explainmodel.ipynb

{
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  "metadata": {
    "colab": {
      "name": "working shap.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8ubWtno3ttb",
        "colab_type": "code",
        "outputId": "4361c543-950c-4381-9134-d0c9680ff672",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "source": [
        "!pip install shap"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting shap\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/57/43/08f152a59a1d60f0328b476bdd58c791498989981ab9c6d595ec5448a86a/shap-0.32.1.tar.gz (259kB)\n",
            "\r\u001b[K     |█▎                              | 10kB 13.8MB/s eta 0:00:01\r\u001b[K     |██▌                             | 20kB 3.1MB/s eta 0:00:01\r\u001b[K     |███▉                            | 30kB 4.5MB/s eta 0:00:01\r\u001b[K     |█████                           | 40kB 3.1MB/s eta 0:00:01\r\u001b[K     |██████▎                         | 51kB 3.9MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 61kB 4.6MB/s eta 0:00:01\r\u001b[K     |████████▉                       | 71kB 5.3MB/s eta 0:00:01\r\u001b[K     |██████████▏                     | 81kB 5.9MB/s eta 0:00:01\r\u001b[K     |███████████▍                    | 92kB 6.6MB/s eta 0:00:01\r\u001b[K     |████████████▋                   | 102kB 5.1MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 112kB 5.1MB/s eta 0:00:01\r\u001b[K     |███████████████▏                | 122kB 5.1MB/s eta 0:00:01\r\u001b[K     |████████████████▍               | 133kB 5.1MB/s eta 0:00:01\r\u001b[K     |█████████████████▊              | 143kB 5.1MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 153kB 5.1MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 163kB 5.1MB/s eta 0:00:01\r\u001b[K     |█████████████████████▌          | 174kB 5.1MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 184kB 5.1MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 194kB 5.1MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▎      | 204kB 5.1MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▌     | 215kB 5.1MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▉    | 225kB 5.1MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 235kB 5.1MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▍ | 245kB 5.1MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▋| 256kB 5.1MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 266kB 5.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from shap) (1.17.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from shap) (1.3.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from shap) (0.21.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from shap) (0.25.3)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.6/dist-packages (from shap) (4.28.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->shap) (0.14.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->shap) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->shap) (2.6.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->shap) (1.12.0)\n",
            "Building wheels for collected packages: shap\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shap: filename=shap-0.32.1-cp36-cp36m-linux_x86_64.whl size=376823 sha256=a75a52eba70e549bee6cb2b4adeb93cff5a6d28ea10b241aa9dd5104a6d81b89\n",
            "  Stored in directory: /root/.cache/pip/wheels/8e/b2/50/8fadb5a59789cb5bdeb01b800223be540651ae92915172050b\n",
            "Successfully built shap\n",
