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notebook.tex
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notebook.tex
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% Default to the notebook output style
% Inherit from the specified cell style.
\documentclass[11pt]{article}
\usepackage[T1]{fontenc}
% Nicer default font (+ math font) than Computer Modern for most use cases
\usepackage{mathpazo}
% Basic figure setup, for now with no caption control since it's done
% automatically by Pandoc (which extracts  syntax from Markdown).
\usepackage{graphicx}
% We will generate all images so they have a width \maxwidth. This means
% that they will get their normal width if they fit onto the page, but
% are scaled down if they would overflow the margins.
\makeatletter
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
\else\Gin@nat@width\fi}
\makeatother
\let\Oldincludegraphics\includegraphics
% Set max figure width to be 80% of text width, for now hardcoded.
\renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
% Ensure that by default, figures have no caption (until we provide a
% proper Figure object with a Caption API and a way to capture that
% in the conversion process - todo).
\usepackage{caption}
\DeclareCaptionLabelFormat{nolabel}{}
\captionsetup{labelformat=nolabel}
\usepackage{adjustbox} % Used to constrain images to a maximum size
\usepackage{xcolor} % Allow colors to be defined
\usepackage{enumerate} % Needed for markdown enumerations to work
\usepackage{geometry} % Used to adjust the document margins
\usepackage{amsmath} % Equations
\usepackage{amssymb} % Equations
\usepackage{textcomp} % defines textquotesingle
% Hack from http://tex.stackexchange.com/a/47451/13684:
\AtBeginDocument{%
\def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
}
\usepackage{upquote} % Upright quotes for verbatim code
\usepackage{eurosym} % defines \euro
\usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
\usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
\usepackage{fancyvrb} % verbatim replacement that allows latex
\usepackage{grffile} % extends the file name processing of package graphics
% to support a larger range
% The hyperref package gives us a pdf with properly built
% internal navigation ('pdf bookmarks' for the table of contents,
% internal cross-reference links, web links for URLs, etc.)
\usepackage{hyperref}
\usepackage{longtable} % longtable support required by pandoc >1.10
\usepackage{booktabs} % table support for pandoc > 1.12.2
\usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
\usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
% normalem makes italics be italics, not underlines
% Colors for the hyperref package
\definecolor{urlcolor}{rgb}{0,.145,.698}
\definecolor{linkcolor}{rgb}{.71,0.21,0.01}
\definecolor{citecolor}{rgb}{.12,.54,.11}
% ANSI colors
\definecolor{ansi-black}{HTML}{3E424D}
\definecolor{ansi-black-intense}{HTML}{282C36}
\definecolor{ansi-red}{HTML}{E75C58}
\definecolor{ansi-red-intense}{HTML}{B22B31}
\definecolor{ansi-green}{HTML}{00A250}
\definecolor{ansi-green-intense}{HTML}{007427}
\definecolor{ansi-yellow}{HTML}{DDB62B}
\definecolor{ansi-yellow-intense}{HTML}{B27D12}
\definecolor{ansi-blue}{HTML}{208FFB}
\definecolor{ansi-blue-intense}{HTML}{0065CA}
\definecolor{ansi-magenta}{HTML}{D160C4}
\definecolor{ansi-magenta-intense}{HTML}{A03196}
\definecolor{ansi-cyan}{HTML}{60C6C8}
\definecolor{ansi-cyan-intense}{HTML}{258F8F}
\definecolor{ansi-white}{HTML}{C5C1B4}
\definecolor{ansi-white-intense}{HTML}{A1A6B2}
% commands and environments needed by pandoc snippets
% extracted from the output of `pandoc -s`
\providecommand{\tightlist}{%
\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
% Add ',fontsize=\small' for more characters per line
\newenvironment{Shaded}{}{}
\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
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\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
\newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
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\newcommand{\RegionMarkerTok}[1]{{#1}}
\newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\NormalTok}[1]{{#1}}
% Additional commands for more recent versions of Pandoc
\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
\newcommand{\ImportTok}[1]{{#1}}
\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
\newcommand{\BuiltInTok}[1]{{#1}}
\newcommand{\ExtensionTok}[1]{{#1}}
\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
% Define a nice break command that doesn't care if a line doesn't already
% exist.
