Google's TensorFlow engine has a unique way of solving problems. This unique way allows us to solve machine learning problems very efficiently. Machine learning is used in almost all areas of life and work, but some of the more famous areas are computer vision, speech recognition, language translations, healthcare, and many more. We will cover the basic steps to understand how TensorFlow operates and eventually build up to production code techniques later in this book. These fundamentals are important to understanding recipes for the rest of this book.

At first, computation in TensorFlow may seem needlessly complicated. But there is a reason for it: because of how TensorFlow treats computation, developing more complicated algorithms is relatively easy. This recipe will guide us through the pseudocode of a TensorFlow algorithm.

Currently, TensorFlow is supported on Linux, macOS, and Windows. The code for this book has been created and run on a Linux system, but should run on any other system as well. The code for the book is available on GitHub at https://github.com/nfmcclure/tensorflow_cookbook as well as the Packt repository: https://github.com/PacktPublishing/TensorFlow-Machine-Learning-Cookbook-Second-Edition.

Throughout this book, we will only concern ourselves with the Python library wrapper of TensorFlow, although most of the original core code for TensorFlow is written in C++. This book will use Python 3.6+ (https://www.python.org) and TensorFlow 1.10.0+ (https://www.tensorflow.org). While TensorFlow can run on the CPU, most algorithms run faster if processed on the GPU, and it is supported on graphics cards with Nvidia Compute Capability v4.0+ (v5.1 recommended).

Popular GPUs for TensorFlow are Nvidia Tesla architectures and Pascal architectures with at least 4 GB of video RAM. To run on a GPU, you will also need to download and install the Nvidia CUDA toolkit as well as version 5.x+ (https://developer.nvidia.com/cuda-downloads).

Some of the recipes in this chapter will rely on a current installation of the SciPy, NumPy, and scikit-learn Python packages. These accompanying packages are also included in the Anaconda package (https://www.continuum.io/downloads).

Here, we will introduce the general flow of TensorFlow algorithms. Most recipes will follow this outline:

1. Import or generate dataset: All of our machine learning algorithms will depend on datasets. In this book, we will either generate data or use an outside source of datasets. Sometimes, it is better to rely on generated data because we just want to know the expected outcome. Most of the time, we will access public datasets for the given recipe. The details on accessing these datasets are in additional resources, section 8 of this chapter.
2. Transform and normalize data: Normally, input datasets do not come into the picture. TensorFlow expects that we need to transform TensorFlow so that they get the accepted shape. The data is usually not in the correct dimension or type that our algorithms expect. We will have to transform our data before we can use it. Most algorithms also expect normalized data and we will look at this here as well. TensorFlow has built-in functions that can normalize data for you, as follows:

import tensorflow as tf
data = tf.nn.batch_norm_with_global_normalization(...) 

3. Partition the dataset into train, test, and validation sets: We generally want to test our algorithms on different sets that we have trained on. Also, many algorithms require hyperparameter tuning, so we set aside a validation set for determining the best set of hyperparameters.
4. Set algorithm parameters (hyperparameters): Our algorithms usually have a set of parameters that we hold constant throughout the procedure. For example, this can be the number of iterations, the learning rate, or other fixed parameters of our choosing. It is considered good practice to initialize these together so that the reader or user can easily find them, as follows:

learning_rate = 0.01 
batch_size = 100 
iterations = 1000

5. Initialize variables and placeholders: TensorFlow depends on knowing what it can and cannot modify. TensorFlow will modify/adjust the variables (model weights/biases) during optimization to minimize a loss function. To accomplish this, we feed in data through placeholders. We need to initialize both variables and placeholders with size and type so that TensorFlow knows what to expect. TensorFlow also needs to know the type of data to expect. For most of this book, we will use float32. TensorFlow also provides float64 and float16. Note that more bytes used for precision results in slower algorithms, but less used results in less precision. See the following code:

a_var = tf.constant(42) 
x_input = tf.placeholder(tf.float32, [None, input_size]) 
y_input = tf.placeholder(tf.float32, [None, num_classes]) 

6. Define the model structure: After we have the data, and have initialized our variables and placeholders, we have to define the model. This is done by building a computational graph. We will talk more in depth about computational graphs in the Operations in a Computational Graph TensorFlow recipe in Chapter 2, The TensorFlow Way. The model for this example will be a linear model ():

y_pred = tf.add(tf.mul(x_input, weight_matrix), b_matrix) 

