Arno  Bradtke

Arno Bradtke

1604137500

Types of Regularization Techniques To Avoid Overfitting

Regularization is a set of techniques which can help avoid overfitting in neural networks, thereby improving the accuracy of deep learning models when it is fed entirely new data from the problem domain. There are various regularization techniques, some of the most popular ones are — L1, L2, dropout, early stopping, and data augmentation.

Why is Regularization Required?

The characteristic of a good machine learning model is its ability to generalise well from the training data to any data from the problem domain; this allows it to make good predictions on the data that model has never seen. To define generalisation, it refers to how well the model has learnt the concepts to apply to any data rather than just with the specific data it was trained on during the training process.

#data-science

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Types of Regularization Techniques To Avoid Overfitting
Arvel  Parker

Arvel Parker

1593156510

Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object

a**=25+**85j

type**(a)**

output**:<class’complex’>**

b**={1:10,2:“Pinky”****}**

id**(b)**

output**:**238989244168

Built-in data types in Python

a**=str(“Hello python world”)****#str**

b**=int(18)****#int**

c**=float(20482.5)****#float**

d**=complex(5+85j)****#complex**

e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**

f**=tuple((“python”,“easy”,“learning”))****#tuple**

g**=range(10)****#range**

h**=dict(name=“Vidu”,age=36)****#dict**

i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**

j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**

k**=bool(18)****#bool**

l**=bytes(8)****#bytes**

m**=bytearray(8)****#bytearray**

n**=memoryview(bytes(18))****#memoryview**

Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger

age**=**18

print**(age)**

Output**:**18

Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).

String

The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.

“Hello”+“python”

output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Tia  Gottlieb

Tia Gottlieb

1598048580

Regularization Technique

What is Regularization Technique?

It’s a technique mainly used to overcome the over-fitting issue during the model fitting. This is done by adding a penalty as the model’s complexity gets increased. Regularization parameter λ penalizes all the regression parameters except the intercept so that the model generalizes the data and it will avoid the over-fitting (i.e. it helps to keep the parameters regular or normal). This will make the fit more generalized to unseen data.

Image for post

Over-fitting means while training the model using the training data, the model reads all the observation and learns from it and model becomes too complex. But while validating the same model using the testing data, the fit becomes worse.

hat does the Regularization Technique do?

The basic concept is we don’t want huge weight for the regression coefficients. The simple regression equation is y= β0+β1x , where y is the response variable or dependent variable or target variable, x is the feature variable or independent variable and β’s are the regression coefficient parameter or unknown parameter.

A small change in the weight to the parameters makes a larger difference in the target variable, thus it ensures that not too much weight is added. In this, not too much weight to any feature is given, and zero weight is given to the least significant feature.

Working of Regularization

Thus regularization will add the penalty for the higher terms and this will decrease the importance given to the higher terms and will bring the model towards less complex.

Regularization equation:

Min(Σ(yi-βi*xi)² + λ/2 * Σ (|βi|)^p )

where p=1,2,…. and i=1,…,n. Mostly the popular values of p chosen would be 1 or 2. Thus selecting the feature is done by regularization.

#overfitting #machine-learning #data-science #regularization #deep learning

Openshift Sandbox/Kata Containers

In this article, I will walk you through Openshift Sandbox containers based on Kata containers and how this is different from the traditional Openshift containers. 

Containers graphic.

Sandbox/kata containers are useful for users for the following scenarios: 

  1. Run 3rd party/untrusted application.
  2. Ensure kernel level isolation.
  3. Proper isolation through VM boundaries.

Prerequisites

You will need to install the following technologies before beginning this exercise:

Create the KataConfig

Create the KataConfig  CR and label the node on which Sandbox containers will be running. I have used sandbox=true label. 

apiVersion: kataconfiguration.openshift.io/v1

kind: KataConfig

metadata:

 name: cluster-kataconfig

spec:

   kataConfigPoolSelector:

     matchLabels:

        sandbox: 'true'

Verify the deployment:

oc describe kataconfig cluster-kataconfig

Name:         cluster-kataconfig

…..

