commit 5b3ebf8645148e92f2b1a714547cdfc87c16c3f9
Author: dukantic <aubindo@outlook.fr>
Date:   Sun Mar 29 20:21:25 2026 +0200

    init lfs

diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..d865b51
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+  Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so.  This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software.  The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable.  Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products.  If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy.  The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+  A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
+computer or modifying a private copy.  Propagation includes copying,
+distribution (with or without modification), making available to the
+public, and in some countries other activities as well.
+
+  To "convey" a work means any kind of propagation that enables other
+parties to make or receive copies.  Mere interaction with a user through
+a computer network, with no transfer of a copy, is not conveying.
+
+  An interactive user interface displays "Appropriate Legal Notices"
+to the extent that it includes a convenient and prominently visible
+feature that (1) displays an appropriate copyright notice, and (2)
+tells the user that there is no warranty for the work (except to the
+extent that warranties are provided), that licensees may convey the
+work under this License, and how to view a copy of this License.  If
+the interface presents a list of user commands or options, such as a
+menu, a prominent item in the list meets this criterion.
+
+  1. Source Code.
+
+  The "source code" for a work means the preferred form of the work
+for making modifications to it.  "Object code" means any non-source
+form of a work.
+
+  A "Standard Interface" means an interface that either is an official
+standard defined by a recognized standards body, or, in the case of
+interfaces specified for a particular programming language, one that
+is widely used among developers working in that language.
+
+  The "System Libraries" of an executable work include anything, other
+than the work as a whole, that (a) is included in the normal form of
+packaging a Major Component, but which is not part of that Major
+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
+(if any) on which the executable work runs, or a compiler used to
+produce the work, or an object code interpreter used to run it.
+
+  The "Corresponding Source" for a work in object code form means all
+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
+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
+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.
+
+  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.
+
+  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.
+
+  Conveying under any other circumstances is permitted solely under
+the conditions stated below.  Sublicensing is not allowed; section 10
+makes it unnecessary.
+
+  3. Protecting Users' Legal Rights From Anti-Circumvention Law.
+
+  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.
+
+  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.
+
+  4. Conveying Verbatim Copies.
+
+  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;
+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.
+
+  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.
+
+  5. Conveying Modified Source Versions.
+
+  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".
+
+    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.
+
+  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>.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..75bca59
--- /dev/null
+++ b/README.md
@@ -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%)  
+![linear_data_linear](images/linear_data_linear.png)  
+
+#### Moon Data (Accuracy: 80.9%)  
+![moon_data_linear](images/moon_data_linear.png)  
+
+#### Two Circles (Accuracy: 33.0%)  
+![twocircles_data_linear](images/twocircles_data_linear.png)  
+
+### General Perceptron  
+
+#### Linear Data (Accuracy: 100.0%)  
+![linear_data](images/linear_data.GIF)  
+
+#### Moon Data (Accuracy: 97.4%)  
+![moon_data](images/moon_data.GIF)  
+
+#### Two Circles (Accuracy: 100.0%)  
+![twocircles_data](images/twocircles_data.GIF)  
+
+## 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:  
+
+![mnist](images/mnist.png)  
+
+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):  
+
+![mnist_error](images/mnist_error.png)  
+
+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%)
+![linear_data_linear](images/linear_data_linear.png) 
+
+#### Moon Data (Accurancy 80.9%)
+
+![moon_data_linear](images/moon_data_linear.png)  
+
+#### Two Circles (Accuracy: 33.0%)
+
+![twocircles_data_linear](images/twocircles_data_linear.png)
+
+### Perceptron Générale
+
+#### Linear Data (Accuracy: 100.0%)
+
+![linear_data](images/linear_data.GIF) 
+
+#### Moon Data (Accuracy: 97.4%)
+
+![moon_data](images/moon_data.GIF)  
+
+#### Two Circles (Accuracy: 100.0%)
+![twocircles_data](images/twocircles_data.GIF)
+
+## 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 :
+
+![mnist](images/mnist.png)
+
+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) :
+
+![mnist_error](images/mnist_error.png)
+
+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".
