Confusion matrix visualization in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of confusion matrix plot code

What will be the output of the following code snippet that plots a confusion matrix using TensorFlow and Matplotlib?

TensorFlow

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

true_labels = [0, 1, 2, 2, 0, 1]
pred_labels = [0, 2, 2, 2, 0, 0]

cm = tf.math.confusion_matrix(true_labels, pred_labels, num_classes=3).numpy()

plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
plt.xlabel('Predicted label')
plt.ylabel('True label')
plt.xticks(np.arange(3), ['Class 0', 'Class 1', 'Class 2'])
plt.yticks(np.arange(3), ['Class 0', 'Class 1', 'Class 2'])
plt.show()

A A 3x3 heatmap plot showing counts with highest values on diagonal except for class 1

B A 3x3 heatmap plot with all zeros

C A bar chart showing counts of predicted labels

D A line plot showing true vs predicted labels

💡 Hint

Think about how confusion matrices represent counts of true vs predicted labels.

The code computes a confusion matrix for 3 classes and plots it as a heatmap. The matrix shows counts of predictions vs true labels. Since class 1 has some misclassifications, the diagonal is not perfect.

❓ Model Choice

intermediate

Best model output for confusion matrix visualization

You have a multi-class classification problem with 4 classes. Which TensorFlow function will correctly compute the confusion matrix to visualize model performance?

A tf.math.confusion_matrix(labels, predictions, num_classes=4)

B tf.keras.metrics.ConfusionMatrix(labels, predictions)

C tf.confusion_matrix(labels, predictions)

D tf.metrics.confusion_matrix(labels, predictions)

💡 Hint

Check the TensorFlow API for confusion matrix functions.

The correct function is tf.math.confusion_matrix which takes labels and predictions and optional num_classes argument.

❓ Metrics

advanced

Interpreting confusion matrix metrics

Given this confusion matrix for a 3-class problem: [[5, 2, 0], [1, 7, 1], [0, 2, 6]] What is the precision for class 1 (index 1)?

A 7 / (1 + 7 + 1) = 0.78

B 7 / (5 + 1 + 0) = 1.17

C 7 / (2 + 7 + 2) = 0.64

D 7 / (7 + 1 + 2) = 0.70

💡 Hint

Precision = True Positives / (True Positives + False Positives). Look at the predicted column for class 1.

Precision for class 1 is TP / (TP + FP). TP is cm[1][1] = 7. FP is sum of column 1 excluding TP: 2 + 2 = 4. So precision = 7 / (7 + 4) = 7/11 ≈ 0.636, closest to option C's calculation.

🔧 Debug

advanced

Debugging confusion matrix visualization code

What error will this code raise? import tensorflow as tf true = [0, 1, 2] pred = [0, 1] cm = tf.math.confusion_matrix(true, pred) print(cm.numpy())

A TypeError: unsupported operand type(s)

B ValueError: Shapes of true and pred do not match

C IndexError: list index out of range

D No error, prints confusion matrix

💡 Hint

Check if true and pred lists have the same length.

The true and pred lists have different lengths, so TensorFlow raises a ValueError about shape mismatch.

🧠 Conceptual

expert

Choosing visualization method for confusion matrix

You want to visualize a confusion matrix for a 10-class classification problem with highly imbalanced classes. Which approach is best to clearly show the model's performance?

A Plot raw counts heatmap with a color scale from 0 to max count

B Plot a line chart of accuracy over epochs

C Plot a bar chart of total predictions per class

D Plot normalized confusion matrix showing percentages per true class

💡 Hint

Normalization helps compare classes with different sample sizes.

Normalized confusion matrices show proportions per true class, making it easier to interpret performance when classes are imbalanced.

Practice

1. What does a confusion matrix primarily show in machine learning?

easy

A. The size of the training dataset

B. The speed of the training process

C. The number of layers in a neural network

D. How many times each class was predicted correctly or wrongly

Solution

1. Step 1: Understand the purpose of a confusion matrix

   A confusion matrix is a table used to describe the performance of a classification model by showing correct and incorrect predictions for each class.

2. Step 2: Match the description to the options

   The description 'How many times each class was predicted correctly or wrongly' matches the purpose of a confusion matrix.

3. Final Answer:

   How many times each class was predicted correctly or wrongly -> Option D

4. Quick Check:

   Confusion matrix = correct and wrong predictions [OK]

Hint: Confusion matrix counts correct and wrong predictions per class [OK]

Common Mistakes:

• Confusing confusion matrix with training speed
• Thinking it shows model architecture details
• Assuming it shows dataset size

2. Which TensorFlow function is used to create a confusion matrix from true and predicted labels?

easy

A. tf.data.Dataset.from_tensor_slices

B. tf.keras.layers.Dense

C. tf.math.confusion_matrix

D. tf.image.resize

Solution

1. Step 1: Identify TensorFlow functions related to confusion matrix

   The function to create a confusion matrix is specifically designed to compare true and predicted labels.

2. Step 2: Match the function to the options

   tf.math.confusion_matrix is the correct TensorFlow function for this purpose, while others relate to layers, datasets, or image processing.

