Evaluating the Model and Visualizing the Predictions

Welcome back! Now that you've trained your Convolutional Neural Network (CNN) model, it's time to evaluate its performance and visualize its predictions. This lesson will guide you through the process of assessing how well your model performs on unseen data and how to interpret its predictions. Whether you're familiar with model evaluation or this is your first time, this lesson will provide you with the knowledge and skills needed to evaluate a CNN model effectively.

In this lesson, you will learn how to evaluate the performance of your CNN model using various metrics. We will cover the steps involved in assessing the model's accuracy and visualizing its predictions. You will also learn how to interpret the results and understand the model's strengths and weaknesses.

Here's a glimpse of the code you'll be working with:

# Evaluate the model
loss, accuracy = model.evaluate(x_test, y_test)
print(f"Test accuracy: {accuracy:.4f}")

# Visualize predictions
def show_predictions(model, x_test, y_test, categories):
    predictions = model.predict(x_test)
    predicted_labels = np.argmax(predictions, axis=1)

    plt.figure(figsize=(10, 10))
    for i in range(9):
        plt.subplot(3, 3, i + 1)
        plt.imshow(x_test[i].reshape(28, 28), cmap='gray')
        plt.title(f"True: {categories[y_test[i]]}\nPred: {categories[predicted_labels[i]]}")
        plt.axis('off')
    plt.tight_layout()
    plt.savefig('static/images/predictions.png')

show_predictions(model, x_test, y_test, categories)

This code snippet demonstrates how to evaluate your model's accuracy and visualize its predictions. You will understand each step and how it contributes to assessing the model's performance.