diff --git a/data/linear_data_eval.csv b/data/linear_data_eval.csv
new file mode 100644
index 0000000..2ca16dd
--- /dev/null
+++ b/data/linear_data_eval.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c397c8008c26015cd169a26338766818cba1f82198912b2a5db5d546e9fe4d8
+size 7641
diff --git a/data/linear_data_train.csv b/data/linear_data_train.csv
new file mode 100644
index 0000000..eb69786
--- /dev/null
+++ b/data/linear_data_train.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f1850d656667a07c8a8ce4eb59c15fc3a78e65ea49a27734d4c2c11f6aa22d0
+size 38125
diff --git a/data/moon_data_eval.csv b/data/moon_data_eval.csv
new file mode 100644
index 0000000..8ca7f53
--- /dev/null
+++ b/data/moon_data_eval.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:381f736b9a6e6aab5ca8ffe9f4b701cb90b64b67a7b81cbc540a09ae29426358
+size 32171
diff --git a/data/moon_data_train.csv b/data/moon_data_train.csv
new file mode 100644
index 0000000..444f928
--- /dev/null
+++ b/data/moon_data_train.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb9f707cdedff00287ca9315331f7ae4030ca6b62fd71c3bd65c256dc054ef0d
+size 64383
diff --git a/data/twocircles_data_eval.csv b/data/twocircles_data_eval.csv
new file mode 100644
index 0000000..b71c658
--- /dev/null
+++ b/data/twocircles_data_eval.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fef1fc1dfa6a1686338161fb6f59bfe4fff3938503e3f7101414a56547ae1515
+size 3718
diff --git a/data/twocircles_data_train.csv b/data/twocircles_data_train.csv
new file mode 100644
index 0000000..4b28f1f
--- /dev/null
+++ b/data/twocircles_data_train.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4f2d4f3c35e8dd243b1073a38f9d668d72acc7e9bdcd824ed9b257718fd7f7b
+size 18700
diff --git a/images/linear_data.GIF b/images/linear_data.GIF
new file mode 100644
index 0000000..01e5d16
--- /dev/null
+++ b/images/linear_data.GIF
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cfb2d2c6d8c466a24acb130c277c09660e328f1267eccfc86b4219edd65f78b
+size 405389
diff --git a/images/linear_data_linear.png b/images/linear_data_linear.png
new file mode 100644
index 0000000..6b794e4
--- /dev/null
+++ b/images/linear_data_linear.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09fe289ce7e6453348a67dd17419d337b6b9ae71e90032b4c9d04120ded83a1a
+size 34741
diff --git a/images/linear_data_linear.png~ b/images/linear_data_linear.png~
new file mode 100644
index 0000000..ff2b2ce
--- /dev/null
+++ b/images/linear_data_linear.png~
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb7e1e9750a5185388124f55f3cab63939a4c38e9096ffcd14137d4c72a700dc
+size 12733
diff --git a/images/mnist.png b/images/mnist.png
new file mode 100644
index 0000000..29dc0b7
--- /dev/null
+++ b/images/mnist.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36ea32d36c40bf8fdf17bcc6524968e0e88487195ec5cc2074f86730fbcf6690
+size 29155
diff --git a/images/mnist.png~ b/images/mnist.png~
new file mode 100644
index 0000000..578139d
--- /dev/null
+++ b/images/mnist.png~
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:400b2bb61101bda65d350405e10ee632ad9d4a5cd71bfe3bb5e7a297c0112700
+size 26201
diff --git a/images/mnist_error.png b/images/mnist_error.png
new file mode 100644
index 0000000..f34084c
--- /dev/null
+++ b/images/mnist_error.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:646c70b30f19c677b33bb65403ae2d45850a3a90e8d48762be2c8f9093f24b8d
+size 29758
diff --git a/images/mnist_error.png~ b/images/mnist_error.png~
new file mode 100644
index 0000000..4ff6420
--- /dev/null
+++ b/images/mnist_error.png~
