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GNU GENERAL PUBLIC LICENSE
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Version 3, 29 June 2007
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Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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Everyone is permitted to copy and distribute verbatim copies
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of this license document, but changing it is not allowed.
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Preamble
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The GNU General Public License is a free, copyleft license for
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software and other kinds of works.
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The licenses for most software and other practical works are designed
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to take away your freedom to share and change the works. By contrast,
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the GNU General Public License is intended to guarantee your freedom to
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share and change all versions of a program--to make sure it remains free
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software for all its users. We, the Free Software Foundation, use the
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GNU General Public License for most of our software; it applies also to
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any other work released this way by its authors. You can apply it to
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your programs, too.
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|
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|
When we speak of free software, we are referring to freedom, not
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price. Our General Public Licenses are designed to make sure that you
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have the freedom to distribute copies of free software (and charge for
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them if you wish), that you receive source code or can get it if you
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|
want it, that you can change the software or use pieces of it in new
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free programs, and that you know you can do these things.
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|
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|
To protect your rights, we need to prevent others from denying you
|
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|
these rights or asking you to surrender the rights. Therefore, you have
|
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|
certain responsibilities if you distribute copies of the software, or if
|
||||||
|
you modify it: responsibilities to respect the freedom of others.
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|
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|
For example, if you distribute copies of such a program, whether
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|
gratis or for a fee, you must pass on to the recipients the same
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|
freedoms that you received. You must make sure that they, too, receive
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|
or can get the source code. And you must show them these terms so they
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know their rights.
|
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|
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|
Developers that use the GNU GPL protect your rights with two steps:
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(1) assert copyright on the software, and (2) offer you this License
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|
giving you legal permission to copy, distribute and/or modify it.
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|
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|
For the developers' and authors' protection, the GPL clearly explains
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that there is no warranty for this free software. For both users' and
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|
authors' sake, the GPL requires that modified versions be marked as
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changed, so that their problems will not be attributed erroneously to
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authors of previous versions.
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Some devices are designed to deny users access to install or run
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modified versions of the software inside them, although the manufacturer
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can do so. This is fundamentally incompatible with the aim of
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protecting users' freedom to change the software. The systematic
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pattern of such abuse occurs in the area of products for individuals to
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use, which is precisely where it is most unacceptable. Therefore, we
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have designed this version of the GPL to prohibit the practice for those
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products. If such problems arise substantially in other domains, we
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stand ready to extend this provision to those domains in future versions
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|
of the GPL, as needed to protect the freedom of users.
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||||||
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|
||||||
|
Finally, every program is threatened constantly by software patents.
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|
States should not allow patents to restrict development and use of
|
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|
software on general-purpose computers, but in those that do, we wish to
|
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|
avoid the special danger that patents applied to a free program could
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make it effectively proprietary. To prevent this, the GPL assures that
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patents cannot be used to render the program non-free.
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|
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The precise terms and conditions for copying, distribution and
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|
modification follow.
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|
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|
TERMS AND CONDITIONS
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||||||
|
0. Definitions.
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||||||
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|
||||||
|
"This License" refers to version 3 of the GNU General Public License.
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"Copyright" also means copyright-like laws that apply to other kinds of
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works, such as semiconductor masks.
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"The Program" refers to any copyrightable work licensed under this
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License. Each licensee is addressed as "you". "Licensees" and
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"recipients" may be individuals or organizations.
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|
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To "modify" a work means to copy from or adapt all or part of the work
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in a fashion requiring copyright permission, other than the making of an
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exact copy. The resulting work is called a "modified version" of the
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earlier work or a work "based on" the earlier work.
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|
||||||
|
A "covered work" means either the unmodified Program or a work based
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|
on the Program.
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||||||
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|
||||||
|
To "propagate" a work means to do anything with it that, without
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|
permission, would make you directly or secondarily liable for
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||||||
|
infringement under applicable copyright law, except executing it on a
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computer or modifying a private copy. Propagation includes copying,
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distribution (with or without modification), making available to the
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public, and in some countries other activities as well.
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To "convey" a work means any kind of propagation that enables other
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parties to make or receive copies. Mere interaction with a user through
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a computer network, with no transfer of a copy, is not conveying.
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|
||||||
|
An interactive user interface displays "Appropriate Legal Notices"
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||||||
|
to the extent that it includes a convenient and prominently visible
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||||||
|
feature that (1) displays an appropriate copyright notice, and (2)
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tells the user that there is no warranty for the work (except to the
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extent that warranties are provided), that licensees may convey the
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work under this License, and how to view a copy of this License. If
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the interface presents a list of user commands or options, such as a
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menu, a prominent item in the list meets this criterion.
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||||||
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|
||||||
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1. Source Code.
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||||||
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The "source code" for a work means the preferred form of the work
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for making modifications to it. "Object code" means any non-source
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form of a work.
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|
||||||
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A "Standard Interface" means an interface that either is an official
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standard defined by a recognized standards body, or, in the case of
|
||||||
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interfaces specified for a particular programming language, one that
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||||||
|
is widely used among developers working in that language.
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||||||
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|
||||||
|
The "System Libraries" of an executable work include anything, other
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||||||
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than the work as a whole, that (a) is included in the normal form of
|
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|
packaging a Major Component, but which is not part of that Major
|
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Component, and (b) serves only to enable use of the work with that
|
||||||
|
Major Component, or to implement a Standard Interface for which an
|
||||||
|
implementation is available to the public in source code form. A
|
||||||
|
"Major Component", in this context, means a major essential component
|
||||||
|
(kernel, window system, and so on) of the specific operating system
|
||||||
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(if any) on which the executable work runs, or a compiler used to
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|
produce the work, or an object code interpreter used to run it.
|
||||||
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|
||||||
|
The "Corresponding Source" for a work in object code form means all
|
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|
the source code needed to generate, install, and (for an executable
|
||||||
|
work) run the object code and to modify the work, including scripts to
|
||||||
|
control those activities. However, it does not include the work's
|
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System Libraries, or general-purpose tools or generally available free
|
||||||
|
programs which are used unmodified in performing those activities but
|
||||||
|
which are not part of the work. For example, Corresponding Source
|
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|
includes interface definition files associated with source files for
|
||||||
|
the work, and the source code for shared libraries and dynamically
|
||||||
|
linked subprograms that the work is specifically designed to require,
|
||||||
|
such as by intimate data communication or control flow between those
|
||||||
|
subprograms and other parts of the work.
|
||||||
|
|
||||||
|
The Corresponding Source need not include anything that users
|
||||||
|
can regenerate automatically from other parts of the Corresponding
|
||||||
|
Source.
|
||||||
|
|
||||||
|
The Corresponding Source for a work in source code form is that
|
||||||
|
same work.
|
||||||
|
|
||||||
|
2. Basic Permissions.
|
||||||
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|
||||||
|
All rights granted under this License are granted for the term of
|
||||||
|
copyright on the Program, and are irrevocable provided the stated
|
||||||
|
conditions are met. This License explicitly affirms your unlimited
|
||||||
|
permission to run the unmodified Program. The output from running a
|
||||||
|
covered work is covered by this License only if the output, given its
|
||||||
|
content, constitutes a covered work. This License acknowledges your
|
||||||
|
rights of fair use or other equivalent, as provided by copyright law.
|
||||||
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|
||||||
|
You may make, run and propagate covered works that you do not
|
||||||
|
convey, without conditions so long as your license otherwise remains
|
||||||
|
in force. You may convey covered works to others for the sole purpose
|
||||||
|
of having them make modifications exclusively for you, or provide you
|
||||||
|
with facilities for running those works, provided that you comply with
|
||||||
|
the terms of this License in conveying all material for which you do
|
||||||
|
not control copyright. Those thus making or running the covered works
|
||||||
|
for you must do so exclusively on your behalf, under your direction
|
||||||
|
and control, on terms that prohibit them from making any copies of
|
||||||
|
your copyrighted material outside their relationship with you.
|
||||||
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|
||||||
|
Conveying under any other circumstances is permitted solely under
|
||||||
|
the conditions stated below. Sublicensing is not allowed; section 10
|
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|
makes it unnecessary.
|
||||||
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|
||||||
|
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
||||||
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|
||||||
|
No covered work shall be deemed part of an effective technological
|
||||||
|
measure under any applicable law fulfilling obligations under article
|
||||||
|
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||||
|
similar laws prohibiting or restricting circumvention of such
|
||||||
|
measures.
|
||||||
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|
||||||
|
When you convey a covered work, you waive any legal power to forbid
|
||||||
|
circumvention of technological measures to the extent such circumvention
|
||||||
|
is effected by exercising rights under this License with respect to
|
||||||
|
the covered work, and you disclaim any intention to limit operation or
|
||||||
|
modification of the work as a means of enforcing, against the work's
|
||||||
|
users, your or third parties' legal rights to forbid circumvention of
|
||||||
|
technological measures.
|
||||||
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|
||||||
|
4. Conveying Verbatim Copies.
|
||||||
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|
||||||
|
You may convey verbatim copies of the Program's source code as you
|
||||||
|
receive it, in any medium, provided that you conspicuously and
|
||||||
|
appropriately publish on each copy an appropriate copyright notice;
|
||||||
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keep intact all notices stating that this License and any
|
||||||
|
non-permissive terms added in accord with section 7 apply to the code;
|
||||||
|
keep intact all notices of the absence of any warranty; and give all
|
||||||
|
recipients a copy of this License along with the Program.
|
||||||
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|
||||||
|
You may charge any price or no price for each copy that you convey,
|
||||||
|
and you may offer support or warranty protection for a fee.
|
||||||
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|
||||||
|
5. Conveying Modified Source Versions.
|
||||||
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|
||||||
|
You may convey a work based on the Program, or the modifications to
|
||||||
|
produce it from the Program, in the form of source code under the
|
||||||
|
terms of section 4, provided that you also meet all of these conditions:
|
||||||
|
|
||||||
|
a) The work must carry prominent notices stating that you modified
|
||||||
|
it, and giving a relevant date.
|
||||||
|
|
||||||
|
b) The work must carry prominent notices stating that it is
|
||||||
|
released under this License and any conditions added under section
|
||||||
|
7. This requirement modifies the requirement in section 4 to
|
||||||
|
"keep intact all notices".
|
||||||
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|
||||||
|
c) You must license the entire work, as a whole, under this
|
||||||
|
License to anyone who comes into possession of a copy. This
|
||||||
|
License will therefore apply, along with any applicable section 7
|
||||||
|
additional terms, to the whole of the work, and all its parts,
|
||||||
|
regardless of how they are packaged. This License gives no
|
||||||
|
permission to license the work in any other way, but it does not
|
||||||
|
invalidate such permission if you have separately received it.
|
||||||
|
|
||||||
|
d) If the work has interactive user interfaces, each must display
|
||||||
|
Appropriate Legal Notices; however, if the Program has interactive
|
||||||
|
interfaces that do not display Appropriate Legal Notices, your
|
||||||
|
work need not make them do so.
|
||||||
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|
||||||
|
A compilation of a covered work with other separate and independent
|
||||||
|
works, which are not by their nature extensions of the covered work,
|
||||||
|
and which are not combined with it such as to form a larger program,
|
||||||
|
in or on a volume of a storage or distribution medium, is called an
|
||||||
|
"aggregate" if the compilation and its resulting copyright are not
|
||||||
|
used to limit the access or legal rights of the compilation's users
|
||||||
|
beyond what the individual works permit. Inclusion of a covered work
|
||||||
|
in an aggregate does not cause this License to apply to the other
|
||||||
|
parts of the aggregate.
|
||||||
|
|
||||||
|
6. Conveying Non-Source Forms.
|
||||||
|
|
||||||
|
You may convey a covered work in object code form under the terms
|
||||||
|
of sections 4 and 5, provided that you also convey the
|
||||||
|
machine-readable Corresponding Source under the terms of this License,
|
||||||
|
in one of these ways:
|
||||||
|
|
||||||
|
a) Convey the object code in, or embodied in, a physical product
|
||||||
|
(including a physical distribution medium), accompanied by the
|
||||||
|
Corresponding Source fixed on a durable physical medium
|
||||||
|
customarily used for software interchange.
|
||||||
|
|
||||||
|
b) Convey the object code in, or embodied in, a physical product
|
||||||
|
(including a physical distribution medium), accompanied by a
|
||||||
|
written offer, valid for at least three years and valid for as
|
||||||
|
long as you offer spare parts or customer support for that product
|
||||||
|
model, to give anyone who possesses the object code either (1) a
|
||||||
|
copy of the Corresponding Source for all the software in the
|
||||||
|
product that is covered by this License, on a durable physical
|
||||||
|
medium customarily used for software interchange, for a price no
|
||||||
|
more than your reasonable cost of physically performing this
|
||||||
|
conveying of source, or (2) access to copy the
|
||||||
|
Corresponding Source from a network server at no charge.
|
||||||
|
|
||||||
|
c) Convey individual copies of the object code with a copy of the
|
||||||
|
written offer to provide the Corresponding Source. This
|
||||||
|
alternative is allowed only occasionally and noncommercially, and
|
||||||
|
only if you received the object code with such an offer, in accord
|
||||||
|
with subsection 6b.
|
||||||
|
|
||||||
|
d) Convey the object code by offering access from a designated
|
||||||
|
place (gratis or for a charge), and offer equivalent access to the
|
||||||
|
Corresponding Source in the same way through the same place at no
|
||||||
|
further charge. You need not require recipients to copy the
|
||||||
|
Corresponding Source along with the object code. If the place to
|
||||||
|
copy the object code is a network server, the Corresponding Source
|
||||||
|
may be on a different server (operated by you or a third party)
|
||||||
|
that supports equivalent copying facilities, provided you maintain
|
||||||
|
clear directions next to the object code saying where to find the
|
||||||
|
Corresponding Source. Regardless of what server hosts the
|
||||||
|
Corresponding Source, you remain obligated to ensure that it is
|
||||||
|
available for as long as needed to satisfy these requirements.
