LIME/LIME.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8abfec63-b585-44c5-87c7-d2694c4df0aa",
   "metadata": {},
   "source": [
    "# Interpretable Machine Learning with LIME for Image Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c5c8ac2-2ce2-4917-8f2d-d369c4e75c81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-18 21:06:42.986462: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-01-18 21:06:43.164196: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:43.164231: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-01-18 21:06:44.403643: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:44.403802: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:44.403814: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "# 导入包\n",
    "import os\n",
    "import keras\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.imagenet_utils import decode_predictions\n",
    "from skimage.io import imread\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57325aa4-3177-4c61-9980-02105168e75f",
   "metadata": {},
   "source": [
    "## 载入模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3795c1db-3859-4e46-a195-95625fff1522",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-18 21:06:49.748418: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:49.748560: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:49.748642: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:49.748716: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:49.748798: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:49.748883: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:49.748957: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:49.749033: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
      "2023-01-18 21:06:49.749073: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1934] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2023-01-18 21:06:49.749752: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"inception_v3\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 299, 299, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 149, 149, 32  864         ['input_1[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 149, 149, 32  96         ['conv2d[0][0]']                 \n",
      " alization)                     )                                                                 \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 149, 149, 32  0           ['batch_normalization[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 147, 147, 32  9216        ['activation[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 147, 147, 32  96         ['conv2d_1[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 147, 147, 32  0           ['batch_normalization_1[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 147, 147, 64  18432       ['activation_1[0][0]']           \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 147, 147, 64  192        ['conv2d_2[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 147, 147, 64  0           ['batch_normalization_2[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 73, 73, 64)   0           ['activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 73, 73, 80)   5120        ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 73, 73, 80)  240         ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 73, 73, 80)   0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 71, 71, 192)  138240      ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 71, 71, 192)  576        ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 71, 71, 192)  0           ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 35, 35, 192)  0          ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 35, 35, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 35, 35, 64)  192         ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 35, 35, 64)   0           ['batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 35, 35, 48)   9216        ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 35, 35, 96)   55296       ['activation_8[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 35, 35, 48)  144         ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 35, 35, 96)  288         ['conv2d_9[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 35, 35, 48)   0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 35, 35, 96)   0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " average_pooling2d (AveragePool  (None, 35, 35, 192)  0          ['max_pooling2d_1[0][0]']        \n",
      " ing2D)                                                                                           \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 35, 35, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 35, 35, 64)   76800       ['activation_6[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 35, 35, 96)   82944       ['activation_9[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 35, 35, 32)   6144        ['average_pooling2d[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 35, 35, 64)  192         ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 35, 35, 64)  192         ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 35, 35, 96)  288         ['conv2d_10[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 35, 35, 32)  96          ['conv2d_11[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 35, 35, 64)   0           ['batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 35, 35, 64)   0           ['batch_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 35, 35, 32)   0           ['batch_normalization_11[0][0]'] \n",
      "                                                                                                  \n",
      " mixed0 (Concatenate)           (None, 35, 35, 256)  0           ['activation_5[0][0]',           \n",
      "                                                                  'activation_7[0][0]',           \n",
      "                                                                  'activation_10[0][0]',          \n",
      "                                                                  'activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)             (None, 35, 35, 64)   16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, 35, 35, 64)  192         ['conv2d_15[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_15[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 35, 35, 48)   12288       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, 35, 35, 96)   55296       ['activation_15[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 35, 35, 48)  144         ['conv2d_13[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_16 (BatchN  (None, 35, 35, 96)  288         ['conv2d_16[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 35, 35, 48)   0           ['batch_normalization_13[0][0]'] \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_16[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_1 (AveragePo  (None, 35, 35, 256)  0          ['mixed0[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 35, 35, 64)   16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, 35, 35, 64)   76800       ['activation_13[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, 35, 35, 96)   82944       ['activation_16[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, 35, 35, 64)   16384       ['average_pooling2d_1[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 35, 35, 64)  192         ['conv2d_12[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, 35, 35, 64)  192         ['conv2d_14[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_17 (BatchN  (None, 35, 35, 96)  288         ['conv2d_17[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_18 (BatchN  (None, 35, 35, 64)  192         ['conv2d_18[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_12[0][0]'] \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_14[0][0]'] \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_17[0][0]'] \n",