            "Installing collected packages: shap\n",
            "Successfully installed shap-0.32.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S1wn7fji3vdM",
        "colab_type": "code",
        "outputId": "efbd67ae-a1b8-4a73-f174-245fcfbc6ae5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mtf33yuL30IX",
        "colab_type": "code",
        "outputId": "d085e497-db53-43ed-cc21-d116f0d49794",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        }
      },
      "source": [
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "\n",
        "#import data\n",
        "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-3-38dc9437001a>:4: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please write your own downloading logic.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use urllib or similar directly.\n",
            "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
            "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.one_hot on tensors.\n",
            "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
            "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
            "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
            "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IVn5yHn39IW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import modules\n",
        "import sys\n",
        "import tensorflow as tf\n",
        "from PIL import Image,ImageFilter\n",
        "import shap"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeauJyty4Awb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def imageprepare(argv):\n",
        "    \"\"\"\n",
        "    This function returns the pixel values.\n",
        "    The imput is a png file location.\n",
        "    \"\"\"\n",
        "    im = Image.open(argv).convert('L')\n",
        "    width = float(im.size[0])\n",
        "    height = float(im.size[1])\n",
        "    newImage = Image.new('L', (28, 28), (255)) #creates white canvas of 28x28 pixels\n",
        "    \n",
        "    if width > height: #check which dimension is bigger\n",
        "        #Width is bigger. Width becomes 20 pixels.\n",
        "        nheight = int(round((20.0/width*height),0)) #resize height according to ratio width\n",
        "        if (nheight == 0): #rare case but minimum is 1 pixel\n",
        "            nheight = 1\n",
        "        # resize and sharpen\n",
        "        img = im.resize((20,nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\n",
        "        wtop = int(round(((28 - nheight)/2),0)) #caculate horizontal pozition\n",
        "        newImage.paste(img, (4, wtop)) #paste resized image on white canvas\n",
        "    else:\n",
        "        #Height is bigger. Heigth becomes 20 pixels. \n",
        "        nwidth = int(round((20.0/height*width),0)) #resize width according to ratio height\n",
        "        if (nwidth == 0): #rare case but minimum is 1 pixel\n",
        "            nwidth = 1\n",
        "         # resize and sharpen\n",
        "        img = im.resize((nwidth,20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\n",
        "        wleft = int(round(((28 - nwidth)/2),0)) #caculate vertical pozition\n",
        "        newImage.paste(img, (wleft, 4)) #paste resized image on white canvas\n",
        "    \n",
        "    #newImage.save(\"sample.png\")\n",
        "\n",
        "    tv = list(newImage.getdata()) #get pixel values\n",
        "    \n",
