├── LICENSE ├── README.md ├── explainable.ipynb └── imgs ├── 20200901-172749.110257 └── events.out.tfevents.1598974069.LAPTOP-HR1GD033.12144.14150.v2 ├── 20200902-180345.009202 └── events.out.tfevents.1599062625.LAPTOP-HR1GD033.36384.8640.v2 └── cat.jpg /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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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 | # explainable 2 | explainable 3 |

Ahoy 👋, I'm Ehsan Paydar

4 |

A passionate engineering master's student artificial intelligence originally from Iran

5 | 6 | - 🌱 I’m currently learning about other kind model AI 7 | 8 | - 👨‍💻 All of my projects are available at [info@epsoft.ir](info@epsoft.ir) 9 | 10 | - 📝 I regularly write articles on [epsoft98@gmail.com](epsoft98@gmail.com) 11 | 12 | - 📫 How to reach me **epsoft98@gmail.com** 13 | 14 |

Connect with me:

15 |

16 | EPSOFT 17 |

18 | 19 |

Languages and Tools:

20 |

aws d3js docker illustrator postgresql python

21 | -------------------------------------------------------------------------------- /explainable.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### تفسیر و توضیح مدل" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "#pip install tf-explain\n" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 3, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import tensorflow as tf\n", 26 | "from tensorflow import keras\n", 27 | "import seaborn as sns\n", 28 | "import numpy as np\n", 29 | "%matplotlib inline" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 4, 35 | "metadata": {}, 36 | "outputs": [], 37 | "source": [ 38 | "path = '../data/flowers'\n", 39 | "img = tf.keras.preprocessing.image.load_img('../data/flowers/daisy/100080576_f52e8ee070_n.jpg', target_size=(150, 150))\n", 40 | "img = tf.keras.preprocessing.image.img_to_array(img)\n", 41 | "data = img.reshape(1,*img.shape)" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 7, 47 | "metadata": {}, 48 | "outputs": [ 49 | { 50 | "name": "stdout", 51 | "output_type": "stream", 52 | "text": [ 53 | "Found 2162 images belonging to 5 classes.\n", 54 | "Found 2161 images belonging to 5 classes.\n" 55 | ] 56 | } 57 | ], 58 | "source": [ 59 | "img_gen_train = keras.preprocessing.image.ImageDataGenerator(rescale=1./255,validation_split=0.5)\n", 60 | "\n", 61 | "train_data = img_gen_train.flow_from_directory('../data/flowers',\n", 62 | " batch_size = 8,\n", 63 | " seed =42,\n", 64 | " target_size = (150,150),\n", 65 | " subset = 'training' \n", 66 | " )\n", 67 | "\n", 68 | "val_data = img_gen_train.flow_from_directory('../data/flowers',\n", 69 | " batch_size = 8,\n", 70 | " seed =42,\n", 71 | " target_size = (150,150),\n", 72 | " subset = 'validation' )" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 20, 78 | "metadata": {}, 79 | "outputs": [ 80 | { 81 | "name": "stdout", 82 | "output_type": "stream", 83 | "text": [ 84 | "(150, 150, 3)\n", 85 | "(5,)\n", 86 | "0\n", 87 | "(150, 150, 3)\n", 88 | "(5,)\n", 89 | "3\n", 90 | "(150, 150, 3)\n", 91 | "(5,)\n", 92 | "2\n", 93 | "(150, 150, 3)\n", 94 | "(5,)\n", 95 | "0\n", 96 | "(150, 150, 3)\n", 97 | "(5,)\n", 98 | "3\n", 99 | "(150, 150, 3)\n", 100 | "(5,)\n", 101 | "2\n", 102 | "(150, 150, 3)\n", 103 | "(5,)\n", 104 | "3\n", 105 | "(150, 150, 3)\n", 106 | "(5,)\n", 107 | "3\n" 108 | ] 109 | } 110 | ], 111 | "source": [ 112 | "(x,y) = val_data.next()\n", 113 | "(x,y) = val_data.next()\n", 114 | "for el, label in zip(x,y):\n", 115 | " print(el.shape)\n", 116 | " print(label.shape)\n", 117 | " print(np.argmax(label))\n", 118 | "\n", 119 | "validation_class_zero = (np.array([\n", 120 | " el for el, label in zip(x,y)\n", 121 | " if np.all(np.argmax(label) == 