pain_2D_4layer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import st_remux as st\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Config\n",
    "nopain = 'Pain_data/final_data/no_pain'\n",
    "highpain = 'Pain_data/final_data/high_pain'\n",
    "\n",
    "fs = 4000\n",
    "win_len = 40\n",
    "\n",
    "use_filter = 0   # weird results if we use python filtering\n",
    "\n",
    "use_matlab_backend = 0\n",
    "\n",
    "#high = np.genfromtxt(highpain, delimiter=',')\n",
    "#no = np.genfromtxt(nopain, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using native backend\n",
      "['.DS_Store', 'pain_large.csv']\n",
      "ydim=16 xdim=40\n",
      "['.DS_Store', 'nopain.csv']\n",
      "ydim=16 xdim=40\n"
     ]
    }
   ],
   "source": [
    "if use_matlab_backend:\n",
    "    print('Using MATLAB signal processing backend')\n",
    "    # high_dir = 'datasets/high/'\n",
    "    # low_dir = 'datasets/low/'\n",
    "\n",
    "    high_dir = 'EMG_data/power_spectrum_for_cnn_input/high/'\n",
    "    low_dir = 'EMG_data/power_spectrum_for_cnn_input/low/'\n",
    "\n",
    "    def generate_3darray(directory):\n",
    "        files = os.listdir(directory)\n",
    "        dims = np.genfromtxt(directory + files[0], delimiter=',').shape\n",
    "        length = len(files)\n",
    "        arr = np.zeros((length, dims[0], dims[1]))\n",
    "\n",
    "        for idx, val in enumerate(files):\n",
    "            arr[idx] = np.genfromtxt(directory + val, delimiter=',')\n",
    "\n",
    "        return arr\n",
    "\n",
    "    hi = generate_3darray(high_dir)\n",
    "    lo = generate_3darray(low_dir)\n",
    "    \n",
    "else: \n",
    "    print('Using native backend')\n",
    "    hi = st.raw_to_arr(highpain, fs, win_len, 0)\n",
    "    no = st.raw_to_arr(nopain, fs, win_len, 0)\n",
    "\n",
    "X = np.concatenate((hi, no))\n",
    "\n",
    "hi_Y = np.ones(len(hi), dtype=int)\n",
    "no_Y = np.zeros(len(no), dtype=int)\n",
    "\n",
    "Y = np.concatenate((hi_Y, no_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (2572, 16, 40) (2572,)\n",
      "Testing data shape:  (286, 16, 40) (286,)\n"
     ]
    }
   ],
   "source": [
    "print('Training data shape: ', train_X.shape, train_Y.shape)\n",
    "print('Testing data shape: ', test_X.shape, test_Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of outputs:  2\n",
      "Output classes:  [0 1]\n"
     ]
    }
   ],
   "source": [
    "classes = np.unique(train_Y)\n",
    "nClasses = len(classes)\n",
    "print('Total number of outputs: ', nClasses)\n",
    "print('Output classes: ', classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Ground Truth: 1')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,2])\n",
    "\n",
    "# Display first image in training set\n",
    "plt.subplot(121)\n",
    "plt.pcolormesh(train_X[0,:,:], cmap='jet')\n",
    "plt.title(\"Ground Truth: {}\".format(train_Y[0]))\n",
    "\n",
    "# Display second image in testing set\n",
    "plt.subplot(122)\n",
    "plt.pcolormesh(test_X[1], cmap='jet')\n",
    "plt.title(\"Ground Truth: {}\".format(test_Y[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2572, 16, 40, 1), (286, 16, 40, 1))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ydim = train_X[0].shape[0]\n",
    "xdim = train_X[0].shape[1]\n",
    "train_X = train_X.reshape(-1, ydim, xdim, 1)\n",
    "test_X = test_X.reshape(-1, ydim, xdim, 1)\n",
    "train_X.shape, test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train_X.astype('float32')\n",
    "test_X = test_X.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original label:  0\n",
      "After conversion to one-hot:  [1. 0.]\n"
     ]
    }
   ],
   "source": [
    "# Change labels from categorical to one-hot encoding\n",
    "train_Y_one_hot = to_categorical(train_Y)\n",
    "test_Y_one_hot = to_categorical(test_Y)\n",
    "\n",
    "# See the difference?\n",
    "print('Original label: ', train_Y[0])\n",
    "print('After conversion to one-hot: ', train_Y_one_hot[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Y_one_hot.shape\n",
    "test_Y_one_hot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2057, 16, 40, 1), (515, 16, 40, 1), (2057, 2), (515, 2))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Partition data for 80% training and 20% validation\n",
    "# should reduce overfitting and boost test performance\n",
    "\n",
    "train_X, valid_X, train_label, valid_label = train_test_split(train_X, train_Y_one_hot, test_size=0.2)\n",
    "\n",
    "train_X.shape, valid_X.shape, train_label.shape, valid_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's get down to business\n",
    "import keras\n",
    "from keras.models import Sequential, Input, Model\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.advanced_activations import LeakyReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "batch_size = 32\n",
    "epochs = 50\n",
    "num_classes = nClasses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "pain_model = Sequential()\n",
    "\n",
    "pain_model.add(Conv2D(32, kernel_size=(3, 3), activation='linear', input_shape=(ydim, xdim, 1), padding='same'))\n",
    "#pain_model.add(BatchNormalization())\n",
    "\n",
    "pain_model.add(\n",
    "    Conv2D(\n",
    "        32, \n",
    "        kernel_size=(3, 3), \n",
    "        activation='linear',  \n",
    "        padding='same'))\n",
    "pain_model.add(LeakyReLU(alpha=0.1))\n",
    "#pain_model.add(BatchNormalization())\n",
    "pain_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
    "pain_model.add(Dropout(0.25))\n",
    "\n",
    "pain_model.add(\n",
    "    Conv2D(\n",
    "        64,\n",
    "        (3, 3),\n",
    "        activation='linear',\n",
    "        padding='same'))\n",
    "pain_model.add(LeakyReLU(alpha=0.1))\n",
    "#pain_model.add(BatchNormalization())\n",
    "pain_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
    "pain_model.add(Dropout(0.25))\n",
    "\n",
    "pain_model.add(\n",
    "    Conv2D(\n",
    "        128,\n",
    "        (3, 3),\n",
    "        activation='linear',\n",
    "        padding='same'))\n",
    "pain_model.add(LeakyReLU(alpha=0.1))\n",
    "#pain_model.add(BatchNormalization())\n",
    "pain_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
    "pain_model.add(Dropout(0.25))\n",
    "pain_model.add(Flatten())\n",
    "\n",