7. Declare the loss functions: After defining the model, we must be able to evaluate the output. This is where we declare the loss function. The loss function is very important as it tells us how far off our predictions are from the actual values. The different types of loss functions are explored in greater detail in the Implementing Back Propagation recipe in Chapter 2, The TensorFlow Way. Here, we implement the mean squared error for n-points, that is, :

loss = tf.reduce_mean(tf.square(y_actual - y_pred)) 

8. Initialize and train the model: Now that we have everything in place, we need to create an instance of our graph, feed in the data through the placeholders, and let TensorFlow change the variables to better predict our training data. Here is one way to initialize the computational graph:

with tf.Session(graph=graph) as session: 
... 
session.run(...) 
... 
Note that we can also initiate our graph with:
session = tf.Session(graph=graph) 
session.run(...) 

9. Evaluate the model: Once we have built and trained the model, we should evaluate the model by looking at how well it does with new data through some specified criteria. We evaluate on the train and test set and these evaluations will allow us to see if the model is under overfitting. We will address this in later recipes.
10. Tune hyperparameters: Most of the time, we will want to go back and change some of the hyperparameters based on the model's performance. We then repeat the previous steps with different hyperparameters and evaluate the model on the validation set.
11. Deploy/predict new outcomes: It is also important to know how to make predictions on new and unseen data. We can do this with all of our models once we have them trained.

In TensorFlow, we have to set up the data, variables, placeholders, and model before we can tell the program to train and change the variables to improve predictions. TensorFlow accomplishes this through computational graphs. These computational graphs are directed graphs with no recursion, which allows for computational parallelism. To do this, we need to create a loss function for TensorFlow to minimize. TensorFlow accomplishes this by modifying the variables in the computational graph. TensorFlow knows how to modify the variables because it keeps track of the computations in the model and automatically computes the variable gradients (how to change each variable) to minimize the loss. Because of this, we can see how easy it can be to make changes and try different data sources.

For more TensorFlow introduction and resources, see the official documentation and tutorials as well:

• A better place to start is the official Python API documentation: https://www.tensorflow.org/api_docs/python/
• There are also tutorials available: https://www.tensorflow.org/tutorials/
• An unofficial collection of TensorFlow tutorials, projects, presentations, and code repositories: https://github.com/jtoy/awesome-tensorflow

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. To do this, first, we must learn how to create tensors.

When we create a tensor and declare it as 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 running an operation to initialize the variables. See the next section, on variables and placeholders, for more information.

Here, we will cover the main ways that we can create tensors in TensorFlow:

1. Fixed tensors:

• • In the following code, we are creating a zero-filled tensor:

zero_tsr = tf.zeros([row_dim, col_dim])

• • In the following code, we are creating a one-filled tensor:

ones_tsr = tf.ones([row_dim, col_dim]) 

• • In the following code, we are creating a constant-filled tensor:

filled_tsr = tf.fill([row_dim, col_dim], 42) 

• • In the following code, we are creating a tensor out of an existing constant:

constant_tsr = tf.constant([1,2,3])

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) 

3. Sequence tensors: TensorFlow allows us to specify tensors that contain defined intervals. The following functions behave very similarly to the NumPy's linspace() outputs and range() outputs. See the following function:

linear_tsr = tf.linspace(start=0, stop=1, start=3) 

The resultant tensor has a sequence of [0.0, 0.5, 1.0]. Note that this function includes the specified stop value. See the following function for more information:

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, you can run the following code:

randnorm_tsr = tf.random_normal([row_dim, col_dim], mean=0.0, stddev=1.0) 

There are also times where we want 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:

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 can help us: random_shuffle() and random_crop(). The following code performs this:

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 images 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]) 

Once we have decided how to create the tensors, 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])) 

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

Placeholders and variables are key tools in with regard to using computational graphs in TensorFlow. We must understand the difference between them and when to best use them to our advantage.

One of the most important distinctions to make with data is whether it is a placeholder or a variable. Variables are the model parameters of the algorithm, and TensorFlow keeps track of how to change these to optimize the algorithm. Placeholders are objects that allow you to feed in data of a specific type and shape, or that depend on the results of the computational graph, such as the expected outcome of a computation.