Status:

  Installation Status:

    Is In Progress:  false

    Completed:

      Completed Nodes Count:  3

      Completed Nodes List:

        master0

        master1

        master2

    Failed:

    Inprogress:

  Prev Mcp Generation:  2

  Runtime Class:        kata

  Total Nodes Count:    3

  Un Installation Status:

    Completed:

    Failed:

    In Progress:

      Status:

  Upgrade Status:

Events:  <none>

Verify a new machine config(mc) and machine config pool(MCP) would have been created with the name Sandbox:

oc get mc |grep sandbox

50-enable-sandboxed-containers-extension

Verify the node configuration. Login to the Node label sandbox=true:

sh-4.4# cat /etc/crio/crio.conf.d/50-kata

[crio.runtime.runtimes.kata]

  runtime_path = "/usr/bin/containerd-shim-kata-v2"

  runtime_type = "vm"

  runtime_root = "/run/vc"

  privileged_without_host_devices = true

 Verify the Runtimeclass:

→ oc get runtimeclass

NAME   HANDLER   AGE

kata   kata      5d14h

This completes the deployment of the Sandbox container using Operator. 

Deploying the Application on Sandbox vs Regular Containers.

Let's try to deploy Sandbox and Regular containers from the same image and will verify the difference.

I have used a sample application image(quay.io/shailendra14k/getotp) based on spring boot for testing. 

#Regular POD definition:

apiVersion: apps/v1

kind: Deployment

metadata:

 name: webapp-deployment-6.0

 labels:

   app: webapp

   version: v6.0

spec:

 replicas: 2

 selector:

   matchLabels:

     app: webapp

 template:

   metadata:

     labels:

       app: webapp

       version: v6.0

   spec:

     containers:

     - name: webapp

       image: quay.io/shailendra14k/getotp:6.0

       imagePullPolicy: Always

       ports:

       - containerPort: 8180

Version 6.0 is Normal and 6.1 has the runtimeclass=kata. 

apiVersion: apps/v1

kind: Deployment

metadata:

 name: webapp-deployment-6.1

 labels:

   app: webapp

   version: v6.1

spec:

 replicas: 1

 selector:

   matchLabels:

     app: webapp

 template:

   metadata:

     labels:

       app: webapp

       version: v6.1

   Spec:

     runtimeClassName: kata

       containers:

     - name: webapp

       image: quay.io/shailendra14k/getotp:6.1

       imagePullPolicy: Always

       ports:

       - containerPort: 8180

 Deploy the application and verify the status:

➜  ~ oc get pods

NAME                                     READY   STATUS    RESTARTS   AGE

webapp-deployment-6.0-5d78fcd8db-ck7g7   1/1     Running   0          11m

webapp-deployment-6.1-6587f8997b-7f5p5   1/1     Running   0          11m

Compare the Uptime of Both Containers 

#Regular containers:

➜  ~ oc exec -it webapp-deployment-6.0-5d78fcd8db-ck7g7 -- cat /proc/uptime

416625.14 4640515.30

#Sandbox containers:

➜  ~ oc exec -it webapp-deployment-6.1-6587f8997b-7f5p5 -- cat /proc/uptime

670.63 658.26

You can observe the difference, which is huge, the uptime of the regular container Kernel is the same as the Node kernel(416625.14s= 4.8 Days). However, the Sandbox container kernel uptime is the time of the creation of the Pod(670.63s=11min)

Compare the Process on the Nodes

Log in to the node, where both containers are running. Use the oc debug node/<node-Name> 

#Regular containers:


sh-4.4# ps -eaf |grep 10008

1000800+  852898  852878  0 07:23 ?        00:00:08 java -jar /home/jboss/test.jar

1000800+ is the UID for the container.

#Sandbox containers:

 First, fetch the sandbox id using the crictl inspect command:

➜  ~oc get pods webapp-deployment-6.1-6587f8997b-7f5p5  -o jsonpath='{.status.containerStatuses[0]}'

{"containerID":"cri-o://b0768d7fbfd2d656b9900ba0b16b6078eb625b412784809ce516f9111a211e10" …..



