index.tex

% Options for packages loaded elsewhere
\PassOptionsToPackage{unicode}{hyperref}
\PassOptionsToPackage{hyphens}{url}
\PassOptionsToPackage{dvipsnames,svgnames,x11names}{xcolor}
%
\documentclass[
  letterpaper,
  DIV=11,
  numbers=noendperiod]{scrreprt}

\usepackage{amsmath,amssymb}
\usepackage{iftex}
\ifPDFTeX
  \usepackage[T1]{fontenc}
  \usepackage[utf8]{inputenc}
  \usepackage{textcomp} % provide euro and other symbols
\else % if luatex or xetex
  \usepackage{unicode-math}
  \defaultfontfeatures{Scale=MatchLowercase}
  \defaultfontfeatures[\rmfamily]{Ligatures=TeX,Scale=1}
\fi
\usepackage{lmodern}
\ifPDFTeX\else  
    % xetex/luatex font selection
\fi
% Use upquote if available, for straight quotes in verbatim environments
\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
\IfFileExists{microtype.sty}{% use microtype if available
  \usepackage[]{microtype}
  \UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts
}{}
\makeatletter
\@ifundefined{KOMAClassName}{% if non-KOMA class
  \IfFileExists{parskip.sty}{%
    \usepackage{parskip}
  }{% else
    \setlength{\parindent}{0pt}
    \setlength{\parskip}{6pt plus 2pt minus 1pt}}
}{% if KOMA class
  \KOMAoptions{parskip=half}}
\makeatother
\usepackage{xcolor}
\setlength{\emergencystretch}{3em} % prevent overfull lines
\setcounter{secnumdepth}{5}
% Make \paragraph and \subparagraph free-standing
\makeatletter
\ifx\paragraph\undefined\else
  \let\oldparagraph\paragraph
  \renewcommand{\paragraph}{
    \@ifstar
      \xxxParagraphStar
      \xxxParagraphNoStar
  }
  \newcommand{\xxxParagraphStar}[1]{\oldparagraph*{#1}\mbox{}}
  \newcommand{\xxxParagraphNoStar}[1]{\oldparagraph{#1}\mbox{}}
\fi
\ifx\subparagraph\undefined\else
  \let\oldsubparagraph\subparagraph
  \renewcommand{\subparagraph}{
    \@ifstar
      \xxxSubParagraphStar
      \xxxSubParagraphNoStar
  }
  \newcommand{\xxxSubParagraphStar}[1]{\oldsubparagraph*{#1}\mbox{}}
  \newcommand{\xxxSubParagraphNoStar}[1]{\oldsubparagraph{#1}\mbox{}}
\fi
\makeatother

\usepackage{color}
\usepackage{fancyvrb}
\newcommand{\VerbBar}{|}
\newcommand{\VERB}{\Verb[commandchars=\\\{\}]}
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
% Add ',fontsize=\small' for more characters per line
\usepackage{framed}
\definecolor{shadecolor}{RGB}{241,243,245}
\newenvironment{Shaded}{\begin{snugshade}}{\end{snugshade}}
\newcommand{\AlertTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.40,0.45,0.13}{#1}}
\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
\newcommand{\BuiltInTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
\newcommand{\CharTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
\newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{\textit{#1}}}
\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{#1}}
\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{\textbf{#1}}}
\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{\textit{#1}}}
\newcommand{\ErrorTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
\newcommand{\ExtensionTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.28,0.35,0.67}{#1}}
\newcommand{\ImportTok}[1]{\textcolor[rgb]{0.00,0.46,0.62}{#1}}
\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{\textbf{#1}}}
\newcommand{\NormalTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.68,0.00,0.00}{#1}}
\newcommand{\RegionMarkerTok}[1]{\textcolor[rgb]{0.00,0.23,0.31}{#1}}
\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{#1}}
\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
\newcommand{\StringTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.07,0.07,0.07}{#1}}
\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.13,0.47,0.30}{#1}}
\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.37,0.37,0.37}{\textit{#1}}}

\providecommand{\tightlist}{%
  \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}\usepackage{longtable,booktabs,array}
\usepackage{calc} % for calculating minipage widths
% Correct order of tables after \paragraph or \subparagraph
\usepackage{etoolbox}
\makeatletter
\patchcmd\longtable{\par}{\if@noskipsec\mbox{}\fi\par}{}{}
\makeatother
% Allow footnotes in longtable head/foot
\IfFileExists{footnotehyper.sty}{\usepackage{footnotehyper}}{\usepackage{footnote}}
\makesavenoteenv{longtable}
\usepackage{graphicx}
\makeatletter
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth\else\Gin@nat@width\fi}
\def\maxheight{\ifdim\Gin@nat@height>\textheight\textheight\else\Gin@nat@height\fi}
\makeatother
% Scale images if necessary, so that they will not overflow the page
% margins by default, and it is still possible to overwrite the defaults
% using explicit options in \includegraphics[width, height, ...]{}
\setkeys{Gin}{width=\maxwidth,height=\maxheight,keepaspectratio}
% Set default figure placement to htbp
\makeatletter
\def\fps@figure{htbp}
\makeatother
% definitions for citeproc citations
\NewDocumentCommand\citeproctext{}{}
\NewDocumentCommand\citeproc{mm}{%
  \begingroup\def\citeproctext{#2}\cite{#1}\endgroup}
\makeatletter
 % allow citations to break across lines
 \let\@cite@ofmt\@firstofone
 % avoid brackets around text for \cite:
 \def\@biblabel#1{}
 \def\@cite#1#2{{#1\if@tempswa , #2\fi}}
\makeatother
\newlength{\cslhangindent}
\setlength{\cslhangindent}{1.5em}
\newlength{\csllabelwidth}
\setlength{\csllabelwidth}{3em}
\newenvironment{CSLReferences}[2] % #1 hanging-indent, #2 entry-spacing
 {\begin{list}{}{%
  \setlength{\itemindent}{0pt}
  \setlength{\leftmargin}{0pt}
  \setlength{\parsep}{0pt}
  % turn on hanging indent if param 1 is 1
  \ifodd #1
   \setlength{\leftmargin}{\cslhangindent}
   \setlength{\itemindent}{-1\cslhangindent}
  \fi
  % set entry spacing
  \setlength{\itemsep}{#2\baselineskip}}}
 {\end{list}}
\usepackage{calc}
\newcommand{\CSLBlock}[1]{\hfill\break\parbox[t]{\linewidth}{\strut\ignorespaces#1\strut}}
\newcommand{\CSLLeftMargin}[1]{\parbox[t]{\csllabelwidth}{\strut#1\strut}}
\newcommand{\CSLRightInline}[1]{\parbox[t]{\linewidth - \csllabelwidth}{\strut#1\strut}}
\newcommand{\CSLIndent}[1]{\hspace{\cslhangindent}#1}

\KOMAoption{captions}{tableheading}
\makeatletter
\@ifpackageloaded{bookmark}{}{\usepackage{bookmark}}
\makeatother
\makeatletter
\@ifpackageloaded{caption}{}{\usepackage{caption}}
\AtBeginDocument{%
\ifdefined\contentsname
  \renewcommand*\contentsname{Table of contents}
\else
  \newcommand\contentsname{Table of contents}
\fi
\ifdefined\listfigurename
  \renewcommand*\listfigurename{List of Figures}
\else
  \newcommand\listfigurename{List of Figures}
\fi
\ifdefined\listtablename
  \renewcommand*\listtablename{List of Tables}
\else
  \newcommand\listtablename{List of Tables}
\fi
\ifdefined\figurename
  \renewcommand*\figurename{Figure}
\else
  \newcommand\figurename{Figure}
\fi
\ifdefined\tablename
  \renewcommand*\tablename{Table}
\else
  \newcommand\tablename{Table}
\fi
}
\@ifpackageloaded{float}{}{\usepackage{float}}
\floatstyle{ruled}
\@ifundefined{c@chapter}{\newfloat{codelisting}{h}{lop}}{\newfloat{codelisting}{h}{lop}[chapter]}
\floatname{codelisting}{Listing}
\newcommand*\listoflistings{\listof{codelisting}{List of Listings}}
\makeatother
\makeatletter
\makeatother
\makeatletter
\@ifpackageloaded{caption}{}{\usepackage{caption}}
\@ifpackageloaded{subcaption}{}{\usepackage{subcaption}}
\makeatother

\ifLuaTeX
  \usepackage{selnolig}  % disable illegal ligatures
\fi
\usepackage{bookmark}

\IfFileExists{xurl.sty}{\usepackage{xurl}}{} % add URL line breaks if available
\urlstyle{same} % disable monospaced font for URLs
\hypersetup{
  pdftitle={Applied Artificial Intelligence and Deep Learning Book},
  pdfauthor={Tim Mayer; Biplov Bhandari; David Saah},
  colorlinks=true,
  linkcolor={blue},
  filecolor={Maroon},
  citecolor={Blue},
  urlcolor={Blue},
  pdfcreator={LaTeX via pandoc}}


\title{Applied Artificial Intelligence and Deep Learning Book}
\author{Tim Mayer \and Biplov Bhandari \and David Saah}
\date{2025-09-18}

\begin{document}
\maketitle

\renewcommand*\contentsname{Table of contents}
{
\hypersetup{linkcolor=}
\setcounter{tocdepth}{2}
\tableofcontents
}

\bookmarksetup{startatroot}

\chapter*{Introduction}\label{introduction}
\addcontentsline{toc}{chapter}{Introduction}

\markboth{Introduction}{Introduction}

The NASA EarthRISE program harnesses NASA Earth Action capabilities to
deliver trusted solutions for sustained benefits to society.
Collabroting with State and Local partners and striving to strengthens
the capaciies of cof parterns to use satellite data and geospatial
technology to address critical challenges. EarthRISE co-develops
innovative solutions through a network of partners to improve resilience
and sustainable resource management across scales. Additionally,
EarthRISE focuses on developing participate in innovative knowledge
products such as the SAR Handbook and the GEE book designed to support
capacity building in applying Remote Sensing and geospatial approaches
to address challenges.

The focus of the EarthRISE Applied Artificial Intelligence and Deep
Learning Book is to provide practitioners with a wide variety of applied
examples of Remote Sensing Artificial Intelligence and Deep Learning
approaches. With each chapter focusing on a specific problem set such as
object detection of downscaling using Deep Learning. Additionally,
throughout the books chapters various examples are provided spanning the
aforementioned NASA Earth Action thematic areas. Thereby providing a
wide variety of thematic applications to complement reader's domain
specific practical knowledge such as agronomy or forestry etc.

We suspect readers are coming to this virtual book with preexisting
geospatial expertise. However, limited Artificial Intelligence and Deep
Learning knowledge

Each chapter contains both the theoretical background as well as a
practical hand-on section facilitated through virtual notebooks.
Finally, this book spans a variety of platforms such as TensorFlow and
PyTorch to provide readers with a wide set of examples.

\part{Curriculum}

\chapter{Data Preparation}\label{data-preparation}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Print out the Python version used by this environment.}
\ImportTok{import}\NormalTok{ sys}

\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f\textquotesingle{}}\SpecialCharTok{\{}\NormalTok{sys}\SpecialCharTok{.}\NormalTok{version}\OperatorTok{=}\SpecialCharTok{\}}\SpecialStringTok{\textquotesingle{}}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
sys.version='3.11.0 | packaged by conda-forge | (main, Jan 14 2023, 12:26:40) [Clang 14.0.6 ]'
\end{verbatim}

Insert text here

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\chapter{Semantic Segmentation (Crop
Mapping)}\label{semantic-segmentation-crop-mapping}

\section{Rice mapping in Bhutan with U-Net using high resolution
satellite
imagery}\label{rice-mapping-in-bhutan-with-u-net-using-high-resolution-satellite-imagery}

\includegraphics{index_files/mediabag/colab_logo_32px.png}
\href{https://colab.research.google.com/github/SERVIR/SERVIR-Applied-Deep-Learning-Book/blob/main/03_Semantic_Segmentation/01__Crop_Mapping/notebooks/Rice_Mapping_Bhutan_2021.ipynb}{Run
in Colab} \includegraphics{index_files/mediabag/GitHub-Mark-32px.png}
\href{https://github.com/SERVIR/SERVIR-Applied-Deep-Learning-Book/blob/main/03_Semantic_Segmentation/01__Crop_Mapping/notebooks/Rice_Mapping_Bhutan_2021.ipynb}{View
on GitHub}

\section{\texorpdfstring{Note: This notebook is meant to be run in
Colab. You can still run this locally, make sure you have
\href{https://developers.google.com/workspace/drive/api/quickstart/python}{Google
Drive API} installed and path adjusted in relevant
places.}{Note: This notebook is meant to be run in Colab. You can still run this locally, make sure you have Google Drive API installed and path adjusted in relevant places.}}\label{note-this-notebook-is-meant-to-be-run-in-colab.-you-can-still-run-this-locally-make-sure-you-have-google-drive-api-installed-and-path-adjusted-in-relevant-places.}

This notebook is also available in this github repo:
\url{https://github.com/SERVIR/servir-aces}. Navigate to the
\texttt{notebook} folder.

\section{Setup environment}\label{setup-environment}

\begin{Shaded}
\begin{Highlighting}[]
\ImportTok{from}\NormalTok{ google.colab }\ImportTok{import}\NormalTok{ drive}
\NormalTok{drive.mount(}\StringTok{"/content/drive"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\OperatorTok{!}\NormalTok{pip install servir}\OperatorTok{{-}}\NormalTok{aces}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
Collecting servir-aces
  Downloading servir_aces-0.0.14-py2.py3-none-any.whl (32 kB)
Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from servir-aces) (1.25.2)
Requirement already satisfied: tensorflow>=2.9.3 in /usr/local/lib/python3.10/dist-packages (from servir-aces) (2.15.0)
Requirement already satisfied: earthengine-api in /usr/local/lib/python3.10/dist-packages (from servir-aces) (0.1.399)
Collecting python-dotenv>=1.0.0 (from servir-aces)
  Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from servir-aces) (3.7.1)
Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.4.0)
Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.6.3)
Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (24.3.25)
Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (0.5.4)
Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (0.2.0)
Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (3.9.0)
Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (18.1.1)
Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (0.2.0)
Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (3.3.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (24.0)
Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (3.20.3)
Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (67.7.2)
Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.16.0)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (2.4.0)
Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (4.11.0)
Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.14.1)
Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (0.36.0)
Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (1.62.2)
Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (2.15.2)
Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (2.15.0)
Requirement already satisfied: keras<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.9.3->servir-aces) (2.15.0)
Requirement already satisfied: google-cloud-storage in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (2.8.0)
Requirement already satisfied: google-api-python-client>=1.12.1 in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (2.84.0)
Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (2.27.0)
Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (0.1.1)
Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (0.22.0)
Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from earthengine-api->servir-aces) (2.31.0)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (1.2.1)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (4.51.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (1.4.5)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (9.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (3.1.2)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->servir-aces) (2.8.2)
Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow>=2.9.3->servir-aces) (0.43.0)
Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client>=1.12.1->earthengine-api->servir-aces) (2.11.1)
Requirement already satisfied: uritemplate<5,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client>=1.12.1->earthengine-api->servir-aces) (4.1.1)
Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth>=1.4.1->earthengine-api->servir-aces) (5.3.3)
Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth>=1.4.1->earthengine-api->servir-aces) (0.4.0)
Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth>=1.4.1->earthengine-api->servir-aces) (4.9)
Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (1.2.0)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (3.6)
Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (0.7.2)
Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (3.0.2)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->earthengine-api->servir-aces) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->earthengine-api->servir-aces) (3.7)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->earthengine-api->servir-aces) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->earthengine-api->servir-aces) (2024.2.2)
Requirement already satisfied: google-cloud-core<3.0dev,>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from google-cloud-storage->earthengine-api->servir-aces) (2.3.3)
Requirement already satisfied: google-resumable-media>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from google-cloud-storage->earthengine-api->servir-aces) (2.7.0)
Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.12.1->earthengine-api->servir-aces) (1.63.0)
Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (1.3.1)
Requirement already satisfied: google-crc32c<2.0dev,>=1.0 in /usr/local/lib/python3.10/dist-packages (from google-resumable-media>=2.3.2->google-cloud-storage->earthengine-api->servir-aces) (1.5.0)
Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=1.4.1->earthengine-api->servir-aces) (0.6.0)
Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (2.1.5)
Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow>=2.9.3->servir-aces) (3.2.2)
Installing collected packages: python-dotenv, servir-aces
Successfully installed python-dotenv-1.0.1 servir-aces-0.0.14
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\OperatorTok{!}\NormalTok{git clone https:}\OperatorTok{//}\NormalTok{github.com}\OperatorTok{/}\NormalTok{SERVIR}\OperatorTok{/}\NormalTok{servir}\OperatorTok{{-}}\NormalTok{aces}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
Cloning into 'servir-aces'...
remote: Enumerating objects: 740, done.
remote: Counting objects: 100% (116/116), done.
remote: Compressing objects: 100% (78/78), done.
remote: Total 740 (delta 46), reused 68 (delta 38), pack-reused 624
Receiving objects: 100% (740/740), 5.07 MiB | 16.12 MiB/s, done.
Resolving deltas: 100% (468/468), done.
\end{verbatim}

\subsection{Download datasets}\label{download-datasets}

For this chapter, we have already prepared and exported the training
datasets. They can be found at the google cloud storage and we will use
\texttt{gsutil} to get the dataset in our workspace. The dataset has
\texttt{training}, \texttt{testing}, and \texttt{validation}
subdirectory. Let's start by downloading these datasets in our
workspace.

If you're looking to produce your own datasets, you can follow this
\href{https://colab.research.google.com/drive/1LCFLeSCu969wIW8TD68-j4hfIu7WiRIK?usp=sharing}{notebook}
which was used to produce these training, testing, and validation
datasets provided in this notebook.

\begin{Shaded}
\begin{Highlighting}[]
\OperatorTok{!}\NormalTok{mkdir }\OperatorTok{{-}}\NormalTok{p content}\OperatorTok{/}\NormalTok{datasets}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\OperatorTok{!}\NormalTok{gsutil }\OperatorTok{{-}}\NormalTok{m cp }\OperatorTok{{-}}\NormalTok{r gs:}\OperatorTok{//}\NormalTok{dl}\OperatorTok{{-}}\NormalTok{book}\OperatorTok{/}\NormalTok{chapter}\OperatorTok{{-}}\DecValTok{1}\NormalTok{ content}\OperatorTok{/}\NormalTok{datasets}\OperatorTok{/}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
If you experience problems with multiprocessing on MacOS, they might be related to https://bugs.python.org/issue33725. You can disable multiprocessing by editing your .boto config or by adding the following flag to your command: `-o "GSUtil:parallel_process_count=1"`. Note that multithreading is still available even if you disable multiprocessing.

Copying gs://dl-book/chapter-1/.DS_Store...
Copying gs://dl-book/chapter-1/dnn_planet_wo_indices/testing/testing.tfrecord.gz...
Copying gs://dl-book/chapter-1/dnn_planet_wo_indices/training/training.tfrecord.gz...
Copying gs://dl-book/chapter-1/dnn_planet_wo_indices/validation/validation.tfrecord.gz...
Copying gs://dl-book/chapter-1/images/image_202100000.tfrecord.gz...            
Copying gs://dl-book/chapter-1/images/image_202100001.tfrecord.gz...            
Copying gs://dl-book/chapter-1/images/image_202100002.tfrecord.gz...            
Copying gs://dl-book/chapter-1/images/image_202100003.tfrecord.gz...            
Copying gs://dl-book/chapter-1/models/dnn_v1/config.env...                      
Copying gs://dl-book/chapter-1/images/image_202100004.tfrecord.gz...            
Copying gs://dl-book/chapter-1/images/image_202100005.tfrecord.gz...            
Copying gs://dl-book/chapter-1/images/image_2021mixer.json...                   
Copying gs://dl-book/chapter-1/models/dnn_v1/aces/keras_metadata.pb...          
Copying gs://dl-book/chapter-1/models/dnn_v1/aces/fingerprint.pb...
Copying gs://dl-book/chapter-1/models/dnn_v1/aces/saved_model.pb...             
Copying gs://dl-book/chapter-1/models/dnn_v1/aces/variables/variables.data-00000-of-00001...
Copying gs://dl-book/chapter-1/models/dnn_v1/aces/variables/variables.index...
Copying gs://dl-book/chapter-1/models/dnn_v1/config.json...                     
Copying gs://dl-book/chapter-1/models/dnn_v1/evaluation.txt...                  
Copying gs://dl-book/chapter-1/models/dnn_v1/logs/train/events.out.tfevents.1713307528.b5c4c6e407b9.190.2.v2...
Copying gs://dl-book/chapter-1/models/dnn_v1/logs/validation/events.out.tfevents.1713307537.b5c4c6e407b9.190.3.v2...
Copying gs://dl-book/chapter-1/models/dnn_v1/model.png...
Copying gs://dl-book/chapter-1/models/dnn_v1/model.txt...                       
Copying gs://dl-book/chapter-1/models/dnn_v1/modelCheckpoint/fingerprint.pb...  
Copying gs://dl-book/chapter-1/models/dnn_v1/modelCheckpoint/variables/variables.data-00000-of-00001...
Copying gs://dl-book/chapter-1/models/dnn_v1/modelCheckpoint/keras_metadata.pb...
Copying gs://dl-book/chapter-1/models/unet_v1/aces/fingerprint.pb...            
Copying gs://dl-book/chapter-1/models/unet_v1/aces/keras_metadata.pb...         
Copying gs://dl-book/chapter-1/models/dnn_v1/model.pkl...                       
Copying gs://dl-book/chapter-1/models/dnn_v1/modelCheckpoint/variables/variables.index...
Copying gs://dl-book/chapter-1/models/dnn_v1/parameters.txt...                  
Copying gs://dl-book/chapter-1/models/dnn_v1/prediction/prediction_dnn_v1.TFRecord...
Copying gs://dl-book/chapter-1/models/unet_v1/model.pkl...
Copying gs://dl-book/chapter-1/models/dnn_v1/trained-model/fingerprint.pb...
Copying gs://dl-book/chapter-1/models/dnn_v1/modelCheckpoint/saved_model.pb...  
==> NOTE: You are downloading one or more large file(s), which would            
run significantly faster if you enabled sliced object downloads. This
feature is enabled by default but requires that compiled crcmod be
installed (see "gsutil help crcmod").