The main way to create a variable is by using the Variable() function, which takes a tensor as an input and outputs a variable. This is only the declaration, and we still need to initialize the variable. Initializing is what puts the variable with the corresponding methods on the computational graph. Here is an example of creating and initializing a variable:

my_var = tf.Variable(tf.zeros([2,3])) 
sess = tf.Session() 
initialize_op = tf.global_variables_initializer() 
sess.run(initialize_op) 

To see what the computational graph looks like after creating and initializing a variable, see the following part of this recipe. Placeholders are just holding the position for data to be fed into the graph. Placeholders get data from a feed_dict argument in the session. To put a placeholder into the graph, we must perform at least one operation on the placeholder. In the following code snippet, we initialize the graph, declare x to be a placeholder (of a predefined size), and define y as the identity operation on x, which just returns x. We then create data to feed into the x placeholder and run the identity operation. The code is shown here, and the resultant graph is in the following section:

sess = tf.Session() 
x = tf.placeholder(tf.float32, shape=[2,2]) 
y = tf.identity(x) 
x_vals = np.random.rand(2,2) 
sess.run(y, feed_dict={x: x_vals}) 
# Note that sess.run(x, feed_dict={x: x_vals}) will result in a self-referencing error. 

The computational graph of initializing a variable as a tensor of zeros is shown in the following diagram:

Here, we can see what the computational graph looks like in detail with just one variable, initialized all to zeros. The grey shaded region is a very detailed view of the operations and constants that were involved. The main computational graph with less detail is the smaller graph outside of the grey region in the upper-right corner. For more details on creating and visualizing graphs, see the first section of Chapter 10, Taking TensorFlow to Production. Similarly, the computational graph of feeding a NumPy array into a placeholder can be seen in the following diagram:

The grey shaded region is a very detailed view of the operations and constants that were involved. The main computational graph with less detail is the smaller graph outside of the grey region in the upper-right corner.

During the run of the computational graph, we have to tell TensorFlow when to initialize the variables we have created. While each variable has an initializer method, the most common way to do this is with the helper function, that is, global_variables_initializer(). This function creates an operation in the graph that initializes all the variables we have created, as follows:

initializer_op = tf.global_variables_initializer() 

But if we want to initialize a variable based on the results of initializing another variable, we have to initialize variables in the order we want, as follows:

sess = tf.Session() 
first_var = tf.Variable(tf.zeros([2,3])) 
sess.run(first_var.initializer) 
second_var = tf.Variable(tf.zeros_like(first_var)) 
# 'second_var' depends on the 'first_var'
sess.run(second_var.initializer)

Understanding how TensorFlow works with matrices is very important in understanding the flow of data through computational graphs.

Many algorithms depend on matrix operations. TensorFlow gives us easy-to-use operations to perform such matrix calculations. For all of the following examples, we first create a graph session by running the following code:

import tensorflow as tf 
sess = tf.Session() 

We will proceed with the recipe as follows:

1. Creating matrices: We can create two-dimensional matrices from NumPy arrays or nested lists, as we described in the Creating and using tensors recipe. We can also use the tensor creation functions and specify a two-dimensional shape for functions such as zeros(), ones(), truncated_normal(), and so on. TensorFlow also allows us to create a diagonal matrix from a one-dimensional array or list with the diag() function, as follows:

identity_matrix = tf.diag([1.0, 1.0, 1.0]) 
A = tf.truncated_normal([2, 3]) 
B = tf.fill([2,3], 5.0) 
C = tf.random_uniform([3,2]) 
D = tf.convert_to_tensor(np.array([[1., 2., 3.],[-3., -7., -1.],[0., 5., -2.]])) 
print(sess.run(identity_matrix)) 
[[ 1.  0.  0.] 
 [ 0.  1.  0.] 
 [ 0.  0.  1.]] 
print(sess.run(A)) 
[[ 0.96751703  0.11397751 -0.3438891 ] 
 [-0.10132604 -0.8432678   0.29810596]] 
print(sess.run(B)) 
[[ 5.  5.  5.] 
 [ 5.  5.  5.]] 
print(sess.run(C)) 
[[ 0.33184157  0.08907614] 
 [ 0.53189191  0.67605299] 
 [ 0.95889051 0.67061249]] 
print(sess.run(D)) 
[[ 1.  2.  3.] 
 [-3. -7. -1.] 
 [ 0.  5. -2.]] 