        "    #normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\n",
        "    tva = [ (255-x)*1.0/255.0 for x in tv] \n",
        "    i =np.asarray(tva) \n",
        "    i=i.reshape(1,len(tva))\n",
        "    return i\n",
        "    #print(tva)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBWi4Wi4G6Z_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predictint_layerwise(imvalue):\n",
        "    \"\"\"\n",
        "    This function returns the predicted integer.\n",
        "    The input is the pixel values from the imageprepare() function.\n",
        "    \"\"\"\n",
        "    \n",
        "    # Define the model (same as when creating the model file)\n",
        "    x = tf.placeholder(tf.float32, [None, 784])\n",
        "    W = tf.Variable(tf.zeros([784, 10]))\n",
        "    b = tf.Variable(tf.zeros([10]))\n",
        "    y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
        "\n",
        "    init_op = tf.global_variables_initializer()\n",
        "    saver = tf.train.Saver()\n",
        "    \n",
        "    \"\"\"\n",
        "    Load the model.ckpt file\n",
        "    file is stored in the same directory as this python script is started\n",
        "    Use the model to predict the integer. Integer is returend as list.\n",
        "    Based on the documentatoin at\n",
        "    https://www.tensorflow.org/versions/master/how_tos/variables/index.html\n",
        "    \"\"\"\n",
        "    with tf.Session() as sess:\n",
        "        sess.run(init_op)\n",
        "        saver.restore(sess, \"/content/drive/My Drive/model2/model.ckpt\")  \n",
        "        ops_list = tf.get_default_graph().get_operations()\n",
        "        tensors_list2 = [tensor for op in ops_list for tensor in op.inputs]\n",
        "        print(tensors_list2)\n",
        "        tensors_list1 = [tensor for op in ops_list for tensor in op.outputs]\n",
        "        print(tensors_list1)\n",
        "        to_explain = mnist.test.images[1:3]\n",
        "        IN = tf.get_default_graph().get_tensor_by_name('Placeholder:0')\n",
        "        OUT = tf.get_default_graph().get_tensor_by_name('Softmax:0')\n",
        "        tensor_dict = {}\n",
        "        tensor_dict['IN'] = IN\n",
        "        tensor_dict['OUT'] = OUT\n",
        "        #only_image = sess.run(tensor_dict,feed_dict={x: [imvalue]})\n",
        "        #new_dict = sess.run(tensor_dict, feed_dict={x: mnist.test.images})\n",
        "        #print(new_dict)\n",
        "        e = shap.GradientExplainer((IN, OUT),mnist.test.images[0:2000],sess)\n",
        "        shap_values = e.shap_values([imvalue],nsamples=200, ranked_outputs=2)\n",
        "        #shap_values, indexes = e.shap_values(new_dict, ranked_outputs=2)       \n",
        "        return shap_values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apVGyiTfq-ZX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xSOJY2aoq-IU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predictint_layerwise(imvalue,model_path):\n",
        "    \"\"\"\n",
        "    This function returns the predicted integer.\n",
        "    The input is the pixel values from the imageprepare() function.\n",
        "    \"\"\"\n",
        "    tf.reset_default_graph()\n",
        "    # Define the model (same as when creating the model file)\n",
        "    x = tf.placeholder(tf.float32, [None, 784])\n",
        "    W = tf.Variable(tf.zeros([784, 10]))\n",
        "    b = tf.Variable(tf.zeros([10]))\n",
