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
What is GEEK
Buddha Community
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
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
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.
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
1650962340
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.
Sandbox/kata containers are useful for users for the following scenarios:
- Run 3rd party/untrusted application.
- Ensure kernel level isolation.
- 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-extensionVerify 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 = trueVerify 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: 8180Version 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: 8180Deploy 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 11mCompare 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
670.63 658.26You 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.jar1000800+ 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=1For 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
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
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