|
||||||
|
|
||||||
|
e) Convey the object code using peer-to-peer transmission, provided
|
||||||
|
you inform other peers where the object code and Corresponding
|
||||||
|
Source of the work are being offered to the general public at no
|
||||||
|
charge under subsection 6d.
|
||||||
|
|
||||||
|
A separable portion of the object code, whose source code is excluded
|
||||||
|
from the Corresponding Source as a System Library, need not be
|
||||||
|
included in conveying the object code work.
|
||||||
|
|
||||||
|
A "User Product" is either (1) a "consumer product", which means any
|
||||||
|
tangible personal property which is normally used for personal, family,
|
||||||
|
or household purposes, or (2) anything designed or sold for incorporation
|
||||||
|
into a dwelling. In determining whether a product is a consumer product,
|
||||||
|
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||||
|
product received by a particular user, "normally used" refers to a
|
||||||
|
typical or common use of that class of product, regardless of the status
|
||||||
|
of the particular user or of the way in which the particular user
|
||||||
|
actually uses, or expects or is expected to use, the product. A product
|
||||||
|
is a consumer product regardless of whether the product has substantial
|
||||||
|
commercial, industrial or non-consumer uses, unless such uses represent
|
||||||
|
the only significant mode of use of the product.
|
||||||
|
|
||||||
|
"Installation Information" for a User Product means any methods,
|
||||||
|
procedures, authorization keys, or other information required to install
|
||||||
|
and execute modified versions of a covered work in that User Product from
|
||||||
|
a modified version of its Corresponding Source. The information must
|
||||||
|
suffice to ensure that the continued functioning of the modified object
|
||||||
|
code is in no case prevented or interfered with solely because
|
||||||
|
modification has been made.
|
||||||
|
|
||||||
|
If you convey an object code work under this section in, or with, or
|
||||||
|
specifically for use in, a User Product, and the conveying occurs as
|
||||||
|
part of a transaction in which the right of possession and use of the
|
||||||
|
User Product is transferred to the recipient in perpetuity or for a
|
||||||
|
fixed term (regardless of how the transaction is characterized), the
|
||||||
|
Corresponding Source conveyed under this section must be accompanied
|
||||||
|
by the Installation Information. But this requirement does not apply
|
||||||
|
if neither you nor any third party retains the ability to install
|
||||||
|
modified object code on the User Product (for example, the work has
|
||||||
|
been installed in ROM).
|
||||||
|
|
||||||
|
The requirement to provide Installation Information does not include a
|
||||||
|
requirement to continue to provide support service, warranty, or updates
|
||||||
|
for a work that has been modified or installed by the recipient, or for
|
||||||
|
the User Product in which it has been modified or installed. Access to a
|
||||||
|
network may be denied when the modification itself materially and
|
||||||
|
adversely affects the operation of the network or violates the rules and
|
||||||
|
protocols for communication across the network.
|
||||||
|
|
||||||
|
Corresponding Source conveyed, and Installation Information provided,
|
||||||
|
in accord with this section must be in a format that is publicly
|
||||||
|
documented (and with an implementation available to the public in
|
||||||
|
source code form), and must require no special password or key for
|
||||||
|
unpacking, reading or copying.
|
||||||
|
|
||||||
|
7. Additional Terms.
|
||||||
|
|
||||||
|
"Additional permissions" are terms that supplement the terms of this
|
||||||
|
License by making exceptions from one or more of its conditions.
|
||||||
|
Additional permissions that are applicable to the entire Program shall
|
||||||
|
be treated as though they were included in this License, to the extent
|
||||||
|
that they are valid under applicable law. If additional permissions
|
||||||
|
apply only to part of the Program, that part may be used separately
|
||||||
|
under those permissions, but the entire Program remains governed by
|
||||||
|
this License without regard to the additional permissions.
|
||||||
|
|
||||||
|
When you convey a copy of a covered work, you may at your option
|
||||||
|
remove any additional permissions from that copy, or from any part of
|
||||||
|
it. (Additional permissions may be written to require their own
|
||||||
|
removal in certain cases when you modify the work.) You may place
|
||||||
|
additional permissions on material, added by you to a covered work,
|
||||||
|
for which you have or can give appropriate copyright permission.
|
||||||
|
|
||||||
|
Notwithstanding any other provision of this License, for material you
|
||||||
|
add to a covered work, you may (if authorized by the copyright holders of
|
||||||
|
that material) supplement the terms of this License with terms:
|
||||||
|
|
||||||
|
a) Disclaiming warranty or limiting liability differently from the
|
||||||
|
terms of sections 15 and 16 of this License; or
|
||||||
|
|
||||||
|
b) Requiring preservation of specified reasonable legal notices or
|
||||||
|
author attributions in that material or in the Appropriate Legal
|
||||||
|
Notices displayed by works containing it; or
|
||||||
|
|
||||||
|
c) Prohibiting misrepresentation of the origin of that material, or
|
||||||
|
requiring that modified versions of such material be marked in
|
||||||
|
reasonable ways as different from the original version; or
|
||||||
|
|
||||||
|
d) Limiting the use for publicity purposes of names of licensors or
|
||||||
|
authors of the material; or
|
||||||
|
|
||||||
|
e) Declining to grant rights under trademark law for use of some
|
||||||
|
trade names, trademarks, or service marks; or
|
||||||
|
|
||||||
|
f) Requiring indemnification of licensors and authors of that
|
||||||
|
material by anyone who conveys the material (or modified versions of
|
||||||
|
it) with contractual assumptions of liability to the recipient, for
|
||||||
|
any liability that these contractual assumptions directly impose on
|
||||||
|
those licensors and authors.
|
||||||
|
|
||||||
|
All other non-permissive additional terms are considered "further
|
||||||
|
restrictions" within the meaning of section 10. If the Program as you
|
||||||
|
received it, or any part of it, contains a notice stating that it is
|
||||||
|
governed by this License along with a term that is a further
|
||||||
|
restriction, you may remove that term. If a license document contains
|
||||||
|
a further restriction but permits relicensing or conveying under this
|
||||||
|
License, you may add to a covered work material governed by the terms
|
||||||
|
of that license document, provided that the further restriction does
|
||||||
|
not survive such relicensing or conveying.
|
||||||
|
|
||||||
|
If you add terms to a covered work in accord with this section, you
|
||||||
|
must place, in the relevant source files, a statement of the
|
||||||
|
additional terms that apply to those files, or a notice indicating
|
||||||
|
where to find the applicable terms.
|
||||||
|
|
||||||
|
Additional terms, permissive or non-permissive, may be stated in the
|
||||||
|
form of a separately written license, or stated as exceptions;
|
||||||
|
the above requirements apply either way.
|
||||||
|
|
||||||
|
8. Termination.
|
||||||
|
|
||||||
|
You may not propagate or modify a covered work except as expressly
|
||||||
|
provided under this License. Any attempt otherwise to propagate or
|
||||||
|
modify it is void, and will automatically terminate your rights under
|
||||||
|
this License (including any patent licenses granted under the third
|
||||||
|
paragraph of section 11).
|
||||||
|
|
||||||
|
However, if you cease all violation of this License, then your
|
||||||
|
license from a particular copyright holder is reinstated (a)
|
||||||
|
provisionally, unless and until the copyright holder explicitly and
|
||||||
|
finally terminates your license, and (b) permanently, if the copyright
|
||||||
|
holder fails to notify you of the violation by some reasonable means
|
||||||
|
prior to 60 days after the cessation.
|
||||||
|
|
||||||
|
Moreover, your license from a particular copyright holder is
|
||||||
|
reinstated permanently if the copyright holder notifies you of the
|
||||||
|
violation by some reasonable means, this is the first time you have
|
||||||
|
received notice of violation of this License (for any work) from that
|
||||||
|
copyright holder, and you cure the violation prior to 30 days after
|
||||||
|
your receipt of the notice.
|
||||||
|
|
||||||
|
Termination of your rights under this section does not terminate the
|
||||||
|
licenses of parties who have received copies or rights from you under
|
||||||
|
this License. If your rights have been terminated and not permanently
|
||||||
|
reinstated, you do not qualify to receive new licenses for the same
|
||||||
|
material under section 10.
|
||||||
|
|
||||||
|
9. Acceptance Not Required for Having Copies.
|
||||||
|
|
||||||
|
You are not required to accept this License in order to receive or
|
||||||
|
run a copy of the Program. Ancillary propagation of a covered work
|
||||||
|
occurring solely as a consequence of using peer-to-peer transmission
|
||||||
|
to receive a copy likewise does not require acceptance. However,
|
||||||
|
nothing other than this License grants you permission to propagate or
|
||||||
|
modify any covered work. These actions infringe copyright if you do
|
||||||
|
not accept this License. Therefore, by modifying or propagating a
|
||||||
|
covered work, you indicate your acceptance of this License to do so.
|
||||||
|
|
||||||
|
10. Automatic Licensing of Downstream Recipients.
|
||||||
|
|
||||||
|
Each time you convey a covered work, the recipient automatically
|
||||||
|
receives a license from the original licensors, to run, modify and
|
||||||
|
propagate that work, subject to this License. You are not responsible
|
||||||
|
for enforcing compliance by third parties with this License.
|
||||||
|
|
||||||
|
An "entity transaction" is a transaction transferring control of an
|
||||||
|
organization, or substantially all assets of one, or subdividing an
|
||||||
|
organization, or merging organizations. If propagation of a covered
|
||||||
|
work results from an entity transaction, each party to that
|
||||||
|
transaction who receives a copy of the work also receives whatever
|
||||||
|
licenses to the work the party's predecessor in interest had or could
|
||||||
|
give under the previous paragraph, plus a right to possession of the
|
||||||
|
Corresponding Source of the work from the predecessor in interest, if
|
||||||
|
the predecessor has it or can get it with reasonable efforts.
|
||||||
|
|
||||||
|
You may not impose any further restrictions on the exercise of the
|
||||||
|
rights granted or affirmed under this License. For example, you may
|
||||||
|
not impose a license fee, royalty, or other charge for exercise of
|
||||||
|
rights granted under this License, and you may not initiate litigation
|
||||||
|
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||||
|
any patent claim is infringed by making, using, selling, offering for
|
||||||
|
sale, or importing the Program or any portion of it.
|
||||||
|
|
||||||
|
11. Patents.
|
||||||
|
|
||||||
|
A "contributor" is a copyright holder who authorizes use under this
|
||||||
|
License of the Program or a work on which the Program is based. The
|
||||||
|
work thus licensed is called the contributor's "contributor version".
|
||||||
|
|
||||||
|
A contributor's "essential patent claims" are all patent claims
|
||||||
|
owned or controlled by the contributor, whether already acquired or
|
||||||
|
hereafter acquired, that would be infringed by some manner, permitted
|
||||||
|
by this License, of making, using, or selling its contributor version,
|
||||||
|
but do not include claims that would be infringed only as a
|
||||||
|
consequence of further modification of the contributor version. For
|
||||||
|
purposes of this definition, "control" includes the right to grant
|
||||||
|
patent sublicenses in a manner consistent with the requirements of
|
||||||
|
this License.
|
||||||
|
|
||||||
|
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||||
|
patent license under the contributor's essential patent claims, to
|
||||||
|
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||||
|
propagate the contents of its contributor version.
|
||||||
|
|
||||||
|
In the following three paragraphs, a "patent license" is any express
|
||||||
|
agreement or commitment, however denominated, not to enforce a patent
|
||||||
|
(such as an express permission to practice a patent or covenant not to
|
||||||
|
sue for patent infringement). To "grant" such a patent license to a
|
||||||
|
party means to make such an agreement or commitment not to enforce a
|
||||||
|
patent against the party.
|
||||||
|
|
||||||
|
If you convey a covered work, knowingly relying on a patent license,
|
||||||
|
and the Corresponding Source of the work is not available for anyone
|
||||||
|
to copy, free of charge and under the terms of this License, through a
|
||||||
|
publicly available network server or other readily accessible means,
|
||||||
|
then you must either (1) cause the Corresponding Source to be so
|
||||||
|
available, or (2) arrange to deprive yourself of the benefit of the
|
||||||
|
patent license for this particular work, or (3) arrange, in a manner
|
||||||
|
consistent with the requirements of this License, to extend the patent
|
||||||
|
license to downstream recipients. "Knowingly relying" means you have
|
||||||
|
actual knowledge that, but for the patent license, your conveying the
|
||||||
|
covered work in a country, or your recipient's use of the covered work
|
||||||
|
in a country, would infringe one or more identifiable patents in that
|
||||||
|
country that you have reason to believe are valid.
|
||||||
|
|
||||||
|
If, pursuant to or in connection with a single transaction or
|
||||||
|
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||||
|
covered work, and grant a patent license to some of the parties
|
||||||
|
receiving the covered work authorizing them to use, propagate, modify
|
||||||
|
or convey a specific copy of the covered work, then the patent license
|
||||||
|
you grant is automatically extended to all recipients of the covered
|
||||||
|
work and works based on it.
|
||||||
|
|
||||||
|
A patent license is "discriminatory" if it does not include within
|
||||||
|
the scope of its coverage, prohibits the exercise of, or is
|
||||||
|
conditioned on the non-exercise of one or more of the rights that are
|
||||||
|
specifically granted under this License. You may not convey a covered
|
||||||
|
work if you are a party to an arrangement with a third party that is
|
||||||
|
in the business of distributing software, under which you make payment
|
||||||
|
to the third party based on the extent of your activity of conveying
|
||||||
|
the work, and under which the third party grants, to any of the
|
||||||
|
parties who would receive the covered work from you, a discriminatory
|
||||||
|
patent license (a) in connection with copies of the covered work
|
||||||
|
conveyed by you (or copies made from those copies), or (b) primarily
|
||||||
|
for and in connection with specific products or compilations that
|
||||||
|
contain the covered work, unless you entered into that arrangement,
|
||||||
|
or that patent license was granted, prior to 28 March 2007.
