1.For New TFLite Beginner

一、 Getting Started for ML Beginners

This document explains how to use machine learning to classify (categorize) Iris flowers by species.  This document dives deeply into the TensorFlow code to do exactly that, explaining ML fundamentals along the way.

If the following list describes you, then you are in the right place:

You know little to nothing about machine learning.

You want to learn how to write TensorFlow programs.

You can code (at least a little) in Python.

Fortunately, someone has already created [a data set of 120 Iris

flowers]( https://en.wikipedia.org/wiki/Iris_flower_data_set)

with the sepal and petal measurements.  This data set has become

one of the canonical introductions to machine learning classification problems.

(The [MNIST database]( https://en.wikipedia.org/wiki/MNIST_database),

which contains handwritten digits, is another popular classification

problem.) The first 5 entries of the Iris data set

look as follows:

| Sepal length | sepal width | petal length | petal width | species

| ---          | ---         | ---          | ---         | ---

|6.4           | 2.8         | 5.6          | 2.2         | 2

|5.0           | 2.3         | 3.3          | 1.0         | 1

|4.9           | 2.5         | 4.5          | 1.7         | 2

|4.9           | 3.1         | 1.5          | 0.1         | 0

|5.7           | 3.8         | 1.7          | 0.3         | 0

Let's introduce some terms:

*   The last column (species) is called the

[**label**]( https://developers.google.com/machine-learning/glossary/#label);

the first four columns are called

[**features**]( https://developers.google.com/machine-learning/glossary/#feature).

Features are characteristics of an example, while the label is

the thing we're trying to predict.

*   An [**example**]( https://developers.google.com/machine-learning/glossary/#example)

consists of the set of features and the label for one sample

flower. The preceding table shows 5 examples from a data set of

120 examples.

Each label is naturally a string (for example, "setosa"), but machine learning

typically relies on numeric values. Therefore, someone mapped each string to

a number.  Here's the representation scheme:

* 0 represents setosa

* 1 represents versicolor

* 2 represents virginica

二、 Models and training

A **model** is the relationship between features

and the label.  For the Iris problem, the model defines the relationship

between the sepal and petal measurements and the predicted Iris species. Some

simple models can be described with a few lines of algebra, but complex machine

learning models have a large number of parameters that are difficult to

summarize.

Could you determine the relationship between the four features and the

Iris species *without* using machine learning?  That is, could you use

traditional programming techniques (for example, a lot of conditional

statements) to create a model?  Maybe. You could play with the data set

long enough to determine the right relationships of petal and sepal

measurements to particular species.  However, a good machine learning

approach *determines the model for you*.  That is, if you feed enough

representative examples into the right machine learning model type, the program

will determine the relationship between sepals, petals, and species.

**Training** is the stage of machine learning in which the model is

gradually optimized (learned).  The Iris problem is an example

of [**supervised machine

learning**]( https://developers.google.com/machine-learning/glossary/#supervised_machine_learning)

in which a model is trained from examples that contain labels.  (In

[**unsupervised machine

learning**]( https://developers.google.com/machine-learning/glossary/#unsupervised_machine_learning),

the examples don't contain labels. Instead, the model typically finds

patterns among the features.)

三、Get the sample program

1. Install TensorFlow

An example of [ Installing TensorFlow on Windows]

1.Windows requirements:

* 64-bit, x86 desktops or laptops

* Windows 7 or later

2.Install TensorFlow with CPU support only

* [Python 3.6.x 64-bit from python.org]( Python Release Python 3.6.2 | Python.org)

python -m pip install --upgrad pip

2. Activate your TensorFlow environment

3. Install or upgrade pandas by issuing the following 

command:pip install pandas