├── .gitignore ├── LICENSE ├── README.md ├── cnn.py ├── data ├── 0_100.jpg ├── 0_104.jpg ├── 0_106.jpg ├── 0_107.jpg ├── 0_108.jpg ├── 0_109.jpg ├── 0_110.jpg ├── 0_113.jpg ├── 0_114.jpg ├── 0_115.jpg ├── 0_117.jpg ├── 0_119.jpg ├── 0_122.jpg ├── 0_123.jpg ├── 0_124.jpg ├── 0_126.jpg ├── 0_127.jpg ├── 0_131.jpg ├── 0_132.jpg ├── 0_133.jpg ├── 0_134.jpg ├── 0_135.jpg ├── 0_136.jpg ├── 0_138.jpg ├── 0_139.jpg ├── 0_141.jpg ├── 0_17.jpg ├── 0_27.jpg ├── 0_30.jpg ├── 0_38.jpg ├── 0_39.jpg ├── 0_45.jpg ├── 0_53.jpg ├── 0_57.jpg ├── 0_59.jpg ├── 0_6.jpg ├── 0_60.jpg ├── 0_63.jpg ├── 0_67.jpg ├── 0_69.jpg ├── 0_7.jpg ├── 0_70.jpg ├── 0_73.jpg ├── 0_81.jpg ├── 0_84.jpg ├── 0_85.jpg ├── 0_89.jpg ├── 0_9.jpg ├── 0_91.jpg ├── 0_97.jpg ├── 1_0.jpg ├── 1_1.jpg ├── 1_101.jpg ├── 1_102.jpg ├── 1_103.jpg ├── 1_12.jpg ├── 1_13.jpg ├── 1_14.jpg ├── 1_21.jpg ├── 1_22.jpg ├── 1_24.jpg ├── 1_25.jpg ├── 1_26.jpg ├── 1_28.jpg ├── 1_31.jpg ├── 1_32.jpg ├── 1_33.jpg ├── 1_34.jpg ├── 1_35.jpg ├── 1_37.jpg ├── 1_40.jpg ├── 1_44.jpg ├── 1_46.jpg ├── 1_47.jpg ├── 1_48.jpg ├── 1_50.jpg ├── 1_52.jpg ├── 1_54.jpg ├── 1_56.jpg ├── 1_58.jpg ├── 1_64.jpg ├── 1_65.jpg ├── 1_66.jpg ├── 1_71.jpg ├── 1_72.jpg ├── 1_74.jpg ├── 1_75.jpg ├── 1_76.jpg ├── 1_77.jpg ├── 1_78.jpg ├── 1_79.jpg ├── 1_8.jpg ├── 1_80.jpg ├── 1_83.jpg ├── 1_87.jpg ├── 1_93.jpg ├── 1_94.jpg ├── 1_96.jpg ├── 1_98.jpg ├── 1_99.jpg ├── 2_10.jpg ├── 2_105.jpg ├── 2_11.jpg ├── 2_111.jpg ├── 2_112.jpg ├── 2_116.jpg ├── 2_118.jpg ├── 2_120.jpg ├── 2_121.jpg ├── 2_125.jpg ├── 2_128.jpg ├── 2_129.jpg ├── 2_130.jpg ├── 2_137.jpg ├── 2_140.jpg ├── 2_142.jpg ├── 2_143.jpg ├── 2_144.jpg ├── 2_145.jpg ├── 2_146.jpg ├── 2_147.jpg ├── 2_148.jpg ├── 2_149.jpg ├── 2_15.jpg ├── 2_16.jpg ├── 2_18.jpg ├── 2_19.jpg ├── 2_2.jpg ├── 2_20.jpg ├── 2_23.jpg ├── 2_29.jpg ├── 2_3.jpg ├── 2_36.jpg ├── 2_4.jpg ├── 2_41.jpg ├── 2_42.jpg ├── 2_43.jpg ├── 2_49.jpg ├── 2_5.jpg ├── 2_51.jpg ├── 2_55.jpg ├── 2_61.jpg ├── 2_62.jpg ├── 2_68.jpg ├── 2_82.jpg ├── 2_86.jpg ├── 2_88.jpg ├── 2_90.jpg ├── 2_92.jpg └── 2_95.jpg ├── demo.png └── download_cifar.py /.gitignore: -------------------------------------------------------------------------------- 1 | /.idea/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. We, the Free Software Foundation, use the 18 | GNU General Public License for most of our software; it applies also to 19 | any other work released this way by its authors. You can apply it to 20 | your programs, too. 21 | 22 | When we speak of free software, we are referring to freedom, not 23 | price. Our General Public Licenses are designed to make sure that you 24 | have the freedom to distribute copies of free software (and charge for 25 | them if you wish), that you receive source code or can get it if you 26 | want it, that you can change the software or use pieces of it in new 27 | free programs, and that you know you can do these things. 28 | 29 | To protect your rights, we need to prevent others from denying you 30 | these rights or asking you to surrender the rights. Therefore, you have 31 | certain responsibilities if you distribute copies of the software, or if 32 | you modify it: responsibilities to respect the freedom of others. 33 | 34 | For example, if you distribute copies of such a program, whether 35 | gratis or for a fee, you must pass on to the recipients the same 36 | freedoms that you received. You must make sure that they, too, receive 37 | or can get the source code. And you must show them these terms so they 38 | know their rights. 39 | 40 | Developers that use the GNU GPL protect your rights with two steps: 41 | (1) assert copyright on the software, and (2) offer you this License 42 | giving you legal permission to copy, distribute and/or modify it. 43 | 44 | For the developers' and authors' protection, the GPL clearly explains 45 | that there is no warranty for this free software. For both users' and 46 | authors' sake, the GPL requires that modified versions be marked as 47 | changed, so that their problems will not be attributed erroneously to 48 | authors of previous versions. 49 | 50 | Some devices are designed to deny users access to install or run 51 | modified versions of the software inside them, although the manufacturer 52 | can do so. This is fundamentally incompatible with the aim of 53 | protecting users' freedom to change the software. The systematic 54 | pattern of such abuse occurs in the area of products for individuals to 55 | use, which is precisely where it is most unacceptable. Therefore, we 56 | have designed this version of the GPL to prohibit the practice for those 57 | products. If such problems arise substantially in other domains, we 58 | stand ready to extend this provision to those domains in future versions 59 | of the GPL, as needed to protect the freedom of users. 60 | 61 | Finally, every program is threatened constantly by software patents. 62 | States should not allow patents to restrict development and use of 63 | software on general-purpose computers, but in those that do, we wish to 64 | avoid the special danger that patents applied to a free program could 65 | make it effectively proprietary. To prevent this, the GPL assures that 66 | patents cannot be used to render the program non-free. 67 | 68 | The precise terms and conditions for copying, distribution and 69 | modification follow. 70 | 71 | TERMS AND CONDITIONS 72 | 73 | 0. Definitions. 74 | 75 | "This License" refers to version 3 of the GNU General Public License. 76 | 77 | "Copyright" also means copyright-like laws that apply to other kinds of 78 | works, such as semiconductor masks. 79 | 80 | "The Program" refers to any copyrightable work licensed under this 81 | License. Each licensee is addressed as "you". "Licensees" and 82 | "recipients" may be individuals or organizations. 83 | 84 | To "modify" a work means to copy from or adapt all or part of the work 85 | in a fashion requiring copyright permission, other than the making of an 86 | exact copy. The resulting work is called a "modified version" of the 87 | earlier work or a work "based on" the earlier work. 88 | 89 | A "covered work" means either the unmodified Program or a work based 90 | on the Program. 91 | 92 | To "propagate" a work means to do anything with it that, without 93 | permission, would make you directly or secondarily liable for 94 | infringement under applicable copyright law, except executing it on a 95 | computer or modifying a private copy. Propagation includes copying, 96 | distribution (with or without modification), making available to the 97 | public, and in some countries other activities as well. 98 | 99 | To "convey" a work means any kind of propagation that enables other 100 | parties to make or receive copies. Mere interaction with a user through 101 | a computer network, with no transfer of a copy, is not conveying. 102 | 103 | An interactive user interface displays "Appropriate Legal Notices" 104 | to the extent that it includes a convenient and prominently visible 105 | feature that (1) displays an appropriate copyright notice, and (2) 106 | tells the user that there is no warranty for the work (except to the 107 | extent that warranties are provided), that licensees may convey the 108 | work under this License, and how to view a copy of this License. If 109 | the interface presents a list of user commands or options, such as a 110 | menu, a prominent item in the list meets this criterion. 111 | 112 | 1. Source Code. 113 | 114 | The "source code" for a work means the preferred form of the work 115 | for making modifications to it. "Object code" means any non-source 116 | form of a work. 117 | 118 | A "Standard Interface" means an interface that either is an official 119 | standard defined by a recognized standards body, or, in the case of 120 | interfaces specified for a particular programming language, one that 121 | is widely used among developers working in that language. 