|
||||||
|
|
||||||
|
Nothing in this License shall be construed as excluding or limiting
|
||||||
|
any implied license or other defenses to infringement that may
|
||||||
|
otherwise be available to you under applicable patent law.
|
||||||
|
|
||||||
|
12. No Surrender of Others' Freedom.
|
||||||
|
|
||||||
|
If conditions are imposed on you (whether by court order, agreement or
|
||||||
|
otherwise) that contradict the conditions of this License, they do not
|
||||||
|
excuse you from the conditions of this License. If you cannot convey a
|
||||||
|
covered work so as to satisfy simultaneously your obligations under this
|
||||||
|
License and any other pertinent obligations, then as a consequence you may
|
||||||
|
not convey it at all. For example, if you agree to terms that obligate you
|
||||||
|
to collect a royalty for further conveying from those to whom you convey
|
||||||
|
the Program, the only way you could satisfy both those terms and this
|
||||||
|
License would be to refrain entirely from conveying the Program.
|
||||||
|
|
||||||
|
13. Use with the GNU Affero General Public License.
|
||||||
|
|
||||||
|
Notwithstanding any other provision of this License, you have
|
||||||
|
permission to link or combine any covered work with a work licensed
|
||||||
|
under version 3 of the GNU Affero General Public License into a single
|
||||||
|
combined work, and to convey the resulting work. The terms of this
|
||||||
|
License will continue to apply to the part which is the covered work,
|
||||||
|
but the special requirements of the GNU Affero General Public License,
|
||||||
|
section 13, concerning interaction through a network will apply to the
|
||||||
|
combination as such.
|
||||||
|
|
||||||
|
14. Revised Versions of this License.
|
||||||
|
|
||||||
|
The Free Software Foundation may publish revised and/or new versions of
|
||||||
|
the GNU General Public License from time to time. Such new versions will
|
||||||
|
be similar in spirit to the present version, but may differ in detail to
|
||||||
|
address new problems or concerns.
|
||||||
|
|
||||||
|
Each version is given a distinguishing version number. If the
|
||||||
|
Program specifies that a certain numbered version of the GNU General
|
||||||
|
Public License "or any later version" applies to it, you have the
|
||||||
|
option of following the terms and conditions either of that numbered
|
||||||
|
version or of any later version published by the Free Software
|
||||||
|
Foundation. If the Program does not specify a version number of the
|
||||||
|
GNU General Public License, you may choose any version ever published
|
||||||
|
by the Free Software Foundation.
|
||||||
|
|
||||||
|
If the Program specifies that a proxy can decide which future
|
||||||
|
versions of the GNU General Public License can be used, that proxy's
|
||||||
|
public statement of acceptance of a version permanently authorizes you
|
||||||
|
to choose that version for the Program.
|
||||||
|
|
||||||
|
Later license versions may give you additional or different
|
||||||
|
permissions. However, no additional obligations are imposed on any
|
||||||
|
author or copyright holder as a result of your choosing to follow a
|
||||||
|
later version.
|
||||||
|
|
||||||
|
15. Disclaimer of Warranty.
|
||||||
|
|
||||||
|
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||||
|
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||||
|
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||||
|
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||||
|
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||||
|
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||||
|
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||||
|
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||||
|
|
||||||
|
16. Limitation of Liability.
|
||||||
|
|
||||||
|
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||||
|
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||||
|
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||||
|
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||||
|
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||||
|
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||||
|
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||||
|
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||||
|
SUCH DAMAGES.
|
||||||
|
|
||||||
|
17. Interpretation of Sections 15 and 16.
|
||||||
|
|
||||||
|
If the disclaimer of warranty and limitation of liability provided
|
||||||
|
above cannot be given local legal effect according to their terms,
|
||||||
|
reviewing courts shall apply local law that most closely approximates
|
||||||
|
an absolute waiver of all civil liability in connection with the
|
||||||
|
Program, unless a warranty or assumption of liability accompanies a
|
||||||
|
copy of the Program in return for a fee.
|
||||||
|
|
||||||
|
END OF TERMS AND CONDITIONS
|
||||||
|
|
||||||
|
How to Apply These Terms to Your New Programs
|
||||||
|
|
||||||
|
If you develop a new program, and you want it to be of the greatest
|
||||||
|
possible use to the public, the best way to achieve this is to make it
|
||||||
|
free software which everyone can redistribute and change under these terms.
|
||||||
|
|
||||||
|
To do so, attach the following notices to the program. It is safest
|
||||||
|
to attach them to the start of each source file to most effectively
|
||||||
|
state the exclusion of warranty; and each file should have at least
|
||||||
|
the "copyright" line and a pointer to where the full notice is found.
|
||||||
|
|
||||||
|
<one line to give the program's name and a brief idea of what it does.>
|
||||||
|
Copyright (C) 2025 dukantic
|
||||||
|
|
||||||
|
This program is free software: you can redistribute it and/or modify
|
||||||
|
it under the terms of the GNU General Public License as published by
|
||||||
|
the Free Software Foundation, either version 3 of the License, or
|
||||||
|
(at your option) any later version.
|
||||||
|
|
||||||
|
This program is distributed in the hope that it will be useful,
|
||||||
|
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
|
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
|
GNU General Public License for more details.
|
||||||
|
|
||||||
|
You should have received a copy of the GNU General Public License
|
||||||
|
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
|
Also add information on how to contact you by electronic and paper mail.
|
||||||
|
|
||||||
|
If the program does terminal interaction, make it output a short
|
||||||
|
notice like this when it starts in an interactive mode:
|
||||||
|
|
||||||
|
<program> Copyright (C) 2025 dukantic
|
||||||
|
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||||
|
This is free software, and you are welcome to redistribute it
|
||||||
|
under certain conditions; type `show c' for details.
|
||||||
|
|
||||||
|
The hypothetical commands `show w' and `show c' should show the appropriate
|
||||||
|
parts of the General Public License. Of course, your program's commands
|
||||||
|
might be different; for a GUI interface, you would use an "about box".
|
||||||
|
|
||||||
|
You should also get your employer (if you work as a programmer) or school,
|
||||||
|
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||||
|
For more information on this, and how to apply and follow the GNU GPL, see
|
||||||
|
<https://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
|
The GNU General Public License does not permit incorporating your program
|
||||||
|
into proprietary programs. If your program is a subroutine library, you
|
||||||
|
may consider it more useful to permit linking proprietary applications with
|
||||||
|
the library. If this is what you want to do, use the GNU Lesser General
|
||||||
|
Public License instead of this License. But first, please read
|
||||||
|
<https://www.gnu.org/licenses/why-not-lgpl.html>.
|
||||||
@@ -0,0 +1,217 @@
|
|||||||
|
> Dorian HAMDANI, Aubin DORIVAL, Rémi SCHIRRA
|
||||||
|
|
||||||
|
# Table of Contents
|
||||||
|
|
||||||
|
- [🇬🇧 Perceptron Report](#-perceptron-report)
|
||||||
|
- [Introduction](#introduction)
|
||||||
|
- [Running the Program](#running-the-program)
|
||||||
|
- [Build](#build)
|
||||||
|
- [Execution](#execution)
|
||||||
|
- [Structure](#structure)
|
||||||
|
- [Comparison](#comparison)
|
||||||
|
- [Linear Perceptron](#linear-perceptron)
|
||||||
|
- [Linear Data](#linear-data-accuracy-1000)
|
||||||
|
- [Moon Data](#moon-data-accuracy-809)
|
||||||
|
- [Two Circles](#two-circles-accuracy-330)
|
||||||
|
- [General Perceptron](#general-perceptron)
|
||||||
|
- [Linear Data](#linear-data-accuracy-1000-1)
|
||||||
|
- [Moon Data](#moon-data-accuracy-974)
|
||||||
|
- [Two Circles](#two-circles-accuracy-1000)
|
||||||
|
- [MNIST Usage](#mnist-usage)
|
||||||
|
- [Our Experience](#our-experience)
|
||||||
|
|
||||||
|
- [🇫🇷 Rapport Perceptron](#-rapport-perceptron)
|
||||||
|
- [Introduction](#introduction-1)
|
||||||
|
- [Lancer le programme](#lancer-le-programme)
|
||||||
|
- [Build](#build-1)
|
||||||
|
- [Exécution](#exécution)
|
||||||
|
- [Structure](#structure-1)
|
||||||
|
- [Comparaison](#comparaison)
|
||||||
|
- [Perceptron Linéaire](#perceptron-lineaire)
|
||||||
|
- [Linear Data](#linear-data-accuracy-1000-2)
|
||||||
|
- [Moon Data](#moon-data-accurancy-809)
|
||||||
|
- [Two Circles](#two-circles-accuracy-330-1)
|
||||||
|
- [Perceptron Générale](#perceptron-générale)
|
||||||
|
- [Linear Data](#linear-data-accuracy-1000-3)
|
||||||
|
- [Moon Data](#moon-data-accuracy-974-1)
|
||||||
|
- [Two Circles](#two-circles-accuracy-1000-1)
|
||||||
|
- [Utilisation MNIST](#utilisation-mnist)
|
||||||
|
- [Notre expérience](#notre-expérience)
|
||||||
|
|
||||||
|
|
||||||
|
# 🇬🇧 Perceptron Report
|
||||||
|
|
||||||
|
## Introduction
|
||||||
|
This report presents the perceptron project, implemented in Java, that we successfully carried out without particular difficulties.
|
||||||
|
|
||||||
|
## Running the Program
|
||||||
|
|
||||||
|
### Build
|
||||||
|
```bash
|
||||||
|
javac -d build src/main/java/fr/perceptron/*.java
|
||||||
|
```
|
||||||
|
|
||||||
|
### Execution
|
||||||
|
|
||||||
|
MNIST (training):
|
||||||
|
```bash
|
||||||
|
java -cp build fr.perceptron.MnistMain
|
||||||
|
```
|
||||||
|
|
||||||
|
MNIST (results):
|
||||||
|
```bash
|
||||||
|
java -cp build fr.perceptron.MnistWindow
|
||||||
|
```
|
||||||
|
|
||||||
|
2D Dataset:
|
||||||
|
```bash
|
||||||
|
java -cp build fr.perceptron.Main
|
||||||
|
```
|
||||||
|
|
||||||
|
## Structure
|
||||||
|
We structured the project as follows:
|
||||||
|
- Main
|
||||||
|
- NeuralNetwork
|
||||||
|
- Layers
|
||||||
|
- DataSet
|
||||||
|
- DataPoint
|
||||||
|
|
||||||
|
The main execution file allows choosing the layer sizes used in the perceptron, as well as the dataset. The program will convert them into an easy-to-use format: a `DataSet` composed of `DataPoint`.
|
||||||
|
|
||||||
|
## Comparison
|
||||||
|
|
||||||
|
We first developed a linear version of the perceptron, without any modular approach. Then, in the second part, we designed a more flexible version, which allowed us to adapt the perceptron to different situations much more easily. Below is a comparison of the results obtained with the same dataset on both versions of the perceptron:
|
||||||
|
|
||||||
|
### Linear Perceptron
|
||||||
|
|
||||||
|
#### Linear Data (Accuracy: 100.0%)
|
||||||
|
|
||||||
|
|
||||||
|
#### Moon Data (Accuracy: 80.9%)
|
||||||
|
|
||||||
|
|
||||||
|
#### Two Circles (Accuracy: 33.0%)
|
||||||
|
|
||||||
|
|
||||||
|
### General Perceptron
|
||||||
|
|
||||||
|
#### Linear Data (Accuracy: 100.0%)
|
||||||
|
|
||||||
|
|
||||||
|
#### Moon Data (Accuracy: 97.4%)
|
||||||
|
|
||||||
|
|
||||||
|
#### Two Circles (Accuracy: 100.0%)
|
||||||
|
|
||||||
|
|
||||||
|
## MNIST Usage
|
||||||
|
On 2D datasets, once the perceptron is trained on the data, we can display a window that predicts the data separation for each pixel of the screen:
|
||||||
|
|
||||||
|
On the handwritten digit dataset, after training the perceptron, we can visualize the results on dataset images or even on a digit drawn by hand:
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
However, some cases can be ambiguous and lead to a detection error, as in this example (a 2 is predicted while the image is actually labeled as a 7):
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
Nevertheless, the perceptron achieved a success rate of 97.24%.
|
||||||
|
|
||||||
|
## Our Experience
|
||||||
|
Starting from very abstract knowledge in the field of artificial intelligence, this perceptron project taught us a lot.
|
||||||
|
|
||||||
|
First, regarding understanding, a significant amount of time was devoted to studying the course material, as it was necessary to fully grasp and connect the mathematical formulas.
|
||||||
|
|
||||||
|
The implementation, which we initially thought would be quick, also required a lot of time, due to the rigor involved and the complexity of debugging. However, the object-oriented Java implementation approach allowed us to realize certain points: training a perceptron is actually similar to solving a system, and we also understood why AI training is performed on the GPU: because the calculations are simple and intuitively parallelizable.
|
||||||
|
|
||||||
|
Finally, one problem we encountered was the interpretation of the results. We initially thought that the perceptron would output the equation of a line/hyperplane (linear or not) separating the data, but in fact, the perceptron classifies new data based on what it has “learned.”
|
||||||
|
|
||||||
|
|
||||||
|
# 🇫🇷 Rapport Perceptron
|
||||||
|
## Introduction
|
||||||
|
Ce rapport présente le projet de perceptron, implémenté en Java que nous avons mené à bien, sans difficultés particulière.