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eccb2b5ccce733adf4d8046c5f57d6dfcb8e33163be685555dce3ca7bc523f2b
+size 28771
diff --git a/images/moon_data.GIF b/images/moon_data.GIF
new file mode 100644
index 0000000..3026458
--- /dev/null
+++ b/images/moon_data.GIF
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:491320c93e8500e54a3a5a713bfa721064a7270e5203345bb5fd2453f7803d72
+size 2796660
diff --git a/images/moon_data_linear.png b/images/moon_data_linear.png
new file mode 100644
index 0000000..b3d6581
--- /dev/null
+++ b/images/moon_data_linear.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37bda6d73d035c5242edd5057b03263188dbe903be67731d21e62e04313ceed6
+size 85746
diff --git a/images/moon_data_linear.png~ b/images/moon_data_linear.png~
new file mode 100644
index 0000000..66dde91
--- /dev/null
+++ b/images/moon_data_linear.png~
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:062827fa53df3575981185277a613c0af09fdfddb5303a0e0a7d7948db85f8e5
+size 21052
diff --git a/images/twocircles_data.GIF b/images/twocircles_data.GIF
new file mode 100644
index 0000000..4df49f9
--- /dev/null
+++ b/images/twocircles_data.GIF
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:674abdd2c44d28c8938a7fafae24c12961160c65321ceb813518c0e808d3f4e0
+size 1316500
diff --git a/images/twocircles_data_linear.png b/images/twocircles_data_linear.png
new file mode 100644
index 0000000..4f637c9
--- /dev/null
+++ b/images/twocircles_data_linear.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f58ddd9aa712865ee54b4629f1f8a9765788172c73b6e017c9ce509fea73bc8
+size 24068
diff --git a/images/twocircles_data_linear.png~ b/images/twocircles_data_linear.png~
new file mode 100644
index 0000000..82e2499
--- /dev/null
+++ b/images/twocircles_data_linear.png~
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e2f32603688d0bd02f83a00102a8f8ab24e7492a8769f714986dd14c995e71d
+size 11564
diff --git a/mnist/cours_technologie_web.pdf b/mnist/cours_technologie_web.pdf
new file mode 100644
index 0000000..de12775
--- /dev/null
+++ b/mnist/cours_technologie_web.pdf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7452d5f45ecfdc0af96831ed55679f7ee7ce060b805a72d8fa97de8d6055a9b5
+size 4336
diff --git a/mnist/t10k-images-idx3-ubyte.gz b/mnist/t10k-images-idx3-ubyte.gz
new file mode 100644
index 0000000..aa17dfe
--- /dev/null
+++ b/mnist/t10k-images-idx3-ubyte.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d422c7b0a1c1c79245a5bcf07fe86e33eeafee792b84584aec276f5a2dbc4e6
+size 1648877
diff --git a/mnist/t10k-labels-idx1-ubyte.gz b/mnist/t10k-labels-idx1-ubyte.gz
new file mode 100644
index 0000000..d1995be
--- /dev/null
+++ b/mnist/t10k-labels-idx1-ubyte.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f7ae60f92e00ec6debd23a6088c31dbd2371eca3ffa0defaefb259924204aec6
+size 4542
diff --git a/mnist/train-images-idx3-ubyte.gz b/mnist/train-images-idx3-ubyte.gz
new file mode 100644
index 0000000..9e9852c
--- /dev/null
+++ b/mnist/train-images-idx3-ubyte.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:440fcabf73cc546fa21475e81ea370265605f56be210a4024d2ca8f203523609
+size 9912422
diff --git a/mnist/train-labels-idx1-ubyte.gz b/mnist/train-labels-idx1-ubyte.gz
new file mode 100644
index 0000000..a7ebf9b
--- /dev/null
+++ b/mnist/train-labels-idx1-ubyte.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3552534a0a558bbed6aed32b30c495cca23d567ec52cac8be1a0730e8010255c
+size 28881
diff --git a/nn_256_128.dat b/nn_256_128.dat
new file mode 100644
index 0000000..8f01ea9
Binary files /dev/null and b/nn_256_128.dat differ
