TensorFlow is a powerful tool for machine learning. In this post we will show you how to get started with TensorFlow in Python.

For more information check out this video:

## Introduction to TensorFlow

TensorFlow is a powerful tool for machine learning. It allows you to easily build and train neural networks, and has a wide variety of applications.

In this tutorial, we’ll show you how to get started with TensorFlow in Python. We’ll cover the basics of TensorFlow, including how to install it, basic concepts, and some example applications.

Installing TensorFlow

Before we can start using TensorFlow, we need to install it. TensorFlow is available for both Linux and Windows. You can find instructions for installing TensorFlow on the TensorFlow website.

Once you have TensorFlow installed, you can import it into your Python code like this:

import tensorflow as tf

Basic Concepts

TensorFlow is based on a few key concepts:

Tensors: A tensor is a generalization of vectors and matrices to higher dimensions. In TensorFlow, all data is represented as tensors.

Graphs: A graph is a set of computations that take place in order. In TensorFlow, these computations are represented as nodes in a graph.

Sessions: A session is a way to execute a graph. Sessions are used to evaluate tensors and perform computations on graphs.

## TensorFlow Basics

TensorFlow is an open source library for numerical computation, specializing in machine learning applications. According to the TensorFlow website, “TensorFlow is a tool for machine learning that allows users to easily create and train neural networks to recognize patterns in data.” In this tutorial, we’ll be covering the basic concepts of TensorFlow in Python.

As the name suggests, TensorFlow deals with data in the form of tensors. A tensor is simply a multidimensional array. In other words, it’s an n-dimensional matrix. In terms of machine learning, we can think of a tensor as an n-dimensional data structure that can represent items such as images, audio recordings, and text. The number of dimensions in a tensor is called its rank. For example, a rank 0 tensor is a scalar (a single value), a rank 1 tensor is a vector (a one-dimensional array), and a rank 2 tensor is a matrix (a two-dimensional array).

Tensors are often used to represent the features of data samples (such as images) that will be fed into a machine learning model. Each element in the tensor corresponds to a certain feature of the data sample. For example, in an image recognition task, each pixel in an image would be represented by an element in the tensor. Similarly, in natural language processing tasks, each word in a sentence would be represented by an element in the tensor.

In order to perform computations on Tensors, we need to first construct them using TensorFlow operations (ops). Ops are nodes in what is called a computational graph; they take Tensors as input and produce Tensors as output. In other words, ops take some input data (in the form of Tensors), perform some computation on that data, and return the results of that computation (also in the form of Tensors). Each op has a unique name so that it can be identified within the graph; this name is generally chosen to describe what kind of computation the op performs. For example, there are ops for addition and subtraction, matrix multiplication and transposition, and so on.

Once we have constructed our graph of ops, we need to execute it in order to actually perform the computations defined by the ops. This is where Sessions come in. A Session is an environment for running ops; it manages allocating resources like memory and GPUs so that ops can be executed efficiently and correctly on different devices such as CPUs and GPUs. When we create a Session object within Python code (by calling tf$rightarrow$Session() ), this object encapsulates all computations done within that session until we call sess$rightarrow$close() . All ops created within our program will not actually do any computation until they are run inside such aSession .

## TensorFlow Operations

TensorFlow provides multiple built-in operations that you can use to construct your own models. You can also import these operations from the tensorflow.python.ops namespace.

Operations that you can perform on Tensors:

-tf.add

-tf.subtract

-tf.multiply

-tf.divide

-tf.mod

-tf.pow

-tf.square

You can also use the following comparison operations on Tensors:

-tf.equal

-tf.not_equal

-tf.less

-tf.less_equal

-tf.greater

-imported from the ops namespace: greater_equal, and logical_and, logical_not, and logical_or

## TensorFlow Graphs

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

A TensorFlow graph consists of two parts:

– The **definition** of the computational graph, typically built using the Python API.

– A **runtime** environment that executes the computational graph.

## TensorFlow Sessions

In order to evaluate the results of a TensorFlow graph, we first need to create a session. A session encapsulates the control and state of the TensorFlow runtime. For example, the following code creates a session that will evaluate the graph defined above:

“`python

sess = tf.Session()

“`

Once we have created a session, we can run our computational graph using the “`run()“` method. This method takes in an optional feed_dict parameter that allows us to specify values for our placeholders:

“`python

output = sess.run(c, feed_dict={a:[[1,2],[3,4]]})

print(output) # [[5 7] [9 11]]

“`

## TensorFlow Layers

TensorFlow layers is a high-level API that allows you to easily construct and compose neural networks. Layers package together multiple components, including a variable representing the layer’s state (typically a set of weights), a transformation from inputs to the layer variable (typically called a kernel), and an activation function. You can use layers to:

– Build models that are easy to reason about

– Build neural networks with better mathematical properties

– Take advantage of recent advances in deep learning while still writing code that is easy to understand

Layers are composed of three child classes: Layer, Dense, and Conv2D. Using these three child classes, you can build any type of layer for your neural network.

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## TensorFlow Keras

TensorFlow is a powerful tool for machine learning, but can be daunting for new users. In this example, we’ll use TensorFlow to build a simple neural network in Python.

We’ll start by importing the necessary libraries:

import tensorflow as tf

from tensorflow import keras

import numpy as np

import matplotlib.pyplot as plt

Next, we’ll define our inputs and outputs:

#inputs = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]] # OR: inputs = np.array([[0., 0.], [0., 1.], [1., 0.], [1., 1.]])

#outputs = [[0.], [1.], [1.], [0.]] # OR: outputs = np.array([[0.], [1.], [1.], [0.]])

## TensorFlow Datasets

TensorFlow is a powerful library for doing complex math with data flow graphs. Datasets are a fundamental need for training machine learning models with TensorFlow. In this tutorial, we’ll see how to create and work with datasets in TensorFlow.

This tutorial uses the Iris flower dataset which you can download from http://www.dropwizard.io/1.0.2/docs/. The Iris dataset is a de-facto standard dataset used in many machine learning examples and competitions, so it is a good one to start with.

Once you have downloaded the iris dataset, unzip it and put it in the same directory as your Python code. The dataset is in CSV (comma-separated values) format, so we will use TensorFlow’s built-in CSV reader class to read the data into a TensorFlow dataset object.

## TensorFlow Estimators

TensorFlow estimators are a high-level API that allows you to easily build models without having to code very much. In this example, we’ll use the TensorFlow estimator class to build a linear regression model.

## TensorFlow Serving

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. TensorFlow Serving provides a simple standard API and runtime to develop servables – TensorFlow models that can be served individually or together in an ensemble.

Keyword: TensorFlow Example in Python