      "                                                                                                  \n",
      " activation_18 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_18[0][0]'] \n",
      "                                                                                                  \n",
      " mixed1 (Concatenate)           (None, 35, 35, 288)  0           ['activation_12[0][0]',          \n",
      "                                                                  'activation_14[0][0]',          \n",
      "                                                                  'activation_17[0][0]',          \n",
      "                                                                  'activation_18[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)             (None, 35, 35, 64)   18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_22 (BatchN  (None, 35, 35, 64)  192         ['conv2d_22[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_22 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_22[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_20 (Conv2D)             (None, 35, 35, 48)   13824       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_23 (Conv2D)             (None, 35, 35, 96)   55296       ['activation_22[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_20 (BatchN  (None, 35, 35, 48)  144         ['conv2d_20[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_23 (BatchN  (None, 35, 35, 96)  288         ['conv2d_23[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_20 (Activation)     (None, 35, 35, 48)   0           ['batch_normalization_20[0][0]'] \n",
      "                                                                                                  \n",
      " activation_23 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_23[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_2 (AveragePo  (None, 35, 35, 288)  0          ['mixed1[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)             (None, 35, 35, 64)   18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)             (None, 35, 35, 64)   76800       ['activation_20[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_24 (Conv2D)             (None, 35, 35, 96)   82944       ['activation_23[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_25 (Conv2D)             (None, 35, 35, 64)   18432       ['average_pooling2d_2[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_19 (BatchN  (None, 35, 35, 64)  192         ['conv2d_19[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_21 (BatchN  (None, 35, 35, 64)  192         ['conv2d_21[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_24 (BatchN  (None, 35, 35, 96)  288         ['conv2d_24[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_25 (BatchN  (None, 35, 35, 64)  192         ['conv2d_25[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_19 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_19[0][0]'] \n",
      "                                                                                                  \n",
      " activation_21 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_21[0][0]'] \n",
      "                                                                                                  \n",
      " activation_24 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_24[0][0]'] \n",
      "                                                                                                  \n",
      " activation_25 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_25[0][0]'] \n",
      "                                                                                                  \n",
      " mixed2 (Concatenate)           (None, 35, 35, 288)  0           ['activation_19[0][0]',          \n",
      "                                                                  'activation_21[0][0]',          \n",
      "                                                                  'activation_24[0][0]',          \n",
      "                                                                  'activation_25[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_27 (Conv2D)             (None, 35, 35, 64)   18432       ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_27 (BatchN  (None, 35, 35, 64)  192         ['conv2d_27[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_27 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_27[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_28 (Conv2D)             (None, 35, 35, 96)   55296       ['activation_27[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_28 (BatchN  (None, 35, 35, 96)  288         ['conv2d_28[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_28 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_28[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_26 (Conv2D)             (None, 17, 17, 384)  995328      ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_29 (Conv2D)             (None, 17, 17, 96)   82944       ['activation_28[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_26 (BatchN  (None, 17, 17, 384)  1152       ['conv2d_26[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_29 (BatchN  (None, 17, 17, 96)  288         ['conv2d_29[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_26 (Activation)     (None, 17, 17, 384)  0           ['batch_normalization_26[0][0]'] \n",
      "                                                                                                  \n",
      " activation_29 (Activation)     (None, 17, 17, 96)   0           ['batch_normalization_29[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 17, 17, 288)  0          ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed3 (Concatenate)           (None, 17, 17, 768)  0           ['activation_26[0][0]',          \n",
      "                                                                  'activation_29[0][0]',          \n",
      "                                                                  'max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)             (None, 17, 17, 128)  98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_34 (BatchN  (None, 17, 17, 128)  384        ['conv2d_34[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_34 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_34[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)             (None, 17, 17, 128)  114688      ['activation_34[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_35 (BatchN  (None, 17, 17, 128)  384        ['conv2d_35[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_35 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_35[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_31 (Conv2D)             (None, 17, 17, 128)  98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_36 (Conv2D)             (None, 17, 17, 128)  114688      ['activation_35[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_31 (BatchN  (None, 17, 17, 128)  384        ['conv2d_31[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_36 (BatchN  (None, 17, 17, 128)  384        ['conv2d_36[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_31 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_31[0][0]'] \n",