0)\n", 122 | "][0:5]), None)\n" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 21, 128 | "metadata": {}, 129 | "outputs": [ 130 | { 131 | "data": { 132 | "text/plain": [ 133 | "(array([[[[0.29803923, 0.09803922, 0. ],\n", 134 | " [0.36862746, 0.13333334, 0. ],\n", 135 | " [0.42352945, 0.15294118, 0. ],\n", 136 | " ...,\n", 137 | " [0.02745098, 0.05490196, 0.08627451],\n", 138 | " [0.04705883, 0.0627451 , 0.10980393],\n", 139 | " [0.10980393, 0.10196079, 0.18431373]],\n", 140 | " \n", 141 | " [[0.31764707, 0.10588236, 0. ],\n", 142 | " [0.3803922 , 0.14509805, 0.00392157],\n", 143 | " [0.43529415, 0.16470589, 0.00392157],\n", 144 | " ...,\n", 145 | " [0.0509804 , 0.07058824, 0.09411766],\n", 146 | " [0.05882353, 0.06666667, 0.11764707],\n", 147 | " [0.1137255 , 0.10588236, 0.18823531]],\n", 148 | " \n", 149 | " [[0.36078432, 0.1254902 , 0. ],\n", 150 | " [0.43137258, 0.16862746, 0.00392157],\n", 151 | " [0.4784314 , 0.18823531, 0. ],\n", 152 | " ...,\n", 153 | " [0.16862746, 0.14509805, 0.18431373],\n", 154 | " [0.1137255 , 0.10196079, 0.14509805],\n", 155 | " [0.12156864, 0.10588236, 0.16470589]],\n", 156 | " \n", 157 | " ...,\n", 158 | " \n", 159 | " [[0.6901961 , 0.52156866, 0.5568628 ],\n", 160 | " [0.6901961 , 0.52156866, 0.54901963],\n", 161 | " [0.69411767, 0.5176471 , 0.5294118 ],\n", 162 | " ...,\n", 163 | " [0.4784314 , 0.4156863 , 0.56078434],\n", 164 | " [0.40784317, 0.40784317, 0.65882355],\n", 165 | " [0.43137258, 0.44705886, 0.73333335]],\n", 166 | " \n", 167 | " [[0.69803923, 0.5372549 , 0.5294118 ],\n", 168 | " [0.69803923, 0.5372549 , 0.5294118 ],\n", 169 | " [0.70980394, 0.5372549 , 0.5254902 ],\n", 170 | " ...,\n", 171 | " [0.3137255 , 0.3019608 , 0.5254902 ],\n", 172 | " [0.37254903, 0.3803922 , 0.47058827],\n", 173 | " [0.35686275, 0.34901962, 0.49411768]],\n", 174 | " \n", 175 | " [[0.69803923, 0.5411765 , 0.50980395],\n", 176 | " [0.70980394, 0.5411765 , 0.5137255 ],\n", 177 | " [0.7137255 , 0.54509807, 0.5176471 ],\n", 178 | " ...,\n", 179 | " [0.34117648, 0.32941177, 0.60784316],\n", 180 | " [0.35686275, 0.36862746, 0.43529415],\n", 181 | " [0.2784314 , 0.28627452, 0.28235295]]],\n", 182 | " \n", 183 | " \n", 184 | " [[[0. , 0. , 0. ],\n", 185 | " [0. , 0. , 0. ],\n", 186 | " [0. , 0. , 0. ],\n", 187 | " ...,\n", 188 | " [0. , 0. , 0. ],\n", 189 | " [0. , 0. , 0. ],\n", 190 | " [0. , 0. , 0. ]],\n", 191 | " \n", 192 | " [[0. , 0. , 0. ],\n", 193 | " [0. , 0. , 0. ],\n", 194 | " [0. , 0. , 0. ],\n", 195 | " ...,\n", 196 | " [0. , 0. , 0. ],\n", 197 | " [0. , 0. , 0. ],\n", 198 | " [0. , 0. , 0. ]],\n", 199 | " \n", 200 | " [[0. , 0. , 0. ],\n", 201 | " [0. , 0. , 0. ],\n", 202 | " [0. , 0. , 0. ],\n", 203 | " ...,\n", 204 | " [0. , 0. , 0. ],\n", 205 | " [0. , 0. , 0. ],\n", 206 | " [0. , 0. , 0. ]],\n", 207 | " \n", 208 | " ...,\n", 209 | " \n", 210 | " [[0. , 0. , 0. ],\n", 211 | " [0. , 0. , 0. ],\n", 212 | " [0. , 0. , 0. ],\n", 213 | " ...,\n", 214 | " [0. , 0. , 0. ],\n", 215 | " [0. , 0. , 0. ],\n", 216 | " [0. , 0. , 0. ]],\n", 217 | " \n", 218 | " [[0. , 0. , 0. ],\n", 219 | " [0. , 0. , 0. ],\n", 220 | " [0. , 0. , 0. ],\n", 221 | " ...,\n", 222 | " [0. , 0. , 0. ],\n", 223 | " [0. , 0. , 0. ],\n", 224 | " [0. , 0. , 0. ]],\n", 225 | " \n", 226 | " [[0. , 0. , 0. ],\n", 227 | " [0. , 0. , 0. ],\n", 228 | " [0. , 0. , 0. ],\n", 229 | " ...,\n", 230 | " [0. , 0. , 0. ],\n", 231 | " [0. , 0. , 0. ],\n", 232 | " [0. , 0. , 0. ]]]], dtype=float32), None)" 233 | ] 234 | }, 235 | "execution_count": 21, 236 | "metadata": {}, 237 | "output_type": "execute_result" 238 | } 239 | ], 240 | "source": [ 241 | "validation_class_zero" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": 16, 