Copying gs://dl-book/chapter-1/models/unet_v1/aces/saved_model.pb...
Copying gs://dl-book/chapter-1/models/dnn_v1/trained-model/keras_metadata.pb... 
Copying gs://dl-book/chapter-1/models/dnn_v1/trained-model/saved_model.pb...    
Copying gs://dl-book/chapter-1/models/dnn_v1/trained-model/variables/variables.index...
Copying gs://dl-book/chapter-1/models/dnn_v1/trained-model/variables/variables.data-00000-of-00001...
Copying gs://dl-book/chapter-1/models/dnn_v1/training.png...                    
Copying gs://dl-book/chapter-1/models/unet_v1/aces/variables/variables.data-00000-of-00001...
Copying gs://dl-book/chapter-1/models/unet_v1/model.png...                      
Copying gs://dl-book/chapter-1/models/unet_v1/logs/train/events.out.tfevents.1713299324.b5c4c6e407b9.190.0.v2...
Copying gs://dl-book/chapter-1/models/unet_v1/config.env...                     
Copying gs://dl-book/chapter-1/models/unet_v1/evaluation.txt...
Copying gs://dl-book/chapter-1/models/unet_v1/config.json...                    
Copying gs://dl-book/chapter-1/models/unet_v1/model.txt...                      
Copying gs://dl-book/chapter-1/models/unet_v1/logs/validation/events.out.tfevents.1713299558.b5c4c6e407b9.190.1.v2...
Copying gs://dl-book/chapter-1/models/unet_v1/aces/variables/variables.index... 
Copying gs://dl-book/chapter-1/models/unet_v1/modelCheckpoint/fingerprint.pb... 
Copying gs://dl-book/chapter-1/models/unet_v1/modelCheckpoint/keras_metadata.pb...
Copying gs://dl-book/chapter-1/models/unet_v1/modelCheckpoint/saved_model.pb... 
Copying gs://dl-book/chapter-1/models/unet_v1/modelCheckpoint/variables/variables.data-00000-of-00001...
Copying gs://dl-book/chapter-1/models/unet_v1/modelCheckpoint/variables/variables.index...
Copying gs://dl-book/chapter-1/models/unet_v1/prediction/prediction_unet_v1.TFRecord...
Copying gs://dl-book/chapter-1/models/unet_v1/parameters.txt...
Copying gs://dl-book/chapter-1/models/unet_v1/trained-model/fingerprint.pb...   
Copying gs://dl-book/chapter-1/models/unet_v1/trained-model/keras_metadata.pb...
Copying gs://dl-book/chapter-1/models/unet_v1/trained-model/saved_model.pb...   
Copying gs://dl-book/chapter-1/models/unet_v1/trained-model/variables/variables.data-00000-of-00001...
Copying gs://dl-book/chapter-1/models/unet_v1/trained-model/variables/variables.index...
Copying gs://dl-book/chapter-1/models/unet_v1/training.png...                   
Copying gs://dl-book/chapter-1/prediction/prediction_dnn_v1.TFRecord...         
Copying gs://dl-book/chapter-1/prediction/prediction_unet_v1.TFRecord...        
Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00000-of-00008.tfrecord.gz...
Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00001-of-00008.tfrecord.gz...
Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00003-of-00008.tfrecord.gz...
Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00002-of-00008.tfrecord.gz...
Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00004-of-00008.tfrecord.gz...
Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00005-of-00008.tfrecord.gz...
Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00006-of-00008.tfrecord.gz...
Copying gs://dl-book/chapter-1/training_data/testing_10/testing__256x256-00007-of-00008.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00000-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00001-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00002-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00003-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00004-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00005-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00006-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00007-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00008-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00009-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00010-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00011-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00012-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00013-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00014-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00015-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00016-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00017-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00018-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00019-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00020-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00021-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00022-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00023-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00024-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00025-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00026-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00027-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00028-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00029-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00030-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00031-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00032-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00033-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00034-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00035-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00036-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00037-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00000-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00001-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00002-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00003-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00004-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00005-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00006-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00007-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00008-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00009-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00010-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00011-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00012-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00013-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00014-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00015-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00016-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00017-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00018-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00019-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00020-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00021-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00022-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00023-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00024-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00025-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00026-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00027-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00028-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00029-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00030-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00031-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00032-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00033-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00034-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00035-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00036-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00037-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00000-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00001-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00002-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00003-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00004-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00005-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00006-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00007-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00008-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00009-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00010-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00011-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00012-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00013-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00014-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00015-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00016-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00017-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00018-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00019-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00020-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00021-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00022-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00023-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00024-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00025-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00026-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00027-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00028-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00029-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00030-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00031-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00032-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00033-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00034-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00035-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00036-of-00038.tfrecord.gz...
Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00037-of-00038.tfrecord.gz...
/ [187/192 files][ 16.3 GiB/ 16.3 GiB]  99% Done  43.1 MiB/s ETA 00:00:00       
\end{verbatim}

\subsection{Setup config file
variables}\label{setup-config-file-variables}

Now the repo is downloaded. We will create an environment file file to
place point to our training data and customize parameters for the model.
To do this, we make a copy of the \texttt{.env.example} file provided.

Under the hood, all the configuration provided via the environment file
are parsed as a config object and can be accessed programatically.

Note current version does not expose all the model intracacies through
the environment file but future version may include those depending on
the need.

\begin{Shaded}
\begin{Highlighting}[]
\OperatorTok{!}\NormalTok{cp servir}\OperatorTok{{-}}\NormalTok{aces}\OperatorTok{/}\NormalTok{.env.example servir}\OperatorTok{{-}}\NormalTok{aces}\OperatorTok{/}\NormalTok{config.env}
\end{Highlighting}
\end{Shaded}

Okay, now we have the \texttt{config.env} file, we will use this to
provide our environments and parameters.

Note there are several parameters that can be changed. Let's start by
changing the \texttt{BASEDIR} and \texttt{OUTPUT\_DIR} as below.

\begin{verbatim}
BASEDIR = "/content/"
OUTPUT_DIR = "/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output"
\end{verbatim}

We will start by training a \href{https://insert-citation-here}{U-Net}
model using the
\texttt{dl-book/chapter-1/unet\_256x256\_planet\_wo\_indices} dataset
inside the \texttt{dataset} folder for this exercise. Let's go ahead and
change our DATADIR in the \texttt{config.env} file as below.

\begin{verbatim}
DATADIR = "datasets/unet_256x256_planet_wo_indices"
\end{verbatim}

These datasets have RGBN from Planetscope mosiac. Since we are trying to
map the rice fields, we use growing season and pre-growing season
information. Thus, we have 8 optical bands, namely \texttt{red\_before},
\texttt{green\_before}, \texttt{blue\_before}, \texttt{nir\_before},
\texttt{red\_during}, \texttt{green\_during}, \texttt{blue\_during}, and
\texttt{nir\_during}. In adidition, you can use \texttt{USE\_ELEVATION}
and \texttt{USE\_S1} config to include the topographic and radar
information. Since this datasets have toppgraphic and radar features, so
we won't be settting these config values. Similarly, these datasets are
tiled to 256x256 pixels, so let's also change that.

\begin{verbatim}
# For model training, USE_ELEVATION extends FEATURES with "elevation" & "slope"
# USE_S1 extends FEATURES with "vv_asc_before", "vh_asc_before", "vv_asc_during", "vh_asc_during",
# "vv_desc_before", "vh_desc_before", "vv_desc_during", "vh_desc_during"
# In case these are not useful and you have other bands in your training data, you can do set
# USE_ELEVATION and USE_S1 to False and update FEATURES to include needed bands
USE_ELEVATION = False
USE_S1 = False

PATCH_SHAPE = (256, 256)
\end{verbatim}

Next, we need to calculate the size of the traiing, testing and
validation dataset. For this, we know our size before hand. But
\texttt{aces} also provides handful of functions that we can use to
calculate this. See this
\href{https://colab.research.google.com/drive/12WgDI3ptFZHmcfOw89fmPSsDwO-sXIUH?usp=sharing}{notebook}
to learn more about how to do it. We will also change the
\texttt{BATCH\_SIZE} to 32; if you have larger memory available, you can
increase the \texttt{BATCH\_SIZE}. You can run for longer
\texttt{EPOCHS} by changing the \texttt{EPOCHS} paramter; we will keep
it to 5 for now.

\begin{verbatim}
# Sizes of the training and evaluation datasets.
TRAIN_SIZE = 8531
TEST_SIZE = 1222
VAL_SIZE = 2404
BATCH_SIZE = 32
EPOCHS = 30
\end{verbatim}

\subsection{Update the config file
programtically}\label{update-the-config-file-programtically}

We can also make a dictionary so we can change these config settings
programatically.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{BASEDIR   }\OperatorTok{=} \StringTok{"/content/"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{OUTPUT\_DIR }\OperatorTok{=} \StringTok{"/content/drive/MyDrive/Colab Notebooks/DL\_Book/Chapter\_1/output"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{DATADIR }\OperatorTok{=} \StringTok{"datasets/unet\_256x256\_planet\_wo\_indices"} \CommentTok{\# @param \{type:"string"\}}
\CommentTok{\# PATCH\_SHAPE, USE\_ELEVATION, USE\_S1, TRAIN\_SIZE, TEST\_SIZE, VAL\_SIZE}
\CommentTok{\# BATCH\_SIZE, EPOCHS are converted to their appropriate type.}
\NormalTok{USE\_ELEVATION }\OperatorTok{=} \StringTok{"False"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{USE\_S1 }\OperatorTok{=} \StringTok{"False"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{PATCH\_SHAPE }\OperatorTok{=} \StringTok{"(256, 256)"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{TRAIN\_SIZE }\OperatorTok{=} \StringTok{"8531"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{TEST\_SIZE }\OperatorTok{=} \StringTok{"1222"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{VAL\_SIZE }\OperatorTok{=} \StringTok{"2404"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{BATCH\_SIZE }\OperatorTok{=} \StringTok{"32"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{EPOCHS }\OperatorTok{=} \StringTok{"30"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{MODEL\_DIR\_NAME }\OperatorTok{=} \StringTok{"unet\_v1"} \CommentTok{\# @param \{type:"string"\}}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{unet\_config\_settings }\OperatorTok{=}\NormalTok{ \{}
    \StringTok{"BASEDIR"}\NormalTok{ : BASEDIR,}
    \StringTok{"OUTPUT\_DIR"}\NormalTok{: OUTPUT\_DIR,}
    \StringTok{"DATADIR"}\NormalTok{: DATADIR,}
    \StringTok{"USE\_ELEVATION"}\NormalTok{: USE\_ELEVATION,}
    \StringTok{"USE\_S1"}\NormalTok{: USE\_S1,}
    \StringTok{"PATCH\_SHAPE"}\NormalTok{: PATCH\_SHAPE,}
    \StringTok{"TRAIN\_SIZE"}\NormalTok{: TRAIN\_SIZE,}
    \StringTok{"TEST\_SIZE"}\NormalTok{: TEST\_SIZE,}
    \StringTok{"VAL\_SIZE"}\NormalTok{: VAL\_SIZE,}
    \StringTok{"BATCH\_SIZE"}\NormalTok{: BATCH\_SIZE,}
    \StringTok{"EPOCHS"}\NormalTok{: EPOCHS,}
    \StringTok{"MODEL\_DIR\_NAME"}\NormalTok{: MODEL\_DIR\_NAME,}
\NormalTok{\}}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\ImportTok{import}\NormalTok{ dotenv}

\NormalTok{config\_file }\OperatorTok{=} \StringTok{"servir{-}aces/config.env"}

\ControlFlowTok{for}\NormalTok{ config\_key }\KeywordTok{in}\NormalTok{ unet\_config\_settings:}
\NormalTok{    dotenv.set\_key(dotenv\_path}\OperatorTok{=}\NormalTok{config\_file,}
\NormalTok{                   key\_to\_set}\OperatorTok{=}\NormalTok{config\_key,}
\NormalTok{                   value\_to\_set}\OperatorTok{=}\NormalTok{unet\_config\_settings[config\_key]}
\NormalTok{                   )}
\end{Highlighting}
\end{Shaded}

\section{U-Net Model}\label{u-net-model}

\subsection{Load config file
variables}\label{load-config-file-variables}

\begin{Shaded}
\begin{Highlighting}[]
\ImportTok{from}\NormalTok{ aces }\ImportTok{import}\NormalTok{ Config, DataProcessor, ModelTrainer, EEUtils}
\end{Highlighting}
\end{Shaded}

Let's load our config file through the \texttt{Config} class.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{unet\_config }\OperatorTok{=}\NormalTok{ Config(config\_file}\OperatorTok{=}\NormalTok{config\_file)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
BASEDIR: /content
DATADIR: /content/datasets/unet_256x256_planet_wo_indices
using features: ['red_before', 'green_before', 'blue_before', 'nir_before', 'red_during', 'green_during', 'blue_during', 'nir_during']
using labels: ['class']
\end{verbatim}

Most of the config in the \texttt{config.env} is now available via the
config instance. Let's check few of them here.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{unet\_config.TRAINING\_DIR, unet\_config.OUTPUT\_DIR, unet\_config.BATCH\_SIZE, unet\_config.TRAIN\_SIZE}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
(PosixPath('/content/datasets/unet_256x256_planet_wo_indices/training'),
 PosixPath('/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output'),
 32,
 8531)
\end{verbatim}

\subsection{\texorpdfstring{Load \texttt{ModelTrainer}
class}{Load ModelTrainer class}}\label{load-modeltrainer-class}

Next, let's make an instance of the \texttt{ModelTrainer} object. The
\texttt{ModelTrainer} class provides various tools for training,
buidling, compiling, and running specified deep learning models.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{unet\_model\_trainer }\OperatorTok{=}\NormalTok{ ModelTrainer(unet\_config, seed}\OperatorTok{=}\DecValTok{42}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
Using seed: 42
\end{verbatim}

\subsection{Train and Save U-Net
model}\label{train-and-save-u-net-model}

\href{https://servir.github.io/servir-aces/model_trainer/}{\texttt{ModelTrainer}}
class provides various functionality. We will use \texttt{train\_model}
function that helps to train the model using the provided configuration
settings.

This method performs the following steps: - Configures memory growth for
TensorFlow. - Creates TensorFlow datasets for training, testing, and
validation. - Builds and compiles the model. - Prepares the output
directory for saving models and results. - Starts the training process.
- Evaluates and prints validation metrics. - Saves training parameters,
plots, and models.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{unet\_model\_trainer.train\_model()}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
****************************************************************************
****************************** Clear Session... ****************************
****************************************************************************
****************************** Configure memory growth... ************************
 > Found 1 GPUs
****************************************************************************
****************************** creating datasets... ************************
Loading dataset from /content/datasets/unet_256x256_planet_wo_indices/training/*
randomly transforming data
Loading dataset from /content/datasets/unet_256x256_planet_wo_indices/validation/*
Loading dataset from /content/datasets/unet_256x256_planet_wo_indices/testing/*
Printing dataset info:
Training
inputs: float32 (32, 256, 256, 8)
tf.Tensor(
[[[[0.073075 0.063275 0.0411   ... 0.050625 0.0274   0.23925 ]
   [0.084775 0.067375 0.047025 ... 0.057675 0.032075 0.242375]
   [0.083625 0.068575 0.045075 ... 0.059275 0.0332   0.2409  ]
   ...
   [0.0702   0.06825  0.04495  ... 0.055025 0.028325 0.26305 ]
   [0.064475 0.066    0.043575 ... 0.0524   0.027075 0.26705 ]
   [0.0676   0.06355  0.04535  ... 0.05375  0.02875  0.263275]]

  [[0.071475 0.062225 0.0388   ... 0.0496   0.025375 0.24155 ]
   [0.07815  0.065025 0.044225 ... 0.0545   0.02905  0.24175 ]
   [0.086025 0.069125 0.046175 ... 0.05855  0.0326   0.2355  ]
   ...
   [0.060775 0.0627   0.041875 ... 0.051575 0.029725 0.267475]
   [0.061375 0.06225  0.04225  ... 0.0513   0.02685  0.268375]
   [0.06845  0.064075 0.043925 ... 0.052925 0.028575 0.267975]]

  [[0.0677   0.0605   0.038625 ... 0.04835  0.024825 0.236075]
   [0.078375 0.0629   0.04215  ... 0.0524   0.02855  0.237375]
   [0.0857   0.065725 0.04635  ... 0.05705  0.030975 0.235375]
   ...
   [0.07     0.062775 0.04485  ... 0.053425 0.0292   0.27015 ]
   [0.0607   0.060675 0.041175 ... 0.053075 0.026275 0.27025 ]
   [0.068    0.0667   0.045375 ... 0.055475 0.029375 0.262725]]

  ...

  [[0.083525 0.06785  0.044125 ... 0.06365  0.0331   0.234825]
   [0.097825 0.07235  0.047925 ... 0.06675  0.03365  0.2363  ]
   [0.1092   0.082125 0.05385  ... 0.072125 0.036225 0.2486  ]
   ...
   [0.08935  0.088725 0.067575 ... 0.079675 0.042425 0.38085 ]
   [0.093725 0.0875   0.06355  ... 0.07565  0.04185  0.344525]
   [0.0937   0.089675 0.066775 ... 0.07465  0.043025 0.330925]]

  [[0.0893   0.0732   0.04715  ... 0.065    0.0351   0.233525]
   [0.091325 0.073425 0.047475 ... 0.0653   0.032675 0.238325]
   [0.096775 0.07645  0.051625 ... 0.06875  0.0344   0.252825]
   ...
   [0.0836   0.084875 0.061975 ... 0.07825  0.042875 0.38785 ]
   [0.08865  0.083825 0.060675 ... 0.0765   0.042525 0.3522  ]
   [0.0909   0.084475 0.061975 ... 0.0769   0.043275 0.342625]]

  [[0.092075 0.078    0.050925 ... 0.06565  0.03555  0.235275]
   [0.0805   0.0705   0.043325 ... 0.063925 0.03215  0.243875]
   [0.086925 0.074025 0.0495   ... 0.067475 0.03345  0.26095 ]
   ...
   [0.081075 0.078725 0.056425 ... 0.07505  0.0398   0.37805 ]
   [0.0865   0.079375 0.05845  ... 0.076175 0.0439   0.3619  ]
   [0.0886   0.077775 0.057725 ... 0.076175 0.042825 0.3439  ]]]


 [[[0.076525 0.0703   0.04595  ... 0.055225 0.028025 0.25075 ]
   [0.072025 0.0658   0.0446   ... 0.05555  0.02795  0.24755 ]
   [0.0669   0.06225  0.038125 ... 0.05245  0.027125 0.241425]
   ...
   [0.054175 0.050575 0.029475 ... 0.04845  0.022375 0.23045 ]
   [0.05465  0.052375 0.031125 ... 0.04935  0.024375 0.2282  ]
   [0.052525 0.052725 0.029275 ... 0.048325 0.02325  0.229475]]

  [[0.0784   0.065975 0.0441   ... 0.0594   0.031425 0.241175]
   [0.075475 0.066225 0.044975 ... 0.05505  0.02915  0.2405  ]
   [0.073375 0.063225 0.044475 ... 0.05435  0.029375 0.243575]
   ...
   [0.047325 0.05035  0.027125 ... 0.04535  0.022275 0.2235  ]
   [0.046475 0.051075 0.026425 ... 0.047025 0.021025 0.2348  ]
   [0.04295  0.050275 0.02575  ... 0.044525 0.01955  0.240875]]

  [[0.065825 0.0619   0.04045  ... 0.053225 0.026425 0.236775]
   [0.07745  0.062725 0.040725 ... 0.0573   0.030725 0.2439  ]
   [0.075525 0.063775 0.0434   ... 0.05595  0.030125 0.25005 ]
   ...
   [0.046675 0.048325 0.02605  ... 0.0475   0.0219   0.23165 ]
   [0.046825 0.04955  0.026425 ... 0.0471   0.02055  0.243125]
   [0.04435  0.0498   0.0253   ... 0.04675  0.020775 0.239925]]

  ...

  [[0.028025 0.041275 0.01945  ... 0.039375 0.015675 0.22205 ]
   [0.0245   0.040675 0.018025 ... 0.039575 0.016475 0.2187  ]
   [0.02185  0.03435  0.01665  ... 0.034025 0.015    0.20335 ]
   ...
   [0.1155   0.09395  0.0714   ... 0.058625 0.0275   0.335675]
   [0.117225 0.09435  0.0699   ... 0.05885  0.028175 0.34795 ]
   [0.1168   0.093275 0.06865  ... 0.0585   0.02895  0.353275]]

  [[0.032025 0.04075  0.020675 ... 0.04025  0.015525 0.2328  ]
   [0.024525 0.038175 0.018025 ... 0.03785  0.015075 0.21255 ]
   [0.0227   0.03625  0.016425 ... 0.035    0.015075 0.204675]
   ...
   [0.11625  0.093825 0.071275 ... 0.058625 0.02685  0.34765 ]
   [0.115325 0.092175 0.06915  ... 0.05855  0.02745  0.3572  ]
   [0.1143   0.091225 0.067325 ... 0.05835  0.028925 0.357825]]

  [[0.033325 0.04015  0.0212   ... 0.037875 0.015575 0.220525]
   [0.027225 0.038525 0.01925  ... 0.03625  0.014825 0.207775]
   [0.02625  0.03785  0.01885  ... 0.035675 0.015175 0.209825]
   ...
   [0.1132   0.09225  0.0699   ... 0.057875 0.027175 0.352875]
   [0.1116   0.090575 0.0685   ... 0.0585   0.027325 0.36045 ]
   [0.110325 0.089725 0.06665  ... 0.059425 0.02975  0.35485 ]]]


 [[[0.076325 0.0714   0.0511   ... 0.05685  0.027375 0.3285  ]
   [0.078825 0.066725 0.044825 ... 0.05665  0.03155  0.3196  ]
   [0.1038   0.0806   0.060575 ... 0.07545  0.048225 0.2805  ]
   ...
   [0.02885  0.040825 0.022125 ... 0.037725 0.016275 0.17815 ]
   [0.0286   0.0422   0.02355  ... 0.039625 0.016675 0.191225]
   [0.02775  0.04375  0.022175 ... 0.043325 0.0181   0.203775]]

  [[0.06785  0.062075 0.04025  ... 0.04975  0.026175 0.31845 ]
   [0.07785  0.06515  0.041575 ... 0.055275 0.033675 0.29555 ]
   [0.099375 0.0823   0.062    ... 0.076125 0.047775 0.27305 ]
   ...
   [0.026425 0.040625 0.021825 ... 0.037175 0.0163   0.180075]
   [0.0283   0.04245  0.02205  ... 0.04045  0.017175 0.192025]
   [0.02925  0.0436   0.022975 ... 0.043725 0.0179   0.20435 ]]

  [[0.064725 0.0621   0.0413   ... 0.05105  0.02655  0.30515 ]
   [0.08075  0.067625 0.0489   ... 0.0599   0.033625 0.28425 ]
   [0.1018   0.078725 0.060025 ... 0.0735   0.043225 0.2772  ]
   ...
   [0.0277   0.0412   0.020975 ... 0.03765  0.01625  0.184425]
   [0.02835  0.043125 0.021675 ... 0.040175 0.017375 0.19335 ]
   [0.030575 0.043325 0.023375 ... 0.04225  0.0173   0.200575]]

  ...

  [[0.06545  0.054525 0.034075 ... 0.05745  0.028325 0.244075]
   [0.06275  0.053075 0.03125  ... 0.055625 0.027675 0.247475]
   [0.060875 0.05235  0.030725 ... 0.053875 0.026575 0.247275]
   ...
   [0.04905  0.0508   0.031375 ... 0.039275 0.018625 0.184025]
   [0.047775 0.04855  0.03135  ... 0.038075 0.017725 0.173025]
   [0.048475 0.052025 0.0336   ... 0.0377   0.018625 0.172875]]

  [[0.061575 0.051675 0.03085  ... 0.052975 0.02525  0.244675]
   [0.056875 0.050975 0.027025 ... 0.051675 0.023125 0.243075]
   [0.051075 0.05215  0.027025 ... 0.052125 0.022625 0.2422  ]
   ...
   [0.051525 0.05075  0.031625 ... 0.039625 0.021775 0.1806  ]
   [0.0485   0.049475 0.031275 ... 0.03685  0.01885  0.181675]
   [0.054275 0.054875 0.036125 ... 0.037525 0.0198   0.171425]]

  [[0.055875 0.051075 0.02745  ... 0.04885  0.02285  0.2407  ]
   [0.056    0.052725 0.0285   ... 0.053175 0.02415  0.24375 ]
   [0.0544   0.05275  0.02815  ... 0.0555   0.0232   0.24885 ]
   ...
   [0.05005  0.051775 0.031    ... 0.03915  0.019525 0.1762  ]
   [0.048825 0.051275 0.0324   ... 0.036175 0.018375 0.18395 ]
   [0.0513   0.051225 0.031875 ... 0.0385   0.020625 0.177575]]]


 ...