2. Addition, subtraction, and multiplication: To add, subtract, or multiply matrices of the same dimension, TensorFlow uses the following function:

print(sess.run(A+B)) 
[[ 4.61596632  5.39771316  4.4325695 ] 
 [ 3.26702736  5.14477345  4.98265553]] 
print(sess.run(B-B)) 
[[ 0.  0.  0.] 
 [ 0.  0.  0.]] 
Multiplication 
print(sess.run(tf.matmul(B, identity_matrix))) 
[[ 5.  5.  5.] 
 [ 5.  5.  5.]] 

It is important to note that the matmul() function has arguments that specify whether or not to transpose the arguments before multiplication or whether each matrix is sparse.

3. The transpose: Transpose a matrix (flip the columns and rows) as follows:

print(sess.run(tf.transpose(C))) 
[[ 0.67124544  0.26766731  0.99068872] 
 [ 0.25006068  0.86560275  0.58411312]] 

Again, it is worth mentioning that reinitializing gives us different values than before.

4. Determinant: To calculate the determinant, use the following:

print(sess.run(tf.matrix_determinant(D))) 
-38.0 

5. Inverse: To find the inverse of a square matrix, see the following:

print(sess.run(tf.matrix_inverse(D))) 
[[-0.5        -0.5        -0.5       ] 
 [ 0.15789474  0.05263158  0.21052632] 
 [ 0.39473684  0.13157895  0.02631579]] 

6. Decompositions: For the Cholesky decomposition, use the following:

print(sess.run(tf.cholesky(identity_matrix))) 
[[ 1.  0.  1.] 
 [ 0.  1.  0.] 
 [ 0.  0.  1.]] 

7. Eigenvalues and eigenvectors: For eigenvalues and eigenvectors, use the following code:

print(sess.run(tf.self_adjoint_eig(D)) 
[[-10.65907521  -0.22750691   2.88658212] 
 [  0.21749542   0.63250104  -0.74339638] 
 [  0.84526515   0.2587998    0.46749277] 
 [ -0.4880805    0.73004459   0.47834331]] 

Note that the self_adjoint_eig() function outputs the eigenvalues in the first row and the subsequent vectors in the remaining vectors. In mathematics, this is known as the eigendecomposition of a matrix.

TensorFlow provides all the tools for us to get started with numerical computations and adding such computations to our graphs. This notation might seem quite heavy for simple matrix operations. Remember that we are adding these operations to the graph and telling TensorFlow which tensors to run through those operations. While this might seem verbose now, it helps us understand the notation in later chapters when this way of computation will make it easier to accomplish our goals.

Now, we must learn what other operations we can add to a TensorFlow graph.

Besides the standard arithmetic operations, TensorFlow provides us with more operations that we should be aware of and how to use them before proceeding. Again, we can create a graph session by running the following code:

import tensorflow as tf 
sess = tf.Session() 

TensorFlow has the standard operations on tensors, that is, add(), sub(), mul(), and div(). Note that all of the operations in this section will evaluate the inputs element-wise, unless specified otherwise:

1. TensorFlow provides some variations of div() and the relevant functions.
2. It is worth mentioning that div() returns the same type as the inputs. This means that it really returns the floor of the division (akin to Python 2) if the inputs are integers. To return the Python 3 version, which casts integers into floats before dividing and always returning a float, TensorFlow provides the truediv() function, as follows:

print(sess.run(tf.div(3, 4))) 
0 
print(sess.run(tf.truediv(3, 4))) 
0.75 

3. If we have floats and want integer division, we can use the floordiv() function. Note that this will still return a float, but it will be rounded down to the nearest integer. This function is as follows:

print(sess.run(tf.floordiv(3.0,4.0))) 
0.0 

4. Another important function is mod(). This function returns the remainder after division. It is as follows:

print(sess.run(tf.mod(22.0, 5.0))) 
2.0 

5. The cross product between two tensors is achieved by the cross() function. Remember that the cross product is only defined for two three-dimensional vectors, so it only accepts two three-dimensional tensors. The following code illustrates this use:

print(sess.run(tf.cross([1., 0., 0.], [0., 1., 0.]))) 
[ 0.  0.  1.0]