        "    y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
        "\n",
        "    init_op = tf.global_variables_initializer()\n",
        "    saver = tf.train.Saver()\n",
        "    \n",
        "    \"\"\"\n",
        "    Load the model.ckpt file\n",
        "    file is stored in the same directory as this python script is started\n",
        "    Use the model to predict the integer. Integer is returend as list.\n",
        "    Based on the documentatoin at\n",
        "    https://www.tensorflow.org/versions/master/how_tos/variables/index.html\n",
        "    \"\"\"\n",
        "    with tf.Session() as sess:\n",
        "        sess.run(init_op)\n",
        "        saver.restore(sess, model_path)\n",
        "        IN = tf.get_default_graph().get_tensor_by_name('Placeholder:0')\n",
        "        OUT = tf.get_default_graph().get_tensor_by_name('Softmax:0')\n",
        "        e = shap.GradientExplainer((IN, OUT),mnist.test.images[0:2000],sess)\n",
        "        shap_values = e.shap_values(imvalue,nsamples=200, ranked_outputs=2)\n",
        "        return shap_values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rICDckq14Kms",
        "colab_type": "code",
        "outputId": "15b32bee-5ac7-482c-d488-67d0d65aa57d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "imvalue = imageprepare(\"/content/1.png\")\n",
        "shap_values=predictint_layerwise(imvalue,\"/content/drive/My Drive/model2/model.ckpt\")\n",
        "reshaped_value=[]\n",
        "for x in shap_values[0]:\n",
        "    reshaped_value.append(x.reshape(x.shape[0], 28, 28,1))\n",
        "img_reshaped = imvalue.reshape(1,28,28,1)\n",
        "shap.image_plot(reshaped_value,img_reshaped,shap_values[1])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from /content/drive/My Drive/model2/model.ckpt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Al7oBe1dGOjKKTvpQ1ci/3WQKiZZ3+oPTvRjbZS2OfX3uUjVmp297U11veZFT\nrgoAn0q3Wl/4vhheoAoIFxgvpe7bxKwFgN0mqQoIG50S1S77D+l9Oy3zWveNjV6vWqZbNUu/QXQ1\nT8hKVxYVEuUpV+Jqiw8ns437/cWGHR3tqHFrfLGQG0IypF8fZjbMRCTtf31rampGurxcTVs9W73H\n45R6deIBAKBku35PLrcss75aJ9QA0NanXwvFklS75eVobPSvV9F0YMeOHXqhgyUqsa32SrBgwQJ/\nrDyTCDTtCZyUMBRmYpunwxnH8cZ5vnhbayu8Qf84dl0XiOgPJzMbZiJZ5x+L7e3t1uva1LvqxAO8\nb/ES6/qWva2TwGLoHHhuncobAADbO9QlOVDk6XbIZU4ZGmb6892yaBrNzeokBTSEXDWOpbwaCxcu\n9MVCg5kPnLYPaYUcx/zKgIiIiJgQEBERERMCIiIiwgSpIchVFGgrouvIXlZ2pFyNHGwNOGKxWF73\nHw6HVSzX/TuOzquKiuyX5mxvb7fGg1pa9PdTub5bSqVUHQ327lVNvNDc3GydX12Ck0Yt2LUtnH1K\nd0WTvm59LRJHS0W1b1o3FIJUzSkNLjNZWwuvr8U3iJMVKZz01m9VB8JbkiddGoxFE9YGbYh6xTOC\nsT0vr3okGHvhtDmq6AsAUHHGrGBIXMsOEyl1vZB/PzBu1EHYUusY6JoHAFtCyaQTmNRNZusfhqxN\nGOkoCXft8BUQem170LC/xVcNHXJDiItRL4ZVSQ/dbWt885umJrRZXrfbw93qxWw+atR0ALBGelUx\nzFZ3UHXH/FxykS6wARA2apdDJUJqcNaZSLQy5W+gWOQ5boWn53/H9A0kA/XBnb37UqHBKjXtRMQj\nBERERDQxjhAQTWahUAihQG49fHpsvfF/Muju7sF+z7/bCYCQp852QjKZgBfb7Yt1dXcj1qM+BCFR\nrKu7cx05C6X1EaHeQf2Je+8+XYUNAPGQvv9IQp1thXhfH7q6/JXgPa2t2G85ypbubtNHutwwKvb5\nq84le1aZ7WgXj3SNje1I6fBRWqnxn1nS2dmJov3+o60igj6jj1YODSXQKf7xEYvFrNdC8EI6luto\nbVWXPssg4ejH0G/U2ZEAgMGBQRUrEn1f/V4/inr8+8eAeOiznJXRke5Q4ziGhPWILTDxxjGPEBAR\nERETAiIiImJCQERERJggNQS2KnnAXtFvk+sSnbYzCvK9/+3bt+c1L2D/zmfnzp15z2+Tq01xvmwt\nnunoSQRa7yYkMyafk07fF5gd6Q6g1/89pYgg3d2iBmFJdTUixl8H4HZ1YkmoURUcrPa61IDZWAzr\naSkfGSw/Ixj7sDtL9brui/AM62sAAA/BSURBVHWtts3/q8Hn9Y7p6cKE/tW/7nQCHdrESyHdrc+g\nsUonD9QMUGGZ2A5fEUCqowN9iPg+UAoEMSTUODCSRKMp8r3XlEkJdpu4HvOWt6SGdMT6PrXQqSwJ\nxv7d263agl9rmxlAmYTUaTjbvD71phGG44Tg3w+fi/Z0NnutapkxJLwUJu+YnRAJAdFkZkso801m\ngWxCa0lqcxVeWZfh6fnb2nKc4hrWbYptKfW+Vv2CBwCYr9vL5qIS0+B1IEY7Px01R2IcG8tIisVi\nKBP13p33OuzevdsyJbDQnaZitg9nuVo1l0n+b3/BD5e7U/Z1SuVIYCfLOOZXBkRERMSEgIiIiJgQ\nEBEREQDJVZAHACKS+59Eo2SMsXfKOYpExH4N4AmmJlCgNWyFW6u+fF3klKvYAqdMfbFfAtfa0WWa\nE63MZ536TMp6bfsh6C9Kb0ls2ByM2a4NP9kZY9TjHA+TZRznYquFCRbiAvb9wLYPAMAyt1IV1zZ5\ncdVtaJPXax3HcaTUCmytna6m8/o79PW+c/DiXZOiO1auccwjBERERMSEgIiIiJgQEBEREZgQEBER\nEdiYiGhCsHV4A4BV6VZ1zfe9ZlAV67WaQXW5w+lSFLEts9ZE1DJ7kFLLjJuUtZgqAd39pQT6MnW2\nArE+SyFXdpm2ME1ktprVHI2nbNvXKalS4yNmKcp7HB1qvALAEyWOKiAMz1haFIx5Az3WD76hqlnF\nap26moeCMQkXq/m9eMxeaDhJigpz4RECIiIiYkJARERETAiIiIgITAiIiIgI7FRI44idCo8eWye4\nMkuhHwCUiT0e1GfsBYDWaS3FgsdioSA7FR5llkLFUM1ca8fNfKViu/LvmDnKq3FOVuxUSERERDkx\nISAiIiImBERERMSEgIiIiMCEgIiIiMDWxUTHBFtFf652yDFjjxMVnKXKP9W+dWqU/k8APEJARERE\nTAiIiIiICQERERGBCQERERHhXVoXExER0dTAIwRERETEhICIiIiYEBARERGYEBARERGYEBARERGY\nEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBAR\nERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGY\nEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBAR\nERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGYEBARERGA0KH+OfeWFjP8uyOAZ4CQA6Q8\nIOICiTQQDQFDqcw0pRFBf8KgPCLoTRhURAW9QwaVRYLuQYPqYgedAx5qSxzsj3uoK83c1pc6aO/3\nML3MQVufhxnlLvb1pjGzwsXe3jRmVbho6UmjsdJFU3cac6pc7OlKY05VCLu7U5hXFcKurhTmV4ew\nszOFBTUhbO9MYWFNCNtjKRxXG8a2/Sksqgthy/4UltSFsLkjhSX1IWxuT2HptBA2tadw/LQwNrYl\nAQFOmB7G+tYkTpwRxrrWJE6aHsY7rUmcPCOCt1sTmdt9SZwyM4y1+5I4dWYYa/YmcVpDBKv3JrC8\nIYK3WhJYPitze/qI2zebEzijMYo3mhM4ozGCN5oTOLMxgtebEjhrdva2MYLXmhI4a3YUrzcN4ew5\nUbzWlMDZsyN4dU8CZ8/J3J5z4DaKV/YMYcXszO3w344IVsyJ4JU9CayYE8XLe4bwnuztijlRvLJ7\nCCvmRvHy7iG8Z24UL+9OZG8z8Vdy3o64T+tt5v+vZtflwG1TAufMHn5skexjyjzGzGM9+BycOfvg\nc/NmcwKOCM6YFcGbgedzdfZ5zjzvUazZm8BpDRGs3ZvAKQ0RvL0vgVNmHtxew9txXXb7Dm/nDW1J\nnDA9MwaWTQ9jY3sSx9dHsLkjiaX14QO3W/YnsaQujK3ZMTU8xnbEUlhYe3AM7upMYV5NCLu7UphX\nHcKe7hTmVoXR3J2C6wgaKzPju6EiM94bKly09aUxoyyE9v40ppe56IinUV/qIpbdX4b3n+5BD9XF\nLnqGPFQWOegb8lBR5CCe8FAWdTCQNCiNCIZSBkVhB4m0QVFIkPIMIq7AMwYhR7L7thzc6T0vc5tK\nA44DJFJAyAUGE0AkBMSHgKII0D8IFEeB3oHMtOXFQFcfUFEKxHqB6lKgoweoKQfau4HaCqC1C6iv\nAPZ1AtOqgJYYMKMaaN4PzKwG9nQAs2qB3e1AYx2wqw2YXQfsaAXmTQO27wPmTQe27QXmzwC2tAAL\nZwCbW4DjZgKbmoFFDcDGJmBxA7BhD7CkEVi/B1jaCKzbDSxrBN7ZBSybA7y9EzhhLrB2J3DiHGDN\nDuCkeZkXure2A6fMA97cBpy6AHhjG3DaAuCNrZnb17cCpy8EXtsCnH4c8OoW4IzjgFc3Z25f2Qyc\nuQh4eRNw1uLs7SLgpU3A2YuBlzYCZy8B/rgRWLE4c3vOEuDFDcCKpdnbJcALG4D3LAVeWA+85/js\n7Yi/n18PrMzenns88Py67N/rgJXLgOfWZR7Pucdnfj93WeB2RHx4nufX2ac/sOz1/ttc6/bicHwD\n8J7sYztnycHHOnz70sbsc5J9boafs1eyz9nwc/nKZsAg8/y+tiXz/L++FVi+MLtdFh7cPsPb7a3t\nwKnzD27P1TuAk+cBa3YCJ83NjoE5B8fEut3A8bOB9bszY2ZDE7BkVmZMLZl1cIxtbs6MuS17M2Nw\neExu35cZqztagbnTsmO4Pjuma4Gm7Bhv2p/Zz2ZUAXs7gelV2f2j8uD+0tED1JRl9qeqssz+VVkK\ndPUDFcVAzwBQVgT0DQIl0ey+GQYGk5l9dXjfTaUPvoEDQM/9I3Z4Px4hICIiIiYERERExISAiIiI\nwISAiIiIwISAiIiIwISAiIiIwISAiIiIwISAiIiIwISAiIiIwISAiIiIAIgx5t2nmgRE5BpjzJ2F\nXg+y4/aZ2Lh9Jj5uo4ntWNg+x9IRgmsKvQJ0SNw+Exu3z8THbTSxTfrtcywlBERERHSYmBAQERHR\nMZUQTOrvbqYAbp+Jjdtn4uM2mtgm/fY5ZooKiYiI6PAdS0cIiIiI6DBNmoRARGpEZJWIbMneVueY\n7ursNFtE5OpsrEREHheRjSKyTkR+OL5rPzWMZRtl4/8gIntEpG/81vrYJyJ/IiKbRGSriHzD8v+o\niDyQ/f8rIjJvxP9uzMY3icjF47neU8Xhbh8RqRWRZ0SkT0RuH+/1nkrGsI0+ICJviMjb2dsLxnvd\nR8UYMyl+APwIwDeyv38DwC2WaWoAbM/eVmd/rwZQAuD87DQRAM8DuKTQj+lY+xnLNsr+7xwAMwH0\nFfqxHCs/AFwA2wAsyI79NQCWBab5KwD/mv394wAeyP6+LDt9FMD87HLcQj+mY+lnjNunFMC5AK4D\ncHuhH8ux+jPGbXQagIbs7ycCaC704znUz6Q5QgDgCgD3Zn+/F8CHLdNcDGCVMSZmjOkEsArAnxhj\n4saYZwDAGJMA8CaAxnFY56nmsLcRABhjXjbG7B2XNZ06zgKw1RizPTv270dmO400crv9CsCFIiLZ\n+P