 [[[0.059125 0.0521   0.0284   ... 0.046025 0.019975 0.234825]
   [0.06905  0.055875 0.0304   ... 0.04825  0.021725 0.237375]
   [0.0699   0.05865  0.031125 ... 0.051375 0.022725 0.23655 ]
   ...
   [0.034575 0.04225  0.0247   ... 0.03785  0.019175 0.157225]
   [0.029975 0.038475 0.023925 ... 0.034475 0.014425 0.175175]
   [0.025325 0.03555  0.02115  ... 0.0325   0.0144   0.157   ]]

  [[0.04895  0.051125 0.02935  ... 0.04475  0.0215   0.2242  ]
   [0.0563   0.05555  0.032025 ... 0.04655  0.0231   0.224225]
   [0.055875 0.0564   0.032875 ... 0.04815  0.023    0.232925]
   ...
   [0.0347   0.0392   0.0209   ... 0.035425 0.015675 0.17295 ]
   [0.031875 0.0362   0.02055  ... 0.029625 0.013925 0.14845 ]
   [0.028125 0.03385  0.020825 ... 0.026825 0.01315  0.13235 ]]

  [[0.0486   0.0514   0.028275 ... 0.046925 0.022425 0.22335 ]
   [0.05655  0.053425 0.028925 ... 0.047275 0.022825 0.219525]
   [0.0573   0.055525 0.0294   ... 0.0482   0.022275 0.2325  ]
   ...
   [0.024925 0.0378   0.019675 ... 0.032725 0.01405  0.18365 ]
   [0.031925 0.033875 0.0206   ... 0.03015  0.014075 0.169075]
   [0.0316   0.032025 0.019625 ... 0.0268   0.012925 0.136325]]

  ...

  [[0.067175 0.0628   0.039875 ... 0.052775 0.0307   0.2282  ]
   [0.080275 0.071475 0.050425 ... 0.0566   0.0342   0.217525]
   [0.07215  0.068375 0.045875 ... 0.056375 0.034375 0.2167  ]
   ...
   [0.03785  0.041425 0.023875 ... 0.043775 0.019575 0.213625]
   [0.03475  0.0394   0.02255  ... 0.04455  0.02     0.217375]
   [0.032625 0.039025 0.02305  ... 0.043425 0.01985  0.229575]]

  [[0.07875  0.068475 0.0437   ... 0.056175 0.0339   0.22795 ]
   [0.08205  0.073825 0.0498   ... 0.057775 0.035225 0.2253  ]
   [0.08115  0.07405  0.0505   ... 0.059475 0.03475  0.2217  ]
   ...
   [0.03895  0.043275 0.026075 ... 0.044775 0.021    0.2286  ]
   [0.03795  0.038525 0.02265  ... 0.04295  0.018625 0.22255 ]
   [0.03365  0.038425 0.02355  ... 0.042    0.0189   0.225125]]

  [[0.089    0.076325 0.0531   ... 0.05915  0.0333   0.228925]
   [0.084925 0.075775 0.050825 ... 0.05925  0.0363   0.236375]
   [0.08475  0.077325 0.050925 ... 0.0591   0.03615  0.225875]
   ...
   [0.040075 0.0416   0.025975 ... 0.044    0.020425 0.234125]
   [0.038075 0.036475 0.022375 ... 0.042175 0.01925  0.21895 ]
   [0.0349   0.036575 0.0241   ... 0.041525 0.0202   0.223625]]]


 [[[0.039875 0.055875 0.031825 ... 0.046725 0.0206   0.2473  ]
   [0.041225 0.053475 0.031675 ... 0.04425  0.01995  0.2442  ]
   [0.038    0.0509   0.030125 ... 0.04345  0.018975 0.252075]
   ...
   [0.079575 0.068025 0.048175 ... 0.0623   0.0347   0.275575]
   [0.093775 0.08395  0.063975 ... 0.12865  0.096575 0.214425]
   [0.102475 0.09315  0.07065  ... 0.124725 0.11835  0.17915 ]]

  [[0.039875 0.055025 0.034025 ... 0.0453   0.020225 0.25715 ]
   [0.039625 0.053725 0.032925 ... 0.0437   0.01945  0.250625]
   [0.03925  0.051775 0.031525 ... 0.0442   0.018825 0.2608  ]
   ...
   [0.080175 0.073025 0.052975 ... 0.06945  0.0391   0.219825]
   [0.09105  0.0811   0.05875  ... 0.09675  0.067    0.133375]
   [0.08775  0.0791   0.053775 ... 0.097075 0.066325 0.1061  ]]

  [[0.04015  0.05545  0.0358   ... 0.046    0.020325 0.2604  ]
   [0.0386   0.053425 0.035075 ... 0.04415  0.0186   0.259075]
   [0.038875 0.0541   0.035    ... 0.04585  0.0204   0.2731  ]
   ...
   [0.09545  0.086025 0.06205  ... 0.08275  0.050225 0.117975]
   [0.07805  0.07245  0.05015  ... 0.08905  0.06075  0.088825]
   [0.075975 0.07035  0.04505  ... 0.09075  0.064575 0.082325]]

  ...

  [[0.041475 0.041475 0.021175 ... 0.03885  0.015775 0.209025]
   [0.039625 0.040275 0.021525 ... 0.0381   0.01435  0.199925]
   [0.034975 0.040175 0.020375 ... 0.0356   0.014575 0.1891  ]
   ...
   [0.0552   0.048575 0.034275 ... 0.037725 0.020475 0.150825]
   [0.046975 0.04565  0.03075  ... 0.0352   0.01815  0.137475]
   [0.049075 0.04705  0.031375 ... 0.03935  0.02075  0.1534  ]]

  [[0.0475   0.04265  0.024375 ... 0.039125 0.0159   0.2042  ]
   [0.048075 0.042075 0.0262   ... 0.039575 0.015975 0.1975  ]
   [0.0455   0.041725 0.02305  ... 0.0391   0.0166   0.203425]
   ...
   [0.054875 0.04825  0.0329   ... 0.036975 0.020325 0.14335 ]
   [0.04635  0.0461   0.0307   ... 0.0349   0.018575 0.1444  ]
   [0.0477   0.045825 0.030225 ... 0.038175 0.0193   0.14945 ]]

  [[0.047625 0.042275 0.025025 ... 0.039375 0.016775 0.2007  ]
   [0.04795  0.043    0.02435  ... 0.039425 0.01655  0.198825]
   [0.057725 0.04625  0.03155  ... 0.0416   0.0185   0.20395 ]
   ...
   [0.0496   0.04615  0.03035  ... 0.036125 0.01925  0.138325]
   [0.0501   0.047175 0.030225 ... 0.0391   0.0216   0.158675]
   [0.04975  0.048025 0.030475 ... 0.038725 0.021075 0.1527  ]]]


 [[[0.09655  0.074775 0.050975 ... 0.0516   0.023025 0.261275]
   [0.092725 0.072675 0.0496   ... 0.058225 0.0292   0.208175]
   [0.080925 0.064725 0.04845  ... 0.08235  0.050425 0.170475]
   ...
   [0.047575 0.051725 0.026375 ... 0.044925 0.017175 0.256825]
   [0.055575 0.052925 0.030125 ... 0.048075 0.018    0.27485 ]
   [0.055525 0.0531   0.0318   ... 0.04635  0.01725  0.256675]]

  [[0.095525 0.07545  0.05235  ... 0.053225 0.022625 0.271925]
   [0.0957   0.075225 0.05265  ... 0.057725 0.02675  0.219325]
   [0.0937   0.071825 0.05245  ... 0.0824   0.05045  0.18085 ]
   ...
   [0.042775 0.048825 0.02565  ... 0.043875 0.016375 0.257325]
   [0.050625 0.051    0.028075 ... 0.04785  0.017925 0.282775]
   [0.0558   0.052    0.029675 ... 0.046875 0.017275 0.268275]]

  [[0.09525  0.076025 0.0528   ... 0.0533   0.021625 0.2891  ]
   [0.09735  0.0765   0.053    ... 0.055425 0.024675 0.244825]
   [0.09475  0.075125 0.05085  ... 0.071575 0.040575 0.1881  ]
   ...
   [0.038275 0.0477   0.0243   ... 0.043325 0.016    0.2494  ]
   [0.04245  0.050225 0.0255   ... 0.046025 0.01685  0.259525]
   [0.0483   0.052175 0.02775  ... 0.04545  0.017225 0.249375]]

  ...

  [[0.033875 0.045775 0.029025 ... 0.0404   0.018975 0.2029  ]
   [0.0357   0.04645  0.028025 ... 0.041925 0.0196   0.20415 ]
   [0.036975 0.046825 0.02825  ... 0.04005  0.018575 0.19235 ]
   ...
   [0.116775 0.0982   0.080175 ... 0.08415  0.06735  0.2857  ]
   [0.104525 0.09055  0.071025 ... 0.0795   0.0627   0.310825]
   [0.0975   0.082025 0.059075 ... 0.06885  0.045825 0.324375]]

  [[0.035775 0.042825 0.02835  ... 0.039125 0.0173   0.20685 ]
   [0.03505  0.0427   0.028275 ... 0.0397   0.017525 0.2041  ]
   [0.03665  0.0459   0.027125 ... 0.041575 0.0189   0.20055 ]
   ...
   [0.10555  0.088325 0.06645  ... 0.081425 0.059475 0.288725]
   [0.10945  0.091575 0.072325 ... 0.084475 0.057925 0.306175]
   [0.096675 0.0814   0.060425 ... 0.069775 0.04325  0.323975]]

  [[0.0381   0.0465   0.027175 ... 0.0385   0.0179   0.199175]
   [0.036325 0.04335  0.027625 ... 0.037975 0.016925 0.1999  ]
   [0.036475 0.047725 0.029125 ... 0.043325 0.019775 0.21835 ]
   ...
   [0.1108   0.1004   0.0796   ... 0.0981   0.084725 0.291575]
   [0.0959   0.0824   0.06165  ... 0.07685  0.0528   0.318575]
   [0.093025 0.07815  0.0585   ... 0.06915  0.0448   0.32745 ]]]], shape=(32, 256, 256, 8), dtype=float32)
outputs: float32 (32, 256, 256, 5)
tf.Tensor(
[[[[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]]

  ...

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [0. 1. 0. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]]


 [[[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]]

  ...

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]]]


 [[[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  ...

  [[0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]]]


 ...


 [[[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  ...

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]]


 [[[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [0. 0. 0. 1. 0.]
   [0. 0. 0. 1. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [0. 0. 0. 0. 1.]
   [0. 0. 0. 0. 1.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 0. 0. 0. 1.]
   [0. 0. 0. 0. 1.]
   [0. 0. 0. 0. 1.]]

  ...

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]]


 [[[0. 0. 1. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 0. 0. 0. 1.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 0. 0. 0. 1.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  ...

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 0. 0. 1. 0.]
   [0. 1. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 0. 0. 1. 0.]
   [0. 1. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 0. 0. 1. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]]], shape=(32, 256, 256, 5), dtype=float32)
Testing
inputs: float32 (1, 256, 256, 8)
tf.Tensor(
[[[[0.0853   0.0767   0.052625 ... 0.084725 0.048225 0.266675]
   [0.08645  0.076725 0.05415  ... 0.0815   0.049725 0.256475]
   [0.0881   0.07945  0.05675  ... 0.0833   0.049725 0.267   ]
   ...
   [0.041725 0.046875 0.027925 ... 0.04645  0.019175 0.2598  ]
   [0.03835  0.044725 0.024125 ... 0.04525  0.018175 0.2606  ]
   [0.0354   0.03985  0.021875 ... 0.044    0.017925 0.260925]]

  [[0.08945  0.072675 0.047475 ... 0.084925 0.045675 0.253325]
   [0.096    0.07225  0.048375 ... 0.088875 0.049475 0.25065 ]
   [0.10235  0.0735   0.0509   ... 0.088175 0.050675 0.269075]
   ...
   [0.042225 0.0459   0.026575 ... 0.04655  0.01875  0.265025]
   [0.040375 0.044525 0.02595  ... 0.04585  0.0186   0.26045 ]
   [0.03615  0.041075 0.022125 ... 0.044825 0.017775 0.263675]]

  [[0.087625 0.0762   0.0522   ... 0.084775 0.0459   0.243175]
   [0.09235  0.07215  0.048425 ... 0.0871   0.04725  0.243725]
   [0.104925 0.074375 0.05205  ... 0.0889   0.048275 0.25105 ]
   ...
   [0.04065  0.041975 0.023275 ... 0.043425 0.018075 0.25435 ]
   [0.0382   0.04225  0.02305  ... 0.0432   0.017725 0.254725]
   [0.037025 0.042925 0.022875 ... 0.046575 0.018425 0.259875]]

  ...

  [[0.074575 0.06     0.03945  ... 0.05635  0.03315  0.198025]
   [0.082    0.06205  0.040675 ... 0.058675 0.033075 0.198625]
   [0.080225 0.06355  0.0416   ... 0.059775 0.03395  0.206025]
   ...
   [0.09965  0.082725 0.06805  ... 0.067325 0.05815  0.27725 ]
   [0.0889   0.0679   0.0468   ... 0.0563   0.034875 0.29495 ]
   [0.07205  0.059575 0.04125  ... 0.05235  0.03185  0.3116  ]]

  [[0.0768   0.06205  0.039975 ... 0.058175 0.0334   0.197525]
   [0.0797   0.0638   0.041675 ... 0.060425 0.035925 0.1993  ]
   [0.08345  0.063725 0.04135  ... 0.0606   0.03585  0.2044  ]
   ...
   [0.110425 0.089975 0.071475 ... 0.083225 0.07175  0.261625]
   [0.0995   0.076725 0.053175 ... 0.060975 0.043725 0.29315 ]
   [0.07945  0.06385  0.0462   ... 0.059675 0.038375 0.32095 ]]

  [[0.074075 0.0615   0.0395   ... 0.0591   0.03185  0.200825]
   [0.0771   0.06265  0.040775 ... 0.059825 0.033975 0.204725]
   [0.0835   0.063125 0.0417   ... 0.059825 0.034325 0.2     ]
   ...
   [0.118575 0.0944   0.070325 ... 0.09795  0.078    0.272   ]
   [0.11975  0.0899   0.063575 ... 0.077975 0.05495  0.306325]
   [0.0861   0.068475 0.049775 ... 0.063225 0.039625 0.3163  ]]]], shape=(1, 256, 256, 8), dtype=float32)
outputs: float32 (1, 256, 256, 5)
tf.Tensor(
[[[[1. 0. 0. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 1. 0. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 0. 1. 0.]
   [0. 0. 0. 1. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 1. 0. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  ...

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [0. 0. 1. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [0. 0. 0. 1. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   ...
   [0. 0. 0. 1. 0.]
   [0. 0. 1. 0. 0.]
   [1. 0. 0. 0. 0.]]]], shape=(1, 256, 256, 5), dtype=float32)
Validation
inputs: float32 (1, 256, 256, 8)
tf.Tensor(
[[[[0.053275 0.043025 0.0284   ... 0.042575 0.01925  0.2313  ]
   [0.0535   0.04265  0.0293   ... 0.043975 0.0191   0.246425]
   [0.049125 0.042675 0.027125 ... 0.042275 0.019325 0.228225]
   ...
   [0.0724   0.064525 0.044325 ... 0.0504   0.0264   0.202325]
   [0.07395  0.0651   0.04495  ... 0.05235  0.02625  0.211175]
   [0.075975 0.0647   0.04615  ... 0.0523   0.027625 0.2079  ]]

  [[0.053025 0.042325 0.02895  ... 0.041625 0.018475 0.239625]
   [0.051225 0.0413   0.029    ... 0.042    0.018375 0.238775]
   [0.04785  0.04345  0.02785  ... 0.042625 0.019825 0.21835 ]
   ...
   [0.067    0.059125 0.042375 ... 0.049375 0.023475 0.18365 ]
   [0.0679   0.06215  0.042125 ... 0.050825 0.0246   0.197125]
   [0.066575 0.062775 0.041925 ... 0.049875 0.0247   0.199775]]

  [[0.04975  0.03945  0.0265   ... 0.040925 0.01785  0.243675]
   [0.050625 0.040725 0.027925 ... 0.040825 0.018625 0.236075]
   [0.0546   0.04545  0.029725 ... 0.043575 0.021075 0.20885 ]
   ...
   [0.069075 0.0611   0.0435   ... 0.050075 0.02435  0.186325]
   [0.07345  0.063225 0.0452   ... 0.052325 0.02595  0.19745 ]
   [0.068175 0.06035  0.04155  ... 0.04985  0.023925 0.1912  ]]

  ...

  [[0.064425 0.062275 0.037175 ... 0.0576   0.027975 0.265325]
   [0.058075 0.059925 0.03495  ... 0.05475  0.02585  0.26375 ]
   [0.040675 0.053675 0.028975 ... 0.0482   0.02065  0.250575]
   ...
   [0.0937   0.09025  0.072    ... 0.0486   0.02375  0.2789  ]
   [0.094125 0.091525 0.072925 ... 0.04795  0.02335  0.273275]
   [0.09135  0.08855  0.067875 ... 0.04985  0.023425 0.282475]]

  [[0.063175 0.05715  0.03525  ... 0.054475 0.0265   0.2553  ]
   [0.0581   0.0556   0.032875 ... 0.0511   0.0242   0.246625]
   [0.0396   0.0509   0.027975 ... 0.0464   0.020075 0.23445 ]
   ...
   [0.09535  0.0905   0.076275 ... 0.048725 0.0235   0.289175]
   [0.093725 0.09015  0.0717   ... 0.048325 0.02345  0.279575]
   [0.09145  0.088125 0.068475 ... 0.0493   0.023075 0.290275]]

  [[0.04605  0.05285  0.0288   ... 0.048925 0.021625 0.2413  ]
   [0.03955  0.051625 0.028325 ... 0.046975 0.020875 0.2319  ]
   [0.0431   0.052225 0.03135  ... 0.042275 0.02     0.221325]
   ...
   [0.099075 0.085075 0.06545  ... 0.051925 0.02575  0.298475]
   [0.100175 0.08775  0.0678   ... 0.05005  0.024175 0.28905 ]
   [0.09685  0.0912   0.07425  ... 0.049975 0.023375 0.290425]]]], shape=(1, 256, 256, 8), dtype=float32)
outputs: float32 (1, 256, 256, 5)
tf.Tensor(
[[[[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]
   [1. 0. 0. 0. 0.]]

  ...

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]]

  [[0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   [0. 0. 1. 0. 0.]
   ...
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]
   [0. 1. 0. 0. 0.]]]], shape=(1, 256, 256, 5), dtype=float32)
****************************************************************************
************************ building and compiling model... *******************
DERIVE_FEATURES: False
Model: "unet"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 input_1 (InputLayer)        [(None, None, None, 8)]      0         []                            
                                                                                                  
 conv2d (Conv2D)             (None, None, None, 32)       2336      ['input_1[0][0]']             
                                                                                                  
 batch_normalization (Batch  (None, None, None, 32)       128       ['conv2d[0][0]']              
 Normalization)                                                                                   
                                                                                                  
 activation (Activation)     (None, None, None, 32)       0         ['batch_normalization[0][0]'] 
                                                                                                  
 activation_1 (Activation)   (None, None, None, 32)       0         ['activation[0][0]']          
                                                                                                  
 separable_conv2d (Separabl  (None, None, None, 64)       2400      ['activation_1[0][0]']        
 eConv2D)                                                                                         
                                                                                                  
 batch_normalization_1 (Bat  (None, None, None, 64)       256       ['separable_conv2d[0][0]']    
 chNormalization)                                                                                 
                                                                                                  
 activation_2 (Activation)   (None, None, None, 64)       0         ['batch_normalization_1[0][0]'
                                                                    ]                             
                                                                                                  
 separable_conv2d_1 (Separa  (None, None, None, 64)       4736      ['activation_2[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_2 (Bat  (None, None, None, 64)       256       ['separable_conv2d_1[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 max_pooling2d (MaxPooling2  (None, None, None, 64)       0         ['batch_normalization_2[0][0]'
 D)                                                                 ]                             
                                                                                                  
 conv2d_1 (Conv2D)           (None, None, None, 64)       2112      ['activation[0][0]']          
                                                                                                  
 add (Add)                   (None, None, None, 64)       0         ['max_pooling2d[0][0]',       
                                                                     'conv2d_1[0][0]']            
                                                                                                  
 activation_3 (Activation)   (None, None, None, 64)       0         ['add[0][0]']                 
                                                                                                  
 separable_conv2d_2 (Separa  (None, None, None, 128)      8896      ['activation_3[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_3 (Bat  (None, None, None, 128)      512       ['separable_conv2d_2[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 activation_4 (Activation)   (None, None, None, 128)      0         ['batch_normalization_3[0][0]'
                                                                    ]                             
                                                                                                  
 separable_conv2d_3 (Separa  (None, None, None, 128)      17664     ['activation_4[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_4 (Bat  (None, None, None, 128)      512       ['separable_conv2d_3[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 max_pooling2d_1 (MaxPoolin  (None, None, None, 128)      0         ['batch_normalization_4[0][0]'
 g2D)                                                               ]                             
                                                                                                  
 conv2d_2 (Conv2D)           (None, None, None, 128)      8320      ['add[0][0]']                 
                                                                                                  
 add_1 (Add)                 (None, None, None, 128)      0         ['max_pooling2d_1[0][0]',     
                                                                     'conv2d_2[0][0]']            
                                                                                                  
 activation_5 (Activation)   (None, None, None, 128)      0         ['add_1[0][0]']               
                                                                                                  
 separable_conv2d_4 (Separa  (None, None, None, 256)      34176     ['activation_5[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_5 (Bat  (None, None, None, 256)      1024      ['separable_conv2d_4[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 activation_6 (Activation)   (None, None, None, 256)      0         ['batch_normalization_5[0][0]'
                                                                    ]                             
                                                                                                  
 separable_conv2d_5 (Separa  (None, None, None, 256)      68096     ['activation_6[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_6 (Bat  (None, None, None, 256)      1024      ['separable_conv2d_5[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 max_pooling2d_2 (MaxPoolin  (None, None, None, 256)      0         ['batch_normalization_6[0][0]'
 g2D)                                                               ]                             
                                                                                                  
 conv2d_3 (Conv2D)           (None, None, None, 256)      33024     ['add_1[0][0]']               
                                                                                                  
 add_2 (Add)                 (None, None, None, 256)      0         ['max_pooling2d_2[0][0]',     
                                                                     'conv2d_3[0][0]']            
                                                                                                  
