CNN-0%overlap-Acc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN Fatiuge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.compat.v1  as tf\n",
    "import numpy as np\n",
    "import os,sys\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "import scipy.io as scio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17174, 200, 9)\n",
      "(17174, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_mat = scio.loadmat('X_train-3Classes-0%overlap.mat')\n",
    "key_name = list(X_train_mat.keys())[-1]\n",
    "X_train=X_train_mat[key_name]\n",
    "print(X_train.shape)\n",
    "\n",
    "Y_train_mat = scio.loadmat('Y_train-3Classes-0%overlap.mat')\n",
    "key_name = list(Y_train_mat.keys())[-1]\n",
    "Y_train=Y_train_mat[key_name]\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17174, 200, 3)\n",
      "(17174, 1)\n"
     ]
    }
   ],
   "source": [
    "#只取前列数据\n",
    "X_train=X_train[:,:,0:3]#只取前\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15456, 200, 3)\n",
      "(15456, 1)\n",
      "(1718, 200, 3)\n",
      "(1718, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_train, Y_train, test_size = 0.1, random_state = 0)\n",
    "#X_test, X_valid, Y_test, Y_valid = train_test_split(X_test, Y_test, test_size = 0.5, random_state = 0)\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train.reshape(-1,200,3,1)\n",
    "X_test=X_test.reshape(-1,200,3,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Anaconda3\\envs\\tensorflow1\\lib\\site-packages\\tensorflow\\python\\compat\\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "tf.set_random_seed(42)\n",
    "# Configuring a session\n",
    "session_conf = tf.ConfigProto(\n",
    "    intra_op_parallelism_threads=1,\n",
    "    inter_op_parallelism_threads=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Keras\n",
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras import regularizers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout,Flatten, Reshape, Softmax\n",
    "from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, TimeDistributed,LSTM,Bidirectional,BatchNormalization\n",
    "#from keras import backend as K\n",
    "from tensorflow.compat.v1.keras import backend as K\n",
    "from attention import Attention\n",
    "sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)\n",
    "K.set_session(sess)\n",
    "\n",
    "# callbacks:\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import TensorBoard\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.callbacks import ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utility function to count the number of classes\n",
    "def _count_classes(y):\n",
    "    return len(set([tuple(category) for category in y]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 3 15456\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# Initializing parameters\n",
    "epochs = 200\n",
    "batch_size = 256\n",
    "timesteps = 200\n",
    "input_dim = len(X_train[0][0])\n",
    "n_classes = _count_classes(Y_train)\n",
    "#input_shape = (timesteps, input_dim)\n",
    "\n",
    "print(timesteps,input_dim,len(X_train))\n",
    "print(n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**LSTM Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 100, 3, 32)        608       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 3, 32)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 50, 3, 64)         18496     \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 50, 3, 128)        24704     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 3, 128)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 25, 1, 128)        49280     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 3200)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 3)                 9603      \n",
      "=================================================================\n",
      "Total params: 102,691\n",
      "Trainable params: 102,691\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Initiliazing the sequential model\n",
    "input_shape = (256, 200, 3, 1)\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32,(18,1),activation='relu',strides=(2,1),padding='SAME',input_shape=(200,3,1)))\n",
    "model.add(MaxPooling2D(pool_size=(2, 1)))\n",
    "model.add(Conv2D(64, (9, 1), activation='relu',padding='SAME'))\n",
    "model.add(Conv2D(128, (3, 1), activation='relu',padding='SAME'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 1)))\n",
    "model.add(Conv2D(128, (1, 3), activation='relu', padding='VALID'))\n",
    "model.add(Flatten())\n",
    "\n",
    "# Adding a dense output layer with sigmoid activation\n",
    "model.add(Dense(n_classes, activation='softmax',kernel_regularizer=regularizers.l2(0.01),\n",
    "                activity_regularizer=regularizers.l1(0.01)))\n",
    "\n",
    "model.summary()\n",
    "#-------------------\n",
    "# Compiling the model\n",
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])#二分类：optimizer='adam', loss='binary_crossentropy'；多分类时激活函数改为softmax。损失函数改为：loss=‘categorical_crossentropy’\n",
    "\n",
    "filepath=\"CNN-weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5\"\n",
    "\n",
    "mc = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "\n",
    "tb = TensorBoard(log_dir='./logs',  # log 目录\n",
