Open Source For You — December 2017

How To Developers

http://www.OpenSourceForU.com | OPEN SOURCE FOR YOU | DECEMBER 2017 | 65

Image classification with pre-trained models

An image classification code with pre-trained ResNet50 is as

follows (https://keras.io/applications/):

from keras.applications.resnet50

import ResNet50 from keras.preprocessing

import image from keras.applications.resnet50

import preprocess_input, decode_predictions

import numpy as np

model = ResNet50(weights=’imagenet’)

img_path = ‘elephant.jpg’

img = image.load_img(img_path, target_size=(224, 224))

x = image.img_to_array(img)

x = np.expand_dims(x, axis=0)

x = preprocess_input(x)

preds = model.predict(x)

# decode the results into a list of tuples (class,

description, probability) # (one such list for each sample in

the batch)

print(‘Predicted:’, decode_predictions(preds, top=3)[0])

# Predicted: [(u’n02504013’, u’Indian_elephant’, 0.82658225),

(u’n01871265’, u’tusker’, 0.1122357), (u’n02504458’,

u’African_elephant’, 0.061040461)]

The simplicity with which the classification tasks are

carried out can be inferred from the above code.

Overall, Keras is a simple, extensible and easy-to-

implement neural network API, which can be used to build

deep learning applications with high level abstraction.

By: Dr K.S. Kuppusamy

The author is an assistant professor of computer science

at the School of Engineering and Technology, Pondicherry

Central University. He has 12+ years of teaching and research

experience in academia and in industry. He can be reached at

[email protected].

All the clients you find on GitHub implement this
signature technique, so you should not have to do it manually.
Now that you have explored the API through the UI and you
understand how to make low level calls, pick your favourite
client or use CloudMonkey. This is a sub-project of Apache
CloudStack and gives operators/developers the ability to use
any of the API methods.
Testing the AWS API interface: While the native
CloudStack API is not a standard, CloudStack provides an
AWS EC2 compatible interface. A great advantage of this is
that existing tools written with EC2 libraries can be reused
against a CloudStack based cloud. In the installation section,
we described how to run this interface by installing packages.
In this section, we find out how to compile the interface with
Maven and test it with the Python Boto module.
Using a running management server (with DevCloud for
instance), start the AWS API interface in a separate shell with
the following command:

mvn -Pawsapi -pl :cloud-awsapi jetty:run

Log into the CloudStack UI http://localhost:8080/client, go
to Service Offerings and edit one of the compute offerings to have
the name m1.small or any of the other AWS EC2 instance types.
With access and secret keys generated for a user, you should
now be able to use the Python Boto module:

import boto
import boto.ec2

accesskey=”2IUSA5xylbsPSnBQ

FoWXKg3RvjHgsufcKhC1SeiCbeEc0obKwUlwJamB_

gFmMJkFHYHTIafpUx0pHcfLvt-dzw”

secretkey=”oxV5Dhhk5ufNowey 7OVHgWxCBVS4deTl9qL0EqMthfP

Buy3ScHPo2fifDxw1aXeL5cyH10hnLOKjyKphcXGeDA”

region = boto.ec2.regioninfo.RegionInfo(name=”ROOT”,

endpoint=”localhost”)

conn = boto.connect_ec2(aws_access_key_id=accesskey, aws_

secret_access_key=secretkey, is_secure=False, region=region,

port=7080, path=”/awsapi”, api_version=”2012-08-15”)

images=conn.get_all_images()

print images

res = images[0].run(instance_type=’m1.small’,security_

groups=[‘default’])

Note the new api_version number in the connection

object, and also note that there was no need to perform a user

registration as in the case of previous CloudStack releases.

Let us thank those at Apache for contributing yet another

outstanding product to the open source community, along

with the detailed documentation they have provided for

CloudStack. All the contents, samples and pictures in this

article are extracts from CloudStack online documentation

and you may explore more about it at http://docs.cloudstack.

apache.org/en/latest/.

By: Somanath T.V.

The author has 14+ years’ experience in the IT industry and

is currently doing research in machine learning and related

areas, along with his tenure at SS Consulting, Kochi. He can

be reached at [email protected].

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