 activation_7 (Activation)   (None, None, None, 256)      0         ['add_2[0][0]']               
                                                                                                  
 conv2d_transpose (Conv2DTr  (None, None, None, 256)      590080    ['activation_7[0][0]']        
 anspose)                                                                                         
                                                                                                  
 batch_normalization_7 (Bat  (None, None, None, 256)      1024      ['conv2d_transpose[0][0]']    
 chNormalization)                                                                                 
                                                                                                  
 activation_8 (Activation)   (None, None, None, 256)      0         ['batch_normalization_7[0][0]'
                                                                    ]                             
                                                                                                  
 conv2d_transpose_1 (Conv2D  (None, None, None, 256)      590080    ['activation_8[0][0]']        
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_8 (Bat  (None, None, None, 256)      1024      ['conv2d_transpose_1[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 up_sampling2d_1 (UpSamplin  (None, None, None, 256)      0         ['add_2[0][0]']               
 g2D)                                                                                             
                                                                                                  
 up_sampling2d (UpSampling2  (None, None, None, 256)      0         ['batch_normalization_8[0][0]'
 D)                                                                 ]                             
                                                                                                  
 conv2d_4 (Conv2D)           (None, None, None, 256)      65792     ['up_sampling2d_1[0][0]']     
                                                                                                  
 add_3 (Add)                 (None, None, None, 256)      0         ['up_sampling2d[0][0]',       
                                                                     'conv2d_4[0][0]']            
                                                                                                  
 activation_9 (Activation)   (None, None, None, 256)      0         ['add_3[0][0]']               
                                                                                                  
 conv2d_transpose_2 (Conv2D  (None, None, None, 128)      295040    ['activation_9[0][0]']        
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_9 (Bat  (None, None, None, 128)      512       ['conv2d_transpose_2[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 activation_10 (Activation)  (None, None, None, 128)      0         ['batch_normalization_9[0][0]'
                                                                    ]                             
                                                                                                  
 conv2d_transpose_3 (Conv2D  (None, None, None, 128)      147584    ['activation_10[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_10 (Ba  (None, None, None, 128)      512       ['conv2d_transpose_3[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 up_sampling2d_3 (UpSamplin  (None, None, None, 256)      0         ['add_3[0][0]']               
 g2D)                                                                                             
                                                                                                  
 up_sampling2d_2 (UpSamplin  (None, None, None, 128)      0         ['batch_normalization_10[0][0]
 g2D)                                                               ']                            
                                                                                                  
 conv2d_5 (Conv2D)           (None, None, None, 128)      32896     ['up_sampling2d_3[0][0]']     
                                                                                                  
 add_4 (Add)                 (None, None, None, 128)      0         ['up_sampling2d_2[0][0]',     
                                                                     'conv2d_5[0][0]']            
                                                                                                  
 activation_11 (Activation)  (None, None, None, 128)      0         ['add_4[0][0]']               
                                                                                                  
 conv2d_transpose_4 (Conv2D  (None, None, None, 64)       73792     ['activation_11[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_11 (Ba  (None, None, None, 64)       256       ['conv2d_transpose_4[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 activation_12 (Activation)  (None, None, None, 64)       0         ['batch_normalization_11[0][0]
                                                                    ']                            
                                                                                                  
 conv2d_transpose_5 (Conv2D  (None, None, None, 64)       36928     ['activation_12[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_12 (Ba  (None, None, None, 64)       256       ['conv2d_transpose_5[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 up_sampling2d_5 (UpSamplin  (None, None, None, 128)      0         ['add_4[0][0]']               
 g2D)                                                                                             
                                                                                                  
 up_sampling2d_4 (UpSamplin  (None, None, None, 64)       0         ['batch_normalization_12[0][0]
 g2D)                                                               ']                            
                                                                                                  
 conv2d_6 (Conv2D)           (None, None, None, 64)       8256      ['up_sampling2d_5[0][0]']     
                                                                                                  
 add_5 (Add)                 (None, None, None, 64)       0         ['up_sampling2d_4[0][0]',     
                                                                     'conv2d_6[0][0]']            
                                                                                                  
 activation_13 (Activation)  (None, None, None, 64)       0         ['add_5[0][0]']               
                                                                                                  
 conv2d_transpose_6 (Conv2D  (None, None, None, 32)       18464     ['activation_13[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_13 (Ba  (None, None, None, 32)       128       ['conv2d_transpose_6[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 activation_14 (Activation)  (None, None, None, 32)       0         ['batch_normalization_13[0][0]
                                                                    ']                            
                                                                                                  
 conv2d_transpose_7 (Conv2D  (None, None, None, 32)       9248      ['activation_14[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_14 (Ba  (None, None, None, 32)       128       ['conv2d_transpose_7[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 up_sampling2d_7 (UpSamplin  (None, None, None, 64)       0         ['add_5[0][0]']               
 g2D)                                                                                             
                                                                                                  
 up_sampling2d_6 (UpSamplin  (None, None, None, 32)       0         ['batch_normalization_14[0][0]
 g2D)                                                               ']                            
                                                                                                  
 conv2d_7 (Conv2D)           (None, None, None, 32)       2080      ['up_sampling2d_7[0][0]']     
                                                                                                  
 add_6 (Add)                 (None, None, None, 32)       0         ['up_sampling2d_6[0][0]',     
                                                                     'conv2d_7[0][0]']            
                                                                                                  
 final_conv (Conv2D)         (None, None, None, 5)        1445      ['add_6[0][0]']               
                                                                                                  
==================================================================================================
Total params: 2060997 (7.86 MB)
Trainable params: 2057221 (7.85 MB)
Non-trainable params: 3776 (14.75 KB)
__________________________________________________________________________________________________
None
****************************************************************************
************************ preparing output directory... *********************
> Saving models and results at /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1...
****************************************************************************
****************************** training model... ***************************
Epoch 1/30
266/266 [==============================] - ETA: 0s - loss: 0.9676 - precision: 0.7271 - recall: 0.5957 - categorical_accuracy: 0.6706 - one_hot_io_u: 0.4242
Epoch 1: val_loss improved from inf to 2.63403, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 299s 971ms/step - loss: 0.9676 - precision: 0.7271 - recall: 0.5957 - categorical_accuracy: 0.6706 - one_hot_io_u: 0.4242 - val_loss: 2.6340 - val_precision: 0.3634 - val_recall: 0.3629 - val_categorical_accuracy: 0.3636 - val_one_hot_io_u: 0.0727
Epoch 2/30
266/266 [==============================] - ETA: 0s - loss: 0.6959 - precision: 0.8066 - recall: 0.6767 - categorical_accuracy: 0.7436 - one_hot_io_u: 0.5043
Epoch 2: val_loss improved from 2.63403 to 1.23879, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 263s 958ms/step - loss: 0.6959 - precision: 0.8066 - recall: 0.6767 - categorical_accuracy: 0.7436 - one_hot_io_u: 0.5043 - val_loss: 1.2388 - val_precision: 0.5670 - val_recall: 0.4644 - val_categorical_accuracy: 0.5205 - val_one_hot_io_u: 0.2377
Epoch 3/30
266/266 [==============================] - ETA: 0s - loss: 0.6352 - precision: 0.8285 - recall: 0.7046 - categorical_accuracy: 0.7664 - one_hot_io_u: 0.5343
Epoch 3: val_loss improved from 1.23879 to 0.62282, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 256s 966ms/step - loss: 0.6352 - precision: 0.8285 - recall: 0.7046 - categorical_accuracy: 0.7664 - one_hot_io_u: 0.5343 - val_loss: 0.6228 - val_precision: 0.8355 - val_recall: 0.7063 - val_categorical_accuracy: 0.7714 - val_one_hot_io_u: 0.5354
Epoch 4/30
266/266 [==============================] - ETA: 0s - loss: 0.5988 - precision: 0.8402 - recall: 0.7216 - categorical_accuracy: 0.7797 - one_hot_io_u: 0.5531
Epoch 4: val_loss improved from 0.62282 to 0.60250, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 252s 949ms/step - loss: 0.5988 - precision: 0.8402 - recall: 0.7216 - categorical_accuracy: 0.7797 - one_hot_io_u: 0.5531 - val_loss: 0.6025 - val_precision: 0.8380 - val_recall: 0.7265 - val_categorical_accuracy: 0.7809 - val_one_hot_io_u: 0.5544
Epoch 5/30
266/266 [==============================] - ETA: 0s - loss: 0.5687 - precision: 0.8498 - recall: 0.7352 - categorical_accuracy: 0.7904 - one_hot_io_u: 0.5689
Epoch 5: val_loss improved from 0.60250 to 0.55160, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 285s 1s/step - loss: 0.5687 - precision: 0.8498 - recall: 0.7352 - categorical_accuracy: 0.7904 - one_hot_io_u: 0.5689 - val_loss: 0.5516 - val_precision: 0.8510 - val_recall: 0.7493 - val_categorical_accuracy: 0.7973 - val_one_hot_io_u: 0.5788
Epoch 6/30
266/266 [==============================] - ETA: 0s - loss: 0.5453 - precision: 0.8571 - recall: 0.7454 - categorical_accuracy: 0.7985 - one_hot_io_u: 0.5813
Epoch 6: val_loss improved from 0.55160 to 0.52872, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 284s 1s/step - loss: 0.5453 - precision: 0.8571 - recall: 0.7454 - categorical_accuracy: 0.7985 - one_hot_io_u: 0.5813 - val_loss: 0.5287 - val_precision: 0.8590 - val_recall: 0.7552 - val_categorical_accuracy: 0.8039 - val_one_hot_io_u: 0.5882
Epoch 7/30
266/266 [==============================] - ETA: 0s - loss: 0.5278 - precision: 0.8624 - recall: 0.7530 - categorical_accuracy: 0.8045 - one_hot_io_u: 0.5905
Epoch 7: val_loss improved from 0.52872 to 0.50506, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 262s 988ms/step - loss: 0.5278 - precision: 0.8624 - recall: 0.7530 - categorical_accuracy: 0.8045 - one_hot_io_u: 0.5905 - val_loss: 0.5051 - val_precision: 0.8651 - val_recall: 0.7671 - val_categorical_accuracy: 0.8122 - val_one_hot_io_u: 0.5986
Epoch 8/30
266/266 [==============================] - ETA: 0s - loss: 0.5123 - precision: 0.8671 - recall: 0.7595 - categorical_accuracy: 0.8098 - one_hot_io_u: 0.5988
Epoch 8: val_loss improved from 0.50506 to 0.49242, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 264s 995ms/step - loss: 0.5123 - precision: 0.8671 - recall: 0.7595 - categorical_accuracy: 0.8098 - one_hot_io_u: 0.5988 - val_loss: 0.4924 - val_precision: 0.8745 - val_recall: 0.7668 - val_categorical_accuracy: 0.8169 - val_one_hot_io_u: 0.6102
Epoch 9/30
266/266 [==============================] - ETA: 0s - loss: 0.5018 - precision: 0.8700 - recall: 0.7641 - categorical_accuracy: 0.8133 - one_hot_io_u: 0.6049
Epoch 9: val_loss did not improve from 0.49242
266/266 [==============================] - 276s 1s/step - loss: 0.5018 - precision: 0.8700 - recall: 0.7641 - categorical_accuracy: 0.8133 - one_hot_io_u: 0.6049 - val_loss: 0.5025 - val_precision: 0.8643 - val_recall: 0.7700 - val_categorical_accuracy: 0.8133 - val_one_hot_io_u: 0.6085
Epoch 10/30
266/266 [==============================] - ETA: 0s - loss: 0.4895 - precision: 0.8736 - recall: 0.7693 - categorical_accuracy: 0.8175 - one_hot_io_u: 0.6118
Epoch 10: val_loss improved from 0.49242 to 0.47591, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 268s 1s/step - loss: 0.4895 - precision: 0.8736 - recall: 0.7693 - categorical_accuracy: 0.8175 - one_hot_io_u: 0.6118 - val_loss: 0.4759 - val_precision: 0.8705 - val_recall: 0.7823 - val_categorical_accuracy: 0.8220 - val_one_hot_io_u: 0.6160
Epoch 11/30
266/266 [==============================] - ETA: 0s - loss: 0.4791 - precision: 0.8766 - recall: 0.7737 - categorical_accuracy: 0.8210 - one_hot_io_u: 0.6176
Epoch 11: val_loss improved from 0.47591 to 0.46856, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 263s 992ms/step - loss: 0.4791 - precision: 0.8766 - recall: 0.7737 - categorical_accuracy: 0.8210 - one_hot_io_u: 0.6176 - val_loss: 0.4686 - val_precision: 0.8763 - val_recall: 0.7808 - val_categorical_accuracy: 0.8242 - val_one_hot_io_u: 0.6214
Epoch 12/30
266/266 [==============================] - ETA: 0s - loss: 0.4726 - precision: 0.8783 - recall: 0.7765 - categorical_accuracy: 0.8231 - one_hot_io_u: 0.6215
Epoch 12: val_loss did not improve from 0.46856
266/266 [==============================] - 255s 960ms/step - loss: 0.4726 - precision: 0.8783 - recall: 0.7765 - categorical_accuracy: 0.8231 - one_hot_io_u: 0.6215 - val_loss: 0.4780 - val_precision: 0.8752 - val_recall: 0.7766 - val_categorical_accuracy: 0.8217 - val_one_hot_io_u: 0.6226
Epoch 13/30
266/266 [==============================] - ETA: 0s - loss: 0.4617 - precision: 0.8814 - recall: 0.7812 - categorical_accuracy: 0.8269 - one_hot_io_u: 0.6277
Epoch 13: val_loss improved from 0.46856 to 0.45125, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 266s 1s/step - loss: 0.4617 - precision: 0.8814 - recall: 0.7812 - categorical_accuracy: 0.8269 - one_hot_io_u: 0.6277 - val_loss: 0.4513 - val_precision: 0.8810 - val_recall: 0.7881 - val_categorical_accuracy: 0.8299 - val_one_hot_io_u: 0.6327
Epoch 14/30
266/266 [==============================] - ETA: 0s - loss: 0.4553 - precision: 0.8830 - recall: 0.7839 - categorical_accuracy: 0.8290 - one_hot_io_u: 0.6312
Epoch 14: val_loss improved from 0.45125 to 0.44229, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 263s 992ms/step - loss: 0.4553 - precision: 0.8830 - recall: 0.7839 - categorical_accuracy: 0.8290 - one_hot_io_u: 0.6312 - val_loss: 0.4423 - val_precision: 0.8813 - val_recall: 0.7953 - val_categorical_accuracy: 0.8335 - val_one_hot_io_u: 0.6370
Epoch 15/30
266/266 [==============================] - ETA: 0s - loss: 0.4488 - precision: 0.8847 - recall: 0.7867 - categorical_accuracy: 0.8312 - one_hot_io_u: 0.6348
Epoch 15: val_loss did not improve from 0.44229
266/266 [==============================] - 258s 973ms/step - loss: 0.4488 - precision: 0.8847 - recall: 0.7867 - categorical_accuracy: 0.8312 - one_hot_io_u: 0.6348 - val_loss: 0.4579 - val_precision: 0.8785 - val_recall: 0.7875 - val_categorical_accuracy: 0.8283 - val_one_hot_io_u: 0.6359
Epoch 16/30
266/266 [==============================] - ETA: 0s - loss: 0.4440 - precision: 0.8859 - recall: 0.7889 - categorical_accuracy: 0.8328 - one_hot_io_u: 0.6378
Epoch 16: val_loss did not improve from 0.44229
266/266 [==============================] - 265s 998ms/step - loss: 0.4440 - precision: 0.8859 - recall: 0.7889 - categorical_accuracy: 0.8328 - one_hot_io_u: 0.6378 - val_loss: 0.4840 - val_precision: 0.8707 - val_recall: 0.7798 - val_categorical_accuracy: 0.8210 - val_one_hot_io_u: 0.6273
Epoch 17/30
266/266 [==============================] - ETA: 0s - loss: 0.4378 - precision: 0.8875 - recall: 0.7916 - categorical_accuracy: 0.8349 - one_hot_io_u: 0.6414
Epoch 17: val_loss did not improve from 0.44229
266/266 [==============================] - 285s 1s/step - loss: 0.4378 - precision: 0.8875 - recall: 0.7916 - categorical_accuracy: 0.8349 - one_hot_io_u: 0.6414 - val_loss: 0.4461 - val_precision: 0.8819 - val_recall: 0.7904 - val_categorical_accuracy: 0.8314 - val_one_hot_io_u: 0.6374
Epoch 18/30
266/266 [==============================] - ETA: 0s - loss: 0.4336 - precision: 0.8884 - recall: 0.7935 - categorical_accuracy: 0.8363 - one_hot_io_u: 0.6438
Epoch 18: val_loss improved from 0.44229 to 0.42199, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 278s 1s/step - loss: 0.4336 - precision: 0.8884 - recall: 0.7935 - categorical_accuracy: 0.8363 - one_hot_io_u: 0.6438 - val_loss: 0.4220 - val_precision: 0.8887 - val_recall: 0.8013 - val_categorical_accuracy: 0.8401 - val_one_hot_io_u: 0.6492
Epoch 19/30
266/266 [==============================] - ETA: 0s - loss: 0.4294 - precision: 0.8894 - recall: 0.7953 - categorical_accuracy: 0.8376 - one_hot_io_u: 0.6465
Epoch 19: val_loss improved from 0.42199 to 0.41151, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 282s 1s/step - loss: 0.4294 - precision: 0.8894 - recall: 0.7953 - categorical_accuracy: 0.8376 - one_hot_io_u: 0.6465 - val_loss: 0.4115 - val_precision: 0.8920 - val_recall: 0.8044 - val_categorical_accuracy: 0.8432 - val_one_hot_io_u: 0.6546
Epoch 20/30
266/266 [==============================] - ETA: 0s - loss: 0.4241 - precision: 0.8907 - recall: 0.7977 - categorical_accuracy: 0.8394 - one_hot_io_u: 0.6488
Epoch 20: val_loss did not improve from 0.41151
266/266 [==============================] - 258s 970ms/step - loss: 0.4241 - precision: 0.8907 - recall: 0.7977 - categorical_accuracy: 0.8394 - one_hot_io_u: 0.6488 - val_loss: 0.4151 - val_precision: 0.8930 - val_recall: 0.8001 - val_categorical_accuracy: 0.8419 - val_one_hot_io_u: 0.6524
Epoch 21/30
266/266 [==============================] - ETA: 0s - loss: 0.4196 - precision: 0.8919 - recall: 0.7998 - categorical_accuracy: 0.8410 - one_hot_io_u: 0.6520
Epoch 21: val_loss did not improve from 0.41151
266/266 [==============================] - 259s 977ms/step - loss: 0.4196 - precision: 0.8919 - recall: 0.7998 - categorical_accuracy: 0.8410 - one_hot_io_u: 0.6520 - val_loss: 0.4322 - val_precision: 0.8861 - val_recall: 0.7996 - val_categorical_accuracy: 0.8377 - val_one_hot_io_u: 0.6480
Epoch 22/30
266/266 [==============================] - ETA: 0s - loss: 0.4171 - precision: 0.8923 - recall: 0.8010 - categorical_accuracy: 0.8418 - one_hot_io_u: 0.6533
Epoch 22: val_loss did not improve from 0.41151
266/266 [==============================] - 260s 982ms/step - loss: 0.4171 - precision: 0.8923 - recall: 0.8010 - categorical_accuracy: 0.8418 - one_hot_io_u: 0.6533 - val_loss: 0.4217 - val_precision: 0.8869 - val_recall: 0.8027 - val_categorical_accuracy: 0.8398 - val_one_hot_io_u: 0.6500
Epoch 23/30
266/266 [==============================] - ETA: 0s - loss: 0.4134 - precision: 0.8931 - recall: 0.8027 - categorical_accuracy: 0.8430 - one_hot_io_u: 0.6551
Epoch 23: val_loss improved from 0.41151 to 0.40218, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 272s 1s/step - loss: 0.4134 - precision: 0.8931 - recall: 0.8027 - categorical_accuracy: 0.8430 - one_hot_io_u: 0.6551 - val_loss: 0.4022 - val_precision: 0.8928 - val_recall: 0.8101 - val_categorical_accuracy: 0.8464 - val_one_hot_io_u: 0.6619
Epoch 24/30
266/266 [==============================] - ETA: 0s - loss: 0.4117 - precision: 0.8936 - recall: 0.8035 - categorical_accuracy: 0.8436 - one_hot_io_u: 0.6563
Epoch 24: val_loss did not improve from 0.40218
266/266 [==============================] - 260s 981ms/step - loss: 0.4117 - precision: 0.8936 - recall: 0.8035 - categorical_accuracy: 0.8436 - one_hot_io_u: 0.6563 - val_loss: 0.4047 - val_precision: 0.8892 - val_recall: 0.8128 - val_categorical_accuracy: 0.8460 - val_one_hot_io_u: 0.6579
Epoch 25/30
266/266 [==============================] - ETA: 0s - loss: 0.4064 - precision: 0.8948 - recall: 0.8058 - categorical_accuracy: 0.8454 - one_hot_io_u: 0.6594
Epoch 25: val_loss improved from 0.40218 to 0.39190, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 298s 1s/step - loss: 0.4064 - precision: 0.8948 - recall: 0.8058 - categorical_accuracy: 0.8454 - one_hot_io_u: 0.6594 - val_loss: 0.3919 - val_precision: 0.8960 - val_recall: 0.8134 - val_categorical_accuracy: 0.8497 - val_one_hot_io_u: 0.6662
Epoch 26/30
266/266 [==============================] - ETA: 0s - loss: 0.4038 - precision: 0.8954 - recall: 0.8070 - categorical_accuracy: 0.8462 - one_hot_io_u: 0.6605
Epoch 26: val_loss improved from 0.39190 to 0.38542, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 280s 1s/step - loss: 0.4038 - precision: 0.8954 - recall: 0.8070 - categorical_accuracy: 0.8462 - one_hot_io_u: 0.6605 - val_loss: 0.3854 - val_precision: 0.8982 - val_recall: 0.8159 - val_categorical_accuracy: 0.8520 - val_one_hot_io_u: 0.6693
Epoch 27/30
266/266 [==============================] - ETA: 0s - loss: 0.4007 - precision: 0.8961 - recall: 0.8085 - categorical_accuracy: 0.8473 - one_hot_io_u: 0.6627
Epoch 27: val_loss improved from 0.38542 to 0.38379, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 283s 1s/step - loss: 0.4007 - precision: 0.8961 - recall: 0.8085 - categorical_accuracy: 0.8473 - one_hot_io_u: 0.6627 - val_loss: 0.3838 - val_precision: 0.8978 - val_recall: 0.8175 - val_categorical_accuracy: 0.8527 - val_one_hot_io_u: 0.6711
Epoch 28/30
266/266 [==============================] - ETA: 0s - loss: 0.3972 - precision: 0.8969 - recall: 0.8101 - categorical_accuracy: 0.8485 - one_hot_io_u: 0.6647
Epoch 28: val_loss improved from 0.38379 to 0.37968, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 264s 996ms/step - loss: 0.3972 - precision: 0.8969 - recall: 0.8101 - categorical_accuracy: 0.8485 - one_hot_io_u: 0.6647 - val_loss: 0.3797 - val_precision: 0.8977 - val_recall: 0.8211 - val_categorical_accuracy: 0.8543 - val_one_hot_io_u: 0.6748
Epoch 29/30
266/266 [==============================] - ETA: 0s - loss: 0.3962 - precision: 0.8970 - recall: 0.8105 - categorical_accuracy: 0.8488 - one_hot_io_u: 0.6653
Epoch 29: val_loss improved from 0.37968 to 0.37328, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 263s 989ms/step - loss: 0.3962 - precision: 0.8970 - recall: 0.8105 - categorical_accuracy: 0.8488 - one_hot_io_u: 0.6653 - val_loss: 0.3733 - val_precision: 0.8989 - val_recall: 0.8249 - val_categorical_accuracy: 0.8568 - val_one_hot_io_u: 0.6785
Epoch 30/30
266/266 [==============================] - ETA: 0s - loss: 0.3934 - precision: 0.8977 - recall: 0.8119 - categorical_accuracy: 0.8498 - one_hot_io_u: 0.6666
Epoch 30: val_loss improved from 0.37328 to 0.37270, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/modelCheckpoint
266/266 [==============================] - 265s 998ms/step - loss: 0.3934 - precision: 0.8977 - recall: 0.8119 - categorical_accuracy: 0.8498 - one_hot_io_u: 0.6666 - val_loss: 0.3727 - val_precision: 0.9007 - val_recall: 0.8224 - val_categorical_accuracy: 0.8565 - val_one_hot_io_u: 0.6781
****************************************************************************
****************************** evaluating model... *************************
************************************************
************************************************
Validation
1222/1222 [==============================] - 36s 22ms/step - loss: 0.3723 - precision: 0.9009 - recall: 0.8225 - categorical_accuracy: 0.8566 - one_hot_io_u: 0.6781
loss: 0.3722515106201172
precision: 0.9008649587631226
recall: 0.8225432634353638
categorical_accuracy: 0.8566350340843201
one_hot_io_u: 0.6780571341514587


