Error analysis patterns in TensorFlow

Introduction

Error analysis helps us find where a machine learning model makes mistakes. This lets us improve the model step by step.

After training a model to understand what types of errors it makes

When you want to improve model accuracy by fixing common mistakes

To check if the model is biased or unfair to certain groups

When debugging why the model performs poorly on some data

To decide if more data or different features are needed

Syntax

TensorFlow

1. Get model predictions on test data
2. Compare predictions with true labels
3. Identify error types (e.g., false positives, false negatives)
4. Group errors by patterns (e.g., by class, feature, or input type)
5. Analyze and visualize errors to find improvement areas

This is a general workflow, not a fixed code syntax.

You can use TensorFlow and Python tools like pandas and matplotlib to help analyze errors.

Examples

Find which test samples the model predicted incorrectly.

TensorFlow

predictions = model.predict(test_images)
errors = predictions != test_labels
error_indices = np.where(errors)[0]

Separate errors into false positives and false negatives for binary classification.

TensorFlow

false_positives = (predictions == 1) & (test_labels == 0)
false_negatives = (predictions == 0) & (test_labels == 1)

Visualize which classes have more errors.

TensorFlow

import matplotlib.pyplot as plt
plt.hist(test_labels[error_indices])
plt.title('Error distribution by class')
plt.show()

Sample Model

This program trains a simple model on handwritten digits, finds which test images were misclassified, counts errors by digit class, and shows a bar chart of errors per class.

TensorFlow

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

# Load example dataset
mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Normalize images
train_images, test_images = train_images / 255.0, test_images / 255.0

# Build simple model
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

# Compile model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Train model
model.fit(train_images, train_labels, epochs=3, verbose=0)

# Predict on test data
logits = model.predict(test_images)
predictions = np.argmax(logits, axis=1)

# Find errors
errors = predictions != test_labels
error_indices = np.where(errors)[0]

# Count errors per class
error_classes, counts = np.unique(test_labels[error_indices], return_counts=True)

# Print error counts
print('Errors per class:')
for cls, count in zip(error_classes, counts):
    print(f'Class {cls}: {count} errors')

# Visualize error distribution
plt.bar(error_classes, counts)
plt.xlabel('Class')
plt.ylabel('Number of errors')
plt.title('Error distribution by class')
plt.show()

Important Notes

Look for patterns like certain classes having more errors or errors happening on similar images.

Use visual tools like confusion matrices or error histograms to understand mistakes better.

Error analysis is an ongoing process to improve your model step by step.

Summary

Error analysis finds where and why a model makes mistakes.

It helps improve model accuracy and fairness.

Use predictions and true labels to identify and group errors.