3GmCFjzA4AW7PLoyPnsLePMabfGPMCgMHxW90paSzb6C1jTEs2vg5AsYhEx2WtD8NkSgimj3iz\n2AdgumWaWQD2jPi7KRs7QESqAHwIwFNHYyWnuCOyjeiIyuf5PjCNMSYFoBtAbZ7z0tiMZfvQ+DhS\n2+ijAN40xgwdpfUcs1ChV2AkEXkSwAzLv7458g9jjBGRUZ8eISIhAPcB+JkxZvvhreXUdrS3ERHR\nsUZETgBwC4APFnpdDmVCJQTGmIty/U9EWkVkpjFmr4jMBNBmmawZwHkj/m4E8OyIv+8EsMUY89Mj\nsLpT0jhsIzqymgHMHvF3YzZmm6YpmzRXAtif57w0NmPZPjQ+xrSNRKQRwK8BfMYYs+3or+7hm0xf\nGTwKYLgi/WoAj1im+T2AD4pIdbbC/YPZGETkZmQ20l+Pw7pOVWPaRnRUvAZgkYjMF5EIMgVPjwam\nGbndrgTwtMlUQT0K4OPZCur5ABYBeHWc1nuqGMv2ofFx2Nso+xX148gUW784bmt8uApd1ZjvDzLf\nxzwFYAuAJwHUZONnAPh/I6b7PDLFT1sBfC4bawRgAGwAsDr784VCP6Zj7Wcs2ygb/xEy38952dvv\nFvoxHQs/AC4FsBmZSulvZmPfA/Cn2d+LAPxXdnu8CmDBiHm/mZ1vE3hmzkTcPjsBxAD0ZfeZZeO9\n/lPh53C3EYCbAPSPeN9ZDWBaoR9Prh92KiQiIqJJ9ZUBERERHSVMCIiIiIgJARERETEhICIiIjAh\nICIiIjAhIJqwROSb2atzrhWR1SJydjb+rIicMWK6eSLyTmDen4pIs4g4I2KfFZH27LLWi8gXj8A6\nnicij411OURUeBOqUyERZYjICgCXA1hujBkSkTpkrrSWz7wOgI8g01v9/QCeGfHvB4wxXxKRaQDW\nicijxpjWI7z6RDQJ8QgB0cQ0E0CHyV4IxRjTYQ5eNe3dnIfMldXuAPAJ2wTGmDZkmqzMHRkXkZez\nfdeH/35WRM4QkbNE5CUReUtE/igiS4LLFJHvisgNI/5+Z8R14a8SkVezRyf+r4i4eT4WIhonTAiI\nJqY/AJgtIptF5F9E5P2B//9n9s11NYDfBv73CWQu4vVrAJeJSDi4cBFZgMz13bcG/vUAgD/PTjMT\nwExjzOsANgJ4rzHmNADfBvCDfB+IiBwP4GMAVhpjTgWQBvCpfOcnovHBhIBoAjLG9AE4HcA1ANoB\nPCAinx0xyaeMMadm32AvHQ5me61fCuBhY0wPgFcAXDxivo9lk4j7AFxrjIkF7vpBZHqxA5nE4FfZ\n3ysB/Fe2VuFWACcgfxdmH8tr2fu+EJlkhIgmENYQEE1Qxpg0MleCfFZE3kbm4in3vMtsFwOoAvC2\niABACYABAMOFfw8YY750iPtsFpH9InIyMp/qr8v+6/sAnjHGfCT7NcCzltlT8H/IKMreCoB7jTE3\nvsu6E1EB8QgB0QQkIktEZNGI0KkAduUx6yeQuXDXPGPMPADzAXxAREpGcfcPAPgagEpjzNpsrBIH\nL/n62Rzz7QSwPLv+y7P3DWQueHVltpARIlIjInOtSyCigmFCQDQxlQG4N3t64FoAywB891AzZN/0\n/wSZy60CAIwx/QBeAPChUdz3r5C5xOuDI2I/AvCPIvIWch9Z/G8ANSKyDsCXkLk6HIwx65G56tsf\nso9lFTJFk0Q0gfBqh0RERMQjBERERMSEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiI\niMCEgIiIiAD8fx3Qd7svx2y6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7Kchl7SMR3D",
        "colab_type": "code",
        "outputId": "a8202dc1-0619-4fa5-8022-755f5ba1e8bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "shap_values[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MnuFmE4JeCTP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "i =np.asarray(shap_values) \n",
        "k=i.reshape(28,28,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gYGlhb75VFu",
        "colab_type": "code",
        "outputId": "5f51521c-ae9f-4d33-fc2f-0ddc6446e414",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "j=[]\n",
        "for x in shap_values[0]:\n",
        "  k = x.reshape(x.shape[0], 28, 28,1)  \n",
        "  j.append(k)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-9337630df712>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mshap_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'float' object is not iterable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBL1OevZD3X3",
        "colab_type": "code",
        "outputId": "a29470c2-3d37-4abd-c1d9-d060b234e5a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "i = mnist.test.images.reshape(mnist.test.images.shape[0], 28, 28,1) \n",
        "print(i.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0MXtMVPCcuO",
        "colab_type": "code",
        "outputId": "9725ca06-9f8f-414a-fe43-b58d16497f49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "source": [
