├── .gitattributes ├── .gitignore ├── FaceRank_with_keras ├── FaceRank预训练模型测试.ipynb ├── README.md └── faceRank_with_keras.py ├── LICENSE ├── Train_Result.md ├── Trained_Models.md ├── cang.jpg ├── cn_readme.md ├── find_faces_in_picture.py ├── readme.md ├── resize_image.py ├── run_model.py ├── stack_data.py ├── t_find_faces_in_picture.py ├── t_resize_image.py ├── toturial.md ├── train_model.py └── wechatgroup.jpg /.gitattributes: -------------------------------------------------------------------------------- 1 | *.ipynb linguist-language=python 2 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | #images ignore 2 | face_image/* 3 | resize/* 4 | test_face/* 5 | test_resize/* 6 | test_web/* 7 | web_image/* 8 | 9 | #model ignore 10 | model/* 11 | 12 | .idea/* 13 | 14 | # Byte-compiled / optimized / DLL files 15 | __pycache__/ 16 | *.py[cod] 17 | *$py.class 18 | 19 | # C extensions 20 | *.so 21 | 22 | # Distribution / packaging 23 | .Python 24 | env/ 25 | build/ 26 | develop-eggs/ 27 | dist/ 28 | downloads/ 29 | eggs/ 30 | .eggs/ 31 | lib/ 32 | lib64/ 33 | parts/ 34 | sdist/ 35 | var/ 36 | wheels/ 37 | *.egg-info/ 38 | .installed.cfg 39 | *.egg 40 | 41 | # PyInstaller 42 | # Usually these files are written by a python script from a template 43 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 44 | *.manifest 45 | *.spec 46 | 47 | # Installer logs 48 | pip-log.txt 49 | pip-delete-this-directory.txt 50 | 51 | # Unit test / coverage reports 52 | htmlcov/ 53 | .tox/ 54 | .coverage 55 | .coverage.* 56 | .cache 57 | nosetests.xml 58 | coverage.xml 59 | *,cover 60 | .hypothesis/ 61 | 62 | # Translations 63 | *.mo 64 | *.pot 65 | 66 | # Django stuff: 67 | *.log 68 | local_settings.py 69 | 70 | # Flask stuff: 71 | instance/ 72 | .webassets-cache 73 | 74 | # Scrapy stuff: 75 | .scrapy 76 | 77 | # Sphinx documentation 78 | docs/_build/ 79 | 80 | # PyBuilder 81 | target/ 82 | 83 | # Jupyter Notebook 84 | .ipynb_checkpoints 85 | 86 | # pyenv 87 | .python-version 88 | 89 | # celery beat schedule file 90 | celerybeat-schedule 91 | 92 | # dotenv 93 | .env 94 | 95 | # virtualenv 96 | .venv/ 97 | venv/ 98 | ENV/ 99 | 100 | # Spyder project settings 101 | .spyderproject 102 | 103 | # Rope project settings 104 | .ropeproject 105 | -------------------------------------------------------------------------------- /FaceRank_with_keras/FaceRank预训练模型测试.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# FaceRank 预训练模型测试" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [ 15 | { 16 | "name": "stderr", 17 | "output_type": "stream", 18 | "text": [ 19 | "Using TensorFlow backend.\n" 20 | ] 21 | } 22 | ], 23 | "source": [ 24 | "from keras.models import Sequential\n", 25 | "from keras.layers.core import Dense, Dropout, Flatten, Activation\n", 26 | "from keras.layers.convolutional import Conv2D, MaxPooling2D\n", 27 | "from keras.preprocessing.image import load_img, img_to_array\n", 28 | "from keras.utils import np_utils\n", 29 | "import os\n", 30 | "import numpy as np" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 2, 36 | "metadata": { 37 | "collapsed": true 38 | }, 39 | "outputs": [], 40 | "source": [ 41 | "def load_dataset(filedir):\n", 42 | " \"\"\"\n", 43 | " 读取数据\n", 44 | " :param filedir:\n", 45 | " :return:\n", 46 | " \"\"\"\n", 47 | " image_data_list = []\n", 48 | " label = []\n", 49 | " train_image_list = os.listdir(filedir + '/train')\n", 50 | " for img in train_image_list:\n", 51 | " url = os.path.join(filedir + '/train/' + img)\n", 52 | " image = load_img(url, target_size=(128, 128))\n", 53 | " image_data_list.append(img_to_array(image))\n", 54 | " label.append(img.split('-')[0])\n", 55 | " img_data = np.array(image_data_list)\n", 56 | " img_data = img_data.astype('float32')\n", 57 | " img_data /= 255\n", 58 | " return img_data, label" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 3, 64 | "metadata": { 65 | "collapsed": true 66 | }, 67 | "outputs": [], 68 | "source": [ 69 | "def make_network():\n", 70 | " model = Sequential()\n", 71 | " model.add(Conv2D(32, (3, 3), padding='same', input_shape=(128, 128, 3)))\n", 72 | " model.add(Activation('relu'))\n", 73 | " model.add(Conv2D(32, (3, 3)))\n", 74 | " model.add(Activation('relu'))\n", 75 | " model.add(MaxPooling2D(pool_size=(2, 2)))\n", 76 | " model.add(Dropout(0.5))\n", 77 | "\n", 78 | " model.add(Flatten())\n", 79 | " model.add(Dense(128))\n", 80 | " model.add(Activation('relu'))\n", 81 | " model.add(Dropout(0.5))\n", 82 | " model.add(Dense(11))\n", 83 | " model.add(Activation('softmax'))\n", 84 | "\n", 85 | " return model" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 4, 91 | "metadata": { 92 | "collapsed": true 93 | }, 94 | "outputs": [], 95 | "source": [ 96 | "train_x, train_y = load_dataset('data')\n", 97 | "train_y = np_utils.to_categorical(train_y)\n", 98 | "model = make_network()" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 6, 104 | "metadata": {}, 105 | "outputs": [ 106 | { 107 | "name": "stdout", 108 | "output_type": "stream", 109 | "text": [ 110 | "Epoch 1/100\n", 111 | "120/120 [==============================] - 8s - loss: 2.5834 - acc: 0.1000 \n", 112 | "Epoch 2/100\n", 113 | "120/120 [==============================] - 8s - loss: 2.4510 - acc: 0.0667 \n", 114 | "Epoch 3/100\n", 115 | "120/120 [==============================] - 9s - loss: 2.3724 - acc: 0.1500 \n", 116 | "Epoch 4/100\n", 117 | "120/120 [==============================] - 9s - loss: 2.3495 - acc: 0.0917 \n", 118 | "Epoch 5/100\n", 119 | "120/120 [==============================] - 9s - loss: 2.3209 - acc: 0.1917 \n", 120 | "Epoch 6/100\n", 121 | "120/120 [==============================] - 9s - loss: 2.3506 - acc: 0.1083 \n", 122 | "Epoch 7/100\n", 123 | "120/120 [==============================] - 9s - loss: 2.2856 - acc: 0.1167 \n", 124 | "Epoch 8/100\n", 125 | "120/120 [==============================] - 9s - loss: 2.3031 - acc: 0.1667 \n", 126 | "Epoch 9/100\n", 127 | "120/120 [==============================] - 9s - loss: 2.2044 - acc: 0.2083 \n", 128 | "Epoch 10/100\n", 129 | "120/120 [==============================] - 9s - loss: 2.2749 - acc: 0.1250 \n", 130 | "Epoch 11/100\n", 131 | "120/120 [==============================] - 9s - loss: 2.2521 - acc: 0.1833 \n", 132 | "Epoch 12/100\n", 133 | "120/120 [==============================] - 9s - loss: 2.1797 - acc: 0.2417 \n", 134 | "Epoch 13/100\n", 135 | "120/120 [==============================] - 9s - loss: 2.2485 - acc: 0.1667 \n", 136 | "Epoch 14/100\n", 137 | "120/120 [==============================] - 9s - loss: 2.1828 - acc: 0.2000 \n", 138 | "Epoch 15/100\n", 139 | "120/120 [==============================] - 9s - loss: 2.0940 - acc: 0.2583 \n", 140 | "Epoch 16/100\n", 141 | "120/120 [==============================] - 9s - loss: 2.1489 - acc: 0.2333 \n", 142 | "Epoch 17/100\n", 143 | "120/120 [==============================] - 9s - loss: 2.1168 - acc: 0.2167 \n", 144 | "Epoch 18/100\n", 145 | "120/120 [==============================] - 9s - loss: 