This post walks through a simple Google TensorFlow example.

#### Getting Started

TensorFlow is an open source library for analytics. It’s particularly useful for building deep learning systems for predictive models involving natural language processing, audio, and images.

The TensorFlow site provides instructions for downloading and installing the package. Loosely speaking, here’s what you need to do to get started on a Windows machine:

- Get comfortable with
**Python**. - Install
**docker**. **Run the “development” image for TensorFlow**. The development images contains all of the samples on the TensorFlow site. The command I used was

docker run -i -t gcr.io/tensorflow/tensorflow:latest-devel /bin/bash

Running the development image “latest-devel” will provide you with code for all of the examples on the TensorFlow site. You don’t strictly speaking have to use docker to get started with TensorFlow, but that’s what worked for me.

#### A Simple TensorFlow Program

I think the TensorFlow tutorials are too complicated for a beginner, so I’m going to present a simple TensorFlow example that takes input x, adds one to it, and stores it in an output array y. Many TensorFlow programs, including this one, have four distinct phases:

**Create TensorFlow****objects**that model the calculation you want to carry out,**Get the input data**for the model,**Run the model**using the input data,- Do something with the
**output**.

I have marked these phases in the code below.

import numpy as np

import tensorflow as tf

import math

def add_one():

with tf.Session() as session:

# (1)

x = tf.placeholder(tf.float32, [1], name=’x’) # fed as input below

y = tf.placeholder(tf.float32, [1], name=’y’) # fetched as output below

b = tf.constant(1.0)

y = x + b # here is our ‘model’: add one to the input.

x_in = [2] # (2)

y_final = session.run([y], {x: x_in}) # (3)

print(y_final) # (4)

The first line in add_one creates a TensorFlow **Session** object. Sessions contain “computational graphs” that represent calculations to be carried out. In our example, we want to create a computational graph that represents adding the constant 1.0 to an input array x. Here is a picture:

The next two lines create “placeholders” x and y. A **placeholder** is an interface between a computational graph element and your data. Placeholders can represent input or output, and in my case x represents the value to send in, and y represents the result. The second argument of the placeholder function is the shape of the placeholder, which is a single dimensional Tensor with one entry. You can also provide a name, which is useful for debugging purposes.

The next line creates the **constant** b using tf.constant. As we will see in future examples, there are other TensorFlow functions for addition, multiplication, and so on. Using these helper functions you can assemble a very wide range of functions that involve inputs, outputs, and other intermediate values. In this example, we’re keeping it very simple.

The next line, y = x + b, is the computational model we want TensorFlow to calculate. This line does not actually compute anything, even though it looks like it should. It simply creates data structures (called “**graph elements**”) that represent the addition of x and b, and the assignment of the result to the placeholder y. Each of the items in my picture above is a graph element. These graph elements are processed by the TensorFlow engine when Session.run() is called. Part of the magic of TensorFlow is to efficiently carry out graph element evaluation, even for very large and complicated graphs.

Now that the model is created, we turn to assembling the input and running the model. Our model has one input x, so we create a list x_in that will be associated with the placeholder x. If you think of a TensorFlow model as a function in your favorite programming language, the placeholders are the arguments. Here we want to “pass” x_in as the value for the “parameter” x. This is what happens in the **session.run()** call. The first argument is a list of graph elements that you would like TensorFlow to evaluate. In this case, we’re interested in evaluating the output placeholder y, so that’s what we pass in. Session.run will return an output value for each graph element that you pass in as the first argument, and the value will correspond to the evaluated value for that element. In English this means that y_final is going to be an array that has the result: x + 1. The second argument to run is a dictionary that specifies the values for input placeholders. This is where we associate the input array x_in with the placeholder x.

When Session.run() is called, TensorFlow will determine which elements of the computational graph need to be evaluated based on what you’ve passed in. It will then carry out the computations and then bind result values accordingly. The final line prints out the resulting array.

This example is one of the simplest ones I could think of that includes all four key phases. It’s missing many of the core features of TensorFlow! In particular, machine learning models usually train certain values to predict or classify something, but we’re not doing that here. In my next post I will walk through another example shows how to train parameters in a simple predictive model.

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