    "                 histogram_freq=1,  # 按照何等频率（epoch）来计算直方图，0为不计算\n",
    "                 batch_size=64,     # 用多大量的数据计算直方图\n",
    "                 write_graph=True,  # 是否存储网络结构图\n",
    "                 write_grads=True, # 是否可视化梯度直方图\n",
    "                 write_images=True,# 是否可视化参数\n",
    "                 embeddings_freq=0,\n",
    "                 embeddings_layer_names=None,\n",
    "                 embeddings_metadata=None)\n",
    "\n",
    "es=EarlyStopping(monitor='val_loss', patience=16, verbose=2)\n",
    "rp=ReduceLROnPlateau(\n",
    "    monitor='val_acc',\n",
    "    factor=0.25,\n",
    "    patience=10,#\n",
    "    verbose=2,\n",
    "    mode='auto',\n",
    "    min_delta=0.0001,\n",
    "    cooldown=0,\n",
    "    min_lr=0\n",
    ")\n",
    "\n",
    "# callbacks_list = [mc,tb,es,rp]\n",
    "callbacks_list =[mc,es,rp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13910 samples, validate on 1546 samples\n",
      "Epoch 1/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 1.7819 - acc: 0.4186WARNING:tensorflow:From D:\\Anaconda3\\envs\\tensorflow1\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_v1.py:2048: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.45408, saving model to CNN-weights-improvement-01-0.45.hdf5\n",
      "13910/13910 [==============================] - 29s 2ms/sample - loss: 1.7819 - acc: 0.4186 - val_loss: 1.0560 - val_acc: 0.4541\n",
      "Epoch 2/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 1.0229 - acc: 0.4967\n",
      "Epoch 00002: val_acc did not improve from 0.45408\n",
      "13910/13910 [==============================] - 28s 2ms/sample - loss: 1.0229 - acc: 0.4967 - val_loss: 1.6553 - val_acc: 0.2277\n",
      "Epoch 3/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.9286 - acc: 0.5513\n",
      "Epoch 00003: val_acc improved from 0.45408 to 0.47283, saving model to CNN-weights-improvement-03-0.47.hdf5\n",
      "13910/13910 [==============================] - 28s 2ms/sample - loss: 0.9286 - acc: 0.5513 - val_loss: 1.0373 - val_acc: 0.4728\n",
      "Epoch 4/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.8252 - acc: 0.6260\n",
      "Epoch 00004: val_acc improved from 0.47283 to 0.65783, saving model to CNN-weights-improvement-04-0.66.hdf5\n",
      "13910/13910 [==============================] - 28s 2ms/sample - loss: 0.8252 - acc: 0.6260 - val_loss: 0.7324 - val_acc: 0.6578\n",
      "Epoch 5/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7306 - acc: 0.6703\n",
      "Epoch 00005: val_acc improved from 0.65783 to 0.68111, saving model to CNN-weights-improvement-05-0.68.hdf5\n",
      "13910/13910 [==============================] - 30s 2ms/sample - loss: 0.7306 - acc: 0.6703 - val_loss: 0.7122 - val_acc: 0.6811\n",
      "Epoch 6/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6535 - acc: 0.7155\n",
      "Epoch 00006: val_acc did not improve from 0.68111\n",
      "13910/13910 [==============================] - 39s 3ms/sample - loss: 0.6535 - acc: 0.7155 - val_loss: 0.8059 - val_acc: 0.6035\n",
      "Epoch 7/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5876 - acc: 0.7485\n",
      "Epoch 00007: val_acc did not improve from 0.68111\n",
      "13910/13910 [==============================] - 39s 3ms/sample - loss: 0.5876 - acc: 0.7485 - val_loss: 0.7794 - val_acc: 0.6384\n",
      "Epoch 8/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5510 - acc: 0.7689\n",
      "Epoch 00008: val_acc improved from 0.68111 to 0.75550, saving model to CNN-weights-improvement-08-0.76.hdf5\n",
      "13910/13910 [==============================] - 41s 3ms/sample - loss: 0.5510 - acc: 0.7689 - val_loss: 0.6014 - val_acc: 0.7555\n",
      "Epoch 9/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5000 - acc: 0.7930\n",
      "Epoch 00009: val_acc did not improve from 0.75550\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.5000 - acc: 0.7930 - val_loss: 0.6407 - val_acc: 0.7283\n",
      "Epoch 10/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4601 - acc: 0.8170\n",
      "Epoch 00010: val_acc improved from 0.75550 to 0.77943, saving model to CNN-weights-improvement-10-0.78.hdf5\n",
      "13910/13910 [==============================] - 44s 3ms/sample - loss: 0.4601 - acc: 0.8170 - val_loss: 0.5618 - val_acc: 0.7794\n",
      "Epoch 11/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4308 - acc: 0.8298\n",
      "Epoch 00011: val_acc improved from 0.77943 to 0.78072, saving model to CNN-weights-improvement-11-0.78.hdf5\n",
      "13910/13910 [==============================] - 43s 3ms/sample - loss: 0.4308 - acc: 0.8298 - val_loss: 0.5794 - val_acc: 0.7807\n",
      "Epoch 12/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3911 - acc: 0.8480\n",
      "Epoch 00012: val_acc improved from 0.78072 to 0.82859, saving model to CNN-weights-improvement-12-0.83.hdf5\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.3911 - acc: 0.8480 - val_loss: 0.4946 - val_acc: 0.8286\n",
      "Epoch 13/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3643 - acc: 0.8694\n",
      "Epoch 00013: val_acc did not improve from 0.82859\n",
      "13910/13910 [==============================] - 39s 3ms/sample - loss: 0.3643 - acc: 0.8694 - val_loss: 0.5466 - val_acc: 0.7917\n",