122 | 123 | The "System Libraries" of an executable work include anything, other 124 | than the work as a whole, that (a) is included in the normal form of 125 | packaging a Major Component, but which is not part of that Major 126 | Component, and (b) serves only to enable use of the work with that 127 | Major Component, or to implement a Standard Interface for which an 128 | implementation is available to the public in source code form. A 129 | "Major Component", in this context, means a major essential component 130 | (kernel, window system, and so on) of the specific operating system 131 | (if any) on which the executable work runs, or a compiler used to 132 | produce the work, or an object code interpreter used to run it. 133 | 134 | The "Corresponding Source" for a work in object code form means all 135 | the source code needed to generate, install, and (for an executable 136 | work) run the object code and to modify the work, including scripts to 137 | control those activities. However, it does not include the work's 138 | System Libraries, or general-purpose tools or generally available free 139 | programs which are used unmodified in performing those activities but 140 | which are not part of the work. For example, Corresponding Source 141 | includes interface definition files associated with source files for 142 | the work, and the source code for shared libraries and dynamically 143 | linked subprograms that the work is specifically designed to require, 144 | such as by intimate data communication or control flow between those 145 | subprograms and other parts of the work. 146 | 147 | The Corresponding Source need not include anything that users 148 | can regenerate automatically from other parts of the Corresponding 149 | Source. 150 | 151 | The Corresponding Source for a work in source code form is that 152 | same work. 153 | 154 | 2. Basic Permissions. 155 | 156 | All rights granted under this License are granted for the term of 157 | copyright on the Program, and are irrevocable provided the stated 158 | conditions are met. This License explicitly affirms your unlimited 159 | permission to run the unmodified Program. The output from running a 160 | covered work is covered by this License only if the output, given its 161 | content, constitutes a covered work. This License acknowledges your 162 | rights of fair use or other equivalent, as provided by copyright law. 163 | 164 | You may make, run and propagate covered works that you do not 165 | convey, without conditions so long as your license otherwise remains 166 | in force. You may convey covered works to others for the sole purpose 167 | of having them make modifications exclusively for you, or provide you 168 | with facilities for running those works, provided that you comply with 169 | the terms of this License in conveying all material for which you do 170 | not control copyright. Those thus making or running the covered works 171 | for you must do so exclusively on your behalf, under your direction 172 | and control, on terms that prohibit them from making any copies of 173 | your copyrighted material outside their relationship with you. 174 | 175 | Conveying under any other circumstances is permitted solely under 176 | the conditions stated below. Sublicensing is not allowed; section 10 177 | makes it unnecessary. 178 | 179 | 3. Protecting Users' Legal Rights From Anti-Circumvention Law. 180 | 181 | No covered work shall be deemed part of an effective technological 182 | measure under any applicable law fulfilling obligations under article 183 | 11 of the WIPO copyright treaty adopted on 20 December 1996, or 184 | similar laws prohibiting or restricting circumvention of such 185 | measures. 186 | 187 | When you convey a covered work, you waive any legal power to forbid 188 | circumvention of technological measures to the extent such circumvention 189 | is effected by exercising rights under this License with respect to 190 | the covered work, and you disclaim any intention to limit operation or 191 | modification of the work as a means of enforcing, against the work's 192 | users, your or third parties' legal rights to forbid circumvention of 193 | technological measures. 194 | 195 | 4. Conveying Verbatim Copies. 196 | 197 | You may convey verbatim copies of the Program's source code as you 198 | receive it, in any medium, provided that you conspicuously and 199 | appropriately publish on each copy an appropriate copyright notice; 200 | keep intact all notices stating that this License and any 201 | non-permissive terms added in accord with section 7 apply to the code; 202 | keep intact all notices of the absence of any warranty; and give all 203 | recipients a copy of this License along with the Program. 204 | 205 | You may charge any price or no price for each copy that you convey, 206 | and you may offer support or warranty protection for a fee. 207 | 208 | 5. Conveying Modified Source Versions. 209 | 210 | You may convey a work based on the Program, or the modifications to 211 | produce it from the Program, in the form of source code under the 212 | terms of section 4, provided that you also meet all of these conditions: 213 | 214 | a) The work must carry prominent notices stating that you modified 215 | it, and giving a relevant date. 216 | 217 | b) The work must carry prominent notices stating that it is 218 | released under this License and any conditions added under section 219 | 7. This requirement modifies the requirement in section 4 to 220 | "keep intact all notices". 221 | 222 | c) You must license the entire work, as a whole, under this 223 | License to anyone who comes into possession of a copy. This 224 | License will therefore apply, along with any applicable section 7 225 | additional terms, to the whole of the work, and all its parts, 226 | regardless of how they are packaged. This License gives no 227 | permission to license the work in any other way, but it does not 228 | invalidate such permission if you have separately received it. 229 | 230 | d) If the work has interactive user interfaces, each must display 231 | Appropriate Legal Notices; however, if the Program has interactive 232 | interfaces that do not display Appropriate Legal Notices, your 233 | work need not make them do so. 234 | 235 | A compilation of a covered work with other separate and independent 236 | works, which are not by their nature extensions of the covered work, 237 | and which are not combined with it such as to form a larger program, 238 | in or on a volume of a storage or distribution medium, is called an 239 | "aggregate" if the compilation and its resulting copyright are not 240 | used to limit the access or legal rights of the compilation's users 241 | beyond what the individual works permit. Inclusion of a covered work 242 | in an aggregate does not cause this License to apply to the other 243 | parts of the aggregate. 244 | 245 | 6. Conveying Non-Source Forms. 246 | 247 | You may convey a covered work in object code form under the terms 248 | of sections 4 and 5, provided that you also convey the 249 | machine-readable Corresponding Source under the terms of this License, 250 | in one of these ways: 251 | 252 | a) Convey the object code in, or embodied in, a physical product 253 | (including a physical distribution medium), accompanied by the 254 | Corresponding Source fixed on a durable physical medium 255 | customarily used for software interchange. 256 | 257 | b) Convey the object code in, or embodied in, a physical product 258 | (including a physical distribution medium), accompanied by a 259 | written offer, valid for at least three years and valid for as 260 | long as you offer spare parts or customer support for that product 261 | model, to give anyone who possesses the object code either (1) a 262 | copy of the Corresponding Source for all the software in the 263 | product that is covered by this License, on a durable physical 264 | medium customarily used for software interchange, for a price no 265 | more than your reasonable cost of physically performing this 266 | conveying of source, or (2) access to copy the 267 | Corresponding Source from a network server at no charge. 