6. Here is a compact list of the more common math functions. All of these functions operate element-wise:

7. Specialty mathematical functions: There are some special math functions that get used in machine learning that are worth mentioning, and TensorFlow has built-in functions for them. Again, these functions operate element-wise, unless specified otherwise:

It is important to know which functions are available to us so that we can add them to our computational graphs. We will mainly be concerned with the preceding functions. We can also generate many different custom functions as compositions of the preceding, as follows:

# Tangent function (tan(pi/4)=1) 
print(sess.run(tf.tan(3.1416/4.)))
1.0 

If we wish to add other operations to our graphs that are not listed here, we must create our own from the preceding functions. Here is an example of an operation that wasn't used previously that we can add to our graph. We chose to add a custom polynomial function, , using the following code:

def custom_polynomial(value): 
    return tf.sub(3 * tf.square(value), value) + 10
print(sess.run(custom_polynomial(11))) 
362 

Activation functions are the key for neural networks to approximate non-linear outputs and adapt to non-linear features. They introduce non-linear operations into neural networks. If we are careful as to which activation functions are selected and where we put them, they are very powerful operations that we can tell TensorFlow to fit and optimize.

When we start to use neural networks, we will use activation functions regularly because activation functions are an essential part of any neural network. The goal of an activation function is just to adjust weight and bias. In TensorFlow, activation functions are non-linear operations that act on tensors. They are functions that operate in a similar way to the previous mathematical operations. Activation functions serve many purposes, but the main concept is that they introduce a non-linearity into the graph while normalizing the outputs. Start a TensorFlow graph with the following commands:

import tensorflow as tf 
sess = tf.Session() 

The activation functions live in the neural network (nn) library in TensorFlow. Besides using built-in activation functions, we can also design our own using TensorFlow operations. We can import the predefined activation functions (import tensorflow.nn as nn) or be explicit and write nn in our function calls. Here, we choose to be explicit with each function call:

1. The rectified linear unit, known as ReLU, is the most common and basic way to introduce non-linearity into neural networks. This function is just called max(0,x). It is continuous, but not smooth. It appears as follows:

print(sess.run(tf.nn.relu([-3., 3., 10.]))) 
[  0.  3.  10.] 

2. There are times where we will want to cap the linearly increasing part of the preceding ReLU activation function. We can do this by nesting the max(0,x) function into a min() function. The implementation that TensorFlow has is called the ReLU6 function. This is defined as min(max(0,x),6). This is a version of the hard-sigmoid function and is computationally faster, and does not suffer from vanishing (infinitesimally near zero) or exploding values. This will come in handy when we discuss deeper neural networks in Chapter 8, Convolutional Neural Networks, and Chapter 9, Recurrent Neural Networks. It appears as follows:

print(sess.run(tf.nn.relu6([-3., 3., 10.]))) 
[ 0. 3. 6.]

3. The sigmoid function is the most common continuous and smooth activation function. It is also called a logistic function and has the form . The sigmoid function is not used very often because of its tendency to zero-out the backpropagation terms during training. It appears as follows:

print(sess.run(tf.nn.sigmoid([-1., 0., 1.]))) 
[ 0.26894143  0.5         0.7310586 ] 

4. Another smooth activation function is the hyper tangent. The hyper tangent function is very similar to the sigmoid except that instead of having a range between 0 and 1, it has a range between -1 and 1. This function has the form of the ratio of the hyperbolic sine over the hyperbolic cosine. Another way to write this is . This activation function is as follows:

print(sess.run(tf.nn.tanh([-1., 0., 1.]))) 
[-0.76159418  0.         0.76159418 ] 

5. The softsign function also gets used as an activation function. The form of this function is . The softsign function is supposed to be a continuous (but not smooth) approximation to the sign function. See the following code:

print(sess.run(tf.nn.softsign([-1., 0., -1.]))) 
[-0.5  0.   0.5] 

6. Another function, the softplus function, is a smooth version of the ReLU function. The form of this function is . It appears as follows:

print(sess.run(tf.nn.softplus([-1., 0., -1.]))) 
[ 0.31326166  0.69314718  1.31326163] 

