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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

By : Nick McClure
3.7 (18)
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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

3.7 (18)
By: Nick McClure

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Table of Contents (13 chapters)
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12
Index

Understanding Loss Functions in Linear Regression

It is important to know the effect of loss functions in algorithm convergence. Here we will illustrate how the L1 and L2 loss functions affect convergence in linear regression.

Getting ready

We will use the same iris dataset as in the prior recipe, but we will change our loss functions and learning rates to see how convergence changes.

How to do it…

  1. The start of the program is unchanged from before until we get to our loss function. We load the necessary libraries, start a session, load the data, create placeholders, and define our variables and model. One thing to note is that we are pulling out our learning rate and model iterations. We are doing this because we want to show the effect of quickly changing these parameters. Use the following code:
    import matplotlib.pyplot as plt
    import numpy as np
    import tensorflow as tf
    from sklearn import datasets
    sess = tf.Session()
    iris = datasets.load_iris()
    x_vals = np.array([x[3] for x in iris...

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