Improving the Model Performance

Welcome back! In the previous lesson, you learned how to evaluate your CNN model’s performance and visualize its predictions. You also explored important metrics like accuracy, precision, recall, and the F1 score, and saw how to interpret a classification report and confusion matrix. Now that you know how to measure your model’s strengths and weaknesses, it’s time to take the next step: making your model even better.

In this lesson, you will learn practical techniques to improve your CNN’s performance on sketch recognition tasks. These methods are widely used in real-world deep learning projects to help models generalize better and avoid common pitfalls like overfitting. By the end of this lesson, you’ll be able to apply these improvements to your own models and see the difference in results.

You will focus on two powerful techniques for boosting your model’s performance:

• Dropout Layers: Dropout is a simple but effective way to prevent overfitting. It works by randomly “dropping out” (ignoring) a fraction of the neurons during training, which forces the model to learn more robust features. You’ll see how to add a Dropout layer to your CNN and understand why it helps.

• Early Stopping: Sometimes, training a model for too many epochs can actually make it perform worse on new data. Early stopping is a technique that monitors your model’s performance on the validation set and automatically stops training when the model stops improving. This saves time and helps you get the best version of your model.

Here’s a quick look at how these improvements appear in code:

from tensorflow.keras.layers import Dropout
from tensorflow.keras.callbacks import EarlyStopping

# Add a Dropout layer after a Dense layer
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))  # 50% of neurons will be dropped during training

# Set up EarlyStopping
early_stopping = EarlyStopping(
    patience=10,
    monitor='val_accuracy',
    restore_best_weights=True
)

You’ll also notice some other helpful changes in the code, such as using more training data, adding batch normalization, and making the model a bit deeper. These changes help the model learn better and make the training process more stable.