf.constant([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
slice_tensor = tensor[1][2]

Drag options to blanks, or click blank then click option'

A[0:2,

B[1:3,

C2:]

D1:]

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong row indices like [1:3, 2:] gets the wrong rows.

Using column slice 1:] gets the wrong columns.

To get the first two rows and last two columns, slice rows with [0:2, and columns with 2:].

5fill in blank

hard

Fill both blanks to create a slice selecting the middle row and middle two columns.

TensorFlow

import tensorflow as tf

tensor = tf.constant([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120])
slice_tensor = tensor[1][2]]

Drag options to blanks, or click blank then click option'

A[1,

B[1:2,

C1:3

D]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [1, 1:3] returns a 1D tensor, losing the row dimension.

Using wrong column indices like 2:4 selects wrong columns.

To select the middle row and middle two columns, slice rows with [1:2, and columns with 1:3. The last blank is the closing bracket ].

Practice

1. What does indexing a tensor in TensorFlow do?

easy

A. Selects a single element by its position

B. Changes the shape of the tensor

C. Adds new elements to the tensor

D. Deletes elements from the tensor

Solution

1. Step 1: Understand indexing

   Indexing means picking one element from a tensor by its position, like choosing one item from a list.

2. Step 2: Compare with other options

   Changing shape, adding, or deleting elements are different operations, not indexing.

3. Final Answer:

   Selects a single element by its position -> Option A

4. Quick Check:

   Indexing = single element pick [OK]

Hint: Indexing picks one element, slicing picks many [OK]

Common Mistakes:

• Thinking indexing changes tensor shape
• Confusing indexing with adding elements
• Assuming indexing deletes elements

2. Which of the following is the correct syntax to slice a 1D tensor t from index 2 to 5 (exclusive) in TensorFlow?

easy

A. t[2:5]

B. t.slice(2, 5)

C. t[2, 5]

D. t.slice(2:5)

Solution

1. Step 1: Recall slicing syntax

   TensorFlow uses Python-style slicing: t[start:stop] to get elements from start up to but not including stop.

2. Step 2: Check each option

   t[2:5] uses correct Python slice syntax. t.slice(2, 5) and D use incorrect method calls or syntax. t[2, 5] uses comma which is invalid for 1D slicing.

3. Final Answer:

   t[2:5] -> Option A

4. Quick Check:

   Slice syntax = t[start:stop] [OK]

Hint: Use square brackets with colon for slicing [OK]

Common Mistakes:

• Using commas instead of colons in slices
• Trying to call slice as a method incorrectly
• Confusing slice stop index as inclusive

3. Given the tensor t = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), what is the output of t[1:, :2].numpy()?

medium

A. [[5 6]
[8 9]]

B. [[1 2]
[4 5]]

C. [[4 5 6]
[7 8 9]]

D. [[4 5]
[7 8]]

Solution

1. Step 1: Understand slicing t[1:, :2]

   1: means rows from index 1 to end (rows 1 and 2). :2 means columns from start to index 2 (columns 0 and 1).

2. Step 2: Extract the sliced elements

   Rows 1 and 2 are [[4,5,6], [7,8,9]]. Taking first two columns gives [[4,5], [7,8]].

3. Final Answer:

   [[4 5] [7 8]] -> Option D

4. Quick Check:

   Rows 1+ and cols 0-1 = [[4 5],[7 8]] [OK]

Hint: Remember slice stop is exclusive, so :2 means columns 0 and 1 [OK]

Common Mistakes:

• Including column index 2 mistakenly
• Starting rows from 0 instead of 1
• Confusing rows and columns order

4. What is wrong with this TensorFlow slicing code?

t = tf.constant([10, 20, 30, 40, 50])
slice = t[1:6]

medium

A. Index 6 is out of range, causing an error

B. Slicing with stop index beyond length is allowed, no error

C. Syntax error due to missing colon

D. TensorFlow does not support slicing

Solution

1. Step 1: Check slicing behavior with stop index

   In Python and TensorFlow, slicing stop index can be beyond tensor length without error; it stops at the end.

2. Step 2: Analyze given code

   Tensor t has length 5, slicing 1:6 extracts elements from index 1 to end safely.

3. Final Answer:

   Slicing with stop index beyond length is allowed, no error -> Option B

4. Quick Check:

   Slice stop > length is safe [OK]

Hint: Slice stop can exceed length without error [OK]

Common Mistakes:

• Expecting IndexError for slice stop beyond length
• Confusing slicing with indexing single element
• Thinking slicing syntax is invalid

5. You have a 3D tensor t = tf.constant([[[1,2],[3,4]], [[5,6],[7,8]], [[9,10],[11,12]]]). How do you extract the second element from each 2D matrix (i.e., elements 2, 4, 6, 8, 10, 12) using indexing and slicing?

hard

A. t[1, :, :]

B. t[:, 1, :]

C. t[:, :, 1]

D. t[:, 1]

Solution

1. Step 1: Understand tensor shape and indexing

   The tensor shape is (3, 2, 2): 3 matrices, each 2x2. We want the second element in the last dimension (index 1).

2. Step 2: Apply slicing to get second element in last dimension

   Using t[:, :, 1] selects all matrices (:), all rows (:), and the second element (index 1) in the last dimension.

3. Final Answer:

   t[:, :, 1] -> Option C

4. Quick Check:

   Last dim index 1 selects second elements [OK]

Hint: Use colon for all dims except last, index last dim 1 [OK]

Common Mistakes:

• Mixing row and column indices
• Using incomplete slicing like t[:, 1]
• Selecting wrong dimension index