train_ccn_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9e0eb66",
   "metadata": {},
   "outputs": [],
   "source": [
    "from model_utils import *\n",
    "import librosa\n",
    "import librosa.display\n",
    "import os\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow.keras as keras\n",
    "\n",
    "np.random.seed(1337)\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(1337)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55b8523c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 4936064457600307946\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d87d206",
   "metadata": {},
   "outputs": [],
   "source": [
    "project_dir = r'D:/Sistemas de Informação/TCC/'\n",
    "#project_dir = os.path.dirname(os.path.abspath(__file__))\n",
    "data_dir = project_dir+'data/'\n",
    "audioset_indices_csv = project_dir+'segments/subset_class_labels_indices.csv'\n",
    "footstep_data = data_dir+'train/footstep_data'\n",
    "other_data = data_dir+'train/other_data'\n",
    "\n",
    "train_data = data_dir+'train/train_data'\n",
    "train_csv = project_dir+ 'segments/2400_subset_unbalanced_train_segments.csv'\n",
    "\n",
    "audioset_eval_csv = project_dir+'segments/subset_eval_segments.csv'\n",
    "eval_sub_dir = 'eval/audio'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6e95f74",
   "metadata": {},
   "source": [
    "Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cd2d1ada",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(1243, 60, 420)\n"
     ]
    }
   ],
   "source": [
    "# Load up numpy arrays to save time if they have already been procesed.\n",
    "\n",
    "save_dir = footstep_data+\"/features/\"\n",
    "feature_file = os.path.join(save_dir + '_x.npy')\n",
    "labels_file = os.path.join(save_dir + '_y.npy')\n",
    "train_x = np.load(feature_file)\n",
    "train_y = np.load(labels_file)\n",
    "print(train_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3d932f92",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(1169, 60, 420)\n"
     ]
    }
   ],
   "source": [
    "save_dir = other_data+\"/features/\"\n",
    "feature_file = os.path.join(save_dir + '_x.npy')\n",
    "labels_file = os.path.join(save_dir + '_y.npy')\n",
    "train_xo = np.load(feature_file)\n",
    "train_yo = np.load(labels_file)\n",
    "print(train_xo.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fc8be1e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = np.append(train_x, train_xo, axis=0)\n",
    "train_y = np.append(train_y, train_yo, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c888048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d951785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2412,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "379d09a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1362df9f880>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4509835",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(train_y[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ecaa266f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "# Shuffling features and labels arrays\n",
    "train_x, train_y = shuffle(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "01272851",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "# Splitting the dataset into train and test subsets\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(train_x, train_y, test_size=0.10, random_state=42, stratify=train_y)\n",
    "#X_train, X_test, Y_train, Y_test = StratifiedKFold(train_x, train_y, test_size=0.10, random_state=42, stratify=train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "59a30895",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(60, 420)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(X_train[0])\n",
    "print(X_train[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e7bce75f",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(2170, 60, 420)\n(242, 60, 420)\n(2170,)\n(242,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a51b9b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshaping the data to fit the model\n",
    "\n",
    "X_train = X_train.reshape(2170, 60, 420, 1)\n",
    "X_test = X_test.reshape(242, 60, 420, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4af78709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0.], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from keras.utils import to_categorical\n",
    "\n",
    "#one-hot encode column\n",
    "#Y_train = to_categorical(Y_train)\n",
    "#Y_test = to_categorical(Y_test)\n",
    "\n",
    "#Y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1df4edb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout\n",
    "\n",
    "# Personal note: have to observe and search the best fitting options for my model\n",
    "\n",
    "# Creating the model\n",
    "model = Sequential()\n",
    "\n",
    "#Adding the model layers\n",
    "model.add(Conv2D(filters=80, kernel_size=3, activation='relu', input_shape=\n",
    "                (60, 420, 1)))\n",
    "model.add(MaxPooling2D(pool_size=(4, 3), strides=(1, 3)))\n",
    "#model.add(Dropout(0.75))\n",
    "model.add(Conv2D(filters=80, kernel_size=3, activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(4, 3), strides=(1, 3)))\n",