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [ 250 | "from tf_explain.callbacks import ActivationsVisualizationCallback\n", 251 | "import tf_explain" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 14, 257 | "metadata": {}, 258 | "outputs": [], 259 | "source": [ 260 | "callbacks = [\n", 261 | " ActivationsVisualizationCallback(\n", 262 | " validation_data= (x,y),\n", 263 | " layers_name=[\"activation_1\"],\n", 264 | " output_dir='imgs/',\n", 265 | " ),\n", 266 | "]" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": 10, 272 | "metadata": {}, 273 | "outputs": [ 274 | { 275 | "name": "stdout", 276 | "output_type": "stream", 277 | "text": [ 278 | "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n" 279 | ] 280 | } 281 | ], 282 | "source": [ 283 | "base_model = keras.applications.MobileNet( weights= 'imagenet',\n", 284 | " input_shape = (150,150,3),\n", 285 | " include_top = False)\n", 286 | "def create_transfer_learning_model():\n", 287 | " base_model.trainable = False\n", 288 | " inputs = keras.layers.Input(shape = (150,150,3))\n", 289 | " x = base_model(inputs,training = False)\n", 290 | " x = keras.layers.Flatten()(x)\n", 291 | " x = keras.layers.Dense(128,activation='relu')(x)\n", 292 | " x = keras.layers.Dropout(0.2)(x)\n", 293 | " outputs = keras.layers.Dense(5,activation='softmax' )(x)\n", 294 | " final_model = keras.Model(inputs,outputs)\n", 295 | " return final_model\n", 296 | "\n", 297 | "\n", 298 | "model = create_transfer_learning_model()\n", 299 | "model.compile(optimizer = 'adam',\n", 300 | " loss ='categorical_crossentropy',metrics = ['accuracy'])" 301 | ] 302 | }, 303 | { 304 | "cell_type": "code", 305 | "execution_count": 22, 306 | "metadata": {}, 307 | "outputs": [ 308 | { 309 | "name": "stdout", 310 | "output_type": "stream", 311 | "text": [ 312 | "271/271 [==============================] - 30s 109ms/step - loss: 0.2616 - accuracy: 0.9075\n" 313 | ] 314 | }, 315 | { 316 | "data": { 317 | "text/plain": [ 318 | "" 319 | ] 320 | }, 321 | "execution_count": 22, 322 | "metadata": {}, 323 | "output_type": "execute_result" 324 | } 325 | ], 326 | "source": [ 327 | "model.fit(train_data,epochs=1,\n", 328 | " callbacks=[tf_explain.callbacks.VanillaGradientsCallback(validation_class_zero, class_index=0)])" 329 | ] 330 | }, 331 | { 332 | "cell_type": "markdown", 333 | "metadata": {}, 334 | "source": [ 335 | "### روش smooth-grad" 336 | ] 337 | }, 338 | { 339 | "cell_type": "code", 340 | "execution_count": 36, 341 | "metadata": {}, 342 | "outputs": [], 343 | "source": [ 344 | "(x,y) = val_date.next()\n", 345 | "\n" 346 | ] 347 | }, 348 | { 349 | "cell_type": "code", 350 | "execution_count": 49, 351 | "metadata": {}, 352 | "outputs": [ 353 | { 354 | "ename": "TypeError", 355 | "evalue": "'NoneType' object is not callable", 356 | "output_type": "error", 357 | "traceback": [ 358 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 359 | "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", 360 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;31m# Start explainer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mexplainer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGradCAM\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mgrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexplainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclass_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m281\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 281 is the tabby cat index in ImageNet\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mexplainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\".