3. Final Answer:

   tf.math.confusion_matrix -> Option C

4. Quick Check:

   Confusion matrix function = tf.math.confusion_matrix [OK]

Hint: Use tf.math.confusion_matrix for confusion matrix in TensorFlow [OK]

Common Mistakes:

• Choosing layer or dataset functions instead
• Confusing with image processing functions
• Using non-existent TensorFlow functions

3. What is the output of this code snippet?

import tensorflow as tf
true_labels = [0, 1, 2, 2, 0]
pred_labels = [0, 2, 2, 2, 0]
cm = tf.math.confusion_matrix(true_labels, pred_labels)
print(cm.numpy())

medium

A. [[2 0 0]
[0 0 1]
[0 0 2]]

B. [[2 0 0]
[0 1 0]
[0 0 2]]

C. [[1 0 1]
[0 0 1]
[0 0 2]]

D. [[2 0 0]
[0 0 2]
[0 0 1]]

Solution

1. Step 1: Count true vs predicted labels

   For class 0: true labels are at positions 0 and 4, predicted also 0 both times -> 2 correct.
   For class 1: true label at position 1, predicted is 2 -> 0 correct, 1 predicted as 2.
   For class 2: true labels at positions 2 and 3, predicted both 2 -> 2 correct.

2. Step 2: Build confusion matrix rows

   Row 0 (true 0): predicted 0 twice -> [2,0,0]
   Row 1 (true 1): predicted 2 once -> [0,0,1]
   Row 2 (true 2): predicted 2 twice -> [0,0,2]

3. Final Answer:

   [[2 0 0] [0 0 1] [0 0 2]] -> Option A

4. Quick Check:

   Count true vs predicted labels = [[2 0 0] [0 0 1] [0 0 2]] [OK]

Hint: Count true-predicted pairs per class row-wise [OK]

Common Mistakes:

• Mixing up true and predicted label order
• Counting predicted labels as rows
• Miscounting class occurrences

4. Identify the error in this TensorFlow code for confusion matrix visualization:

import tensorflow as tf
true_labels = [0, 1, 1, 0]
pred_labels = [0, 1, 0, 0]
cm = tf.math.confusion_matrix(true_labels, pred_labels, num_classes=1)
print(cm.numpy())

medium

A. tf.math.confusion_matrix does not accept num_classes argument

B. num_classes should be 2, not 1

C. true_labels and pred_labels must be tensors, not lists

D. print(cm.numpy()) should be print(cm)

Solution

1. Step 1: Check the number of classes in labels

   True and predicted labels only contain 0 and 1, so there are 2 classes total.

2. Step 2: Verify num_classes argument

   Setting num_classes=1 is incorrect because labels include 1, which is not in [0, 1), causing a ValueError (labels out of range).

3. Final Answer:

   num_classes should be 2, not 1 -> Option B

4. Quick Check:

   num_classes must match actual classes = 2 [OK]

Hint: Set num_classes to actual number of classes in labels [OK]

Common Mistakes:

• Using wrong num_classes value
• Thinking lists are invalid inputs
• Misunderstanding print method for tensors

5. You want to visualize a confusion matrix as a heatmap using TensorFlow and Matplotlib. Which code snippet correctly creates and displays the heatmap?

hard

A. import tensorflow as tf
import matplotlib.pyplot as plt
true = [0,1,0,1]
pred = [0,0,0,1]
cm = tf.math.confusion_matrix(true, pred)
plt.imshow(cm, cmap='Blues')
plt.colorbar()
plt.show()

B. import tensorflow as tf
import matplotlib.pyplot as plt
true = [0,1,0,1]
pred = [0,0,0,1]
cm = tf.keras.metrics.ConfusionMatrix(true, pred)
plt.imshow(cm)
plt.show()

C. import tensorflow as tf
import matplotlib.pyplot as plt
true = [0,1,0,1]
pred = [0,0,0,1]
cm = tf.math.confusion_matrix(true, pred)
plt.plot(cm)
plt.show()

D. import tensorflow as tf
import matplotlib.pyplot as plt
true = [0,1,0,1]
pred = [0,0,0,1]
cm = tf.math.confusion_matrix(true, pred)
plt.bar(cm)
plt.show()

Solution

1. Step 1: Generate confusion matrix using TensorFlow

   tf.math.confusion_matrix(true, pred) correctly creates the confusion matrix tensor.

2. Step 2: Visualize matrix using Matplotlib heatmap

   plt.imshow with cmap='Blues' displays the matrix as a heatmap, plt.colorbar adds a color scale, and plt.show() renders the plot.

3. Final Answer:

   Code snippet B correctly creates and displays the heatmap -> Option A

4. Quick Check:

   Use tf.math.confusion_matrix + plt.imshow + plt.colorbar [OK]

Hint: Use plt.imshow with cmap and colorbar for heatmap [OK]

Common Mistakes:

• Using tf.keras.metrics.ConfusionMatrix (does not exist)
• Plotting confusion matrix with plt.plot or plt.bar
• Forgetting to add colorbar for scale