\def\br{\hspace*{\fill} \\* }
% Math Jax compatability definitions
\def\gt{>}
\def\lt{<}
% Document parameters
\title{ResNet50 - Retrain}
% Pygments definitions
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}
% Slightly bigger margins than the latex defaults
\geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
\begin{document}
\maketitle
\section{Models and weights from
GitHub}\label{models-and-weights-from-github}
Source Link: https://github.com/fchollet/deep-learning-models - ResNet50
- VGG16 - VGG19 Working Directory for all the models:
C:\Users\liangx\Documents\Github\_Clone\deep-learning-models
\subsection{Classify images ResNet50}\label{classify-images-resnet50}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}34}]:} \PY{k+kn}{from} \PY{n+nn}{resnet50} \PY{k}{import} \PY{n}{ResNet50}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{preprocessing} \PY{k}{import} \PY{n}{image}
\PY{k+kn}{from} \PY{n+nn}{imagenet\PYZus{}utils} \PY{k}{import} \PY{n}{preprocess\PYZus{}input}\PY{p}{,} \PY{n}{decode\PYZus{}predictions}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{n}{model} \PY{o}{=} \PY{n}{ResNet50}\PY{p}{(}\PY{n}{weights}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{imagenet}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{img\PYZus{}path} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{images/monkey.jpg}\PY{l+s+s1}{\PYZsq{}}
\PY{n}{img} \PY{o}{=} \PY{n}{image}\PY{o}{.}\PY{n}{load\PYZus{}img}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{,} \PY{n}{target\PYZus{}size}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{224}\PY{p}{,} \PY{l+m+mi}{224}\PY{p}{)}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n}{image}\PY{o}{.}\PY{n}{img\PYZus{}to\PYZus{}array}\PY{p}{(}\PY{n}{img}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{expand\PYZus{}dims}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{0}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n}{preprocess\PYZus{}input}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{preds} \PY{o}{=} \PY{n}{model}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Predicted:}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{decode\PYZus{}predictions}\PY{p}{(}\PY{n}{preds}\PY{p}{)}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{:} \PY{p}{[}\PY{p}{[}\PY{l+s+sa}{u}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{n02504458}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+sa}{u}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{African\PYZus{}elephant}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{]}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
Predicted: [[('n02487347', 'macaque', 0.8569621), ('n02486410', 'baboon', 0.058234967), ('n02486261', 'patas', 0.04959094), ('n02484975', 'guenon', 0.013083459), ('n02494079', 'squirrel\_monkey', 0.006898398)]]
\end{Verbatim}
\subsection{Extract features from images
VGG16}\label{extract-features-from-images-vgg16}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor} }]:} \PY{k+kn}{from} \PY{n+nn}{vgg16} \PY{k}{import} \PY{n}{VGG16}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{preprocessing} \PY{k}{import} \PY{n}{image}
\PY{k+kn}{from} \PY{n+nn}{imagenet\PYZus{}utils} \PY{k}{import} \PY{n}{preprocess\PYZus{}input}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{n}{model} \PY{o}{=} \PY{n}{VGG16}\PY{p}{(}\PY{n}{weights}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{imagenet}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{include\PYZus{}top}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
\PY{n}{img\PYZus{}path} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{images/elephant.jpg}\PY{l+s+s1}{\PYZsq{}}
\PY{n}{img} \PY{o}{=} \PY{n}{image}\PY{o}{.}\PY{n}{load\PYZus{}img}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{,} \PY{n}{target\PYZus{}size}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{224}\PY{p}{,} \PY{l+m+mi}{224}\PY{p}{)}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n}{image}\PY{o}{.}\PY{n}{img\PYZus{}to\PYZus{}array}\PY{p}{(}\PY{n}{img}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{expand\PYZus{}dims}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{0}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n}{preprocess\PYZus{}input}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{features} \PY{o}{=} \PY{n}{model}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\end{Verbatim}
\subsection{Extract features from an arbitrary intermediate layer
VGG19}\label{extract-features-from-an-arbitrary-intermediate-layer-vgg19}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor} }]:} \PY{k+kn}{from} \PY{n+nn}{vgg19} \PY{k}{import} \PY{n}{VGG19}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{preprocessing} \PY{k}{import} \PY{n}{image}
\PY{k+kn}{from} \PY{n+nn}{imagenet\PYZus{}utils} \PY{k}{import} \PY{n}{preprocess\PYZus{}input}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{models} \PY{k}{import} \PY{n}{Model}