268 | 269 | c) Convey individual copies of the object code with a copy of the 270 | written offer to provide the Corresponding Source. This 271 | alternative is allowed only occasionally and noncommercially, and 272 | only if you received the object code with such an offer, in accord 273 | with subsection 6b. 274 | 275 | d) Convey the object code by offering access from a designated 276 | place (gratis or for a charge), and offer equivalent access to the 277 | Corresponding Source in the same way through the same place at no 278 | further charge. You need not require recipients to copy the 279 | Corresponding Source along with the object code. If the place to 280 | copy the object code is a network server, the Corresponding Source 281 | may be on a different server (operated by you or a third party) 282 | that supports equivalent copying facilities, provided you maintain 283 | clear directions next to the object code saying where to find the 284 | Corresponding Source. Regardless of what server hosts the 285 | Corresponding Source, you remain obligated to ensure that it is 286 | available for as long as needed to satisfy these requirements. 287 | 288 | e) Convey the object code using peer-to-peer transmission, provided 289 | you inform other peers where the object code and Corresponding 290 | Source of the work are being offered to the general public at no 291 | charge under subsection 6d. 292 | 293 | A separable portion of the object code, whose source code is excluded 294 | from the Corresponding Source as a System Library, need not be 295 | included in conveying the object code work. 296 | 297 | A "User Product" is either (1) a "consumer product", which means any 298 | tangible personal property which is normally used for personal, family, 299 | or household purposes, or (2) anything designed or sold for incorporation 300 | into a dwelling. In determining whether a product is a consumer product, 301 | doubtful cases shall be resolved in favor of coverage. For a particular 302 | product received by a particular user, "normally used" refers to a 303 | typical or common use of that class of product, regardless of the status 304 | of the particular user or of the way in which the particular user 305 | actually uses, or expects or is expected to use, the product. A product 306 | is a consumer product regardless of whether the product has substantial 307 | commercial, industrial or non-consumer uses, unless such uses represent 308 | the only significant mode of use of the product. 309 | 310 | "Installation Information" for a User Product means any methods, 311 | procedures, authorization keys, or other information required to install 312 | and execute modified versions of a covered work in that User Product from 313 | a modified version of its Corresponding Source. The information must 314 | suffice to ensure that the continued functioning of the modified object 315 | code is in no case prevented or interfered with solely because 316 | modification has been made. 317 | 318 | If you convey an object code work under this section in, or with, or 319 | specifically for use in, a User Product, and the conveying occurs as 320 | part of a transaction in which the right of possession and use of the 321 | User Product is transferred to the recipient in perpetuity or for a 322 | fixed term (regardless of how the transaction is characterized), the 323 | Corresponding Source conveyed under this section must be accompanied 324 | by the Installation Information. But this requirement does not apply 325 | if neither you nor any third party retains the ability to install 326 | modified object code on the User Product (for example, the work has 327 | been installed in ROM). 328 | 329 | The requirement to provide Installation Information does not include a 330 | requirement to continue to provide support service, warranty, or updates 331 | for a work that has been modified or installed by the recipient, or for 332 | the User Product in which it has been modified or installed. Access to a 333 | network may be denied when the modification itself materially and 334 | adversely affects the operation of the network or violates the rules and 335 | protocols for communication across the network. 336 | 337 | Corresponding Source conveyed, and Installation Information provided, 338 | in accord with this section must be in a format that is publicly 339 | documented (and with an implementation available to the public in 340 | source code form), and must require no special password or key for 341 | unpacking, reading or copying. 342 | 343 | 7. Additional Terms. 344 | 345 | "Additional permissions" are terms that supplement the terms of this 346 | License by making exceptions from one or more of its conditions. 347 | Additional permissions that are applicable to the entire Program shall 348 | be treated as though they were included in this License, to the extent 349 | that they are valid under applicable law. If additional permissions 350 | apply only to part of the Program, that part may be used separately 351 | under those permissions, but the entire Program remains governed by 352 | this License without regard to the additional permissions. 353 | 354 | When you convey a copy of a covered work, you may at your option 355 | remove any additional permissions from that copy, or from any part of 356 | it. (Additional permissions may be written to require their own 357 | removal in certain cases when you modify the work.) You may place 358 | additional permissions on material, added by you to a covered work, 359 | for which you have or can give appropriate copyright permission. 360 | 361 | Notwithstanding any other provision of this License, for material you 362 | add to a covered work, you may (if authorized by the copyright holders of 363 | that material) supplement the terms of this License with terms: 364 | 365 | a) Disclaiming warranty or limiting liability differently from the 366 | terms of sections 15 and 16 of this License; or 367 | 368 | b) Requiring preservation of specified reasonable legal notices or 369 | author attributions in that material or in the Appropriate Legal 370 | Notices displayed by works containing it; or 371 | 372 | c) Prohibiting misrepresentation of the origin of that material, or 373 | requiring that modified versions of such material be marked in 374 | reasonable ways as different from the original version; or 375 | 376 | d) Limiting the use for publicity purposes of names of licensors or 377 | authors of the material; or 378 | 379 | e) Declining to grant rights under trademark law for use of some 380 | trade names, trademarks, or service marks; or 381 | 382 | f) Requiring indemnification of licensors and authors of that 383 | material by anyone who conveys the material (or modified versions of 384 | it) with contractual assumptions of liability to the recipient, for 385 | any liability that these contractual assumptions directly impose on 386 | those licensors and authors. 387 | 388 | All other non-permissive additional terms are considered "further 389 | restrictions" within the meaning of section 10. If the Program as you 390 | received it, or any part of it, contains a notice stating that it is 391 | governed by this License along with a term that is a further 392 | restriction, you may remove that term. If a license document contains 393 | a further restriction but permits relicensing or conveying under this 394 | License, you may add to a covered work material governed by the terms 395 | of that license document, provided that the further restriction does 396 | not survive such relicensing or conveying. 397 | 398 | If you add terms to a covered work in accord with this section, you 399 | must place, in the relevant source files, a statement of the 400 | additional terms that apply to those files, or a notice indicating 401 | where to find the applicable terms. 