Book Image

Machine Learning with TensorFlow 1.x

By : Quan Hua, Saif Ahmed, Shams Ul Azeem
Book Image

Machine Learning with TensorFlow 1.x

By: Quan Hua, Saif Ahmed, Shams Ul Azeem

Overview of this book

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim. By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.
Table of Contents (13 chapters)
Free Chapter
1
Getting Started with TensorFlow

Automatic fine-tune in production

After running the system for a while, we will have some user-labeled images. We will create a fine-tune process to automatically run every day and fine-tune the latest model with new data.

Let's create a file named finetune.py in the scripts folder.

Loading the user-labeled data

First, we will add the code to download all user-labeled images from the production server:

    import tensorflow as tf 
    import os 
    import json 
    import random 
    import requests 
    import shutil 
    from scipy.misc import imread, imsave 
    from datetime import datetime 
    from tqdm import tqdm 
 
    import nets, models, datasets 
 
 
    def ensure_folder_exists(folder_path): 
    if not...