2.0267 - acc: 0.2750 \n", 146 | "Epoch 19/100\n", 147 | "120/120 [==============================] - 9s - loss: 2.0205 - acc: 0.2917 \n", 148 | "Epoch 20/100\n", 149 | "120/120 [==============================] - 9s - loss: 2.0279 - acc: 0.2667 \n", 150 | "Epoch 21/100\n", 151 | "120/120 [==============================] - 9s - loss: 1.8000 - acc: 0.3583 \n", 152 | "Epoch 22/100\n", 153 | "120/120 [==============================] - 9s - loss: 1.9757 - acc: 0.2583 \n", 154 | "Epoch 23/100\n", 155 | "120/120 [==============================] - 9s - loss: 1.7208 - acc: 0.4417 \n", 156 | "Epoch 24/100\n", 157 | "120/120 [==============================] - 9s - loss: 1.7918 - acc: 0.3750 \n", 158 | "Epoch 25/100\n", 159 | "120/120 [==============================] - 9s - loss: 1.7776 - acc: 0.3667 \n", 160 | "Epoch 26/100\n", 161 | "120/120 [==============================] - 9s - loss: 1.6400 - acc: 0.4250 \n", 162 | "Epoch 27/100\n", 163 | "120/120 [==============================] - 9s - loss: 1.6489 - acc: 0.4333 \n", 164 | "Epoch 28/100\n", 165 | "120/120 [==============================] - 9s - loss: 1.4964 - acc: 0.5000 \n", 166 | "Epoch 29/100\n", 167 | "120/120 [==============================] - 9s - loss: 1.3950 - acc: 0.5500 \n", 168 | "Epoch 30/100\n", 169 | "120/120 [==============================] - 9s - loss: 1.2612 - acc: 0.5917 \n", 170 | "Epoch 31/100\n", 171 | "120/120 [==============================] - 9s - loss: 1.1697 - acc: 0.6417 \n", 172 | "Epoch 32/100\n", 173 | "120/120 [==============================] - 9s - loss: 1.0743 - acc: 0.6833 \n", 174 | "Epoch 33/100\n", 175 | "120/120 [==============================] - 9s - loss: 1.0963 - acc: 0.6500 \n", 176 | "Epoch 34/100\n", 177 | "120/120 [==============================] - 9s - loss: 0.9886 - acc: 0.6917 \n", 178 | "Epoch 35/100\n", 179 | "120/120 [==============================] - 9s - loss: 0.9085 - acc: 0.6917 \n", 180 | "Epoch 36/100\n", 181 | "120/120 [==============================] - 9s - loss: 0.9465 - acc: 0.6917 \n", 182 | "Epoch 37/100\n", 183 | "120/120 [==============================] - 9s - loss: 0.7760 - acc: 0.7917 \n", 184 | "Epoch 38/100\n", 185 | "120/120 [==============================] - 9s - loss: 0.7133 - acc: 0.7917 \n", 186 | "Epoch 39/100\n", 187 | "120/120 [==============================] - 9s - loss: 0.6561 - acc: 0.8000 \n", 188 | "Epoch 40/100\n", 189 | "120/120 [==============================] - 9s - loss: 0.6136 - acc: 0.8167 \n", 190 | "Epoch 41/100\n", 191 | "120/120 [==============================] - 9s - loss: 0.5581 - acc: 0.8500 \n", 192 | "Epoch 42/100\n", 193 | "120/120 [==============================] - 9s - loss: 0.7516 - acc: 0.7667 \n", 194 | "Epoch 43/100\n", 195 | "120/120 [==============================] - 9s - loss: 0.5138 - acc: 0.8500 \n", 196 | "Epoch 44/100\n", 197 | "120/120 [==============================] - 9s - loss: 0.4506 - acc: 0.9000 \n", 198 | "Epoch 45/100\n", 199 | "120/120 [==============================] - 9s - loss: 0.3942 - acc: 0.8833 \n", 200 | "Epoch 46/100\n", 201 | "120/120 [==============================] - 9s - loss: 0.6240 - acc: 0.8083 \n", 202 | "Epoch 47/100\n", 203 | "120/120 [==============================] - 9s - loss: 0.3461 - acc: 0.8917 \n", 204 | "Epoch 48/100\n", 205 | "120/120 [==============================] - 9s - loss: 0.4823 - acc: 0.8500 \n", 206 | "Epoch 49/100\n", 207 | "120/120 [==============================] - 10s - loss: 0.3077 - acc: 0.9500 \n", 208 | "Epoch 50/100\n", 209 | "120/120 [==============================] - 10s - loss: 0.3378 - acc: 0.9167 \n", 210 | "Epoch 51/100\n", 211 | "120/120 [==============================] - 10s - loss: 0.3369 - acc: 0.8833 \n", 212 | "Epoch 52/100\n", 213 | "120/120 [==============================] - 9s - loss: 0.4497 - acc: 0.8583 \n", 214 | "Epoch 53/100\n", 215 | "120/120 [==============================] - 9s - loss: 0.3058 - acc: 0.9250 \n", 216 | "Epoch 54/100\n", 217 | "120/120 [==============================] - 9s - loss: 0.2792 - acc: 0.9500 \n", 218 | "Epoch 55/100\n", 219 | "120/120 [==============================] - 9s - loss: 0.2728 - acc: 0.9417 \n", 220 | "Epoch 56/100\n", 221 | "120/120 [==============================] - 9s - loss: 0.3008 - acc: 0.9250 \n", 222 | "Epoch 57/100\n", 223 | "120/120 [==============================] - 10s - loss: 0.2725 - acc: 0.9333 \n", 224 | "Epoch 58/100\n", 225 | "120/120 [==============================] - 10s - loss: 0.3019 - acc: 0.9083 \n", 226 | "Epoch 59/100\n", 227 | "120/120 [==============================] - 9s - loss: 0.2992 - acc: 0.9167 \n", 228 | "Epoch 60/100\n", 229 | "120/120 [==============================] - 9s - loss: 0.2315 - acc: 0.9333 \n", 230 | "Epoch 61/100\n", 231 | "120/120 [==============================] - 9s - loss: 0.2019 - acc: 0.9500 \n", 232 | "Epoch 62/100\n", 233 | "120/120 [==============================] - 9s - loss: 0.2232 - acc: 0.9500 \n", 234 | "Epoch 63/100\n", 235 | "120/120 [==============================] - 9s - loss: 0.2297 - acc: 0.9333 \n", 236 | "Epoch 64/100\n", 237 | "120/120 [==============================] - 9s - loss: 0.2397 - acc: 0.9167 \n", 238 | "Epoch 65/100\n", 239 | "120/120 [==============================] - 9s - loss: 0.1984 - acc: 0.9333 \n", 240 | "Epoch 66/100\n", 241 | "120/120 [==============================] - 9s - loss: 0.1865 - acc: 0.9250 \n", 242 | "Epoch 67/100\n", 243 | "120/120 [==============================] - 9s - loss: 0.2462 - acc: 0.9250 \n", 244 | "Epoch 68/100\n", 245 | "120/120 [==============================] - 9s - loss: 0.1647 - acc: 0.9583 \n", 246 | "Epoch 69/100\n", 247 | "120/120 [==============================] - 9s - loss: 0.1407 - acc: 0.9583 \n", 248 | "Epoch 70/100\n", 249 | "120/120 [==============================] - 9s - loss: 0.2484 - acc: 0.8917 \n", 250 | "Epoch 71/100\n", 251 | "120/120 [==============================] - 9s - loss: 0.1418 - acc: 0.9583 \n", 252 | "Epoch 72/100\n", 253 | "120/120 [==============================] - 9s - loss: 0.1518 - acc: 0.9500 \n", 254 | "Epoch 73/100\n", 255 | "120/120 [==============================] - 9s - loss: 0.1372 - acc: 0.9667 \n", 256 | "Epoch 74/100\n", 257 | "120/120 [==============================] - 9s - loss: 0.1458 - acc: 0.9583 \n", 258 | "Epoch 75/100\n", 259 | "120/120 [==============================] - 9s - loss: 0.1205 - acc: 0.9667 \n", 260 | "Epoch 76/100\n", 261 | "120/120 [==============================] - 9s - loss: 0.1509 - acc: 0.9417 \n", 262 | "Epoch 77/100\n", 263 | "120/120 [==============================] - 9s - loss: 0.1888 - acc: 0.9167 \n", 264 | "Epoch 78/100\n", 265 | "120/120 [==============================] - 9s - loss: 0.1468 - acc: 0.9500 \n", 266 | "Epoch 79/100\n", 267 | "120/120 [==============================] - 9s - loss: 0.2129 - acc: 0.9250 \n", 268 | "Epoch 80/100\n", 269 | "120/120 [==============================] - 9s - loss: 0.1372 - acc: 0.9583 \n", 270 | "Epoch 81/100\n", 271 | "120/120 [==============================] - 