      "                                                                                                  \n",
      " activation_36 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_36[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_32 (Conv2D)             (None, 17, 17, 128)  114688      ['activation_31[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_37 (Conv2D)             (None, 17, 17, 128)  114688      ['activation_36[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_32 (BatchN  (None, 17, 17, 128)  384        ['conv2d_32[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_37 (BatchN  (None, 17, 17, 128)  384        ['conv2d_37[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_32 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_32[0][0]'] \n",
      "                                                                                                  \n",
      " activation_37 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_37[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_3 (AveragePo  (None, 17, 17, 768)  0          ['mixed3[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_30 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)             (None, 17, 17, 192)  172032      ['activation_32[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_38 (Conv2D)             (None, 17, 17, 192)  172032      ['activation_37[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_39 (Conv2D)             (None, 17, 17, 192)  147456      ['average_pooling2d_3[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_30 (BatchN  (None, 17, 17, 192)  576        ['conv2d_30[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_33 (BatchN  (None, 17, 17, 192)  576        ['conv2d_33[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_38 (BatchN  (None, 17, 17, 192)  576        ['conv2d_38[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_39 (BatchN  (None, 17, 17, 192)  576        ['conv2d_39[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_30 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_30[0][0]'] \n",
      "                                                                                                  \n",
      " activation_33 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_33[0][0]'] \n",
      "                                                                                                  \n",
      " activation_38 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_38[0][0]'] \n",
      "                                                                                                  \n",
      " activation_39 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_39[0][0]'] \n",
      "                                                                                                  \n",
      " mixed4 (Concatenate)           (None, 17, 17, 768)  0           ['activation_30[0][0]',          \n",
      "                                                                  'activation_33[0][0]',          \n",
      "                                                                  'activation_38[0][0]',          \n",
      "                                                                  'activation_39[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_44 (Conv2D)             (None, 17, 17, 160)  122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_44 (BatchN  (None, 17, 17, 160)  480        ['conv2d_44[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_44 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_44[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_45 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_44[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_45 (BatchN  (None, 17, 17, 160)  480        ['conv2d_45[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_45 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_45[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_41 (Conv2D)             (None, 17, 17, 160)  122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_46 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_45[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_41 (BatchN  (None, 17, 17, 160)  480        ['conv2d_41[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_46 (BatchN  (None, 17, 17, 160)  480        ['conv2d_46[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_41 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_41[0][0]'] \n",
      "                                                                                                  \n",
      " activation_46 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_46[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_42 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_41[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_47 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_46[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_42 (BatchN  (None, 17, 17, 160)  480        ['conv2d_42[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_47 (BatchN  (None, 17, 17, 160)  480        ['conv2d_47[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_42 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_42[0][0]'] \n",
      "                                                                                                  \n",
      " activation_47 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_47[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_4 (AveragePo  (None, 17, 17, 768)  0          ['mixed4[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_40 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_43 (Conv2D)             (None, 17, 17, 192)  215040      ['activation_42[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_48 (Conv2D)             (None, 17, 17, 192)  215040      ['activation_47[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_49 (Conv2D)             (None, 17, 17, 192)  147456      ['average_pooling2d_4[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_40 (BatchN  (None, 17, 17, 192)  576        ['conv2d_40[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_43 (BatchN  (None, 17, 17, 192)  576        ['conv2d_43[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_48 (BatchN  (None, 17, 17, 192)  576        ['conv2d_48[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_49 (BatchN  (None, 17, 17, 192)  576        ['conv2d_49[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_40 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_40[0][0]'] \n",
      "                                                                                                  \n",
      " activation_43 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_43[0][0]'] \n",
      "                                                                                                  \n",
      " activation_48 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_48[0][0]'] \n",
      "                                                                                                  \n",