\PY{n}{base\PYZus{}model} \PY{o}{=} \PY{n}{VGG19}\PY{p}{(}\PY{n}{weights}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{imagenet}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{model} \PY{o}{=} \PY{n}{Model}\PY{p}{(}\PY{n+nb}{input}\PY{o}{=}\PY{n}{base\PYZus{}model}\PY{o}{.}\PY{n}{input}\PY{p}{,} \PY{n}{output}\PY{o}{=}\PY{n}{base\PYZus{}model}\PY{o}{.}\PY{n}{get\PYZus{}layer}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{block4\PYZus{}pool}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{o}{.}\PY{n}{output}\PY{p}{)}
\PY{n}{img\PYZus{}path} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{images/elephant.jpg}\PY{l+s+s1}{\PYZsq{}}
\PY{n}{img} \PY{o}{=} \PY{n}{image}\PY{o}{.}\PY{n}{load\PYZus{}img}\PY{p}{(}\PY{n}{img\PYZus{}path}\PY{p}{,} \PY{n}{target\PYZus{}size}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{224}\PY{p}{,} \PY{l+m+mi}{224}\PY{p}{)}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n}{image}\PY{o}{.}\PY{n}{img\PYZus{}to\PYZus{}array}\PY{p}{(}\PY{n}{img}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{expand\PYZus{}dims}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{0}\PY{p}{)}
\PY{n}{x} \PY{o}{=} \PY{n}{preprocess\PYZus{}input}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\PY{n}{block4\PYZus{}pool\PYZus{}features} \PY{o}{=} \PY{n}{model}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{x}\PY{p}{)}
\end{Verbatim}
\section{Loading a Pre-trained Model in
Keras}\label{loading-a-pre-trained-model-in-keras}
Source Link:
https://engmrk.com/kerasapplication-pre-trained-model/?utm\_campaign=News\&utm\_medium=Community\&utm\_source=DataCamp.com
The pre-trained classical models are already available in Keras as
Applications. These models are trained on ImageNet data set for
classifying images into one of 1000 categories or classes. The
pre-trained models are available with Keras in two parts, model
architecture and model weights. Model architectures are downloaded
during Keras installation but model weights are large size files and can
be downloaded on instantiating a model.
Following are the models available in Keras:
\begin{itemize}
\tightlist
\item
Xception
\item
VGG16
\item
VGG19
\item
ResNet50
\item
InceptionV3
\item
InceptionResNetV2
\item
MobileNet
\item
DenseNet
\item
NASNet
\item
MobileNetV2 All of these architectures are compatible with all the
backends (TensorFlow, Theano, and CNTK).
\end{itemize}
For more details and reference, please visit:
https://keras.io/applications
First, we will import the Keras and required model from
keras.applications and then we will instantiate the model architecture
along with the imagenet weights. If you want only model architecture
then instantiate the model with weights as `None'.
\subsection{* VGG16}\label{vgg16}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor} }]:} \PY{k+kn}{import} \PY{n+nn}{keras}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{applications} \PY{k}{import} \PY{n}{vgg16}
\PY{c+c1}{\PYZsh{}Load the VGG16 model}
\PY{n}{vgg\PYZus{}model} \PY{o}{=} \PY{n}{vgg16}\PY{o}{.}\PY{n}{VGG16}\PY{p}{(}\PY{n}{weights}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{imagenet}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\subsection{* ResNet50}\label{resnet50}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}32}]:} \PY{k+kn}{import} \PY{n+nn}{keras}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{applications} \PY{k}{import} \PY{n}{resnet50}
\PY{c+c1}{\PYZsh{}Load the ResNet50 model}
\PY{n}{resnet\PYZus{}model} \PY{o}{=} \PY{n}{resnet50}\PY{o}{.}\PY{n}{ResNet50}\PY{p}{(}\PY{n}{weights}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{imagenet}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\subsection{* MobileNet}\label{mobilenet}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor} }]:} \PY{k+kn}{import} \PY{n+nn}{keras}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{applications} \PY{k}{import} \PY{n}{mobilenet}
\PY{c+c1}{\PYZsh{}Load the MobileNet model}
\PY{n}{mobilenet\PYZus{}model} \PY{o}{=} \PY{n}{mobilenet}\PY{o}{.}\PY{n}{MobileNet}\PY{p}{(}\PY{n}{weights}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{imagenet}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\subsection{* Inception\_v3}\label{inception_v3}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor} }]:} \PY{k+kn}{import} \PY{n+nn}{keras}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{applications} \PY{k}{import} \PY{n}{inception\PYZus{}v3}
\PY{c+c1}{\PYZsh{}Load the Inception\PYZus{}V3 model}
\PY{n}{inception\PYZus{}model} \PY{o}{=} \PY{n}{inception\PYZus{}v3}\PY{o}{.}\PY{n}{InceptionV3}\PY{p}{(}\PY{n}{weights}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{imagenet}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
Keras provides the load\_img() function for loading an image from file
as a PIL image object. The load\_img() function provides additional
arguments that may be useful when loading the image, such as `grayscale`
that allows the image to be loaded in grayscale (defaults to False),
`color\_mode` that allows the image mode or channel format to be
specified (defaults to rgb), and `target\_size` that allows a tuple of
(height, width) to be specified, resizing the image automatically after
being loaded.