9s - loss: 0.1020 - acc: 0.9750 \n", 272 | "Epoch 82/100\n", 273 | "120/120 [==============================] - 9s - loss: 0.1189 - acc: 0.9750 \n", 274 | "Epoch 83/100\n", 275 | "120/120 [==============================] - 9s - loss: 0.1359 - acc: 0.9583 \n", 276 | "Epoch 84/100\n", 277 | "120/120 [==============================] - 9s - loss: 0.1620 - acc: 0.9250 \n", 278 | "Epoch 85/100\n", 279 | "120/120 [==============================] - 9s - loss: 0.1753 - acc: 0.9333 \n", 280 | "Epoch 86/100\n", 281 | "120/120 [==============================] - 9s - loss: 0.1077 - acc: 0.9833 \n", 282 | "Epoch 87/100\n", 283 | "120/120 [==============================] - 9s - loss: 0.0911 - acc: 0.9750 \n", 284 | "Epoch 88/100\n", 285 | "120/120 [==============================] - 9s - loss: 0.1045 - acc: 0.9583 \n", 286 | "Epoch 89/100\n" 287 | ] 288 | }, 289 | { 290 | "name": "stdout", 291 | "output_type": "stream", 292 | "text": [ 293 | "120/120 [==============================] - 9s - loss: 0.1049 - acc: 0.9667 \n", 294 | "Epoch 90/100\n", 295 | "120/120 [==============================] - 9s - loss: 0.1311 - acc: 0.9500 \n", 296 | "Epoch 91/100\n", 297 | "120/120 [==============================] - 9s - loss: 0.2061 - acc: 0.9500 \n", 298 | "Epoch 92/100\n", 299 | "120/120 [==============================] - 9s - loss: 0.1331 - acc: 0.9250 \n", 300 | "Epoch 93/100\n", 301 | "120/120 [==============================] - 9s - loss: 0.1095 - acc: 0.9667 \n", 302 | "Epoch 94/100\n", 303 | "120/120 [==============================] - 9s - loss: 0.1084 - acc: 0.9917 \n", 304 | "Epoch 95/100\n", 305 | "120/120 [==============================] - 9s - loss: 0.1551 - acc: 0.9500 \n", 306 | "Epoch 96/100\n", 307 | "120/120 [==============================] - 8s - loss: 0.1281 - acc: 0.9583 \n", 308 | "Epoch 97/100\n", 309 | "120/120 [==============================] - 9s - loss: 0.0590 - acc: 0.9917 \n", 310 | "Epoch 98/100\n", 311 | "120/120 [==============================] - 9s - loss: 0.1307 - acc: 0.9500 \n", 312 | "Epoch 99/100\n", 313 | "120/120 [==============================] - 9s - loss: 0.1090 - acc: 0.9667 \n", 314 | "Epoch 100/100\n", 315 | "120/120 [==============================] - 10s - loss: 0.1178 - acc: 0.9500 \n" 316 | ] 317 | } 318 | ], 319 | "source": [ 320 | "model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", 321 | "hist = model.fit(train_x, train_y, batch_size=32, epochs=100, verbose=1)" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": 7, 327 | "metadata": {}, 328 | "outputs": [ 329 | { 330 | "name": "stdout", 331 | "output_type": "stream", 332 | "text": [ 333 | "120/120 [==============================] - 2s \n" 334 | ] 335 | }, 336 | { 337 | "data": { 338 | "text/plain": [ 339 | "[0.033411206336071093, 0.98333333333333328]" 340 | ] 341 | }, 342 | "execution_count": 7, 343 | "metadata": {}, 344 | "output_type": "execute_result" 345 | } 346 | ], 347 | "source": [ 348 | "model.evaluate(train_x,train_y)" 349 | ] 350 | }, 351 | { 352 | "cell_type": "code", 353 | "execution_count": 8, 354 | "metadata": { 355 | "collapsed": true 356 | }, 357 | "outputs": [], 358 | "source": [ 359 | "model.save('faceRank.h5')" 360 | ] 361 | }, 362 | { 363 | "cell_type": "code", 364 | "execution_count": 9, 365 | "metadata": { 366 | "collapsed": true 367 | }, 368 | "outputs": [], 369 | "source": [ 370 | "del model" 371 | ] 372 | }, 373 | { 374 | "cell_type": "code", 375 | "execution_count": 10, 376 | "metadata": { 377 | "collapsed": true 378 | }, 379 | "outputs": [], 380 | "source": [ 381 | "from keras.models import load_model" 382 | ] 383 | }, 384 | { 385 | "cell_type": "code", 386 | "execution_count": 11, 387 | "metadata": { 388 | "collapsed": true 389 | }, 390 | "outputs": [], 391 | "source": [ 392 | "model = load_model('faceRank.h5')" 393 | ] 394 | }, 395 | { 396 | "cell_type": "code", 397 | "execution_count": 12, 398 | "metadata": {}, 399 | "outputs": [ 400 | { 401 | "name": "stdout", 402 | "output_type": "stream", 403 | "text": [ 404 | "120/120 [==============================] - 2s \n" 405 | ] 406 | }, 407 | { 408 | "data": { 409 | "text/plain": [ 410 | "[0.033411206336071093, 0.98333333333333328]" 411 | ] 412 | }, 413 | "execution_count": 12, 414 | "metadata": {}, 415 | "output_type": "execute_result" 416 | } 417 | ], 418 | "source": [ 419 | "model.evaluate(train_x,train_y)" 420 | ] 421 | }, 422 | { 423 | "cell_type": "code", 424 | "execution_count": 14, 425 | "metadata": { 426 | "collapsed": true 427 | }, 428 | "outputs": [], 429 | "source": [ 430 | "def load_image(img_url):\n", 431 | " image = load_img(img_url,target_size=(128,128))\n", 432 | " image = img_to_array(image)\n", 433 | " image /= 255\n", 434 | " image = np.expand_dims(image,axis=0)\n", 435 | " return image" 436 | ] 437 | }, 438 | { 439 | "cell_type": "code", 440 | "execution_count": 15, 441 | "metadata": { 442 | "collapsed": true 443 | }, 444 | "outputs": [], 445 | "source": [ 446 | "image = load_image('data/test/9-1.jpg')" 447 | ] 448 | }, 449 | { 450 | "cell_type": "code", 451 | "execution_count": 16, 452 | "metadata": {}, 453 | "outputs": [ 454 | { 455 | "name": "stdout", 456 | "output_type": "stream", 457 | "text": [ 458 | "1/1 [==============================] - 0s\n" 459 | ] 460 | }, 461 | { 462 | "data": { 463 | "text/plain": [ 464 | "array([8], dtype=int64)" 465 | ] 466 | }, 467 | "execution_count": 16, 468 | "metadata": {}, 469 | "output_type": "execute_result" 470 | } 471 | ], 472 | "source": [ 473 | "model.predict_classes(image)" 474 | ] 475 | }, 476 | { 477 | "cell_type": "code", 478 | "execution_count": null, 479 | "metadata": { 480 | "collapsed": true 481 | }, 482 | "outputs": [], 483 | "source": [] 484 | } 485 | ], 486 | "metadata": { 487 | "kernelspec": { 488 | "display_name": "Python 3", 489 | "language": "python", 490 | "name": "python3" 491 | }, 492 | "language_info": { 493 | "codemirror_mode": { 494 | "name": "ipython", 495 | "version": 3 496 | }, 497 | "file_extension": ".py", 498 | "mimetype": "text/x-python", 499 | "name": "python", 500 | "nbconvert_exporter": "python", 501 | "pygments_lexer": "ipython3", 502 | "version": "3.5.3" 503 | } 504 | }, 505 | "nbformat": 4, 506 | "nbformat_minor": 2 507 | } 508 | -------------------------------------------------------------------------------- /FaceRank_with_keras/README.md: -------------------------------------------------------------------------------- 1 | # FaceRank预训练模型测试 2 | 3 | ---------- 4 | 感谢@fendouai辛苦标注图片颜值信息，由于本人擅长使用keras，于是将@fendouai的项目改成keras版本，同时训练模型已经上传，且有使用实例在notebook中，谢谢大家。 5 | 6 | -------------------------------------------------------------------------------- /FaceRank_with_keras/faceRank_with_keras.