    "\"\"\"pain_model.add(Dense(512, activation='linear'))\n",
    "pain_model.add(LeakyReLU(alpha=0.1))\n",
    "#pain_model.add(BatchNormalization())\n",
    "pain_model.add(Dropout(0.3))\"\"\"\n",
    "\n",
    "pain_model.add(Dense(128, activation='linear'))\n",
    "pain_model.add(LeakyReLU(alpha=0.1))\n",
    "#pain_model.add(BatchNormalization())\n",
    "pain_model.add(Dropout(0.3))\n",
    "\n",
    "pain_model.add(Dense(num_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_model.compile(\n",
    "    loss=keras.losses.categorical_crossentropy,\n",
    "    optimizer=keras.optimizers.Adam(),\n",
    "    metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 16, 40, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 40, 32)        9248      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 16, 40, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 20, 32)         0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 8, 20, 32)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 20, 64)         18496     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 8, 20, 64)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 10, 64)         0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 4, 10, 64)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 4, 10, 128)        73856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 4, 10, 128)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 2, 5, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 2, 5, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1280)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               163968    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 266,146\n",
      "Trainable params: 266,146\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "pain_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 2057 samples, validate on 515 samples\n",
      "Epoch 1/50\n",
      "2057/2057 [==============================] - 5s 2ms/step - loss: 0.6403 - accuracy: 0.6675 - val_loss: 0.6435 - val_accuracy: 0.7087\n",
      "Epoch 2/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6314 - accuracy: 0.6777 - val_loss: 0.5941 - val_accuracy: 0.6951\n",
      "Epoch 3/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6187 - accuracy: 0.6835 - val_loss: 0.5991 - val_accuracy: 0.7049\n",
      "Epoch 4/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6145 - accuracy: 0.6928 - val_loss: 0.5926 - val_accuracy: 0.7087\n",
      "Epoch 5/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6120 - accuracy: 0.6942 - val_loss: 0.5786 - val_accuracy: 0.7243\n",
      "Epoch 6/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6103 - accuracy: 0.6957 - val_loss: 0.5690 - val_accuracy: 0.7340\n",
      "Epoch 7/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6109 - accuracy: 0.6947 - val_loss: 0.5721 - val_accuracy: 0.7262\n",
      "Epoch 8/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6059 - accuracy: 0.6962 - val_loss: 0.5722 - val_accuracy: 0.7359\n",
      "Epoch 9/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6039 - accuracy: 0.7005 - val_loss: 0.5589 - val_accuracy: 0.7534\n",
      "Epoch 10/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6039 - accuracy: 0.7064 - val_loss: 0.5559 - val_accuracy: 0.7476\n",
      "Epoch 11/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6022 - accuracy: 0.7039 - val_loss: 0.5568 - val_accuracy: 0.7670\n",
      "Epoch 12/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6035 - accuracy: 0.7039 - val_loss: 0.5699 - val_accuracy: 0.7398\n",
      "Epoch 13/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6048 - accuracy: 0.6918 - val_loss: 0.5615 - val_accuracy: 0.7476\n",
      "Epoch 14/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5953 - accuracy: 0.7035 - val_loss: 0.5495 - val_accuracy: 0.7495\n",
      "Epoch 15/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5968 - accuracy: 0.7083 - val_loss: 0.5497 - val_accuracy: 0.7437\n",
      "Epoch 16/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5967 - accuracy: 0.7010 - val_loss: 0.5707 - val_accuracy: 0.7515\n",
      "Epoch 17/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5941 - accuracy: 0.7039 - val_loss: 0.5430 - val_accuracy: 0.7398\n",
      "Epoch 18/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5905 - accuracy: 0.7127 - val_loss: 0.5584 - val_accuracy: 0.7398\n",
      "Epoch 19/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5979 - accuracy: 0.7078 - val_loss: 0.5640 - val_accuracy: 0.7456\n",
      "Epoch 20/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5903 - accuracy: 0.7000 - val_loss: 0.5512 - val_accuracy: 0.7534\n",
      "Epoch 21/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5914 - accuracy: 0.7083 - val_loss: 0.5578 - val_accuracy: 0.7417\n",
      "Epoch 22/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5841 - accuracy: 0.7122 - val_loss: 0.5446 - val_accuracy: 0.7417\n",
      "Epoch 23/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5894 - accuracy: 0.7127 - val_loss: 0.5606 - val_accuracy: 0.7359\n",
      "Epoch 24/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5786 - accuracy: 0.7112 - val_loss: 0.5425 - val_accuracy: 0.7476\n",
      "Epoch 25/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5824 - accuracy: 0.7030 - val_loss: 0.5641 - val_accuracy: 0.7495\n",
      "Epoch 26/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5745 - accuracy: 0.7229 - val_loss: 0.5503 - val_accuracy: 0.7456\n",
      "Epoch 27/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5774 - accuracy: 0.7195 - val_loss: 0.5616 - val_accuracy: 0.7476\n",
      "Epoch 28/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5770 - accuracy: 0.7156 - val_loss: 0.5440 - val_accuracy: 0.7515\n",
      "Epoch 29/50\n",
      "2057/2057 [==============================] - 5s 2ms/step - loss: 0.5693 - accuracy: 0.7205 - val_loss: 0.5482 - val_accuracy: 0.7437\n",
      "Epoch 30/50\n",
      "2057/2057 [==============================] - 5s 2ms/step - loss: 0.5566 - accuracy: 0.7297 - val_loss: 0.5636 - val_accuracy: 0.7398\n",
      "Epoch 31/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5556 - accuracy: 0.7195 - val_loss: 0.5468 - val_accuracy: 0.7631\n",
      "Epoch 32/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5536 - accuracy: 0.7239 - val_loss: 0.5465 - val_accuracy: 0.7573\n",