      " activation_49 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_49[0][0]'] \n",
      "                                                                                                  \n",
      " mixed5 (Concatenate)           (None, 17, 17, 768)  0           ['activation_40[0][0]',          \n",
      "                                                                  'activation_43[0][0]',          \n",
      "                                                                  'activation_48[0][0]',          \n",
      "                                                                  'activation_49[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_54 (Conv2D)             (None, 17, 17, 160)  122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_54 (BatchN  (None, 17, 17, 160)  480        ['conv2d_54[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_54 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_54[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_55 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_54[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_55 (BatchN  (None, 17, 17, 160)  480        ['conv2d_55[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_55 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_55[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_51 (Conv2D)             (None, 17, 17, 160)  122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_56 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_55[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_51 (BatchN  (None, 17, 17, 160)  480        ['conv2d_51[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_56 (BatchN  (None, 17, 17, 160)  480        ['conv2d_56[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_51 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_51[0][0]'] \n",
      "                                                                                                  \n",
      " activation_56 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_56[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_52 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_51[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_57 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_56[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_52 (BatchN  (None, 17, 17, 160)  480        ['conv2d_52[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_57 (BatchN  (None, 17, 17, 160)  480        ['conv2d_57[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_52 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_52[0][0]'] \n",
      "                                                                                                  \n",
      " activation_57 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_57[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_5 (AveragePo  (None, 17, 17, 768)  0          ['mixed5[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_50 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_53 (Conv2D)             (None, 17, 17, 192)  215040      ['activation_52[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_58 (Conv2D)             (None, 17, 17, 192)  215040      ['activation_57[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_59 (Conv2D)             (None, 17, 17, 192)  147456      ['average_pooling2d_5[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_50 (BatchN  (None, 17, 17, 192)  576        ['conv2d_50[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_53 (BatchN  (None, 17, 17, 192)  576        ['conv2d_53[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_58 (BatchN  (None, 17, 17, 192)  576        ['conv2d_58[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_59 (BatchN  (None, 17, 17, 192)  576        ['conv2d_59[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_50 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_50[0][0]'] \n",
      "                                                                                                  \n",
      " activation_53 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_53[0][0]'] \n",
      "                                                                                                  \n",
      " activation_58 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_58[0][0]'] \n",
      "                                                                                                  \n",
      " activation_59 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_59[0][0]'] \n",
      "                                                                                                  \n",
      " mixed6 (Concatenate)           (None, 17, 17, 768)  0           ['activation_50[0][0]',          \n",
      "                                                                  'activation_53[0][0]',          \n",
      "                                                                  'activation_58[0][0]',          \n",
      "                                                                  'activation_59[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_64 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_64 (BatchN  (None, 17, 17, 192)  576        ['conv2d_64[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_64 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_64[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_65 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_64[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_65 (BatchN  (None, 17, 17, 192)  576        ['conv2d_65[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_65 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_65[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_61 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_66 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_65[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_61 (BatchN  (None, 17, 17, 192)  576        ['conv2d_61[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_66 (BatchN  (None, 17, 17, 192)  576        ['conv2d_66[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_61 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_61[0][0]'] \n",
      "                                                                                                  \n",
      " activation_66 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_66[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_62 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_61[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_67 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_66[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_62 (BatchN  (None, 17, 17, 192)  576        ['conv2d_62[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_67 (BatchN  (None, 17, 17, 192)  576        ['conv2d_67[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_62 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_62[0][0]'] \n",
      "                                                                                                  \n",