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | @Time : 2017/8/1 13:37 4 | @Author : hadxu 5 | """ 6 | 7 | from keras.models import Sequential 8 | from keras.layers.core import Dense, Dropout, Flatten, Activation 9 | from keras.layers.convolutional import Conv2D, MaxPooling2D 10 | from keras.preprocessing.image import load_img, img_to_array 11 | from keras.utils import np_utils 12 | import os 13 | import numpy as np 14 | 15 | 16 | def load_dataset(filedir): 17 | """ 18 | 读取数据 19 | :param filedir: 20 | :return: 21 | """ 22 | image_data_list = [] 23 | label = [] 24 | train_image_list = os.listdir(filedir + '/train') 25 | for img in train_image_list: 26 | url = os.path.join(filedir + '/train/' + img) 27 | image = load_img(url, target_size=(128, 128)) 28 | image_data_list.append(img_to_array(image)) 29 | label.append(img.split('-')[0]) 30 | img_data = np.array(image_data_list) 31 | img_data = img_data.astype('float32') 32 | img_data /= 255 33 | return img_data, label 34 | 35 | 36 | def make_network(): 37 | model = Sequential() 38 | model.add(Conv2D(32, (3, 3), padding='same', input_shape=(128, 128, 3))) 39 | model.add(Activation('relu')) 40 | model.add(Conv2D(32, (3, 3))) 41 | model.add(Activation('relu')) 42 | model.add(MaxPooling2D(pool_size=(2, 2))) 43 | model.add(Dropout(0.5)) 44 | 45 | model.add(Flatten()) 46 | model.add(Dense(128)) 47 | model.add(Activation('relu')) 48 | model.add(Dropout(0.5)) 49 | model.add(Dense(11)) 50 | model.add(Activation('softmax')) 51 | 52 | return model 53 | 54 | 55 | if __name__ == '__main__': 56 | train_x, train_y = load_dataset('data') 57 | train_y = np_utils.to_categorical(train_y) 58 | model = make_network() 59 | model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) 60 | hist = model.fit(train_x, train_y, batch_size=32, epochs=200, verbose=1) 61 | -------------------------------------------------------------------------------- /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. 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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 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) 2017 {name of author} 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 | FaceRank Copyright (C) 2017 fendouai 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 | . -------------------------------------------------------------------------------- /Train_Result.md: -------------------------------------------------------------------------------- 1 | ``` 2 | 3 | (?, 128, 128, 24) 4 | (?, 64, 64, 24) 5 | (?, 64, 64, 96) 6 | (?, 32, 32, 96) 7 | 2017-07-29 18:31:20.497520: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 8 | 2017-07-29 18:31:20.497532: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 9 | 2017-07-29 18:31:20.497536: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 10 | 2017-07-29 18:31:20.497540: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 11 | ['1-1.jpg', '1-10.jpg', '1-2.jpg', '1-3.jpg', '1-4.jpg', '1-5.jpg', '1-7.jpg', '1-8.jpg', '1-9.jpg', '10-1.jpg', '10-10.jpg', '10-11.jpg', '10-12.jpg', '10-13.jpg', '10-14.jpg', '10-15.jpg', '10-16.jpg', '10-18.jpg', '10-19.jpg', '10-2.jpg', '10-20.jpg', '10-3.jpg', '10-4.jpg', '10-5.jpg', '10-6.jpg', '10-7.jpg', '10-8.jpg', '10-9.jpg', '2-1.jpg', '2-10.jpg', '2-3.jpg', '2-4.jpg', '2-5.jpg', '2-6.jpg', '2-8.jpg', '2-9.jpg', '3-1.jpg', '3-10.jpg', '3-2.jpg', '3-3.jpg', '3-4.jpg', '3-6.jpg', '3-7.jpg', '3-8.jpg', '3-9.jpg', '4-1.jpg', '4-10.jpg', '4-2.jpg', '4-3.jpg', '4-4.jpg', '4-5.jpg', '4-6.jpg', '4-7.jpg', '4-8.jpg', '4-9.jpg', '5-1.jpg', '5-10.jpg', '5-2.jpg', '5-3.jpg', '5-6.jpg', '5-7.jpg', '5-8.jpg', '6-1.jpg', '6-10.jpg', '6-2.jpg', '6-4.jpg', '6-5.jpg', '6-6.jpg', '6-7.jpg', '6-8.jpg', '6-9.jpg', '7-1.jpg', '7-10.jpg', '7-2.jpg', '7-3.jpg', '7-4.jpg', '7-5.jpg', '7-6.jpg', '7-7.jpg', '7-8.jpg', '7-9.jpg', '8-10.jpg', '8-11.jpg', '8-12.jpg', '8-13.jpg', '8-14.jpg', '8-15.jpg', '8-16.jpg', '8-17.jpg', '8-18.jpg', '8-19.jpg', '8-2.jpg', '8-20.jpg', '8-3.jpg', '8-4.jpg', '8-5.jpg', '8-6.jpg', '8-7.jpg', '8-8.jpg', '8-9.jpg', '9-1.jpg', '9-10.jpg', '9-11.jpg', '9-12.jpg', '9-13.jpg', '9-14.jpg', '9-15.jpg', '9-16.jpg', '9-17.jpg', '9-18.jpg', '9-19.jpg', '9-2.jpg', '9-20.jpg', '9-3.jpg', '9-4.jpg', '9-5.jpg', '9-6.jpg', '9-7.jpg', '9-8.jpg', '9-9.jpg'] 12 | 120 13 | count: 1 14 | (10, 128, 128, 3) 15 | (10, 10) 16 | (10, 128, 128, 3) 17 | (10, 10) 18 | (10, 128, 128, 3) 19 | (10, 10) 20 | Iter 30, Minibatch Loss= 48161656.000000, Training Accuracy= 0.80000 21 | (10, 128, 128, 3) 22 | (10, 10) 23 | (10, 128, 128, 3) 24 | (10, 10) 25 | (10, 128, 128, 3) 26 | (10, 10) 27 | Iter 60, Minibatch Loss= 321867840.000000, Training Accuracy= 0.00000 28 | (10, 128, 128, 3) 29 | (10, 10) 30 | (10, 128, 128, 3) 31 | (10, 10) 32 | (10, 128, 128, 3) 33 | (10, 10) 34 | Iter 90, Minibatch Loss= 423731744.000000, Training Accuracy= 0.00000 35 | (10, 128, 128, 3) 36 | (10, 10) 37 | (10, 128, 128, 3) 38 | (10, 10) 39 | (10, 128, 128, 3) 40 | (10, 10) 41 | Iter 120, Minibatch Loss= 427380992.000000, Training Accuracy= 0.00000 42 | count: 2 43 | (10, 128, 128, 3) 44 | (10, 10) 45 | (10, 128, 128, 3) 46 | (10, 10) 47 | (10, 128, 128, 3) 48 | (10, 10) 49 | Iter 150, Minibatch Loss= 212897232.000000, Training Accuracy= 0.00000 50 | (10, 128, 128, 3) 51 | (10, 10) 52 | (10, 128, 128, 3) 53 | (10, 10) 54 | (10, 128, 128, 3) 55 | (10, 10) 56 | Iter 180, Minibatch Loss= 46964744.000000, Training Accuracy= 0.10000 57 | (10, 128, 128, 3) 58 | (10, 10) 59 | (10, 128, 128, 3) 60 | (10, 10) 61 | (10, 128, 128, 3) 62 | (10, 10) 63 | Iter 210, Minibatch Loss= 14519466.000000, Training Accuracy= 0.40000 64 | (10, 128, 128, 3) 65 | (10, 10) 66 | (10, 128, 128, 3) 67 | (10, 10) 68 | (10, 128, 128, 3) 69 | (10, 10) 70 | Iter 240, Minibatch Loss= 9990268.000000, Training Accuracy= 0.60000 71 | count: 3 72 | (10, 128, 128, 3) 73 | (10, 10) 74 | (10, 128, 128, 3) 75 | (10, 10) 76 | (10, 128, 128, 3) 77 | (10, 10) 78 | Iter 270, Minibatch Loss= 80894400.000000, Training Accuracy= 0.00000 79 | (10, 128, 128, 3) 80 | (10, 10) 81 | (10, 128, 128, 3) 82 | (10, 10) 83 | (10, 128, 128, 3) 84 | (10, 10) 85 | Iter 300, Minibatch Loss= 55994028.000000, Training Accuracy= 0.00000 86 | (10, 128, 128, 3) 87 | (10, 10) 88 | (10, 128, 128, 3) 89 | (10, 10) 90 | (10, 128, 128, 3) 91 | (10, 10) 92 | Iter 330, Minibatch Loss= 71483504.000000, Training Accuracy= 0.00000 93 | (10, 128, 128, 3) 94 | (10, 10) 95 | (10, 128, 128, 3) 96 | (10, 10) 97 | (10, 128, 128, 3) 98 | (10, 10) 99 | Iter 360, Minibatch Loss= 36483064.000000, Training Accuracy= 0.00000 100 | count: 4 101 | (10, 128, 128, 3) 102 | (10, 10) 103 | (10, 128, 128, 3) 104 | (10, 10) 105 | (10, 128, 128, 3) 106 | (10, 10) 107 | Iter 390, Minibatch Loss= 58645964.000000, Training Accuracy= 0.00000 108 | (10, 128, 128, 3) 109 | (10, 10) 110 | (10, 128, 128, 3) 111 | (10, 10) 112 | (10, 128, 128, 3) 113 | (10, 10) 114 | Iter 420, Minibatch Loss= 48663864.000000, Training Accuracy= 0.00000 115 | (10, 128, 128, 3) 116 | (10, 10) 117 | (10, 128, 128, 3) 118 | (10, 10) 119 | (10, 128, 128, 3) 120 | (10, 10) 121 | Iter 450, Minibatch Loss= 17381402.000000, Training Accuracy= 0.10000 122 | (10, 128, 128, 3) 123 | (10, 10) 124 | (10, 128, 128, 3) 125 | (10, 10) 126 | (10, 128, 128, 3) 127 | (10, 