\section{Load an Image}\label{load-an-image}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}14}]:} \PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{preprocessing}\PY{n+nn}{.}\PY{n+nn}{image} \PY{k}{import} \PY{n}{load\PYZus{}img}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{preprocessing}\PY{n+nn}{.}\PY{n+nn}{image} \PY{k}{import} \PY{n}{img\PYZus{}to\PYZus{}array}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{applications}\PY{n+nn}{.}\PY{n+nn}{imagenet\PYZus{}utils} \PY{k}{import} \PY{n}{decode\PYZus{}predictions}
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
\PY{n}{filename} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{images/elephant.jpg}\PY{l+s+s1}{\PYZsq{}}
\PY{c+c1}{\PYZsh{} load an image in PIL format (width, height)}
\PY{n}{original\PYZus{}image} \PY{o}{=} \PY{n}{load\PYZus{}img}\PY{p}{(}\PY{n}{filename}\PY{p}{,} \PY{n}{target\PYZus{}size}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{224}\PY{p}{,} \PY{l+m+mi}{224}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}15}]:} \PY{c+c1}{\PYZsh{} convert the PIL image (width, height) to a NumPy array (height, width, channel)}
\PY{n}{numpy\PYZus{}image} \PY{o}{=} \PY{n}{img\PYZus{}to\PYZus{}array}\PY{p}{(}\PY{n}{original\PYZus{}image}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}16}]:} \PY{c+c1}{\PYZsh{} Convert the image into 4D Tensor (samples, height, width, channels) by adding an extra dimension to the axis 0.}
\PY{n}{input\PYZus{}image} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{expand\PYZus{}dims}\PY{p}{(}\PY{n}{numpy\PYZus{}image}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{0}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}18}]:} \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{PIL image size = }\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{original\PYZus{}image}\PY{o}{.}\PY{n}{size}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{NumPy image size = }\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{numpy\PYZus{}image}\PY{o}{.}\PY{n}{shape}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Input image size =}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{input\PYZus{}image}\PY{o}{.}\PY{n}{shape}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{imshow}\PY{p}{(}\PY{n}{np}\PY{o}{.}\PY{n}{uint8}\PY{p}{(}\PY{n}{input\PYZus{}image}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
PIL image size = (224, 224)
NumPy image size = (224, 224, 3)
Input image size = (1, 224, 224, 3)
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}18}]:} <matplotlib.image.AxesImage at 0x841ec5c0>
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_22_2.png}
\end{center}
{ \hspace*{\fill} \\}
\section{Predict Image in VGG16}\label{predict-image-in-vgg16}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}28}]:} \PY{c+c1}{\PYZsh{}preprocess for vgg16 (Normalizing the image!!)}
\PY{n}{processed\PYZus{}image\PYZus{}vgg16} \PY{o}{=} \PY{n}{vgg16}\PY{o}{.}\PY{n}{preprocess\PYZus{}input}\PY{p}{(}\PY{n}{input\PYZus{}image}\PY{o}{.}\PY{n}{copy}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} vgg16}
\PY{n}{predictions\PYZus{}vgg16} \PY{o}{=} \PY{n}{vgg\PYZus{}model}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{processed\PYZus{}image\PYZus{}vgg16}\PY{p}{)}
\PY{n}{label\PYZus{}vgg16} \PY{o}{=} \PY{n}{decode\PYZus{}predictions}\PY{p}{(}\PY{n}{predictions\PYZus{}vgg16}\PY{p}{)}
\PY{n+nb}{print} \PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{label\PYZus{}vgg16 =}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{label\PYZus{}vgg16}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
label\_vgg16 = [[('n02504458', 'African\_elephant', 0.8856153), ('n01871265', 'tusker', 0.1080491), ('n02504013', 'Indian\_elephant', 0.0062642987), ('n01704323', 'triceratops', 6.8866946e-05), ('n02408429', 'water\_buffalo', 7.891501e-07)]]