|
||||||
|
|
||||||
|
## Lancer le programme
|
||||||
|
### Build
|
||||||
|
```bash
|
||||||
|
javac -d build src/main/java/fr/perceptron/*.java
|
||||||
|
```
|
||||||
|
### Exécution
|
||||||
|
MNIST (entraînement) :
|
||||||
|
```bash
|
||||||
|
java -cp build fr.perceptron.MnistMain
|
||||||
|
```
|
||||||
|
MNIST (résultats) :
|
||||||
|
```
|
||||||
|
java -cp build fr.perceptron.MnistWindow
|
||||||
|
```
|
||||||
|
|
||||||
|
Jeu de donnée 2D :
|
||||||
|
```bash
|
||||||
|
java -cp build fr.perceptron.Main
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
|
## Structure
|
||||||
|
Nous avons structuré le projet de la manière suivante :
|
||||||
|
- Main
|
||||||
|
- NeuralNetwork
|
||||||
|
- Layers
|
||||||
|
- DataSet
|
||||||
|
- DataPoint
|
||||||
|
|
||||||
|
Le fichier d'exécution principal permet le choix des tailles des couches utilisées dans le perceptron, et des données utilisée. Le programme se chargera de les convertir en un format facile à utiliser : un DataSet composé de DataPoint.
|
||||||
|
|
||||||
|
## Comparaison
|
||||||
|
|
||||||
|
Nous avons commencé par développer une version linéaire du perceptron, sans aucune approche modulaire. Ensuite, dans la deuxième partie, nous avons conçu une version
|
||||||
|
plus flexible, ce qui nous a permis d'adapter le perceptron à différentes situations de manière bien plus aisée. Voici une comparaison des résultats obtenus avec le
|
||||||
|
même jeu de données sur les deux versions du perceptron :
|
||||||
|
### Perceptron Lineaire
|
||||||
|
|
||||||
|
#### Linear Data (Accuracy: 100.0%)
|
||||||
|
|
||||||
|
|
||||||
|
#### Moon Data (Accurancy 80.9%)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#### Two Circles (Accuracy: 33.0%)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
### Perceptron Générale
|
||||||
|
|
||||||
|
#### Linear Data (Accuracy: 100.0%)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#### Moon Data (Accuracy: 97.4%)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#### Two Circles (Accuracy: 100.0%)
|
||||||
|
|
||||||
|
|
||||||
|
## Utilisation MNIST
|
||||||
|
Sur les jeux de données 2D, nous pouvons, après avoir entraîné le perceptron sur les données, afficher un fenêtre qui prédit la séparation des données pour chaque pixel de l'écran :
|
||||||
|
|
||||||
|
|
||||||
|
Sur le jeu de donnée d'écriture manuscrite, après entraînement du perceptron, on peut visualiser les résultats sur des images du jeu de donnée ou bien un chiffre dessiné à la main :
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
Néanmoins, certains cas peuvent être ambigües et provoquer une erreur de détection comme ici (un 2 est prédit alors que l'image est marquée étant un 7) :
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
Le perceptron a tout de même un taux de 97.24% de bonne réponses.
|
||||||
|
|
||||||
|
## Notre expérience
|
||||||
|
A partir de nos connaissances très abstraites sur le domaine de l'intelligence artificielle, ce projet de perceptron nous a beaucoup apprit.
|
||||||
|
|
||||||
|
A commencer par la compréhension, une période de temps non négligeable a été consacrée à la lecture du cours, il a fallu réussir à comprendre et lier les formules mathématiques entre elles.
|
||||||
|
|
||||||
|
L'implémentation, que nous pensions au départ être rapide, a également demandé beaucoup de temps, en raison de la rigueur demandée et la détection de bug plus complexe. L'approche d'implémentation en Java (POO) nous a néanmoins permit de réaliser certains points : l'entraînement d'un perceptron ressemble en fait à la résolution d'un système, et nous avons aussi comprit pourquoi l'entraînement d'IA est exécutée sur la carte graphique : car les calculs effectués sont simples et intuitivement parallélisable.
|
||||||
|
|
||||||
|
Pour finir, un problème que nous avons rencontré, a été l'interprétation des résultats. Nous pensions que le perceptron renverrai l'équation d'une droite / hyperplan (linéaire ou non) séparant les données, mais en fait, le perceptron classifie une donnée nouvelle en fonction de ce qu'il a "apprit".
|
||||||
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|
|||||||
|
|
||||||
|
package fr.perceptron;
|
||||||
|
|
||||||
|
import java.io.Serializable;
|
||||||
|
|
||||||
|
public class DataPoint implements Serializable {
|
||||||
|
|
||||||
|
public double[] inputs;
|
||||||
|
public double[] expectedOutputs;
|
||||||
|
|
||||||
|
public DataPoint() {
|
||||||
|
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,155 @@
|
|||||||
|
|
||||||
|
package fr.perceptron;
|
||||||
|
|
||||||
|
import java.io.BufferedReader;
|
||||||
|
import java.io.FileReader;
|
||||||
|
import java.io.IOException;
|
||||||
|
import java.nio.file.Files;
|
||||||
|
import java.nio.file.Paths;
|
||||||
|
import java.util.Random;
|
||||||
|
import java.util.stream.Stream;
|
||||||
|
|
||||||
|
public class DataSet {
|
||||||
|
|
||||||
|
public DataPoint[] points;
|
||||||
|
public int numPoints;
|
||||||
|
public int numBatches;
|
||||||
|
public int currentBatch;
|
||||||
|
|
||||||
|
public DataSet(int numBatches) {
|
||||||
|
this.numBatches = numBatches;
|
||||||
|
this.currentBatch = 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
public void loadFromArray(double[][] inputs, double[] labels) {
|
||||||
|
if (inputs.length != labels.length) return;
|
||||||
|
int num = inputs.length;
|
||||||
|
|
||||||
|
this.points = new DataPoint[num];
|
||||||
|
this.numPoints = num;
|
||||||
|
|
||||||
|
for (int i=0; i<num; i++) {
|
||||||
|
DataPoint p = new DataPoint();
|
||||||
|
p.inputs = inputs[i];
|
||||||
|
p.expectedOutputs = new double[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||||
|
p.expectedOutputs[(int) labels[i]] = 1;
|
||||||
|
this.points[i] = p;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public void loadDataSet(String path) {
|
||||||
|
int numLines = 0;
|
||||||
|
// count the number of lines in the file
|
||||||
|
try (Stream<String> lines = Files.lines(Paths.get(path))) {
|
||||||
|
numLines = (int) lines.count();
|
||||||
|
} catch (IOException e) {
|
||||||
|
e.printStackTrace();
|
||||||
|
}
|
||||||
|
|
||||||
|
// create the array of DataPoints
|
||||||
|
this.points = new DataPoint[numLines];
|
||||||
|
this.numPoints = numLines;
|
||||||
|
|
||||||
|
// read the file again and fill the array
|
||||||
|
try (BufferedReader br = new BufferedReader(new FileReader(path))) {
|
||||||
|
String line;
|
||||||
|
int i = 0;
|
||||||
|
while ((line = br.readLine()) != null) {
|
||||||
|
String[] values = line.split(",");
|
||||||
|
DataPoint point = new DataPoint();
|
||||||
|
point.inputs = new double[values.length - 1];
|
||||||
|
point.expectedOutputs = new double[1];
|
||||||
|
|
||||||
|
// fill the inputs
|
||||||
|
for (int j = 1; j < values.length; ++j) {
|
||||||
|
point.inputs[j - 1] = Double.parseDouble(values[j]);
|
||||||
|
}
|
||||||
|
|
||||||
|
// fill the expected output
|
||||||
|
point.expectedOutputs[0] = Double.parseDouble(values[0]);
|
||||||
|
this.points[i] = point;
|
||||||
|
i++;
|
||||||
|
}
|
||||||
|
} catch (IOException e) {
|
||||||
|
System.err.println("Error reading file: " + e.getMessage());
|
||||||
|
} catch (NumberFormatException e) {
|
||||||
|
System.err.println("Error parsing number: " + e.getMessage());
|
||||||
|
}
|
||||||
|
|
||||||
|
// check if the number of points is correct
|
||||||
|
if (this.points.length != numLines) {
|
||||||
|
System.err.println("Warning: file has " + numLines
|
||||||
|
+ " lines but " + this.points.length + " points were created.");
|
||||||
|
}
|
||||||
|
|
||||||
|
// reset the current batch
|
||||||
|
this.currentBatch = 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
public DataSet generateBatch() {
|
||||||
|
|
||||||
|
// if the current batch is greater than the number of batches,
|
||||||
|
// return null
|
||||||
|
if (this.currentBatch >= this.numBatches) {
|
||||||
|
return null;
|
||||||
|
}
|
||||||
|
|
||||||
|
int batchSize = this.numPoints / this.numBatches;
|
||||||
|
// make sure the batch size is correct for the last batch
|
||||||
|
if (this.currentBatch == this.numBatches - 1) {
|
||||||
|
batchSize = this.numPoints - (this.numBatches - 1) * batchSize;
|
||||||
|
}
|
||||||
|
|
||||||
|
// generate a batch of data points
|
||||||
|
DataSet batch = new DataSet(0);
|
||||||
|
batch.points = new DataPoint[batchSize];
|
||||||
|
batch.numPoints = batchSize;
|
||||||
|
|
||||||
|
// fill the batch with the points
|
||||||
|
int batchStart = this.currentBatch * batchSize;
|
||||||
|
for (int i = 0; i < batchSize; ++i) {
|
||||||
|
// get the index of the point in the original data set
|
||||||
|
int index = batchStart + i;
|
||||||
|
batch.points[i] = new DataPoint();
|
||||||
|
batch.points[i].inputs = this.points[index].inputs;
|
||||||
|
batch.points[i].expectedOutputs = this.points[index].expectedOutputs;
|
||||||
|
}
|
||||||
|
|
||||||
|
// increment the current batch
|
||||||
|
this.currentBatch++;
|
||||||
|
|
||||||
|
return batch;
|
||||||
|
}
|
||||||
|
|
||||||
|
public void resetBatches() {
|
||||||
|
this.currentBatch = 0;
|
||||||
|
shuffle();
|
||||||
|
}
|
||||||
|
|
||||||
|
public void shuffle() {
|
||||||
|
Random rnd = new Random();
|
||||||
|
for (int i = numPoints - 1; i > 0; i--) {
|
||||||
|
int index = rnd.nextInt(i + 1);
|
||||||
|
DataPoint temp = points[i];
|
||||||
|
points[i] = points[index];
|
||||||
|
points[index] = temp;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Ensure that the number of points per batch is set to 'size'
|
||||||
|
// and update the number of batches accordingly
|
||||||
|
// also reset the current batch to 0
|
||||||
|
public void batchesOf(int size) {
|
||||||
|
this.currentBatch = 0;
|
||||||
|
this.numBatches = this.numPoints / size;
|
||||||
|
if (this.numPoints % size != 0) {
|
||||||
|
this.numBatches++;
|
||||||
|
}
|
||||||
|
System.out.println("Number of batches: " + this.numBatches);
|
||||||
|
System.out.println("Batch size: " + size);
|
||||||
|
}
|
||||||
|
|
||||||
|
public void setNumBatches(int numBatches) {
|
||||||
|
this.numBatches = numBatches;
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,101 @@
|
|||||||
|
|
||||||
|
package fr.perceptron;
|
||||||
|
|
||||||
|
import java.io.Serializable;
|
||||||
|
import java.util.Random;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* The class Layer is one layer of the perceptron
|
||||||
|
*/
|
||||||
|
|
||||||
|
public class Layer implements Serializable {
|
||||||
|
|
||||||
|
public int numInputNeurons;
|
||||||
|
public int numNeurons;
|
||||||
|
|
||||||
|
public double[] weights;
|
||||||
|
public double[] biases;
|
||||||
|
public double[] rawLayerData;
|
||||||
|
|
||||||
|
private transient Random random;
|
||||||
|
|
||||||
|
public Layer(int numInputNeurons, int numNeurons, Random random) {
|
||||||
|
|
||||||
|
if (numInputNeurons < 1 || numNeurons < 1) {
|
||||||
|
throw new IllegalArgumentException("Invalid number of neurons");
|
||||||
|
}
|
||||||
|
|
||||||
|
this.random = random;
|
||||||
|
|
||||||
|
this.weights = new double[numInputNeurons * numNeurons];
|
||||||
|
this.biases = new double[numNeurons];
|
||||||
|
this.rawLayerData = new double[numNeurons];
|
||||||
|
this.numInputNeurons = numInputNeurons;
|
||||||
|
this.numNeurons = numNeurons;
|
||||||
|
|
||||||
|
this.initRandomWeights();
|
||||||
|
// this.initRandomBiases();
|
||||||
|
}
|
||||||
|
|
||||||
|
public Layer(int numInputNeurons, int numNeurons) {
|
||||||
|
this(numInputNeurons, numNeurons, new Random());
|
||||||
|
}
|
||||||
|
|
||||||
|
public void initRandomWeights() {
|
||||||
|
|
||||||
|
for (int i = 0; i < this.weights.length; ++i) {
|
||||||
|