      "Epoch 33/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5491 - accuracy: 0.7351 - val_loss: 0.5683 - val_accuracy: 0.7476\n",
      "Epoch 34/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5471 - accuracy: 0.7229 - val_loss: 0.5764 - val_accuracy: 0.7417\n",
      "Epoch 35/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5432 - accuracy: 0.7312 - val_loss: 0.5667 - val_accuracy: 0.7301\n",
      "Epoch 36/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5416 - accuracy: 0.7360 - val_loss: 0.5564 - val_accuracy: 0.7573\n",
      "Epoch 37/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5217 - accuracy: 0.7448 - val_loss: 0.5607 - val_accuracy: 0.7398\n",
      "Epoch 38/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5217 - accuracy: 0.7423 - val_loss: 0.5759 - val_accuracy: 0.7320\n",
      "Epoch 39/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5139 - accuracy: 0.7550 - val_loss: 0.5766 - val_accuracy: 0.7417\n",
      "Epoch 40/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5041 - accuracy: 0.7535 - val_loss: 0.5782 - val_accuracy: 0.7107\n",
      "Epoch 41/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5021 - accuracy: 0.7671 - val_loss: 0.5788 - val_accuracy: 0.7262\n",
      "Epoch 42/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.4856 - accuracy: 0.7574 - val_loss: 0.5985 - val_accuracy: 0.7340\n",
      "Epoch 43/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.4750 - accuracy: 0.7715 - val_loss: 0.6115 - val_accuracy: 0.7243\n",
      "Epoch 44/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.4871 - accuracy: 0.7696 - val_loss: 0.6100 - val_accuracy: 0.7262\n",
      "Epoch 45/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.4550 - accuracy: 0.7769 - val_loss: 0.6551 - val_accuracy: 0.7262\n",
      "Epoch 46/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.4623 - accuracy: 0.7783 - val_loss: 0.6303 - val_accuracy: 0.6854\n",
      "Epoch 47/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.4452 - accuracy: 0.8002 - val_loss: 0.6270 - val_accuracy: 0.7165\n",
      "Epoch 48/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.4174 - accuracy: 0.8051 - val_loss: 0.6673 - val_accuracy: 0.7029\n",
      "Epoch 49/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.4037 - accuracy: 0.8133 - val_loss: 0.6566 - val_accuracy: 0.7087\n",
      "Epoch 50/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.4083 - accuracy: 0.8216 - val_loss: 0.6668 - val_accuracy: 0.7087\n"
     ]
    }
   ],
   "source": [
    "pain_train = pain_model.fit(train_X,train_label,\n",
    "    batch_size=batch_size,\n",
    "    epochs=epochs,\n",
    "    verbose=1,\n",
    "    validation_data=(valid_X, valid_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_eval = pain_model.evaluate(test_X, test_Y_one_hot, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss:  0.6550697184109188\n",
      "Test accuracy:  0.6993007063865662\n"
     ]
    }
   ],
   "source": [
    "print('Test loss: ', test_eval[0])\n",
    "print('Test accuracy: ', test_eval[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Aa1EivXf8UlhYSNu2bWnfvj1HHHEE3btXuCRV5pprruHiiy8mPz+fwsJC2rdvzwEHHFAuT5MmTbjkkkto3749eXl5HHfccaXHxowZw5VXXsltt91G3bp1ee211zj99NOZNWsWXbp0oU6dOpxxxhn85S9/4c9//jMXXHABzz//PCeeeGJEmS666CLOOOMMunTpQmFhIa1btwYgPz+fm266iRNOOIHatWvTuXNn/vWvfwFQVFTEyJEjueCCCxJ+jQzDiEyNWyO3S5cuGrqIyvz58znmmGNSJFHNori4mOLiYurVq8fixYvp168fixcvTju3ybFjxzJp0iRfrqzRsHvDSCcS7U4ejIjMUNUusfKll6Yw2LZtGyeddBLFxcWoKk8//XTaKfyrr76aDz74gHfffTfVohhG0kh2uIVIWE/fSGvs3jDShRYtwk/CystzHjtVxW9PPy2ibBqGYaQ7yXYnj4QpfcMwjCSQbHfySJjSNwzDSAKpdCcPxpS+YRhGEigqglGjnA1fxH2OGpXcQVwwpe+L3r17V5ho9dhjjzF06NCo5+23334ArFq1ivPPPz9i2aED16E89thjbA8KuH3aaaexadMmP6IbhlGDSGa4hUiY0vfBoEGDGDt2bLm0sWPHMmjQIF/nH3bYYVFntMYiVOm//fbbNGrUqNLlJRtVLQ3nYBhGajGl74Pzzz+ft956i127XFTo5cuXs2rVKnr06FHqN19YWEiHDh2YMGFChfOXL19O+/btARciYeDAgeTn53PBBReUhj4A578eCMs8YsQIAJ544glWrVrFiSeeWDortkWLFvz8888APPLII7Rv35727duXhmVevnw5xxxzDFdccQXt2rWjX79+5eoJ8Oabb3LcccfRqVMn+vbty5o1awA3F+DSSy+lQ4cO5Ofnl4ZxePfddyksLKSgoICTTjoJcOsLPPTQQ6Vltm/fnuXLl5fKMHToUAoLC/nhhx/Ctg9g2rRp/PrXv6agoICuXbuydetWevbsWS5kdPfu3Zk9e3Zcv5thGGHwE4ozmVus0MrXXqvaq1dit2uvDR+qNJjTTjtNx48fr6ouvPGNN96oqi6E8ubNm1VVdd26ddqqVSstKSlRVdUGDRqoquqyZcu0Xbt2qqr68MMP66WXXqqqqrNmzdKcnBydNm2aqpaFHi4uLtZevXrprFmzVFU1Ly9P161bVypLYH/69Onavn173bZtm27dulXbtm2rX3/9tS5btkxzcnJKwyIPGDBA//Of/1Ro04YNG0plfeaZZ/SGG25QVdWbbrpJrw26KBs2bNC1a9dq8+bNdenSpeVkDQ41rararl07XbZsmS5btkxFRL/88svSY+Hat2vXLm3ZsmVpeOrNmzfrnj179N///nepDAsXLtRw94WqhVY2aiaJCJUcLyQwtLJBeRNPsGlHVbn11lvJz8+nb9++/Pjjj6U95nB88sknXHjhhYCLTZOfn196bNy4cRQWFtKpUyfmzp0bNphaMJ999hnnnHMODRo0YL/99uPcc8/l008/BaBly5Z07NgRiBy+eeXKlZxyyil06NCBBx98kLlz5wLwwQcflFvFq3HjxkydOpUTTjiBli1bAv7CL+fl5XH88cdHbd/ChQs59NBDS8NTN2zYkNq1azNgwADeeust9uzZw3PPPcfgwYNj1mcYNYFkLuRUGdJr/j7gWTCSztlnn80NN9zA119/zY4dOygsLARcALN169YxY8YM6tSpQ4sWLcKGUw4mXLjhZcuW8dBDDzFt2jQaN27M4MGDY5ajUWZTB8IygwvNHM68c80113DDDTdw5plnMmXKFO66667SckNlDJcG5cMvQ/kQzMHhlyO1L1K59evX5+STT2bChAmMGzcu5mC3YdQUoi10noqB21Csp++T/fbbj969e3PZZZeVG8ANhBWuU6cOkydP5vtw86yDOOGEE0oXP//2229L7dRbtmyhQYMGHHDAAaxZs4Z33nmn9Jz999+frVu3hi1r/PjxbN++nV9++YU33niDnj17+m7T5s2badbMrXz5wgsvlKb369ePv//976X7GzdupFu3bnz88ccsW7YMKB9++euvvwbg66+/Lj0eSqT2tWnThlWrVjFt2jQAtm7dWrp2wOWXX84f//hHjj32WF9vFoZRE6gpM28jYUo/DgYNGsSsWbNKV64CFyJ4+vTpdOnShTFjxsRcEOTqq69m27Zt5Ofn88ADD9C1a1fArYLVqVMn2rVrx2WXXVYuLPOQIUM49dRTK4Q3LiwsZPDgwXTt2pXjjjuOyy+/nE6dOvluz1133cWAAQPo2bMnTZs2LU2//fbb2bhxI+3bt6egoIDJkydz0EEHMWrUKM4991wKCgpKQyKfd955bNiwgY4dO/LPf/6To446KmxdkdpXt25dXnnlFa655hoKCgo4+eSTS98WOnfuTMOGDS3mvpFW1JSZt5GwgGtGjWXVqlX07t2bBQsWUKtW+P6J3RtGTSM0mia4mbfVPRHLAq4Zac3o0aM57rjjuOeeeyIqfMOoidSUmbeR8PVvEpH+IrJQRJaIyC1hjueKyGQR+UZEZovIaUHHhnvnLRSRUxIpvJG5XHzxxfzwww8MGDAg1aIYRljGjHHhkmvVcp/B3jk1YeZtJGJ674hIDvAUcDKwEpgmIhPVLZEY4Hbc2rn/FJG2uKUVW3jfBwLtgMOAD0TkKFXdG6+gkbw8jOylppkmjeyhpiyIUhn89PS7AktUdamq7gbGAmeF5FGgoff9AGCV9/0sYKyq7lLVZcASr7y4qFevHuvXr7c/uVGKqrJ+/Xrq1auXalGMLCSaW2ZNx4+ffjPgh6D9lcBxIXnuAt4TkWuABkDfoHOnhpzbLLQCERkCDAHIDTPE3bx5c1auXMm6det8iGtkC/Xq1aN58+apFsPIQmq6W2Y0/Cj9cDaV0C73IODfqvqwiHQD/iMi7X2ei6qOAkaB894JPV6nTp3SmaCGYRipJjc3/NKHNcUtMxp+zDsrgcOD9ptTZr4J8HtgHICqfgnUA5r6PNcwDCOtqCkLolQGP0p/GtBaRFqKSF3cwOzEkDwrgJMAROQYnNJf5+UbKCL7iEhLoDXwVaKENwzDSAU13S0zGjHNO6paLCLDgElADvCcqs4VkZG4qG4TgT8Bz4jI9TjzzWAv6ttcERkHzAOKgT9UxnPHMAyjplFUlB5KPpS0mJFrGIZhRMdm5BqGYRgVMKVvGIaRRZjSNwzDyCJM6RuGYWQRpvQNwzCyCFP6hmEYWYQpfcMwjCzClL5hGEYWYUrfMAwjizClbxiGkUWY0jcMw8giTOkbhmFkEab0DcMwsghT+oZhGFmEKX3DMIwswpS+YRhGFuFL6YtIfxFZKCJLROSWMMcfFZGZ3rZIRDYFHXtAROaKyHwReUJEwi2WbhiGYSSBmMslikgO8BRwMm6h82kiMlFV5wXyqOr1QfmvATp5338NdAfyvcOfAb2AKQmS3zAMw4gDPz39rsASVV2qqruBscBZUfIPAl72vitukfS6wD5AHWBN5cU1DMMwqoIfpd8M+CFof6WXVgERyQNaAh8BqOqXwGRgtbdNUtX5VRHYMAwjmYwZAy1aQK1a7nPMmFRLVDX8KP1wNvhIq6kPBF5V1b0AInIkcAzQHPeg6CMiJ1SoQGSIiEwXkenr1q3zJ7lhGEY1M2YMDBkC338Pqu5zyJD0Vvx+lP5K4PCg/ebAqgh5B1Jm2gE4B5iqqttUdRvwDnB86EmqOkpVu6hql4MOOsif5IZhGAkkXI/+tttg+/by+bZvd+npih+lPw1oLSItRaQuTrFPDM0kIkcDjYEvg5JXAL1EpLaI1MEN4pp5xzCMGkWkHv3334fPv2JFcuVLJDGVvqoWA8OASTiFPU5V54rISBE5MyjrIGCsqgabfl4FvgPmALOAWar6ZsKkNwzDSACRevQ5OeHz5+ZWv0zVhZTX0amnS5cuOn369FSLYRhGFlGrluvhh6N+/fIPhPr1YdQoKCpKjmx+EZEZqtolVj6bkWsYRtYTqeeel+cUfF4eiJTt1zSFHw+m9A3DyHruucf14IOpX9+lFxXB8uVQUuI+01nhgyl9wzAylHj864uKMq9HH4mYYRgMwzDSjYA3TsAWH/DGgciKvKgoM5V8KNbTNwwj48hE//pEYUrfMIyMI5If/YoVmRdWIV5M6RuGkXFE8sY58MDMC6sQL6b0DcPIOCJ544CZfUzpG4ZR44nXJBPJG2fDhvD50zmsQrzYjFzDMGo0oZ44UPlZsS1ahI+nk5fnfPDTGZuRm0W88w6sXJlqKQyjekikJ060SVjZgin9NGfTJjj9dLjvvlRLYhjVQzRPnHjJpklYkbDJWWnOp5+66eGzZqVaEsOoHnJzw5tkKhvpMlsmYUXCevppzscfu885cyJHCTSMdMZMMonFlH6aM2WK+9yyJbs8EIzswUwyicWUfhqzaRN88w307ev258xJrTxGcpg6texhny1kWqTLVGJKP4357DP3Jxg2zO3Pnp1aeYzkMHQonHIKfPll7LyGEYovpS8i/UVkoYgsEZFbwhx/VERmetsiEdkUdCxXRN4TkfkiMk9EWiRO/OxmyhTYZx+nAFq0MKWfDezY4X7n3bvh7LPNpGfET0ylLyI5wFPAqUBbYJCItA3Oo6rXq2pHVe0IPAm8HnR4NPCgqh4DdAXWJkr4bGfKFDj+eKhXDzp0MPNONjBzJuzdC/feCzt3wplnwrZtqZbKSCf89PS7AktUdamq7gbGAmdFyT8IeBnAezjUVtX3AVR1m6puj3Ku4ZPNm509v1cvt5+fDwsXwq5dqZXLqF6++sp9XnIJvPKKe9BfdJEz8xmGH/wo/WbAD0H7K720CohIHtAS+MhLOgrYJCKvi8g3IvKg9+YQet4QEZkuItPXrVsXXwuylIA9v3dvt9+hg+sBzp+fUrGMambaNDjsMLf17w8PPwzjx8Mdd6RaMiNd8KP0JUxaJI/wgcCrqrrX268N9ARuBI4FjgAGVyhMdZSqdlHVLgcddJAPkYwpU6BuXWfeAdfTB7PrZzrTpsGxx5btX3stXH65M/e89FLq5EoV2R4bvzL4UforgcOD9psDqyLkHYhn2gk69xvPNFQMjAcKKyOoUZ6APX/ffd1+69ZuUNeUfuayaRMsWlRe6YvAU085M99ll8H//pc6+ZJNIBBbNsfGrwx+lP40oLWItBSRujjFPjE0k4gcDTQGvgw5t7GIBLrvfYB5VRPZ2LwZvv66zLQDULs2tG1rg7mZzIwZ7jNY6YN743v1VWjWDM49t2JwskTy5z/DWdFG9JKILYlYOWIqfa+HPgyYBMwHxqnqXBEZKSJnBmUdBIzVoFjNnpnnRuBDEZmDMxU9k8gGZCMBe35gEDdAfr719DOZwCBulzDBc5s2hdGjYdUq+H//r/pkePttePNNWL++auUkwiyTyEBsWYWq1qitc+fOakTnz39WrVtX9Zdfyqc//LAqqK5dmxq5jOrlnHNUjzwyep6+fVUPPrjivZEIdu5Uzclx99jYsZUv58UXVevXd+UEtvr1XXo85OWVLyOw5eVVXrZ0BpiuPnSszchNQ6ZMgeOOqxiEqkMH92kmnswkdBA3HCNGwNq11dPbnz/feYgBTJpU+XKimWXieQOwQGyVw5R+DSSaz/WWLc62G2zPD5CpHjwWPRR++sktlBNL6ffo4WIx/e1vibftB+6rDh2c0q/s7xLJ/BIYiPU7MGuB2CqHKf0axsSJzj4bacXIUP/8YH71KzjooMzq6X//PRx8sLNXZzPTprnPWEofynr7Tz+dWBnmzHEeYn/4gxs7mDu3cuVEioOfkxP/wKwFYosfU/o1jFdegY0bnYfEjz9WPB7qnx9KJg3mqsI118DPP8MDD2R3j/+rr5xS7NQpdt4ePeCkkxLf258923mI/eY3br+yJp5IZpmA6SgUG5hNLKb0axAlJfDee/DrXzszztlnV/zTTpkCXbtW/NMEyM93PbBIf6BEM2aMCwdRHYwf7zxFunVzbfr00+qpJxksXQpPPAHFxZU7f9o0aNcOGjTwl3/ECFizJrG9/dmz3f3VvLlT/pVV+pHMMnl54fNXdoUsIwJ+RnuTuWWz986MGc77YPSZ2LfDAAAgAElEQVRo1QkTVEVUL7hAtaTEHd+82XlP3H575DKee86VsXBh9cv78ceuruOPT3zZW7aoNmumWlDg2t24sepvf5v4epLBpElOflB96KH4zy8pUT3wQNXf/z6+8/r0Uf3VrxLjybN2rZP/4Yfd/nXXqe6zT2K9hBLl1ZOtYN476Ueg59Svn4ueeN99ztzzl7+49M8/dz34cPb8AIHB3Mra9f32RHfvhquucl4WU6e6yWKJ5I47nN346aehYUO49FJ4/XVYvTqx9cSiKiYlVWeWOvVU1zvu0wfuvDP8eq/RWLYMNmzwZ88PJtDbHzUqvvPCEbifAh5ip5zigvt98knVyw5gA7NJws+TIZlbNvf0e/VS7dixbL+kRPXii12PZ9w41ZtuUq1TJ3rvavt21Vq1VO+803+9O3a43tQJJ6jWq6f64Yexz/nrX51cL73kemPx9kKjMW2aa8PQoWVpixe7+u6+O3H1RKKkRPWLL1QvuUR1//3d29bWrfGVsW2bOw/cG8q2barff6/aoIHq6aeXvb35YexYV86MGfHJoOp6+4cc4u6LqvDYY06Gn35y+7/84nr6111XtXKNxIHPnn7KlXzolq1Kf8sW1dq1VW++uXz6jh2q3bqp7ruvm3TSo0fsso4+WvXss2Pnmz9f9frrnekAVFu1clvjxqqLFkU+b/Fi94cfMMDtX3GFk2/Dhth1xmLPHtXCQqeoNm0qf+yUU1QPO0x19+6q1xOOjRtVn3xStUMHdz32289dx1q1VNu3V12yxF85S5eq5uc789z995dX8A895Mp+7TX/ct1wg7velWl3wAT36KPxnxvMZZepHnRQ+bSTT1Y95piqlWskDlP6acaECe7X+Oijisd++kk1N9cdj2bPDzBggFPekfjmG9erB/egGTBA9YMPVPfuVf3uO9UmTdyDY+PGiueWlLg/e8OGqj/+WFYeqD7yiL+2RiPQo3zllYrHAtcoHoXph+3byx5coNq5s+qoUWW9+/fecw/Cxo1V3303ejnPP+8eoo0ahc+7Z48bpzjsMDdW4YeePas2bnLiie4hGu/bSjBduqiedFL5tMADbMWKypdrJA5T+mnG0KHu1X/nzvDHZ85UbdfOKdhYjBzpftlwf/IdO1Rbt3YDfPffX/a6HsyUKe5hcMopTkkFM2aMK/vvfy+f/utfu3L37o0tXyR++MH1rk89Nbz5o7jYPfz69Kl8HeH4+99dmy67THX69PB5vvvOvQHUqqX6t7+Vl2/uXNU//tEpenAmusWLI9f3v/+5t4BrroktW3Gxuy/85I3E5587uc89t3K/T3GxeyBef3359DlzXHufeabyshmJw5R+mtGqlepvfpOYssaPd7/s1KkVj915pzv23nvRy3jmGZfv2mvL0jZscHFdunZ1iiCYF190+SdNqrzc55zjlMvSpZHz3Huvq2fevMrXE0xJiWrbtq53H8vOvm2bs8+Ds9e/8IIzt4Ebaxk40L2p+bHX/+EPTvF/9VX0fAHFOnq0/zaF45FH/L8phrJwoTv3uefKp5eUOA+rrl2d6VHEfZq3TWowpZ9GLFnifoknnkhMeUuXuvJGjSqfPn++U05FRf7Kufba8uUMGeJcRsO9bezc6Wy+Z51VOZlHj3Z13X9/9Hxr1rhgc1Xp+QYzZUp4hRaJkhIno4g7r3Vr1QcfjD/I3aZNqoceqtqpU8W3qWACLrjz58dXfji5L7tMSwff4+HVV9154d6CAmZCc7NMPab004inntKE+tbv3evMJMOGlaWVlDjvoEaNwpt0wrFnjzPx1K7tFB2o/ulPkfMPH+7MCN9/H5+8X3zhFHmfPv4GK4uK3JhCVWzUAQYMcLb6eP3Nv/7aDZLG44UTyrhxGnMs5KqrXFurYjYLsGuXGx+oV8+ZmPxy553udw3nAdS0aUWln82RLlOJKf004swzVVu0qJoCCaVbN6fkAzz/fPjefyw2bVJt08adm5sbXdEuX+6Uw623+i//+++dyahVK9X16/2d8/nnTp6nn/ZfTzh+/NE90G64oWrlVJaSEtXTTnM2+wULwufp3DmxYxhr17p77dBDVVeu9HfO2We7gf1whFP44N6EXnzRzD7JxJR+mrBrl+uVX3llYsu98krXgy0pUV23znnkdO9euR7j4sXOe+T992PnPfNMp8QjDUgHs3Wr82Rp2DA+G31JiRsszc+v2oPyrrvcPyDaoGt1s2yZ+20aNVJ9++3yx3budOa4UDfeqjJnjrvnOnf294bTqlWZe24okWLaN2lis2uTjV+lbzNyU8yXX8K2bW6GYyLp0MEFblu1yi1xt3mzi7FeqxK/+JFHOjn79o2dd+hQF+Hx9dej5yspgYsucjM9X3kFjjnGvzwirp7Zs91KTqtWVdx2745exp49brbnKae49qWKFi1cXJ28PBfI7L77ymYBz5rl5Ix3Jm4s2reHl192s6gvvTT6rONt2+C778pm4oZyzz1uqc5gAnGhYkXMjPUbGdWEnycD0B9YCCwBbglz/FFgprctAjaFHG8I/Aj8PVZd2dbTHz7cDY6GTkSqKp984npXN93kPm+5JbHlR2LvXre6U/fu0fPdequT67HHKlfPtm2qBxwQ2bzQqpUb9I1EYHBy4sTK1Z9ogmfwnn++ewsKuJJWlx/8gw+68v/1r8h5pk51ecaPj5wn4BEWsOW/+GLZQHc4s4+q6qpV7k303/9OaJOyGhJl3gFygO+AI4C6wCygbZT81wDPhaQ9DrxkSr8inTv7m2UbLxs2lP3RWrasnuXzIhFYtnHmzPDHA+6dV1xRNfPM1KnOrh+6PfaYc/3s3j2ymenEE90YRajraSopKVF94IGyGcB9+7r5FIkc6wmtr6DAbZHqGDXK/VbffRe5nD17nInu8svL0mItZXjddW4/0WbNbCaRSr8bMClofzgwPEr+L4CTg/Y7A2OBwab0y7N2rev5/OUv1VP+4Ye7X/idd6qn/EisX+88RLp0cTF5grdLL3UhBXr1cuMZ1UXAM+aSSyoqtHnz3LF7762++qtCcFTO00+v3rqeftrV8/nn4Y8PG+bs/7HGgs45x91vgXzRImauXu3uDyjvbGBUjUQq/fOBZ4P2L4qkvIE8YDWQ4+3XAqYAh0dT+sAQYDowPTc3NxnXp0YQmN0aa4JOZbn99vKTq5LJnXe6iTvhtp493eBydTNihLu+Dz5YPn3YMOciGs38k2q++865y44bV731bNvmeum/+1344716OU+wWLz0krvWwbNzI3nvXH+9M2n27u3eZIzEkEilPyCM0n8yQt6bg48Bw4CbvO/W0w/h4oudl0NNMjFkEnv3Oq8TEdU333RpW7e6yJl+J6hlA3/8o/MSCn0IlpS4N44hQ2KXUVLiJmo1bhz9YRro5Q8eXDamkIhAfYZ/pe/Hl2Ol11MP0BxYFSHvQODloP1uwDARWQ48BFwsIvf7qDPjUXWrZJ18slsGz0g8tWrBv/8NhYUwaBB8+61b6WvrVrfOq+EYOtR5CT37bPn0VaucB1hgjYYxY5y3Ua1a7jN4wXIR5x22bRv86U+R63rgAVfXbbdBmzYubeHCRLbGiEmspwJQG1gKtKRsILddmHxHA8sBiVDOYKynX8rMma6X8/zzqZYk81m50k1GatHCTTTr2LH6BkdrGn4nSJ10krPJB791vv22u0c//tj/qla33+6OffBBxTpWr3YD7Jdc4vYXLbL/QCIhUT19VS3GmWkmAfOBcao6V0RGisiZQVkHAWO9yo0YBK+SZVQvzZq59XZ/+gkWLHA9W5FUS1X9jBkDQ4a4lbpU3eeQIeV76AGGDoUffoD//rcsbfZs99mhg+uZx/K7B7j1Vjfv4aqrYOfO8scefND55t9+u9tv2RLq1LGeftLx82RI5pYNPf3du1WPOEL1uONSLUl28frrLpLptm2pliQ5xHKbDGbPHjfI3q9fWVpRkev9q8b2uw/m/ffdsTvuKEv76afyvfwAbdv6W/DHiA02Ize57NjhP++LL8LSpWU9HiM5nHMOvPUWNGiQakmSw4oV/tNr14Yrr3TjTIsXu7TZs8tm4ubmhi8rXHrfvm5d2/vvd29W4Hr5u3ZVfDNo06YsT1WI5/+X7ZjSTwCrV7uFr++4I3bePXvgr3+Fzp3dtHvDqC7iUdQAV1zhlP8//+nMMAsWlA3i3nNPWXiFAPXru/RwPPywe7hedZUzq/3jH3DhhdC6dfl8bdrAkiXuf1FZHnoIDjnElWP4wM/rQDK3dDTvBGYX5uREnoUaIBAfvaZM/zcyF7+Dr8FccIHLc8ghLn/TpmX5442aGZjNG1hxLNy6y4F1FCJFGY3FkiVlE70GD65cGZkCFmUzOQT8js85x/1Bjjsu8uzFPXtcTJjCwuzxHjFSS7yKOuB9k4jomHv3ulAY4OakhOOrrzRmbJ9IlJS4MYj993erluXk+F+8PhMxpZ8kArMLFy8u67X84x/h8wZi2k+YkFQRjTQkVbHoc3MrKv2qLIoyf75bMyDSEpibN7vyY62YFo7ALOAnn3QB3OrVc2E+shVT+kkgeHahqut59Onjoj+uWlU+b6CX36mT9fKN6FTGLJMo4vHSSRSHHRa/aWbDBhfC4dhjy+YWXHttdvf2/Sp9G8itAg8+WDa7EJzv9z//6TwJrr++fN4xY1xc8rvuyg4fcaPy+PWJrw7iHfxNBEcfHb8Hz/DhsG4dPP102Yz2m25yA9H33pt4GTMJU/qV5KefnIK/8MLyi3AcdZT7c77yCrz7rksrLoa//AU6dYIzzkiNvEb6EI+rZaKJ10snEQTcNtXntM4vvnDK/rrr3H8qwGGHObfTF15wLtFGeEzpV5LQ2YXB3Hyz670MHep6aIFe/ogR1ss3YpOK3naAoiK3olhenrtX8/LcflFR9dXZpg1s2uR67rHYs8cp9sMPh7vvrnj85ptdb786H1Jpjx8bUDK3dLDpB2YXRvJIUFWdMsXZQv/8Z7eSVDbFezGqRipt+qlg0iQtjfETi/vvj+0Mcc01bsH7aAu/ZCKYTb/6CMwujDajtlcvGDzY5V2yxHr5hn9S0dtOJYFom7Hs+suWud792WfDmWdGznfzzc7OH822v2qVW5853BY6npJx+HkyJHOr6T19P738AOvWuXj51ss3jMjs3eveZK6/Pnq+Sy5RbdDA35rBgd5+sKvorl2q//d/bhnKSGsrg+p551WpOSkD6+lXD356+QGaNoWZM+GDD6yXb1Qv0WLd13Rq1YrtwbNnD0ycCOef7+z5sbj5Zlfuvfe6Qd3hw92YyIABsGgRjBwJr75aceveHebNS1zbaiR+ngzJ3GpyT3/tWtfLv+iiVEtipJJUTZyKJk9lxgBqUjsGDVJt2TLy8Q8/dO164w3/ZQ4bVjbvoFYt1bPOUv3vf6OvVHf99e7apeObOTY5K/H84x/uis2enWpJjFRREwdZ4wmhHKCmtePuu52C3r49/PFrrnEdrl9+8V/mqlUuTMPdd7uFdPzw6KPuWvz8s/96agp+lb6Zd+Jg/HgXJbB9+1RLYqSKVE6cikRl/PprWjvatHGPnkBY52BU3X+vX7+KcwiiceihbrGiO+90C+n4IS/PfX7/vf960g1fSl9E+ovIQhFZIiK3hDn+qIjM9LZFIrLJS+8oIl+KyFwRmS0iFyS6Acli82aYPNl5Dph9PntJ9MSpRNjiK+PXn8oJYOE4+mj3Gc6u/803blWvs86qfjkC1yxV1yEZxFT6IpIDPAWcCrQFBolI2+A8qnq9qnZU1Y7Ak8Dr3qHtwMWq2g7oDzwmIo0S2YBk8c47bjDp7LNTLYmRSqIp2HgVeDzLGQafE1pHZWbRpnICWDhat3adqXBKf8IE197TT69+OQI9/UxW+rGN/tANmBS0PxwYHiX/F8DJEY7NAlpHq6+m2vQvuMAFeIo2CGRkPpFs4VdfHb+NPF5bfDQ7fLyDsjXNpq/qFq4fNKhien6+6gknJEeGkhI3dnDDDcmpL5GQqIFc4Hzg2aD9i4C/R8ibB6wGcsIc64pbWL1WtPpqotLfudPF7L788lRLYtQEwinYygymxhvRsjJ1xNuOVNK/v1trIpjvvnNtfOSR5Mlx9NGq55+fvPoShV+lX9vHy0A4C3ak0EgDgVdVdW+5AkQOBf4DXKKqJRUqEBkCDAHITdX7ZRQmT4atW820YziKiirOjr3oovB5o5kJcnPDDxhG+gsk2g4frh2ppE0b+OQTKClx5hxwph1Ijj0/QF6eDeSuBIKnQzQHVkXIOxB4OThBRBoC/wVuV9Wp4U5S1VGq2kVVuxx00EE+REouEya49T5POinVkhg1lcrYyOO1xdc0O3yiadPGeRD9+GNZ2vjxbnH2I45Inhy5uZlt0/ej9KcBrUWkpYjUxSn2iaGZRORooDHwZVBaXeANYLSq/l9iRE4uJSVO6Z96KtSrl2ppjJpKNAUeaYA33hg7qQh7nExCY/D8/DN89lny37Dz8mDNGti5M7n1JouYSl9Vi4FhwCScTX6cqs4VkZEiEhz2aBAw1rMtBfgtcAIwOMils2MC5a92pk2D1avNtGNEJ5ICh+geOkVFsHy561wsXx7d3JLpgdhClf5bb7nrkuz/XuDN6YcfYuf9/nsX6jmtgrT5Mfwnc6tpA7m33OKWYNuwIdWSGOlIogdfM5mSEtVGjVSHDnX7Z52levjhyQ+JMHmy+40++CB23sAM3tGjq12smGAzchPDhAnQuzc0bpxqSYx0pKZNgqrJiJQFXtu+Hd57LzWTIeOZlRt4K0mrAHepFqAms3AhzJ9vpp1EkcpIkKmqO9MHXxNNYOnE995za02n4r/XrJl70Ph5MAeU/vvvu3GAdMCUfhRS4S6WqVRm9mll6wlV7smqOxyZPviaaNq0cQucjB4NjRpBz57Jl6FuXbferl+lf/zxbuzhlVeqX7aE4McGlMytJtn0u3WrOFnEqBzJsG1HmmXapEn0uqt7klJNmwRVk3njjbIJahdemDo5unVT7dMnep6NG52sDzzgFko69tjkyBYJzKZfNX76CaZONdNOokiGbTtS5Mj16yPXnYy3gHg8dLKdgAePamr/e3589RcudJ9t2rjfdNq08FFCaxqm9CPw5pupv/EyiWTYtuN9gOTm1rwQw9lOq1ZQuzbssw+cckrq5MjLc/dTSYX4AWUE7Plt2sCgQW4cIB0GdE3pR2D8eDcL0GLnJ4Zk2LYjPUCaNIlct3nX1Czq1HEzcE87DfbbL3Vy5ObC7t2wdm3kPAsWOHlbtnSDv717O6VfbqZSDcSUfhi2bnXr2lrsfEciPF+iTSyqTPnxhBh+/PHIdZt3Tc3j3Xfh+edTK4OfEMsLFsCRR7o3E4ALL4QlS5yZp0bjx/CfzK2yA7krVqj26qX6zjuVOr0cTzzhBmg+/rjqZaU71R2CtzLlZ3qIYSP1zJrl7oVx4yLnadNG9ZxzyvY3bVLdZx+3tGMkdu92k8969gy/XXll5WUm29bI3bVL9cADVX/3u0qdrqouVv4tt7ir0rNnzYqdnyoPkOr2uqlM+ZkeYthIPQHPnIceCn98927V2rVVhw8vn37eeaoHH6y6Z0/484YOdeX26KF64okVt8Bs5MqQdUpf1T0l69dX3bo1/nM3bFA95RR3RYYMcTH0awqp7I3GG/M9GeVXt0yGoarasGHkXvuCBe6ee+GF8umvv+7Sw1kcnnrKHbvxxsTLqupf6WeUTb+oyHleBCZV+eXbb+HYY+Gjj+Dpp922zz7VI2NlSKWHSXXbvCtTvtnhjWQQzW0z2HMnmNNOc5PKQselPvwQ/vhHt+Tj/fcnXtZ4yCil3727G4B58UX/57z2mptR98svMGWK89GuaUTzMIk0CJqosAOV8bqJp+7KlG+zXI1kEHDbDEdA6QcWdA+wzz4wYAC88YbTKeB89wcMcA+IMWMgJ6f6ZPaFn9eBZG5VnZE7fLiLirlmTey8Tz7pXreOP171xx+rVG21EsmG3aRJ4tZrjWbXjsfmXdmB2Xht6smww5utP7u5+mo3ThiOwYNVDz00/LEpU9x9P2aMGxs4+mjVpk1Vly6tPllV/Zt3Uq7kQ7eqKv1vv3WteuKJ6PnWr3fr3vbvX7Ps9+GIN7xATk749MosuB0vmRJK2Lx6jPvuc797uDHC4493A6/h2LvXhYTu189tdeokxxMwa5W+qmpBgepxx0XPc/vtrvVz5lS5uqQQrtcZaUAz0paMBbczZZA1Ux5eRuV56SX3m8+dWz49EPf/6qsjn3vTTWX3zLPPVq+cAfwq/Yyy6QcoKoL//S9yHIwNG+CJJ5ydLV1m3IaL3xJp4DKSzTDRC26Hs91nyiCrzdQ1Avds6G++bh1s2lRxEDeYiy92s3VvuAF+//vqk7Ey+FL6ItJfRBaKyBIRuSXM8UeDlkNcJCKbgo5dIiKLve2SRAofiUAcjJdeCn/8scdgyxa4445kSOOfeAdfIw1oDhlS/QtuRwpUdtppiR1ktTj4RqqINCs30iBuMO3auQXeH3qoemSrErFeBYAc4DvgCKAuMAtoGyX/NcBz3vcDgaXeZ2Pve+No9SUqtPKJJ6q2bl1xqbUNG5z/7fnnJ6SahFFZG3KkwcbqHnyNZv5I1ABoKu3qZtM3iovd+Nitt5ZPf/ppdz8sX54auSJBomz6QDdgUtD+cGB4lPxfACd73wcBTwcdexoYFK2+RCn9Z591rfvqq/Lpd97p0mfPTkg1CSPVNuR4FXUybPfpdk2MzCMvr2Jc/+uvV913XzdgW5Pwq/T9mHeaAcHrwq/00iogInlAS+CjeM4VkSEiMl1Epq9bt86HSLE57zy3Ak6wz/7Gjc60c955LpJfTSLVNuR4Y76nMlRyTb0mRuaRl1dxrdwFC5xpp1aajoj6ETtcnEmNkHcg8Kqq7o3nXFUdpapdVLXLQQcd5EOk2DRq5Ga/jR0LxcUuLWDLv/POhFSRUNLNhpzKUMk19ZoYmUe4WbkLFkQfxK3p+FH6K4HDg/abA6si5B0IvFzJcxNOUZGLh/3hh260/fHH4dxzIT8/WRKEJ56wwDV1lmm0UMmJIt2uiZF55OXBypWw1+vG7tjh3vrSWen7senXxg3AtqRsILddmHxHA8sBCUo7EFiGG8Rt7H0/MFp9iVwjd8cO50970UWqI0Y4e/DMmQkrvpREDZqaDbkidk2MVBIYtF2xwu0HQi6PHZtaucKBT5t+bR8PhWIRGQZMwnnyPKeqc0VkpFfJRC/rIGCsV3ng3A0i8hcgsKzASFXdUPlHVHzUqwfnnw8vv+wWOjj3XCgoSGwdAdfFQEC0gOtigNtuc6+HubmuhxoteJrZjStSVGTXxEgdwb76hx8eOdBaWuHnyZDMLZE9fVXVyZPLetRV7eWH63XGGxcn3tmyhmGkjnnz3P9zzBi3f/fd7r/6yy+plSscZPOM3GBOOMEttjxggL9efrSoleEmI4WO7AdYvz58jz7e2bKGYaSO0Fm5CxY4O3/oWFM6EdO8k+7UqgUzZjhTTyyimWoimWVycsoGefywd6+7YYLLssFJw6iZNGgATZqUV/ppbdohw+LpR+KAA/wtihLN3h7JNzygxIOpX9/dKOEIeLlUp9dLNFIV1sAw0pXcXNcBLCmBhQtN6WcU0SYDRTK/RFLijz8e2d0wVZN+IpmoTPEbRmQCvvo//ug6gdFi7qQDpvSDiDYZKJrPeDglngw/9nhJ5bKLhpGuBGblzp/v9q2nn0HEUuzxKvGaNo0/1WENDCMdyc2FrVtduHYwpZ9RxFLsNU2Jx4uFNTCM+AmEWH7vPTc++KtfpVaeqmJKP4R0V+zRsLAGhhE/gU7Rl1+6Xr6EiyiWRpjSzyJq4jiDYdR0Akp/7970N+1AFvjpG+WxsAaGER8HH+xcvnftygylbz19wzCMKNSq5eLugCl9wzCMrCAwmGtKP42xmamGYfglN9eFXDniiFRLUnWy0qYfLcaO2bsNwwhl2DDo1s0twZruiJaFv68RdOnSRadPn16tdbRoET46Zl6ec9M0DMNIN0Rkhqp2iZUvK807NjPVMIxsJSuVvs1MNQwjW/Gl9EWkv4gsFJElInJLhDy/FZF5IjJXRF4KSn/AS5svIk+IpH4+m81MNQwjW4mp9EUkB3gKOBVoCwwSkbYheVoDw4HuqtoOuM5L/zXQHcgH2gPHAr0S2YDKYDNTDcPIVvx473QFlqjqUgARGQucBcwLynMF8JSqbgRQ1bVeugL1gLqAAHWANYkRvWrYzFTDMLIRP+adZsAPQfsrvbRgjgKOEpHPRWSqiPQHUNUvgcnAam+bpKrzQysQkSEiMl1Epq9bt64y7YiI+eMbhmGU4aenH84GH+rnWRtoDfQGmgOfikh7oClwjJcG8L6InKCqn5QrTHUUMAqcy6Zv6WNg/viGYRjl8dPTXwkcHrTfHFgVJs8EVd2jqsuAhbiHwDnAVFXdpqrbgHeA46sutj9spSjDMIzy+FH604DWItJSROoCA4GJIXnGAycCiEhTnLlnKbAC6CUitUWkDm4Qt4J5p7owf3zDMIzyxFT6qloMDAMm4RT2OFWdKyIjReRML9skYL2IzMPZ8P+squuBV4HvgDnALGCWqr5ZDe0Ii/njG4ZhlCejwzCE2vTB+eObe6ZhGJmGhWHA/PENwzBCyfgom+aPbxiGUUZG9/QNwzCM8pjSNwzDyCJM6RuGYWQRpvQNwzCyCFP6hmEYWUTGKH0LrGYYhhGbjHDZtMBqhmEY/siInr4FVjMMw/BHRih9C6xmGIbhj4xQ+hZYzTAMwx8ZofRtoXPDMAx/ZITSt8BqhmEY/sgI7x2wwGqGYRh+yIievmEYhuEPX0pfRPqLyEIRWSIit0TI81sRmScic0XkpaD0XBF5T0Tme8dbJEZ0wzAMI15imndEJAd4CjgZtwD6NBGZqKrzgvK0BoYD3VV1o4gcHFTEaLQniLoAAASMSURBVOAeVX1fRPYDShLaAsMwDMM3fnr6XYElqrpUVXcDY4GzQvJcATylqhsBVHUtgIi0BWqr6vte+jZVDZlGZRiGYSQLP0q/GfBD0P5KLy2Yo4CjRORzEZkqIv2D0jeJyOsi8o2IPOi9ORiGYRgpwI/3joRJC11NvTbQGugNNAc+FZH2XnpPoBOwAngFGAz8q1wFIkMAL1oO20RkoT/xw9IU+LkK56cr1u7swtqdXfhpd56fgvwo/ZXA4UH7zYFVYfJMVdU9wDJPabf20r9R1aUAIjIeOJ4Qpa+qo4BRfgSOhYhM97MifKZh7c4urN3ZRSLb7ce8Mw1oLSItRaQuMBCYGJJnPHCiJ1xTnFlnqXduYxE5yMvXB5iHYRiGkRJiKn1VLQaGAZOA+cA4VZ0rIiNF5Ewv2yRgvYjMAyYDf1bV9aq6F7gR+FBE5uBMRc9UR0MMwzCM2PiakauqbwNvh6TdGfRdgRu8LfTc94H8qokZFwkxE6Uh1u7swtqdXSSs3eL0tWEYhpENWBgGwzCMLMKUvmEYRhaRMUrfT3ygTEFEnhORtSLybVDagSLyvogs9j4bp1LGRCMih4vIZC+G01wRudZLz/R21xORr0Rkltfuu730liLyP6/dr3iedRmHiOR4Ezvf8vazpd3LRWSOiMwUkeleWkLu9YxQ+kHxgU4F2gKDvBAQmcq/gf4habcAH6pqa+BDbz+TKAb+pKrH4OZ6/MH7jTO93buAPqpaAHQE+ovI8cDfgEe9dm8Efp9CGauTa3FegwGypd0AJ6pqxyD//ITc6xmh9PEXHyhjUNVPgA0hyWcBL3jfXwDOTqpQ1YyqrlbVr73vW3GKoBmZ325V1W3ebh1vU9ycl1e99IxrN4CINAd+Azzr7QtZ0O4oJORezxSl7yc+UKbzK1VdDU5BAgfHyJ+2eOG5OwH/Iwva7Zk4ZgJrgfeB74BN3hwayNz7/THgJsoi8zYhO9oN7sH+nojM8MLUQILu9UxZOctPfCAjA/DCc78GXKeqW1znL7PxJjl2FJFGwBvAMeGyJVeq6kVETgfWquoMEekdSA6TNaPaHUR3VV3lhal/X0QWJKrgTOnp+4kPlOmsEZFDAbzPtSmWJ+GISB2cwh+jqq97yRnf7gCqugmYghvTaCQigU5bJt7v3YEzRWQ5zlzbB9fzz/R2A6Cqq7zPtbgHfVcSdK9nitL3Ex8o05kIXOJ9vwSYkEJZEo5nz/0XMF9VHwk6lOntPsjr4SMi+wJ9ceMZk4HzvWwZ125VHa6qzVW1Be7//JGqFpHh7QYQkQYisn/gO9AP+JYE3esZMyNXRE7D9QRygOdU9Z4Ui1RtiMjLuDDWTYE1wAhc0LtxQC4ujPUAVQ0d7E1bRKQH8CkwhzIb7604u34mtzsfN2iXg+ukjVPVkSJyBK4HfCDwDXChqu5KnaTVh2feuVFVT8+GdnttfMPbrQ28pKr3iEgTEnCvZ4zSNwzDMGKTKeYdwzAMwwem9A3DMLIIU/qGYRhZhCl9wzCMLMKUvmEYRhZhSt8wDCOLMKVvGIaRRfx/pRUQR3/Y8NAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3HHGEG1unQwfXyKN6dejZ0/XlSaVOnJH2yK0ig4FGr00b98sNVa4fzOjRLvdeOlCPHg3XXht8n/z8suv27XPBvaio7PoRIyzoGxMPd94Jkya5lnz+4ZBr1XJDtKxeDZddltj0JUrKFu/UrOnu6tEE/XADtEXblrd0wPezFkLGVL5Zs2DiRJfT37vXleG/+irccgs0bQqNG0OvXolOZWKkbPEOuJnsFyxwj3EV5S/TL/0UUKdO8Nx+RkbwwB+qOChexT6TJ7unjQ0b3I1s9Gh78jCppaDAFeWouqEW6tRJdIriI60HXPPLynJNs0q3yIlFqKeAp58O3hJoyJDoWwiFewKI9ukg2PbWE9mkg4cfdhm9559Pn4AfFVVNqtfJJ5+sXpk6VRVU58/37JBBvfaaasuWqiLu52uvhV4v4tIU7FW3btnl115zr3CflT5HqO0zM4Oft2XLyv1+jImXxYtVq1dXHTAg0SmJPyBHI4ixKV28s2yZa5L1yiuhK2LjLVQLoVDFQbG0HApV5BSKiOuhaExVVlQEZ57ppjFcvtz9f6QTT4t3RKSHiKwQkVUiMjzENr8XkWUislREXg9YP0BEVvpeAyK/hIpr08bNdhNNZW5lC9UxLFzFb6hhI/LzgxcVRRPwIf0GnDKpafx4mDsXxo5Nv4AfjXKDvohkAOOBi4EsoK+IZJXapg1wL3CWqrYFbvWtbwT8BTgNOBX4i4g09PQKwqhRw7XgSaa2uKHqBvw5+tJatPAuKGdmJuecwdaayVTUhg1w331w0UXQt2+iU5PcIsnpnwqsUtU1qrofmAKUbuz0J2C8qu4AUNVtvvUXAZ+o6nbfZ58APbxJemTCjcGzZAl8/XU8U+P06+fm2Cwudj/79Qs/iXuoz0LlZkIF96efjm3O4MrscWyVy6aiVGHYMPfz+efTb9TMqJVX6A/0AV4MWL4WeLbUNu8CY4CvgW+AHr71dwIjA7a7H7gzyDmGADlATosWLTyt3HjwQVfJuXdvybqdO1Vvukm1WjVXkTlypGphoaenjUmoCuFQn0VbwRvLuUOd44YbQp87Gi1bWuWyqZh333V/M3/9a6JTklhEWJEbSdC/MkjQf6bUNh8A04AaQGtgI9AAuCtI0L8j3Pm8bL2jqvr22+4qc3JUi4tVX39dtWlTF9z+/GfVgQPd5z16qObne3rquIg2uIc6RqgAHiooZ2SEDtbRpClUayaRWL4Nk47OOUe1dWvVAwcSnZLE8jLonwHMDFi+F7i31DbPAwMDlj8FTgH6Ai8ErH8B6BvufF4H/eXLS3Lz55/v3p98suq8ee7z4mLV559XrVFDtVUr1QULPD19lRAutx2uiWm0TU+jPbcx5cnNdX8vY8YkOiWJ52XQrw6s8eXgawKLgbaltukBvOx73xj4HsgEGgFrgYa+11qgUbjzeR309+93AR1U69dXHT8+eFHON9+oNmumWru26ksveZqEpBcutx1tTj/cE0Aw4Z4ywvHiCcdUfddf7/5nf/wx0SlJPM+CvjsWlwD/BVYDI3zrRgE9fe8FeBJYBiwBrg7Y94/AKt9rUHnn8jroq6oOHao6aJDqDz+E327rVtXu3d23cuON7ikgHYTLbUdbph8q9y8SXSc2v2jrMkz62LHD/d4HDUp0SpKDp0E/nq/KCPrROHBA9eab3TczZUpCkxI35QXRaIJ1qBtIZmb0gdrLnsX2ZJB6xo51v/fK7nFfVVjQr4DCQtXOnVWbN1fdsyfRqYkPr4Kil4E61A0k3NNENGmywF91FRWptmmjesYZiU5J8og06Kf0gGuxysiAZ56BjRvhkUcSnZr4CNZ3INbjRDsrWSjhPgsmVCe2ESPCT4Npqp5//xtWrnTt8010UnrsnYrq3x/eesv16D3uuESnpmoLNyvZunXR7RPt8NTVqrn8fWk25lDV1bMnzJnjMga1aiU6NcnBhlb2wJgxbjKW225LdEqqvnA9jqPdJ1zP4mC9hEM9AdiYQ1XT2rXwwQfwpz9ZwI9JJGVA8XwlQ5l+oMcec2XAH32U6JRUfbHUG0Szj9e9h63yNzndfbdrGrxhQ6JTklywoZW9sX+/m1RZFb791uX8TXIKV4Q0enR0M4aFminNJrZPrIICaN4czj0X3n470alJLpEW71jQj8CMGXDxxfDYY27OTZOcvCy7j6UOwlS+l16CQYPgs89c4DclrEzfQz16wOWXw0MPwebNiU6NCSXWsvtg9QChWg5F26LIeEcVnn3WTYPavXuiU1N1WdCP0FNPwYEDltNPZrFUFoca2rlRo+Dbh7uBVOYQ1Olu/36Xu58/H2680YZPrpBICv7j+Uq2itxAI0a4SsD33090Skwo0Va+etWDuLKHoE5127erfvqp6hNPqPbv74ZD6dBB9ZhjVA87rOS7q1dP9aefEp3a5IRV5Hpv3z445xz47juYPRu6dEl0ikxFhasHePXV4JW/kyeXXT9iRPRzHye6bkAVvvkGOnVy8ypX5nl27oStW2HbtpLXli2uccTChYd+d82aQevWrj9Gw4buqatRI/f+lFPcy5RlFbmVZMsWOP10V9TzzTfW1ruqi7bCNlSrntI9fsuT6I5hqnD//e6Gdeml8O67UL269+eZPx8uvDB4j2wRN491586Hvpo08T4d6SDSoF8Jv+bUdtRR8NFHcOaZ7p/lq6+gfv1Ep8rEavTo4EE8VD1AqCEdQuXoQ61PZGZBFe65Bx5/HM44Az78EG6/HcaN8/48t93mbiZPPglHHglHHFHyaty4cm40phyRlAHF85XMZfqB/v1v1erVVS+4wI3ZH8r27arffqu6ZImb8GHRItWFC93IgNu3xy+9JjQvZvoKNrR0MpbpFxer3nKLS8ewYW7gsttuc8tPP+3tud57zx33uee8Pa4JDhtls/JNmuS+wT/+8dCx9/fvV50+XfWKK0omcAn2OvJI1by8hCXfxKC8uQe8mC+gshQVuZsQqN56a8nfbGGhaq9ebs7o6dO9OdeBA6onnqh6/PHhM0XGOxb04+R//sd9iw8/7HLzd9zhgjmoHnGE6u23q775pupbb7n5et95R3XaNNWXX3ZPCgMHVl7aFi6s3OkfCwtV27dXfeCByjtHsvFymOZ4DvlcVKR63XXuHHffXXaCoD173DSidet6Mz79hAnuXP/8Z8WPZSJjQT9OiotVr7225J+2enXV3/7WPdqWl8MZPtztM2uW9+n64QfVhg1Va9VSnTHD++OruvGIQPXYY9NnljFV73Ln8ZofuLBQ9Q9/cMceOTL072rzZtdE8uijVb//Pvbz7dmj2rSp6plnptffRaJZ0I+jX35xOfqxY1W3bYt8v717VVu3Vj3hBNWff/Y2TVdfrVqzpmrbti7wz5zp7fFVVXv3LglUy5Z5f/xUF25uYi+99JI77oMPlr9tbq5rC9+xY+zt4R96yJ3v669j29/ExoJ+FfHxx5H/Q0bqww/dMUeNchNGd+rkJo/+17+8O8fmzW6kw7593bkee8y7Y6eLeOX0zz7bla1HmuueMcP9bi+/3BULRWPrVtXDD3dPuya+LOhXIVdd5XLjK1ZU/Fi7d6u2aKGaleWeQFRd4O/Y0QX+Tz6p+DlUVf/3f91fz4oV7qZy9tkVP2YyFgUUFFReuuJRpr9ypTvuI49Et9+4cW6/v/wluv2GDXM3jO++i24/U3EW9KuQzZtV69dXPe+8igeYW25xxQOlH63z8ly39tq1XXPTiigqcsVS3bu75fvvdy0/fvwxtuP9/LPqPfeoNmig+uWXFUubl3btUm3c2JWDV5ZQ9QPFxS6AfvBBxY4/YoT73WzaFN1+xcWqAwa4CPHee5Ht89