fashion_mnist/shap-fashion_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "init_g = tf.global_variables_initializer()\n",
    "init_l = tf.local_variables_initializer()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init_g)\n",
    "    sess.run(init_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 test samples\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "num_classes = 10\n",
    "epochs = 5\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(_, _), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
    "\n",
    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1).astype('float32')\n",
    "x_test /= 255\n",
    "\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(x_test[i].reshape(img_rows, img_cols), cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[y_test[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:131: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               1179776   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "preds (Dense)                (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,199,882\n",
      "Trainable params: 1,199,882\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model = load_model('fashion_mnist-gs.h5')\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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 random\n",
    "\n",
    "random_idx = random.choice(range(len(x_test)))\n",
    "to_explain_img = x_test[random_idx]\n",
    "to_explain_label = int(y_test[random_idx])\n",
    "\n",
    "plt.figure()\n",
    "plt.title(f'Predicted: {to_explain_label} - {class_names[to_explain_label]}')\n",
    "plt.imshow(to_explain_img.reshape(img_rows, img_cols), cmap=plt.cm.binary)\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SHAP - GRADIENT EXPLAINER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "FailedPreconditionError",
     "evalue": "Attempting to use uninitialized value preds/kernel\n\t [[node preds/kernel/read (defined at /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:402) ]]\n\nOriginal stack trace for 'preds/kernel/read':\n  File \"/usr/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.6/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/traitlets/config/application.py\", line 664, in launch_instance\n    app.start()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 612, in start\n    self.io_loop.start()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 149, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/lib/python3.6/asyncio/base_events.py\", line 438, in run_forever\n    self._run_once()\n  File \"/usr/lib/python3.6/asyncio/base_events.py\", line 1451, in _run_once\n    handle._run()\n  File \"/usr/lib/python3.6/asyncio/events.py\", line 145, in _run\n    self._callback(*self._args)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/ioloop.py\", line 690, in <lambda>\n    lambda f: self._run_callback(functools.partial(callback, future))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/ioloop.py\", line 743, in _run_callback\n    ret = callback()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 787, in inner\n    self.run()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 748, in run\n    yielded = self.gen.send(value)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 365, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 268, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 545, in execute_request\n    user_expressions, allow_stdin,\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 306, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2867, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2895, in _run_cell\n    return runner(coro)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3072, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3263, in run_ast_nodes\n    if (await self.run_code(code, result,  async_=asy)):\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3343, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-3-eeb7f0086b4b>\", line 3, in <module>\n    model = load_model('fashion_mnist-gs.h5')\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/saving.py\", line 419, in load_model\n    model = _deserialize_model(f, custom_objects, compile)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/saving.py\", line 225, in _deserialize_model\n    model = model_from_config(model_config, custom_objects=custom_objects)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/saving.py\", line 458, in model_from_config\n    return deserialize(config, custom_objects=custom_objects)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/layers/__init__.py\", line 55, in deserialize\n    printable_module_name='layer')\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/utils/generic_utils.py\", line 145, in deserialize_keras_object\n    list(custom_objects.items())))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/sequential.py\", line 301, in from_config\n    model.add(layer)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/sequential.py\", line 181, in add\n    output_tensor = layer(self.outputs[0])\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/base_layer.py\", line 431, in __call__\n    self.build(unpack_singleton(input_shapes))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/layers/core.py\", line 866, in build\n    constraint=self.kernel_constraint)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/legacy/interfaces.py\", line 91, in wrapper\n    return func(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/base_layer.py\", line 252, in add_weight\n    constraint=constraint)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\", line 402, in variable\n    v = tf.Variable(value, dtype=tf.as_dtype(dtype), name=name)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 259, in __call__\n    return cls._variable_v1_call(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 220, in _variable_v1_call\n    shape=shape)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 198, in <lambda>\n    previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py\", line 2511, in default_variable_creator\n    shape=shape)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 263, in __call__\n    return super(VariableMetaclass, cls).