#From the node

sh-4.4# crictl inspect b0768d7fbfd2d656b9900ba0b16b6078eb625b412784809ce516f9111a211e10 | jq -r '.info.sandboxID'

7740c8967dd6ad50ecd8c31558c3c844bbe7ac4e7ca1115e7f91eec974737270

Fetch the process id using the SandboxId:

sh-4.4# ps aux | grep 7740c8967dd6ad50ecd8c31558c3c844bbe7ac4e7ca1115e7f91eec974737270

root      852850  0.0  0.1 1337556 34816 ?       Sl   07:23   0:00 /usr/bin/containerd-shim-kata-v2 -namespace default -address  -publish-binary /usr/bin/crio -id 

7740c8967dd6ad50ecd8c31558c3c844bbe7ac4e7ca1115e7f91eec974737270

 

root      852859  0.0  0.0 122804  4776 ?        Sl   07:23   0:00 /usr/libexec/virtiofsd --fd=3 -o source=/run/kata-containers/shared/sandboxes/7740c8967dd6ad50ecd8c31558c3c844bbe7ac4e7ca1115e7f91eec974737270/shared -o cache=auto --syslog -o no_posix_lock -d --thread-pool-size=1

root      852865  0.9  1.8 2465200 603492 ?      Sl   07:23   0:15 /usr/libexec/qemu-kiwi -name sandbox-7740c8967dd6ad50ecd8c31558c3c844bbe7ac4e7ca1115e7f91eec974737270 -uuid ae09b8a0-1f89-4196-8402-cdcb471675bd -machine q35,accel=kvm,kernel_irqchip -cpu 

…… /run/vc/vm/7740c8967dd6ad50ecd8c31558c3c844bbe7ac4e7ca1115e7f91eec974737270/qemu.log -smp 1,cores=1,threads=1,sockets=12,maxcpus=12

 

root      852873  0.0  0.2 2514884 75800 ?       Sl   07:23   0:00 /usr/libexec/virtiofsd --fd=3 -o source=/run/kata-containers/shared/sandboxes/7740c8967dd6ad50ecd8c31558c3c844bbe7ac4e7ca1115e7f91eec974737270/shared -o cache=auto --syslog -o no_posix_lock -d --thread-pool-size=1

For the regular container, the process runs directly on the Node host kernel; However, for the Sandbox, the containers run inside the VMs.  

Conclusion

Thank you for reading! We saw how the Sandbox containers are deployed on Openshift and its comparison with the regular containers.  

Source: https://dzone.com/articles/openshift-sandboxkata-containers

#openshift #sandbox 

Oleta  Becker

Oleta Becker

1602903600

Regularization Techniques

This short article talks about the regularization techniques, the advantages, meanings, way to apply them, and why are necessary. In this paper, I’m not going to explain how to design or how are the neural networks anything about forward or backpropagation, weights, bias (threshold), normalization, but maybe in the next article, I’m going to covert those topics. However, you need those concepts to understand regularization techniques.

First, we need to understand what is the problem with Neural Networks. When we are designing and creating a Neural Network we have a goal to apply them, for example, if I want to recognize the numbers between 0 to 9 (My goal), I should understand that I need to use samples with a lot of ways to write these numbers (0–9) to train the model and also samples to test the model. This is so important because like you know we have different ways to write the numbers, the lines and/or circles could be perfect in some cases or maybe not, maybe this occurs for a lot of factors like age, sickness, alcohol levels in blood, anxiety, the technique to write, and more. What do you think of doctors’ writing? yeah, that’s another topic, back to the problem we need to choose very well our samples trying to get the data which represent the future possible datasets, we are going to have many problems but in this case, we are going to talk only about “overfitting”.

To understand overfitting is necessary to know the meaning of bias and variance I recommend this video because It’s a very good explanation https://www.youtube.com/watch?v=EuBBz3bI-aA

#dropout-regularization #regularization #deep-learning

Arno  Bradtke

Arno Bradtke

1604137500

Types of Regularization Techniques To Avoid Overfitting

Regularization is a set of techniques which can help avoid overfitting in neural networks, thereby improving the accuracy of deep learning models when it is fed entirely new data from the problem domain. There are various regularization techniques, some of the most popular ones are — L1, L2, dropout, early stopping, and data augmentation.

Why is Regularization Required?

The characteristic of a good machine learning model is its ability to generalise well from the training data to any data from the problem domain; this allows it to make good predictions on the data that model has never seen. To define generalisation, it refers to how well the model has learnt the concepts to apply to any data rather than just with the specific data it was trained on during the training process.

#data-science