TensorFlow

TENSORFLOW: ( working progress )

tensorflow/tensorflowtensorflow - Computation using data flow graphs for scalable machine learning

Definition:

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) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

Google present TensorFlow:

Technical part:

First, we start with a simply question:

“How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”

The answer is: Machine learning

Machine learning

Machine learning studies computer algorithms for learning to do stuff. We might, for instance, be interested in learning to complete a task, or to make accurate predictions, or to behave intelligently. The learning that is being done is always based on some sort of observations or data, such as examples (the most common case in this course), direct experience, or instruction. So in general, machine learning is about learning to do better in the future based on what was experienced in the past. The emphasis of machine learning is on automatic methods. In other words, the goal is to devise learning algorithms that do the learning automatically without human intervention or assistance.

Definition:

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” -- Tom Mitchell, Carnegie Mellon University

Machine learning with R

install.packages("caret")

install.packages("lattice")

install.packages("ggplot2")

#Download Library

library(caret)

library(lattice)

library(ggplot2)

#import Library

Dataset: Iris

Data Set Information:

This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.

Predicted attribute: class of iris plant.

This is an exceedingly simple domain.

This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick '@' espeedaz.net ). The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.

Attribute Information:

1. sepal length in cm

2. sepal width in cm

3. petal length in cm

4. petal width in cm

5. class:

-- Iris Setosa

-- Iris Versicolour

-- Iris Virginica

Source:

Creator: R.A. Fisher

Donor: Michael Marshall (MARSHALL%PLU '@' io.arc.nasa.gov)

Raw Data

Iris.csv

Different way to import the dataset in Rstudio:

iris <- iris # you already find in your Rstudio (no joke :D)

Train & Test:

Creation of validation:

Teorich part:

validation_index <- createDataPartition(iris$Species, p=0.80, list=FALSE) #Partition

validation <- iris[-validation_index,] #Test

dataset <- iris[validation_index,] #Train Ciuf Ciuf

Now it's time to understand the dataset, play with it, discover what it's hidden

Dimension:

[I] dim(iris)

[O] ## [1] 150 5

Attributes:

sapply(iris,class)

## Sepal.Length Sepal.Width Petal.Length Petal.Width Species

## "numeric" "numeric" "numeric" "numeric" "factor"

Head of dataset:

head(iris)

## Sepal.Length Sepal.Width Petal.Length Petal.Width Species

##  1 5.1 3.5 1.4 0.2 setosa

##  2 4.9 3.0 1.4 0.2 setosa

##  3 4.7 3.2 1.3 0.2 setosa

##  4 4.6 3.1 1.5 0.2 setosa

##  5 5.0 3.6 1.4 0.2 setosa

##  6 5.4 3.9 1.7 0.4 setosa

Tail of dataset:

tail(iris)

## Sepal.Length Sepal.Width Petal.Length Petal.Width Species

## 145 6.7 3.3 5.7 2.5 virginica

## 146 6.7 3.0 5.2 2.3 virginica

## 147 6.3 2.5 5.0 1.9 virginica

## 148 6.5 3.0 5.2 2.0 virginica

## 149 6.2 3.4 5.4 2.3 virginica

## 150 5.9 3.0 5.1 1.8 virginica

Levels of class:

levels(iris$Species)

## [1] "setosa" "versicolor" "virginica"

Distribution of class:

percentage <- prop.table(table(iris$Species)) * 100

Variable of percentual:

cbind(freq=table(iris$Species), percentage=percentage)

##  freq               percentage

##  setosa      50      33.33333

##  versicolor  50      33.33333

##  virginica   50      33.33333

Summary of dataset:

Description :

summary is a generic function used to produce result summaries of the results of various model fitting functions. The function invokes particular methods which depend on the class of the first argument.

summary(iris)

Create an Input & Output

X = INPUT

Y = OUTPUT

x <- iris[,1:4]

y <- iris[,5]

Let's plot

par(mfrow=c(1,4))

for(i in 1:4) {

boxplot(x[,i], main=names(iris)[i], col = c("Orange"))

plot(y, col = c("Orange","Red","Light green")) #Colorazione Goleador

Feature plot

Descrition:

Details

This function ``stacks'' data to get it into a form compatible with lattice and creates the plots

Value

An object of class ``trellis''. The `update' method can be used to update components of the object and the `print' method (usually called by default) will plot it on an appropriate plotting device.

featurePlot(x = iris[, 1:4], y = iris$Species, plot = "pairs", ## Add a key at the top auto.key = list(columns = 3))

Scatterplot Matrix with Ellipses:

featurePlot(x = iris[, 1:4], y = iris$Species, plot = "ellipse", ## Add a key at the top auto.key = list(columns = 3))

featurePlot(x=x, y=y, plot="box", col = c("Orange","Red","Light green"))

Density parts:

Creation scales variable:

scales <- list(x=list(relation="free"), y=list(relation="free"))

Let's plot:

TENSORFLOW PROJECTS

Images recognition

Tensorboard

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