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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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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Started with TensorFlow
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1. Getting Started with TensorFlow
Introduction
How TensorFlow Works
Declaring Tensors
Using Placeholders and Variables
Working with Matrices
Declaring Operations
Implementing Activation Functions
Working with Data Sources
Additional Resources
2
2. The TensorFlow Way
2. The TensorFlow Way
Introduction
Operations in a Computational Graph
Layering Nested Operations
Working with Multiple Layers
Implementing Loss Functions
Implementing Back Propagation
Working with Batch and Stochastic Training
Combining Everything Together
Evaluating Models
3
3. Linear Regression
3. Linear Regression
Introduction
Using the Matrix Inverse Method
Implementing a Decomposition Method
Learning The TensorFlow Way of Linear Regression
Understanding Loss Functions in Linear Regression
Implementing Deming regression
Implementing Lasso and Ridge Regression
Implementing Elastic Net Regression
Implementing Logistic Regression
4
4. Support Vector Machines
4. Support Vector Machines
Introduction
Working with a Linear SVM
Reduction to Linear Regression
Working with Kernels in TensorFlow
Implementing a Non-Linear SVM
Implementing a Multi-Class SVM
5
5. Nearest Neighbor Methods
5. Nearest Neighbor Methods
Introduction
Working with Nearest Neighbors
Working with Text-Based Distances
Computing with Mixed Distance Functions
Using an Address Matching Example
Using Nearest Neighbors for Image Recognition
6
6. Neural Networks
6. Neural Networks
Introduction
Implementing Operational Gates
Working with Gates and Activation Functions
Implementing a One-Layer Neural Network
Implementing Different Layers
Using a Multilayer Neural Network
Improving the Predictions of Linear Models
Learning to Play Tic Tac Toe
7
7. Natural Language Processing
7. Natural Language Processing
Introduction
Working with bag of words
Implementing TF-IDF
Working with Skip-gram Embeddings
Working with CBOW Embeddings
Making Predictions with Word2vec
Using Doc2vec for Sentiment Analysis
8
8. Convolutional Neural Networks
8. Convolutional Neural Networks
Introduction
Implementing a Simpler CNN
Implementing an Advanced CNN
Retraining Existing CNNs models
Applying Stylenet/Neural-Style
Implementing DeepDream
9
9. Recurrent Neural Networks
9. Recurrent Neural Networks
Introduction
Implementing RNN for Spam Prediction
Implementing an LSTM Model
Stacking multiple LSTM Layers
Creating Sequence-to-Sequence Models
Training a Siamese Similarity Measure
10
10. Taking TensorFlow to Production
10. Taking TensorFlow to Production
Introduction
Implementing unit tests
Using Multiple Executors
Parallelizing TensorFlow
Taking TensorFlow to Production
Productionalizing TensorFlow – An Example
11
11. More with TensorFlow
11. More with TensorFlow
Introduction
Visualizing graphs in Tensorboard
There's more…
Working with a Genetic Algorithm
Clustering Using K-Means
Solving a System of ODEs
12
Index
Index

Declaring Tensors

Tensors are the primary data structure that TensorFlow uses to operate on the computational graph. We can declare these tensors as variables and or feed them in as placeholders. First we must know how to create tensors.

Getting ready

When we create a tensor and declare it to be a variable, TensorFlow creates several graph structures in our computation graph. It is also important to point out that just by creating a tensor, TensorFlow is not adding anything to the computational graph. TensorFlow does this only after creating available out of the tensor. See the next section on variables and placeholders for more information.

How to do it…

Here we will cover the main ways to create tensors in TensorFlow:

  1. Fixed tensors:
    • Create a zero filled tensor. Use the following:
      zero_tsr = tf.zeros([row_dim, col_dim])
    • Create a one filled tensor. Use the following:
      ones_tsr = tf.ones([row_dim, col_dim])
    • Create a constant filled tensor. Use the following:
      filled_tsr = tf.fill([row_dim, col_dim], 42)
    • Create a tensor out of an existing constant. Use the following:
      constant_tsr = tf.constant([1,2,3])

    Note

    Note that the tf.constant() function can be used to broadcast a value into an array, mimicking the behavior of tf.fill() by writing tf.constant(42, [row_dim, col_dim])

  2. Tensors of similar shape:
    • We can also initialize variables based on the shape of other tensors, as follows:
      zeros_similar = tf.zeros_like(constant_tsr)
ones_similar = tf.ones_like(constant_tsr)

    Note

    Note, that since these tensors depend on prior tensors, we must initialize them in order. Attempting to initialize all the tensors all at once willwould result in an error. See the section There's more… at the end of the next chapter on variables and placeholders.

  3. Sequence tensors:
    • TensorFlow allows us to specify tensors that contain defined intervals. The following functions behave very similarly to the range() outputs and numpy's linspace() outputs. See the following function:
      linear_tsr = tf.linspace(start=0, stop=1, start=3)
    • The resulting tensor is the sequence [0.0, 0.5, 1.0]. Note that this function includes the specified stop value. See the following function:
      integer_seq_tsr = tf.range(start=6, limit=15, delta=3)
    • The result is the sequence [6, 9, 12]. Note that this function does not include the limit value.
  4. Random tensors:
    • The following generated random numbers are from a uniform distribution:
      randunif_tsr = tf.random_uniform([row_dim, col_dim], minval=0, maxval=1)
    • Note that this random uniform distribution draws from the interval that includes the minval but not the maxval (minval <= x < maxval).
    • To get a tensor with random draws from a normal distribution, as follows:
      randnorm_tsr = tf.random_normal([row_dim, col_dim], mean=0.0, stddev=1.0)
    • There are also times when we wish to generate normal random values that are assured within certain bounds. The truncated_normal() function always picks normal values within two standard deviations of the specified mean. See the following:
      runcnorm_tsr = tf.truncated_normal([row_dim, col_dim], mean=0.0, stddev=1.0)
    • We might also be interested in randomizing entries of arrays. To accomplish this, there are two functions that help us: random_shuffle() and random_crop(). See the following:
      shuffled_output = tf.random_shuffle(input_tensor)
cropped_output = tf.random_crop(input_tensor, crop_size)
    • Later on in this book, we will be interested in randomly cropping an image of size (height, width, 3) where there are three color spectrums. To fix a dimension in the cropped_output, you must give it the maximum size in that dimension:
      cropped_image = tf.random_crop(my_image, [height/2, width/2, 3])

How it works…

Once we have decided on how to create the tensors, then we may also create the corresponding variables by wrapping the tensor in the Variable() function, as follows. More on this in the next section:

my_var = tf.Variable(tf.zeros([row_dim, col_dim]))

There's more…

We are not limited to the built-in functions. We can convert any numpy array to a Python list, or constant to a tensor using the function convert_to_tensor(). Note that this function also accepts tensors as an input in case we wish to generalize a computation inside a function.

End of Section 4
Next Section
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