this.weights[i] = random.nextDouble() * 2 - 1.0; // [-1.0; 1.0[ range
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public void initRandomBiases() {
|
||||||
|
for (int i = 0; i < this.biases.length; ++i) {
|
||||||
|
this.biases[i] = random.nextDouble() * 2 - 1.0; // [-1.0; 1.0[ range
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public double[] forward(double[] inputs) {
|
||||||
|
double[] outputs = new double[this.numNeurons];
|
||||||
|
|
||||||
|
for (int numOutput = 0; numOutput < this.numNeurons; ++numOutput) {
|
||||||
|
|
||||||
|
outputs[numOutput] = this.biases[numOutput];
|
||||||
|
for (int numInput = 0; numInput < this.numInputNeurons; ++numInput) {
|
||||||
|
outputs[numOutput] += this.getWeight(numInput, numOutput) * inputs[numInput];
|
||||||
|
}
|
||||||
|
this.rawLayerData[numOutput] = outputs[numOutput];
|
||||||
|
}
|
||||||
|
|
||||||
|
return activationFunction(outputs);
|
||||||
|
}
|
||||||
|
|
||||||
|
public static double activationFunction(double x) {
|
||||||
|
// Sigmoid function
|
||||||
|
return 1.0d / (1.0d + Math.exp(-x));
|
||||||
|
}
|
||||||
|
|
||||||
|
public static double activationDerivativeFunction(double x){
|
||||||
|
double temp = activationFunction(x);
|
||||||
|
return temp * (1.0d - temp);
|
||||||
|
}
|
||||||
|
|
||||||
|
public static double[] activationFunction(double[] inputs) {
|
||||||
|
double[] outputs = new double[inputs.length];
|
||||||
|
for (int i = 0; i < inputs.length; ++i) {
|
||||||
|
outputs[i] = activationFunction(inputs[i]);
|
||||||
|
}
|
||||||
|
return outputs;
|
||||||
|
}
|
||||||
|
|
||||||
|
public double getWeight(int neuronIn, int neuronOut) {
|
||||||
|
return this.weights[neuronOut * this.numInputNeurons + neuronIn];
|
||||||
|
}
|
||||||
|
public void setWeight(int neuronIn, int neuronOut, double v) {
|
||||||
|
this.weights[neuronOut * this.numInputNeurons + neuronIn] = v;
|
||||||
|
}
|
||||||
|
|
||||||
|
@Override
|
||||||
|
public String toString() {
|
||||||
|
return String.format("Layer[%d, %d]", this.numInputNeurons, this.numNeurons);
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,137 @@
|
|||||||
|
package fr.perceptron;
|
||||||
|
|
||||||
|
import java.io.FileInputStream;
|
||||||
|
import java.io.FileOutputStream;
|
||||||
|
import java.io.IOException;
|
||||||
|
import java.io.InputStream;
|
||||||
|
import java.io.ObjectInputStream;
|
||||||
|
import java.io.ObjectOutputStream;
|
||||||
|
import java.util.zip.GZIPInputStream;
|
||||||
|
|
||||||
|
public class MNIST {
|
||||||
|
|
||||||
|
private static double[] labels_train;
|
||||||
|
private static double[][] images_train;
|
||||||
|
private static double[] labels_eval;
|
||||||
|
private static double[][] images_eval;
|
||||||
|
|
||||||
|
public static double[] getEvalLabels() {
|
||||||
|
return labels_eval;
|
||||||
|
}
|
||||||
|
|
||||||
|
public static double[][] getEvalImages() {
|
||||||
|
return images_eval;
|
||||||
|
}
|
||||||
|
|
||||||
|
public static double[] getTrainLabels() {
|
||||||
|
return labels_train;
|
||||||
|
}
|
||||||
|
|
||||||
|
public static double[][] getTrainImages() {
|
||||||
|
return images_train;
|
||||||
|
}
|
||||||
|
|
||||||
|
public static void loadEval() {
|
||||||
|
final String trainImagesPath = "./mnist/t10k-images-idx3-ubyte.gz";
|
||||||
|
final String trainLabelsPath = "./mnist/t10k-labels-idx1-ubyte.gz";
|
||||||
|
|
||||||
|
try {
|
||||||
|
InputStream imgIn = new GZIPInputStream(new FileInputStream(trainImagesPath));
|
||||||
|
InputStream lblIn = new GZIPInputStream(new FileInputStream(trainLabelsPath));
|
||||||
|
|
||||||
|
byte[] tempBuffer = new byte[16];
|
||||||
|
imgIn.read(tempBuffer, 0, 16);
|
||||||
|
lblIn.read(tempBuffer, 0, 8); // Les labels n'ont que 8 octets d'en-tête
|
||||||
|
|
||||||
|
byte[] dataBuffer = new byte[1];
|
||||||
|
labels_eval = new double[10000];
|
||||||
|
images_eval = new double[10000][784];
|
||||||
|
|
||||||
|
System.out.println("Start loading MNIST set.");
|
||||||
|
|
||||||
|
for (int i = 0; i < 10000; i++) {
|
||||||
|
lblIn.read(dataBuffer, 0, 1);
|
||||||
|
labels_eval[i] = dataBuffer[0] & 0xFF;
|
||||||
|
|
||||||
|
for (int j = 0; j < 784; j++) {
|
||||||
|
imgIn.read(dataBuffer, 0, 1);
|
||||||
|
double pixelVal = (dataBuffer[0] & 0xFF) / 255.0;
|
||||||
|
images_eval[i][j] = pixelVal;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
System.out.println("MNIST set loaded.");
|
||||||
|
imgIn.close();
|
||||||
|
lblIn.close();
|
||||||
|
|
||||||
|
} catch (IOException e) {
|
||||||
|
e.printStackTrace();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public static void loadTrain() {
|
||||||
|
final String trainImagesPath = "./mnist/train-images-idx3-ubyte.gz";
|
||||||
|
final String trainLabelsPath = "./mnist/train-labels-idx1-ubyte.gz";
|
||||||
|
|
||||||
|
try {
|
||||||
|
InputStream imgIn = new GZIPInputStream(new FileInputStream(trainImagesPath));
|
||||||
|
InputStream lblIn = new GZIPInputStream(new FileInputStream(trainLabelsPath));
|
||||||
|
|
||||||
|
byte[] tempBuffer = new byte[16];
|
||||||
|
imgIn.read(tempBuffer, 0, 16);
|
||||||
|
lblIn.read(tempBuffer, 0, 8); // Les labels n'ont que 8 octets d'en-tête
|
||||||
|
|
||||||
|
byte[] dataBuffer = new byte[1];
|
||||||
|
labels_train = new double[60000];
|
||||||
|
images_train = new double[60000][784];
|
||||||
|
|
||||||
|
System.out.println("Start loading MNIST set.");
|
||||||
|
|
||||||
|
for (int i = 0; i < 60000; i++) {
|
||||||
|
lblIn.read(dataBuffer, 0, 1);
|
||||||
|
labels_train[i] = dataBuffer[0] & 0xFF;
|
||||||
|
|
||||||
|
for (int j = 0; j < 784; j++) {
|
||||||
|
imgIn.read(dataBuffer, 0, 1);
|
||||||
|
double pixelVal = (dataBuffer[0] & 0xFF) / 255.0;
|
||||||
|
images_train[i][j] = pixelVal;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
System.out.println("MNIST set loaded.");
|
||||||
|
imgIn.close();
|
||||||
|
lblIn.close();
|
||||||
|
|
||||||
|
} catch (IOException e) {
|
||||||
|
e.printStackTrace();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public static void save() {
|
||||||
|
try {
|
||||||
|
System.out.println("Saving set to cache...");
|
||||||
|
ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream("data.dat"));
|
||||||
|
oos.writeObject(labels_train);
|
||||||
|
oos.writeObject(images_train);
|
||||||
|
oos.writeObject(labels_eval);
|
||||||
|
oos.writeObject(images_eval);
|
||||||
|
|
||||||
|
oos.close();
|
||||||
|
System.err.println("Set saved.");
|
||||||
|
|
||||||
|
} catch (Exception e) {
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public static void load() {
|
||||||
|
try {
|
||||||
|
System.out.println("Loading cache...");
|
||||||
|
ObjectInputStream ois = new ObjectInputStream(new FileInputStream("data.dat"));
|
||||||
|
labels_train = (double[]) ois.readObject();
|
||||||
|
images_train = (double[][]) ois.readObject();
|
||||||
|
labels_eval = (double[]) ois.readObject();
|
||||||
|
images_eval = (double[][]) ois.readObject();
|
||||||
|
ois.close();
|
||||||
|
System.out.println("Cache loaded.");
|
||||||
|
} catch (Exception e) {
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,90 @@
|
|||||||
|
package fr.perceptron;
|
||||||
|
|
||||||
|
import java.io.BufferedWriter;
|
||||||
|
import java.io.FileWriter;
|
||||||
|
|
||||||
|
public class Main {
|
||||||
|
|
||||||
|
public static void main(String[] args) {
|
||||||
|
|
||||||
|
int[] layerSizes = { 2, 50, 10, 1 };
|
||||||
|
if (args.length > 0) {
|
||||||
|
layerSizes = new int[args.length];
|
||||||
|
for (int i = 0; i < args.length; ++i) {
|
||||||
|
layerSizes[i] = Integer.parseInt(args[i]);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
String file = "data/twocircles_data_";
|
||||||
|
DataSet data = new DataSet(20);
|
||||||
|
DataSet eval = new DataSet(1);
|
||||||
|
data.loadDataSet(file + "train.csv");
|
||||||
|
eval.loadDataSet(file + "eval.csv");
|
||||||
|
|
||||||
|
NeuralNetwork nn = new NeuralNetwork(layerSizes);
|
||||||
|
System.out.println(nn + "\n");
|
||||||
|
|
||||||
|
Window window = new Window();
|
||||||
|
window.init(nn, data);
|
||||||
|
|
||||||
|
long startTime = System.currentTimeMillis();
|
||||||
|
long endTime, epochStartTime, elapsedTime;
|
||||||
|
double totalCost = 0.0;
|
||||||
|
|
||||||
|
double[] learnRate;
|
||||||
|
double[] NNCost;
|
||||||
|
learnRate = new double[500];
|
||||||
|
NNCost = new double[500];
|
||||||
|
double oldCost;
|
||||||
|
|
||||||
|
// Train the Neural Network
|
||||||
|
for (int i = 0; i < 500; i++) {
|
||||||
|
System.out.println("Epoch " + i);
|
||||||
|
epochStartTime = System.currentTimeMillis();
|
||||||
|
nn.train(data);
|
||||||
|
elapsedTime = System.currentTimeMillis();
|
||||||
|
System.out.println("Time elapsed: " + (elapsedTime - epochStartTime) + " ms");
|
||||||
|
oldCost = totalCost;
|
||||||
|
totalCost = nn.evaluateTotalCost(data);
|
||||||
|
System.out.println("Total cost: " + totalCost + " Learning rate: " + nn.learningRate);
|
||||||
|
window.update();
|
||||||
|
learnRate[i] = nn.learningRate;
|
||||||
|
NNCost[i] = totalCost;
|
||||||
|
if (totalCost < NeuralNetwork.COST_TOLERANCE) break;
|
||||||
|
if (Math.abs(oldCost - totalCost) < NeuralNetwork.COST_VARIATION_TOLERANCE) break;
|
||||||
|
data.resetBatches();
|
||||||
|
}
|
||||||
|
|
||||||
|
endTime = System.currentTimeMillis() - startTime;
|
||||||
|
System.out.println("Total training time: " + endTime + " ms (" + (endTime / 1000.0) + " s)");
|
||||||
|
window.update();
|
||||||
|
|
||||||
|
int correct = 0;
|
||||||
|
DataSet d = eval;
|
||||||
|
for (int i = 0; i < d.points.length; i++) {
|
||||||
|
double res = nn.calculateOutputs(d.points[i].inputs)[0];
|
||||||
|
correct += (res > 0.5 ? 1.0 : 0.0) == d.points[i].expectedOutputs[0] ? 1 : 0;
|
||||||
|
}
|
||||||
|
System.out.println("Correct points on eval set: " + correct + " / " + d.points.length);
|
||||||
|
System.out.println("Accuracy: " + (correct * 100.0 / d.points.length) + "%");
|
||||||
|
|
||||||
|
// Save the Neural Network
|
||||||
|
saveStats(learnRate, NNCost, null, "stats.txt");
|
||||||
|
}
|
||||||
|
|
||||||
|
public static void saveStats(double[] learnRate, double[] NNCost, double[] accuracies, String filename) {
|
||||||
|
// Save learn rate and cost in a text file, where each line contains:
|
||||||
|
// learnRate[i], NNCost[i]
|
||||||
|
try (BufferedWriter writer = new BufferedWriter(new FileWriter("stats/" + filename))) {
|
||||||
|
for (int i = 0; i < learnRate.length; i++) {
|
||||||
|
writer.write(learnRate[i] + "," + NNCost[i]);
|
||||||
|
if (accuracies != null) {
|
||||||
|
writer.write("," + accuracies[i]);
|
||||||
|
}
|
||||||
|
writer.newLine();
|
||||||
|
}
|
||||||
|
} catch (Exception e) {
|
||||||
|
e.printStackTrace();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,101 @@
|
|||||||
|
|
||||||
|
package fr.perceptron;
|
||||||
|
|
||||||
|
import java.io.File;
|
||||||
|
import java.io.IOException;
|
||||||
|
|
||||||
|
public class MnistMain {
|
||||||
|
|
||||||
|
public static void main(String[] args) {
|
||||||
|
|
||||||
|
int[] layerSizes = {
|
||||||
|
784, // 28 * 28 = 784
|
||||||
|
512, // 256 neurons in the hidden layer 1
|
||||||
|
// 128, // 128 neurons in the hidden layer 2
|
||||||
|
10 // 10 classes (0-9 digits)
|
||||||
|
};
|
||||||
|
if (args.length > 0) {
|
||||||
|
layerSizes = new int[args.length];
|
||||||
|
for (int i = 0; i < args.length; ++i) {
|
||||||
|
layerSizes[i] = Integer.parseInt(args[i]);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!new File("data.dat").exists()) {
|
||||||
|
MNIST.loadTrain();
|
||||||
|
MNIST.loadEval();
|
||||||
|
MNIST.save();
|
||||||
|
} else {
|
||||||
|
MNIST.load();
|
||||||
|
}
|
||||||
|
|
||||||
|
DataSet data = new DataSet(1);
