Book Image

TensorFlow Developer Certificate Guide

By : Oluwole Fagbohun
4 (2)
Book Image

TensorFlow Developer Certificate Guide

4 (2)
By: Oluwole Fagbohun

Overview of this book

The TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries. You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction. To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you’ll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional.
Table of Contents (20 chapters)
1
Part 1 – Introduction to TensorFlow
6
Part 2 – Image Classification with TensorFlow
12
Part 3 – Natural Language Processing with TensorFlow
15
Part 4 – Time Series with TensorFlow

Early stopping

In Chapter 6, Improving the Model, we introduced the concept of early stopping as an effective way of preventing overfitting. It does this by halting training when the model’s performance fails to improve over a defined number of epochs, as indicated in Figure 8.3. This way, we prevent our model from overfitting.

Figure 8.3 – A learning curve showing early stopping

Figure 8.3 – A learning curve showing early stopping

Let’s recreate the same baseline model, but this time, we will apply a built-in callback to stop training when the validation accuracy fails to improve. We will use the same build and compile steps as in the first model and then add a callback when we fit the model:

#Fit the model
# Add an early stopping callback
callbacks = [tf.keras.callbacks.EarlyStopping(
    monitor="val_accuracy", patience=3,
    restore_best_weights=True)]
history_2 = model_2.fit(train_data,
    epochs=20,
 ...