7. The Exponential Linear Unit (ELU) is very similar to the softplus function except that the bottom asymptote is -1 instead of 0. The form is if x < 0 else x. It appears as follows:

print(sess.run(tf.nn.elu([-1., 0., -1.]))) 
[-0.63212055  0.          1.        ] 

These activation functions are ways that we can introduce non-linearities in neural networks or other computational graphs in the future. It is important to note where in our network we are using activation functions. If the activation function has a range between 0 and 1 (sigmoid), then the computational graph can only output values between 0 and 1. If the activation functions are inside and hidden between nodes, then we want to be aware of the effect that the range can have on our tensors as we pass them through. If our tensors were scaled to have a mean of zero, we will want to use an activation function that preserves as much variance as possible around zero. This would imply that we want to choose an activation function such as the hyperbolic tangent (tanh) or the softsign. If the tensors are all scaled to be positive, then we would ideally choose an activation function that preserves variance in the positive domain.

Here are two graphs that illustrate the different activation functions. The following graphs show the ReLU, ReLU6, softplus, exponential LU, sigmoid, softsign, and hyperbolic tangent functions:

Here, we can see four of the activation functions: softplus, ReLU, ReLU6, and exponential LU. These functions flatten out to the left of zero and linearly increase to the right of zero, with the exception of ReLU6, which has a maximum value of six:

Here are the sigmoid, hyperbolic tangent (tanh), and softsign activation functions. These activation functions are all smooth and have a S n shape. Note that there are two horizontal asymptotes for these functions.

For most of this book, we will rely on the use of datasets to fit machine learning algorithms. This section has instructions on how to access each of these datasets through TensorFlow and Python.

In TensorFlow, some of the datasets that we will use are built into Python libraries, some will require a Python script to download, and some will be manually downloaded through the internet. Almost all of these datasets will require an active internet connection so that you can retrieve them.

1. Iris data: This dataset is arguably the most classic dataset used in machine learning and maybe all of statistics. It is a dataset that measures sepal length, sepal width, petal length, and petal width of three different types of iris flowers: Iris setosa, Iris virginica, and Iris versicolor. There are 150 measurements overall, which means that there are 50 measurements of each species. To load the dataset in Python, we will use scikit-learn's dataset function, as follows:

from sklearn import datasets 
iris = datasets.load_iris() 
print(len(iris.data)) 
150 
print(len(iris.target)) 
150 
print(iris.data[0]) # Sepal length, Sepal width, Petal length, Petal width 
[ 5.1 3.5 1.4 0.2] 
print(set(iris.target)) # I. setosa, I. virginica, I. versicolor 
{0, 1, 2} 

2. Birth weight data: This data was originally from Baystate Medical Center, Springfield, Mass 1986 (1). This dataset contains the measure of child birth weight and other demographic and medical measurements of the mother and family history. There are 189 observations of eleven variables. The following code shows you how you can access this data in Python:

import requests
birthdata_url = 'https://github.com/nfmcclure/tensorflow_cookbook/raw/master/01_Introduction/07_Working_with_Data_Sources/birthweight_data/birthweight.dat' 
birth_file = requests.get(birthdata_url) 
birth_data = birth_file.text.split('\r\n') 
birth_header = birth_data[0].split('\t') 
birth_data = [[float(x) for x in y.split('\t') if len(x)>=1] for y in birth_data[1:] if len(y)>=1]
print(len(birth_data)) 
189 
print(len(birth_data[0])) 
9

3. Boston Housing data: Carnegie Mellon University maintains a library of datasets in their StatLib Library. This data is easily accessible via The University of California at Irvine's machine learning Repository (https://archive.ics.uci.edu/ml/index.php). There are 506 observations of house worth, along with various demographic data and housing attributes (14 variables). The following code shows you how to access this data in Python, via the Keras library:

from keras.datasets import boston_housing
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
housing_header = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] 
print(x_train.shape[0]) 
404 
print(x_train.shape[1]) 
13 

4. MNIST handwriting data: The MNIST (Mixed National Institute of Standards and Technology) dataset is a subset of the larger NIST handwriting database. The MNIST handwriting dataset is hosted on Yann LeCun's website (https://yann.lecun.com/exdb/mnist/). It is a database of 70,000 images of single-digit numbers (0-9) with about 60,000 annotated for a training set and 10,000 for a test set. This dataset is used so often in image recognition that TensorFlow provides built-in functions to access this data. In machine learning, it is also important to provide validation data to prevent overfitting (target leakage). Because of this, TensorFlow sets aside 5,000 images of the train set into a validation set. The following code shows you how to access this data in Python:

from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("MNIST_data/"," one_hot=True) 
print(len(mnist.train.images)) 
55000 
print(len(mnist.test.images)) 
10000 
print(len(mnist.validation.images)) 
5000 
print(mnist.train.labels[1,:]) # The first label is a 3 
[ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.] 