      "Epoch 14/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3401 - acc: 0.8783\n",
      "Epoch 00014: val_acc did not improve from 0.82859\n",
      "13910/13910 [==============================] - 51s 4ms/sample - loss: 0.3401 - acc: 0.8783 - val_loss: 0.4938 - val_acc: 0.8273\n",
      "Epoch 15/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3143 - acc: 0.8935\n",
      "Epoch 00015: val_acc did not improve from 0.82859\n",
      "13910/13910 [==============================] - 50s 4ms/sample - loss: 0.3143 - acc: 0.8935 - val_loss: 0.4790 - val_acc: 0.8279\n",
      "Epoch 16/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2896 - acc: 0.9016\n",
      "Epoch 00016: val_acc did not improve from 0.82859\n",
      "13910/13910 [==============================] - 39s 3ms/sample - loss: 0.2896 - acc: 0.9016 - val_loss: 0.7109 - val_acc: 0.7497\n",
      "Epoch 17/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2812 - acc: 0.9078\n",
      "Epoch 00017: val_acc did not improve from 0.82859\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.2812 - acc: 0.9078 - val_loss: 0.5343 - val_acc: 0.8008\n",
      "Epoch 18/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2565 - acc: 0.9193\n",
      "Epoch 00018: val_acc did not improve from 0.82859\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.2565 - acc: 0.9193 - val_loss: 0.7458 - val_acc: 0.7348\n",
      "Epoch 19/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2433 - acc: 0.9294\n",
      "Epoch 00019: val_acc improved from 0.82859 to 0.84347, saving model to CNN-weights-improvement-19-0.84.hdf5\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.2433 - acc: 0.9294 - val_loss: 0.4572 - val_acc: 0.8435\n",
      "Epoch 20/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2231 - acc: 0.9349\n",
      "Epoch 00020: val_acc did not improve from 0.84347\n",
      "13910/13910 [==============================] - 39s 3ms/sample - loss: 0.2231 - acc: 0.9349 - val_loss: 0.5784 - val_acc: 0.8266\n",
      "Epoch 21/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2175 - acc: 0.9390\n",
      "Epoch 00021: val_acc improved from 0.84347 to 0.86481, saving model to CNN-weights-improvement-21-0.86.hdf5\n",
      "13910/13910 [==============================] - 39s 3ms/sample - loss: 0.2175 - acc: 0.9390 - val_loss: 0.4459 - val_acc: 0.8648\n",
      "Epoch 22/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1984 - acc: 0.9486\n",
      "Epoch 00022: val_acc did not improve from 0.86481\n",
      "13910/13910 [==============================] - 39s 3ms/sample - loss: 0.1984 - acc: 0.9486 - val_loss: 0.6623 - val_acc: 0.7937\n",
      "Epoch 23/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1947 - acc: 0.9490\n",
      "Epoch 00023: val_acc did not improve from 0.86481\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.1947 - acc: 0.9490 - val_loss: 0.5396 - val_acc: 0.8331\n",
      "Epoch 24/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1943 - acc: 0.9485\n",
      "Epoch 00024: val_acc did not improve from 0.86481\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.1943 - acc: 0.9485 - val_loss: 0.5232 - val_acc: 0.8182\n",
      "Epoch 25/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1752 - acc: 0.9612\n",
      "Epoch 00025: val_acc did not improve from 0.86481\n",
      "13910/13910 [==============================] - 37s 3ms/sample - loss: 0.1752 - acc: 0.9612 - val_loss: 0.4593 - val_acc: 0.8551\n",
      "Epoch 26/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1649 - acc: 0.9602\n",
      "Epoch 00026: val_acc did not improve from 0.86481\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.1649 - acc: 0.9602 - val_loss: 0.6196 - val_acc: 0.7827\n",
      "Epoch 27/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1583 - acc: 0.9626\n",
      "Epoch 00027: val_acc did not improve from 0.86481\n",
      "13910/13910 [==============================] - 37s 3ms/sample - loss: 0.1583 - acc: 0.9626 - val_loss: 0.4742 - val_acc: 0.8571\n",
      "Epoch 28/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1529 - acc: 0.9630\n",
      "Epoch 00028: val_acc did not improve from 0.86481\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.1529 - acc: 0.9630 - val_loss: 0.4849 - val_acc: 0.8532\n",
      "Epoch 29/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1373 - acc: 0.9712\n",
      "Epoch 00029: val_acc did not improve from 0.86481\n",
      "13910/13910 [==============================] - 37s 3ms/sample - loss: 0.1373 - acc: 0.9712 - val_loss: 0.7332 - val_acc: 0.7911\n",
      "Epoch 30/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1553 - acc: 0.9649\n",
      "Epoch 00030: val_acc did not improve from 0.86481\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.1553 - acc: 0.9649 - val_loss: 0.4763 - val_acc: 0.8571\n",
      "Epoch 31/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1384 - acc: 0.9708\n",
      "Epoch 00031: val_acc did not improve from 0.86481\n",
      "\n",
      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "13910/13910 [==============================] - 40s 3ms/sample - loss: 0.1384 - acc: 0.9708 - val_loss: 0.4866 - val_acc: 0.8616\n",
      "Epoch 32/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0621 - acc: 0.9983\n",