|
||||||
|
DataSet eval = new DataSet(1);
|
||||||
|
data.loadFromArray(MNIST.getTrainImages(), MNIST.getTrainLabels());
|
||||||
|
data.batchesOf(1000);
|
||||||
|
eval.loadFromArray(MNIST.getEvalImages(), MNIST.getEvalLabels());
|
||||||
|
|
||||||
|
String cachedNN = "nn.dat";
|
||||||
|
NeuralNetwork nn;
|
||||||
|
System.out.println("Try loading cached NN...");
|
||||||
|
try {
|
||||||
|
nn = NeuralNetwork.load(cachedNN);
|
||||||
|
} catch (ClassNotFoundException | IOException e) {
|
||||||
|
System.out.println("Loading failed, creating new NN...");
|
||||||
|
nn = new NeuralNetwork(layerSizes);
|
||||||
|
}
|
||||||
|
System.out.println("Done.");
|
||||||
|
System.out.println(nn + "\n");
|
||||||
|
|
||||||
|
int numEpochs = 500;
|
||||||
|
long startTime = System.currentTimeMillis();
|
||||||
|
long endTime, epochStartTime, elapsedTime;
|
||||||
|
double[] learnRates = new double[numEpochs];
|
||||||
|
double[] dataAccuracies = new double[numEpochs];
|
||||||
|
// double[] NNCost = new double[numEpochs];
|
||||||
|
double[] evalAccuracies = new double[numEpochs];
|
||||||
|
// double cost = 0.0;
|
||||||
|
|
||||||
|
// Train the Neural Network
|
||||||
|
for (int i = nn.epoch; i < numEpochs; i++) {
|
||||||
|
System.out.println("Epoch " + i);
|
||||||
|
epochStartTime = System.currentTimeMillis();
|
||||||
|
nn.train(data);
|
||||||
|
elapsedTime = System.currentTimeMillis() - epochStartTime;
|
||||||
|
System.out.println("Time elapsed: " + elapsedTime + "ms (" + (elapsedTime / 1000.0) + "s)");
|
||||||
|
// cost = nn.evaluateTotalCost(data);
|
||||||
|
double dataAccuracy = nn.evaluateAccuracy(data);
|
||||||
|
double evalAccuracy = nn.evaluateAccuracy(eval);
|
||||||
|
System.out.println(String.format("Learning rate: %.10f", nn.learningRate));
|
||||||
|
System.out.println(String.format("Train data accuracy: %.5f%%", dataAccuracy * 100));
|
||||||
|
System.out.println(String.format("Eval data accuracy: %.5f%%", evalAccuracy * 100));
|
||||||
|
learnRates[i] = nn.learningRate;
|
||||||
|
// NNCost[i] = cost;
|
||||||
|
dataAccuracies[i] = dataAccuracy;
|
||||||
|
evalAccuracies[i] = evalAccuracy;
|
||||||
|
data.resetBatches();
|
||||||
|
// Main.saveStats(learnRates, NNCost, dataAccuracies, "stats.txt");
|
||||||
|
Main.saveStats(learnRates, dataAccuracies, evalAccuracies, "stats.txt");
|
||||||
|
try {
|
||||||
|
nn.save(cachedNN);
|
||||||
|
} catch (IOException e) {
|
||||||
|
System.out.println(e);
|
||||||
|
e.printStackTrace();
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
endTime = System.currentTimeMillis() - startTime;
|
||||||
|
System.out.println("Total training time: " + endTime + "ms (" + (endTime / 1000.0) + "s)");
|
||||||
|
// Main.saveStats(learnRates, NNCost, dataAccuracies, "stats.txt");
|
||||||
|
Main.saveStats(learnRates, dataAccuracies, evalAccuracies, "stats.txt");
|
||||||
|
|
||||||
|
try {
|
||||||
|
nn.save(cachedNN);
|
||||||
|
} catch (IOException e) {
|
||||||
|
System.out.println(e);
|
||||||
|
e.printStackTrace();
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,330 @@
|
|||||||
|
package fr.perceptron;
|
||||||
|
|
||||||
|
import javax.swing.BorderFactory;
|
||||||
|
import javax.swing.BoxLayout;
|
||||||
|
import javax.swing.JButton;
|
||||||
|
import javax.swing.JComponent;
|
||||||
|
import javax.swing.JFrame;
|
||||||
|
import javax.swing.JLabel;
|
||||||
|
import javax.swing.JPanel;
|
||||||
|
|
||||||
|
import java.awt.BorderLayout;
|
||||||
|
import java.awt.Color;
|
||||||
|
import java.awt.Dimension;
|
||||||
|
import java.awt.FlowLayout;
|
||||||
|
import java.awt.Graphics;
|
||||||
|
import java.awt.event.MouseAdapter;
|
||||||
|
import java.awt.event.MouseEvent;
|
||||||
|
import java.awt.event.MouseMotionAdapter;
|
||||||
|
import java.awt.image.BufferedImage;
|
||||||
|
import java.io.File;
|
||||||
|
import java.io.IOException;
|
||||||
|
import java.util.Arrays;
|
||||||
|
import java.util.Random;
|
||||||
|
|
||||||
|
public class MnistWindow {
|
||||||
|
private int PXSIZE = 20;
|
||||||
|
private DataSet data;
|
||||||
|
private final JFrame frame;
|
||||||
|
private final DrawingPanel panel;
|
||||||
|
private final Random random = new Random();
|
||||||
|
private BarComponent[] bars;
|
||||||
|
private int currentPointIndex = 0;
|
||||||
|
private JLabel expectedResult;
|
||||||
|
private JLabel predictedResult;
|
||||||
|
|
||||||
|
private NeuralNetwork nn;
|
||||||
|
|
||||||
|
public MnistWindow(DataSet data, NeuralNetwork nn) {
|
||||||
|
this.data = data;
|
||||||
|
this.nn = nn;
|
||||||
|
|
||||||
|
frame = new JFrame("MNIST Viewer");
|
||||||
|
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
|
||||||
|
|
||||||
|
panel = new DrawingPanel(Arrays.copyOf(data.points[0].inputs, data.points[0].inputs.length));
|
||||||
|
bars = new BarComponent[10];
|
||||||
|
|
||||||
|
JPanel rightPanel = new JPanel();
|
||||||
|
rightPanel.setLayout(new BoxLayout(rightPanel, BoxLayout.Y_AXIS));
|
||||||
|
|
||||||
|
// Create digit bars
|
||||||
|
for (int i = 0; i < 10; i++) {
|
||||||
|
JPanel barPanel = new JPanel(new FlowLayout(FlowLayout.LEFT, 5, 0));
|
||||||
|
barPanel.add(new JLabel(Integer.toString(i)));
|
||||||
|
BarComponent bar = new BarComponent();
|
||||||
|
bar.setPreferredSize(new Dimension(200, 20));
|
||||||
|
bars[i] = bar;
|
||||||
|
barPanel.add(bar);
|
||||||
|
rightPanel.add(barPanel);
|
||||||
|
}
|
||||||
|
|
||||||
|
expectedResult = new JLabel();
|
||||||
|
predictedResult = new JLabel();
|
||||||
|
rightPanel.add(predictedResult);
|
||||||
|
rightPanel.add(expectedResult);
|
||||||
|
|
||||||
|
double acc = 0;
|
||||||
|
for (DataPoint d : data.points) {
|
||||||
|
double expected = getDataValue(d.expectedOutputs);
|
||||||
|
double predicted = getDataValue(softmax(nn.calculateOutputs(d.inputs)));
|
||||||
|
if (expected == predicted) {
|
||||||
|
acc += 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
acc /= data.points.length;
|
||||||
|
|
||||||
|
// Create buttons
|
||||||
|
JPanel buttonPanel = new JPanel();
|
||||||
|
JButton resetButton = new JButton("Reset");
|
||||||
|
resetButton.addActionListener(e -> panel.reset());
|
||||||
|
JButton nextButton = new JButton("Next");
|
||||||
|
nextButton.addActionListener(e -> showRandomPoint(false));
|
||||||
|
JButton nextErrorButton = new JButton("Next error");
|
||||||
|
nextErrorButton.addActionListener(e -> showRandomPoint(true));
|
||||||
|
buttonPanel.add(resetButton);
|
||||||
|
buttonPanel.add(nextButton);
|
||||||
|
buttonPanel.add(nextErrorButton);
|
||||||
|
rightPanel.add(buttonPanel);
|
||||||
|
|
||||||
|
rightPanel.add(new JLabel("Global accuracy : " + acc*100 + "%"));
|
||||||
|
|
||||||
|
// Setup main layout
|
||||||
|
JPanel mainPanel = new JPanel(new BorderLayout(10, 10));
|
||||||
|
mainPanel.setBorder(BorderFactory.createEmptyBorder(10, 10, 10, 10));
|
||||||
|
mainPanel.add(panel, BorderLayout.WEST);
|
||||||
|
mainPanel.add(rightPanel, BorderLayout.CENTER);
|
||||||
|
|
||||||
|
frame.add(mainPanel);
|
||||||
|
frame.pack();
|
||||||
|
frame.setVisible(true);
|
||||||
|
|
||||||
|
predict(false);
|
||||||
|
//showPoint(currentPointIndex); // Show initial point
|
||||||
|
}
|
||||||
|
|
||||||
|
public double[] softmax(double outputs[]) {
|
||||||
|
double[] result = new double[outputs.length];
|
||||||
|
double sum = 0;
|
||||||
|
for (double v : outputs) {
|
||||||
|
sum += v;
|
||||||
|
}
|
||||||
|
for (int i=0; i<result.length; i++) {
|
||||||
|
result[i] = outputs[i]/sum;
|
||||||
|
}
|
||||||
|
return result;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Update le tout pour le dessin custom
|
||||||
|
private void predictCustom() {
|
||||||
|
double[] inputs = panel.getData();
|
||||||
|
double[] outputs = softmax(nn.calculateOutputs(inputs));
|
||||||
|
|
||||||
|
String v = String.valueOf(getDataValue(outputs));
|
||||||
|
expectedResult.setText("");
|
||||||
|
predictedResult.setText("Predicted : " + v);
|
||||||
|
|
||||||
|
updateBars(outputs);
|
||||||
|
}
|
||||||
|
|
||||||
|
private void predict(boolean findError) {
|
||||||
|
DataPoint point = data.points[currentPointIndex];
|
||||||
|
panel.setData(Arrays.copyOf(point.inputs, point.inputs.length));
|
||||||
|
double[] inputs = panel.getData();
|
||||||
|
double[] outputs = softmax(nn.calculateOutputs(inputs));
|
||||||
|
double[] expectedOutputs = point.expectedOutputs;
|
||||||
|
|
||||||
|
expectedResult.setText("Expected : " + getDataValue(expectedOutputs));
|
||||||
|
predictedResult.setText("Predicted : " + getDataValue(outputs));
|
||||||
|
|
||||||
|
updateBars(outputs);
|
||||||
|
|
||||||
|
if (findError && (getDataValue(expectedOutputs) == getDataValue(outputs))) {
|
||||||
|
showRandomPoint(true);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
private int getDataValue(double[] d) {
|
||||||
|
double max = 0;
|
||||||
|
int imax = 0;
|
||||||
|
for (int i=0; i<d.length; i++) {
|
||||||
|
double v = d[i];
|
||||||
|
if (v>max) {
|
||||||
|
max = v;
|
||||||
|
imax = i;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return imax;
|
||||||
|
}
|
||||||
|
|
||||||
|
private void showRandomPoint(boolean findError) {
|
||||||
|
currentPointIndex = random.nextInt(data.points.length);
|
||||||
|
//showPoint(currentPointIndex);
|
||||||
|
predict(findError);
|
||||||
|
}
|
||||||
|
|
||||||
|
private void updateBars(double[] values) {
|
||||||
|
for (int i = 0; i < 10; i++) {
|
||||||
|
bars[i].setValue(values[i]);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
private BufferedImage createImageFromData(double[] data) {
|
||||||
|
BufferedImage image = new BufferedImage(28, 28, BufferedImage.TYPE_INT_RGB);
|
||||||
|
for (int y = 0; y < 28; y++) {
|
||||||
|
for (int x = 0; x < 28; x++) {
|
||||||
|
int value = (int) (data[y * 28 + x] * 255);
|
||||||
|
value = Math.max(0, Math.min(255, value));
|
||||||
|
int rgb = new Color(value, value, value).getRGB();
|
||||||
|
image.setRGB(x, y, rgb);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return image;
|
||||||
|
}
|
||||||
|
|
||||||
|
private class DrawingPanel extends JPanel {
|
||||||
|
private double[] data;
|
||||||
|
private BufferedImage currentImage;
|
||||||
|
|
||||||
|
private DrawingPanel(double[] data) {
|
||||||
|
this.data = data;
|
||||||
|
this.currentImage = createImageFromData(data);
|
||||||
|
setupMouseListeners();
|
||||||
|
setPreferredSize(new Dimension(28*PXSIZE, 28*PXSIZE));
|
||||||
|
}
|
||||||
|
|
||||||
|
public double[] getData() {
|
||||||
|
return data;
|
||||||
|
}
|
||||||
|
|
||||||
|
public void setData(double[] newData) {
|
||||||
|
this.data = Arrays.copyOf(newData, newData.length);
|
||||||
|
this.currentImage = createImageFromData(this.data);
|
||||||
|
repaint();
|
||||||
|
}
|
||||||
|
|
||||||
|
private void setupMouseListeners() {
|
||||||
|
addMouseListener(new MouseAdapter() {
|
||||||
|
@Override
|
||||||
|
public void mousePressed(MouseEvent e) {
|
||||||
|
updateCell(e.getX(), e.getY());
|
||||||
|
}
|
||||||
|
});
|
||||||
|
|
||||||
|
addMouseMotionListener(new MouseMotionAdapter() {
|
||||||
|
@Override
|
||||||
|
public void mouseDragged(MouseEvent e) {
|
||||||
|
updateCell(e.getX(), e.getY());
|
||||||
|
}
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
private void updateCell(int x, int y) {
|
||||||
|
int col = x / PXSIZE;
|
||||||
|
int row = y / PXSIZE;
|
||||||
|
int radius = 2;
|
||||||
|
double sigma = radius / 2.8;
|
||||||
|
|
||||||
|
if (col >= 0 && col < 28 && row >= 0 && row < 28) {
|
||||||
|
for (int dr = -radius; dr <= radius; dr++) {
|
||||||
|
for (int dc = -radius; dc <= radius; dc++) {
|
||||||
|
int c = col + dc;
|
||||||
|
int r = row + dr;
|
||||||
|
|
||||||
|
if (c >= 0 && c < 28 && r >= 0 && r < 28) {
|
||||||