10) 128 | Iter 480, Minibatch Loss= 2577538.500000, Training Accuracy= 0.70000 129 | count: 5 130 | (10, 128, 128, 3) 131 | (10, 10) 132 | (10, 128, 128, 3) 133 | (10, 10) 134 | (10, 128, 128, 3) 135 | (10, 10) 136 | Iter 510, Minibatch Loss= 15052680.000000, Training Accuracy= 0.40000 137 | (10, 128, 128, 3) 138 | (10, 10) 139 | (10, 128, 128, 3) 140 | (10, 10) 141 | (10, 128, 128, 3) 142 | (10, 10) 143 | Iter 540, Minibatch Loss= 18420312.000000, Training Accuracy= 0.20000 144 | (10, 128, 128, 3) 145 | (10, 10) 146 | (10, 128, 128, 3) 147 | (10, 10) 148 | (10, 128, 128, 3) 149 | (10, 10) 150 | Iter 570, Minibatch Loss= 23141172.000000, Training Accuracy= 0.10000 151 | (10, 128, 128, 3) 152 | (10, 10) 153 | (10, 128, 128, 3) 154 | (10, 10) 155 | (10, 128, 128, 3) 156 | (10, 10) 157 | Iter 600, Minibatch Loss= 10837658.000000, Training Accuracy= 0.40000 158 | count: 6 159 | (10, 128, 128, 3) 160 | (10, 10) 161 | (10, 128, 128, 3) 162 | (10, 10) 163 | (10, 128, 128, 3) 164 | (10, 10) 165 | Iter 630, Minibatch Loss= 21745000.000000, Training Accuracy= 0.30000 166 | (10, 128, 128, 3) 167 | (10, 10) 168 | (10, 128, 128, 3) 169 | (10, 10) 170 | (10, 128, 128, 3) 171 | (10, 10) 172 | Iter 660, Minibatch Loss= 17480332.000000, Training Accuracy= 0.40000 173 | (10, 128, 128, 3) 174 | (10, 10) 175 | (10, 128, 128, 3) 176 | (10, 10) 177 | (10, 128, 128, 3) 178 | (10, 10) 179 | Iter 690, Minibatch Loss= 17633370.000000, Training Accuracy= 0.10000 180 | (10, 128, 128, 3) 181 | (10, 10) 182 | (10, 128, 128, 3) 183 | (10, 10) 184 | (10, 128, 128, 3) 185 | (10, 10) 186 | Iter 720, Minibatch Loss= 10235282.000000, Training Accuracy= 0.30000 187 | count: 7 188 | (10, 128, 128, 3) 189 | (10, 10) 190 | (10, 128, 128, 3) 191 | (10, 10) 192 | (10, 128, 128, 3) 193 | (10, 10) 194 | Iter 750, Minibatch Loss= 6799557.000000, Training Accuracy= 0.80000 195 | (10, 128, 128, 3) 196 | (10, 10) 197 | (10, 128, 128, 3) 198 | (10, 10) 199 | (10, 128, 128, 3) 200 | (10, 10) 201 | Iter 780, Minibatch Loss= 4268240.000000, Training Accuracy= 0.70000 202 | (10, 128, 128, 3) 203 | (10, 10) 204 | (10, 128, 128, 3) 205 | (10, 10) 206 | (10, 128, 128, 3) 207 | (10, 10) 208 | Iter 810, Minibatch Loss= 575766.312500, Training Accuracy= 0.90000 209 | (10, 128, 128, 3) 210 | (10, 10) 211 | (10, 128, 128, 3) 212 | (10, 10) 213 | (10, 128, 128, 3) 214 | (10, 10) 215 | Iter 840, Minibatch Loss= 6839501.000000, Training Accuracy= 0.50000 216 | count: 8 217 | (10, 128, 128, 3) 218 | (10, 10) 219 | (10, 128, 128, 3) 220 | (10, 10) 221 | (10, 128, 128, 3) 222 | (10, 10) 223 | Iter 870, Minibatch Loss= 19500750.000000, Training Accuracy= 0.30000 224 | (10, 128, 128, 3) 225 | (10, 10) 226 | (10, 128, 128, 3) 227 | (10, 10) 228 | (10, 128, 128, 3) 229 | (10, 10) 230 | Iter 900, Minibatch Loss= 11227581.000000, Training Accuracy= 0.60000 231 | (10, 128, 128, 3) 232 | (10, 10) 233 | (10, 128, 128, 3) 234 | (10, 10) 235 | (10, 128, 128, 3) 236 | (10, 10) 237 | Iter 930, Minibatch Loss= 14566576.000000, Training Accuracy= 0.20000 238 | (10, 128, 128, 3) 239 | (10, 10) 240 | (10, 128, 128, 3) 241 | (10, 10) 242 | (10, 128, 128, 3) 243 | (10, 10) 244 | Iter 960, Minibatch Loss= 5684557.500000, Training Accuracy= 0.40000 245 | count: 9 246 | (10, 128, 128, 3) 247 | (10, 10) 248 | (10, 128, 128, 3) 249 | (10, 10) 250 | (10, 128, 128, 3) 251 | (10, 10) 252 | Iter 990, Minibatch Loss= 7205131.000000, Training Accuracy= 0.80000 253 | (10, 128, 128, 3) 254 | (10, 10) 255 | (10, 128, 128, 3) 256 | (10, 10) 257 | (10, 128, 128, 3) 258 | (10, 10) 259 | Iter 1020, Minibatch Loss= 5167798.000000, Training Accuracy= 0.70000 260 | (10, 128, 128, 3) 261 | (10, 10) 262 | (10, 128, 128, 3) 263 | (10, 10) 264 | (10, 128, 128, 3) 265 | (10, 10) 266 | Iter 1050, Minibatch Loss= 0.000000, Training Accuracy= 1.00000 267 | (10, 128, 128, 3) 268 | (10, 10) 269 | (10, 128, 128, 3) 270 | (10, 10) 271 | (10, 128, 128, 3) 272 | (10, 10) 273 | Iter 1080, Minibatch Loss= 394604.187500, Training Accuracy= 0.90000 274 | count: 10 275 | (10, 128, 128, 3) 276 | (10, 10) 277 | (10, 128, 128, 3) 278 | (10, 10) 279 | (10, 128, 128, 3) 280 | (10, 10) 281 | Iter 1110, Minibatch Loss= 3477911.250000, Training Accuracy= 0.70000 282 | (10, 128, 128, 3) 283 | (10, 10) 284 | (10, 128, 128, 3) 285 | (10, 10) 286 | (10, 128, 128, 3) 287 | (10, 10) 288 | Iter 1140, Minibatch Loss= 293735.093750, Training Accuracy= 0.90000 289 | (10, 128, 128, 3) 290 | (10, 10) 291 | (10, 128, 128, 3) 292 | (10, 10) 293 | (10, 128, 128, 3) 294 | (10, 10) 295 | Iter 1170, Minibatch Loss= 3111549.500000, Training Accuracy= 0.80000 296 | (10, 128, 128, 3) 297 | (10, 10) 298 | (10, 128, 128, 3) 299 | (10, 10) 300 | (10, 128, 128, 3) 301 | (10, 10) 302 | Iter 1200, Minibatch Loss= 0.000000, Training Accuracy= 1.00000 303 | Optimization Finished! 304 | 305 | Process finished with exit code 0 306 | 307 | 308 | ``` 309 | -------------------------------------------------------------------------------- /Trained_Models.md: -------------------------------------------------------------------------------- 1 | ## Models as programs 2 | 3 | https://blog.keras.io/the-future-of-deep-learning.html 4 | 5 | 之前学习了 Keras 作者的这篇博客，机器学习应该是程序，我感觉更应该是 API，可以直接调用。 6 | 所以 FaceRank 项目，不仅提供了原始模型，数据集工具，模型持久化，并且提供训练好的模型，以及可以直接运行的代码。 7 | 最后的效果就是 输入图片，输出分数，实现机器学习模型的函数化，类库化。 8 | 9 | 相关教程说明： 10 | http://www.tensorflownews.com/2017/07/29/facerank-tensorflow-cnn/ 11 | http://www.tensorflownews.com/2017/08/02/facerank-tensorflow-face_recognition/ 12 | 13 | ## Tensorflow 14 | * 130 张数据集已经训练的模型下载 15 | http://www.tensorflownews.com/2017/08/03/facerank-tensorflow-cnn-model/ 16 | 17 | * 欢迎提交更多的模型，欢迎 PR。 18 | 19 | ## 数据集 20 | * 网上的一个人脸数据集（格式和 FaceRank 的并不完全一样，需要重新处理。） 21 | http://www.hcii-lab.net/data/SCUT-FBP/EN/introduce.html 22 | -------------------------------------------------------------------------------- /cang.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fendouai/FaceRank/e55ff9bbdac62174e239e101e577a4bb11f0482a/cang.jpg -------------------------------------------------------------------------------- /cn_readme.md: -------------------------------------------------------------------------------- 1 | ## FaceRank-人脸打分基于 TensorFlow 的 CNN 模型 2 | 3 | ## 结果图片 4 | 如有侵权，请通知删除，结果由 FaceRank AI 输出。 5 | ![Result Pic](https://github.com/fendouai/FaceRank/blob/master/cang.jpg) 6 | 7 | ## 隐私 8 | 因为隐私问题，训练图片集并不提供，稍微可能会放一些卡通图片。 9 | 10 | 11 | ## 数据集 12 | * 130张 128*128 张网络图片，图片名： 1-3.jpg 表示 分值为 1 的第 3 张图。 13 | 你可以把符合这个格式的图片放在 resize_images 来训练模型。 14 | 15 | ## 模型 16 | 人脸打分基于 TensorFlow 的 CNN 模型 代码参考 : https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py 17 | 18 | ## 运行 19 | 安装好 TensorFlow 之后，直接运行 train_model.py. 20 | * 训练模型 21 | * 保存模型到 model 文件夹 22 | 23 | ## 测试 24 | 运行完 train_model.py 之后,直接运行 run_model.py 来测试. 25 | 26 | ## 下载 27 | 训练好的模型可以在以下网址下载： 28 | http://www.tensorflownews.com/ 29 | 30 | ## 模型效果 31 | * 训练过程 32 | 你可以看训练过程： Train_Result.md ,这里有损失函数和准确率变化过程。 33 | * 测试结果 34 | 结果并不非常好，但是增加数据集之后有所改善。 