\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"grad_cam.png\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 361 | "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tf_explain\\core\\grad_cam.py\u001b[0m in \u001b[0;36mexplain\u001b[1;34m(self, validation_data, model, class_index, layer_name, colormap, image_weight)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 52\u001b[0m outputs, guided_grads = GradCAM.get_gradients_and_filters(\n\u001b[1;32m---> 53\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayer_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclass_index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 54\u001b[0m )\n\u001b[0;32m 55\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", 362 | "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 778\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 779\u001b[0m \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 782\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 363 | "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 805\u001b[0m \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 806\u001b[0m \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 807\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 808\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 809\u001b[0m \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 364 | "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object is not callable" 365 | ] 366 | } 367 | ], 368 | "source": [ 369 | "model = tf.keras.applications.vgg16.VGG16(weights=\"imagenet\", include_top=True)\n", 370 | "\n", 371 | "# Load a sample image (or multiple ones)\n", 372 | "img = tf.keras.preprocessing.image.load_img('imgs/cat.jpg', target_size=(224, 224))\n", 373 | "img = tf.keras.preprocessing.image.img_to_array(img)\n", 374 | "data = ([img], None)\n", 375 | "\n", 376 | "# Start explainer\n", 377 | "explainer = GradCAM()\n", 378 | "grid = explainer.explain(data, model, class_index=281) # 281 is the tabby cat index in ImageNet\n", 379 | "\n", 380 | "explainer.save(grid, \".\", \"grad_cam.png\")" 381 | ] 382 | }, 383 | { 384 | "cell_type": "code", 385 | "execution_count": 5, 386 | "metadata": {}, 387 | "outputs": [ 388 | { 389 | "name": "stdout", 390 | "output_type": "stream", 391 | "text": [ 392 | "Model: \"vgg16\"\n", 393 | "_________________________________________________________________\n", 394 | "Layer (type) Output Shape Param # \n", 395 | "=================================================================\n", 396 | "input_1 (InputLayer) [(None, 224, 224, 3)] 0 \n", 397 | "_________________________________________________________________\n", 398 | "block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 \n", 399 | "_________________________________________________________________\n", 400 | "block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 \n", 401 | "_________________________________________________________________\n", 402 | "block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 \n", 403 | "_________________________________________________________________\n", 404 | "block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 \n", 405 | "_________________________________________________________________\n", 406 | "block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 \n", 407 | "_________________________________________________________________\n", 408 | "block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 \n", 409 | "_________________________________________________________________\n", 410 | "block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 \n", 411 | "_________________________________________________________________\n", 412 | "block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 \n", 413 | "_________________________________________________________________\n", 