****************************************************************************
****************************** saving parameters... ************************
****************************************************************************
*************** saving model config and history object... ******************
****************************************************************************
****************************** saving plots... *****************************
Saving plots and model visualization at /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1...
****************************************************************************
****************************** saving models... ****************************
****************************************************************************
\end{verbatim}

\includegraphics{03_Semantic_Segmentation/01__Crop_Mapping/notebooks/Rice_Mapping_Bhutan_2021_files/figure-pdf/cell-16-output-2.png}

\subsection{Save the config file}\label{save-the-config-file}

\begin{Shaded}
\begin{Highlighting}[]
\ImportTok{from}\NormalTok{ pathlib }\ImportTok{import}\NormalTok{ Path}
\ImportTok{import}\NormalTok{ shutil}

\NormalTok{config\_file }\OperatorTok{=}\NormalTok{ Path(config\_file)}
\NormalTok{drive\_config\_file }\OperatorTok{=}\NormalTok{ Path(unet\_config.MODEL\_DIR }\OperatorTok{/} \SpecialStringTok{f"}\SpecialCharTok{\{}\BuiltInTok{str}\NormalTok{(config\_file)}\SpecialCharTok{.}\NormalTok{split(}\StringTok{\textquotesingle{}/\textquotesingle{}}\NormalTok{)[}\OperatorTok{{-}}\DecValTok{1}\NormalTok{]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}

\CommentTok{\# Create the target directory if it doesn\textquotesingle{}t exist}
\NormalTok{drive\_config\_file.parent.mkdir(parents}\OperatorTok{=}\VariableTok{True}\NormalTok{, exist\_ok}\OperatorTok{=}\VariableTok{True}\NormalTok{)}

\CommentTok{\# Copy the file}
\NormalTok{shutil.copy(config\_file, drive\_config\_file)}

\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"File copied from }\SpecialCharTok{\{}\NormalTok{config\_file}\SpecialCharTok{\}}\SpecialStringTok{ to }\SpecialCharTok{\{}\NormalTok{drive\_config\_file}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
File copied from servir-aces/config.env to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/config.env
\end{verbatim}

\subsection{Load the logs files via
TensorBoard}\label{load-the-logs-files-via-tensorboard}

Tensorboard provides a unique way to view and interact with the logs
while the model is being trained. Learn more
\href{https://www.tensorflow.org/tensorboard/get_started}{here}. Here we
only show you how you can load them to tensorboard with our training
logs.

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Load the TensorBoard notebook extension}
\OperatorTok{\%}\NormalTok{load\_ext tensorboard}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{log\_dir\_unet }\OperatorTok{=} \SpecialStringTok{f"}\SpecialCharTok{\{}\BuiltInTok{str}\NormalTok{(unet\_config.MODEL\_DIR)}\SpecialCharTok{\}}\SpecialStringTok{/logs"}
\NormalTok{log\_dir\_unet}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
'/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/logs'
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\OperatorTok{\%}\NormalTok{tensorboard }\OperatorTok{{-}{-}}\NormalTok{logdir }\StringTok{"}\SpecialCharTok{\{log\_dir\_unet\}}\StringTok{"}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
Reusing TensorBoard on port 6007 (pid 5630), started 0:02:00 ago. (Use '!kill 5630' to kill it.)
\end{verbatim}

\begin{verbatim}
<IPython.core.display.Javascript object>
\end{verbatim}

\subsection{Load the Saved U-Net
Model}\label{load-the-saved-u-net-model}

Load the saved model

\begin{Shaded}
\begin{Highlighting}[]
\ImportTok{import}\NormalTok{ tensorflow }\ImportTok{as}\NormalTok{ tf}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{unet\_model }\OperatorTok{=}\NormalTok{ tf.keras.models.load\_model(}\SpecialStringTok{f"}\SpecialCharTok{\{}\BuiltInTok{str}\NormalTok{(unet\_config.MODEL\_DIR)}\SpecialCharTok{\}}\SpecialStringTok{/trained{-}model"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\BuiltInTok{print}\NormalTok{(unet\_model.summary())}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
Model: "unet"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 input_1 (InputLayer)        [(None, None, None, 8)]      0         []                            
                                                                                                  
 conv2d (Conv2D)             (None, None, None, 32)       2336      ['input_1[0][0]']             
                                                                                                  
 batch_normalization (Batch  (None, None, None, 32)       128       ['conv2d[0][0]']              
 Normalization)                                                                                   
                                                                                                  
 activation (Activation)     (None, None, None, 32)       0         ['batch_normalization[0][0]'] 
                                                                                                  
 activation_1 (Activation)   (None, None, None, 32)       0         ['activation[0][0]']          
                                                                                                  
 separable_conv2d (Separabl  (None, None, None, 64)       2400      ['activation_1[0][0]']        
 eConv2D)                                                                                         
                                                                                                  
 batch_normalization_1 (Bat  (None, None, None, 64)       256       ['separable_conv2d[0][0]']    
 chNormalization)                                                                                 
                                                                                                  
 activation_2 (Activation)   (None, None, None, 64)       0         ['batch_normalization_1[0][0]'
                                                                    ]                             
                                                                                                  
 separable_conv2d_1 (Separa  (None, None, None, 64)       4736      ['activation_2[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_2 (Bat  (None, None, None, 64)       256       ['separable_conv2d_1[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 max_pooling2d (MaxPooling2  (None, None, None, 64)       0         ['batch_normalization_2[0][0]'
 D)                                                                 ]                             
                                                                                                  
 conv2d_1 (Conv2D)           (None, None, None, 64)       2112      ['activation[0][0]']          
                                                                                                  
 add (Add)                   (None, None, None, 64)       0         ['max_pooling2d[0][0]',       
                                                                     'conv2d_1[0][0]']            
                                                                                                  
 activation_3 (Activation)   (None, None, None, 64)       0         ['add[0][0]']                 
                                                                                                  
 separable_conv2d_2 (Separa  (None, None, None, 128)      8896      ['activation_3[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_3 (Bat  (None, None, None, 128)      512       ['separable_conv2d_2[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 activation_4 (Activation)   (None, None, None, 128)      0         ['batch_normalization_3[0][0]'
                                                                    ]                             
                                                                                                  
 separable_conv2d_3 (Separa  (None, None, None, 128)      17664     ['activation_4[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_4 (Bat  (None, None, None, 128)      512       ['separable_conv2d_3[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 max_pooling2d_1 (MaxPoolin  (None, None, None, 128)      0         ['batch_normalization_4[0][0]'
 g2D)                                                               ]                             
                                                                                                  
 conv2d_2 (Conv2D)           (None, None, None, 128)      8320      ['add[0][0]']                 
                                                                                                  
 add_1 (Add)                 (None, None, None, 128)      0         ['max_pooling2d_1[0][0]',     
                                                                     'conv2d_2[0][0]']            
                                                                                                  
 activation_5 (Activation)   (None, None, None, 128)      0         ['add_1[0][0]']               
                                                                                                  
 separable_conv2d_4 (Separa  (None, None, None, 256)      34176     ['activation_5[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_5 (Bat  (None, None, None, 256)      1024      ['separable_conv2d_4[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 activation_6 (Activation)   (None, None, None, 256)      0         ['batch_normalization_5[0][0]'
                                                                    ]                             
                                                                                                  
 separable_conv2d_5 (Separa  (None, None, None, 256)      68096     ['activation_6[0][0]']        
 bleConv2D)                                                                                       
                                                                                                  
 batch_normalization_6 (Bat  (None, None, None, 256)      1024      ['separable_conv2d_5[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 max_pooling2d_2 (MaxPoolin  (None, None, None, 256)      0         ['batch_normalization_6[0][0]'
 g2D)                                                               ]                             
                                                                                                  
 conv2d_3 (Conv2D)           (None, None, None, 256)      33024     ['add_1[0][0]']               
                                                                                                  
 add_2 (Add)                 (None, None, None, 256)      0         ['max_pooling2d_2[0][0]',     
                                                                     'conv2d_3[0][0]']            
                                                                                                  
 activation_7 (Activation)   (None, None, None, 256)      0         ['add_2[0][0]']               
                                                                                                  
 conv2d_transpose (Conv2DTr  (None, None, None, 256)      590080    ['activation_7[0][0]']        
 anspose)                                                                                         
                                                                                                  
 batch_normalization_7 (Bat  (None, None, None, 256)      1024      ['conv2d_transpose[0][0]']    
 chNormalization)                                                                                 
                                                                                                  
 activation_8 (Activation)   (None, None, None, 256)      0         ['batch_normalization_7[0][0]'
                                                                    ]                             
                                                                                                  
 conv2d_transpose_1 (Conv2D  (None, None, None, 256)      590080    ['activation_8[0][0]']        
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_8 (Bat  (None, None, None, 256)      1024      ['conv2d_transpose_1[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 up_sampling2d_1 (UpSamplin  (None, None, None, 256)      0         ['add_2[0][0]']               
 g2D)                                                                                             
                                                                                                  
 up_sampling2d (UpSampling2  (None, None, None, 256)      0         ['batch_normalization_8[0][0]'
 D)                                                                 ]                             
                                                                                                  
 conv2d_4 (Conv2D)           (None, None, None, 256)      65792     ['up_sampling2d_1[0][0]']     
                                                                                                  
 add_3 (Add)                 (None, None, None, 256)      0         ['up_sampling2d[0][0]',       
                                                                     'conv2d_4[0][0]']            
                                                                                                  
 activation_9 (Activation)   (None, None, None, 256)      0         ['add_3[0][0]']               
                                                                                                  
 conv2d_transpose_2 (Conv2D  (None, None, None, 128)      295040    ['activation_9[0][0]']        
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_9 (Bat  (None, None, None, 128)      512       ['conv2d_transpose_2[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 activation_10 (Activation)  (None, None, None, 128)      0         ['batch_normalization_9[0][0]'
                                                                    ]                             
                                                                                                  
 conv2d_transpose_3 (Conv2D  (None, None, None, 128)      147584    ['activation_10[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_10 (Ba  (None, None, None, 128)      512       ['conv2d_transpose_3[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 up_sampling2d_3 (UpSamplin  (None, None, None, 256)      0         ['add_3[0][0]']               
 g2D)                                                                                             
                                                                                                  
 up_sampling2d_2 (UpSamplin  (None, None, None, 128)      0         ['batch_normalization_10[0][0]
 g2D)                                                               ']                            
                                                                                                  
 conv2d_5 (Conv2D)           (None, None, None, 128)      32896     ['up_sampling2d_3[0][0]']     
                                                                                                  
 add_4 (Add)                 (None, None, None, 128)      0         ['up_sampling2d_2[0][0]',     
                                                                     'conv2d_5[0][0]']            
                                                                                                  
 activation_11 (Activation)  (None, None, None, 128)      0         ['add_4[0][0]']               
                                                                                                  
 conv2d_transpose_4 (Conv2D  (None, None, None, 64)       73792     ['activation_11[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_11 (Ba  (None, None, None, 64)       256       ['conv2d_transpose_4[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 activation_12 (Activation)  (None, None, None, 64)       0         ['batch_normalization_11[0][0]
                                                                    ']                            
                                                                                                  
 conv2d_transpose_5 (Conv2D  (None, None, None, 64)       36928     ['activation_12[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_12 (Ba  (None, None, None, 64)       256       ['conv2d_transpose_5[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 up_sampling2d_5 (UpSamplin  (None, None, None, 128)      0         ['add_4[0][0]']               
 g2D)                                                                                             
                                                                                                  
 up_sampling2d_4 (UpSamplin  (None, None, None, 64)       0         ['batch_normalization_12[0][0]
 g2D)                                                               ']                            
                                                                                                  
 conv2d_6 (Conv2D)           (None, None, None, 64)       8256      ['up_sampling2d_5[0][0]']     
                                                                                                  
 add_5 (Add)                 (None, None, None, 64)       0         ['up_sampling2d_4[0][0]',     
                                                                     'conv2d_6[0][0]']            
                                                                                                  
 activation_13 (Activation)  (None, None, None, 64)       0         ['add_5[0][0]']               
                                                                                                  
 conv2d_transpose_6 (Conv2D  (None, None, None, 32)       18464     ['activation_13[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_13 (Ba  (None, None, None, 32)       128       ['conv2d_transpose_6[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 activation_14 (Activation)  (None, None, None, 32)       0         ['batch_normalization_13[0][0]
                                                                    ']                            
                                                                                                  
 conv2d_transpose_7 (Conv2D  (None, None, None, 32)       9248      ['activation_14[0][0]']       
 Transpose)                                                                                       
                                                                                                  
 batch_normalization_14 (Ba  (None, None, None, 32)       128       ['conv2d_transpose_7[0][0]']  
 tchNormalization)                                                                                
                                                                                                  
 up_sampling2d_7 (UpSamplin  (None, None, None, 64)       0         ['add_5[0][0]']               
 g2D)                                                                                             
                                                                                                  
 up_sampling2d_6 (UpSamplin  (None, None, None, 32)       0         ['batch_normalization_14[0][0]
 g2D)                                                               ']                            
                                                                                                  
 conv2d_7 (Conv2D)           (None, None, None, 32)       2080      ['up_sampling2d_7[0][0]']     
                                                                                                  
 add_6 (Add)                 (None, None, None, 32)       0         ['up_sampling2d_6[0][0]',     
                                                                     'conv2d_7[0][0]']            
                                                                                                  
 final_conv (Conv2D)         (None, None, None, 5)        1445      ['add_6[0][0]']               
                                                                                                  
==================================================================================================
Total params: 2060997 (7.86 MB)
Trainable params: 2057221 (7.85 MB)
Non-trainable params: 3776 (14.75 KB)
__________________________________________________________________________________________________
None
\end{verbatim}

\subsection{Inference using Saved U-Net
Model}\label{inference-using-saved-u-net-model}

Now we can use the saved model to start the export of the prediction of
the image. For prediction, you would need to first prepare your image
data. We have already exported the image needed here, which we will use
for now. See
\href{https://colab.research.google.com/drive/1MZexam3GZKsQySQO9Jk_RPNyyMLmciEq?usp=sharing}{this
notebook} to understand how we did it.

In addition,
\href{https://colab.research.google.com/drive/19AZ-I7YrGNSsWkqbzjNLw3MXVeV4N2uX?usp=sharing}{this
notebook} shows how you can then use the image to predict from the saved
Model.

In any case, you now have the prediction in the Earth Engine as image.

\section{DNN Model}\label{dnn-model}

\subsection{Setup any changes in the config file for DNN
Model}\label{setup-any-changes-in-the-config-file-for-dnn-model}

There are few config variables that needs to be changed for running a
DNN model. First would be the data itself so let's change the
\texttt{DATADIR}. We also need to change our output directory using
\texttt{MODEL\_DIR\_NAME}. This is the sub-directory inside the
\texttt{OUTPUT\_DIR} for this model run. We also need to specify this is
the DNN model that we want to run. We have \texttt{MODEL\_TYPE}
parameter for that. Currently, it supports unet, dnn, and cnn (case
sensitive) models; default being unet. Make other changes, as
appropriate.

\begin{verbatim}
DATADIR = "datasets/dnn_planet_wo_indices"
MODEL_DIR_NAME = "dnn_v1"
MODEL_TYPE = "dnn"
\end{verbatim}

\subsection{Update the config file
programtically}\label{update-the-config-file-programtically-1}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{DATADIR }\OperatorTok{=} \StringTok{"datasets/dnn\_planet\_wo\_indices"} \CommentTok{\# @param \{type:"string"\}}
\CommentTok{\# PATCH\_SHAPE, USE\_ELEVATION, USE\_S1, TRAIN\_SIZE, TEST\_SIZE, VAL\_SIZE}
\CommentTok{\# BATCH\_SIZE, EPOCHS are converted to their appropriate type.}
\NormalTok{MODEL\_DIR\_NAME }\OperatorTok{=} \StringTok{"dnn\_v1"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{MODEL\_TYPE }\OperatorTok{=} \StringTok{"dnn"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{BATCH\_SIZE }\OperatorTok{=} \StringTok{"32"} \CommentTok{\# @param \{type:"string"\}}
\NormalTok{EPOCHS }\OperatorTok{=} \StringTok{"30"} \CommentTok{\# @param \{type:"string"\}}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{dnn\_config\_settings }\OperatorTok{=}\NormalTok{ \{}
    \StringTok{"DATADIR"}\NormalTok{: DATADIR,}
    \StringTok{"MODEL\_DIR\_NAME"}\NormalTok{: MODEL\_DIR\_NAME,}
    \StringTok{"MODEL\_TYPE"}\NormalTok{: MODEL\_TYPE,}
    \StringTok{"BATCH\_SIZE"}\NormalTok{: BATCH\_SIZE,}
    \StringTok{"EPOCHS"}\NormalTok{: EPOCHS,}
\NormalTok{\}}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\ControlFlowTok{for}\NormalTok{ config\_key }\KeywordTok{in}\NormalTok{ dnn\_config\_settings:}
\NormalTok{    dotenv.set\_key(dotenv\_path}\OperatorTok{=}\NormalTok{config\_file,}
\NormalTok{                   key\_to\_set}\OperatorTok{=}\NormalTok{config\_key,}
\NormalTok{                   value\_to\_set}\OperatorTok{=}\NormalTok{dnn\_config\_settings[config\_key]}
\NormalTok{                   )}
\end{Highlighting}
\end{Shaded}

\subsection{Load config file variables for DNN
Model}\label{load-config-file-variables-for-dnn-model}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{dnn\_config }\OperatorTok{=}\NormalTok{ Config(config\_file}\OperatorTok{=}\NormalTok{config\_file, override}\OperatorTok{=}\VariableTok{True}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
BASEDIR: /content
DATADIR: /content/datasets/dnn_planet_wo_indices
using features: ['red_before', 'green_before', 'blue_before', 'nir_before', 'red_during', 'green_during', 'blue_during', 'nir_during']
using labels: ['class']
\end{verbatim}

Most of the config in the \texttt{config.env} is now available via the
config instance. Let's check few of them here.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{dnn\_config.TRAINING\_DIR, dnn\_config.OUTPUT\_DIR, dnn\_config.BATCH\_SIZE, dnn\_config.MODEL\_TYPE}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
(PosixPath('/content/datasets/dnn_planet_wo_indices/training'),
 PosixPath('/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output'),
 32,
 'dnn')
\end{verbatim}

\subsection{\texorpdfstring{Load \texttt{ModelTrainer}
class}{Load ModelTrainer class}}\label{load-modeltrainer-class-1}

Next, let's make an instance of the \texttt{ModelTrainer} object. The
\texttt{ModelTrainer} class provides various tools for training,
buidling, compiling, and running specified deep learning models.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{dnn\_model\_trainer }\OperatorTok{=}\NormalTok{ ModelTrainer(dnn\_config, seed}\OperatorTok{=}\DecValTok{42}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
Using seed: 42
\end{verbatim}

\subsection{Train and Save DNN model}\label{train-and-save-dnn-model}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{dnn\_model\_trainer.train\_model()}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
****************************************************************************
****************************** Clear Session... ****************************
****************************************************************************
****************************** Configure memory growth... ************************
 > Found 1 GPUs
****************************************************************************
****************************** creating datasets... ************************
Loading dataset from /content/datasets/dnn_planet_wo_indices/training/*
Loading dataset from /content/datasets/dnn_planet_wo_indices/validation/*
Loading dataset from /content/datasets/dnn_planet_wo_indices/testing/*
Printing dataset info:
Training
inputs: float32 (32, 1, 8)
tf.Tensor(
[[[0.06445  0.0383   0.09815  0.06755  0.269975 0.207325 0.11135
   0.060025]]

 [[0.075925 0.02705  0.08695  0.054775 0.235575 0.291625 0.1049
   0.0364  ]]

 [[0.043625 0.025    0.064175 0.04265  0.22     0.225    0.062025
   0.03195 ]]

 [[0.07915  0.05365  0.1054   0.093425 0.257325 0.28345  0.1119
   0.079675]]

 [[0.06945  0.025825 0.10755  0.062125 0.245125 0.28365  0.116975
   0.0485  ]]

 [[0.092425 0.07485  0.10285  0.09645  0.238575 0.252075 0.123025
   0.094825]]

 [[0.0555   0.02955  0.087325 0.09095  0.230625 0.298175 0.08075
   0.0524  ]]

 [[0.0643   0.021275 0.085875 0.0431   0.248075 0.27395  0.098625
   0.0284  ]]

 [[0.0747   0.047675 0.094625 0.072125 0.253125 0.267225 0.09725
   0.063825]]

 [[0.0626   0.023575 0.083675 0.04705  0.20645  0.2539   0.10465
   0.0317  ]]

 [[0.072725 0.030975 0.10185  0.0666   0.26585  0.38635  0.11875
   0.045725]]

 [[0.0713   0.0289   0.09355  0.06415  0.236675 0.2564   0.10215
   0.049475]]

 [[0.086175 0.077875 0.10745  0.0709   0.263475 0.289175 0.1188
   0.075975]]

 [[0.079575 0.027525 0.102325 0.054775 0.253875 0.2761   0.111325
   0.03985 ]]

 [[0.08825  0.0803   0.1016   0.0862   0.265025 0.260925 0.115625
   0.092325]]

 [[0.08025  0.10325  0.1034   0.13695  0.2283   0.246475 0.109975
   0.124   ]]

 [[0.077775 0.029875 0.0953   0.0546   0.235325 0.266    0.122125
   0.0469  ]]

 [[0.0778   0.024025 0.103525 0.053975 0.23395  0.263625 0.116025
   0.036825]]

 [[0.089475 0.070675 0.10515  0.09125  0.257725 0.254375 0.132675
   0.09745 ]]

 [[0.0785   0.026275 0.105575 0.051025 0.2552   0.2522   0.120125
   0.034825]]

 [[0.07945  0.045775 0.094475 0.0652   0.264175 0.335825 0.1147
   0.059275]]

 [[0.04725  0.031125 0.08165  0.065325 0.23025  0.2299   0.0981
   0.060525]]

 [[0.07475  0.02425  0.10205  0.04945  0.263225 0.178625 0.112775
   0.0373  ]]

 [[0.079875 0.0259   0.105875 0.05245  0.2505   0.269425 0.12245
   0.034975]]

 [[0.0746   0.033975 0.104075 0.0598   0.25375  0.29345  0.117325
   0.04885 ]]

 [[0.067125 0.026625 0.093575 0.05095  0.255925 0.231575 0.10545
   0.0415  ]]

 [[0.076325 0.02685  0.10615  0.05585  0.25805  0.276325 0.116725
   0.0417  ]]

 [[0.061275 0.023975 0.086025 0.041375 0.199425 0.255125 0.111
   0.030825]]

 [[0.059725 0.0203   0.0877   0.0434   0.230125 0.251975 0.105675
   0.027225]]

 [[0.07205  0.02285  0.094025 0.04865  0.2084   0.247225 0.119175
   0.03225 ]]

 [[0.0405   0.023025 0.06625  0.0505   0.218225 0.258275 0.0802
   0.03875 ]]

 [[0.07005  0.023125 0.111925 0.0475   0.2716   0.2495   0.124725
   0.0313  ]]], shape=(32, 1, 8), dtype=float32)
outputs: float32 (32, 1, 5)
tf.Tensor(
[[[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 0. 0. 1. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 0. 0. 1. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[1. 0. 0. 0. 0.]]