//ujqtG26IOpnGAxb0q5i//c39Nl55JfZjzJnjgsef/xz887w819qmTh03zkmsPvnEpXXy5JLzguqrr0Z/rPnzXb2Df1yV4493uetk8Pe/68HK+UjrLPbuVV2/vuLnfustd+5WrWJv8lhYqNqsmerFF8e2/759rkVPvXqqy5eXv32fPm6cnB9+iO18pmIs6FcxRUWqp53mcpax5Jj373c5+WbNXA41lG3bXOA//HDV1atjS+uVV6o2alQSnIuKXGuNq66KLr0PPOAC6tFHu5ZA//qX+4u8997Y0lWevDzVKVMif5o6/XTV445zTyDnn1/+fvv3q55xhuqvfqWanx97OvfvV23TxrW+AtWJE2M7zowZbv+pU2NPy4YNqk2auMYGof6uiotdc2RIr+a7ycaCfhW0eLErDx04MPpinkcecb/NadPK33bDBlecdNZZLjcYjW3bXIezW289dP1117ljRpIr/fZb1S5dXHr79z+0Z/LAge47WLgwunRFol8/d87PPy9/26VL3bZPPKE6frx7/+ab4fe59149WDb/0EOxp/O559wxmjQpedJ4+eXoj3PVVe7mXNGWYZ9/7n4nPXuWrdj96ivVc85x6TzxRFenZBLDgn4Vdffd7rfSurUrS46kQmzlSldWf8UVkZ/n1VfdeR59NLr0Pf6422/p0kPXT5vm1n/2Wfj933/fNSVt0sR1VCstP991buvSxfXqDGf+/MjnSV2ypKSJ5CWXlL/9nXe6YPvDD+7G2LmzeyIJ1Yzx3/92xx88WPXSS90T2969kaUt0J497uZZrVrJDQTcd/baa5H3EcjPd40Dbrop+jQE8/TTh+bkFy5036O/Z/mzz5Y0HDCJYUG/ijpwwJXr/+Y3Jf/4p5ziWlNs2eIqfefOdT0dn35a9a67VNu1c0UK0VTWFRe7MtgaNdx4QJHuc/zx7gmhtN27XZC57bbQ+//8syujbt/eNe0LxV+eHaoZaHGx6ujRLvBF2sfht79139Gtt7pjL1kSetv9+11v6t69S9b93/+5/e68s+z2W7e64q2TTnKB/ssv3bbjxpWfrtIefvjQYB/4atQodGuf0jcDfyWsVz2yi4tLOnhddJH72bChyzTs2ePNOUzFWNBPAZs2ueKFTp1CB4KaNV2581tvRX/8H390wap9+8gC5+efu3O+9FLwz3v0UP31r0MXTY0d6/Yvr9locbEL0rVruxYhgX76yT3RgGq3bofmPkOZN69kux9/dIFywIDQ2/ufWkpPlnPddS73/+23JeuKitx116rlOjb5de3qms5GUwmbl+cqTUP9rkO9gk34Uq2aO7+X9u1Tzc52lbUjRqju2OHt8U3FWNBPMUuWuJzv3/7mBsVasMCVr1e0iad/KIW77ip/22uucUUPoYot/GXfwYqkdu50wek3v4ksXZs2uXN161ZSjvzf/7r+B9Wqqf71r+7a/T2Pw7Uuuegid25/ReRNN7knnFBDDVx+uepRR5UtXtq2zeVuu3cv+d6feMJd8/jxh277wQdufTRl8bfe6q7t6KOjD/zBXg0bRn7uSO3b536XJvl4GvSBHsAKYBUwPMjnA4E8YJHvNTjgs6KA9dPLO5cF/fi7/npXLPDFF6G38ZcRDxsWepv1691f1OOPl/1s5Ej3WTSDeb34otvnhRdcEK1f3wXvwOamP/zgWtd06xb8Bjh7tjvGmDEl69audRWTwYpq/D2Nhw8Pnqbnn3fHe/119wRRo4Z7Kil97uJi9wSVlRVZr9a1a93Na/Dg4J22ataMLfCb9OFZ0AcygNXAsUBNYDGQVWqbgcCzIfbfE0lC/C8L+vG3e7crImrVKnSzPH/RTHnl/x06uAAcaPNmF8T69o0uXcXFrsNa7druptS5s+q6dWW387en/8c/yu5/9tmuCKv000nfvq4opXSu9dFH3bFC9Y4uLHRFHEcd5b6zY44J3Txz8mR3rHffLf9ar73WXefGjW65dBn9K6+4J4DS4/XUretuhMECfrjhnk3q8TLonwHMDFi+F7i31DYW9Ku4r792RQv9+rkc/yefuDF8/vlP17b9+ONdhXJ5RoxwOeXAZpjXX+9yxLH0C1i1ylVgXntt6GKloiIX3Bs2PLSC2N9O/dlny+6zYIGWqSz2V1R37Ro+TXPnuiBarZp7kgjlwAHXCuu008IXwy1a5I53zz3hz+u/iTRpcmgQD/ZkUKtW+RO42A0htXgZ9PsALwYsX1s6wPuC/hYgF3gbOCbgs0IgB/gG6B3iHEN82+S08Lr2yUTsvvuC5xiMyGLCAAAX40lEQVT9r1AVuIH8rVz8vXW/+87dBCrSdLC8ppuqrsdsjRrupqXqgmx2tgtmoZoSXnCBy7H7K7G/+sqlfdKk8s83fnxkvaf9Pa1DDZ9dXOx6zDZsWP5MaoWF7qbUsWPZIiN/AAf3fb/8cvkTviTTjF6m4rwM+lcGCfrPlNomE6jlez8U+Czgs6N9P48F1gHHhTuf5fQTp7jYNTf89FP3c84c1x576VJXrBJJpXFRkWvu6C/K+d3vXO/fcE00veKf0GbGDPeEAuF7s86ceeg2gwa5tHrZwWjfPvd9XHTRoeuLi13b/jPP1JD1IMG88krJDXjTJteC5uef3fG2bHEB3//EEG7o5niN8GniJ67FO6W2zwB2hfjsJaBPuPNZ0K/6Bg0qGTwNvB02OpyCApcTbt3aVaCecEL4p4TiYpdrPukkV5dx2GGuWabX/COS+tvMz5pV0ou1eXP3NBDpEMYHDrhmsaWDdbVqrkgnsPVUuMAe7oZgxT5Vk5dBvzqwBmgdUJHbttQ2RwW8/y3wje99w4AngMbAytKVwKVfFvSrvnfecX9ZRx/tcrnx7Jo/a1ZJACtv2ARVF9CgpO3/f/7jfZp27nQdw7p3Vz333JLv5plnYhtcbvNml+N/4QXVp55yHdVGjnQT+TzzTMl24YpwQt0QgrX5D9UBzG4GycXrJpuXAP/1teIZ4Vs3Cujpe/8IsNR3Q5gFnOhbfyawxLd+CXBdeeeyoF/1/fRTSRPD0u3X4+Guu1yHqUhyz/v3u05M4MaOqayx8++5x52jaVPXEmrfvso5T2nhJmsPFtxDtQQKdzMwycHToB/PlwX91NCzpytqiXVY4Hh66in3nxDYlt9re/e6kShjGY+nsgS7IYQq9gn1sjqA5BFp0Be3bfLIzs7WnJycRCfDVNCePVBYCA0aJDol5SsogGefhaFDoV69RKcmsVq1gvXrI99eBIqLKy05JgoiMl9Vs8vbrlo8EmPSz+GHV42AD1CnDtx1lwV8gNGjoW7dQ9fVrQuZmcG3b9Gi8tNkvGVB3xhzUL9+MGECtGzpcvEtW7rlp58OfjMYPTox6TSxq57oBBhjkku/fu4VzIgRsGGDy+GPHh16O5O8LOgbYyIS7mZgqg4r3jHGmDRiQd8YY9KIBX1jjEkjFvSNMSaNWNA3xpg0YkHfGGPSiAV9Y4xJIxb0jTEVMnmyG7OnWjX3c/Lk8OtNYlnnLGNMzCZPhiFDYN8+t7x+vVv++mt4+eWy68E6eCWajbJpjIlZqFE5MzKgqKjs+pYtYd26yk5VerJRNo0xlW7DhuDrgwX8cNub+LGgb4yJWaihlTMyotvexI8FfWNMzEKNvz9kiA3FnKws6BtjYhZq/P2//S34en8lrrXsSRyryDXGxFXpFj/gngICbwomelaRa4xJSiNGHBrwwS2PGJGY9KQbC/rGmLgK1YLHWvbEhwV9Y0xchWrBYy174sOCvjEmrkK1+LGWPfFhQd8YE1ehWvxYJW582Ng7xpi4s0nWE8dy+saYpGHt9yufBX1jTFLwt99fvx5US0bmnDzZbgZesuIdY0xSCNV+/5ZboKDAhmn2SkQ5fRHpISIrRGSViAwP8vlAEckTkUW+1+CAzwaIyErfa4CXiTfGpI5Q7fTz860zl5fKzemLSAYwHvgNsBGYJyLTVXVZqU3fVNUbS+3bCPgLkA0oMN+37w5PUm+MSRktWgQfmz8U68wVm0hy+qcCq1R1jaruB6YAvSI8/kXAJ6q63RfoPwF6xJZUY0wqC9V+PzMz+PbWmSs2kQT9ZsD3AcsbfetK+52I5IrI2yJyTDT7isgQEckRkZy8vLwIk26MSSWh2u8//bR15vJSJBW5EmRd6aE53wfeUNVfRGQo8DJwXoT7oqoTgAngRtmMIE3GmBQUrv3+iBGuSKdFCxfwrRI3NpEE/Y3AMQHLzYHNgRuoan7A4t+BxwL27V5q38+jTaQxJr1ZZy7vRFK8Mw9oIyKtRaQmcDUwPXADETkqYLEnsNz3fiZwoYg0FJGGwIW+dcYYYxKg3Jy+qhaKyI24YJ0BTFTVpSIyCshR1enAzSLSEygEtgMDfftuF5GHcDcOgFGqur0SrsMYY0wEbOYsY4xJATZzljHGBJHuQzpY0DfGVFnRBvBw4/ukCwv6xpgqKZYAbvPzWtA3xlRRsQRwm5/Xgr4xpoqKJYDb/LwW9I0xVVQsAdzm57Wgb4ypomIJ4DY/r02iYoypovyBOtoxedJ9SAcL+saYKivdA3gsrHjHGJOS0r0TViiW0zfGpBx/G36bV7csy+kbY1KOdcIKzYK+MSblWCes0CzoG2NSjnXCCs2CvjEm5VgnrNAs6BtjUo51wgrNWu8YY1KSteEPznL6xhiTRizoG2NMGrGgb4wxYaRaz14r0zfGmBBSsWev5fSNMSaEVOzZa0HfGGNCSMWevRb0jTEmhFTs2WtB3xhjCF5hm4o9ey3oG2PSnr/Cdv16UD20wjbVevaKqiY6DYfIzs7WnJycRCfDGJNGWrVygb60li1h3bp4pyY2IjJfVbPL285y+saYtJeKFbahRBT0RaSHiKwQkVUiMjzMdn1EREUk27fcSkQKRGSR7/W8Vwk3xhivpGKFbSjlBn0RyQDGAxcDWUBfEckKsl094GZgTqmPVqtqJ99rqAdpNsYYT6VihW0okeT0TwVWqeoaVd0PTAF6BdnuIWAM8LOH6TPGmEqXTkMxRxL0mwHfByxv9K07SEQ6A8eo6gdB9m8tIgtF5AsROTvYCURkiIjkiEhOXl5epGk3xhjP9OvnKm2Li93PVAz4EFnQlyDrDjb5EZFqwFPAHUG22wK0UNXOwO3A6yLyqzIHU52gqtmqmt2kSZPIUm6MMQlUVQdiiyTobwSOCVhuDmwOWK4HtAM+F5F1wOnAdBHJVtVfVDUfQFXnA6uB471IuDHGJEqodv1VIfBHEvTnAW1EpLWI1ASuBqb7P1TVXaraWFVbqWor4Bugp6rmiEgTX0UwInIs0AZY4/lVGGNMHFXlgdjKHVpZVQtF5EZgJpABTFTVpSIyCshR1elhdj8HGCUihUARMFRVt3uRcGOMSZSq3K7feuQaY0yUkrEHr/XINcaYSlKV2/VXiZmzDhw4wMaNG/n5Z+sCUBXUrl2b5s2bU6NGjUQnxZhK4W/OOWKEK9Jp0cIF/KrQzLNKFO+sXbuWevXqkZmZiUiwFqQmWagq+fn57N69m9atWyc6OcakjZQq3vn5558t4FcRIkJmZqY9lRmTpKpE0Acs4Fch9rsyJnlVmaBvjDGm4lIy6HvdPTo/P59OnTrRqVMnmjZtSrNmzQ4u79+/P6JjDBo0iBUrVoTdZvz48Uz2qEtf165dWbRokSfHMsakjirReica/u7R/t5ygdOexVqznpmZeTCAPvDAAxx++OHceeedh2yjqqgq1aoFv49OmjSp3PMMGzYstgQaY0yEUi6nH8/u0atWraJdu3YMHTqULl26sGXLFoYMGUJ2djZt27Zl1KhRB7f157wLCwtp0KABw4cPp2PHjpxxxhls27YNgJEjRzJ27NiD2w8fPpxTTz2VE044gf/85z8A7N27l9/97nd07NiRvn37kp2dXW6O/rXXXqN9+/a0a9eO++67D4DCwkKuvfbag+vHjRsHwFNPPUVWVhYdO3akf//+nn9nxpjESrmcfry7Ry9btoxJkybx/PNuUrBHH32URo0aUVhYyLnnnkufPn3Iyjp0zpldu3bRrVs3Hn30UW6//XYmTpzI8OFlJyRTVebOncv06dMZNWoUM2bM4JlnnqFp06a88847LF68mC5duoRN38aNGxk5ciQ5OTnUr1+fCy64gA8++IAmTZrw448/smTJEgB27twJwJgxY1i/fj01a9Y8uM4YkzpSLqcf72nPjjvuOE455ZSDy2+88QZdunShS5cuLF++nGXLlpXZp06dOlx88cUAnHzyyawL0W/7iiuuKLPNV199xdVXXw1Ax44dadu2bdj0zZkzh/POO4/GjRtTo0YNrrnmGmbPns2vf/1rVqxYwS233MLMmTOpX78+AG3btqV///5MnjzZOlcZk4JSLujHu3v0YYcddvD9ypUrefrpp/nss8/Izc2lR48eQdur16xZ8+D7jIwMCgsLgx67Vq1aZbaJtjNdqO0zMzPJzc2la9eujBs3juuvvx6AmTNnMnToUObOnUt2djZFRUVRnc8Yk9xSLugnctqzn376iXr16vGrX/2KLVu2MHPmTM/P0bVrV6ZOnQrAkiVLgj5JBDr99NOZNWsW+fn5FBYWMmXKFLp160ZeXh6qypVXXsmDDz7IggULKCoqYuPGjZx33nk8/vjj5OXlsa90BYkxpkpLuTJ9cAE+EWNgdOnShaysLNq1a8exxx7LWWed5fk5brrpJv7whz/QoUMHunTpQrt27Q4WzQTTvHlzRo0aRffu3VFVLr/8ci699FIWLFjAddddh6oiIjz22GMUFhZyzTXXsHv3boqLi7nnnnuoV6+e59dgjEmcKjH2zvLlyznppJMSlKLkUlhYSGFhIbVr12blypVceOGFrFy5kurVk+v+bb8zY+Ir0rF3kitSmHLt2bOH888/n8LCQlSVF154IekCvjEmeVm0qGIaNGjA/PnzE50MY0wYkycn77DLFvSNMcZDlTEqgJdSrvWOMcYkUrJPmm5B3xhjPJTsk6Zb0DfGGA/Fe1SAaFnQj0D37t3LdLQaO3Ysf/7zn8Pud/jhhwOwefNm+vTpE/LYpZuoljZ27NhDOkldcsklnoyL88ADD/DEE09U+DjGmBLJPmm6Bf0I9O3blylTphyybsqUKfTt2zei/Y8++mjefvvtmM9fOuh/9NFHNGjQIObjGWMqTyyjAng9B0g4Va71zq23gtdzg3TqBL4RjYPq06cPI0eO5JdffqFWrVqsW7eOzZs307VrV/bs2UOvXr3YsWMHBw4c4OGHH6ZXr16H7L9u3Touu+wyvv32WwoKChg0aBDLli3jpJNOoqCg4OB2N9xwA/PmzaOgoIA+ffrw4IMPMm7cODZv3sy5555L48aNmTVrFq1atSInJ4fGjRvz5JNPMnHiRAAGDx7Mrbfeyrp167j44ovp2rUr//nPf2jWrBnvvfcederUCXmNixYtYujQoezbt4/jjjuOiRMn0rBhQ8aNG8fzzz9P9erVycrKYsqUKXzxxRfccsstgJsacfbs2dZz15gA0YwKEO/WPpbTj0BmZiannnoqM2bMAFwu/6qrrkJEqF27NtOmTWPBggXMmjWLO+64I+ygaM899xx169YlNzeXESNGHNLmfvTo0eTk5JCbm8sXX3xBbm4uN998M0cffTSzZs1i1qxZhxxr/vz5TJo0iTlz5vDNN9/w97//nYULFwJu8Ldhw4axdOlSGjRowDvvvBP2Gv/whz/w2GOPkZubS/v27XnwwQcBN1T0woULyc3NPTh89BNPPMH48eNZtGgRX375ZdibiTEmvHi39qlyOf1wOfLK5C/i6dWrF1OmTDmYu1ZV7rvvPmbPnk21atXYtGkTW7dupWnTpkGPM3v2bG6++WYAOnToQIcOHQ5+NnXqVCZMmEBhYSFbtmxh2bJlh3xe2ldffcVvf/vbgyN9XnHFFXz55Zf07NmT1q1b06lTJyD88M3gxvffuXMn3bp1A2DAgAFceeWVB9PYr18/evfuTe/evQE466yzuP322+nXrx9XXHEFzZs3j+QrNMYEEe/WPpbTj1Dv3r359NNPWbBgAQUFBQcnL5k8eTJ5eXnMnz+fRYsWceSRRwYdTjmQiJRZt3btWp544gk+/fRTcnNzufTSS8s9TrgnCv+wzBB++ObyfPjhhwwbNoz58+dz8sknU1hYyPDhw3nxxRcpKCjg9NNP57vvvovp2Makm2Bl9/Fu7WNBP0KHH3443bt3549//OMhFbi7du3iiCOOoEaNGsyaNYv169eHPc4555xzcPLzb7/9ltzcXMANy3zYYYdRv359tm7dyscff3xwn3r16rF79+6gx3r33XfZt28fe/fuZdq0aZx99tlRX1v9+vVp2LAhX375JQCvvvoq3bp1o7i4mO+//55zzz2XMWPGsHPnTvbs2cPq1atp374999xzD9nZ2Rb0jYmAv+x+/XpQLSm7v+SS+Lb2qXLFO4nUt29frrjiikNa8vTr14/LL7+c7OxsOnXqxIknnhj2GDfccAODBg2iQ4cOdOrUiVNPPRVws2B17tyZtm3blhmWeciQIVx88cUcddRRh5Trd+nShYEDBx48xuDBg+ncuXPYopxQXn755YMVucceeyyTJk2iqKiI/v37s2vXLlSV2267jQYNGnD//fcza9YsMjIyyMrKOjgLmDEmtFBl9x995Fr3xGusnoiGVhaRHsDTQAbwoqo+GmK7PsBbwCmqmuNbdy9wHVAE3KyqYWcWsaGVU4P9zow5VLVqLodfmggUF1f8+JEOrVxu8Y6IZADjgYuBLKCviGQF2a4ecDMwJ2BdFnA10BboAfzNdzxjjEkrydJTN5Iy/VOBVaq6RlX3A1OAXkG2ewgYAwTWPvYCpqjqL6q6FljlO54xxqSVZOmpG0nQbwZ8H7C80bfuIBHpDByjqh9Eu69v/yEikiMiOXl5eUETkWwzfJnQ7HdlTFmJnL87UCQVuWXbF8LB/2oRqQY8BQyMdt+DK1QnABPAlemX/rx27drk5+eTmZkZtLmjSR6qSn5+PrVr1050UoxJOomavztQJEF/I3BMwHJzYHPAcj2gHfC5LyA3BaaLSM8I9o1I8+bN2bhxI6GeAkxyqV27tnXYMiZJRRL05wFtRKQ1sAlXMXuN/0NV3QU09i+LyOfAnaqaIyIFwOsi8iRwNNAGmBttImvUqEHr1q2j3c0YY0wp5QZ9VS0UkRuBmbgmmxNVdamIjAJyVHV6mH2XishUYBlQCAxT1SKP0m6MMSZKEbXTj6dg7fSNMcaE51k7fWOMMakj6XL6IpIHhB/AJrzGwI8eJacqsetOL3bd6SWS626pqk3KO1DSBf2KEpGcSB5xUo1dd3qx604vXl63Fe8YY0wasaBvjDFpJBWD/oREJyBB7LrTi113evHsulOuTN8YY0xoqZjTN8YYE4IFfWOMSSMpE/RFpIeIrBCRVSIyPNHpqUwiMlFEtonItwHrGonIJyKy0vezYSLT6DUROUZEZonIchFZKiK3+Nan+nXXFpG5IrLYd90P+ta3FpE5vut+U0RqJjqtlUFEMkRkoYh84FtOl+teJyJLRGSRiPhnIfTkbz0lgn6ks3ulkJdwM5EFGg58qqptgE99y6mkELhDVU8CTgeG+X7HqX7dvwDnqWpHoBPQQ0ROBx4DnvJd9w7clKSp6BZgecByulw3wLmq2imgfb4nf+spEfSJfHavlKCqs4HtpVb3Al72vX8Z6B3XRFUyVd2iqgt873fjAkEzUv+6VVX3+BZr+F4KnAe87VufctcNICLNgUuBF33LQhpcdxie/K2nStCPaIauFHekqm4BFyCBIxKcnkojIq2Azrj5mFP+un1FHIuAbcAnwGpgp6oW+jZJ1b/3scDdgH/a8EzS47rB3dj/JSLzRWSIb50nf+uRjKdfFUQ0Q5ep+kTkcOAd4FZV/SkdZlLzDUfeSUQaANOAk4JtFt9UVS4RuQzYpqrzRaS7f3WQTVPqugOcpaqbReQI4BMR+c6rA6dKTt+TGbqquK0ichSA7+e2BKfHcyJSAxfwJ6vqP32rU/66/VR1J/A5rk6jgYj4M22p+Pd+FtBTRNbhimvPw+X8U/26AVDVzb6f23A3+lPx6G89VYL+wdm9fLX5VwMhJ3dJUdOBAb73A4D3EpgWz/nKc/8BLFfVJwM+SvXrbuLL4SMidYALcPUZs4A+vs1S7rpV9V5Vba6qrXD/z5+paj9S/LoBROQwEannfw9cCHyLR3/rKdMjV0QuweUE/LN7jU5wkiqNiLwBdMcNt7oV+AvwLjAVaAFsAK5U1dKVvVWWiHQFvgSWUFLGex+uXD+Vr7sDrtIuA5dJm6qqo0TkWFwOuBGwEOivqr8kLqWVx1e8c6eqXpYO1+27xmm+xerA66o6WkQy8eBvPWWCvjHGmPKlSvGOMcaYCFjQN8aYNGJB3xhj0ogFfWOMSSMW9I0xJo1Y0DfGmDRiQd8YY9LI/wOu88EOylfOugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = pain_train.history['accuracy']\n",
    "val_accuracy = pain_train.history['val_accuracy']\n",
    "loss = pain_train.history['loss']\n",
    "val_loss = pain_train.history['val_loss']\n",
    "epoch = range(len(accuracy))\n",
    "plt.plot(epoch, accuracy, 'bo', label='Training accuracy')\n",
    "plt.plot(epoch, val_accuracy, 'b', label='Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epoch, loss, 'bo', label='Training loss')\n",
    "plt.plot(epoch, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_classes = pain_model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_classes = np.argmax(np.round(predicted_classes), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((286,), (286,))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_classes.shape, test_Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 200 correct labels\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correct = np.where(predicted_classes==test_Y)[0]\n",
    "print(\"Found %d correct labels\" % len(correct))\n",
    "for i, correct, in enumerate(correct[:9]):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    plt.pcolormesh(test_X[correct].reshape(ydim, xdim), cmap='jet')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[correct], test_Y[correct]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 86 incorrect labels\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "incorrect = np.where(predicted_classes!=test_Y)[0]\n",
    "print(\"Found %d incorrect labels\" % len(incorrect))\n",
    "for i, incorrect, in enumerate(incorrect[:9]):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    plt.pcolormesh(test_X[incorrect].reshape(ydim, xdim), cmap='jet')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], test_Y[incorrect]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "    Class 0       0.73      0.84      0.78       182\n",
      "    Class 1       0.62      0.45      0.52       104\n",
      "\n",
      "avg / total       0.69      0.70      0.69       286\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "target_names = [\"Class {}\".format(i) for i in range(num_classes)]\n",
    "print(classification_report(test_Y, predicted_classes, target_names=target_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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}