      " activation_67 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_67[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_6 (AveragePo  (None, 17, 17, 768)  0          ['mixed6[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_60 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_63 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_62[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_68 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_67[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_69 (Conv2D)             (None, 17, 17, 192)  147456      ['average_pooling2d_6[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_60 (BatchN  (None, 17, 17, 192)  576        ['conv2d_60[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_63 (BatchN  (None, 17, 17, 192)  576        ['conv2d_63[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_68 (BatchN  (None, 17, 17, 192)  576        ['conv2d_68[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_69 (BatchN  (None, 17, 17, 192)  576        ['conv2d_69[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_60 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_60[0][0]'] \n",
      "                                                                                                  \n",
      " activation_63 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_63[0][0]'] \n",
      "                                                                                                  \n",
      " activation_68 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_68[0][0]'] \n",
      "                                                                                                  \n",
      " activation_69 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_69[0][0]'] \n",
      "                                                                                                  \n",
      " mixed7 (Concatenate)           (None, 17, 17, 768)  0           ['activation_60[0][0]',          \n",
      "                                                                  'activation_63[0][0]',          \n",
      "                                                                  'activation_68[0][0]',          \n",
      "                                                                  'activation_69[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_72 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_72 (BatchN  (None, 17, 17, 192)  576        ['conv2d_72[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_72 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_72[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_73 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_72[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_73 (BatchN  (None, 17, 17, 192)  576        ['conv2d_73[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_73 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_73[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_70 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_74 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_73[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_70 (BatchN  (None, 17, 17, 192)  576        ['conv2d_70[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_74 (BatchN  (None, 17, 17, 192)  576        ['conv2d_74[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_70 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_70[0][0]'] \n",
      "                                                                                                  \n",
      " activation_74 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_74[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_71 (Conv2D)             (None, 8, 8, 320)    552960      ['activation_70[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_75 (Conv2D)             (None, 8, 8, 192)    331776      ['activation_74[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_71 (BatchN  (None, 8, 8, 320)   960         ['conv2d_71[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_75 (BatchN  (None, 8, 8, 192)   576         ['conv2d_75[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_71 (Activation)     (None, 8, 8, 320)    0           ['batch_normalization_71[0][0]'] \n",
      "                                                                                                  \n",
      " activation_75 (Activation)     (None, 8, 8, 192)    0           ['batch_normalization_75[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 768)   0           ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed8 (Concatenate)           (None, 8, 8, 1280)   0           ['activation_71[0][0]',          \n",
      "                                                                  'activation_75[0][0]',          \n",
      "                                                                  'max_pooling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_80 (Conv2D)             (None, 8, 8, 448)    573440      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_80 (BatchN  (None, 8, 8, 448)   1344        ['conv2d_80[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_80 (Activation)     (None, 8, 8, 448)    0           ['batch_normalization_80[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_77 (Conv2D)             (None, 8, 8, 384)    491520      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_81 (Conv2D)             (None, 8, 8, 384)    1548288     ['activation_80[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_77 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_77[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_81 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_81[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_77 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_77[0][0]'] \n",
      "                                                                                                  \n",
      " activation_81 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_81[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_78 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_77[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_79 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_77[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_82 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_81[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_83 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_81[0][0]']          \n",
      "                                                                                                  \n",
      " average_pooling2d_7 (AveragePo  (None, 8, 8, 1280)  0           ['mixed8[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_76 (Conv2D)             (None, 8, 8, 320)    409600      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_78 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_78[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_79 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_79[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_82 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_82[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_83 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_83[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_84 (Conv2D)             (None, 8, 8, 192)    245760      ['average_pooling2d_7[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_76 (BatchN  (None, 8, 8, 320)   960         ['conv2d_76[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_78 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_78[0][0]'] \n",
      "                                                                                                  \n",