 [[0. 0. 1. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 0. 0. 1. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 0. 0. 1. 0.]]

 [[0. 0. 0. 1. 0.]]

 [[0. 0. 0. 1. 0.]]

 [[0. 0. 0. 1. 0.]]

 [[0. 0. 0. 1. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 0. 1. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 1. 0. 0. 0.]]

 [[0. 0. 0. 0. 1.]]

 [[0. 0. 1. 0. 0.]]

 [[0. 1. 0. 0. 0.]]], shape=(32, 1, 5), dtype=float32)
Testing
inputs: float32 (1, 1, 8)
tf.Tensor(
[[[0.06205  0.0342   0.081075 0.0639   0.24245  0.251675 0.086575
   0.054175]]], shape=(1, 1, 8), dtype=float32)
outputs: float32 (1, 1, 5)
tf.Tensor([[[1. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)
Validation
inputs: float32 (1, 1, 8)
tf.Tensor(
[[[0.067225 0.031725 0.092275 0.07245  0.23165  0.2267   0.103025
   0.05155 ]]], shape=(1, 1, 8), dtype=float32)
outputs: float32 (1, 1, 5)
tf.Tensor([[[0. 0. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)
****************************************************************************
************************ building and compiling model... *******************
INITIAL_BIAS: None
Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_layer (InputLayer)    [(None, None, 8)]         0         
                                                                 
 dense (Dense)               (None, None, 256)         2304      
                                                                 
 dropout (Dropout)           (None, None, 256)         0         
                                                                 
 dense_1 (Dense)             (None, None, 128)         32896     
                                                                 
 dropout_1 (Dropout)         (None, None, 128)         0         
                                                                 
 dense_2 (Dense)             (None, None, 64)          8256      
                                                                 
 dropout_2 (Dropout)         (None, None, 64)          0         
                                                                 
 dense_3 (Dense)             (None, None, 32)          2080      
                                                                 
 dropout_3 (Dropout)         (None, None, 32)          0         
                                                                 
 dense_4 (Dense)             (None, None, 5)           165       
                                                                 
=================================================================
Total params: 45701 (178.52 KB)
Trainable params: 45701 (178.52 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
None
****************************************************************************
************************ preparing output directory... *********************
> Saving models and results at /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1...
****************************************************************************
****************************** training model... ***************************
Epoch 1/30
264/266 [============================>.] - ETA: 0s - loss: 1.2595 - precision: 0.6700 - recall: 0.2397 - categorical_accuracy: 0.4964 - one_hot_io_u: 0.2151
Epoch 1: val_loss improved from inf to 1.15955, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 31s 99ms/step - loss: 1.2623 - precision: 0.6685 - recall: 0.2401 - categorical_accuracy: 0.4960 - one_hot_io_u: 0.2148 - val_loss: 1.1595 - val_precision: 0.6827 - val_recall: 0.4064 - val_categorical_accuracy: 0.5807 - val_one_hot_io_u: 0.2866
Epoch 2/30
265/266 [============================>.] - ETA: 0s - loss: 0.9196 - precision: 0.7593 - recall: 0.5601 - categorical_accuracy: 0.6874 - one_hot_io_u: 0.4127
Epoch 2: val_loss improved from 1.15955 to 0.87784, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 16s 60ms/step - loss: 0.9206 - precision: 0.7592 - recall: 0.5596 - categorical_accuracy: 0.6871 - one_hot_io_u: 0.4125 - val_loss: 0.8778 - val_precision: 0.7574 - val_recall: 0.6090 - val_categorical_accuracy: 0.6968 - val_one_hot_io_u: 0.4428
Epoch 3/30
266/266 [==============================] - ETA: 0s - loss: 0.8111 - precision: 0.7857 - recall: 0.6387 - categorical_accuracy: 0.7218 - one_hot_io_u: 0.4734
Epoch 3: val_loss did not improve from 0.87784
266/266 [==============================] - 23s 89ms/step - loss: 0.8111 - precision: 0.7857 - recall: 0.6387 - categorical_accuracy: 0.7218 - one_hot_io_u: 0.4734 - val_loss: 0.8998 - val_precision: 0.8096 - val_recall: 0.4193 - val_categorical_accuracy: 0.7288 - val_one_hot_io_u: 0.4815
Epoch 4/30
261/266 [============================>.] - ETA: 0s - loss: 0.7535 - precision: 0.8003 - recall: 0.6697 - categorical_accuracy: 0.7462 - one_hot_io_u: 0.5193
Epoch 4: val_loss improved from 0.87784 to 0.82494, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 16s 61ms/step - loss: 0.7584 - precision: 0.7999 - recall: 0.6640 - categorical_accuracy: 0.7440 - one_hot_io_u: 0.5169 - val_loss: 0.8249 - val_precision: 0.8067 - val_recall: 0.5383 - val_categorical_accuracy: 0.7317 - val_one_hot_io_u: 0.4902
Epoch 5/30
265/266 [============================>.] - ETA: 0s - loss: 0.7328 - precision: 0.8096 - recall: 0.6695 - categorical_accuracy: 0.7466 - one_hot_io_u: 0.5352
Epoch 5: val_loss improved from 0.82494 to 0.74722, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 18s 66ms/step - loss: 0.7328 - precision: 0.8096 - recall: 0.6694 - categorical_accuracy: 0.7467 - one_hot_io_u: 0.5352 - val_loss: 0.7472 - val_precision: 0.8216 - val_recall: 0.6514 - val_categorical_accuracy: 0.7579 - val_one_hot_io_u: 0.5588
Epoch 6/30
264/266 [============================>.] - ETA: 0s - loss: 0.7254 - precision: 0.8092 - recall: 0.6757 - categorical_accuracy: 0.7493 - one_hot_io_u: 0.5396
Epoch 6: val_loss improved from 0.74722 to 0.69727, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 16s 61ms/step - loss: 0.7268 - precision: 0.8086 - recall: 0.6749 - categorical_accuracy: 0.7487 - one_hot_io_u: 0.5391 - val_loss: 0.6973 - val_precision: 0.8324 - val_recall: 0.6755 - val_categorical_accuracy: 0.7696 - val_one_hot_io_u: 0.5790
Epoch 7/30
265/266 [============================>.] - ETA: 0s - loss: 0.7175 - precision: 0.8121 - recall: 0.6877 - categorical_accuracy: 0.7531 - one_hot_io_u: 0.5439
Epoch 7: val_loss improved from 0.69727 to 0.69563, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 16s 61ms/step - loss: 0.7175 - precision: 0.8121 - recall: 0.6878 - categorical_accuracy: 0.7532 - one_hot_io_u: 0.5440 - val_loss: 0.6956 - val_precision: 0.8141 - val_recall: 0.6905 - val_categorical_accuracy: 0.7562 - val_one_hot_io_u: 0.5521
Epoch 8/30
265/266 [============================>.] - ETA: 0s - loss: 0.7065 - precision: 0.8111 - recall: 0.6887 - categorical_accuracy: 0.7545 - one_hot_io_u: 0.5516
Epoch 8: val_loss improved from 0.69563 to 0.67199, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 24s 92ms/step - loss: 0.7066 - precision: 0.8109 - recall: 0.6885 - categorical_accuracy: 0.7542 - one_hot_io_u: 0.5513 - val_loss: 0.6720 - val_precision: 0.8114 - val_recall: 0.7196 - val_categorical_accuracy: 0.7716 - val_one_hot_io_u: 0.5806
Epoch 9/30
266/266 [==============================] - ETA: 0s - loss: 0.6927 - precision: 0.8161 - recall: 0.6925 - categorical_accuracy: 0.7579 - one_hot_io_u: 0.5574
Epoch 9: val_loss improved from 0.67199 to 0.66129, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 24s 92ms/step - loss: 0.6927 - precision: 0.8161 - recall: 0.6925 - categorical_accuracy: 0.7579 - one_hot_io_u: 0.5574 - val_loss: 0.6613 - val_precision: 0.8124 - val_recall: 0.7205 - val_categorical_accuracy: 0.7704 - val_one_hot_io_u: 0.5848
Epoch 10/30
264/266 [============================>.] - ETA: 0s - loss: 0.6922 - precision: 0.8143 - recall: 0.6990 - categorical_accuracy: 0.7613 - one_hot_io_u: 0.5631
Epoch 10: val_loss did not improve from 0.66129
266/266 [==============================] - 23s 87ms/step - loss: 0.6936 - precision: 0.8139 - recall: 0.6985 - categorical_accuracy: 0.7607 - one_hot_io_u: 0.5626 - val_loss: 0.6650 - val_precision: 0.8180 - val_recall: 0.7234 - val_categorical_accuracy: 0.7720 - val_one_hot_io_u: 0.5882
Epoch 11/30
263/266 [============================>.] - ETA: 0s - loss: 0.6978 - precision: 0.8120 - recall: 0.6963 - categorical_accuracy: 0.7553 - one_hot_io_u: 0.5598
Epoch 11: val_loss did not improve from 0.66129
266/266 [==============================] - 23s 87ms/step - loss: 0.6984 - precision: 0.8117 - recall: 0.6962 - categorical_accuracy: 0.7553 - one_hot_io_u: 0.5595 - val_loss: 0.6796 - val_precision: 0.8190 - val_recall: 0.7076 - val_categorical_accuracy: 0.7633 - val_one_hot_io_u: 0.5847
Epoch 12/30
263/266 [============================>.] - ETA: 0s - loss: 0.6804 - precision: 0.8173 - recall: 0.7035 - categorical_accuracy: 0.7622 - one_hot_io_u: 0.5677
Epoch 12: val_loss improved from 0.66129 to 0.65464, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 17s 63ms/step - loss: 0.6806 - precision: 0.8177 - recall: 0.7042 - categorical_accuracy: 0.7626 - one_hot_io_u: 0.5679 - val_loss: 0.6546 - val_precision: 0.8114 - val_recall: 0.7230 - val_categorical_accuracy: 0.7745 - val_one_hot_io_u: 0.5955
Epoch 13/30
261/266 [============================>.] - ETA: 0s - loss: 0.6858 - precision: 0.8154 - recall: 0.6991 - categorical_accuracy: 0.7605 - one_hot_io_u: 0.5657
Epoch 13: val_loss did not improve from 0.65464
266/266 [==============================] - 15s 56ms/step - loss: 0.6863 - precision: 0.8152 - recall: 0.6998 - categorical_accuracy: 0.7609 - one_hot_io_u: 0.5665 - val_loss: 0.6637 - val_precision: 0.8033 - val_recall: 0.7338 - val_categorical_accuracy: 0.7712 - val_one_hot_io_u: 0.5907
Epoch 14/30
264/266 [============================>.] - ETA: 0s - loss: 0.6880 - precision: 0.8123 - recall: 0.6987 - categorical_accuracy: 0.7601 - one_hot_io_u: 0.5650
Epoch 14: val_loss did not improve from 0.65464
266/266 [==============================] - 24s 90ms/step - loss: 0.6894 - precision: 0.8119 - recall: 0.6984 - categorical_accuracy: 0.7595 - one_hot_io_u: 0.5648 - val_loss: 0.7179 - val_precision: 0.7723 - val_recall: 0.6801 - val_categorical_accuracy: 0.7309 - val_one_hot_io_u: 0.5505
Epoch 15/30
263/266 [============================>.] - ETA: 0s - loss: 0.6748 - precision: 0.8167 - recall: 0.7041 - categorical_accuracy: 0.7643 - one_hot_io_u: 0.5692
Epoch 15: val_loss improved from 0.65464 to 0.64675, saving model to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/modelCheckpoint
266/266 [==============================] - 16s 60ms/step - loss: 0.6758 - precision: 0.8164 - recall: 0.7037 - categorical_accuracy: 0.7639 - one_hot_io_u: 0.5688 - val_loss: 0.6467 - val_precision: 0.8123 - val_recall: 0.7363 - val_categorical_accuracy: 0.7745 - val_one_hot_io_u: 0.5883
Epoch 16/30
265/266 [============================>.] - ETA: 0s - loss: 0.6859 - precision: 0.8126 - recall: 0.7015 - categorical_accuracy: 0.7598 - one_hot_io_u: 0.5653
Epoch 16: val_loss did not improve from 0.64675
266/266 [==============================] - 24s 90ms/step - loss: 0.6850 - precision: 0.8129 - recall: 0.7020 - categorical_accuracy: 0.7602 - one_hot_io_u: 0.5664 - val_loss: 0.6541 - val_precision: 0.8152 - val_recall: 0.7284 - val_categorical_accuracy: 0.7720 - val_one_hot_io_u: 0.5856
Epoch 17/30
264/266 [============================>.] - ETA: 0s - loss: 0.6713 - precision: 0.8203 - recall: 0.7104 - categorical_accuracy: 0.7682 - one_hot_io_u: 0.5777
Epoch 17: val_loss did not improve from 0.64675
266/266 [==============================] - 14s 54ms/step - loss: 0.6715 - precision: 0.8205 - recall: 0.7103 - categorical_accuracy: 0.7683 - one_hot_io_u: 0.5778 - val_loss: 0.6520 - val_precision: 0.8122 - val_recall: 0.7413 - val_categorical_accuracy: 0.7754 - val_one_hot_io_u: 0.5897
Epoch 18/30
261/266 [============================>.] - ETA: 0s - loss: 0.6766 - precision: 0.8158 - recall: 0.6999 - categorical_accuracy: 0.7611 - one_hot_io_u: 0.5662
Epoch 18: val_loss did not improve from 0.64675
266/266 [==============================] - 24s 91ms/step - loss: 0.6716 - precision: 0.8175 - recall: 0.7020 - categorical_accuracy: 0.7632 - one_hot_io_u: 0.5692 - val_loss: 0.6723 - val_precision: 0.8073 - val_recall: 0.7354 - val_categorical_accuracy: 0.7745 - val_one_hot_io_u: 0.5787
Epoch 19/30
266/266 [==============================] - ETA: 0s - loss: 0.6681 - precision: 0.8207 - recall: 0.7084 - categorical_accuracy: 0.7657 - one_hot_io_u: 0.5756
Epoch 19: val_loss did not improve from 0.64675
266/266 [==============================] - 14s 54ms/step - loss: 0.6681 - precision: 0.8207 - recall: 0.7084 - categorical_accuracy: 0.7657 - one_hot_io_u: 0.5756 - val_loss: 0.7133 - val_precision: 0.7844 - val_recall: 0.7250 - val_categorical_accuracy: 0.7558 - val_one_hot_io_u: 0.5393
Epoch 20/30
263/266 [============================>.] - ETA: 0s - loss: 0.6686 - precision: 0.8171 - recall: 0.7109 - categorical_accuracy: 0.7676 - one_hot_io_u: 0.5786
Epoch 20: val_loss did not improve from 0.64675
266/266 [==============================] - 15s 57ms/step - loss: 0.6676 - precision: 0.8176 - recall: 0.7113 - categorical_accuracy: 0.7680 - one_hot_io_u: 0.5788 - val_loss: 0.6900 - val_precision: 0.8031 - val_recall: 0.7429 - val_categorical_accuracy: 0.7725 - val_one_hot_io_u: 0.5750
Epoch 21/30
262/266 [============================>.] - ETA: 0s - loss: 0.6652 - precision: 0.8173 - recall: 0.7029 - categorical_accuracy: 0.7663 - one_hot_io_u: 0.5746
Epoch 21: val_loss did not improve from 0.64675
266/266 [==============================] - 23s 89ms/step - loss: 0.6622 - precision: 0.8185 - recall: 0.7044 - categorical_accuracy: 0.7676 - one_hot_io_u: 0.5755 - val_loss: 0.6969 - val_precision: 0.8025 - val_recall: 0.7354 - val_categorical_accuracy: 0.7700 - val_one_hot_io_u: 0.5710
Epoch 22/30
265/266 [============================>.] - ETA: 0s - loss: 0.6699 - precision: 0.8170 - recall: 0.7060 - categorical_accuracy: 0.7678 - one_hot_io_u: 0.5758
Epoch 22: val_loss did not improve from 0.64675
266/266 [==============================] - 17s 63ms/step - loss: 0.6699 - precision: 0.8172 - recall: 0.7063 - categorical_accuracy: 0.7679 - one_hot_io_u: 0.5761 - val_loss: 0.7111 - val_precision: 0.8088 - val_recall: 0.7354 - val_categorical_accuracy: 0.7708 - val_one_hot_io_u: 0.5744
Epoch 23/30
262/266 [============================>.] - ETA: 0s - loss: 0.6639 - precision: 0.8248 - recall: 0.7085 - categorical_accuracy: 0.7693 - one_hot_io_u: 0.5784
Epoch 23: val_loss did not improve from 0.64675
266/266 [==============================] - 23s 87ms/step - loss: 0.6620 - precision: 0.8255 - recall: 0.7099 - categorical_accuracy: 0.7703 - one_hot_io_u: 0.5791 - val_loss: 0.7080 - val_precision: 0.7946 - val_recall: 0.7500 - val_categorical_accuracy: 0.7712 - val_one_hot_io_u: 0.5708
Epoch 24/30
262/266 [============================>.] - ETA: 0s - loss: 0.6667 - precision: 0.8214 - recall: 0.7093 - categorical_accuracy: 0.7672 - one_hot_io_u: 0.5761
Epoch 24: val_loss did not improve from 0.64675
266/266 [==============================] - 24s 90ms/step - loss: 0.6651 - precision: 0.8218 - recall: 0.7104 - categorical_accuracy: 0.7681 - one_hot_io_u: 0.5768 - val_loss: 0.7125 - val_precision: 0.7986 - val_recall: 0.7371 - val_categorical_accuracy: 0.7687 - val_one_hot_io_u: 0.5655
Epoch 25/30
266/266 [==============================] - ETA: 0s - loss: 0.6620 - precision: 0.8192 - recall: 0.7146 - categorical_accuracy: 0.7716 - one_hot_io_u: 0.5834
Epoch 25: val_loss did not improve from 0.64675
266/266 [==============================] - 24s 90ms/step - loss: 0.6620 - precision: 0.8192 - recall: 0.7146 - categorical_accuracy: 0.7716 - one_hot_io_u: 0.5834 - val_loss: 0.7170 - val_precision: 0.7933 - val_recall: 0.7454 - val_categorical_accuracy: 0.7612 - val_one_hot_io_u: 0.5481
Epoch 26/30
264/266 [============================>.] - ETA: 0s - loss: 0.6630 - precision: 0.8221 - recall: 0.7129 - categorical_accuracy: 0.7707 - one_hot_io_u: 0.5811
Epoch 26: val_loss did not improve from 0.64675
266/266 [==============================] - 14s 55ms/step - loss: 0.6636 - precision: 0.8220 - recall: 0.7131 - categorical_accuracy: 0.7708 - one_hot_io_u: 0.5811 - val_loss: 0.6966 - val_precision: 0.7934 - val_recall: 0.7446 - val_categorical_accuracy: 0.7675 - val_one_hot_io_u: 0.5624
Epoch 27/30
266/266 [==============================] - ETA: 0s - loss: 0.6562 - precision: 0.8221 - recall: 0.7137 - categorical_accuracy: 0.7681 - one_hot_io_u: 0.5774
Epoch 27: val_loss did not improve from 0.64675
266/266 [==============================] - 24s 91ms/step - loss: 0.6562 - precision: 0.8221 - recall: 0.7137 - categorical_accuracy: 0.7681 - one_hot_io_u: 0.5774 - val_loss: 0.6592 - val_precision: 0.8102 - val_recall: 0.7388 - val_categorical_accuracy: 0.7750 - val_one_hot_io_u: 0.5840
Epoch 28/30
264/266 [============================>.] - ETA: 0s - loss: 0.6545 - precision: 0.8258 - recall: 0.7097 - categorical_accuracy: 0.7717 - one_hot_io_u: 0.5830
Epoch 28: val_loss did not improve from 0.64675
266/266 [==============================] - 14s 54ms/step - loss: 0.6543 - precision: 0.8259 - recall: 0.7099 - categorical_accuracy: 0.7720 - one_hot_io_u: 0.5832 - val_loss: 0.6637 - val_precision: 0.8178 - val_recall: 0.7359 - val_categorical_accuracy: 0.7766 - val_one_hot_io_u: 0.5848
Epoch 29/30
264/266 [============================>.] - ETA: 0s - loss: 0.6576 - precision: 0.8259 - recall: 0.7109 - categorical_accuracy: 0.7687 - one_hot_io_u: 0.5800
Epoch 29: val_loss did not improve from 0.64675
266/266 [==============================] - 16s 61ms/step - loss: 0.6561 - precision: 0.8265 - recall: 0.7112 - categorical_accuracy: 0.7689 - one_hot_io_u: 0.5802 - val_loss: 0.6871 - val_precision: 0.7955 - val_recall: 0.7329 - val_categorical_accuracy: 0.7633 - val_one_hot_io_u: 0.5514
Epoch 30/30
266/266 [==============================] - ETA: 0s - loss: 0.6533 - precision: 0.8234 - recall: 0.7118 - categorical_accuracy: 0.7696 - one_hot_io_u: 0.5826
Epoch 30: val_loss did not improve from 0.64675
266/266 [==============================] - 15s 56ms/step - loss: 0.6533 - precision: 0.8234 - recall: 0.7118 - categorical_accuracy: 0.7696 - one_hot_io_u: 0.5826 - val_loss: 0.6730 - val_precision: 0.8023 - val_recall: 0.7359 - val_categorical_accuracy: 0.7683 - val_one_hot_io_u: 0.5576
****************************************************************************
****************************** evaluating model... *************************
************************************************
************************************************
Validation
1219/1219 [==============================] - 7s 6ms/step - loss: 0.6585 - precision: 0.8038 - recall: 0.7293 - categorical_accuracy: 0.7736 - one_hot_io_u: 0.5689
loss: 0.6584734320640564
precision: 0.8037974834442139
recall: 0.7292863130569458
categorical_accuracy: 0.7735849022865295
one_hot_io_u: 0.5688682794570923