    "model.add(Conv2D(filters=80, kernel_size=3, activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(4, 3), strides=(1, 3)))\n",
    "#model.add(Dropout(0.75))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c9e5bfbf",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_7\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_18 (Conv2D)           (None, 58, 418, 80)       800       \n_________________________________________________________________\nmax_pooling2d_18 (MaxPooling (None, 55, 139, 80)       0         \n_________________________________________________________________\nconv2d_19 (Conv2D)           (None, 55, 139, 80)       57680     \n_________________________________________________________________\nmax_pooling2d_19 (MaxPooling (None, 52, 46, 80)        0         \n_________________________________________________________________\nconv2d_20 (Conv2D)           (None, 52, 46, 80)        57680     \n_________________________________________________________________\nmax_pooling2d_20 (MaxPooling (None, 49, 15, 80)        0         \n_________________________________________________________________\nflatten_6 (Flatten)          (None, 58800)             0         \n_________________________________________________________________\ndense_6 (Dense)              (None, 1)                 58801     \n=================================================================\nTotal params: 174,961\nTrainable params: 174,961\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "320488f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import visualkeras\n",
    "\n",
    "visualkeras.layered_view(model, to_file='model.png').show() # write and show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1d388ff3",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'network' from 'tensorflow.python.keras.engine' (D:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
      "  File \u001b[0;32m\"<ipython-input-129-16f11b98ee25>\"\u001b[0m, line \u001b[0;32m10\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n    plot_model(model, to_file=project_dir+'model.png', show_shapes=True, rankdir='TB')\n",
      "  File \u001b[0;32m\"D:\\Anaconda\\lib\\site-packages\\plot_model\\plot_model.py\"\u001b[0m, line \u001b[0;32m453\u001b[0m, in \u001b[0;35mplot_model\u001b[0m\n    dot = model_to_dot(model,\n",
      "\u001b[1;36m  File \u001b[1;32m\"D:\\Anaconda\\lib\\site-packages\\plot_model\\plot_model.py\"\u001b[1;36m, line \u001b[1;32m91\u001b[1;36m, in \u001b[1;35mmodel_to_dot\u001b[1;36m\u001b[0m\n\u001b[1;33m    from tensorflow.python.keras.engine import network\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m\u001b[1;31m:\u001b[0m cannot import name 'network' from 'tensorflow.python.keras.engine' (D:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\__init__.py)\n"
     ]
    }
   ],
   "source": [
    "#from keras.utils.vis_utils import plot_model\n",
    "from plot_model import plot_model\n",
    "\n",
    "# The problem: https://stackoverflow.com/questions/47605558/importerror-failed-to-import-pydot-you-must-install-pydot-and-graphviz-for-py\n",
    "# Experiencing problems to install graphviz, it seems the releases in windows are not that supported.\n",
    "# I should get back to this problem later, another solution is to plot the model by hand\n",
    "# Don't forget this, it might be good to use it on the paper\n",
    "\n",
    "# Model visualization \n",
    "plot_model(model, to_file=project_dir+'model.png', show_shapes=True, rankdir='TB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "124a5d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model.compile(optimizer=keras.optimizers.Adam(), loss='binary_crossentropy', metrics=['binary_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a882e1d6",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/10\n",
      "17/17 [==============================] - 103s 6s/step - loss: 0.5811 - binary_accuracy: 0.7005 - val_loss: 0.5869 - val_binary_accuracy: 0.7107\n",
      "Epoch 2/10\n",
      "17/17 [==============================] - 99s 6s/step - loss: 0.5530 - binary_accuracy: 0.7189 - val_loss: 0.5987 - val_binary_accuracy: 0.7066\n",
      "Epoch 3/10\n",
      "17/17 [==============================] - 101s 6s/step - loss: 0.5439 - binary_accuracy: 0.7240 - val_loss: 0.6090 - val_binary_accuracy: 0.7025\n",
      "Epoch 4/10\n",
      "17/17 [==============================] - 98s 6s/step - loss: 0.5404 - binary_accuracy: 0.7194 - val_loss: 0.5787 - val_binary_accuracy: 0.7231\n",
      "Epoch 5/10\n",
      "17/17 [==============================] - 99s 6s/step - loss: 0.5388 - binary_accuracy: 0.7332 - val_loss: 0.6062 - val_binary_accuracy: 0.6901\n",
      "Epoch 6/10\n",
      "17/17 [==============================] - 99s 6s/step - loss: 0.5069 - binary_accuracy: 0.7419 - val_loss: 0.5783 - val_binary_accuracy: 0.7066\n",
      "Epoch 7/10\n",
      "17/17 [==============================] - 100s 6s/step - loss: 0.5016 - binary_accuracy: 0.7475 - val_loss: 0.6266 - val_binary_accuracy: 0.6736\n",
      "Epoch 8/10\n",