\end{Verbatim}
\section{Predict Image in ResNet50}\label{predict-image-in-resnet50}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}37}]:} \PY{c+c1}{\PYZsh{}preprocess for resnet50 (Normalizing the image!!)}
\PY{n}{processed\PYZus{}image\PYZus{}resnet50} \PY{o}{=} \PY{n}{resnet50}\PY{o}{.}\PY{n}{preprocess\PYZus{}input}\PY{p}{(}\PY{n}{input\PYZus{}image}\PY{o}{.}\PY{n}{copy}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} resnet50}
\PY{n}{predictions\PYZus{}resnet50} \PY{o}{=} \PY{n}{resnet\PYZus{}model}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{processed\PYZus{}image\PYZus{}resnet50}\PY{p}{)}
\PY{n}{label\PYZus{}resnet50} \PY{o}{=} \PY{n}{decode\PYZus{}predictions}\PY{p}{(}\PY{n}{predictions\PYZus{}resnet50}\PY{p}{)}
\PY{n+nb}{print} \PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{label\PYZus{}resnet50 =}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{label\PYZus{}resnet50}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
label\_resnet50 = [[('n02504458', 'African\_elephant', 0.80959266), ('n01871265', 'tusker', 0.13150445), ('n02504013', 'Indian\_elephant', 0.057454366), ('n09428293', 'seashore', 0.0003063112), ('n01704323', 'triceratops', 9.2910755e-05)]]
\end{Verbatim}
\section{Predict Image in
Inception\_v3}\label{predict-image-in-inception_v3}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}38}]:} \PY{c+c1}{\PYZsh{}preprocess for inception\PYZus{}v3 (Normalizing the image!!)}
\PY{n}{processed\PYZus{}image\PYZus{}inception\PYZus{}v3} \PY{o}{=} \PY{n}{inception\PYZus{}v3}\PY{o}{.}\PY{n}{preprocess\PYZus{}input}\PY{p}{(}\PY{n}{input\PYZus{}image}\PY{o}{.}\PY{n}{copy}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} inception\PYZus{}v3}
\PY{n}{predictions\PYZus{}inception\PYZus{}v3} \PY{o}{=} \PY{n}{inception\PYZus{}model}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{processed\PYZus{}image\PYZus{}inception\PYZus{}v3}\PY{p}{)}
\PY{n}{label\PYZus{}inception\PYZus{}v3} \PY{o}{=} \PY{n}{decode\PYZus{}predictions}\PY{p}{(}\PY{n}{predictions\PYZus{}inception\PYZus{}v3}\PY{p}{)}
\PY{n+nb}{print} \PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{label\PYZus{}inception\PYZus{}v3 =}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{label\PYZus{}inception\PYZus{}v3}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
label\_inception\_v3 = [[('n02504458', 'African\_elephant', 0.9970175), ('n01871265', 'tusker', 0.0029755807), ('n02504013', 'Indian\_elephant', 6.558931e-06), ('n03840681', 'ocarina', 8.458902e-08), ('n03271574', 'electric\_fan', 2.7554375e-08)]]
\end{Verbatim}
\section{Predict Image in MobileNet}\label{predict-image-in-mobilenet}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}39}]:} \PY{c+c1}{\PYZsh{}preprocess for mobilenet (Normalizing the image!!)}
\PY{n}{processed\PYZus{}image\PYZus{}mobilenet} \PY{o}{=} \PY{n}{mobilenet}\PY{o}{.}\PY{n}{preprocess\PYZus{}input}\PY{p}{(}\PY{n}{input\PYZus{}image}\PY{o}{.}\PY{n}{copy}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} mobilenet}
\PY{n}{predictions\PYZus{}mobilenet} \PY{o}{=} \PY{n}{mobilenet\PYZus{}model}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{processed\PYZus{}image\PYZus{}mobilenet}\PY{p}{)}
\PY{n}{label\PYZus{}mobilenet} \PY{o}{=} \PY{n}{decode\PYZus{}predictions}\PY{p}{(}\PY{n}{predictions\PYZus{}mobilenet}\PY{p}{)}
\PY{n+nb}{print} \PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{label\PYZus{}mobilenet = }\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{label\PYZus{}mobilenet}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
label\_mobilenet = [[('n02504458', 'African\_elephant', 0.6129278), ('n01871265', 'tusker', 0.38332123), ('n02504013', 'Indian\_elephant', 0.0031316064), ('n04507155', 'umbrella', 0.00022650174), ('n03000247', 'chain\_mail', 5.6946195e-05)]]
\end{Verbatim}
% Add a bibliography block to the postdoc
\end{document}