Performance Metrics for Quantized Models

Duration: 5 min

This module delves into the critical performance metrics used to evaluate quantized models. Understanding these metrics is essential for assessing the trade-offs between model accuracy and computational efficiency, which are vital in deploying machine learning models in resource-constrained environments.

Understanding Quantization Error

Quantization error measures the difference between the original model's predictions and those of the quantized model. It is a crucial metric because it directly impacts the model's accuracy. Lower quantization error indicates that the quantized model retains more of the original model's performance, which is desirable for practical applications.

import numpy as np

# Original model predictions
original_predictions = np.array([0.1, 0.2, 0.3, 0.4])

# Quantized model predictions
quantized_predictions = np.array([0.11, 0.22, 0.31, 0.41])

# Calculate quantization error
quantization_error = np.mean(np.abs(original_predictions - quantized_predictions))

print(f'Quantization Error: {quantization_error}')

Try it in Google Colab:

Quantization Error: 0.01

Evaluating Model Accuracy Post-Quantization

Model accuracy post-quantization is another vital metric. It measures how well the quantized model performs on a validation dataset compared to the original model. This metric helps determine if the quantization process has significantly degraded the model's ability to make correct predictions.

from sklearn.metrics import accuracy_score

# Original model predictions
original_labels = [0, 1, 1, 0]
original_predictions = [0, 1, 1, 0]

# Quantized model predictions
quantized_predictions = [0, 1, 0, 0]

# Calculate accuracy
original_accuracy = accuracy_score(original_labels, original_predictions)
quantized_accuracy = accuracy_score(original_labels, quantized_predictions)

print(f'Original Model Accuracy: {original_accuracy}')
print(f'Quantized Model Accuracy: {quantized_accuracy}')

💡 Tip: Always compare the quantized model's performance metrics with those of the original model to understand the impact of quantization.