35 | 36 | ``` 37 | (?, 128, 128, 24) 38 | (?, 64, 64, 24) 39 | (?, 64, 64, 96) 40 | (?, 32, 32, 96) 41 | 42 | ['1-1.jpg', '1-2.jpg', '10-1.jpg', '10-2.jpg', '2-1.jpg', '2-2.jpg', '3-1.jpg', '3-2.jpg', '4-1.jpg', '4-2.jpg', '5-1.jpg', '5-2.jpg', '6-1.jpg', '6-2.jpg', '7-1.jpg', '7-2.jpg', '8-1.jpg', '8-2.jpg', '9-1.jpg', '9-2.jpg'] 43 | 20 44 | (10, 128, 128, 3) 45 | [3 2 8 6 5 8 0 4 7 7] 46 | (10, 128, 128, 3) 47 | [2 6 6 6 5 8 7 8 7 5] 48 | Test Finished! 49 | ``` 50 | ## 支持 51 | * 提交 issue 52 | * QQ 群: 522785813 53 | * 知乎:https://zhuanlan.zhihu.com/TensorFlownews 54 | * 博客:http://www.tensorflownews.com/ 55 | 56 | ##后续计划 57 | * 图片像素要提高 58 | * 增加数据集 59 | * 在临近的层次，用公用的图片：比如1-3；4-6；7-9 用相似或者相同图片。 60 | 61 | ## 微信群： 62 | ![Result Pic](https://github.com/fendouai/FaceRank/blob/master/wechatgroup.jpg) 63 | 64 | 如果二维码过期，请到这里 http://www.tensorflownews.com/ 会保持更新。 65 | -------------------------------------------------------------------------------- /find_faces_in_picture.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import face_recognition 3 | import os 4 | print("h") 5 | def find_and_save_face(web_file,face_file): 6 | # Load the jpg file into a numpy array 7 | image = face_recognition.load_image_file(web_file) 8 | print(image.dtype) 9 | # Find all the faces in the image 10 | face_locations = face_recognition.face_locations(image) 11 | 12 | print("I found {} face(s) in this photograph.".format(len(face_locations))) 13 | 14 | for face_location in face_locations: 15 | 16 | # Print the location of each face in this image 17 | top, right, bottom, left = face_location 18 | print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right)) 19 | 20 | # You can access the actual face itself like this: 21 | face_image = image[top:bottom, left:right] 22 | pil_image = Image.fromarray(face_image) 23 | pil_image.save(face_file) 24 | print("h") 25 | list = os.listdir("web_image/") 26 | print(list) 27 | 28 | for image in list: 29 | id_tag = image.find(".") 30 | name=image[0:id_tag] 31 | print(name) 32 | 33 | web_file = "./web_image/" +image 34 | face_file="./face_image/"+name+".jpg" 35 | 36 | im=Image.open("./web_image/"+image) 37 | try: 38 | find_and_save_face(web_file, face_file) 39 | except: 40 | print("fail") 41 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | ## Face Rank - Rank Face by CNN Model based on TensorFlow 2 | 3 | ## Keras Version 4 | https://github.com/fendouai/FaceRank/tree/master/FaceRank_with_keras 5 | 6 | ## RankFace 7 | 8 | A deep learning based model to judge the AQ, Appearance Quotient, of faces. (For Chinese Young Girls Only) https://github.com/Entropy-xcy/RankFace 9 | 10 | ## 中文说明（QQ群：522785813） 11 | 12 | 项目总体说明：https://github.com/fendouai/FaceRank/blob/master/cn_readme.md 13 | 14 | 运行详细说明：https://github.com/fendouai/FaceRank/blob/master/toturial.md 15 | 16 | Gitee(速度更快) 17 | 18 | 项目总体说明：https://gitee.com/fendouai/FaceRank/blob/master/cn_readme.md 19 | 20 | 运行详细说明：https://gitee.com/fendouai/FaceRank/blob/master/toturial.md 21 | 22 | ## Result Pic 23 | ![Result Pic](./cang.jpg) 24 | 25 | ## Privacy 26 | Because of privacy，the training images dataset is not provided. 27 | maybe some carton images will be given later. 28 | 29 | ## Dataset 30 | * 130 pictures with size 128*128 from web with tag 31 | image: 1-3.jpg means rank 1,3st train pic 32 | you can add your own pics to the resize_images folder 33 | 34 | ## Model 35 | Model is CNN based on TensorFlow based on : https://github.com/aymericdamien/TensorFlow-Examples/ 36 | 37 | ## Run 38 | After you installed TensorFlow ,just run train_model.py. 39 | * train the model 40 | * save the model to model dir 41 | 42 | ## Test 43 | After you run the train_model.py ,just run the run_model.py to test. 44 | 45 | ## Download 46 | The model is trained can be download at 47 | http://www.tensorflownews.com/ 48 | 49 | ## WechatGroup 50 | 51 | If it is out of time,you can go to http://www.tensorflownews.com/ ,I will update the wechat group qcode here. 52 | 53 | ## Thanks 54 | @HadXu develop the keras version 55 | https://github.com/fendouai/FaceRank/tree/master/FaceRank_with_keras 56 | 57 | -------------------------------------------------------------------------------- /resize_image.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import os 3 | 4 | list = os.listdir("./face_image") 5 | print(list) 6 | 7 | for image in list: 8 | id_tag = image.find(".") 9 | name=image[0:id_tag] 10 | print(name) 11 | 12 | im=Image.open("./face_image/"+image) 13 | out = im.resize((128, 128)) 14 | #out.show() 15 | out.save("./resize_image/"+name+".jpg") 16 | 17 | -------------------------------------------------------------------------------- /run_model.py: -------------------------------------------------------------------------------- 1 | ''' 2 | A Convolutional Network implementation example using TensorFlow library. 3 | This example is using the MNIST database of handwritten digits 4 | (http://yann.lecun.com/exdb/mnist/) 5 | 6 | Author: Aymeric Damien 7 | Project: https://github.com/aymericdamien/TensorFlow-Examples/ 8 | ''' 9 | 10 | from __future__ import print_function 11 | import os 12 | import matplotlib.pyplot as plt 13 | import tensorflow as tf 14 | from PIL import Image 15 | import numpy 16 | import tensorflow as tf 17 | 18 | # Import MNIST data 19 | from tensorflow.examples.tutorials.mnist import input_data 20 | 21 | # Parameters 22 | learning_rate = 0.001 23 | training_iters = 3000 24 | batch_size = 10 25 | display_step = 2 26 | 27 | # Network Parameters 28 | n_input = 128*128 # MNIST data input (img shape: 28*28) 29 | n_classes = 10 # MNIST total classes (0-9 digits) 30 | dropout = 0.75 # Dropout, probability to keep units 31 | 32 | # tf Graph input 33 | x = tf.placeholder(tf.float32, [None, 128, 128, 3]) 34 | y = tf.placeholder(tf.float32, [None, n_classes]) 35 | keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) 36 | 37 | 38 | # Create some wrappers for simplicity 39 | def conv2d(x, W, b, strides=1): 40 | # Conv2D wrapper, with bias and relu activation 41 | x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') 42 | x = tf.nn.bias_add(x, b) 43 | return tf.nn.relu(x) 44 | 45 | 46 | def maxpool2d(x, k=2): 47 | # MaxPool2D wrapper 48 | return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], 49 | padding='SAME') 50 | 51 | 52 | # Create model 53 | def conv_net(x, weights, biases, dropout): 54 | # Reshape input picture 55 | x = tf.reshape(x, shape=[-1, 128, 128, 3]) 56 | 57 | # Convolution Layer 58 | conv1 = conv2d(x, weights['wc1'], biases['bc1']) 59 | print(conv1.shape) 60 | # Max Pooling (down-sampling) 61 | conv1 = maxpool2d(conv1, k=2) 62 | print(conv1.shape) 63 | # Convolution Layer 64 | conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) 65 | print(conv2.shape) 66 | # Max Pooling (down-sampling) 67 | conv2 = maxpool2d(conv2, k=2) 68 | print(conv2.shape) 69 | # Fully connected layer 70 | # Reshape conv2 output to fit fully connected layer input 71 | fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) 