diff --git a/src/main/java/fr/perceptron/DataPoint.java b/src/main/java/fr/perceptron/DataPoint.java
new file mode 100644
index 0000000..2a59777
--- /dev/null
+++ b/src/main/java/fr/perceptron/DataPoint.java
@@ -0,0 +1,14 @@
+
+package fr.perceptron;
+
+import java.io.Serializable;
+
+public class DataPoint implements Serializable {
+    
+    public double[] inputs;
+    public double[] expectedOutputs;
+
+    public DataPoint() {
+        
+    }
+}
diff --git a/src/main/java/fr/perceptron/DataSet.java b/src/main/java/fr/perceptron/DataSet.java
new file mode 100644
index 0000000..4a7da20
--- /dev/null
+++ b/src/main/java/fr/perceptron/DataSet.java
@@ -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;
+    }
+}
diff --git a/src/main/java/fr/perceptron/Layer.java b/src/main/java/fr/perceptron/Layer.java
new file mode 100644
index 0000000..d762530
--- /dev/null
+++ b/src/main/java/fr/perceptron/Layer.java
@@ -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);
+    }
+}
diff --git a/src/main/java/fr/perceptron/MNIST.java b/src/main/java/fr/perceptron/MNIST.java
new file mode 100644
index 0000000..086363c
--- /dev/null
+++ b/src/main/java/fr/perceptron/MNIST.java
@@ -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) {
+        }
+    }
+}
diff --git a/src/main/java/fr/perceptron/Main.java b/src/main/java/fr/perceptron/Main.java
new file mode 100644
index 0000000..9d05352
--- /dev/null
+++ b/src/main/java/fr/perceptron/Main.java
@@ -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();
+        }
+    }
+}
diff --git a/src/main/java/fr/perceptron/MnistMain.java b/src/main/java/fr/perceptron/MnistMain.java
new file mode 100644
index 0000000..6e73a0d
--- /dev/null
+++ b/src/main/java/fr/perceptron/MnistMain.java
@@ -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;
+        }
+    }
+}
diff --git a/src/main/java/fr/perceptron/MnistWindow.java b/src/main/java/fr/perceptron/MnistWindow.java
new file mode 100644
index 0000000..8a41d5d
--- /dev/null
+++ b/src/main/java/fr/perceptron/MnistWindow.java
@@ -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.");
+        }
+        
+    }
+}
\ No newline at end of file
diff --git a/src/main/java/fr/perceptron/NeuralNetwork.java b/src/main/java/fr/perceptron/NeuralNetwork.java
new file mode 100644
index 0000000..82ab8fd
--- /dev/null
+++ b/src/main/java/fr/perceptron/NeuralNetwork.java
@@ -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;
+        }
+    }
+}
diff --git a/src/main/java/fr/perceptron/Window.java b/src/main/java/fr/perceptron/Window.java
new file mode 100644
index 0000000..7942f3c
--- /dev/null
+++ b/src/main/java/fr/perceptron/Window.java
@@ -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();
+        }
+    }
+}
\ No newline at end of file
diff --git a/stats/stats.py b/stats/stats.py
new file mode 100644
index 0000000..1069121
--- /dev/null
+++ b/stats/stats.py
@@ -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
\ No newline at end of file
diff --git a/stats/stats.txt b/stats/stats.txt
new file mode 100644
index 0000000..8063382
--- /dev/null
+++ b/stats/stats.txt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c35caf85c56fb191a3bca238229f1baa1062a3efc1a4263ecaac4f79dbffb77
+size 4738
diff --git a/stats/stats_256_128.txt b/stats/stats_256_128.txt
new file mode 100644
index 0000000..e26c5ed
--- /dev/null
+++ b/stats/stats_256_128.txt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b55c5d35857b38ad568ffdb61a6528c2b4823f2e0747765687becc842917f13
+size 21305