****************************************************************************
****************************** saving parameters... ************************
****************************************************************************
*************** saving model config and history object... ******************
****************************************************************************
****************************** saving plots... *****************************
Saving plots and model visualization at /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1...
****************************************************************************
****************************** saving models... ****************************
****************************************************************************
\end{verbatim}

\includegraphics{03_Semantic_Segmentation/01__Crop_Mapping/notebooks/Rice_Mapping_Bhutan_2021_files/figure-pdf/cell-30-output-2.png}

\subsection{Save the config file}\label{save-the-config-file-1}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{drive\_config\_file }\OperatorTok{=}\NormalTok{ Path(dnn\_config.MODEL\_DIR }\OperatorTok{/} \SpecialStringTok{f"}\SpecialCharTok{\{}\BuiltInTok{str}\NormalTok{(config\_file)}\SpecialCharTok{.}\NormalTok{split(}\StringTok{\textquotesingle{}/\textquotesingle{}}\NormalTok{)[}\OperatorTok{{-}}\DecValTok{1}\NormalTok{]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}

\CommentTok{\# Create the target directory if it doesn\textquotesingle{}t exist}
\NormalTok{drive\_config\_file.parent.mkdir(parents}\OperatorTok{=}\VariableTok{True}\NormalTok{, exist\_ok}\OperatorTok{=}\VariableTok{True}\NormalTok{)}

\CommentTok{\# Copy the file}
\NormalTok{shutil.copy(config\_file, drive\_config\_file)}

\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"File copied from }\SpecialCharTok{\{}\NormalTok{config\_file}\SpecialCharTok{\}}\SpecialStringTok{ to }\SpecialCharTok{\{}\NormalTok{drive\_config\_file}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
File copied from servir-aces/config.env to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/config.env
\end{verbatim}

\subsection{Load the logs files via
TensorBoard}\label{load-the-logs-files-via-tensorboard-1}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{log\_dir\_dnn }\OperatorTok{=} \SpecialStringTok{f"}\SpecialCharTok{\{}\BuiltInTok{str}\NormalTok{(dnn\_config.MODEL\_DIR)}\SpecialCharTok{\}}\SpecialStringTok{/logs"}
\NormalTok{log\_dir\_dnn}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
'/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/dnn_v1/logs'
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\OperatorTok{\%}\NormalTok{tensorboard }\OperatorTok{{-}{-}}\NormalTok{logdir }\StringTok{"}\SpecialCharTok{\{log\_dir\_dnn\}}\StringTok{"}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.Javascript object>
\end{verbatim}

\subsection{Load the Saved DNN Model}\label{load-the-saved-dnn-model}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{dnn\_model }\OperatorTok{=}\NormalTok{ tf.keras.models.load\_model(}\SpecialStringTok{f"}\SpecialCharTok{\{}\BuiltInTok{str}\NormalTok{(dnn\_config.MODEL\_DIR)}\SpecialCharTok{\}}\SpecialStringTok{/trained{-}model"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\BuiltInTok{print}\NormalTok{(dnn\_model.summary())}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_layer (InputLayer)    [(None, None, 8)]         0         
                                                                 
 dense (Dense)               (None, None, 256)         2304      
                                                                 
 dropout (Dropout)           (None, None, 256)         0         
                                                                 
 dense_1 (Dense)             (None, None, 128)         32896     
                                                                 
 dropout_1 (Dropout)         (None, None, 128)         0         
                                                                 
 dense_2 (Dense)             (None, None, 64)          8256      
                                                                 
 dropout_2 (Dropout)         (None, None, 64)          0         
                                                                 
 dense_3 (Dense)             (None, None, 32)          2080      
                                                                 
 dropout_3 (Dropout)         (None, None, 32)          0         
                                                                 
 dense_4 (Dense)             (None, None, 5)           165       
                                                                 
=================================================================
Total params: 45701 (178.52 KB)
Trainable params: 45701 (178.52 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
None
\end{verbatim}

\subsection{Inference using Saved DNN
Model}\label{inference-using-saved-dnn-model}

Now we can use the saved model to start the export of the prediction of
the image. For prediction, you would need to first prepare your image
data. We have already exported the image needed here, which we will use
for now. See
\href{https://colab.research.google.com/drive/1MZexam3GZKsQySQO9Jk_RPNyyMLmciEq?usp=sharing}{this
notebook} to understand how we did it.

In addition,
\href{https://colab.research.google.com/drive/1c59eWgw637n1aQfyIobOKIIulqKSXeqv?usp=sharing}{this
notebook} shows how you can then use the image to predict from the saved
Model.

In any case, you now have the prediction in the Earth Engine as image.

\section{Independent Validation}\label{independent-validation}

For independent validation, we will use a file that we have prepared.
These files were collected using
\href{https://app.collect.earth/home}{Collect Earth Online} by SCO and
NASA DEVELOP interns. We will be using GEE here. Before we do that,
let's make changes in our config file.

We will make sure our \texttt{GCS\_PROJECT} is setup correctly.

\begin{verbatim}
GCS_PROJECT = "servir-ee"
\end{verbatim}

\subsection{Update the config file}\label{update-the-config-file}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{GCS\_PROJECT }\OperatorTok{=} \StringTok{"servir{-}ee"} \CommentTok{\# @param \{type:"string"\}}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{config\_settings }\OperatorTok{=}\NormalTok{ \{}
    \StringTok{"GCS\_PROJECT"}\NormalTok{: GCS\_PROJECT,}
\NormalTok{\}}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\ControlFlowTok{for}\NormalTok{ config\_key }\KeywordTok{in}\NormalTok{ config\_settings:}
\NormalTok{    dotenv.set\_key(dotenv\_path}\OperatorTok{=}\NormalTok{config\_file,}
\NormalTok{                   key\_to\_set}\OperatorTok{=}\NormalTok{config\_key,}
\NormalTok{                   value\_to\_set}\OperatorTok{=}\NormalTok{config\_settings[config\_key]}
\NormalTok{                   )}
\end{Highlighting}
\end{Shaded}

\subsection{Load config file variable}\label{load-config-file-variable}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{config }\OperatorTok{=}\NormalTok{ Config(config\_file}\OperatorTok{=}\NormalTok{config\_file, override}\OperatorTok{=}\VariableTok{True}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
BASEDIR: /content
DATADIR: /content/datasets/dnn_planet_wo_indices
using features: ['red_before', 'green_before', 'blue_before', 'nir_before', 'red_during', 'green_during', 'blue_during', 'nir_during']
using labels: ['class']
\end{verbatim}

\subsection{Import earthengine and geemap for
visualization}\label{import-earthengine-and-geemap-for-visualization}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Import, authenticate and initialize the Earth Engine library.}
\ImportTok{import}\NormalTok{ ee}
\NormalTok{ee.Authenticate()}
\NormalTok{EEUtils.initialize\_session(use\_highvolume}\OperatorTok{=}\VariableTok{True}\NormalTok{, project}\OperatorTok{=}\NormalTok{config.GCS\_PROJECT)}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\ImportTok{import}\NormalTok{ geemap}

\NormalTok{Map }\OperatorTok{=}\NormalTok{ geemap.Map()}
\end{Highlighting}
\end{Shaded}

\subsection{Class Information and
Masking}\label{class-information-and-masking}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# CLASS}
\CommentTok{\# 0 {-} cropland etc.}
\CommentTok{\# 1 {-} rice}
\CommentTok{\# 2 {-} forest}
\CommentTok{\# 3 {-} Built up}
\CommentTok{\# 4 {-} Others (includes water body)}
\NormalTok{l1 }\OperatorTok{=}\NormalTok{ ee.FeatureCollection(}\StringTok{"projects/servir{-}sco{-}assets/assets/Bhutan/BT\_Admin\_1"}\NormalTok{)}
\NormalTok{paro }\OperatorTok{=}\NormalTok{ l1.}\BuiltInTok{filter}\NormalTok{(ee.Filter.eq(}\StringTok{"ADM1\_EN"}\NormalTok{, }\StringTok{"Paro"}\NormalTok{))}


\CommentTok{\# mask the rice growing zone}
\CommentTok{\# in Paro, rice grows upto 2600 m asl (double check to make sure??)}
\NormalTok{dem }\OperatorTok{=}\NormalTok{ ee.Image(}\StringTok{"MERIT/DEM/v1\_0\_3"}\NormalTok{) }\CommentTok{\# ee.Image(\textquotesingle{}USGS/SRTMGL1\_003\textquotesingle{})}
\NormalTok{dem }\OperatorTok{=}\NormalTok{ dem.clip(paro)}
\NormalTok{rice\_zone }\OperatorTok{=}\NormalTok{ dem.gte(}\DecValTok{0}\NormalTok{).And(dem.lte(}\DecValTok{2600}\NormalTok{))}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\subsection{Model: U-Net}\label{model-u-net}

\subsubsection{Load and visualize the prediction
output}\label{load-and-visualize-the-prediction-output}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{UNET\_RGBN }\OperatorTok{=}\NormalTok{ ee.Image(}\StringTok{"projects/servir{-}ee/assets/dl{-}book/chapter{-}1/prediction/prediction\_unet\_v1"}\NormalTok{)}
\NormalTok{UNET\_RGBN }\OperatorTok{=}\NormalTok{ UNET\_RGBN.updateMask(rice\_zone)}
\NormalTok{Map.centerObject(UNET\_RGBN, }\DecValTok{11}\NormalTok{)}
\NormalTok{Map.addLayer(UNET\_RGBN.clip(paro), \{}\StringTok{"bands"}\NormalTok{: [}\StringTok{"prediction"}\NormalTok{], }\StringTok{"min"}\NormalTok{:}\DecValTok{0}\NormalTok{, }\StringTok{"max"}\NormalTok{:}\DecValTok{4}\NormalTok{, }\StringTok{"palette"}\NormalTok{: [}\StringTok{"FFFF00"}\NormalTok{, }\StringTok{"FFC0CB"}\NormalTok{, }\StringTok{"267300"}\NormalTok{, }\StringTok{"E60000"}\NormalTok{, }\StringTok{"005CE6"}\NormalTok{]\}, }\StringTok{"UNET\_RGBN"}\NormalTok{)}
\NormalTok{Map}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{verbatim}
Map(center=[27.378354616518475, 89.42005508391453], controls=(WidgetControl(options=['position', 'transparent_…
\end{verbatim}

\subsubsection{Calculate classification
metrics}\label{calculate-classification-metrics}

Remapping to rice and non-rice output

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{UNET\_RGBN\_remapped }\OperatorTok{=}\NormalTok{ UNET\_RGBN.remap([}\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{], [}\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{], }\DecValTok{0}\NormalTok{, }\StringTok{"prediction"}\NormalTok{)}
\NormalTok{Map.addLayer(UNET\_RGBN\_remapped, \{}\StringTok{"min"}\NormalTok{: }\DecValTok{0}\NormalTok{, }\StringTok{"max"}\NormalTok{: }\DecValTok{1}\NormalTok{, }\StringTok{"palette"}\NormalTok{: [}\StringTok{"cfcf00"}\NormalTok{, }\StringTok{"267300"}\NormalTok{]\}, }\StringTok{"UNET\_RGBN\_remapped"}\NormalTok{)}
\NormalTok{Map}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{verbatim}
Map(bottom=220961.0, center=[27.378354616518475, 89.42005508391453], controls=(WidgetControl(options=['positio…
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{sampling\_geom }\OperatorTok{=}\NormalTok{ ee.FeatureCollection(}\StringTok{"projects/servir{-}ee/assets/dl{-}book/chapter{-}1/data/sampledGeom"}\NormalTok{)}
\NormalTok{ceo\_final\_data }\OperatorTok{=}\NormalTok{ ee.FeatureCollection(}\StringTok{"projects/servir{-}ee/assets/dl{-}book/chapter{-}1/data/ceoData"}\NormalTok{)}
\NormalTok{ceo\_final\_data }\OperatorTok{=}\NormalTok{ ee.FeatureCollection(ceo\_final\_data.}\BuiltInTok{filter}\NormalTok{(ee.Filter.bounds(sampling\_geom).Not()))}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{prediction\_unet }\OperatorTok{=}\NormalTok{ UNET\_RGBN\_remapped.sampleRegions(}
\NormalTok{    collection }\OperatorTok{=}\NormalTok{ ceo\_final\_data,}
\NormalTok{    scale }\OperatorTok{=} \DecValTok{10}\NormalTok{,}
\NormalTok{    geometries }\OperatorTok{=} \VariableTok{True}
\NormalTok{)}

\CommentTok{\# print("predictionOutputUnet", prediction\_unet.getInfo())}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{error\_matrix\_unet }\OperatorTok{=}\NormalTok{ prediction\_unet.errorMatrix(actual}\OperatorTok{=}\StringTok{"rice"}\NormalTok{, predicted}\OperatorTok{=}\StringTok{"remapped"}\NormalTok{)}
\NormalTok{test\_acc\_unet }\OperatorTok{=}\NormalTok{ error\_matrix\_unet.accuracy()}
\NormalTok{test\_kappa\_unet }\OperatorTok{=}\NormalTok{ error\_matrix\_unet.kappa()}
\NormalTok{test\_recall\_producer\_acc\_unet }\OperatorTok{=}\NormalTok{ error\_matrix\_unet.producersAccuracy().get([}\DecValTok{1}\NormalTok{, }\DecValTok{0}\NormalTok{])}
\NormalTok{test\_precision\_consumer\_acc\_unet }\OperatorTok{=}\NormalTok{ error\_matrix\_unet.consumersAccuracy().get([}\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{])}
\NormalTok{f1\_unet }\OperatorTok{=}\NormalTok{ error\_matrix\_unet.fscore().get([}\DecValTok{1}\NormalTok{])}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\BuiltInTok{print}\NormalTok{(}\StringTok{"error\_matrix\_unet"}\NormalTok{, error\_matrix\_unet.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"test\_acc\_unet"}\NormalTok{, test\_acc\_unet.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"test\_kappa\_unet"}\NormalTok{, test\_kappa\_unet.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"test\_recall\_producer\_acc\_unet"}\NormalTok{, test\_recall\_producer\_acc\_unet.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"test\_precision\_consumer\_acc\_unet"}\NormalTok{, test\_precision\_consumer\_acc\_unet.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"f1\_unet"}\NormalTok{, f1\_unet.getInfo())}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{verbatim}
error_matrix_unet [[1191, 29], [33, 50]]
test_acc_unet 0.9524174980813507
test_kappa_unet 0.5919321924312524
test_recall_producer_acc_unet 0.6024096385542169
test_precision_consumer_acc_unet 0.6329113924050633
f1_unet 0.6172839506172839
\end{verbatim}

\subsubsection{Calculate Probability
Distribution}\label{calculate-probability-distribution}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{prob\_output\_unet }\OperatorTok{=}\NormalTok{ UNET\_RGBN.select([}\StringTok{"prediction"}\NormalTok{, }\StringTok{"others\_etc"}\NormalTok{, }\StringTok{"cropland\_etc"}\NormalTok{, }\StringTok{"urban"}\NormalTok{, }\StringTok{"forest"}\NormalTok{, }\StringTok{"rice"}\NormalTok{]) }\OperatorTok{\textbackslash{}}
\NormalTok{                            .rename([}\StringTok{"prediction\_class"}\NormalTok{, }\StringTok{"others\_prob"}\NormalTok{, }\StringTok{"cropland\_prob"}\NormalTok{, }\StringTok{"urban\_prob"}\NormalTok{, }\StringTok{"forest\_prob"}\NormalTok{, }\StringTok{"rice\_prob"}\NormalTok{]) }\OperatorTok{\textbackslash{}}
\NormalTok{                            .sampleRegions(collection}\OperatorTok{=}\NormalTok{ceo\_final\_data, scale}\OperatorTok{=}\DecValTok{10}\NormalTok{, geometries}\OperatorTok{=}\VariableTok{True}\NormalTok{)}

\CommentTok{\# print("prob\_output\_unet", prob\_output\_unet.getInfo())}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{prob\_output\_unet }\OperatorTok{=}\NormalTok{ prob\_output\_unet.getInfo()}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\subsection{Model: DNN}\label{model-dnn}

\subsubsection{Load and visualize the prediction
output}\label{load-and-visualize-the-prediction-output-1}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{DNN\_RGBN }\OperatorTok{=}\NormalTok{ ee.Image(}\StringTok{"projects/servir{-}ee/assets/dl{-}book/chapter{-}1/prediction/prediction\_dnn\_v1"}\NormalTok{)}
\NormalTok{DNN\_RGBN }\OperatorTok{=}\NormalTok{ DNN\_RGBN.updateMask(rice\_zone)}
\NormalTok{Map.centerObject(DNN\_RGBN)}
\NormalTok{Map.addLayer(DNN\_RGBN.clip(paro), \{}\StringTok{"bands"}\NormalTok{: [}\StringTok{"prediction"}\NormalTok{], }\StringTok{"min"}\NormalTok{:}\DecValTok{0}\NormalTok{, }\StringTok{"max"}\NormalTok{:}\DecValTok{4}\NormalTok{, }\StringTok{"palette"}\NormalTok{: [}\StringTok{"FFFF00"}\NormalTok{, }\StringTok{"FFC0CB"}\NormalTok{, }\StringTok{"267300"}\NormalTok{, }\StringTok{"E60000"}\NormalTok{, }\StringTok{"005CE6"}\NormalTok{]\}, }\StringTok{"DNN\_RGBN"}\NormalTok{)}
\NormalTok{Map}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{verbatim}
Map(bottom=220961.0, center=[27.378354616518475, 89.42005508391453], controls=(WidgetControl(options=['positio…
\end{verbatim}

\subsubsection{Calculate classification
metrics}\label{calculate-classification-metrics-1}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{DNN\_RGBN\_remapped }\OperatorTok{=}\NormalTok{ DNN\_RGBN.remap([}\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{], [}\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{], }\DecValTok{0}\NormalTok{, }\StringTok{"prediction"}\NormalTok{)}
\NormalTok{Map.addLayer(DNN\_RGBN\_remapped, \{}\StringTok{"min"}\NormalTok{: }\DecValTok{0}\NormalTok{, }\StringTok{"max"}\NormalTok{: }\DecValTok{1}\NormalTok{, }\StringTok{"palette"}\NormalTok{: [}\StringTok{"cfcf00"}\NormalTok{, }\StringTok{"267300"}\NormalTok{]\}, }\StringTok{"DNN\_RGBN\_remapped"}\NormalTok{)}
\NormalTok{Map}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{verbatim}
Map(bottom=220961.0, center=[27.37845188654284, 89.42005507220328], controls=(WidgetControl(options=['position…
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{prediction\_dnn }\OperatorTok{=}\NormalTok{ DNN\_RGBN\_remapped.sampleRegions(}
\NormalTok{    collection }\OperatorTok{=}\NormalTok{ ceo\_final\_data,}
\NormalTok{    scale }\OperatorTok{=} \DecValTok{10}\NormalTok{,}
\NormalTok{    geometries }\OperatorTok{=} \VariableTok{True}
\NormalTok{)}

\CommentTok{\# print("predictionOutputDNN", prediction\_dnn.getInfo())}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{error\_matrix\_dnn }\OperatorTok{=}\NormalTok{ prediction\_dnn.errorMatrix(actual}\OperatorTok{=}\StringTok{"rice"}\NormalTok{, predicted}\OperatorTok{=}\StringTok{"remapped"}\NormalTok{)}
\NormalTok{test\_acc\_dnn }\OperatorTok{=}\NormalTok{ error\_matrix\_dnn.accuracy()}
\NormalTok{test\_kappa\_dnn }\OperatorTok{=}\NormalTok{ error\_matrix\_dnn.kappa()}
\NormalTok{test\_recall\_producer\_acc\_dnn }\OperatorTok{=}\NormalTok{ error\_matrix\_dnn.producersAccuracy().get([}\DecValTok{1}\NormalTok{, }\DecValTok{0}\NormalTok{])}
\NormalTok{test\_precision\_consumer\_acc\_dnn }\OperatorTok{=}\NormalTok{ error\_matrix\_dnn.consumersAccuracy().get([}\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{])}
\NormalTok{f1\_dnn }\OperatorTok{=}\NormalTok{ error\_matrix\_dnn.fscore().get([}\DecValTok{1}\NormalTok{])}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\BuiltInTok{print}\NormalTok{(}\StringTok{"error\_matrix\_dnn"}\NormalTok{, error\_matrix\_dnn.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"test\_acc\_dnn"}\NormalTok{, test\_acc\_dnn.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"test\_kappa\_dnn"}\NormalTok{, test\_kappa\_dnn.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"test\_recall\_producer\_acc\_dnn"}\NormalTok{, test\_recall\_producer\_acc\_dnn.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"test\_precision\_consumer\_acc\_dnn"}\NormalTok{, test\_precision\_consumer\_acc\_dnn.getInfo())}
\BuiltInTok{print}\NormalTok{(}\StringTok{"f1\_dnn"}\NormalTok{, f1\_dnn.getInfo())}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{verbatim}
error_matrix_dnn [[1175, 45], [20, 63]]
test_acc_dnn 0.9501151189562548
test_kappa_dnn 0.6332676611314382
test_recall_producer_acc_dnn 0.7590361445783133
test_precision_consumer_acc_dnn 0.5833333333333334
f1_dnn 0.6596858638743456
\end{verbatim}

\subsubsection{Calculate Probability
Distribution}\label{calculate-probability-distribution-1}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{prob\_output\_dnn }\OperatorTok{=}\NormalTok{ DNN\_RGBN.select([}\StringTok{"prediction"}\NormalTok{, }\StringTok{"others\_etc"}\NormalTok{, }\StringTok{"cropland\_etc"}\NormalTok{, }\StringTok{"urban"}\NormalTok{, }\StringTok{"forest"}\NormalTok{, }\StringTok{"rice"}\NormalTok{]) }\OperatorTok{\textbackslash{}}
\NormalTok{                            .rename([}\StringTok{"prediction\_class"}\NormalTok{, }\StringTok{"others\_prob"}\NormalTok{, }\StringTok{"cropland\_prob"}\NormalTok{, }\StringTok{"urban\_prob"}\NormalTok{, }\StringTok{"forest\_prob"}\NormalTok{, }\StringTok{"rice\_prob"}\NormalTok{]) }\OperatorTok{\textbackslash{}}
\NormalTok{                            .sampleRegions(collection}\OperatorTok{=}\NormalTok{ceo\_final\_data, scale}\OperatorTok{=}\DecValTok{10}\NormalTok{, geometries}\OperatorTok{=}\VariableTok{True}\NormalTok{)}

\CommentTok{\# print("prob\_output\_dnn", prob\_output\_dnn.getInfo())}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{prob\_output\_dnn }\OperatorTok{=}\NormalTok{ prob\_output\_dnn.getInfo()}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\section{Figures and Plots}\label{figures-and-plots}