|
double distance = Math.sqrt(dc * dc + dr * dr);
|
||||||
|
|
||||||
|
if (distance <= radius) {
|
||||||
|
// gauss
|
||||||
|
double exponent = -(distance * distance) / (2 * sigma * sigma);
|
||||||
|
double falloff = Math.exp(exponent);
|
||||||
|
|
||||||
|
int index = r * 28 + c;
|
||||||
|
data[index] = Math.min(1.0, Math.max(data[index]+falloff/2, falloff));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
currentImage = createImageFromData(data);
|
||||||
|
repaint();
|
||||||
|
predictCustom();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public void reset() {
|
||||||
|
Arrays.fill(data, 0.0);
|
||||||
|
currentImage = createImageFromData(data);
|
||||||
|
repaint();
|
||||||
|
}
|
||||||
|
|
||||||
|
@Override
|
||||||
|
protected void paintComponent(Graphics g) {
|
||||||
|
super.paintComponent(g);
|
||||||
|
if (currentImage == null) return;
|
||||||
|
g.drawImage(currentImage, 0, 0, 28*PXSIZE, 28*PXSIZE, null);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
private class BarComponent extends JComponent {
|
||||||
|
private double value = 0.0;
|
||||||
|
|
||||||
|
public void setValue(double value) {
|
||||||
|
this.value = Math.max(0.0, Math.min(1.0, value));
|
||||||
|
repaint();
|
||||||
|
}
|
||||||
|
|
||||||
|
@Override
|
||||||
|
protected void paintComponent(Graphics g) {
|
||||||
|
super.paintComponent(g);
|
||||||
|
int width = getWidth();
|
||||||
|
int height = getHeight();
|
||||||
|
|
||||||
|
// Draw background
|
||||||
|
g.setColor(Color.WHITE);
|
||||||
|
g.fillRect(0, 0, width, height);
|
||||||
|
|
||||||
|
// Draw filled bar
|
||||||
|
g.setColor(Color.BLACK);
|
||||||
|
int fillWidth = (int) (value * (width - 2));
|
||||||
|
g.fillRect(1, 1, fillWidth, height - 2);
|
||||||
|
|
||||||
|
// Draw border
|
||||||
|
g.setColor(Color.BLACK);
|
||||||
|
g.drawRect(0, 0, width - 1, height - 1);
|
||||||
|
}
|
||||||
|
|
||||||
|
@Override
|
||||||
|
public Dimension getPreferredSize() {
|
||||||
|
return new Dimension(200, 20);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public static void main(String[] args) {
|
||||||
|
if (!new File("data.dat").exists()) {
|
||||||
|
MNIST.loadTrain();
|
||||||
|
MNIST.loadEval();
|
||||||
|
MNIST.save();
|
||||||
|
}
|
||||||
|
else {
|
||||||
|
MNIST.load();
|
||||||
|
}
|
||||||
|
|
||||||
|
DataSet eval = new DataSet(1);
|
||||||
|
eval.loadFromArray(MNIST.getEvalImages(), MNIST.getEvalLabels());
|
||||||
|
|
||||||
|
String cachedNN = "nn_256_128.dat";
|
||||||
|
NeuralNetwork nn;
|
||||||
|
System.out.println("Try loading cached NN...");
|
||||||
|
try {
|
||||||
|
nn = NeuralNetwork.load(cachedNN);
|
||||||
|
System.out.println("Done.");
|
||||||
|
System.out.println(nn + "\n");
|
||||||
|
new MnistWindow(eval, nn);
|
||||||
|
} catch (ClassNotFoundException | IOException e) {
|
||||||
|
System.out.println(e);
|
||||||
|
System.out.println("Loading failed.");
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,215 @@
|
|||||||
|
package fr.perceptron;
|
||||||
|
|
||||||
|
import java.io.FileInputStream;
|
||||||
|
import java.io.FileOutputStream;
|
||||||
|
import java.io.IOException;
|
||||||
|
import java.io.ObjectInputStream;
|
||||||
|
import java.io.ObjectOutputStream;
|
||||||
|
import java.io.Serializable;
|
||||||
|
import java.util.Random;
|
||||||
|
// import java.util.concurrent.ExecutorService;
|
||||||
|
// import java.util.concurrent.Executors;
|
||||||
|
// import java.util.concurrent.Future;
|
||||||
|
|
||||||
|
public class NeuralNetwork implements Serializable {
|
||||||
|
|
||||||
|
public int[] sizes; // number of neurons for each layer of the neural network
|
||||||
|
public Layer[] layers;
|
||||||
|
|
||||||
|
private transient Random random;
|
||||||
|
private static final double initialLearningRate = 0.1d;
|
||||||
|
private static final double decayRate = 0.95d;
|
||||||
|
private static final double decayFactor = 10.0d;
|
||||||
|
public static final double COST_TOLERANCE = 10e-5;
|
||||||
|
public static final double COST_VARIATION_TOLERANCE = 10e-7;
|
||||||
|
public int epoch;
|
||||||
|
public double learningRate;
|
||||||
|
|
||||||
|
public NeuralNetwork(int[] layerSizes) {
|
||||||
|
this.sizes = layerSizes;
|
||||||
|
// the "input layer" doesn't truly exist so we don't add it
|
||||||
|
this.layers = new Layer[layerSizes.length - 1];
|
||||||
|
// init to a fixed seed to debug
|
||||||
|
this.random = new Random(0);
|
||||||
|
|
||||||
|
// set the epoch to 0 and the learning rate to the initial value
|
||||||
|
this.epoch = 0;
|
||||||
|
this.learningRate = initialLearningRate;
|
||||||
|
|
||||||
|
// create each layer with the number of input neurons corresponding
|
||||||
|
// to the number of output neurons of the previous index
|
||||||
|
for (int i = 0; i < layerSizes.length - 1; ++i) {
|
||||||
|
layers[i] = new Layer(layerSizes[i], layerSizes[i + 1], this.random);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// calculates the output of the Neural Network based on given input values
|
||||||
|
// the inputs are forwarded through each layer of the NN, using the forward()
|
||||||
|
// method of each layer.
|
||||||
|
public double[] calculateOutputs(double[] inputs) {
|
||||||
|
double[] layerOutput = inputs;
|
||||||
|
|
||||||
|
// forward the inputs through each layer
|
||||||
|
for (int i = 0; i < this.layers.length; ++i) {
|
||||||
|
layerOutput = this.layers[i].forward(layerOutput);
|
||||||
|
}
|
||||||
|
|
||||||
|
return layerOutput;
|
||||||
|
}
|
||||||
|
|
||||||
|
// returns an int that corresponds to the index of the highest activation
|
||||||
|
// value of an output. (i.e. the index of the max of all values of the array)
|
||||||
|
public int classify(double[] networkOutput) {
|
||||||
|
int maxActivationIndex = 0;
|
||||||
|
double maxActivation = Double.MIN_VALUE;
|
||||||
|
|
||||||
|
for (int i = 0; i < networkOutput.length; ++i) {
|
||||||
|
if (networkOutput[i] > maxActivation) {
|
||||||
|
maxActivation = networkOutput[i];
|
||||||
|
maxActivationIndex = i;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return maxActivationIndex;
|
||||||
|
}
|
||||||
|
|
||||||
|
public int classify(double input) {
|
||||||
|
// return 1 if input > 0 else 0
|
||||||
|
return (input > 0.5 ? 1 : 0);
|
||||||
|
}
|
||||||
|
|
||||||
|
// calculates the cost of an evaluation by comparing the values predicted
|
||||||
|
// with the expected output. The formula used for the cost is the
|
||||||
|
// mean square error, defined as :
|
||||||
|
// E(w) = 1/2 * sum( (expected - predicted) ^ 2)
|
||||||
|
public double calculateCost(double[] expected, double[] outputs) {
|
||||||
|
|
||||||
|
double cost = 0;
|
||||||
|
for (int i = 0; i < outputs.length; ++i) {
|
||||||
|
cost += Math.pow(expected[i] - outputs[i], 2);
|
||||||
|
}
|
||||||
|
return 0.5 * cost;
|
||||||
|
}
|
||||||
|
|
||||||
|
public void train(DataSet data) {
|
||||||
|
// Train the Neural Network using the given data set
|
||||||
|
// the data set is split into batches, and each batch is used to
|
||||||
|
// update the weights and biases of the Neural Network
|
||||||
|
// This implementation is sequential, but it could be parallelized (TODO)
|
||||||
|
DataSet currentSet = data.generateBatch();
|
||||||
|
|
||||||
|
while(currentSet != null) {
|
||||||
|
System.out.print(String.format("Batch %2d/%d\r", data.currentBatch, data.numBatches));
|
||||||
|
// for each point in the batch, calculate the outputs and update
|
||||||
|
// the gradients of the Neural Network
|
||||||
|
for (DataPoint currentPoint : currentSet.points) {
|
||||||
|
double[] values = this.calculateOutputs(currentPoint.inputs);
|
||||||
|
this.updateGradients(currentPoint, values);
|
||||||
|
}
|
||||||
|
currentSet = data.generateBatch();
|
||||||
|
}
|
||||||
|
System.out.println();
|
||||||
|
// update the epoch and learning rate
|
||||||
|
this.epoch++;
|
||||||
|
this.learningRate = initialLearningRate
|
||||||
|
* Math.pow(decayRate, this.epoch / decayFactor);
|
||||||
|
}
|
||||||
|
|
||||||
|
public void updateGradients(DataPoint point, double[] values) {
|
||||||
|
// update the gradients of the Neural Network
|
||||||
|
int finalLayer = this.layers.length - 1;
|
||||||
|
double[][] layerErrors = new double[finalLayer + 1][];
|
||||||
|
layerErrors[finalLayer] = new double[this.layers[finalLayer].numNeurons];
|
||||||
|
|
||||||
|
// calculate the delta for the output layer
|
||||||
|
for (int i = 0; i < layerErrors[finalLayer].length; i++) {
|
||||||
|
layerErrors[finalLayer][i] = -(point.expectedOutputs[i] - values[i])
|
||||||
|
* Layer.activationDerivativeFunction(this.layers[finalLayer].rawLayerData[i]);
|
||||||
|
}
|
||||||
|
|
||||||
|
// calculate the delta for the hidden layers
|
||||||
|
for (int i = finalLayer - 1; i >= 0; i--) {
|
||||||
|
int layerSize = this.layers[i].numNeurons;
|
||||||
|
layerErrors[i] = new double[layerSize];
|
||||||
|
for (int j = 0; j < layerSize; j++) {
|
||||||
|
layerErrors[i][j] = 0;
|
||||||
|
for (int k = 0; k < this.layers[i+1].numNeurons; k++) {
|
||||||
|
layerErrors[i][j] += this.layers[i + 1].getWeight(j, k) * layerErrors[i + 1][k];
|
||||||
|
}
|
||||||
|
layerErrors[i][j] *= Layer.activationDerivativeFunction(layers[i].rawLayerData[j]);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// update the weights and biases of each layer
|
||||||
|
for (int l = 0; l < this.layers.length; l++) { // for each layer
|
||||||
|
for (int j = 0; j < this.layers[l].numNeurons; j++) { // for each neuron
|
||||||
|
for (int i = 0; i < this.layers[l].numInputNeurons; i++) { // for each input neuron
|
||||||
|
double w = this.layers[l].getWeight(i, j);
|
||||||
|
// update the weight using the delta of the neuron
|
||||||
|
// find the raw data of the neuron
|
||||||
|
// if l == 0, the raw data is the input data
|
||||||
|
double rawData = (l == 0) ? point.inputs[i] : layers[l - 1].rawLayerData[i];
|
||||||
|
// update the weight
|
||||||
|
// w = w - learningRate * delta * f(rawData)
|
||||||
|
w = w - this.learningRate * layerErrors[l][j]
|
||||||
|
* Layer.activationFunction(rawData);
|
||||||
|
// set the new weight
|
||||||
|
this.layers[l].setWeight(i, j, w);
|
||||||
|
}
|
||||||
|
// update the biases
|
||||||
|
this.layers[l].biases[j] -= this.learningRate * layerErrors[l][j];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public double evaluateTotalCost(DataSet data) {
|
||||||
|
double res = 0.0;
|
||||||
|
|
||||||
|
for (int i = 0; i < data.numPoints; ++i) {
|
||||||
|
DataPoint point = data.points[i];
|
||||||
|
double[] outputs = calculateOutputs(point.inputs);
|
||||||
|
res += calculateCost(point.expectedOutputs, outputs);
|
||||||
|
}
|
||||||
|
|
||||||
|
return res / data.numPoints;
|
||||||
|
}
|
||||||
|
|
||||||
|
public double evaluateAccuracy(DataSet data) {
|
||||||
|
int correct = 0;
|
||||||
|
for (int i = 0; i < data.numPoints; ++i) {
|
||||||
|
DataPoint point = data.points[i];
|
||||||
|
double[] outputs = calculateOutputs(point.inputs);
|
||||||
|
int predicted = classify(outputs);
|
||||||
|
int expected = classify(point.expectedOutputs);
|
||||||
|
if (predicted == expected) {
|
||||||
|
correct++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return (double) correct / data.numPoints;
|
||||||
|
}
|
||||||
|
|
||||||
|
@Override
|
||||||
|
public String toString() {
|
||||||
|
StringBuilder sb = new StringBuilder();
|
||||||
|
sb.append("NeuralNetwork{\n");
|
||||||
|
for (int i = 0; i < this.layers.length; ++i) {
|
||||||
|
sb.append(" " + this.layers[i].toString() + "\n");
|
||||||
|
}
|
||||||
|
sb.append("}");
|
||||||
|
return sb.toString();
|
||||||
|
}
|
||||||
|
|
||||||
|
public void save(String filename) throws IOException {
|