402 | 403 | Additional terms, permissive or non-permissive, may be stated in the 404 | form of a separately written license, or stated as exceptions; 405 | the above requirements apply either way. 406 | 407 | 8. Termination. 408 | 409 | You may not propagate or modify a covered work except as expressly 410 | provided under this License. Any attempt otherwise to propagate or 411 | modify it is void, and will automatically terminate your rights under 412 | this License (including any patent licenses granted under the third 413 | paragraph of section 11). 414 | 415 | However, if you cease all violation of this License, then your 416 | license from a particular copyright holder is reinstated (a) 417 | provisionally, unless and until the copyright holder explicitly and 418 | finally terminates your license, and (b) permanently, if the copyright 419 | holder fails to notify you of the violation by some reasonable means 420 | prior to 60 days after the cessation. 421 | 422 | Moreover, your license from a particular copyright holder is 423 | reinstated permanently if the copyright holder notifies you of the 424 | violation by some reasonable means, this is the first time you have 425 | received notice of violation of this License (for any work) from that 426 | copyright holder, and you cure the violation prior to 30 days after 427 | your receipt of the notice. 428 | 429 | Termination of your rights under this section does not terminate the 430 | licenses of parties who have received copies or rights from you under 431 | this License. If your rights have been terminated and not permanently 432 | reinstated, you do not qualify to receive new licenses for the same 433 | material under section 10. 434 | 435 | 9. Acceptance Not Required for Having Copies. 436 | 437 | You are not required to accept this License in order to receive or 438 | run a copy of the Program. Ancillary propagation of a covered work 439 | occurring solely as a consequence of using peer-to-peer transmission 440 | to receive a copy likewise does not require acceptance. However, 441 | nothing other than this License grants you permission to propagate or 442 | modify any covered work. These actions infringe copyright if you do 443 | not accept this License. Therefore, by modifying or propagating a 444 | covered work, you indicate your acceptance of this License to do so. 445 | 446 | 10. Automatic Licensing of Downstream Recipients. 447 | 448 | Each time you convey a covered work, the recipient automatically 449 | receives a license from the original licensors, to run, modify and 450 | propagate that work, subject to this License. You are not responsible 451 | for enforcing compliance by third parties with this License. 452 | 453 | An "entity transaction" is a transaction transferring control of an 454 | organization, or substantially all assets of one, or subdividing an 455 | organization, or merging organizations. If propagation of a covered 456 | work results from an entity transaction, each party to that 457 | transaction who receives a copy of the work also receives whatever 458 | licenses to the work the party's predecessor in interest had or could 459 | give under the previous paragraph, plus a right to possession of the 460 | Corresponding Source of the work from the predecessor in interest, if 461 | the predecessor has it or can get it with reasonable efforts. 462 | 463 | You may not impose any further restrictions on the exercise of the 464 | rights granted or affirmed under this License. For example, you may 465 | not impose a license fee, royalty, or other charge for exercise of 466 | rights granted under this License, and you may not initiate litigation 467 | (including a cross-claim or counterclaim in a lawsuit) alleging that 468 | any patent claim is infringed by making, using, selling, offering for 469 | sale, or importing the Program or any portion of it. 470 | 471 | 11. Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. For 483 | purposes of this definition, "control" includes the right to grant 484 | patent sublicenses in a manner consistent with the requirements of 485 | this License. 486 | 487 | Each contributor grants you a non-exclusive, worldwide, royalty-free 488 | patent license under the contributor's essential patent claims, to 489 | make, use, sell, offer for sale, import and otherwise run, modify and 490 | propagate the contents of its contributor version. 491 | 492 | In the following three paragraphs, a "patent license" is any express 493 | agreement or commitment, however denominated, not to enforce a patent 494 | (such as an express permission to practice a patent or covenant not to 495 | sue for patent infringement). To "grant" such a patent license to a 496 | party means to make such an agreement or commitment not to enforce a 497 | patent against the party. 498 | 499 | If you convey a covered work, knowingly relying on a patent license, 500 | and the Corresponding Source of the work is not available for anyone 501 | to copy, free of charge and under the terms of this License, through a 502 | publicly available network server or other readily accessible means, 503 | then you must either (1) cause the Corresponding Source to be so 504 | available, or (2) arrange to deprive yourself of the benefit of the 505 | patent license for this particular work, or (3) arrange, in a manner 506 | consistent with the requirements of this License, to extend the patent 507 | license to downstream recipients. "Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Tensorflow-CNN-Tutorial 2 | 3 | 这是一个手把手教你用Tensorflow构建卷机网络（CNN）进行图像分类的教程。完整代码可在Github中下载：[https://github.com/hujunxianligong/Tensorflow-CNN-Tutorial](https://github.com/hujunxianligong/Tensorflow-CNN-Tutorial)。教程并没有使用MNIST数据集，而是使用了真实的图片文件，并且教程代码包含了模型的保存、加载等功能，因此希望在日常项目中使用Tensorflow的朋友可以参考这篇教程。 4 | 5 | 6 | ## 概述 7 | 8 | 9 | 10 | + 代码利用卷积网络完成一个图像分类的功能 11 | + 训练完成后，模型保存在model文件中，可直接使用模型进行线上分类 12 | + 同一个代码包括了训练和测试阶段，通过修改train参数为True和False控制训练和测试 13 | 14 | ## 数据准备 15 | 16 | 教程的图片从Cifar数据集中获取，`download_cifar.py`从Keras自带的Cifar数据集中获取了部分Cifar数据集，并将其转换为jpg图片。 17 | 18 | 默认从Cifar数据集中选取了3类图片，每类50张图，分别是 19 | + 0 => 飞机 20 | + 1 => 汽车 21 | + 2 => 鸟 22 | 23 | 图片都放在data文件夹中，按照label_id.jpg进行命名，例如2_111.jpg代表图片类别为2（鸟），id为111。 24 | 25 | 26 | 27 | ![](demo.png) 28 | 29 | ## 导入相关库 30 | 31 | 除了Tensorflow，本教程还需要使用pillow(PIL)，在Windows下PIL可能需要使用conda安装。 32 | 33 | 如果使用`download_cifar.py`自己构建数据集，还需要安装keras。 34 | 35 | 36 | ```python 37 | import os 38 | #图像读取库 39 | from PIL import Image 40 | #矩阵运算库 41 | import numpy as np 42 | import tensorflow as tf 43 | ``` 44 | 45 | ## 配置信息 46 | 47 | 设置了一些变量增加程序的灵活性。图片文件存放在`data_dir`文件夹中，`train`表示当前执行是训练还是测试，`model-path`约定了模型存放的路径。 48 | 49 | ```python 50 | # 数据文件夹 51 | data_dir = "data" 52 | # 训练还是测试 53 | train = True 54 | # 模型文件路径 55 | model_path = "model/image_model" 56 | ``` 57 | 58 | ## 数据读取 59 | 60 | 从图片文件夹中将图片读入numpy的array中。这里有几个细节： 61 | 62 | + pillow读取的图像像素值在0-255之间，需要归一化。 63 | + 在读取图像数据、Label信息的同时，记录图像的路径，方便后期调试。 64 | 65 | ```python 66 | 67 | # 从文件夹读取图片和标签到numpy数组中 68 | # 标签信息在文件名中，例如1_40.jpg表示该图片的标签为1 69 | def read_data(data_dir): 70 | datas = [] 71 | labels = [] 72 | fpaths = [] 73 | for fname in os.listdir(data_dir): 74 | fpath = os.path.join(data_dir, fname) 75 | fpaths.append(fpath) 76 | image = Image.open(fpath) 77 | data = np.array(image) / 255.0 78 | label = int(fname.split("_")[0]) 79 | datas.append(data) 80 | labels.append(label) 81 | 82 | datas = np.array(datas) 83 | labels = np.array(labels) 84 | 85 | print("shape of datas: {}\tshape of labels: {}".format(datas.shape, labels.shape)) 86 | return fpaths, datas, labels 87 | 88 | 89 | fpaths, datas, labels = read_data(data_dir) 90 | 91 | # 计算有多少类图片 92 | num_classes = len(set(labels)) 93 | ``` 94 | 95 | ## 定义placeholder(容器) 96 | 97 | 除了图像数据和Label，Dropout率也要放在placeholder中，因为在训练阶段和测试阶段需要设置不同的Dropout率。 