      " activation_79 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_79[0][0]'] \n",
      "                                                                                                  \n",
      " activation_82 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_82[0][0]'] \n",
      "                                                                                                  \n",
      " activation_83 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_83[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_84 (BatchN  (None, 8, 8, 192)   576         ['conv2d_84[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_76 (Activation)     (None, 8, 8, 320)    0           ['batch_normalization_76[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9_0 (Concatenate)         (None, 8, 8, 768)    0           ['activation_78[0][0]',          \n",
      "                                                                  'activation_79[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 8, 8, 768)    0           ['activation_82[0][0]',          \n",
      "                                                                  'activation_83[0][0]']          \n",
      "                                                                                                  \n",
      " activation_84 (Activation)     (None, 8, 8, 192)    0           ['batch_normalization_84[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9 (Concatenate)           (None, 8, 8, 2048)   0           ['activation_76[0][0]',          \n",
      "                                                                  'mixed9_0[0][0]',               \n",
      "                                                                  'concatenate[0][0]',            \n",
      "                                                                  'activation_84[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_89 (Conv2D)             (None, 8, 8, 448)    917504      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_89 (BatchN  (None, 8, 8, 448)   1344        ['conv2d_89[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_89 (Activation)     (None, 8, 8, 448)    0           ['batch_normalization_89[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_86 (Conv2D)             (None, 8, 8, 384)    786432      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_90 (Conv2D)             (None, 8, 8, 384)    1548288     ['activation_89[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_86 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_86[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_90 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_90[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_86 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_86[0][0]'] \n",
      "                                                                                                  \n",
      " activation_90 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_90[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_87 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_86[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_88 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_86[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_91 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_90[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_92 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_90[0][0]']          \n",
      "                                                                                                  \n",
      " average_pooling2d_8 (AveragePo  (None, 8, 8, 2048)  0           ['mixed9[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_85 (Conv2D)             (None, 8, 8, 320)    655360      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_87 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_87[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_88 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_88[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_91 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_91[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_92 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_92[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_93 (Conv2D)             (None, 8, 8, 192)    393216      ['average_pooling2d_8[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_85 (BatchN  (None, 8, 8, 320)   960         ['conv2d_85[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_87 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_87[0][0]'] \n",
      "                                                                                                  \n",
      " activation_88 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_88[0][0]'] \n",
      "                                                                                                  \n",
      " activation_91 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_91[0][0]'] \n",
      "                                                                                                  \n",
      " activation_92 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_92[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_93 (BatchN  (None, 8, 8, 192)   576         ['conv2d_93[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_85 (Activation)     (None, 8, 8, 320)    0           ['batch_normalization_85[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9_1 (Concatenate)         (None, 8, 8, 768)    0           ['activation_87[0][0]',          \n",
      "                                                                  'activation_88[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 8, 8, 768)    0           ['activation_91[0][0]',          \n",
      "                                                                  'activation_92[0][0]']          \n",
      "                                                                                                  \n",
      " activation_93 (Activation)     (None, 8, 8, 192)    0           ['batch_normalization_93[0][0]'] \n",
      "                                                                                                  \n",
      " mixed10 (Concatenate)          (None, 8, 8, 2048)   0           ['activation_85[0][0]',          \n",
      "                                                                  'mixed9_1[0][0]',               \n",
      "                                                                  'concatenate_1[0][0]',          \n",
      "                                                                  'activation_93[0][0]']          \n",
      "                                                                                                  \n",
      " avg_pool (GlobalAveragePooling  (None, 2048)        0           ['mixed10[0][0]']                \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " predictions (Dense)            (None, 1000)         2049000     ['avg_pool[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,851,784\n",
      "Trainable params: 23,817,352\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# 下载Google_Inception net-v3深度神经网络模型\n",
    "model = InceptionV3(weights='imagenet', include_top=True)\n",
    "\n",
    "# 查看模型架构\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af9a7ae-b94e-47d4-b0c2-cbab47310c1e",
   "metadata": {},
   "source": [