||||||
|
try (ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(filename))) {
|
||||||
|
oos.writeObject(this);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
public static NeuralNetwork load(String filename) throws IOException, ClassNotFoundException {
|
||||||
|
try (ObjectInputStream ois = new ObjectInputStream(new FileInputStream(filename))) {
|
||||||
|
NeuralNetwork nn = (NeuralNetwork) ois.readObject();
|
||||||
|
nn.random = new Random(0);
|
||||||
|
return nn;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,209 @@
|
|||||||
|
package fr.perceptron;
|
||||||
|
|
||||||
|
import javax.swing.JFrame;
|
||||||
|
import javax.swing.JPanel;
|
||||||
|
import java.awt.Color;
|
||||||
|
import java.awt.Dimension;
|
||||||
|
import java.awt.Graphics;
|
||||||
|
import java.awt.event.KeyEvent;
|
||||||
|
import java.awt.event.KeyListener;
|
||||||
|
import java.awt.image.BufferedImage;
|
||||||
|
|
||||||
|
public class Window {
|
||||||
|
|
||||||
|
public Canvas canvas;
|
||||||
|
|
||||||
|
public Window() {}
|
||||||
|
|
||||||
|
public void init(NeuralNetwork nn, DataSet data) {
|
||||||
|
|
||||||
|
// Set the canvas to be the main frame's content pane
|
||||||
|
this.canvas = new Canvas(800, 800);
|
||||||
|
this.canvas.setBackground(Color.white);
|
||||||
|
this.canvas.setPreferredSize(new Dimension(800, 800));
|
||||||
|
this.canvas.setVisible(true);
|
||||||
|
JFrame mainFrame = new JFrame("Perceptron");
|
||||||
|
mainFrame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
|
||||||
|
mainFrame.setSize(800, 800);
|
||||||
|
mainFrame.add(this.canvas);
|
||||||
|
mainFrame.addKeyListener(canvas);
|
||||||
|
mainFrame.setResizable(false);
|
||||||
|
mainFrame.setLocationRelativeTo(null); // Center the window
|
||||||
|
mainFrame.pack();
|
||||||
|
mainFrame.setVisible(true);
|
||||||
|
mainFrame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
|
||||||
|
this.canvas.data = data;
|
||||||
|
this.canvas.nn = nn;
|
||||||
|
this.canvas.updatePixels();
|
||||||
|
}
|
||||||
|
|
||||||
|
public void update() {
|
||||||
|
// Update the canvas with the neural network's output
|
||||||
|
canvas.updatePixels();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
class Canvas extends JPanel implements KeyListener {
|
||||||
|
|
||||||
|
private byte[][] pixels;
|
||||||
|
private int width;
|
||||||
|
private int height;
|
||||||
|
public DataSet data;
|
||||||
|
private float zoomLevel;
|
||||||
|
public NeuralNetwork nn;
|
||||||
|
|
||||||
|
private static final Color[] COLORS = {
|
||||||
|
new Color(0xc06699ff, true), // light blue
|
||||||
|
new Color(0xc0ff6666, true), // light red
|
||||||
|
};
|
||||||
|
|
||||||
|
public Canvas(int width, int height) {
|
||||||
|
setBackground(Color.white);
|
||||||
|
this.pixels = new byte[height][width];
|
||||||
|
this.width = width;
|
||||||
|
this.height = height;
|
||||||
|
// this.zoomLevel = 2.0f; // linear training set
|
||||||
|
// this.zoomLevel = 0.75f; // moon training set
|
||||||
|
this.zoomLevel = 0.125f; // two circles training set
|
||||||
|
|
||||||
|
// Enable double buffering
|
||||||
|
setDoubleBuffered(true);
|
||||||
|
}
|
||||||
|
|
||||||
|
public Dimension getPreferredSize() {
|
||||||
|
return new Dimension(800, 800);
|
||||||
|
}
|
||||||
|
|
||||||
|
public void paintComponent(Graphics g) {
|
||||||
|
super.paintComponent(g);
|
||||||
|
|
||||||
|
// Clear the canvas
|
||||||
|
g.setColor(Color.WHITE);
|
||||||
|
g.fillRect(0, 0, getWidth(), getHeight());
|
||||||
|
|
||||||
|
// Draw the axes
|
||||||
|
this.drawAxis(g);
|
||||||
|
// Draw the points
|
||||||
|
this.drawDataPoints(g);
|
||||||
|
|
||||||
|
// draw the pixels from the pixels array to the canvas
|
||||||
|
// for (int y = 0; y < height; y++) {
|
||||||
|
// for (int x = 0; x < width; x++) {
|
||||||
|
// // Set the color based on the pixel value
|
||||||
|
// g.setColor(COLORS[pixels[y][x]]);
|
||||||
|
// // Draw one pixel
|
||||||
|
// g.fillRect(x, y, 1, 1);
|
||||||
|
// }
|
||||||
|
// }
|
||||||
|
|
||||||
|
// Draw the pixels from the pixels array to the canvas
|
||||||
|
// use a BufferedImage for better performance
|
||||||
|
BufferedImage img = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
|
||||||
|
for (int y = 0; y < height; y++) {
|
||||||
|
for (int x = 0; x < width; x++) {
|
||||||
|
// Set the color based on the pixel value
|
||||||
|
img.setRGB(x, y, COLORS[pixels[y][x]].getRGB());
|
||||||
|
}
|
||||||
|
}
|
||||||
|
g.drawImage(img, 0, 0, null);
|
||||||
|
}
|
||||||
|
|
||||||
|
public void updatePixels() {
|
||||||
|
System.out.println("Updating pixels...");
|
||||||
|
|
||||||
|
int midX = width / 2;
|
||||||
|
int midY = height / 2;
|
||||||
|
|
||||||
|
// Initialize a new pixels array
|
||||||
|
byte[][] newPixels = new byte[height][width];
|
||||||
|
|
||||||
|
// Use parallel processing to compute pixel values
|
||||||
|
java.util.stream.IntStream.range(0, height).parallel().forEach(y -> {
|
||||||
|
for (int x = 0; x < width; x++) {
|
||||||
|
|
||||||
|
// Normalize the pixel coordinates to the range [-2, 2]
|
||||||
|
double[] inputs = {
|
||||||
|
// Normalize the pixel coordinates to the range [-2, 2]
|
||||||
|
(double) (x - midX) / (width / 4 * this.zoomLevel),
|
||||||
|
(double) -(y - midY) / (height / 4 * this.zoomLevel),
|
||||||
|
};
|
||||||
|
|
||||||
|
// Calculate the output of the neural network
|
||||||
|
double[] output = nn.calculateOutputs(inputs);
|
||||||
|
int classification = nn.classify(output[0]);
|
||||||
|
|
||||||
|
// Store the computed value in the temporary array
|
||||||
|
newPixels[y][x] = (byte) classification;
|
||||||
|
}
|
||||||
|
});
|
||||||
|
|
||||||
|
// Replace the old pixels array with the new one
|
||||||
|
this.pixels = newPixels;
|
||||||
|
System.out.println("Done updating pixels.");
|
||||||
|
|
||||||
|
// Repaint the canvas
|
||||||
|
repaint();
|
||||||
|
}
|
||||||
|
|
||||||
|
public void drawAxis(Graphics g) {
|
||||||
|
// Draw the axes
|
||||||
|
g.setColor(Color.BLACK);
|
||||||
|
g.drawLine(0, height / 2, width, height / 2); // X-axis
|
||||||
|
g.drawLine(width / 2, 0, width / 2, height); // Y-axis
|
||||||
|
|
||||||
|
// Add labels to the axes
|
||||||
|
g.drawString("X", width - 20, height / 2 + 15);
|
||||||
|
g.drawString("Y", width / 2 - 15, 15);
|
||||||
|
// g.drawString("0", width / 2 + 5, height / 2 + 15);
|
||||||
|
// g.drawString("1", width - 20, height / 2 - 5);
|
||||||
|
// g.drawString("-1", width / 2 + 5, height - 5);
|
||||||
|
// g.drawString("-1", 5, height / 2 + 15);
|
||||||
|
// g.drawString("1", width / 2 + 5, 15);
|
||||||
|
}
|
||||||
|
|
||||||
|
public void drawDataPoints(Graphics g) {
|
||||||
|
// Draw the data points
|
||||||
|
g.setColor(Color.BLACK);
|
||||||
|
for (int i = 0; i < this.data.points.length; i++) {
|
||||||
|
int x = (int) (this.zoomLevel * this.data.points[i].inputs[0] * (width / 4) + (width / 2));
|
||||||
|
int y = (int) (this.zoomLevel * -this.data.points[i].inputs[1] * (height / 4) + (height / 2));
|
||||||
|
if (this.data.points[i].expectedOutputs[0] == 1.0) {
|
||||||
|
// Draw a circle
|
||||||
|
g.drawOval(x, y, 8, 8);
|
||||||
|
} else {
|
||||||
|
// Draw a cross
|
||||||
|
g.drawLine(x - 4, y, x + 4, y);
|
||||||
|
g.drawLine(x, y - 4, x, y + 4);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
@Override
|
||||||
|
public void keyPressed(KeyEvent e) {
|
||||||
|
|
||||||
|
// Check if the key pressed is UP or DOWN
|
||||||
|
if (e.getKeyCode() == KeyEvent.VK_UP) {
|
||||||
|
this.zoomLevel += 0.125f;
|
||||||
|
System.out.println("Zoom level: " + this.zoomLevel);
|
||||||
|
} else if (e.getKeyCode() == KeyEvent.VK_DOWN) {
|
||||||
|
this.zoomLevel -= 0.125f;
|
||||||
|
System.out.println("Zoom level: " + this.zoomLevel);
|
||||||
|
}
|
||||||
|
if (this.zoomLevel < 0.125f) {
|
||||||
|
this.zoomLevel = 0.125f;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
@Override
|
||||||
|
public void keyReleased(KeyEvent e) {}
|
||||||
|
|
||||||
|
@Override
|
||||||
|
public void keyTyped(KeyEvent e) {
|
||||||
|
|
||||||
|
if (e.getKeyChar() == 'u') {
|
||||||
|
// Update the pixels
|
||||||
|
updatePixels();
|
||||||
|
repaint();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,92 @@
|
|||||||
|
#/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
|
||||||
|
# Simple script to show statistics a Neural Network training session.
|
||||||
|
|
||||||
|
# - Imports
|
||||||
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
|
# Read the data from the file
|
||||||
|
def read_data(file_path: str) -> list:
|
||||||
|
with open(file_path, 'r') as file:
|
||||||
|
lines = file.readlines()
|
||||||
|
return [line.strip() for line in lines if line.strip()]
|
||||||
|
|
||||||
|
# Parse the data from the file
|
||||||
|
# This function takes the data read from the file and parses it.
|
||||||
|
# Each line contains multiple values separated by commas.
|
||||||
|
# The function extracts the values and returns them as a list of lists.
|
||||||
|
def parse_data(data: list, numEntries: int) -> list[list]:
|
||||||
|
parsed_data = [[] for _ in range(numEntries)]
|
||||||
|
for line in data:
|
||||||
|
values = line.split(',')
|
||||||
|
for i in range(numEntries):
|
||||||
|
try:
|
||||||
|
parsed_data[i].append(float(values[i]))
|
||||||
|
except (ValueError, IndexError):
|
||||||
|
print(f"Error parsing line: {line}")
|
||||||
|
continue
|
||||||
|
return parsed_data
|
||||||
|
|
||||||
|
# Plot the data using matplotlib
|
||||||
|
def plot_data(
|
||||||
|
data: list,
|
||||||
|
title: str,
|
||||||
|
xlabel: str,
|
||||||
|
ylabel: str,
|
||||||
|
log_scale: bool = False
|
||||||
|
) -> None:
|
||||||
|
# Plot the data
|
||||||
|
plt.figure(figsize=(10, 5))
|
||||||
|
plt.plot(data, label=title)
|
||||||
|
# Add labels and title
|
||||||
|
plt.xlabel(xlabel)
|
||||||
|
plt.ylabel(ylabel)
|
||||||
|
plt.title(title)
|
||||||
|
plt.legend()
|
||||||
|
plt.grid()
|
||||||
|
# Show the plot
|
||||||
|
if log_scale:
|
||||||
|
plt.yscale('log')
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
def plot_accuracies(
|
||||||
|
learn_rates: list,
|
||||||
|
train: list,
|
||||||
|
eval: list
|
||||||
|
) -> None:
|
||||||
|
# Create 2 subplots, one for the learning rates in relation to the epochs
|
||||||
|
# and one for the training and evaluation accuracies
|
||||||
|
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 10))
|
||||||
|
# Plot the learning rates
|
||||||
|
ax1.plot(learn_rates, label='Learning Rate')
|
||||||
|
ax1.set_xlabel('Epochs')
|
||||||
|
ax1.set_ylabel('Learning Rate')
|
||||||
|
ax1.set_title('Learning Rate vs Epochs')
|
||||||
|
ax1.legend()
|
||||||
|
ax1.grid()
|
||||||
|
# Plot the accuracies
|
||||||
|
ax2.plot(train, label='Training Accuracy')
|
||||||
|
ax2.plot(eval, label='Evaluation Accuracy')
|
||||||
|
ax2.set_xlabel('Epochs')
|
||||||
|
ax2.set_ylabel('Accuracy')
|
||||||
|
ax2.set_title('Training and Evaluation Accuracy vs Epochs')
|
||||||
|
ax2.legend()
|
||||||
|
ax2.grid()
|
||||||
|
# Show the plots
|
||||||
|
plt.tight_layout()
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
# Main function to execute the script
|
||||||
|
def main():
|
||||||
|
# Read the data from the file
|
||||||
|
data = read_data('stats/stats_256_128.txt')
|
||||||
|
# Parse the data
|
||||||
|
learn_rates, data_accuracy, eval_accuracy = parse_data(data, 3)
|
||||||
|
# Plot the accuracies
|
||||||
|
plot_accuracies(learn_rates, data_accuracy, eval_accuracy)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
||||||
|
# - End of script
|
||||||
LFS
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BIN
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Reference in New Issue
Block a user