98 | 99 | ```python 100 | # 定义Placeholder，存放输入和标签 101 | datas_placeholder = tf.placeholder(tf.float32, [None, 32, 32, 3]) 102 | labels_placeholder = tf.placeholder(tf.int32, [None]) 103 | 104 | # 存放DropOut参数的容器，训练时为0.25，测试时为0 105 | dropout_placeholdr = tf.placeholder(tf.float32) 106 | ``` 107 | 108 | ## 定义卷基网络（卷积和Pooling部分） 109 | ```python 110 | # 定义卷积层, 20个卷积核, 卷积核大小为5，用Relu激活 111 | conv0 = tf.layers.conv2d(datas_placeholder, 20, 5, activation=tf.nn.relu) 112 | # 定义max-pooling层，pooling窗口为2x2，步长为2x2 113 | pool0 = tf.layers.max_pooling2d(conv0, [2, 2], [2, 2]) 114 | 115 | # 定义卷积层, 40个卷积核, 卷积核大小为4，用Relu激活 116 | conv1 = tf.layers.conv2d(pool0, 40, 4, activation=tf.nn.relu) 117 | # 定义max-pooling层，pooling窗口为2x2，步长为2x2 118 | pool1 = tf.layers.max_pooling2d(conv1, [2, 2], [2, 2]) 119 | ``` 120 | 121 | ## 定义全连接部分 122 | ```python 123 | # 将3维特征转换为1维向量 124 | flatten = tf.layers.flatten(pool1) 125 | 126 | # 全连接层，转换为长度为100的特征向量 127 | fc = tf.layers.dense(flatten, 400, activation=tf.nn.relu) 128 | 129 | # 加上DropOut，防止过拟合 130 | dropout_fc = tf.layers.dropout(fc, dropout_placeholdr) 131 | 132 | # 未激活的输出层 133 | logits = tf.layers.dense(dropout_fc, num_classes) 134 | 135 | predicted_labels = tf.arg_max(logits, 1) 136 | ``` 137 | 138 | ## 定义损失函数和优化器 139 | 140 | 这里有一个技巧，没有必要给Optimizer传递平均的损失，直接将未平均的损失函数传给Optimizer即可。 141 | 142 | ```python 143 | # 利用交叉熵定义损失 144 | losses = tf.nn.softmax_cross_entropy_with_logits( 145 | labels=tf.one_hot(labels_placeholder, num_classes), 146 | logits=logits 147 | ) 148 | # 平均损失 149 | mean_loss = tf.reduce_mean(losses) 150 | 151 | # 定义优化器，指定要优化的损失函数 152 | optimizer = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(losses) 153 | ``` 154 | 155 | ## 定义模型保存器/载入器 156 | 如果在比较大的数据集上进行长时间训练，建议定期保存模型。 157 | ```python 158 | # 用于保存和载入模型 159 | saver = tf.train.Saver() 160 | ``` 161 | 162 | ## 进入训练/测试执行阶段 163 | ```python 164 | with tf.Session() as sess: 165 | ``` 166 | 167 | 在执行阶段有两条分支： 168 | + 如果trian为True，进行训练。训练需要使用`sess.run(tf.global_variables_initializer())`初始化参数，训练完成后，需要使用`saver.save(sess, model_path)`保存模型参数。 169 | + 如果train为False，进行测试，测试需要使用`saver.restore(sess, model_path)`读取参数。 170 | 171 | ## 训练阶段执行 172 | ```python 173 | if train: 174 | print("训练模式") 175 | # 如果是训练，初始化参数 176 | sess.run(tf.global_variables_initializer()) 177 | # 定义输入和Label以填充容器，训练时dropout为0.25 178 | train_feed_dict = { 179 | datas_placeholder: datas, 180 | labels_placeholder: labels, 181 | dropout_placeholdr: 0.25 182 | } 183 | for step in range(150): 184 | _, mean_loss_val = sess.run([optimizer, mean_loss], feed_dict=train_feed_dict) 185 | if step % 10 == 0: 186 | print("step = {}\tmean loss = {}".format(step, mean_loss_val)) 187 | saver.save(sess, model_path) 188 | print("训练结束，保存模型到{}".format(model_path)) 189 | ``` 190 | 191 | ### 测试阶段执行 192 | ```python 193 | else: 194 | print("测试模式") 195 | # 如果是测试，载入参数 196 | saver.restore(sess, model_path) 197 | print("从{}载入模型".format(model_path)) 198 | # label和名称的对照关系 199 | label_name_dict = { 200 | 0: "飞机", 201 | 1: "汽车", 202 | 2: "鸟" 203 | } 204 | # 定义输入和Label以填充容器，测试时dropout为0 205 | test_feed_dict = { 206 | datas_placeholder: datas, 207 | labels_placeholder: labels, 208 | dropout_placeholdr: 0 209 | } 210 | predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict) 211 | # 真实label与模型预测label 212 | for fpath, real_label, predicted_label in zip(fpaths, labels, predicted_labels_val): 213 | # 将label id转换为label名 214 | real_label_name = label_name_dict[real_label] 215 | predicted_label_name = label_name_dict[predicted_label] 216 | print("{}\t{} => {}".format(fpath, real_label_name, predicted_label_name)) 217 | ``` 218 | -------------------------------------------------------------------------------- /cnn.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | 3 | import os 4 | #图像读取库 5 | from PIL import Image 6 | #矩阵运算库 7 | import numpy as np 8 | import tensorflow as tf 9 | 10 | 11 | # 数据文件夹 12 | data_dir = "data" 13 | # 训练还是测试 14 | train = True 15 | # 模型文件路径 16 | model_path = "model/image_model" 17 | 18 | 19 | # 从文件夹读取图片和标签到numpy数组中 20 | # 标签信息在文件名中，例如1_40.jpg表示该图片的标签为1 21 | def read_data(data_dir): 22 | datas = [] 23 | labels = [] 24 | fpaths = [] 25 | for fname in os.listdir(data_dir): 26 | fpath = os.path.join(data_dir, fname) 27 | fpaths.append(fpath) 28 | image = Image.open(fpath) 29 | data = np.array(image) / 255.0 30 | label = int(fname.split("_")[0]) 31 | datas.append(data) 32 | labels.append(label) 33 | 34 | datas = np.array(datas) 35 | labels = np.array(labels) 36 | 37 | print("shape of datas: {}\tshape of labels: {}".format(datas.shape, labels.shape)) 38 | return fpaths, datas, labels 39 | 40 | 41 | fpaths, datas, labels = read_data(data_dir) 42 | 43 | # 计算有多少类图片 44 | num_classes = len(set(labels)) 45 | 46 | 47 | # 定义Placeholder，存放输入和标签 48 | datas_placeholder = tf.placeholder(tf.float32, [None, 32, 32, 3]) 49 | labels_placeholder = tf.placeholder(tf.int32, [None]) 50 | 51 | # 存放DropOut参数的容器，训练时为0.25，测试时为0 52 | dropout_placeholdr = tf.placeholder(tf.float32) 53 | 54 | # 定义卷积层, 20个卷积核, 卷积核大小为5，用Relu激活 55 | conv0 = tf.layers.conv2d(datas_placeholder, 20, 5, activation=tf.nn.relu) 56 | # 定义max-pooling层，pooling窗口为2x2，步长为2x2 57 | pool0 = tf.layers.max_pooling2d(conv0, [2, 2], [2, 2]) 58 | 59 | # 定义卷积层, 40个卷积核, 卷积核大小为4，用Relu激活 60 | conv1 = tf.layers.conv2d(pool0, 40, 4, activation=tf.nn.relu) 61 | # 定义max-pooling层，pooling窗口为2x2，步长为2x2 62 | pool1 = tf.layers.max_pooling2d(conv1, [2, 2], [2, 2]) 63 | 64 | # 将3维特征转换为1维向量 65 | flatten = tf.layers.flatten(pool1) 66 | 67 | # 全连接层，转换为长度为100的特征向量 68 | fc = tf.layers.dense(flatten, 400, activation=tf.nn.relu) 69 | 70 | # 加上DropOut，防止过拟合 71 | dropout_fc = tf.layers.dropout(fc, dropout_placeholdr) 72 | 73 | # 未激活的输出层 74 | logits = tf.layers.dense(dropout_fc, num_classes) 75 | 76 | predicted_labels = tf.arg_max(logits, 1) 77 | 78 | 79 | # 利用交叉熵定义损失 80 | losses = tf.nn.softmax_cross_entropy_with_logits( 81 | labels=tf.one_hot(labels_placeholder, num_classes), 82 | logits=logits 83 | ) 84 | # 平均损失 85 | mean_loss = tf.reduce_mean(losses) 86 | 87 | # 定义优化器，指定要优化的损失函数 88 | optimizer = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(losses) 89 | 90 | 91 | # 用于保存和载入模型 92 | saver = tf.train.Saver() 93 | 94 | with tf.Session() as sess: 95 | 96 | if train: 97 | print("训练模式") 98 | # 如果是训练，初始化参数 99 | sess.run(tf.global_variables_initializer()) 100 | # 定义输入和Label以填充容器，训练时dropout为0.25 101 | train_feed_dict = { 102 | datas_placeholder: datas, 103 | labels_placeholder: labels, 104 | dropout_placeholdr: 0.25 105 | } 106 | for step in range(150): 107 | _, mean_loss_val = sess.run([optimizer, mean_loss], feed_dict=train_feed_dict) 108 | 109 | if step % 10 == 0: 110 | print("step = {}\tmean loss = {}".format(step, mean_loss_val)) 111 | saver.save(sess, model_path) 112 | print("训练结束，保存模型到{}".format(model_path)) 113 | else: 114 | print("测试模式") 115 | # 如果是测试，载入参数 116 | saver.restore(sess, model_path) 117 | print("从{}载入模型".format(model_path)) 118 | # label和名称的对照关系 119 | label_name_dict = { 120 | 0: "飞机", 121 | 1: "汽车", 122 | 2: "鸟" 123 | } 124 | # 定义输入和Label以填充容器，测试时dropout为0 125 | test_feed_dict = { 126 | datas_placeholder: datas, 127 | labels_placeholder: labels, 128 | dropout_placeholdr: 0 129 | } 130 | predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict) 131 | # 真实label与模型预测label 132 | for fpath, real_label, predicted_label in zip(fpaths, labels, predicted_labels_val): 133 | # 将label id转换为label名 134 | real_label_name = label_name_dict[real_label] 135 | predicted_label_name = label_name_dict[predicted_label] 136 | print("{}\t{} => {}".format(fpath, real_label_name, predicted_label_name)) 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | -------------------------------------------------------------------------------- /data/0_100.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_100.jpg -------------------------------------------------------------------------------- /data/0_104.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_104.jpg -------------------------------------------------------------------------------- /data/0_106.