72 | fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) 73 | fc1 = tf.nn.relu(fc1) 74 | # Apply Dropout 75 | fc1 = tf.nn.dropout(fc1, dropout) 76 | 77 | # Output, class prediction 78 | out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) 79 | return out 80 | 81 | # Store layers weight & bias 82 | weights = { 83 | # 5x5 conv, 1 input, 32 outputs 84 | 'wc1': tf.Variable(tf.random_normal([5, 5, 3, 24])), 85 | # 5x5 conv, 32 inputs, 64 outputs 86 | 'wc2': tf.Variable(tf.random_normal([5, 5, 24, 96])), 87 | # fully connected, 7*7*64 inputs, 1024 outputs 88 | 'wd1': tf.Variable(tf.random_normal([32*32*96, 1024])), 89 | # 1024 inputs, 10 outputs (class prediction) 90 | 'out': tf.Variable(tf.random_normal([1024, n_classes])) 91 | } 92 | 93 | biases = { 94 | 'bc1': tf.Variable(tf.random_normal([24])), 95 | 'bc2': tf.Variable(tf.random_normal([96])), 96 | 'bd1': tf.Variable(tf.random_normal([1024])), 97 | 'out': tf.Variable(tf.random_normal([n_classes])) 98 | } 99 | 100 | # Construct model 101 | pred = conv_net(x, weights, biases, keep_prob) 102 | pred_result=tf.argmax(pred, 1) 103 | # Define loss and optimizer 104 | cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) 105 | optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 106 | 107 | # Evaluate model 108 | correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 109 | accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) 110 | 111 | # Initializing the variables 112 | init = tf.global_variables_initializer() 113 | saver=tf.train.Saver() 114 | 115 | # Launch the graph 116 | with tf.Session() as sess: 117 | saver.restore(sess, "./model/model.ckpt") 118 | step = 1 119 | # Keep training until reach max iterations 120 | list = os.listdir("./test_resize/") 121 | print(list) 122 | print(len(list)) 123 | 124 | for batch_id in range(0, 2): 125 | batch = list[batch_id * 10:batch_id * 10 + 10] 126 | batch_xs = [] 127 | batch_ys = [] 128 | for image in batch: 129 | id_tag = image.find("-") 130 | score = image[0:id_tag] 131 | # print(score) 132 | img = Image.open("./test_resize/" + image) 133 | img_ndarray = numpy.asarray(img, dtype='float32') 134 | img_ndarray = numpy.reshape(img_ndarray, [128, 128, 3]) 135 | # print(img_ndarray.shape) 136 | batch_x = img_ndarray 137 | batch_xs.append(batch_x) 138 | 139 | # print(batch_ys) 140 | batch_xs = numpy.asarray(batch_xs) 141 | print(batch_xs.shape) 142 | 143 | # Run optimization op (backprop) 144 | pred_result_test=sess.run(pred_result, feed_dict={x: batch_xs,keep_prob: 1.}) 145 | print(pred_result_test) 146 | print("Test Finished!") 147 | saver.save(sess,"./model/model.ckpt") -------------------------------------------------------------------------------- /stack_data.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | import numpy 4 | from PIL import Image 5 | import os 6 | 7 | list = os.listdir("./resize_image/") 8 | print(list) 9 | print(len(list)) 10 | for batch_id in range(1, 10): 11 | batch = list[batch_id * 10:batch_id * 10 + 10] 12 | batch_xs=[] 13 | batch_ys=[] 14 | for image in batch: 15 | id_tag = image.find("-") 16 | score = image[0:id_tag] 17 | # print(score) 18 | img = Image.open("./resize_image/" + image) 19 | img_ndarray = numpy.asarray(img, dtype='float32') 20 | img_ndarray = numpy.reshape(img_ndarray, [128, 128, 3]) 21 | # print(img_ndarray.shape) 22 | batch_x = img_ndarray 23 | batch_xs.append(batch_x) 24 | #print(batch_xs) 25 | batch_y = numpy.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) 26 | # print(type(score)) 27 | batch_y[int(score) - 1] = 1 28 | # print(batch_y) 29 | batch_y = numpy.reshape(batch_y, [10,]) 30 | batch_ys.append(batch_y) 31 | #print(batch_ys) 32 | batch_xs=numpy.asarray(batch_xs) 33 | print(batch_xs.shape) 34 | batch_ys = numpy.asarray(batch_ys) 35 | print(batch_ys.shape) -------------------------------------------------------------------------------- /t_find_faces_in_picture.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import face_recognition 3 | import os 4 | 5 | def find_and_save_face(web_file,face_file): 6 | # Load the jpg file into a numpy array 7 | image = face_recognition.load_image_file(web_file) 8 | print(image.dtype) 9 | # Find all the faces in the image 10 | face_locations = face_recognition.face_locations(image) 11 | 12 | print("I found {} face(s) in this photograph.".format(len(face_locations))) 13 | 14 | for face_location in face_locations: 15 | 16 | # Print the location of each face in this image 17 | top, right, bottom, left = face_location 18 | print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right)) 19 | 20 | # You can access the actual face itself like this: 21 | face_image = image[top:bottom, left:right] 22 | pil_image = Image.fromarray(face_image) 23 | pil_image.save(face_file) 24 | 25 | 26 | list = os.listdir("./test_web/") 27 | print(list) 28 | 29 | for image in list: 30 | id_tag = image.find(".") 31 | name=image[0:id_tag] 32 | print(name) 33 | 34 | web_file = "./test_web/" +image 35 | face_file="./test_face/"+name+".jpg" 36 | try: 37 | find_and_save_face(web_file, face_file) 38 | except: 39 | print("fail") 40 | 41 | -------------------------------------------------------------------------------- /t_resize_image.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import os 3 | 4 | list = os.listdir("./test_face") 5 | print(list) 6 | 7 | for image in list: 8 | name_len=len(image) 9 | name=image[0:name_len-3] 10 | print(name) 11 | im=Image.open("./test_face/"+image) 12 | out = im.resize((128, 128)) 13 | #out.show() 14 | out.save("./test_resize/"+name+"jpg") 15 | 16 | -------------------------------------------------------------------------------- /toturial.md: -------------------------------------------------------------------------------- 1 | # FaceRank，最有趣的 TensorFlow 入门实战项目 2 | ## TensorFlow 从观望到入门！ 3 | https://github.com/fendouai/FaceRank 4 | 5 | ## 最有趣？ 6 | 机器学习是不是很无聊，用来用去都是识别字体。能不能帮我找到颜值高的妹子，顺便提高一下姿势水平。 7 | 8 | FaceRank 基于 TensorFlow CNN 模型，提供了一些图片处理的工具集，后续还会提供训练好的模型。给 FaceRank 一个妹子，他给你个分数。 9 | 10 | 从此以后筛选简历，先把头像颜值低的去掉；自动寻找女主颜值高的小电影；自动关注美女；自动排除负分滚粗的相亲对象。从此以后升职加薪，迎娶白富美，走上人生巅峰。 11 | 12 | 苍老师镇楼： 13 | 14 | ![1cbf16b28aa949acadeeff4398829328_th.jpg](http://upload-images.jianshu.io/upload_images/76451-2deffc054e0e3452.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240) 15 | 16 | ## 项目开源： 17 | GitHub:https://github.com/fendouai/FaceRank 18 | 19 | ## 依赖库： 20 | * Tensorflow 21 | 安装：pip install tensorflow 22 | 简介：Tensorflow 是谷歌的机器学习框架，FaceRank 使用了基于它的 CNN 模型。 23 | http://www.tensorflownews.com/2017/07/28/installing-tensorflow-tensorflow/ 24 | * face_recognition 25 | 简介：这个库在项目中，用来从图片中截出人脸，并保存为新文件，方便生成数据集。 26 | 这个库比较难装，如果直接安装失败，建议使用 docker. 27 | The world's simplest facial recognition api for Python and the command line 28 | 安装：pip install face_recognition 29 | 30 | ## 训练数据集生成工具 31 | * 文件夹截图 32 | ![fileinfo.png](http://www.tensorflownews.com/wp-content/uploads/2017/08/fileinfo.png) 33 | 34 | * 标注说明 35 | 文件夹中 1-2.jpg 表明这是 1分的图片，2是第2张。