      "17/17 [==============================] - 100s 6s/step - loss: 0.4991 - binary_accuracy: 0.7447 - val_loss: 0.6338 - val_binary_accuracy: 0.6736\n",
      "Epoch 9/10\n",
      "17/17 [==============================] - 99s 6s/step - loss: 0.4815 - binary_accuracy: 0.7664 - val_loss: 0.6013 - val_binary_accuracy: 0.7231\n",
      "Epoch 10/10\n",
      "17/17 [==============================] - 99s 6s/step - loss: 0.4615 - binary_accuracy: 0.7760 - val_loss: 0.7309 - val_binary_accuracy: 0.6240\n"
     ]
    }
   ],
   "source": [
    "# Trainning the model\n",
    "history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=10, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8d29f60a",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[5.13458252e-02],\n",
       "       [6.42836094e-04],\n",
       "       [3.93387914e-01],\n",
       "       [4.30468917e-01],\n",
       "       [4.67463732e-02],\n",
       "       [9.88540053e-03],\n",
       "       [5.23550808e-01],\n",
       "       [5.99532306e-01],\n",
       "       [1.53818339e-01],\n",
       "       [7.42701086e-05],\n",
       "       [2.29609549e-01],\n",
       "       [4.66762066e-01],\n",
       "       [7.82689452e-03],\n",
       "       [1.24939024e-01],\n",
       "       [1.73337340e-01],\n",
       "       [2.51690567e-01],\n",
       "       [4.55202281e-01],\n",
       "       [6.25813723e-01],\n",
       "       [3.41035068e-01],\n",
       "       [3.75051290e-01]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 77
    }
   ],
   "source": [
    "# Predicting the the sounds with the test sets\n",
    "model.predict(X_test[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3f729582",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0])"
      ]
     },
     "metadata": {},
     "execution_count": 75
    }
   ],
   "source": [
    "# Test results comparison\n",
    "Y_test[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "8/8 - 2s - loss: 0.7309 - binary_accuracy: 0.6240\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Evaluation plot\n",
    "\n",
    "plt.plot(history.history['binary_accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_binary_accuracy'], label='val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.5, 1])\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "test_loss, test_acc = model.evaluate(X_test, Y_test, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.5165289044380188\n"
     ]
    }
   ],
   "source": [
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b602cb40",
   "metadata": {},
   "outputs": [],
   "source": [
    " # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications.vgg16 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.models import Model\n",
    "from matplotlib import pyplot\n",
    "from numpy import expand_dims\n",
    "from matplotlib import pyplot\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d9a9ecf9",
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "conv2d_18 (3, 3, 1, 80)\nconv2d_19 (3, 3, 80, 80)\nconv2d_20 (3, 3, 80, 80)\n"
     ]
    }
   ],
   "source": [
    "for layer in model.layers:\n",
    "    \n",
    "    if 'conv' not in layer.name:\n",
    "        continue    \n",
    "    filters , bias = layer.get_weights()\n",
    "    print(layer.name , filters.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cb952629",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "not enough values to unpack (expected 2, got 0)",
     "output_type": "error",
     "traceback": [
      "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-21-969e53b0c268>\"\u001b[1;36m, line \u001b[1;32m2\u001b[1;36m, in \u001b[1;35m<module>\u001b[1;36m\u001b[0m\n\u001b[1;33m    filters , bias = model.layers[1].get_weights()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m\u001b[1;31m:\u001b[0m not enough values to unpack (expected 2, got 0)\n"
     ]
    }
   ],
   "source": [
    "# retrieve weights from the second hidden layer\n",
    "#filters , bias = model.layers[1].get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "58f73ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize filter values to 0-1 so we can visualize them\n",
    "f_min, f_max = filters.min(), filters.max()\n",
    "filters = (filters - f_min) / (f_max - f_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "517a0668",
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "n_filters =6\n",
    "ix=1\n",
    "fig = pyplot.figure(figsize=(20,15))\n",
    "for i in range(n_filters):\n",
    "    # get the filters\n",
    "    f = filters[:,:,:,i]\n",
    "    for j in range(3):\n",
    "        # subplot for 6 filters and 3 channels\n",
    "        pyplot.subplot(n_filters,3,ix)\n",
    "        pyplot.imshow(f[:,:,j] ,cmap='plasma')\n",
    "        ix+=1\n",
    "#plot the filters \n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f27e38a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python388jvsc74a57bd007efdcd4b820c98a756949507a4d29d7862823915ec7477944641bea022f4f62",
   "display_name": "Python 3.8.8 64-bit"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "metadata": {
   "interpreter": {
    "hash": "b5addd3f4caae7f85af1640a71fe8e618a097dd92002e86286fcb9de2be36611"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}