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_106.jpg -------------------------------------------------------------------------------- /data/0_107.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_107.jpg -------------------------------------------------------------------------------- /data/0_108.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_108.jpg -------------------------------------------------------------------------------- /data/0_109.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_109.jpg -------------------------------------------------------------------------------- /data/0_110.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_110.jpg -------------------------------------------------------------------------------- /data/0_113.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_113.jpg -------------------------------------------------------------------------------- /data/0_114.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_114.jpg -------------------------------------------------------------------------------- /data/0_115.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_115.jpg -------------------------------------------------------------------------------- /data/0_117.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_117.jpg -------------------------------------------------------------------------------- /data/0_119.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_119.jpg -------------------------------------------------------------------------------- /data/0_122.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_122.jpg -------------------------------------------------------------------------------- /data/0_123.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_123.jpg -------------------------------------------------------------------------------- /data/0_124.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_124.jpg -------------------------------------------------------------------------------- /data/0_126.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_126.jpg -------------------------------------------------------------------------------- /data/0_127.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_127.jpg -------------------------------------------------------------------------------- /data/0_131.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_131.jpg -------------------------------------------------------------------------------- /data/0_132.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_132.jpg -------------------------------------------------------------------------------- /data/0_133.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_133.jpg -------------------------------------------------------------------------------- /data/0_134.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_134.jpg -------------------------------------------------------------------------------- /data/0_135.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_135.jpg -------------------------------------------------------------------------------- /data/0_136.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_136.jpg -------------------------------------------------------------------------------- /data/0_138.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_138.jpg -------------------------------------------------------------------------------- /data/0_139.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_139.jpg -------------------------------------------------------------------------------- /data/0_141.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_141.jpg -------------------------------------------------------------------------------- /data/0_17.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_17.jpg -------------------------------------------------------------------------------- /data/0_27.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_27.jpg -------------------------------------------------------------------------------- /data/0_30.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_30.jpg -------------------------------------------------------------------------------- /data/0_38.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_38.jpg -------------------------------------------------------------------------------- /data/0_39.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_39.jpg -------------------------------------------------------------------------------- /data/0_45.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_45.jpg -------------------------------------------------------------------------------- /data/0_53.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_53.jpg -------------------------------------------------------------------------------- /data/0_57.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_57.jpg -------------------------------------------------------------------------------- /data/0_59.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_59.jpg -------------------------------------------------------------------------------- /data/0_6.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_6.jpg -------------------------------------------------------------------------------- /data/0_60.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_60.jpg -------------------------------------------------------------------------------- /data/0_63.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_63.jpg -------------------------------------------------------------------------------- /data/0_67.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_67.jpg -------------------------------------------------------------------------------- /data/0_69.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_69.jpg -------------------------------------------------------------------------------- /data/0_7.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_7.jpg -------------------------------------------------------------------------------- /data/0_70.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_70.jpg -------------------------------------------------------------------------------- /data/0_73.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_73.jpg -------------------------------------------------------------------------------- /data/0_81.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_81.jpg -------------------------------------------------------------------------------- /data/0_84.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_84.jpg -------------------------------------------------------------------------------- /data/0_85.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_85.jpg -------------------------------------------------------------------------------- /data/0_89.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_89.jpg -------------------------------------------------------------------------------- /data/0_9.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_9.jpg -------------------------------------------------------------------------------- /data/0_91.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_91.jpg -------------------------------------------------------------------------------- /data/0_97.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/0_97.jpg -------------------------------------------------------------------------------- /data/1_0.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_0.jpg -------------------------------------------------------------------------------- /data/1_1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_1.jpg -------------------------------------------------------------------------------- /data/1_101.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_101.jpg -------------------------------------------------------------------------------- /data/1_102.