Confusion matrix visualization in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that computes the confusion matrix.

TensorFlow

from sklearn.metrics import [1]

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

Aclassification_report

Broc_auc_score

Caccuracy_score

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Importing accuracy_score instead of confusion_matrix

Using classification_report which summarizes metrics but does not return the matrix

Trying to import from tensorflow instead of sklearn.metrics

The confusion_matrix function from sklearn.metrics is used to compute the confusion matrix.

2fill in blank

medium

Complete the code to compute the confusion matrix from true and predicted labels.

TensorFlow

cm = [1](y_true, y_pred)

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

Aconfusion_matrix

Bmean_squared_error

Caccuracy_score

Droc_curve

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score which returns a scalar accuracy value

Using mean_squared_error which is for regression

Using roc_curve which returns false positive and true positive rates

The confusion_matrix function takes true labels and predicted labels to compute the confusion matrix.

3fill in blank

hard

Fix the error in the code to plot the confusion matrix using matplotlib.

TensorFlow

import matplotlib.pyplot as plt
plt.imshow(cm, cmap=[1])
plt.colorbar()
plt.show()

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

A'viridis'

B'blue'

C'red'

D'gray'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single color name like 'blue' which is invalid for cmap

Passing a list or tuple instead of a string

Omitting the cmap argument causing default colors

The cmap argument expects a colormap name like 'viridis' to color the matrix visually.

4fill in blank

hard

Fill both blanks to add axis labels to the confusion matrix plot.

TensorFlow

plt.xlabel([1])
plt.ylabel([2])

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

A'Predicted label'

B'True label'

C'Accuracy'

D'Loss'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping true and predicted labels

Using metric names like 'Accuracy' instead of axis labels

Omitting axis labels

The x-axis shows predicted labels and the y-axis shows true labels in a confusion matrix plot.

5fill in blank

hard

Fill all three blanks to create a normalized confusion matrix and plot it with labels.

TensorFlow

cm_norm = cm / cm.sum(axis=[1], keepdims=[2])
plt.imshow(cm_norm, cmap=[3])
plt.xlabel('Predicted label')
plt.ylabel('True label')
plt.colorbar()
plt.show()

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

A1

BTrue

C'Blues'

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 which normalizes columns instead of rows

Setting keepdims=False causing shape errors

Using an invalid colormap name

Normalizing by rows (axis=1) with keepdims=True keeps the matrix shape. 'Blues' is a good colormap for normalized matrices.

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