    "## 读取图片并做预处理操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b3ca872-da99-45c5-9f11-3a2f4cd37237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdd705fb110>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.utils import image_utils\n",
    "img_path = \"./mug.jpg\"\n",
    "img = image_utils.load_img(img_path, target_size=(299, 299)) # 导入图片\n",
    "Xi = image_utils.img_to_array(img) # 将图片转化为 array 数据\n",
    "Xi = np.expand_dims(Xi, axis=0) # shape=(1, 299, 299, 3)，相当于batch_size=1\n",
    "Xi = keras.applications.inception_v3.preprocess_input(Xi) # 对图片进行预处理\n",
    "\n",
    "plt.imshow((Xi[0]/2 + 0.5)) # Xi[0]相当于读取整个图片，即(299,299,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81ddc3a-c0a1-46f0-b7da-1e6d5153bbad",
   "metadata": {},
   "source": [
    "## 预测图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f48cc84-6095-4108-bb0d-80d9ef9f1ed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 2s 2s/step\n",
      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json\n",
      "35363/35363 [==============================] - 0s 2us/step\n",
      "('n03063599', 'coffee_mug', 0.7765415)\n",
      "('n07930864', 'cup', 0.049692083)\n",
      "('n03063689', 'coffeepot', 0.011346712)\n",
      "('n04560804', 'water_jug', 0.007289998)\n",
      "('n04579145', 'whiskey_jug', 0.0034664476)\n"
     ]
    }
   ],
   "source": [
    "yhat = model.predict(Xi)\n",
    "for x in decode_predictions(yhat)[0]:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "df9332b5-59f4-4b9b-84db-51f0e37795fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([504, 968, 505, 899, 901])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_pred_classes = yhat[0].argsort()[-5:][::-1]\n",
    "top_pred_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40976ae2-eecb-471e-a7a3-b60d7c707381",
   "metadata": {},
   "source": [
    "# LIME explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fce236d-742d-4b4c-a7f7-203e1106bf1a",
   "metadata": {},
   "source": [
    "## Step1：建立超像素（Super Pixel）产生多个扰动样本\n",
    "使用快速移位分割（quickshift）演算法生成超像素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69a116d5-5ab8-4822-8f2a-39ed87ac3ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import skimage.segmentation as segmentation\n",
    "# 将原始图片分块，superpixels.shape=(299,299)\n",
    "# 在指定位置标记上所属块\n",
    "superpixels = segmentation.quickshift(Xi[0], kernel_size=4, max_dist=200, ratio=0.2)\n",
    "num_superpixels = np.unique(superpixels).shape[0]\n",
    "num_superpixels\n",
    "'''\n",
    "np.unique(superpixels)\n",
    "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
    "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
    "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
    "       51])\n",
    "       \n",
    "superpixels\n",
    "array([[ 0,  0,  0, ...,  4,  4,  4],\n",
    "       [ 0,  0,  0, ...,  4,  4,  4],\n",
    "       [ 0,  0,  0, ...,  4,  4,  4],\n",
    "       ...,\n",
    "       [46, 46, 46, ..., 48, 48, 48],\n",
    "       [46, 46, 46, ..., 48, 48, 48],\n",
    "       [46, 46, 46, ..., 48, 48, 48]])\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e109a4c-f213-40da-8792-f73766d2349d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdbc3e69e10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(segmentation.mark_boundaries(Xi[0]/2 + 0.5, superpixels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30da1345-8344-4ad1-b815-1055d9abd7f8",
   "metadata": {},
   "source": [
    "产生多个随机的扰动样本，对于每个超像素指派{0，1}：0表示灰色；1表示原本色彩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48384ee4-20c0-4d50-beb9-b3905670f57a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 0, 1, 0, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_perturb = 150\n",
    "perturbations = np.random.binomial(1, 0.5, size=(num_perturb, num_superpixels)) # shape=(150, 52)\n",
    "perturbations[0] # 显示第一个扰动样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c2eb353b-1393-47f6-bfdf-728331fe4bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def perturb_image(img, perturbation, segments):\n",
    "    active_pixels = np.where(perturbation == 1)[0] # 指定perturbation值为1时的下标\n",
    "    mask = np.zeros(segments.shape) # (299,299) 全0\n",
    "    for active in active_pixels:\n",
    "        mask[segments == active] = 1 # 根据perturbation值为1所对应的区块，将mask对应的区块值设置为1\n",
    "    perturbed_image = copy.deepcopy(img)\n",
    "    perturbed_image = perturbed_image * mask[:, :, np.newaxis] # (299,299,3) * (299, 299, 1)\n",
    "    '''\n",
    "    以深度为0时为例\n",
    "    （299, 3) * (299, 1)表示前者（共有三列）每列都与后者（只有一列）的列相乘\n",
    "    '''\n",
    "    return perturbed_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "17c0a561-8707-46ab-ba13-f9942b19ce53",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  7,  8, 10, 15, 17, 20, 22, 23, 24, 25, 28,\n",
       "       29, 30, 31, 32, 35, 36, 37, 38, 40, 41, 43, 44, 46, 48, 49, 51])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_pixels = np.where(perturbations[0] == 1)[0]\n",
    "active_pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "108f4d4b-1e33-41b8-9e6d-6fa6b11b7ce5",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask[superpixels == active_pixels[0]] = 1\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "334de5c9-ea68-443b-a8d5-8a2d6f2c532b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True, ..., False, False, False],\n",
       "       [ True,  True,  True, ..., False, False, False],\n",
       "       [ True,  True,  True, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "superpixels == active_pixels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f48072b8-97c6-4c6b-a4a4-b5cf4b967402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x432 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 10, figsize=(30, 6),\n",
    "                      subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                      gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "for i in range(10):\n",
    "    ax[0, i].imshow(perturb_image(Xi[0]/2+0.5, perturbations[i], superpixels))\n",
    "    ax[1, i].imshow(perturb_image(Xi[0]/2+0.5, perturbations[i+9], superpixels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd6c457-f013-476f-bf47-d06434f9a2f5",
   "metadata": {},
   "source": [
    "## Step2：使用ML分类器预测新生成的图像类别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "38258726-2ad3-44b4-b22b-c0c372e3a8dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "(150, 1, 1000)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = []\n",
    "# f(z)\n",
    "for pert in perturbations:\n",
    "    perturbed_img = perturb_image(Xi[0], pert, superpixels)\n",
    "    pred = model.predict(np.expand_dims(perturbed_img, axis=0))\n",
    "    predictions.append(pred)\n",
    "    \n",
    "predictions = np.array(predictions)\n",
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "15f101c9-832f-42ae-89e7-fe15a5a56963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[4.25656835e-05, 8.53286037e-05, 1.59012896e-04, ...,\n",
       "         1.29462263e-04, 2.56847445e-04, 2.71262339e-04]],\n",
       "\n",
       "       [[1.16763695e-04, 2.86074763e-04, 1.54633366e-04, ...,\n",
       "         2.04728189e-04, 1.21867095e-04, 9.35770629e-04]],\n",
       "\n",
       "       [[1.65394595e-05, 3.96391879e-05, 4.38127317e-05, ...,\n",