        "shap.image_plot(j,i[1:2],k[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-ee4112f3d03f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/shap/plots/image.py\u001b[0m in \u001b[0;36mimage_plot\u001b[0;34m(shap_values, x, labels, show, width, aspect, hspace, labelpad)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;31m# make sure labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mshap_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Labels must have same row count as shap_values arrays!\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Labels must have a column for each output in shap_values!\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjB1ejS9Deey",
        "colab_type": "code",
        "outputId": "659fe362-eea6-421e-8869-47fa2784c99b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        }
      },
      "source": [
        "# summarize the effects of all the features\n",
        "shap.summary_plot(shap_values,mnist.test.images[1:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/hzJ1dATwR7VmRgO/oARS/wW42/Z/7qmvERER0bkc4jdAcojf4JFD/JpbDvH7UA7x25Vy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\n",
            "text/plain": [
              "<Figure size 576x684 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qI4OTsxs4EOu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def predictint(imvalue):\n",
        "    \"\"\"\n",
        "    This function returns the predicted integer.\n",
        "    The input is the pixel values from the imageprepare() function.\n",
        "    \"\"\"\n",
        "    \n",
        "    # Define the model (same as when creating the model file)\n",
        "    x = tf.placeholder(tf.float32, [None, 784])\n",
        "    W = tf.Variable(tf.zeros([784, 10]))\n",
        "    b = tf.Variable(tf.zeros([10]))\n",
        "    y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
        "\n",
        "    init_op = tf.global_variables_initializer()\n",
        "    saver = tf.train.Saver()\n",
        "    \n",
        "    \"\"\"\n",
        "    Load the model.ckpt file\n",
        "    file is stored in the same directory as this python script is started\n",
        "    Use the model to predict the integer. Integer is returend as list.\n",
        "    Based on the documentatoin at\n",
        "    https://www.tensorflow.org/versions/master/how_tos/variables/index.html\n",
        "    \"\"\"\n",
        "    with tf.Session() as sess:\n",
        "        sess.run(init_op)\n",
        "        saver.restore(sess, \"/content/drive/My Drive/model2/model.ckpt\")\n",
        "        #print (\"Model restored.\")\n",
        "        ops_list = tf.get_default_graph().get_operations()\n",
        "        tensors_list2 = [tensor for op in ops_list for tensor in op.inputs]\n",
        "        print(tensors_list2)\n",
        "        tensors_list1 = [tensor for op in ops_list for tensor in op.outputs]\n",
        "        print(tensors_list1)\n",
        "        prediction=tf.argmax(y,1)\n",
        "        k= prediction.eval(feed_dict={x: [imvalue]}, session=sess)\n",
        "        IN = tf.get_default_graph().get_tensor_by_name('Placeholder:0')\n",
        "        OUT = tf.get_default_graph().get_tensor_by_name('Softmax:0')\n",
        "        e = shap.GradientExplainer((IN,OUT),mnist.test.images[0:2000],sess)\n",
        "        shap_values = e.shap_values(mnist.test.images[1:5])\n",
        "        print(shap_values[0].shape)\n",
        "        # plot the feature attributions\n",
        "        #shap.image_plot(shap_values, -mnist.test.images[1:5])\n",
        "        return k,shap_values"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}