\subsection{Training and Validation
Plot}\label{training-and-validation-plot}

\begin{Shaded}
\begin{Highlighting}[]
\ImportTok{import}\NormalTok{ matplotlib.pyplot }\ImportTok{as}\NormalTok{ plt}
\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np}
\ImportTok{import}\NormalTok{ pandas }\ImportTok{as}\NormalTok{ pd}
\ImportTok{import}\NormalTok{ pickle}

\OperatorTok{\%}\NormalTok{matplotlib inline}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\ControlFlowTok{with} \BuiltInTok{open}\NormalTok{(unet\_config.MODEL\_DIR }\OperatorTok{/} \StringTok{"model.pkl"}\NormalTok{, }\StringTok{"rb"}\NormalTok{) }\ImportTok{as}\NormalTok{ f:}
\NormalTok{    unet\_model\_metrics }\OperatorTok{=}\NormalTok{ pickle.load(f)}

\ControlFlowTok{with} \BuiltInTok{open}\NormalTok{(dnn\_config.MODEL\_DIR }\OperatorTok{/} \StringTok{"model.pkl"}\NormalTok{, }\StringTok{"rb"}\NormalTok{) }\ImportTok{as}\NormalTok{ f:}
\NormalTok{    dnn\_model\_metrics }\OperatorTok{=}\NormalTok{ pickle.load(f)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Create subplots for different metrics in a 3x4 grid}
\NormalTok{fig, axs }\OperatorTok{=}\NormalTok{ plt.subplots(}\DecValTok{2}\NormalTok{, }\DecValTok{4}\NormalTok{, figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{4}\OperatorTok{*}\DecValTok{7}\NormalTok{, }\DecValTok{6}\OperatorTok{*}\DecValTok{2}\NormalTok{))}

\NormalTok{colors }\OperatorTok{=}\NormalTok{ [}\StringTok{"\#1f77b4"}\NormalTok{, }\StringTok{"\#ff7f0e"}\NormalTok{, }\StringTok{"\#2ca02c"}\NormalTok{, }\StringTok{"\#d62728"}\NormalTok{]}
\NormalTok{metrics }\OperatorTok{=}\NormalTok{ [}\StringTok{"loss"}\NormalTok{, }\StringTok{"precision"}\NormalTok{, }\StringTok{"recall"}\NormalTok{, }\StringTok{"categorical\_accuracy"}\NormalTok{]}
\NormalTok{metrics\_name }\OperatorTok{=}\NormalTok{ [}\StringTok{"Loss"}\NormalTok{, }\StringTok{"Precision"}\NormalTok{, }\StringTok{"Recall"}\NormalTok{, }\StringTok{"Categorical Accuracy"}\NormalTok{]}

\NormalTok{epochs }\OperatorTok{=} \BuiltInTok{range}\NormalTok{(}\DecValTok{1}\NormalTok{, config.EPOCHS }\OperatorTok{+} \DecValTok{1}\NormalTok{)}

\NormalTok{title\_fontsize }\OperatorTok{=} \DecValTok{22}
\NormalTok{label\_fontsize }\OperatorTok{=} \DecValTok{22}
\NormalTok{legend\_fontsize }\OperatorTok{=} \DecValTok{15}
\NormalTok{tick\_fontsize }\OperatorTok{=} \DecValTok{18}
\NormalTok{lw}\OperatorTok{=}\FloatTok{1.5}

\ControlFlowTok{for}\NormalTok{ i }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(}\DecValTok{2}\NormalTok{):}
    \ControlFlowTok{for}\NormalTok{ y }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(}\BuiltInTok{len}\NormalTok{(metrics)):}
        \ControlFlowTok{if}\NormalTok{ i }\OperatorTok{==} \DecValTok{1}\NormalTok{:}
\NormalTok{            axs[i][y].plot(epochs, unet\_model\_metrics[}\SpecialStringTok{f"val\_}\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{], color}\OperatorTok{=}\NormalTok{colors[}\DecValTok{0}\NormalTok{], marker}\OperatorTok{=}\StringTok{"o"}\NormalTok{, lw}\OperatorTok{=}\NormalTok{lw, label}\OperatorTok{=}\SpecialStringTok{f"U{-}Net validate }\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
\NormalTok{            axs[i][y].plot(epochs, dnn\_model\_metrics[}\SpecialStringTok{f"val\_}\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{], color}\OperatorTok{=}\NormalTok{colors[}\DecValTok{1}\NormalTok{], lw}\OperatorTok{=}\NormalTok{lw, marker}\OperatorTok{=}\StringTok{"o"}\NormalTok{, label}\OperatorTok{=}\SpecialStringTok{f"DNN validate }\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
\NormalTok{            axs[i][y].set\_title(}\SpecialStringTok{f"Validate }\SpecialCharTok{\{}\NormalTok{metrics\_name[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{, fontsize}\OperatorTok{=}\NormalTok{title\_fontsize)}
\NormalTok{            axs[i][y].set\_xlabel(}\StringTok{"epochs"}\NormalTok{, fontsize}\OperatorTok{=}\NormalTok{label\_fontsize)}
\NormalTok{            axs[i][y].set\_ylabel(}\SpecialStringTok{f"}\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{, fontsize}\OperatorTok{=}\NormalTok{label\_fontsize)}
\NormalTok{            axs[i][y].grid(linestyle}\OperatorTok{=}\StringTok{"dotted"}\NormalTok{, alpha}\OperatorTok{=}\FloatTok{0.7}\NormalTok{)}
\NormalTok{            axs[i][y].legend(fontsize}\OperatorTok{=}\NormalTok{legend\_fontsize)}
\NormalTok{            axs[i][y].tick\_params(axis}\OperatorTok{=}\StringTok{"both"}\NormalTok{, which}\OperatorTok{=}\StringTok{"major"}\NormalTok{, labelsize}\OperatorTok{=}\NormalTok{tick\_fontsize)}
        \ControlFlowTok{else}\NormalTok{:}
\NormalTok{            axs[i][y].plot(epochs, unet\_model\_metrics[metrics[y]], color}\OperatorTok{=}\NormalTok{colors[}\DecValTok{0}\NormalTok{], lw}\OperatorTok{=}\NormalTok{lw, marker}\OperatorTok{=}\StringTok{"o"}\NormalTok{, label}\OperatorTok{=}\SpecialStringTok{f"U{-}Net train }\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
\NormalTok{            axs[i][y].plot(epochs, dnn\_model\_metrics[metrics[y]], color}\OperatorTok{=}\NormalTok{colors[}\DecValTok{1}\NormalTok{], lw}\OperatorTok{=}\NormalTok{lw, marker}\OperatorTok{=}\StringTok{"o"}\NormalTok{, label}\OperatorTok{=}\SpecialStringTok{f"DNN train }\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
\NormalTok{            axs[i][y].set\_title(}\SpecialStringTok{f"Train }\SpecialCharTok{\{}\NormalTok{metrics\_name[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{, fontsize}\OperatorTok{=}\NormalTok{title\_fontsize)}
\NormalTok{            axs[i][y].set\_xlabel(}\StringTok{"epochs"}\NormalTok{, fontsize}\OperatorTok{=}\NormalTok{label\_fontsize)}
\NormalTok{            axs[i][y].set\_ylabel(}\SpecialStringTok{f"}\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{, fontsize}\OperatorTok{=}\NormalTok{label\_fontsize)}
\NormalTok{            axs[i][y].grid(linestyle}\OperatorTok{=}\StringTok{"dotted"}\NormalTok{, alpha}\OperatorTok{=}\FloatTok{0.7}\NormalTok{)}
\NormalTok{            axs[i][y].legend(fontsize}\OperatorTok{=}\NormalTok{legend\_fontsize)}
\NormalTok{            axs[i][y].tick\_params(axis}\OperatorTok{=}\StringTok{"both"}\NormalTok{, which}\OperatorTok{=}\StringTok{"major"}\NormalTok{, labelsize}\OperatorTok{=}\NormalTok{tick\_fontsize)}


\CommentTok{\# Adjust layout and show the plot}
\NormalTok{plt.tight\_layout()}
\CommentTok{\# plt.savefig("metrics\_plot\_model\_comparison.png", dpi=500, bbox\_inches="tight")}
\NormalTok{plt.show()}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\includegraphics{03_Semantic_Segmentation/01__Crop_Mapping/notebooks/Rice_Mapping_Bhutan_2021_files/figure-pdf/cell-60-output-2.png}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Create subplots for different metrics in a 3x4 grid}
\NormalTok{fig, axs }\OperatorTok{=}\NormalTok{ plt.subplots(}\DecValTok{1}\NormalTok{, }\DecValTok{4}\NormalTok{, figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{4}\OperatorTok{*}\DecValTok{7}\NormalTok{, }\DecValTok{6}\OperatorTok{*}\DecValTok{1}\NormalTok{))}

\NormalTok{colors }\OperatorTok{=}\NormalTok{ [}\StringTok{"\#1f77b4"}\NormalTok{, }\StringTok{"\#ff7f0e"}\NormalTok{, }\StringTok{"\#2ca02c"}\NormalTok{, }\StringTok{"\#d62728"}\NormalTok{]}
\NormalTok{metrics }\OperatorTok{=}\NormalTok{ [}\StringTok{"loss"}\NormalTok{, }\StringTok{"precision"}\NormalTok{, }\StringTok{"recall"}\NormalTok{, }\StringTok{"categorical\_accuracy"}\NormalTok{]}
\NormalTok{metrics\_name }\OperatorTok{=}\NormalTok{ [}\StringTok{"Loss"}\NormalTok{, }\StringTok{"Precision"}\NormalTok{, }\StringTok{"Recall"}\NormalTok{, }\StringTok{"Categorical Accuracy"}\NormalTok{]}

\NormalTok{epochs }\OperatorTok{=} \BuiltInTok{range}\NormalTok{(}\DecValTok{1}\NormalTok{, config.EPOCHS }\OperatorTok{+} \DecValTok{1}\NormalTok{)}

\NormalTok{title\_fontsize }\OperatorTok{=} \DecValTok{22}
\NormalTok{label\_fontsize }\OperatorTok{=} \DecValTok{22}
\NormalTok{legend\_fontsize }\OperatorTok{=} \DecValTok{15}
\NormalTok{tick\_fontsize }\OperatorTok{=} \DecValTok{18}
\NormalTok{lw}\OperatorTok{=}\FloatTok{1.5}

\ControlFlowTok{for}\NormalTok{ y }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(}\BuiltInTok{len}\NormalTok{(metrics)):}
\NormalTok{    axs[y].plot(epochs, unet\_model\_metrics[}\SpecialStringTok{f"val\_}\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{], color}\OperatorTok{=}\NormalTok{colors[}\DecValTok{0}\NormalTok{], marker}\OperatorTok{=}\StringTok{"o"}\NormalTok{, lw}\OperatorTok{=}\NormalTok{lw, label}\OperatorTok{=}\SpecialStringTok{f"U{-}Net validate }\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
\NormalTok{    axs[y].plot(epochs, dnn\_model\_metrics[}\SpecialStringTok{f"val\_}\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{], color}\OperatorTok{=}\NormalTok{colors[}\DecValTok{1}\NormalTok{], lw}\OperatorTok{=}\NormalTok{lw, marker}\OperatorTok{=}\StringTok{"o"}\NormalTok{, label}\OperatorTok{=}\SpecialStringTok{f"DNN validate }\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}

\NormalTok{    axs[y].plot(epochs, unet\_model\_metrics[metrics[y]], color}\OperatorTok{=}\NormalTok{colors[}\DecValTok{2}\NormalTok{], lw}\OperatorTok{=}\NormalTok{lw, marker}\OperatorTok{=}\StringTok{"o"}\NormalTok{, label}\OperatorTok{=}\SpecialStringTok{f"U{-}Net train }\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
\NormalTok{    axs[y].plot(epochs, dnn\_model\_metrics[metrics[y]], color}\OperatorTok{=}\NormalTok{colors[}\DecValTok{3}\NormalTok{], lw}\OperatorTok{=}\NormalTok{lw, marker}\OperatorTok{=}\StringTok{"o"}\NormalTok{, label}\OperatorTok{=}\SpecialStringTok{f"DNN train }\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
\NormalTok{    axs[y].set\_title(}\SpecialStringTok{f"}\SpecialCharTok{\{}\NormalTok{metrics\_name[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{, fontsize}\OperatorTok{=}\NormalTok{title\_fontsize)}
\NormalTok{    axs[y].set\_xlabel(}\StringTok{"epochs"}\NormalTok{, fontsize}\OperatorTok{=}\NormalTok{label\_fontsize)}
\NormalTok{    axs[y].set\_ylabel(}\SpecialStringTok{f"}\SpecialCharTok{\{}\NormalTok{metrics[y]}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{, fontsize}\OperatorTok{=}\NormalTok{label\_fontsize)}
\NormalTok{    axs[y].grid(linestyle}\OperatorTok{=}\StringTok{"dotted"}\NormalTok{, alpha}\OperatorTok{=}\FloatTok{0.7}\NormalTok{)}
\NormalTok{    axs[y].legend(fontsize}\OperatorTok{=}\NormalTok{legend\_fontsize)}
\NormalTok{    axs[y].tick\_params(axis}\OperatorTok{=}\StringTok{"both"}\NormalTok{, which}\OperatorTok{=}\StringTok{"major"}\NormalTok{, labelsize}\OperatorTok{=}\NormalTok{tick\_fontsize)}


\CommentTok{\# Adjust layout and show the plot}
\NormalTok{plt.tight\_layout()}
\CommentTok{\# plt.savefig("metrics\_plot\_model\_comparison.png", dpi=500, bbox\_inches="tight")}
\NormalTok{plt.show()}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\includegraphics{03_Semantic_Segmentation/01__Crop_Mapping/notebooks/Rice_Mapping_Bhutan_2021_files/figure-pdf/cell-61-output-2.png}

\subsection{Probability Distribution
Plot}\label{probability-distribution-plot}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{all\_data }\OperatorTok{=}\NormalTok{ \{\}}

\NormalTok{unet\_data }\OperatorTok{=}\NormalTok{ []}
\NormalTok{dnn\_data }\OperatorTok{=}\NormalTok{ []}

\NormalTok{unet\_rice\_data }\OperatorTok{=}\NormalTok{ []}
\NormalTok{dnn\_rice\_data }\OperatorTok{=}\NormalTok{ []}

\NormalTok{unet\_other\_data }\OperatorTok{=}\NormalTok{ []}
\NormalTok{dnn\_other\_data }\OperatorTok{=}\NormalTok{ []}

\ControlFlowTok{for}\NormalTok{ i, feature }\KeywordTok{in} \BuiltInTok{enumerate}\NormalTok{(prob\_output\_unet[}\StringTok{"features"}\NormalTok{]):}
\NormalTok{    unet\_rice\_prob }\OperatorTok{=} \BuiltInTok{round}\NormalTok{(feature[}\StringTok{"properties"}\NormalTok{][}\StringTok{"rice\_prob"}\NormalTok{], }\DecValTok{5}\NormalTok{)}
\NormalTok{    unet\_other\_prob }\OperatorTok{=} \BuiltInTok{round}\NormalTok{(feature[}\StringTok{"properties"}\NormalTok{][}\StringTok{"cropland\_prob"}\NormalTok{] }\OperatorTok{+} \BuiltInTok{round}\NormalTok{(feature[}\StringTok{"properties"}\NormalTok{][}\StringTok{"forest\_prob"}\NormalTok{] }\OperatorTok{+}\NormalTok{ feature[}\StringTok{"properties"}\NormalTok{][}\StringTok{"others\_prob"}\NormalTok{]}\OperatorTok{+}\NormalTok{ feature[}\StringTok{"properties"}\NormalTok{][}\StringTok{"urban\_prob"}\NormalTok{]), }\DecValTok{5}\NormalTok{)}
\NormalTok{    unet\_data.append([unet\_rice\_prob, unet\_other\_prob])}
\NormalTok{    unet\_rice\_data.append(unet\_rice\_prob)}
\NormalTok{    unet\_other\_data.append(unet\_other\_prob)}

\NormalTok{    dnn\_feature }\OperatorTok{=}\NormalTok{ prob\_output\_dnn[}\StringTok{"features"}\NormalTok{][i]}
\NormalTok{    dnn\_rice\_prob }\OperatorTok{=} \BuiltInTok{round}\NormalTok{(dnn\_feature[}\StringTok{"properties"}\NormalTok{][}\StringTok{"rice\_prob"}\NormalTok{], }\DecValTok{5}\NormalTok{)}
\NormalTok{    dnn\_other\_prob }\OperatorTok{=} \FloatTok{1.} \OperatorTok{{-}} \BuiltInTok{round}\NormalTok{(dnn\_feature[}\StringTok{"properties"}\NormalTok{][}\StringTok{"rice\_prob"}\NormalTok{], }\DecValTok{5}\NormalTok{)}
    \CommentTok{\# dnn\_other\_prob = round(dnn\_feature["properties"]["cropland\_prob"] + dnn\_feature["properties"]["forest\_prob"] + dnn\_feature["properties"]["others\_prob"]+ dnn\_feature["properties"]["urban\_prob"], 5)}
\NormalTok{    dnn\_data.append([dnn\_rice\_prob, dnn\_other\_prob])}
\NormalTok{    dnn\_rice\_data.append(dnn\_rice\_prob)}
\NormalTok{    dnn\_other\_data.append(dnn\_other\_prob)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{fig, (ax1, ax2) }\OperatorTok{=}\NormalTok{ plt.subplots(nrows}\OperatorTok{=}\DecValTok{1}\NormalTok{, ncols}\OperatorTok{=}\DecValTok{2}\NormalTok{, figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{8}\NormalTok{, }\DecValTok{5}\NormalTok{))}

\NormalTok{title\_fontsize }\OperatorTok{=} \DecValTok{22}
\NormalTok{label\_fontsize }\OperatorTok{=} \DecValTok{10}
\NormalTok{tick\_fontsize }\OperatorTok{=} \DecValTok{10}

\CommentTok{\# rectangular box plot}
\NormalTok{bplot1 }\OperatorTok{=}\NormalTok{ ax1.boxplot([unet\_rice\_data, dnn\_rice\_data],}
\NormalTok{                     notch}\OperatorTok{=}\VariableTok{True}\NormalTok{,}
\NormalTok{                     vert}\OperatorTok{=}\VariableTok{True}\NormalTok{,  }\CommentTok{\# vertical box alignment}
\NormalTok{                     patch\_artist}\OperatorTok{=}\VariableTok{True}\NormalTok{,  }\CommentTok{\# fill with color}
\NormalTok{                     labels}\OperatorTok{=}\NormalTok{[}\StringTok{"U{-}Net"}\NormalTok{, }\StringTok{"DNN"}\NormalTok{],}
\NormalTok{                     sym}\OperatorTok{=}\StringTok{"k+"}\NormalTok{)  }\CommentTok{\# will be used to label x{-}ticks}
\NormalTok{ax1.set\_title(}\StringTok{"Rice Probability"}\NormalTok{)}

\CommentTok{\# notch shape box plot}
\NormalTok{bplot2 }\OperatorTok{=}\NormalTok{ ax2.boxplot([unet\_other\_data, dnn\_other\_data],}
\NormalTok{                     notch}\OperatorTok{=}\VariableTok{True}\NormalTok{,  }\CommentTok{\# notch shape}
\NormalTok{                     vert}\OperatorTok{=}\VariableTok{True}\NormalTok{,  }\CommentTok{\# vertical box alignment}
\NormalTok{                     patch\_artist}\OperatorTok{=}\VariableTok{True}\NormalTok{,  }\CommentTok{\# fill with color}
\NormalTok{                     labels}\OperatorTok{=}\NormalTok{[}\StringTok{"U{-}Net"}\NormalTok{, }\StringTok{"DNN"}\NormalTok{],}
\NormalTok{                     sym}\OperatorTok{=}\StringTok{"k+"}\NormalTok{)  }\CommentTok{\# will be used to label x{-}ticks}
\NormalTok{ax2.set\_title(}\StringTok{"Other Probability"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
<IPython.core.display.HTML object>
\end{verbatim}

\begin{verbatim}
Text(0.5, 1.0, 'Other Probability')
\end{verbatim}

\includegraphics{03_Semantic_Segmentation/01__Crop_Mapping/notebooks/Rice_Mapping_Bhutan_2021_files/figure-pdf/cell-63-output-3.png}

\chapter{Object Detection}\label{object-detection}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Print out the Python version used by this environment.}
\ImportTok{import}\NormalTok{ sys}

\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f\textquotesingle{}}\SpecialCharTok{\{}\NormalTok{sys}\SpecialCharTok{.}\NormalTok{version}\OperatorTok{=}\SpecialCharTok{\}}\SpecialStringTok{\textquotesingle{}}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
sys.version='3.9.0 | packaged by conda-forge | (default, Nov 26 2020, 07:55:15) \n[Clang 11.0.0 ]'
\end{verbatim}

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\chapter{Time Series}\label{time-series}

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\chapter{Ecological Processes
Simulation}\label{ecological-processes-simulation}

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\chapter{Transfer Learning}\label{transfer-learning}

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\chapter{Fusion}\label{fusion}

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\chapter{Downscaling}\label{downscaling}

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\chapter{Future of Deep Learning and Foundational
Models}\label{future-of-deep-learning-and-foundational-models}

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\chapter{Ethics and Artificial
Intelligence}\label{ethics-and-artificial-intelligence}

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\chapter*{Conclusions}\label{conclusions}
\addcontentsline{toc}{chapter}{Conclusions}

\markboth{Conclusions}{Conclusions}

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
parturient montes, nascetur ridiculus mus.

Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat
leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque
habitant morbi tristique senectus et netus et malesuada fames ac turpis
egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere
iaculis. Suspendisse et maximus elit. In fringilla gravida ornare.
Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel
neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet
vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor
lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque
ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse
potenti.

Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim
ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies.
Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit
amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit,
eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id,
tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis
efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus
ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum
ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam
id magna ultrices, sodales metus viverra, tempus turpis.

Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
tortor. Donec ut urna nisi.

Aenean placerat luctus tortor vitae molestie. Nulla at aliquet nulla.
Sed efficitur tellus orci, sed fringilla lectus laoreet eget. Vivamus
maximus quam sit amet arcu dignissim, sed accumsan massa ullamcorper.
Sed iaculis tincidunt feugiat. Nulla in est at nunc ultricies dictum ut
vitae nunc. Aenean convallis vel diam at malesuada. Suspendisse arcu
libero, vehicula tempus ultrices a, placerat sit amet tortor. Sed dictum
id nulla commodo mattis. Aliquam mollis, nunc eu tristique faucibus,
purus lacus tincidunt nulla, ac pretium lorem nunc ut enim. Curabitur
eget mattis nisl, vitae sodales augue. Nam felis massa, bibendum sit
amet nulla vel, vulputate rutrum lacus. Aenean convallis odio pharetra
nulla mattis consequat.

\bookmarksetup{startatroot}

\chapter*{References}\label{references}
\addcontentsline{toc}{chapter}{References}

\markboth{References}{References}

\phantomsection\label{refs}
\begin{CSLReferences}{0}{1}
\end{CSLReferences}




\end{document}