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_102.jpg -------------------------------------------------------------------------------- /data/1_103.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_103.jpg -------------------------------------------------------------------------------- /data/1_12.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_12.jpg -------------------------------------------------------------------------------- /data/1_13.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_13.jpg -------------------------------------------------------------------------------- /data/1_14.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_14.jpg -------------------------------------------------------------------------------- /data/1_21.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_21.jpg -------------------------------------------------------------------------------- /data/1_22.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_22.jpg -------------------------------------------------------------------------------- /data/1_24.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_24.jpg -------------------------------------------------------------------------------- /data/1_25.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_25.jpg -------------------------------------------------------------------------------- /data/1_26.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_26.jpg -------------------------------------------------------------------------------- /data/1_28.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_28.jpg -------------------------------------------------------------------------------- /data/1_31.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_31.jpg -------------------------------------------------------------------------------- /data/1_32.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_32.jpg -------------------------------------------------------------------------------- /data/1_33.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_33.jpg -------------------------------------------------------------------------------- /data/1_34.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_34.jpg -------------------------------------------------------------------------------- /data/1_35.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_35.jpg -------------------------------------------------------------------------------- /data/1_37.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_37.jpg -------------------------------------------------------------------------------- /data/1_40.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_40.jpg -------------------------------------------------------------------------------- /data/1_44.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_44.jpg -------------------------------------------------------------------------------- /data/1_46.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_46.jpg -------------------------------------------------------------------------------- /data/1_47.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_47.jpg -------------------------------------------------------------------------------- /data/1_48.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_48.jpg -------------------------------------------------------------------------------- /data/1_50.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_50.jpg -------------------------------------------------------------------------------- /data/1_52.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_52.jpg -------------------------------------------------------------------------------- /data/1_54.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_54.jpg -------------------------------------------------------------------------------- /data/1_56.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_56.jpg -------------------------------------------------------------------------------- /data/1_58.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_58.jpg -------------------------------------------------------------------------------- /data/1_64.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_64.jpg -------------------------------------------------------------------------------- /data/1_65.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_65.jpg -------------------------------------------------------------------------------- /data/1_66.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_66.jpg -------------------------------------------------------------------------------- /data/1_71.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_71.jpg -------------------------------------------------------------------------------- /data/1_72.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_72.jpg -------------------------------------------------------------------------------- /data/1_74.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_74.jpg -------------------------------------------------------------------------------- /data/1_75.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_75.jpg -------------------------------------------------------------------------------- /data/1_76.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_76.jpg -------------------------------------------------------------------------------- /data/1_77.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_77.jpg -------------------------------------------------------------------------------- /data/1_78.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_78.jpg -------------------------------------------------------------------------------- /data/1_79.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_79.jpg -------------------------------------------------------------------------------- /data/1_8.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_8.jpg -------------------------------------------------------------------------------- /data/1_80.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_80.jpg -------------------------------------------------------------------------------- /data/1_83.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_83.jpg -------------------------------------------------------------------------------- /data/1_87.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_87.jpg -------------------------------------------------------------------------------- /data/1_93.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_93.jpg -------------------------------------------------------------------------------- /data/1_94.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_94.jpg -------------------------------------------------------------------------------- /data/1_96.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_96.jpg -------------------------------------------------------------------------------- /data/1_98.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_98.jpg -------------------------------------------------------------------------------- /data/1_99.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/1_99.jpg -------------------------------------------------------------------------------- /data/2_10.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_10.jpg -------------------------------------------------------------------------------- /data/2_105.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_105.jpg -------------------------------------------------------------------------------- /data/2_11.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_11.jpg -------------------------------------------------------------------------------- /data/2_111.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_111.jpg -------------------------------------------------------------------------------- /data/2_112.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_112.jpg -------------------------------------------------------------------------------- /data/2_116.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_116.jpg -------------------------------------------------------------------------------- /data/2_118.