5. Spam-ham text data. UCI's machine learning dataset library also holds a spam-ham text message dataset. We can access this .zip file and get the spam-ham text data as follows:

import requests 
import io 
from zipfile import ZipFile 
zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip' 
r = requests.get(zip_url) 
z = ZipFile(io.BytesIO(r.content)) 
file = z.read('SMSSpamCollection') 
text_data = file.decode() 
text_data = text_data.encode('ascii',errors='ignore') 
text_data = text_data.decode().split('\n') 
text_data = [x.split('\t') for x in text_data if len(x)>=1] 
[text_data_target, text_data_train] = [list(x) for x in zip(*text_data)] 
print(len(text_data_train)) 
5574 
print(set(text_data_target)) 
{'ham', 'spam'} 
print(text_data_train[1]) 
Ok lar... Joking wif u oni... 

6. Movie review data: Bo Pang from Cornell has released a movie review dataset that classifies reviews as good or bad (3). You can find the data on the following website: http://www.cs.cornell.edu/people/pabo/movie-review-data/. To download, extract, and transform this data, we can run the following code:

import requests 
import io 
import tarfile 
movie_data_url = 'http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz' 
r = requests.get(movie_data_url) 
# Stream data into temp object 
stream_data = io.BytesIO(r.content) 
tmp = io.BytesIO() 
while True: 
    s = stream_data.read(16384) 
    if not s: 
        break 
    tmp.write(s) 
    stream_data.close() 
tmp.seek(0) 
# Extract tar file 
tar_file = tarfile.open(fileobj=tmp, mode="r:gz") 
pos = tar_file.extractfile('rt-polaritydata/rt-polarity.pos') 
neg = tar_file.extractfile('rt-polaritydata/rt-polarity.neg') 
# Save pos/neg reviews (Also deal with encoding) 
pos_data = [] 
for line in pos: 
    pos_data.append(line.decode('ISO-8859-1').encode('ascii',errors='ignore').decode()) 
neg_data = [] 
for line in neg: 
    neg_data.append(line.decode('ISO-8859-1').encode('ascii',errors='ignore').decode()) 
tar_file.close() 
print(len(pos_data)) 
5331 
print(len(neg_data)) 
5331 
# Print out first negative review 
print(neg_data[0]) 
simplistic , silly and tedious . 

7. CIFAR-10 image data: The Canadian Institute For Advanced Research has released an image set that contains 80 million labeled colored images (each image is scaled to 32 x 32 pixels). There are 10 different target classes (airplane, automobile, bird, and so on). CIFAR-10 is a subset that includes 60,000 images. There are 50,000 images in the training set, and 10,000 in the test set. Since we will be using this dataset in multiple ways, and because it is one of our larger datasets, we will not run a script each time we need it. To get this dataset, please navigate to http://www.cs.toronto.edu/~kriz/cifar.html and download the CIFAR-10 dataset. We will address how to use this dataset in the appropriate chapters.
8. The works of Shakespeare text data: Project Gutenberg (5) is a project that releases electronic versions of free books. They have compiled all of the works of Shakespeare together. The following code shows you how to access this text file through Python:

import requests 
shakespeare_url = 'http://www.gutenberg.org/cache/epub/100/pg100.txt' 
# Get Shakespeare text 
response = requests.get(shakespeare_url) 
shakespeare_file = response.content 
# Decode binary into string 
shakespeare_text = shakespeare_file.decode('utf-8') 
# Drop first few descriptive paragraphs. 
shakespeare_text = shakespeare_text[7675:] 
print(len(shakespeare_text)) # Number of characters 
5582212

