Confusion matrix visualization in TensorFlow

Introduction

A confusion matrix helps us see how well a model is doing by showing where it gets things right or wrong.

When you want to check how many times your model guessed each class correctly or incorrectly.

When you need to understand which classes your model confuses the most.

When you want a clear picture of your model's performance beyond just accuracy numbers.

Syntax

TensorFlow

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

cm = tf.math.confusion_matrix(labels, predictions)

plt.imshow(cm, cmap='Blues')
plt.colorbar()
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix')
plt.show()

labels are the true class values.

predictions are the model's predicted class values.

Examples

This creates a confusion matrix for 3 classes with some correct and incorrect predictions.

TensorFlow

labels = [0, 1, 2, 2, 0]
predictions = [0, 2, 1, 2, 0]
cm = tf.math.confusion_matrix(labels, predictions)

This shows the confusion matrix with a green color map.

TensorFlow

plt.imshow(cm, cmap='Greens')
plt.colorbar()
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix')
plt.show()

Sample Model

This program calculates the confusion matrix from true labels and predictions, prints it, and then shows it as a colored grid.

TensorFlow

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

# True labels for 5 samples
labels = [0, 1, 2, 2, 0]
# Model predictions for the same samples
predictions = [0, 2, 1, 2, 0]

# Compute confusion matrix
cm = tf.math.confusion_matrix(labels, predictions)

print('Confusion Matrix:')
print(cm.numpy())

# Plot confusion matrix
plt.imshow(cm, cmap='Blues')
plt.colorbar()
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix')
plt.show()

Important Notes

The confusion matrix shape depends on the number of classes.

Diagonal values show correct predictions; off-diagonal show mistakes.

Use color maps to make the matrix easier to read visually.

Summary

A confusion matrix shows how many times each class was predicted correctly or wrongly.

It helps understand model errors beyond just accuracy.

TensorFlow's tf.math.confusion_matrix makes it easy to create this matrix.

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