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_118.jpg -------------------------------------------------------------------------------- /data/2_120.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_120.jpg -------------------------------------------------------------------------------- /data/2_121.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_121.jpg -------------------------------------------------------------------------------- /data/2_125.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_125.jpg -------------------------------------------------------------------------------- /data/2_128.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_128.jpg -------------------------------------------------------------------------------- /data/2_129.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_129.jpg -------------------------------------------------------------------------------- /data/2_130.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_130.jpg -------------------------------------------------------------------------------- /data/2_137.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_137.jpg -------------------------------------------------------------------------------- /data/2_140.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_140.jpg -------------------------------------------------------------------------------- /data/2_142.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_142.jpg -------------------------------------------------------------------------------- /data/2_143.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_143.jpg -------------------------------------------------------------------------------- /data/2_144.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_144.jpg -------------------------------------------------------------------------------- /data/2_145.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_145.jpg -------------------------------------------------------------------------------- /data/2_146.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_146.jpg -------------------------------------------------------------------------------- /data/2_147.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_147.jpg -------------------------------------------------------------------------------- /data/2_148.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_148.jpg -------------------------------------------------------------------------------- /data/2_149.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_149.jpg -------------------------------------------------------------------------------- /data/2_15.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_15.jpg -------------------------------------------------------------------------------- /data/2_16.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_16.jpg -------------------------------------------------------------------------------- /data/2_18.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_18.jpg -------------------------------------------------------------------------------- /data/2_19.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_19.jpg -------------------------------------------------------------------------------- /data/2_2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_2.jpg -------------------------------------------------------------------------------- /data/2_20.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_20.jpg -------------------------------------------------------------------------------- /data/2_23.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_23.jpg -------------------------------------------------------------------------------- /data/2_29.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_29.jpg -------------------------------------------------------------------------------- /data/2_3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_3.jpg -------------------------------------------------------------------------------- /data/2_36.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_36.jpg -------------------------------------------------------------------------------- /data/2_4.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_4.jpg -------------------------------------------------------------------------------- /data/2_41.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_41.jpg -------------------------------------------------------------------------------- /data/2_42.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_42.jpg -------------------------------------------------------------------------------- /data/2_43.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_43.jpg -------------------------------------------------------------------------------- /data/2_49.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_49.jpg -------------------------------------------------------------------------------- /data/2_5.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_5.jpg -------------------------------------------------------------------------------- /data/2_51.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_51.jpg -------------------------------------------------------------------------------- /data/2_55.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_55.jpg -------------------------------------------------------------------------------- /data/2_61.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_61.jpg -------------------------------------------------------------------------------- /data/2_62.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_62.jpg -------------------------------------------------------------------------------- /data/2_68.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_68.jpg -------------------------------------------------------------------------------- /data/2_82.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_82.jpg -------------------------------------------------------------------------------- /data/2_86.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_86.jpg -------------------------------------------------------------------------------- /data/2_88.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_88.jpg -------------------------------------------------------------------------------- /data/2_90.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_90.jpg -------------------------------------------------------------------------------- /data/2_92.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_92.jpg -------------------------------------------------------------------------------- /data/2_95.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/data/2_95.jpg -------------------------------------------------------------------------------- /demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hujunxianligong/Tensorflow-CNN-Tutorial/47ab0c7215fff8d853b1df6682e946a422704cbc/demo.png -------------------------------------------------------------------------------- /download_cifar.py: -------------------------------------------------------------------------------- 1 | from keras.datasets import cifar10 2 | from PIL import Image 3 | import numpy as np 4 | import os 5 | 6 | (x_train, y_train), (x_test, y_test) = cifar10.load_data() 7 | 8 | max_num_datas = 50 9 | num_classes = 3 10 | num_datas_list = np.zeros(num_classes) 11 | 12 | img_dir = "data" 13 | id = 0 14 | 15 | for x, y in zip(x_train, y_train): 16 | 17 | if np.sum(num_datas_list) > max_num_datas * len(num_datas_list): 18 | break 19 | 20 | label = y[0] 21 | if label >= num_classes: 22 | continue 23 | 24 | if num_datas_list[label] == max_num_datas: 25 | continue 26 | 27 | num_datas_list[label] += 1 28 | 29 | img_path = os.path.join(img_dir, "{}_{}.jpg".format(label, id)) 30 | id += 1 31 | img = Image.fromarray(x) 32 | img.save(img_path) 33 | 34 | --------------------------------------------------------------------------------