9. English-German sentence translation data: The Tatoeba project (http://tatoeba.org) collects sentence translations in many languages. Their data has been released under the Creative Commons License. From this data, ManyThings.org (http://www.manythings.org) has compiled sentence-to-sentence translations in text files that are available for download. Here, we will use the English-German translation file, but you can change the URL to whichever languages you would like to use:

import requests 
import io 
from zipfile import ZipFile 
sentence_url = 'http://www.manythings.org/anki/deu-eng.zip' 
r = requests.get(sentence_url) 
z = ZipFile(io.BytesIO(r.content)) 
file = z.read('deu.txt') 
# Format Data 
eng_ger_data = file.decode() 
eng_ger_data = eng_ger_data.encode('ascii',errors='ignore') 
eng_ger_data = eng_ger_data.decode().split('\n') 
eng_ger_data = [x.split('\t') for x in eng_ger_data if len(x)>=1] 
[english_sentence, german_sentence] = [list(x) for x in zip(*eng_ger_data)] 
print(len(english_sentence)) 
137673 
print(len(german_sentence)) 
137673 
print(eng_ger_data[10]) 
['I' won!, 'Ich habe gewonnen!'] 

When it comes time to using one of these datasets in a recipe, we will refer you to this section and assume that the data is loaded in such a way as described in the preceding section. If further data transformation or pre-processing is needed, then such code will be provided in the recipe itself.

Here are additional references for the data resources we use in this book:

• Hosmer, D.W., Lemeshow, S., and Sturdivant, R. X. (2013) Applied Logistic Regression: 3rd Edition
• Lichman, M. (2013). UCI machine learning repository http://archive.ics.uci.edu/ml Irvine, CA: University of California, School of Information and Computer Science
• Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan, Thumbs up? Sentiment Classification using machine learning techniques, Proceedings of EMNLP 2002 http://www.cs.cornell.edu/people/pabo/movie-review-data/
• Krizhevsky. (2009). Learning Multiple Layers of Features from Tiny Images http://www.cs.toronto.edu/~kriz/cifar.html
• Project Gutenberg. Accessed April 2016 http://www.gutenberg.org/

In this section, you will find additional links, documentation sources, and tutorials that are of great assistance to learning and using TensorFlow.

When learning how to use TensorFlow, it helps to know where to turn for assistance or pointers. This section lists resources to get TensorFlow running and to troubleshoot problems.

Here is a list of TensorFlow resources:

• The code for this book is available online at https://github.com/nfmcclure/tensorflow_cookbook and at the Packt repository: https://github.com/PacktPublishing/TensorFlow-Machine-Learning-Cookbook-Second-Edition.

• The official TensorFlow Python API documentation is located at https://www.tensorflow.org/api_docs/python. Here, there is documentation and examples of all of the functions, objects, and methods in TensorFlow.
• TensorFlow's official tutorials are very thorough and detailed. They are located at https://www.tensorflow.org/tutorials/index.html. They start covering image recognition models, and work through Word2Vec, RNN models, and sequence-to-sequence models. They also have additional tutorials for generating fractals and solving PDE systems. Note that they are continually adding more tutorials and examples to this collection.

• TensorFlow's official GitHub repository is available via https://github.com/tensorflow/tensorflow. Here, you can view the open source code and even fork or clone the most current version of the code if you want. You can also see current filed issues if you navigate to the issues directory.
• A public Docker container that is kept current by TensorFlow is available on Dockerhub at https://hub.docker.com/r/tensorflow/tensorflow/.
• A great source for community help is Stack Overflow. There is a tag for TensorFlow. This tag seems to be growing in interest as TensorFlow is gaining more popularity. To view activity on this tag, visit http://stackoverflow.com/questions/tagged/Tensorflow.
• While TensorFlow is very agile and can be used for many things, the most common use of TensorFlow is deep learning. To understand the basis for deep learning, how the underlying mathematics works, and to develop more intuition on deep learning, Google has created an online course that's available on Udacity. To sign up and take the video lecture course, visit https://www.udacity.com/course/deep-learning--ud730.
• TensorFlow has also made a site where you can visually explore training a neural network while changing the parameters and datasets. Visit http://playground.tensorflow.org/ to explore how different settings affect the training of neural networks.
• Geoffrey Hinton teaches an online course called Neural Networks for Machine Learning through Coursera https://www.coursera.org/learn/neural-networks.
• Stanford University has an online syllabus and detailed course notes for Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/.