414 | "block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 \n", 415 | "_________________________________________________________________\n", 416 | "block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 \n", 417 | "_________________________________________________________________\n", 418 | "block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 \n", 419 | "_________________________________________________________________\n", 420 | "block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 \n", 421 | "_________________________________________________________________\n", 422 | "block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 \n", 423 | "_________________________________________________________________\n", 424 | "block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 \n", 425 | "_________________________________________________________________\n", 426 | "block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 \n", 427 | "_________________________________________________________________\n", 428 | "block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 \n", 429 | "_________________________________________________________________\n", 430 | "block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 \n", 431 | "_________________________________________________________________\n", 432 | "block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 \n", 433 | "_________________________________________________________________\n", 434 | "flatten (Flatten) (None, 25088) 0 \n", 435 | "_________________________________________________________________\n", 436 | "fc1 (Dense) (None, 4096) 102764544 \n", 437 | "_________________________________________________________________\n", 438 | "fc2 (Dense) (None, 4096) 16781312 \n", 439 | "_________________________________________________________________\n", 440 | "predictions (Dense) (None, 1000) 4097000 \n", 441 | "=================================================================\n", 442 | "Total params: 138,357,544\n", 443 | "Trainable params: 138,357,544\n", 444 | "Non-trainable params: 0\n", 445 | "_________________________________________________________________\n" 446 | ] 447 | }, 448 | { 449 | "data": { 450 | "text/plain": [ 451 | "array([[[242., 237., 255.],\n", 452 | " [225., 222., 243.],\n", 453 | " [195., 194., 212.],\n", 454 | " ...,\n", 455 | " [232., 232., 234.],\n", 456 | " [232., 232., 234.],\n", 457 | " [232., 232., 234.]],\n", 458 | "\n", 459 | " [[242., 237., 255.],\n", 460 | " [223., 220., 241.],\n", 461 | " [193., 192., 210.],\n", 462 | " ...,\n", 463 | " [232., 232., 234.],\n", 464 | " [232., 232., 234.],\n", 465 | " [232., 232., 234.]],\n", 466 | "\n", 467 | " [[241., 236., 255.],\n", 468 | " [221., 218., 239.],\n", 469 | " [190., 189., 207.],\n", 470 | " ...,\n", 471 | " [232., 232., 234.],\n", 472 | " [232., 232., 234.],\n", 473 | " [232., 232., 234.]],\n", 474 | "\n", 475 | " ...,\n", 476 | "\n", 477 | " [[122., 124., 123.],\n", 478 | " [122., 124., 123.],\n", 479 | " [122., 124., 123.],\n", 480 | " ...,\n", 481 | " [211., 211., 211.],\n", 482 | " [211., 211., 211.],\n", 483 | " [211., 211., 211.]],\n", 484 | "\n", 485 | " [[119., 121., 120.],\n", 486 | " [119., 121., 120.],\n", 487 | " [119., 121., 120.],\n", 488 | " ...,\n", 489 | " [211., 211., 211.],\n", 490 | " [211., 211., 211.],\n", 491 | " [211., 211., 211.]],\n", 492 | "\n", 493 | " [[121., 123., 122.],\n", 494 | " [121., 123., 122.],\n", 495 | " [121., 123., 122.],\n", 496 | " ...,\n", 497 | " [211., 211., 211.],\n", 498 | " [211., 211., 211.],\n", 499 | " [211., 211., 211.]]], dtype=float32)" 500 | ] 501 | }, 502 | "execution_count": 5, 503 | "metadata": {}, 