Aditya adityaknegi

## small_large_filter_compare.csv

          
            Smaller Filter Sizes
            Larger Filter Sizes

            
              It has a smaller receptive field as it looks at very few pixels at once.
              Larger receptive field per layer.

            
              Highly local features extracted without much image overview.
              Quite generic features extracted spread across the image.

            
              Therefore captures smaller, complex features in the image.
              Therefore captures the basic components in the image.

            
              Amount of information extracted will be vast, maybe useful in later layers.
              Amount of information extracted are considerably lesser.

            
              Slow reduction in the image dimension can make the network deep
              Fast reduction in the image dimension makes the network shallow

            
              Better weight sharing
              Poorer weight sharing

            
              In an extreme scenario, using a 1x1 convolution is like treating each pixel as a useful feature.
              Using a image sized filter is equivalent to a fully connected layer.

## foreignKeyFlaskAlchemy.py
from flask import Flask, jsonify, request, make_response
from flask.ext.httpauth import HTTPBasicAuth
from flask_sqlalchemy import SQLAlchemy
from flask import render_template, redirect, url_for
from sqlalchemy import UniqueConstraint, exc

app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///memes.db'
app.config['SECRET_KEY'] = 'HALO'
	Smaller Filter Sizes	Larger Filter Sizes
	It has a smaller receptive field as it looks at very few pixels at once.	Larger receptive field per layer.
	Highly local features extracted without much image overview.	Quite generic features extracted spread across the image.
	Therefore captures smaller, complex features in the image.	Therefore captures the basic components in the image.
	Amount of information extracted will be vast, maybe useful in later layers.	Amount of information extracted are considerably lesser.
	Slow reduction in the image dimension can make the network deep	Fast reduction in the image dimension makes the network shallow
	Better weight sharing	Poorer weight sharing
	In an extreme scenario, using a 1x1 convolution is like treating each pixel as a useful feature.	Using a image sized filter is equivalent to a fully connected layer.
	from flask import Flask, jsonify, request, make_response
	from flask.ext.httpauth import HTTPBasicAuth
	from flask_sqlalchemy import SQLAlchemy
	from flask import render_template, redirect, url_for
	from sqlalchemy import UniqueConstraint, exc

	app = Flask(__name__)
	app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///memes.db'
	app.config['SECRET_KEY'] = 'HALO'