#github project  -  how to loop through a folder containing multiple images and classifying them using Keras and Pre-trained Networks. #tensorflow #keras #CNN #Neuralnet #INCEPTIONV3 #Machinelearning #DeepLearning

ImageClassificationDeepLearning

Here I will show how to loop through a folder containing multiple images and classifying them using Keras and Pre-trained Networks.

#TENSORFLOW #KERAS #NN #NEURALNET #INCEPTIONV3 #MACHINELEARNING #DEEPLEARNING

Our brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images.

How it helps ? 

Let’s say you have a folder wherein Multiple images get uploaded – best example will be like OLX or Quickr which are free Buy & Sell websites. Now, you need to determine if any harmful things ( e.g. Gun etc) are being bought and sold .

SEVERAL PRE-TRAINED NETWORKS :

• VGG16, VGG19, ResNet50, Inception V3, and Xception

State-of-the-art deep learning image classifiers in Keras

Keras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset: VGG16 VGG19 ResNet50 Inception V3 Xception

Inception V3

The goal of the inception module is to act as a “multi-level feature extractor” by computing 1×1, 3×3, and 5×5 convolutions within the same module of the network — the output of these filters are then stacked along the channel dimension and before being fed into the next layer in the network. The original incarnation of this architecture was called GoogLeNet, but subsequent manifestations have simply been called Inception vN where N refers to the version number put out by Google.

Link :https://github.com/saptakbasu7/ImageClassificationDeepLearning 

Did you know TensorFlow is Life-Saving ? Read- on

#MachineLearning #DeepLearning #ML #AI #ArtificialIntelligence #TensorFlow

Beginner’s guide to Tensorflow

Did you know TensorFlow is Life-Saving ? Read- on

INTRODUCTION:

The primary software tool of Deep Learning is TensorFlow. It is an open source artificial intelligence library, using data flow graphs to build models.

 It allows developers to create large-scale neural networks with many layers.

USED FOR:

TensorFlow is mainly used for:

1)  Voice/Sound Recognition

2)  Text Based Applications

3)  Image Recognition

4)  Video Detection

INTERESTING FACTS :

 Nasa ( National Aeronautics and Space Administration) is designing a system with TensorFlow for orbit classification and object clustering of asteroids. As a result, they can classify and predict NEOs (near earth objects). ( So, in a way TensorFlow is life-saving!!! )

NOW TECHNICAL STUFF :

TensorFlow is a library for numerical computation where data flows through the graph.

 Data in TensorFlow is represented by n-dimensional arrays called Tensors.

Graph is made of data(Tensors) and mathematical operations. 

§  Nodes on the graph: represent mathematical operations. 

§  Edges on the graph: represent the Tensors that flow between operations. 

There is one more aspect in which TensorFlow is very different from any other programming language.

In TensorFlow, you first need to create a blueprint of whatever you want to create. While you are creating the graph, variables don’t have any value. Later when you have created the complete graph, you have to run it inside a session, only then the variables have any values.

import tensorflow as tf  

Graph in TensorFlow:

GRAPH is the backbone of TensorFlow and every computation/operation/variables reside on the graph. Everything that happens in the code, resides on a default graph provided by TensorFlow. You can access this graph by:

graph = tf.get_default_graph()

Next big thing, Session!

A GRAPH  is used to define operations, but the operations can  only run within a SESSION. Graphs and sessions are created independently of each other.

Sess = tf.Session()

Tensors in Tensorflow:

TensorFlow holds Data in tensors

i)                Constants

are constants whose value can’t be changed. You can declare a constant like this: 

              a=tf.constant(1.0)

ii)                   Variables

are again Tensors which are like variables in any other language. 

b=tf.Variable(2.0,name=None)

iii)                 PlaceHolders

are tensors which are waiting to be initialized/fed. Placeholders are used for training data which is only fed when the code is actually run inside a session. What is fed to Placeholder is called feed_dict. Feed_dict are key value pairs for holding data:

a = tf.placeholder("float")