504 | "output_type": "execute_result" 505 | } 506 | ], 507 | "source": [ 508 | " model = tf.keras.applications.vgg16.VGG16(weights=\"imagenet\", include_top=True)\n", 509 | "\n", 510 | " img = tf.keras.preprocessing.image.load_img('imgs/cat.jpg', target_size=(224, 224))\n", 511 | " img = tf.keras.preprocessing.image.img_to_array(img)\n", 512 | "\n", 513 | " model.summary()\n", 514 | " data = ([img], None)\n", 515 | "\n", 516 | "img" 517 | ] 518 | }, 519 | { 520 | "cell_type": "code", 521 | "execution_count": 54, 522 | "metadata": {}, 523 | "outputs": [], 524 | "source": [ 525 | "from tf_explain.core.smoothgrad import SmoothGrad\n", 526 | " \n", 527 | "\n" 528 | ] 529 | }, 530 | { 531 | "cell_type": "code", 532 | "execution_count": 55, 533 | "metadata": {}, 534 | "outputs": [], 535 | "source": [ 536 | " tabby_cat_class_index = 281\n", 537 | " explainer = SmoothGrad()\n", 538 | " # Compute SmoothGrad on VGG16\n", 539 | " grid = explainer.explain(data, model, tabby_cat_class_index, 20, 1.0)\n", 540 | " explainer.save(grid, \".\", \"smoothgrad.png\")\n" 541 | ] 542 | }, 543 | { 544 | "cell_type": "code", 545 | "execution_count": 58, 546 | "metadata": {}, 547 | "outputs": [ 548 | { 549 | "name": "stdout", 550 | "output_type": "stream", 551 | "text": [ 552 | "271/271 [==============================] - 20s 75ms/step - loss: 1.3295 - accuracy: 0.7169\n" 553 | ] 554 | }, 555 | { 556 | "data": { 557 | "text/plain": [ 558 | "" 559 | ] 560 | }, 561 | "execution_count": 58, 562 | "metadata": {}, 563 | "output_type": "execute_result" 564 | } 565 | ], 566 | "source": [ 567 | "model.fit(train_data,batch_size=16, epochs=1)" 568 | ] 569 | }, 570 | { 571 | "cell_type": "code", 572 | "execution_count": 59, 573 | "metadata": {}, 574 | "outputs": [ 575 | { 576 | "data": { 577 | "text/plain": [ 578 | "{'daisy': 0, 'dandelion': 1, 'rose': 2, 'sunflower': 3, 'tulip': 4}" 579 | ] 580 | }, 581 | "execution_count": 59, 582 | "metadata": {}, 583 | "output_type": "execute_result" 584 | } 585 | ], 586 | "source": [ 587 | "val_date.class_indices" 588 | ] 589 | }, 590 | { 591 | "cell_type": "code", 592 | "execution_count": 63, 593 | "metadata": {}, 594 | "outputs": [], 595 | "source": [ 596 | "img = tf.keras.preprocessing.image.load_img('../data/flowers/daisy/100080576_f52e8ee070_n.jpg', target_size=(150, 150))\n", 597 | "img = tf.keras.preprocessing.image.img_to_array(img)\n", 598 | "data = ([img], None)" 599 | ] 600 | }, 601 | { 602 | "cell_type": "code", 603 | "execution_count": 67, 604 | "metadata": {}, 605 | "outputs": [], 606 | "source": [ 607 | "tabby_cat_class_index = 0\n", 608 | "explainer = SmoothGrad()\n", 609 | "# Compute SmoothGrad on VGG16\n", 610 | "grid = explainer.explain(data, model, tabby_cat_class_index)\n", 611 | "explainer.save(grid, \".\", \"smoothgrad.png\")" 612 | ] 613 | }, 614 | { 615 | "cell_type": "code", 616 | "execution_count": null, 617 | "metadata": {}, 618 | "outputs": [], 619 | "source": [] 620 | } 621 | ], 622 | "metadata": { 623 | "kernelspec": { 624 | "display_name": "Python 3", 625 | "language": "python", 626 | "name": "python3" 627 | }, 628 | "language_info": { 629 | "codemirror_mode": { 630 | "name": "ipython", 631 | "version": 3 632 | }, 633 | "file_extension": ".py", 634 | "mimetype": "text/x-python", 635 | "name": "python", 636 | "nbconvert_exporter": "python", 637 | "pygments_lexer": "ipython3", 638 | "version": "3.7.4" 639 | } 640 | }, 641 | "nbformat": 4, 642 | "nbformat_minor": 4 643 | } 644 | -------------------------------------------------------------------------------- /imgs/20200901-172749.110257/events.out.tfevents.1598974069.LAPTOP-HR1GD033.12144.14150.v2: 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