也就是 “-”前面的数字就是分数。 36 | 37 | * find_faces_in_picture.py 38 | 这个脚本使用了 face_recognition 来扣人脸，它会从 上图中的 web_image 读取图片，抠图之后保存到 face_image 文件夹。 39 | 40 | * resize_image.py 41 | 这个脚本会读取 face_image 文件夹，并将图片统一处理为 128*128像素。 42 | 43 | ## 训练 44 | 一切都准备好了，直接运行 train_model.py 45 | 这部分内容在 Github 有比较详细说明： 46 | https://github.com/fendouai/FaceRank/ 47 | 48 | ## 模型使用 49 | * FaceRank 内置了模型保存功能，训练之后，以后都可以直接运行 run_model.py 。也就是可以封装成函数或者类库使用，非常方便。 50 | 51 | ## 学习流程 52 | 如果看到这里有很多不懂的话，建议： 53 | * Hello World 54 | https://zhuanlan.zhihu.com/p/27963600 55 | * 基本概念 56 | https://zhuanlan.zhihu.com/p/27986689 57 | * 卷积神经网络 58 | https://zhuanlan.zhihu.com/p/28161292 59 | * 训练好模型参数的保存和恢复代码 60 | https://zhuanlan.zhihu.com/p/27912379 61 | * TensorFlowNews 专栏 62 | https://zhuanlan.zhihu.com/TensorFlownews 63 | * TensorFlowNews 博客 64 | http://www.tensorflownews.com/ 65 | 66 | 欢迎关注我的博客，因为我也还在学习中，现有的教程经常比较大，涉及到的只是比较多，我会经常拆分出小的知识点，我的博客也会把这些小的知识点记录下来。 67 | FaceRank，带你走进 TensorFlow 的世界。 68 | -------------------------------------------------------------------------------- /train_model.py: -------------------------------------------------------------------------------- 1 | ''' 2 | A Convolutional Network implementation example using TensorFlow library. 3 | This example is using the MNIST database of handwritten digits 4 | (http://yann.lecun.com/exdb/mnist/) 5 | 6 | Author: Aymeric Damien 7 | Project: https://github.com/aymericdamien/TensorFlow-Examples/ 8 | ''' 9 | 10 | from __future__ import print_function 11 | import os 12 | import matplotlib.pyplot as plt 13 | import tensorflow as tf 14 | from PIL import Image 15 | import numpy 16 | import tensorflow as tf 17 | 18 | # Import MNIST data 19 | from tensorflow.examples.tutorials.mnist import input_data 20 | 21 | # Parameters 22 | learning_rate = 0.001 23 | training_iters = 3000 24 | batch_size = 10 25 | display_step = 3 26 | 27 | # Network Parameters 28 | n_input = 128*128 # MNIST data input (img shape: 128*128 ) 29 | n_classes = 10 # MNIST total classes (0-9 digits) 30 | dropout = 0.75 # Dropout, probability to keep units 31 | 32 | # tf Graph input 33 | x = tf.placeholder(tf.float32, [None, 128, 128, 3]) 34 | y = tf.placeholder(tf.float32, [None, n_classes]) 35 | keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) 36 | 37 | 38 | # Create some wrappers for simplicity 39 | def conv2d(x, W, b, strides=1): 40 | # Conv2D wrapper, with bias and relu activation 41 | x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') 42 | x = tf.nn.bias_add(x, b) 43 | return tf.nn.relu(x) 44 | 45 | 46 | def maxpool2d(x, k=2): 47 | # MaxPool2D wrapper 48 | return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], 49 | padding='SAME') 50 | 51 | 52 | # Create model 53 | def conv_net(x, weights, biases, dropout): 54 | # Reshape input picture 55 | x = tf.reshape(x, shape=[-1, 128, 128, 3]) 56 | 57 | # Convolution Layer 58 | conv1 = conv2d(x, weights['wc1'], biases['bc1']) 59 | print(conv1.shape) 60 | # Max Pooling (down-sampling) 61 | conv1 = maxpool2d(conv1, k=2) 62 | print(conv1.shape) 63 | # Convolution Layer 64 | conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) 65 | print(conv2.shape) 66 | # Max Pooling (down-sampling) 67 | conv2 = maxpool2d(conv2, k=2) 68 | print(conv2.shape) 69 | # Fully connected layer 70 | # Reshape conv2 output to fit fully connected layer input 71 | fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) 72 | fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) 73 | fc1 = tf.nn.relu(fc1) 74 | # Apply Dropout 75 | fc1 = tf.nn.dropout(fc1, dropout) 76 | 77 | # Output, class prediction 78 | out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) 79 | return out 80 | 81 | # Store layers weight & bias 82 | weights = { 83 | # 5x5 conv, 3 input, 24 outputs 84 | 'wc1': tf.Variable(tf.random_normal([5, 5, 3, 24])), 85 | # 5x5 conv, 24 inputs, 96 outputs 86 | 'wc2': tf.Variable(tf.random_normal([5, 5, 24, 96])), 87 | # fully connected, 32*32*96 inputs, 1024 outputs 88 | 'wd1': tf.Variable(tf.random_normal([32*32*96, 1024])), 89 | # 1024 inputs, 10 outputs (class prediction) 90 | 'out': tf.Variable(tf.random_normal([1024, n_classes])) 91 | } 92 | 93 | biases = { 94 | 'bc1': tf.Variable(tf.random_normal([24])), 95 | 'bc2': tf.Variable(tf.random_normal([96])), 96 | 'bd1': tf.Variable(tf.random_normal([1024])), 97 | 'out': tf.Variable(tf.random_normal([n_classes])) 98 | } 99 | 100 | # Construct model 101 | pred = conv_net(x, weights, biases, keep_prob) 102 | 103 | # Define loss and optimizer 104 | cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) 105 | optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 106 | 107 | # Evaluate model 108 | correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 109 | accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) 110 | 111 | # Initializing the variables 112 | init = tf.global_variables_initializer() 113 | saver=tf.train.Saver() 114 | 115 | 116 | # Launch the graph 117 | with tf.Session() as sess: 118 | sess.run(init) 119 | step = 1 120 | # Keep training until reach max iterations 121 | list = os.listdir("./resize_image/") 122 | print(list) 123 | print(len(list)) 124 | count=0 125 | while count<10: 126 | count = count+1 127 | print("count:",count) 128 | for batch_id in range(0, 12): 129 | batch = list[batch_id * 10:batch_id * 10 + 10] 130 | batch_xs = [] 131 | batch_ys = [] 132 | for image in batch: 133 | id_tag = image.find("-") 134 | score = image[0:id_tag] 135 | # print(score) 136 | img = Image.open("./resize_image/" + image) 137 | img_ndarray = numpy.asarray(img, dtype='float32') 138 | img_ndarray = numpy.reshape(img_ndarray, [128, 128, 3]) 139 | # print(img_ndarray.shape) 140 | batch_x = img_ndarray 141 | batch_xs.append(batch_x) 142 | # print(batch_xs) 143 | batch_y = numpy.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) 144 | # print(type(score)) 145 | batch_y[int(score) - 1] = 1 146 | # print(batch_y) 147 | batch_y = numpy.reshape(batch_y, [10, ]) 148 | batch_ys.append(batch_y) 149 | # print(batch_ys) 150 | batch_xs = numpy.asarray(batch_xs) 151 | print(batch_xs.shape) 152 | batch_ys = numpy.asarray(batch_ys) 153 | print(batch_ys.shape) 154 | 155 | sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, 156 | keep_prob: dropout}) 157 | if step % display_step == 0: 158 | # Calculate batch loss and accuracy 159 | loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_xs, 160 | y: batch_ys, 161 | keep_prob: 1.}) 162 | print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ 163 | "{:.6f}".format(loss) + ", Training Accuracy= " + \ 164 | "{:.5f}".format(acc)) 165 | step += 1 166 | print("Optimization Finished!") 167 | saver.save(sess,"./model/model.ckpt") 168 | 169 | -------------------------------------------------------------------------------- /wechatgroup.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fendouai/FaceRank/e55ff9bbdac62174e239e101e577a4bb11f0482a/wechatgroup.jpg --------------------------------------------------------------------------------