       "         9.69403482e-05, 4.78674592e-05, 3.19203013e-03]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[9.83123391e-05, 1.60723604e-04, 2.46274285e-04, ...,\n",
       "         9.15266864e-05, 1.90243867e-04, 1.77041418e-03]],\n",
       "\n",
       "       [[8.58040767e-06, 2.80533222e-05, 7.87107729e-06, ...,\n",
       "         5.81965332e-06, 4.61912123e-06, 1.38938343e-02]],\n",
       "\n",
       "       [[1.49394575e-04, 1.89053462e-04, 1.05580191e-04, ...,\n",
       "         5.51764060e-05, 7.05753991e-05, 1.42338127e-03]]], dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01f0517-a198-4ebb-b656-1725050ce345",
   "metadata": {},
   "source": [
    "## Step3：计算原始图像与新生成图像之间的距离，并计算每个新生成图像的权重（相似性）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "66a2abc2-77b1-49ee-9f37-af0fc1a32d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "original_image = np.expand_dims(np.ones(num_superpixels), axis=0) # 原始图片每个超像素块x'=1\n",
    "original_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5e4444b0-10a5-453a-b092-4ffb7bfcb556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances = metrics.pairwise_distances(perturbations, original_image, metric='cosine').ravel()\n",
    "distances.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06ade0e-7519-4a9e-bb0c-41c89e473ddf",
   "metadata": {},
   "source": [
    "使用核函数计算权重$\\pi_x(z)=exp(\\frac{-D(x,z)^2}{\\sigma^2})$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0b9406aa-d509-4302-b629-75e11dd71326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_width = 0.25\n",
    "weights = np.sqrt(np.exp(-(distances**2)/kernel_width**2)) # kernel function\n",
    "weights.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a23532-bc34-4cc5-a58b-983a1de33475",
   "metadata": {},
   "source": [
    "## Step4：使用`perturbations`，`predictions`和`weights`去拟合可解释模型（线性）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "05db5f7b-470c-4f5d-90ee-6f45b4febcd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01979167, -0.0132694 , -0.00960429,  0.02001737,  0.00446463,\n",
       "       -0.01788187,  0.01111496, -0.05951688,  0.05674493,  0.02980495,\n",
       "       -0.01471047,  0.03107395,  0.09950637,  0.02866331, -0.00258388,\n",
       "       -0.01708884,  0.0408538 ,  0.00902899,  0.04394271,  0.01965968,\n",
       "        0.01167358,  0.02955862, -0.0074612 ,  0.18335746,  0.09029392,\n",
       "        0.06135452,  0.02953861, -0.05636378,  0.03548036,  0.04555296,\n",
       "       -0.09432192,  0.00202529,  0.21826787,  0.02815464, -0.04952745,\n",
       "        0.01682952,  0.00400754, -0.02832042, -0.04664622,  0.00301374,\n",
       "        0.14174523,  0.15032692,  0.01291304, -0.0156231 , -0.03365997,\n",
       "       -0.03651895,  0.02647397,  0.02110183,  0.04489331, -0.08245459,\n",
       "       -0.14259657, -0.06966448])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "class_to_explain = top_pred_classes[0]\n",
    "simpler_model = LinearRegression()\n",
    "simpler_model.fit(X=perturbations, y=predictions[:,:,class_to_explain], sample_weight=weights)\n",
    "coeff = simpler_model.coef_[0]\n",
    "coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b763b0d1-06b6-417e-944e-ff05eaf9f320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 52)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simpler_model.coef_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "05142b27-ab5d-48e8-b136-20445f0a7c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[:,:,class_to_explain].shape # 取所有深度，所有行，class_to_explain列的数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ecf01da-8143-486c-ab12-2e2be3fad177",
   "metadata": {},
   "source": [
    "计算top features（superpixels）：根据系数由大至小排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9e49f5da-018d-44f2-94ab-4b0f41e4c0f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48, 29,  8, 25, 24, 12, 40, 41, 23, 32])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_top_features = 10\n",
    "top_features = np.argsort(coeff)[-num_top_features:]\n",
    "top_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ac37c3-d69b-4714-897a-df1f9ef3fcca",
   "metadata": {},
   "source": [
    "显示LIME explanation（image with top features）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "388a1124-ebf8-4cec-884f-35aa73e208c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdbc2832850>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import skimage\n",
    "mask = np.zeros(num_superpixels)\n",
    "mask[top_features] = True\n",
    "skimage.io.imshow(perturb_image(Xi[0]/2+0.5, mask, superpixels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "868618c5-112f-4afe-b038-e73b7237aced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 30, 49, 51,  7, 27, 34, 38, 45, 44, 37,  5, 15, 43, 10,  1,  2,\n",
       "       22, 14, 31, 39, 36,  4, 17,  6, 20, 42, 35, 19,  0,  3, 47, 46, 33,\n",
       "       13, 26, 21,  9, 11, 28, 16, 18, 48, 29,  8, 25, 24, 12, 40, 41, 23,\n",
       "       32])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argsort(coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d89e3678-88c8-4d50-bc20-d0ee995c507b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48, 29,  8, 25, 24, 12, 40, 41, 23, 32])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argsort(coeff)[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9494382-15fc-4b3a-b525-e257da3b2f50",
   "metadata": {},
   "source": [
    "# LIME套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e1d3295e-7037-4103-aed9-7c9b0e497f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立解释器\n",
    "from lime import lime_image\n",
    "explainer = lime_image.LimeImageExplainer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dbeb33ce-4e15-49d5-b4d7-879ed12fadc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义图片分割方式\n",
    "def segment_fn(image):\n",
    "    return segmentation.quickshift(image, kernel_size=4, max_dist=200, ratio=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "373aa7f9-737e-45d1-a507-0ea3eea2868e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6eaad572fa564499bfb1e6dd9339e828",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n",
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      "1/1 [==============================] - 1s 1s/step\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 针对被解释实例训练解释器\n",
    "explanation = explainer.explain_instance(Xi[0].astype('double'), model.predict, top_labels=5, num_samples=1000, segmentation_fn=segment_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "009ede50-cbf7-491b-9507-c1a99000b30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdbc20089d0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 进行解释\n",
    "from skimage.segmentation import mark_boundaries\n",
    "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=10, hide_rest=True)\n",
    "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "583750db-ce60-4dc3-9c12-e12fed749028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdbc1d908d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=10, hide_rest=False)\n",
    "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "385595b1-67e4-403f-ba50-0fd5ca00849f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdbc1d89550>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=10, hide_rest=False)\n",
    "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60cc6e94-31ad-466f-a3af-8540890e1a55",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}