__call__(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 1568, in __init__\n    shape=shape)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 1755, in _init_from_args\n    self._snapshot = array_ops.identity(self._variable, name=\"read\")\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py\", line 180, in wrapper\n    return target(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py\", line 86, in identity\n    ret = gen_array_ops.identity(input, name=name)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 4253, in identity\n    \"Identity\", input=input, name=name)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py\", line 788, in _apply_op_helper\n    op_def=op_def)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\", line 507, in new_func\n    return func(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 3616, in create_op\n    op_def=op_def)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 2005, in __init__\n    self._traceback = tf_stack.extract_stack()\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFailedPreconditionError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[0;32m~/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1355\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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   1357\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1340\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1341\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1428\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1429\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFailedPreconditionError\u001b[0m: Attempting to use uninitialized value preds/kernel\n\t [[{{node preds/kernel/read}}]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFailedPreconditionError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-88933a0c2d68>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# we explain the model's predictions on the first three samples of the test set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# shap_values = explainer.shap_values([x_test[:3], x_test[:3]])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mshap_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexplainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mto_explain_img\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[0m\u001b[1;32m      9\u001b[0m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mto_explain_label\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_explain_img\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\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;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float'\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~/workspace/xai-genz/venv/lib/python3.6/site-packages/shap/explainers/_gradient.py\u001b[0m in \u001b[0;36mshap_values\u001b[0;34m(self, X, nsamples, ranked_outputs, output_rank_order, rseed, return_variances)\u001b[0m\n\u001b[1;32m    104\u001b[0m         \u001b[0mwere\u001b[0m \u001b[0mchosen\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0;34m\"top\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m         \"\"\"\n\u001b[0;32m--> 106\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnsamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mranked_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_rank_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_variances\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    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/workspace/xai-genz/venv/lib/python3.6/site-packages/shap/explainers/_gradient.py\u001b[0m in \u001b[0;36mshap_values\u001b[0;34m(self, X, nsamples, ranked_outputs, output_rank_order, rseed, return_variances)\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;31m# rank and determine the model outputs that we will explain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\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--> 206\u001b[0;31m             \u001b[0mmodel_output_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\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    207\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m             \u001b[0mmodel_output_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/workspace/xai-genz/venv/lib/python3.6/site-packages/shap/explainers/_gradient.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, out, model_inputs, X)\u001b[0m\n\u001b[1;32m    317\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras_phase_placeholder\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[1;32m    318\u001b[0m                 \u001b[0mfeed_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras_phase_placeholder\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\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    320\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m             \u001b[0;31m# build inputs that are correctly shaped, typed, and tf-wrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 950\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    951\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1171\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\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[1;32m   1172\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1173\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1174\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m       \u001b[0mresults\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~/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1348\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\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[1;32m   1349\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1350\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1368\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1370\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\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   1371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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;31mFailedPreconditionError\u001b[0m: Attempting to use uninitialized value preds/kernel\n\t [[node preds/kernel/read (defined at /home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:402) ]]\n\nOriginal stack trace for 'preds/kernel/read':\n  File \"/usr/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.6/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/traitlets/config/application.py\", line 664, in launch_instance\n    app.start()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 612, in start\n    self.io_loop.start()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 149, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/lib/python3.6/asyncio/base_events.py\", line 438, in run_forever\n    self._run_once()\n  File \"/usr/lib/python3.6/asyncio/base_events.py\", line 1451, in _run_once\n    handle._run()\n  File \"/usr/lib/python3.6/asyncio/events.py\", line 145, in _run\n    self._callback(*self._args)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/ioloop.py\", line 690, in <lambda>\n    lambda f: self._run_callback(functools.partial(callback, future))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/ioloop.py\", line 743, in _run_callback\n    ret = callback()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 787, in inner\n    self.run()\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 748, in run\n    yielded = self.gen.send(value)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 365, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 268, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 545, in execute_request\n    user_expressions, allow_stdin,\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 306, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2867, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2895, in _run_cell\n    return runner(coro)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3072, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3263, in run_ast_nodes\n    if (await self.run_code(code, result,  async_=asy)):\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3343, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-3-eeb7f0086b4b>\", line 3, in <module>\n    model = load_model('fashion_mnist-gs.h5')\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/saving.py\", line 419, in load_model\n    model = _deserialize_model(f, custom_objects, compile)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/saving.py\", line 225, in _deserialize_model\n    model = model_from_config(model_config, custom_objects=custom_objects)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/saving.py\", line 458, in model_from_config\n    return deserialize(config, custom_objects=custom_objects)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/layers/__init__.py\", line 55, in deserialize\n    printable_module_name='layer')\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/utils/generic_utils.py\", line 145, in deserialize_keras_object\n    list(custom_objects.items())))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/sequential.py\", line 301, in from_config\n    model.add(layer)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/sequential.py\", line 181, in add\n    output_tensor = layer(self.outputs[0])\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/base_layer.py\", line 431, in __call__\n    self.build(unpack_singleton(input_shapes))\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/layers/core.py\", line 866, in build\n    constraint=self.kernel_constraint)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/legacy/interfaces.py\", line 91, in wrapper\n    return func(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/engine/base_layer.py\", line 252, in add_weight\n    constraint=constraint)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\", line 402, in variable\n    v = tf.Variable(value, dtype=tf.as_dtype(dtype), name=name)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 259, in __call__\n    return cls._variable_v1_call(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 220, in _variable_v1_call\n    shape=shape)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 198, in <lambda>\n    previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py\", line 2511, in default_variable_creator\n    shape=shape)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 263, in __call__\n    return super(VariableMetaclass, cls).__call__(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 1568, in __init__\n    shape=shape)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\", line 1755, in _init_from_args\n    self._snapshot = array_ops.identity(self._variable, name=\"read\")\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py\", line 180, in wrapper\n    return target(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py\", line 86, in identity\n    ret = gen_array_ops.identity(input, name=name)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 4253, in identity\n    \"Identity\", input=input, name=name)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py\", line 788, in _apply_op_helper\n    op_def=op_def)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\", line 507, in new_func\n    return func(*args, **kwargs)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 3616, in create_op\n    op_def=op_def)\n  File \"/home/alicia/workspace/xai-genz/venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 2005, in __init__\n    self._traceback = tf_stack.extract_stack()\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "# since we have two inputs we pass a list of inputs to the explainer\n",
    "explainer = shap.GradientExplainer(model, x_test)\n",
    "\n",
    "# we explain the model's predictions on the first three samples of the test set\n",
    "# shap_values = explainer.shap_values([x_test[:3], x_test[:3]])\n",
    "shap_values = explainer.shap_values(np.array([to_explain_img]))\n",
    "shap.image_plot(shap_values[to_explain_label], to_explain_img.reshape(1, 28, 28).astype('float'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicion - 9 Pullover\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'shap_values' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-453ee23c3a31>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Predicion - {y_test[0]} {class_names[to_explain_label]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# here we plot the explanations for all classes for the first input (this is the feed forward input)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m shap.image_plot([shap_values[i] for i in range(len(class_names))], to_explain_img.reshape(1, 28, 28).astype('float'),\n\u001b[0m\u001b[1;32m      4\u001b[0m                labels=np.array(class_names).reshape(1, len(class_names)))\n",
      "\u001b[0;32m<ipython-input-7-453ee23c3a31>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Predicion - {y_test[0]} {class_names[to_explain_label]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# here we plot the explanations for all classes for the first input (this is the feed forward input)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m shap.image_plot([shap_values[i] for i in range(len(class_names))], to_explain_img.reshape(1, 28, 28).astype('float'),\n\u001b[0m\u001b[1;32m      4\u001b[0m                labels=np.array(class_names).reshape(1, len(class_names)))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'shap_values' is not defined"
     ]
    }
   ],
   "source": [
    "print(f'Predicion - {y_test[0]} {class_names[to_explain_label]}')\n",
    "# here we plot the explanations for all classes for the first input (this is the feed forward input)\n",
    "shap.image_plot([shap_values[i] for i in range(len(class_names))], to_explain_img.reshape(1, 28, 28).astype('float'),\n",
    "               labels=np.array(class_names).reshape(1, len(class_names)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}