      "Epoch 00032: val_acc improved from 0.86481 to 0.86546, saving model to CNN-weights-improvement-32-0.87.hdf5\n",
      "13910/13910 [==============================] - 41s 3ms/sample - loss: 0.0621 - acc: 0.9983 - val_loss: 0.5103 - val_acc: 0.8655\n",
      "Epoch 33/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0558 - acc: 0.9985\n",
      "Epoch 00033: val_acc improved from 0.86546 to 0.86805, saving model to CNN-weights-improvement-33-0.87.hdf5\n",
      "13910/13910 [==============================] - 40s 3ms/sample - loss: 0.0558 - acc: 0.9985 - val_loss: 0.5160 - val_acc: 0.8680\n",
      "Epoch 34/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0510 - acc: 0.9987\n",
      "Epoch 00034: val_acc did not improve from 0.86805\n",
      "13910/13910 [==============================] - 39s 3ms/sample - loss: 0.0510 - acc: 0.9987 - val_loss: 0.5239 - val_acc: 0.8590\n",
      "Epoch 35/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0476 - acc: 0.9990\n",
      "Epoch 00035: val_acc improved from 0.86805 to 0.86869, saving model to CNN-weights-improvement-35-0.87.hdf5\n",
      "13910/13910 [==============================] - 39s 3ms/sample - loss: 0.0476 - acc: 0.9990 - val_loss: 0.4970 - val_acc: 0.8687\n",
      "Epoch 36/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0439 - acc: 0.9992\n",
      "Epoch 00036: val_acc did not improve from 0.86869\n",
      "13910/13910 [==============================] - 40s 3ms/sample - loss: 0.0439 - acc: 0.9992 - val_loss: 0.5301 - val_acc: 0.8622\n",
      "Epoch 37/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0423 - acc: 0.9994\n",
      "Epoch 00037: val_acc did not improve from 0.86869\n",
      "13910/13910 [==============================] - 38s 3ms/sample - loss: 0.0423 - acc: 0.9994 - val_loss: 0.5272 - val_acc: 0.8642\n",
      "Epoch 00037: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "#model2.load_weights(\"M2-weights-improvement-23-0.70.hdf5\")\n",
    "history=model.fit(X_train,\n",
    "          Y_train,\n",
    "          batch_size=batch_size,\n",
    "          validation_split=0.1,\n",
    "          validation_data=None,\n",
    "          shuffle=True,\n",
    "          epochs=epochs,\n",
    "          callbacks=callbacks_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Bitstream Vera Sans'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Bitstream Vera Sans'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Bitstream Vera Sans'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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idDfZ+6uwy0v0i9GO+HM6KMl1BuIt24eWVa8MqujrUNJnUOy9G/8eja0fH/XBTxT7O/0m9ksbnkFoBdDdzNqVs47lEkJYGkJ4AB8YXI9K+qIgVZOCdJGKiU0KcoSZrRfbaGYN8OBw7VxUqoJeA2YA25nZxfE7zOwgPJNKysysvpldHH1pSRSbSCR+xtS38IFjPc3svqhlOLHMtmZ2Qtz9Pc1sv8Q+v9FArNgXpUnRtjZmdqqZNUqxPiV5Ag8u9zaz0xOuezDe730lntM5K8zsUjNL1kd8H7x1cAGeWi8d55pZ4SDBKCC8C++T/CtFX+LKFH3xeQv/1fZRM2sYV+56eMs0wMMhhKVxpz4U3d5uZi3jzmmI5/ou7/+ux/E+yGea2ZVmtsrYDjPraGaHx90/zMx2TRzXYD6AdNfobuy9tZ2ZHWUJgx2jc/ePPzYFsaD+/6LuELGyagD/xX+lm0TywZcD8LzgPYFLo23Pl/CrxE34+/Rhi5vwKe56Nc2se/x7IgPSfh0S7GgJE7uZ2bH4c7yYosGg4M/PVLzl/sb41zH6pS32/Nwb2x5C+CeqYy3gTVt14qi6ZrYfFWRm5yTrex51DYxlRppS0evIaizX6WW0aMn1QmopGPco4dza+EDIgP9THIBPRjETn1ikH6WnWkxpe9z+Z0ieei5pPVOo/4Rof9uE7T0pmtzkV7zf/WD8n8b/ou2jU3x+146OX4m3lvbHuwkMi7avAA5OOKc1RekF5+GthS9Gz29sMpfv446/KNo2F28ZjB07I9r+J1GaQjwvdMB/3v4O7w7zWnRMwAd4xeelbksJKScpPpnRz9F1Y5M+FVD6ZEZtS3i+At5Ym8pzO4+iibRep2gypViKwnPS+DuIpdS7P3qtPouem1iqv7msmvN9D0r424k7pgWeEjFEr8dreEaQWI75L0g+mdFnca/HAIq+PI7H+zIH4LhU/j4SjtmGorzZM6L3ywv4BFgTSUijR9H7/R88c8cLeOq82OQ4g4nS6lGU5nAh/p59CR+oGZvIaDolTDRUQl3vpuhv57OovNj7dC5J0l7Gnft43GsaSDJBVtyxx+N/DyF6vWOTgX2GdwEJJExolM77NBOvQ8Ln2YMUn1wuNq9CAdArybV2iXu9RsU9ttImM6qH5/YP+N94bNKzLyh9MqOUP2sp+t8xBv8y+2JUTmyCslXSoWpZs5acV0CLllwvVCBIj45pjLfCjIn+0U0BnsKzCvRhNQzSo33b40HJfDzo+B5vRd81OufbFJ/fWnh2l/54gLEgKm9U9DxtVcJ5a+EZWwZTNKHKVDwIvZm4gBFvVbwx+gc6OXodZuCZKS4DGscd2wifQGUAPpvmwugx/o63XrZNqEdbSgjSo/3b4P+8p0V1nIkHobum+5xH+9MJ0k/Auzb8Hj1Hi/Eg6xXiJiRKsaxYMGfAeXgqvyV4v+VXSD7x0x6UEaTHPed98C8TS6LnfEj0+tYp4Zx6wA3R41kWvfaP4y36A6Pr7pvK30eSspsC10bvjwXR+2UiHlhfBWwSd+y2+EDMb/H+8sui13ow3nc4fjKh9aPzP4pe5yV4QPcL3mLdorR6lVDXQ/EZU2N/AxPxsRVJ3z9x5+0S95p+l8J12uO/UvwZvY8W4p9p7wCnA83K+z7NxOsQHT8ouu4e+Gy7gyj6PBkE7FPKtTbGf/2aSNEkc5+Q0ECQcE5NvDvMoOj5X4Z/4XqPVWdJLqxbqn/3+C93j0Xvj9j4gAn4xFn7lfbcaVkzlthEICIiKTGza/Cf2/uGEM4r63hZPUSp7wghpJuJqFJF3Z/G4+NB1g8+tkHWAGY2CM+A0j14jn2Rak190kVkFWa2frK+zmbWg6LBns8l7hfJlKh/d+IYg6b4ry/rAB8qQBeR6kwpGEUkmTzgHTMbjv/8WoBnGYhNInRbCOHHHNVN1gxPAO3M7Fe8P/j6+LT3TfBuJ/oVR0SqNQXpIpLMCLz/b7doaYj3yfwIeDSEsMp07SIZ9jCeam9LfHKXAvwL4xPA3cEzcIiIVFvqky4iIiIiUsWoT7qIiIiISBWj7i4J1l133dC2bdtcV0NEREREqrmhQ4fOCiEknRlaQXqCtm3bMmTIkFxXQ0RERESqOTObWNI+dXcREREREaliFKSLiIiIiFQxCtJFRERERKoYBekiIiIiIlWMgnQRERERkSpGQbqIiIiISBWjIF1EREREpIpRnvRyWrp0KdOnT2f+/PmsXLky19URqbLq1atHy5YtadasWa6rIiIistpQkF4OS5cu5c8//6RFixZ06NCBOnXqYGa5rpZIlRNCYOHChYwdO5bly5ez/vrr57pKIiIiqwV1dymH6dOn06JFC1q2bEndunUVoIuUwMxo1KgRG2ywAaNGjeKXX37JdZVERERWCwrSy2H+/Pn66V4kDU2bNqVhw4YMGjSI2bNn57o6IiIiVZ6C9HJYuXIlderUyXU1RFYbtWvXxswwMyZNmpTr6oiIiFR5OQ3SzewiM3vNzP4ysxC39C5HWW3M7HEzm2hmy8xshpkNMLNdslB1dXERSUPs76VmzZosWLAgx7URERGp+nI9cLQP0KSihZhZZ2Ag0DRuc3PgYOBAMzslhPBsRa8jIhVjZhQUFOS6GiIiIlVerru7jAD6AecAM8pTgJnVAl6iKED/AA/O74nu1wAeMbONK1ZVqWyDBg0q7CLRtm3bSr9+7969C6/fp0+fSr++iIiIrLlyGqSHEHYLIZwaQngEWFLOYvYDNo/WFwD/CSG8G0K4DG9dB1gLOLtitV3ztG3btjBITWUZNGhQrqssIiIiUi3kurtLJuwZt/5zCCE+2P8G2DvJcbIa2G677fj6668BnxCnsl1zzTWcdtppALRu3brSry8iIlLlheBLQQHk5/ttbCnrfuISK6ek9dL2xy/JtpW1v0sXqGL/66tDkB7fjWV6wr74+5uUVICZnQGcAQrG4r3++ussXbq08H6/fv14+umnAVh//fV57bXXih3fqVOnpOWsWLGCEELaGXGaNGnCrrvummatM6d9+/a0b98+Z9cXEREpZsoUuOgiGD8+vQC0pP2pBMalBcn5+X5bHTz3HJx4Yq5rUUx1CNIbxK0vT9gXf79hSQWEEB4HHgfIy8urJu+2isvLyyt2f+DAgYXrdevWLRZAT5gwgbXXXrvw/t9//81VV13FBx98wKxZs/j5559p1aoV11xzDcOGDWPSpEnMnTsXM2PDDTeke/fuXHXVVWy8cdF3rkGDBtG9e3cA2rRpw4QJEwqv1a5du8LjZs+ezXXXXcebb77JnDlz2HLLLbntttvo0aNH4THPPPMMJ598MgDdunWjb9++XH311QwaNIiVK1fSrVs3HnjgATbddNPCc3r37s2zz/p44xtuuKGwX3qfPn248cYbAejVqxenn3461113HT/88AO1atWiZ8+ePPjgg7Ro0aLY8/fmm29y880388cff9C8eXNOOOEEevXqxRZbbFF4TEjhw+7ll1/mlVde4ffff2fWrFksWrSIxo0bs9VWW9GrVy9OPvnkVbIPTZ8+nfvuu48PP/yQ8ePHk5+fT8uWLdlll1245557itX1yy+/pG/fvnz33XfMmDGD+vXrs8kmm3D00Ufzf//3f2XWT0REsuDHH+HQQ2HatFzXZFVmUKOGLzVrFq2Xdd+s6H58GbH1ZNuSrccvybalsr9Vq1w/i6uoDkH6orj1ugn74u8vrIS6SKRbt26MGTOm2LaZM2fy2GOPrXLsuHHjGDduHG+88QZDhgwpFqinYscddyx2rWHDhnHwwQczevRo2rRps8rxo0ePZocddmDRoqK3zocffsghhxzCiBEjqFEj9aEaX331FS+++CIrV64s3Pbqq68yf/58Pvroo8Jtzz33HL169Sq8P2XKFG6//XY++eSTlK8VM2DAAN55551i2+bMmcNXX33FV199xfDhw/nf//5XuG/IkCH07NlzlUmE/vrrL/766y8uvfTSwiD9hhtu4Kabbip23PLlyxk6dCgrV65UkC4ikguvvAInnwxLl8Luu8Mdd0CdOukHovHbUwmCywqM4wNsybjqEKSPj1tfP2Ffy7j1cZVQF4lMmjSJm266iR122IGJEyey7rrrUrt2bW666SY233xzmjRpQr169fj333/p378/L7zwAnPnzuWee+6hb9++aV1r7ty5PPHEE6y99tpcdNFFTJ06leXLl/Poo49y2223rXL8tGnT6Ny5M9dccw2TJ0/m//7v/1ixYgUjR47k008/LdYCX5a//vqLffbZh/PPP5+hQ4cWtrB//PHH/Pnnn2y++eYsXLiQCy64oPCcnXfemcsvv5xp06Zx5ZVXpvVYAQ4++GC6d+/OBhtsQKNGjSgoKGDChAlcccUVzJo1i4ceeogrr7yS9ddfn2XLlnHkkUcWBugtWrTgqquuomPHjkydOpWXX365sNX9k08+KRagd+/enTPPPJPGjRvzyy+/8P3336ddVxERqYCCArjxRoh9Np92GvTt6wG6VHvVIUj/HLgoWu9sZvVDCIuj+7snHJddVfWbZA76i911112cf/75q2zv3LkzjzzyCEOHDmXWrFnFWqCBcgWCDz/8MEceeSTgrfKxwHf06NFJj69duzbvvPMOG264IQAfffRRYav36NGj0wrS1113XQYMGMBaa63FQQcdRP/+/Rk1alRhWZtvvjkff/wx8+fPB6BOnTq8+eabrLfeegDk5+dz3nnnpfV4e/TowV133UXfvn0ZP348ixcvLtZNJj8/n59++omDDjqIgQMHFnYTqlGjBh999BHbbbdd4bGxLkAATzzxROF6ly5dGDhwYOGvCvvtt19adRQRkQpavBh694bXXvPW6nvugQsvrLqxhmRcToN0M9sXqB/drR+3q7OZzYvWB4cQZpnZM0Csv8CNIYQ+0fqHwBigPdAIeN3MHsGzuXSLjlkKPJqNxyDJHXHEEats69evH6eeemqp582dOzfta+21116F6+uss07h+pw5c5Ie36FDh8IAPdVzSrLTTjux1lprlVpWfFecTTbZpDBAB9IeGLtkyRJ22WUX/vzzz1KPiz2PI0eOLNzWrl27YgF6ovhjDz300LS6/YiISAZNnQqHHAJDh0KjRtC/P6ixZI2T65b0x4FVOw3D+dEC0B0YVFIBIYSVZnYcnhO9CZ43Pf6dHIBzQwjZ7+5SXUY4Z0DLli1X2Xb77bcXrvfs2ZNzzjmHpk2bMmTIEC6++GKAcs1G2axZs8L1WrWK3tIlDcKMPz7Vc1K5dkllxQ/iTBzQma633nqrMEBv0KABt912G506daJWrVqcc845jBgxAijf8ygiIlXAkCFw8ME+QLRdO3j3Xdhyy1zXSnKgWjSVhRCGANsBTwFTgBXAbOBdoFsIoV8Oq7dGShaMTpo0qXD9rrvu4qCDDmLXXXdl4cLqPaY3Po3juHHjmDVrVuH9WB74VMU/hz179uT8889njz32YOutt2bKlCmrHN+xY8fC9b/++otff/11lWNiXybijx0wYMAqgX66X2BERCRNr74Ku+3mAfpuu3lGFwXoa6yctqSHENqmcWxvoHcp+/8CTqtwpSRrNt54Y/744w8A/vvf/3LqqacydOhQbrnllhzXLLv23XdfmjRpwvz581m2bBlHHHEEl156KdOmTePaa69Nq6z4zDefffYZzz//PE2aNOHuu+9O2lVo7733pk2bNkycOJGCggJ69uzJVVddxRZbbMG0adN45ZVXuO2229hmm2047bTTeP311wHPCNOjRw9OP/10GjduzIgRIxg8eDADBgyo2JMhIiKrCsEHiEbJBzjlFHjkEQ0QXcPluruLrEEuvPBCzjrrLAD69+9P//79Adhjjz0YNGhQDmuWXQ0bNuSBBx4oTMEYS5UIsO222/LLL7+kXNaBBx7IxhtvzPjx45k3bx4nnXQS4JNLdejQoXDQakzdunV59dVX6dmzJ3PnzmX69OlceOGFxY659dZbAR+QevXVVxfeHzhwYLHc+Ntss016D1xERMq2ZIkPEH31VR8UevfdcPHFGiAq1aO7i6wezjzzTB555BE6dOhAvXr1aN++Pf/73/+4/vrrc121rDvppJN4/fXX2WabbahTpw4bbLABl112GQ8//HDhMfXr1y+lhKJjPv/8cw477DCaNWtGkyZNOPjggxk8eHCxAanxtt9+e3777Tcuu+wyttpqK+rXr0+9evVo164dxx9/PBtssEHhsbfccgufffYZRxxxBBtuuCG1a9emSZMmdO7cmeOPP77iT4SIiBT5+2/Pe/7qqz5A9N134ZJLFKALAKZ+psXl5eWFIUOGlHrM0KFD6dKlSyXVSKqDEELSfvoPPvhgYQ71bbfdlmHDhlV21SrN0KFD+f777+nYsWPhTLIiImusoUN9gOjff2uA6BrMzIaGEPKS7VN3F5FK8Omnn9KvXz9OOOEEtthiC1auXMkXX3zBddddV3hMrOuKiIhUc6+9Br16eVeX3XaDN96A5s1zXSupYhSki1SCgoKCYv3wEx1yyCFJJ38SEZFqJAS4+Wa44Qa/f/LJ8OijGiAqSSlIF6kEm222GSeeeCI//PAD06ZNY+nSpTRr1oztttuOk046iWOOOabCOdRFRCTOvHnw9dcwaBB8+SX8+y9ssw1st13RUsJYnowJwdMpDh8OI0bAp5/6YgZ33aX+51IqBekilWDjjTfmueeey3U1RESqrzlzPCj/8ksPzH/5ZdVJBkeP9q4mMS1berDeuXNR4N62bfkC58WL4fffPSCPBeXDh8Ps2cWPa9gQXn4ZDjww/WvIGkVBuoiIiKx+Zs+Gr74qCsqHDy8elNeuDTvsAHvsAd26wTrreOA+bJgvv/zirdzTpsEHHxSdt/basO22xVvcO3SA2IzSBQXw119FQXhsGTs2+czja68NW29dtPToAa1bZ+lJkepEQbqIiIhUfbNmeVA+aJAvI0YU31+nDuy4owfke+zh64mpbbfbzvuBgwfb48YVBe2xZcaMomvE1KsHnTpBzZp+3UWLVq1frVoezG+9tR8bC8o33FBdWqRcFKSLiIhIxRQUeB/w2bNXXRYsgJUrIT+/6Dbd9UmT4Lffil+zbl3YaaeioHyHHWCttVKvc40a0L69L0cd5dtifciHDYOffy4K3CdMgJ9+Kjp3gw2KB+Jbbw2bb+51EskQBekiIiLVybRp8PzzRQMU69UrWurWLf1+4rYaNbyvd7LgO36ZOzd5V49MqlfPg/JY95UddvBtmWTmAfgGG8ABBxRtnzsXfv3VH2OnTrDuupm9rkgSCtJFRERWdytWwPvvQ79+3r86P7/y69Ckiff7TlyaNPGuILVqeXeRmjWTr5e2v2lT6NIldy3VTZv6lwORSqQgXUREZHU1ciQ8/TQ895z3pQYPag87DI47Dho3hqVLV12WLUu+PXFffr4HqMmC7/ilaVMfqCkiGaMgXUREZHWyYAH07++t5t9/X7S9Y0c49VQ44QRo0SJ39RORjFCQLiIiUtWF4JlN+vXzPN9Llvj2xo3h2GPhlFOga1dlERGpRmrkugIivXv3xswwM/r06VO4vU+fPoXbe/funVJZe+yxR+E5zzzzTFbqGxO7jpkxYcKErF5LRNZQU6fCrbd6BpI99vBuLUuWFK1Pm+bTym+/vQJ0kWpGLelSop49e/Lxxx8DcOSRR/Lqq68mPW7PPffkiy++AODoo4/mlVdeqbQ6ZsuECRMKg/y1116biy66KKf1EZFKsGQJfPedpxKMpQAsKChaL2lbsvs1apQ9GLK0/XPnwosvwscfe3kAG20EvXv7sskmOXyiRKQyKEiXEvXq1aswSH/33XeZP38+TZo0KXbM5MmTGRQ34UOqLd7xrrnmGk477TQAWleRWdgmTJjAjTfeCECbNm2SBulff/114XrLli0rq2oikkkTJ3pWlPffh88/98GSVUmdOnDood6dZe+9PYAXkTWCgnQp0WGHHUaTJk2YP38+S5cu5fXXX+fUU08tdsyLL75IiHLjbrDBBuyzzz5pX6d9+/a0b98+I3WuTLvuumuuqyAi6VqxAr791oPyDz6A338vvr9zZ2jTxoPhWGt4bEn1fo0axVvXU5m0J/G+mU8ff/zxnj1FRNY8IQQtcUuXLl1CWYYMGVLmMdXFGWecEYAAhG7duq2yf8sttyzcf8UVV4QQQvj444/DkUceGTbffPPQrFmzUKtWrdC4ceOw/fbbh3vuuScsX768WBm9evUqLOOGG24o3H7DDTcUbu/Vq1exc8aPHx+OOOKI0Lhx49CoUaNw4IEHhj/++CN069at8Jynn3668Ph06tSmTZvCMpItf/31VwghJN0W88svv4QTTzwxtG7dOtSpUyc0atQodO3aNdx1111h6dKlpT7+AQMGhB122CHUq1cvrLvuuuGMM84ICxcuTOn1Sve5DyGEefPmhZtvvjnk5eWFxo0bhzp16oRWrVqFI444IowaNarYscOGDQu9evUK7dq1C3Xr1g2NGjUKW221VbjsssvKrNuQIUPCQw89FD7//POUHotIxvzzTwjPPBPCkUeG0KRJCD4M05dGjUI44ogQ+vULYdq0XNdURNYwwJBQQkya86C4qi0K0ov75ptvCgNIMwsTJkwo3Pfzzz8XC1RHjhwZQgjhiiuuKDXIPeSQQ4pdI90gferUqWH99ddfpdymTZuGtm3bJg3S06lTRYP0l19+OdSuXbvE87t06RIWLFiQ9PFvuummSc8588wzU3q90n3ux48fX+w5S1zeeuutwmMff/zxUKtWraTHNWnSpMy6KUiXSpOfH8JPP4Vw440hbL99CGbFA/MOHUK49NIQPvsshGXLcl1bEVmDlRakq7uLlGrnnXdms802Y/To0YQQePHFF7n66qsBeOGFFwqP23777dliiy0A2H333dlwww1p3bo1jRo1wsyYNm0aN9xwA2PHjmXAgAH89NNPdO3atVx1uuaaa5g+fToATZo04bbbbmOjjTbi3nvvLdY/Pl46dXr99df57rvvuOCCCwBYf/31ee211wrLKq3/+fTp0zn11FNZsWIFAPvttx/nnHMOkydP5qqrrmL+/PkMHTqUK6+8kr59+65y/tixYzn22GM5/vjjef/993nkkUcAeOqpp7j77rtp2LBhqc9Nus/98ccfX5iZplGjRlx++eV07dqVmTNn8s4771Az6v86cuRIzj77bPKjWQy33XZbLr74YtZbbz1GjRpF//79S62XSNYUFMA//3jf8nHj4LPP4MMPIfqMAHyWyu7dfZr3/feHjTfOXX1FRFJVUvS+pi4VbUmPb6xJXB57rOi4xx4r/dh4nTuXfNzpp8fXq/RyyuuWW24pbDHt0KFDCCGElStXhpYtWxZuf/jhhwuPX7RoUfjvf/8bunTpEho3bhzMbJWW1wceeKDw+HRa0vPz80OTJk0Kt99///2Fx8+ePTustdZaSVvS063TF198Ubi9TZs2SZ+X+HNjLen3339/4bbmzZuHJUuWFB7/0EMPFe5r3LhxWLly5SqPf8sttwwFBQWFj7V+/fqF+4YPH17ma5XO4/ztt9+KbR8wYECJ5V522WWFx2200UYpd7+Jp5Z0KZf8/BCmTAnhm29CePHFEG65xT/49t03hM02C6Fu3eQfjq1ahXDWWSG8804I5Xi/iohUBtSSLhVx4oknct1111FQUMCoUaMYMmQIc+bMYdq0aQDUrVuXY445BvAvffvvvz9ffvllqWXOnTu3XHWZOXMm8+fPL7y/0047Fa43a9aMDh06MGzYsGLnZLtO8UaNGlW4npeXR7169Qrvxw80XbBgAX///TetWrUqdv6ee+6JRbmOa9SoQdOmTVm8eDEAc+bMKfXa6T7OkSNHFm6rW7cuBxxwQInnxB/bo0cPGjRoUOo1RNIybx788Ye3hE+Y4K3iEyb4MmkSLF9e+vnrrgtt2/qAz7w8bzHfaivlDReR1ZqC9AwLIbXjzjjDl1QMHZracV26pH79dLRq1Yo999yTgQMHAvD8888XCxgPPvhgmjZtCsB3331XGCTWrFmTPn36sPPOO1OnTh1uuukmPv30UwAKYnl/K0FVrFNJmjVrVux+rVpFf6KhjBd3dXqcsoaaOxdGjvTl99+Lbv/+u/TzWrTwALxt26Ildr9NGyijG5iIyOpIQbqkpHfv3oVB+ssvv1zYuhvbFzNp0qTC9W233ZZrr70WgJUrVxbbV17NmzencePGLFiwAIDvv/++sH/1nDlzirVkV6RONWoUTcabTlDboUOHwvWhQ4eydOnSwtb0b775pnBf48aNM55bPd3H2bFjx8L1ZcuW8cEHH3DQQQcVOyaEgJnRsWNHPvjgAwA++eQTFi1aVKw1PXacCABz5hQF4fEBefTr2yrq1YMttvBZNeMD8bZtoXVr0C83IrIGUpAuKTnssMMKg+OZM2cWbl9//fXp0aNH4f2N4wZkDR8+nIcffph27drx+OOP8+eff1a4HjVq1OCwww7j2WefBeD666+nTp06bLjhhtx7770sWbJklXPKU6d14vIS//333zz33HNsvPHGrLXWWnTp0qXE+h111FFcddVVLF68mBkzZvCf//yHs846iylTpnDNNdcUHnfCCScUayXPhHQf55ZbbsmOO+7I999/X1inK664gi5dujB79mzee+89jj32WA466CB69+7NfffdR35+PpMnT6Zbt25ceOGFrLfeeowePZpXXnmFwYMHZ/TxyGpi1izPN/7TT0UB+T//JD+2fn0Pxjt29GXLLf22bVtN0iMikkBBuqSkfv36HHnkkTz11FPFtp9wwgmFGUDA+2HvvPPOfPvtt6xYsYJzzz0XgAYNGtC1a1d++umnCtfllltu4aOPPuKff/5h3rx5nHXWWYXX2HDDDZk6dWqx48tTpw4dOrDRRhsxZcoU8vPz6dWrFwCbbLIJY8eOLbFu66+/Pk899RQnnXQSK1as4P333+f9998vdkyXLl247bbbKvQcJFOex/niiy+yxx57MHnyZBYsWFDsiwT4lw7wgP6hhx7ivPPOIz8/n6FDh3LSSScVHpc4E61Uc2PGwIAB8M478M03RdPWxzRo4MF4LAiPBeRt2vhEPyIiUiZ9WkrK4ru1lLStRo0aDBgwgN69e7PeeuvRoEEDunfvzqBBg4p1r6iIDTfckG+//ZbDDjuMRo0a0bBhQ/bZZx+++uorNt1001WOL0+datasyVtvvcXuu+9O/fr106rfMcccw48//sgJJ5xAq1atqF27Ng0bNqRLly7ceeedDB48mMaNG5frsZemPI9z4403Zvjw4dx444107tyZhg0bUqdOHVq1asURRxxRmFYT4KyzzuKHH37gxBNPpG3bttSpU4eGDRuy1VZbcfrpp2f88UgVUlAA330HV17pAfdmm8H//R98/bW3gO+7L9xxh8/iOWECLFjgLevPPAOXXw4HHgjt2ilAFxFJg5U1GG1Nk5eXF4YMGVLqMUOHDi21y4OIrGro0KF8//33dOzYke7du+e6OlKWJUtg4EBvMX/3XZgxo2jf2mt7vvFDDvGp6/VLiohIuZjZ0BBCXrJ96u4iIiJuxgxvDR8wAD75xAP1mDZtPCg/5BDYbTeoXTt39RQRWQOkHaSbWVOgBT6xycwQQsWTS4uISG6MHQtvveWB+bffFs/jmpcHBx/sgXmnTso7LiJSicoM0s2sBnAIcCTQDVg/Yf90YBDwGvBOCEFJmEVEqrKpU6F/f3j5ZYjv3lenDuy5pwflBx4IG22UuzqKiKzhSgzSzawmcDZwJbABsAj4CfgAmA0Y0AzYFDgYOBaYZma3Ao+GEPKzW3UREUnZ7NnwxhsemH/5ZVGLecOGHpQfeqj3L2/UKKfVFBERV1pL+khgI+AV4Hngq5JayaPW9j2AE4E7gfOALZIdKyIilWThQk+T+NJL8PHHsHKlb69b1wd+Hnust5ivtVZu6ykiIqsoLUh/H7gjhFDCrBRFouD9c+BzM7sKuCJD9RMRkXQsWwYffeQt5u+8UzT4s0YNT5V47LFw2GHKyCIiUsWVGKSHEC4pT4EhhOnAxeWukYiIpCc/HwYN8sD8jTdg3ryifTvv7IH5kUfCeuvlqoYiIpKmKpGC0cwOBs4HugD1gcnAO8CtIYTZaZRzInAqsG1UzjTgY+CWEMLETNY5hIAp04FISgoSZ6SUips+HX75xVvN+/f3+zFbbw3HHQdHHw1t2+aqhiIiUgEpB+lmtimwaQjho7htOwDX4gNInw0hPJ5uBczsRuD6hM2bApcAh5vZ7iGEyWWUYcCL+ODVeK2B04H/mNk+IYSh6dYvmXr16rFw4UIaaYCVSEoWL16sQL288vNhzBgPyOOXfxJ6Im6yibeYH3uszwoqIiKrtXRa0u/Ag/GPAMxsXeBDoCGwBHjEzGaEEN5OtUAz242iAL0AD/j/wPu07wi0BZ4EepRR1DEUBejLgMuBUfhA1hOApsArZrZFCGFlqvUrScuWLRk3bhwtW7akadOm1K5dW63qIkkUFBSwePFiRo8ezfTp08nPz6dBgwa5rlbVtXAhjBjhQfivv/rt8OHFJxWKadwYtt0Wtt/eu7J07ao85iIi1Ug6QXoeEN9SfizQGO9aMhrPlX4h8HYaZV4Ut94vhHAb+BSpwEQ8zeO+ZrZlCOH3Uso5JG69fwjhgaicL4DDgAZ46/xBwFtp1C+pZs2aUadOHX7++Wfq16+vAF2kFAUFBUyfPp0FCxZQUFDARsq97WbPhh9/LN46PmZM8cmEYlq39oA8fmnbVkG5iEg1lk6Q3hz4O+5+T+CbEMJvAGb2CnBNmtfvHrc+OLYSQphsZpOANtGmPYHSgvSmceuL4spZYWbL8SA9dr0KB+kADRs2ZL311uPDDz+kVq1arLXWWgrWRZIIIbB8+XKWLFlCx44dWX/99cs+qTqaPh2+/tpzlH/1lbeYJ6pVy7uqxAfj22wDzZpVcmVFRCTX0gnSFwFrQ+FER7sCD8TtX4K3rKfEzJpSPLiennDIdIqC9E3KKO4PYN9o/XAzewL4Ezgl4RptU61fKjbffHMaN27MqFGj+Oeff8jP1/xNIonMjBYtWrD55puzySabUKNGjVxXqXJMnlwUkH/1Ffz5Z/H9det6F5XOnYsC8o4dfbuIiKzx0gnSfwdOMrPngCPxvuifxu1vA8xMo7zEjqnLS7nfsIyy7gd6A02A9YCfSziuXrKNZnYGcAZA69aty7hUcS1btqRly5ZpnSMi1UwIMH58UVD+5ZcwYULxY+rXh112gd13h27dPECvl/QjSUREJK0g/S5gADAjuj8M+Dpu/76UHBwnsyjhfmLzUfz9haUVFEL4y8y6AY8AO8XtGgvMwgehAswt4fzHifrb5+XlJekQKiISJwQYNap4S/nUqcWPadIEdt3VA/Ldd/cW89q1c1NfERFZ7aQcpIcQ3jezPfFBmvOBh0LwEU5mtg4wBXgujfLmmtlcirqjJHZUjW+eHpdCeb8CO5vZ+njqxRnAJIq39v+aav1ERFaxcqVPFnTnnfBzQpvEOusUtZLvvrvnKq9ZMzf1FBGR1V5akxmFEL4CvkqyfTZweDmu/0XcebsBzwCYWTugVdxxn6dRx+lE/dujFI+xwamBDA0aFZE1zOLF8MwzcM893q0FPCjfZx8PyHffHbbYAtaU/vYiIpJ1ac84amYN8C4l6wEDQwj/lHFKaR6gKEjvbWbjgJHA1XHHDIylXzSzZ4Be0fYbQwh94ur1Gd795lc8V/oOwKV4GkeAZ0IIf1SgriKyppk9G/r2hQcfhFmzfNumm8Jll0GvXupTLiIiWZNWkG5mZwO34VlcArAP8I+ZtcC7lpwfQngi1fJCCF+a2S146sYawC0Jh0wCTkuxuE3wVI3JfAScl2q9RGQNN2EC3HsvPPWUt6KDD/S84go49FB1YxERkaxL+bdZMzsC6It3UTmNohZqQggz8ED40HQrEEK4Fp9w6HNgHp7VZRxwH5AXQpiYYlFPAT/iA0VXALOBgfisoweEEBanWzcRWcP88gscf7y3lj/4oAfo++0HX3wBP/wARxyhAF1ERCqFhWSz2yU70Ox7YFEIYa9ooOhMYO8QwufR/muB00MIbUorp6rLy8sLQ4YMyXU1RKSyhACff+6DQT/5xLfVqgXHHuvdWrbeOrf1ExGRasvMhoYQ8pLtS6e7SyfgilL2TwNapFMxEZGcWbkS3nzTg/OhQ31bgwZw+ulw8cWQ5pwJIiIimZROkJ5P6d1jNmDV3OciIlXLrFnw6qvFM7U0bw4XXghnnw3NmuW2fiIiIqQXpP8K9MAzshRjZjXwWUh/ylC9REQyY9ky+OYb+PRT784ybJh3cQHYZJOiTC1rrZXbeoqIiMRJJ0h/CHjZzG6maNKiGma2OXArsCWld4cREcm+EGDECA/KP/3UZwNdsqRof926sNtucMYZcPjhGggqIiJVUjozjvY3s054usSros0f4VleDOgTQvgw81UUESnD338XBeUDB8I/CdM3bLONTzy0zz4eoKvVXEREqrh0Zxy91szeBI4HOuDB+Rjg+RCCUqKISOVYtAi+/LIoMP/99+L7N9igKCjfe29Yb73c1FNERKSc0p5xNITwM/BzFuoiIlK6L7+EG2+EwYNhxYqi7Q0awB57FAXmW2wBZiUWIyIiuTV5sk8/0aYNdOqkCZyTSTtIFxGpdPPn+2yfjz3m92vUgO23LwrKd9oJ6tTJbR1FRHLs5599wuRZs3yZObNofZdd4Npr/bhFiyAvz9s29t0X9twTmjSpnDo+8IBP5jx8eNG2WrWgY0c47DDo06dy6rE6SDlIN7N+KRwWQginVqA+IiLFvfuup0acOhVq14arr4YLLlCqRFnFl19Cu3ZKcS/Vy/TpPv590qSiZcaMouB7/HhYe20/9oorfFhOMvHtGF99BaNG+fLooz5+focdPGDv0cMD+FoZaMadMwc+/ti/BMR6HY4b5wF6gwaw887+0T5qlG/bbruicydP9rp07uxLly6w7baZ/zKRn+/5BjLxeDMtnRlHC1I4LIQQVutUCZpxVKSKmDHDc5e/8orf32EHb37Zcsvc1kuqpEcegXPOgc02g5EjlbRHqr5Fi4oH3vHLkUf6+xngvffgoINKLmfsWM8mC94b8JdfYN11V13ati36+MzP91b3Tz7xIPq773x+t5iJE4u+7C5YAI0bp/aYQvAhQu+9B++/D99+CwUF/iPoGWf4MX/8AVOmwO67e7Kt2HMxfDjUr+/j/AEGDIBDD131Gptu6kH7fff58CPwgH7OHP/Rdd48v40tnTvDfvv5cT//DOedV3z/woXQvz8cdVRqjzHTMjLjaAhhlYmMzKwmsDFwGT4jac/yVlJEBPBP+RdfhIsugtmz/VP7llvg/PMVeUlSH3zg/3gBRo/2iWSPPDK3dRJZudKD3XHjfJk2DW66qWj/dtvBmDHJz23fvmh98829G0jr1r60agUtW3rg3bw5NG1adOwNN6RWt5o1oWtXX665xgPxQYM8aB8/vvivUdtv7x/L++7ryx57QKNGxcsLAS65xP/2Jk0q2l6rlh+//vpF27bYwpd4DRp4r8V4PXvCTz95YD10qN8OH+5fSsaN8zab+GNHjkz+WM8+uyhIX7HCv5DEM/MvCVVRyi3pZRZk9i4wJYRwdkYKzBG1pIvk0KRJcNZZ8GGUzXXvveHxx70Pg0gSw4Z5Vs1YH9shQ/xn8Z9+0thhyb5Fi7xVOtbS/OWX3qYwbpwH6Pn5xY9fsKAowD3wQPjzz6LgO37ZfPOq0W1rzhzYeGNvcY6pVcu7qey0E/z3v0XdRPbZx7vatGgB++8PBxzg2zLZPWXFCg/GR48u/kX8iCP8C0+TJqsuO+9c9EvEwoX+S0P8/kaNfJhTrpTWkp7JIP1c4IYQQouMFJgjCtJFcqCgwPsrXHmlf4quvTbcey/07q1IS0rVvz+ccIL/VP3EE3D00XDaaXDwwXrrVAVTp8Jnn3nXhrZtc12bivnhB29pjrWMjx3r/cVvugmuu86P+fBDD1BjNtrIu6LElnPPrbwBmpmycqV/6f34Y3/8P/zgH9ngibZ22cXXv/vOW+jz8nIb9K5uMtLdJQXNgIYZLE9E1gSjRsHpp/unPXiTyEMPFf99VKQERx/twd+223r/1nffzXWNJCYEb+387jt47bWiIP2RR+DJJ5O3ILdu7V05qpqpU6FbN1i2rPj22rXh33+L7m+/PbzzjvebbteueqQVrFXLW8132skzr8ybB59/7q9r/GDUxO4qUnEVDtLNbG1gb+BiYGhFyxORNcSKFXDXXT7SaflyD8r79oXDD891zaSKW7EC/vrLB4mCjylOJoQ1szV95cqqkaniu++K+v9uumnR9pEjvX/xz0lmXGnd2ruJxFxwgf+wlpfnv47kymOPeYC+887+A1+sZXyjjYoPlVlnndIHeVYHa6/tH9P6qM6+dFIwFgAl9Y0xYA5wSSYqJSLV3NChcOqp8Ouvfv+UU+Duu4uPgBJJIgQfJPrSS/DGGz6QLdGkSUX5oJ97rnLrl2srV3rw1KED3H67f0kpKMjNmOv77vPbq6/2XzpibrjBuygly2qy4YZFxy1bBg8+WHT/k0+8j3MuXH+9B+Vdu3o+b5HKkE4KxmdYNUgPeHA+Gng5hPBv4nmrG/VJF8miJUv899K77/bIoV07Hxi69965rpmsJu6803NB16vnP7kn+4l90iQPqAoKvN/wmjLuOAQ480zvm9+0qQfJffvCSScVZb+pLH/95a3nNWv65DqxVHnpWLrUEz19+qmPPejSBX78Uf2dpXrJVArG3hmrkYisWULwxLmXXOJRU40avn7TTZ57SyQFr73mATrA88+X3Ae2dWs49lg/5p57fIjDmuDGGz1Ar1fP/9ymT/cBf7NmedKkyuwC88AD/iXp+OPLF6CDP45TT/XX8quv/Ae4N96o3PSaM2d6hhYNkZFc0PdREcmuoUN9urmDD/YAfcstfYaLe+5RgC4p+/ZbOPFEX7/zTvjPf0o//vLL/bZfPw+0qrvHH/cgvUYNb3XeeWc45BBvzf7rL89fXVnmzy/KYX3xxRUvr379ovzf11zjYxIqy003+S8xzz9fedcUiSkxSDez1uVZKrPyIlKFTZzoHU/z8nyWjGbN4H//89FiJY30k9XSkCHeJSFDGX1XMW6cB5zLlnmL8GWXlX3OVlt5nuYlS4r3a66O3nnHJ2wBz5wSG2BZs6b/YAU+Rjtbr0+iBg3g6ad9wuD4ad4r4pRT/AvHmDGeCrAyTJ3qX36WLcvc4xBJR4l90ssYKFqiEMJqPSWg+qSLVNC8eXDbbXD//f7frU4d/2999dWeFkCqlfnzvTvD4sUeyNx0kwfHmcyq8t573nK+554ekKbabePrrz0/d9Om3k+9YTVMElxQ4N+Dhw3zwY033lh8/+LF0KaNd3kZNMjTCK6uBg70lIeV9RjOO8/79B91lP86IZIN5ZrMyMz6UL4g/cayj6q6FKSLlNPy5fDoox6lzZ7t2449Fm69dfWfxURK9Oqrnqs8Xteu/jbo0SNzwfpPP3nGksTpyEsTgnf7+OEHGDCg+qbGmzXLW64vuyz5892njwfvBxzgX3iyqaCgegzsnDLFBx+vWOFT0W+1Va5rJNVVpcw4Wl0oSBdJUwjw1ls+om/sWN+2++6ewaVr19zWTbJu8WJv4QzB+z7fdhvMmOH7vvsOdtyxfOXGMrPEcqGX188/e2Dfvn3Fyqlq/v3XfxlI5UvQzJk+mLZ+fe8u0qxZ9urVvbt/J7/9dlhvvexdZ+hQn64+W1lb1YoulaW0IL0afN8VkZz5/nvYbTefJXTsWNh8c2+yHDRIAfoaon597wN9yCFw0UUwfrz3fz7iiOJDD0aNSq/c66+HbbaBt9+uWP06d65+Afr8+bDrrh5I5ueXfXzz5vDBBz5MJJsB+nff+Z/+gAHZ7Vp0663exeeuu7JT/pQpniXHDK67LjvXEElFuYJ0M2toZhtp4KjIGmr8eO/jsNNO8M03HgX07QsjRnjEtiZO8yiADxq87DJ4/fWit8GIET4BzD77FM1AWZp+/eCWW7yrQaamVQ/B+6hXxo/Hc+b4LwHZsGwZHHaYd8EYOBAWLEjtvO7ds98nPzZ50ZlnZjdx0157+e3//gfTpmW+/MWL/fk68kh1c5HcSitIN7NjzOw3YD4wEfgrySIi1dWcOZ4uokMH74xcr54PCB07Fs45x0d1yRqjTx845pjk07vH+/NP73IycKD3Ed9vP+9jnszAgR7kgX/v69kzM3U95BDvhfXhh5kpryTPPuvdPebNy3zZBQXQqxd88YXn7f744/S7eyxalNoXpXRNmOA5zGvVyv7ESTvs4F9UliyBm2/OfPmbbQYffbTmzVYrVU/KQbqZHQq8hE+A9BhgwMvAa8AKYChwU+arKCI5N3eu9zHfZBNvLlu50qcxHD3amzwbN851DaWShQAvvOD9dRctKv3Y//zHg7hrr/XW3I8+gu239x9dhg0rOu6337ybzMqVnuc8Fqxnwm67+e0dd2SuzERffw2nn+5BXqwbyvLlxfvpl1cIcOml/nw3auRfNtIdjz1tmvdN339/79OeSQ8+6F8ijjkGNtwws2Un89//+gDVJ54oGgqTaXXrZqdckZSFEFJagMHA70A9YF2gANgz2rcV3rp+SKrlVdWlS5cuQURCCPn5IXz2WQjHHRdCvXoheJwQwl57hfDzz2kXV1CQhTqW0/jxIQwbFsLIkUXbCgpCeP/9EN5+O4TXXgvhpZdCePbZEJ58MoRHHgnh99+Ljv3ppxDuvz+EZcsqvepVxsiR/nZYZ50QVqxI/byZM0O44ooQ6tf3808+2bf//XcIrVv7tiOP9LdfJs2fH0KTJl7+d99ltuwQQhg3zp8LCOHCC4u2P/usb6tbN4TTTw/hjz/KV/5dd3k5tWuHMHBg+eu5yy5ezv/+V/4yEs2fH0KjRl7u0KGZK7csJ5/s1zz22MyUN3lyCIccEsKPP2amPJFUAENCSbF3STtWORAWAJdH682iIH2fuP13Ad+nWl5VXRSkyxpv0qQQbrophHbtigJzsxD22SeEDz4oV7S9fHkI3buH8PjjuQ3WCwpCOPvsoofVuXPxfbHtyZZHHy069uGHfds991T+Y6gq7rjDn4OTTirf+f/8E8Ill/gXphBC+PxzD9x32imExYszV894V17pdT7ssMyWO39+CB07etk9exb/0vLjjyEcfHDx99KBB4YwaFDqfwtLl4awzTZ+7ksvVayub7/t5bRpk96Xq9K88YaX2a1bZspL1cSJIdSp49ceNqzi5Z17btGXRJHKUlqQnuKUEADUBKLkxyyJbpvE7f8TODutZnwRqRqWLfNZYvr1846uIRpd17o1nHwy9O5doVznt97q/WjHjfPU6bVqebq+LbbISO1TdvXVPiNj3bqeiCY+vZ+ZdwOoUcO71icuHToUHRvr3tG3r2c0qQ55odP1zjt+W97c4y1awD33FN3v3t27i7RqBWutVfH6JXPBBXDvvZ4x5s8//T1QUfn53sVj5EgfHPvKK8UnW+ra1bOd/Pmn9xR79lnPVf7ee37eyy+XfY26dT1ryiefeErAijjoIH/fjx4Nr73mf48Vdfjh8Mcf3ke8MrVu7fOk/ftvxdM9xmd0uf76zNRPpMJKit4TFzwIvynu/nTgtrj7dwH/pFpeVV3Uki5rlBEjQrjooqLf6cGbpo4+OoRPPglh5coKX+Knn0KoWdOL/uILb3XcffcQ1l03hD//rPhDSNWdd3odatUK4b33KlbWypXeEgneRWZNM2OG/7hSp04ICxbkujbpOf10f91OPTUz5f33v6Gw28+4cWUfP2NGCDfeGELz5iE89ljR9tmz/W8j3pQp2fnl6bHHvM7bbVe1uqGVR6bqH2tFP+qozJQnkipKaUlPp/3nW2DvuPvvABeZ2fXR7KTnAoMq+qVBRLJs/nx47DEfudepk+cxmz0btt4a7r8f/v7bmwP32Qdq1qzQpZYsgRNP9NbGiy+GPfbwVtL69X2WxJ49Yfr0jDyqUj33nA9EBHjmGZ95sSJq1vRkNgAPPVSxslZHH3zg3+i6d09vBtCq4LLLPGNou3aZKe+ss2DffeHNN31ynbI0b+4ttRMneqaWmNtu818RLrsMJk/2X5q6dIHTTvNUlJl00kn+S8awYf4LV0X8/HPlpLUsSXy21/z88tVFrehSZZUUvScuQFfgVmCt6H5z4Be8b3oBMAJolWp5VXVRS7pUSwUF3gn2xBNDWGutolbzJk28k/aQIVlpUrvwQr9Mx44hLFlStP3ff0PIywuF/cKz3Rr755/e8v3gg5krc9YsH09rFsKYMZkrd3UwblwIt94awptv5rom5ZPpAb+Z+NM55JCiP8uaNb2lPTZOOxsDlG++2VvSv/ii/GV8/73XcZ99ct8i/8EHIWyxRQgDBqR/bqwV/eijM18vkbKQiYGjJRYAWwNbAjUqWlZVWBSkS7Xzww/+3yt+5Fr37iG88EL2RugFz0AR614yZMiq+//5J4RNNy36J5/tTCnZ+CIQyy5xySWZL1uqrjFjPENNpgZexvz0UwjHHFPUPWybbVbtApMpy5dXPLA++miv5xVXZKZOFXH//V6XLbdMr5fev/+G0Lixf9n+7bfs1U+kJKUF6enkSU/6u3cIYXgI4fcQQpbmVxORcikogDvvhF128VFdG27oiarHjoXPP4fjj8/eCD385/Stt/afj7t0Sb7/o4/89tNP4dRTMztL4/ffe+7mmGx0yzjvPJ+6Pi8v82VLdi1c6D29nnoqvfPmzYMDD/R867fdltk65eX5QNJx4+DRR31ip2xNQVC7dsUmBp40yWeVrYzJi1Jx5pnQpg38/ju8+GLq5zVs6IN+n3oKttwye/UTKQ/zID6FA83+AV4Engsh/JLNSuVSXl5eGDJkSK6rIVIx06Z5x9OBA/3+xRd7RFHJs3MsW+b9t2uVkkdq6FDo1s0zq7zwAtSpU/Hr/vabzy45d67Pgnj44RUvsyQhVCzYWd3cfbePNTj5ZNhoo1zXpvw+/tjHRLRs6f2/U/nTWLnS36effurTxX/77erXJz/R6NGe8ea669KbhOj//s/fC8cdl15QnE3PPef9/Nu08Ww6moxIVgdmNjSEkLSpJ52Bo+OBi4ChZvarmV1iZhVMeiQiGffhh968O3AgrLsuvP++/xeupP9YU6cWrdetW3qADt7K/uOPPlY1EwH6X3/5QL65c31Gy/KmCEzVmhSgh+Ctz9dfXzkDfrNp3339z2TaNHj++dTOufhiD9CbN4d33139A3TwH9ceewweeCD1c/79Fx5/3Ncvvjg79SqP44/31vCJE/2XiLIMGVI0M6xIVZRykB5C2AnYDB882gi4G5hsZu+Z2ZFmVu5/r2Z2sJl9amZzzGypmY0xs3vMbJ00yqhtZueb2eConJVmttDMfjOzu/WFQqq9Zcvgkku8qW/mTNhrLxg+3O9XkunTPfA55hhYvDj18zp2LMo1vmiR54Qu7/X32ccDrz328CnUa9cuX1npGDPG86V/8032r5VLw4b5l7ANNoDOnXNdm4oxK8r4c9ddZQdrDz/smXzq1IG33qrQtAFVymWX+e2jj8KCBamd8/TTfuxuu1Wtrl41a/qcDAD//a9/mSjJlCneE3DrrSs/v7tIqtKagiOEMDaEcF0IYWOgO/A8sCvQH5huZil8dy3OzG4EBuDpHZsCdYFNgUuAIWbWKsWiXgEeAHaJyqkJNMAHtV4K/JRO0C+yWhk9Gnbe2WdLqVnTu7Z88on/ll9JQoDTT/dsjnPmlK+7++LFsOee0KNH+qnh5s3z88aN89b5AQOgXr3061Aezz/v2Svvv79yrpcr8RMYVYcJnI46yrtGjB7t75eSfPmlT4QEnqpvl10qp36VYfvtvWvYggX+2FIRAjRtWrVa0WMOOgh22sl/wRs1quTjbr8dli/3LLRZHJojUjEljShNdQHqAacC84D8NM/dDQjRkg9cBRwKfBe3/eMUytk07vgAPIIH/ZcAK+O2n11WWcruIquVgoIQnn02hAYNPLVBu3YhfPddTqryxBNehbXXDmHy5PKVUVAQwvnnezmNG4fw66+pn/uf//h5m2/uE8ZUpilTPCNHzZq+Xl117hyq3QRODzzgj2n77UvOdjJvXgg9eoRw5ZWVW7fK8u67/hxstJFnfUnFwoUZmessK8aP9/qVZNIkn4hLGV2kKiBbKRiBPYFngAV4rvSZaZ7/RlwA/UTc9lZRebF9W5ZRTl5CkN4wbt9vcdsvLKtOCtJltTF/fgjHH1+UVvGYYzyayIFx40Jo2NCr8eKLFStr5cqigHuDDUKYMCG188aPD2HPPf0fcC4ceaTX+brrcnP9bJs82R9f/frFc96v7hYuLJpw96uvSj5uxYoQ8vMrr16VKT8/hA4d/Dl4/vlc1yb7zjknKC+6VBmlBelp/2BpZh3M7FYzmwh8ChwLfA4cAWyQZnHd49YHx1ZCCJOBSXH79iyjnN+AaXH37zKzvczsEqBDtG0h8Haa9ROpmn78EbbbztMq1K/vnURfegmaNKn0quTne0aFhQu9+8Cxx1asvJo1vftIt24++WnPnt6FJpn45FTt2sFnn/msjbkQS0P32GM+PKAyfPmlp5nMZOrKkrz7rt/26FF53YgqQ4MG3if91Ve9x1jMihU+3nr5cr9fq1b16OKTTI0aRX3T77675Fk7f/kF+vb1cSOrgwULoE8fH0gaM3kyPPmkj0m47rqcVU0kNSVF74kLcB7wI94tpQAYAlwArJtqGQnlNaV463ePhP3fx+37XwrlbQsMTSgztnwKbJVKvdSSLlVafn4Id9zhMwRBCNtuG8KoUTmt0sMPe1VatvRZODNl7twQOnXysnfeedV5lwoKfLLUW2/N/WyHsfrE6lvRXxNSEWvZhhBeeSX71/vyyxBOOCGE/v2zf61cKygI4Ywz1qzW1qVLvYV5+PCSjzn2WH9Orrqq8upVEbHX8OSTi7apFV2qGjLR3SUKzP8G7qSM7icplrdRQiDdPWH/V3H7nkyhvHbAy9GXiMQgfRFwI1Fe+CTnnhF96RjSunXrzL8CIpkwbZpPzRmLzC680P+z5tiiRT6t9gcfZL7sKVNCaNUqhJNOWrWv7DXX+NNQt24IY8dm/trl8dhjXqeddsr+tVasCGHjjf16HTtW364YlW3+/KLZK+vWDeH773Ndo6ph0qSicRcTJ+a6NqkZO9bbM2rUCOH3333bo49633v1RZeqIlNBek+gRqrHp1BeYkt6z4T9P6Takg6sDUyPO/4UijK7jIrbflFZ9VJLulRJH34YQvPm/ie77ro+0msN8fffq7aU33OPPxU1a4YwYEBu6pXMwoXeevfjj5VzvWXLQmjd2p+LNaGFO5sKCkI46yzvc1+jhj+nL72U61rlTuK4g8svD4VDX1YnsZbzQw8t2rZiRe7qI5KotCA9nTzpH4UQMtbzMYQwF5gbt2n9hEPic8eNK6O4I4BYHvRfQwj9QgiLQgi/45leYo4uV2VFcmX5cu8sut9+nvt8zz3h1199XvIce+ut0vMQZ0rLlkUTBi1YAHvvDZde6vefftonLKoqGjTwPuldu2bvGrNnF00kVKcOXHONr994Y/YmZnnySZ+5dXXpi1weZv5+XrzY+/hfd13Fx1esjn75xVNMnnFG0baFC/19DVUz7WJprr3Wh+28/TZ8/71vK2uCNZGqItfDYOIzIe8WWzGzdniGl5jPyyinedx64hxwTUpYF6naxozxkWz33FM89/kG6Y7PzrxvvoH//McntKmsiUBWrIBdd/XBoeA5yU88sXKuXV6hhAF4FXHBBT7508cf+/3evaF1axg5El5/PfPXy8+HK6/01zt+AF51dM01PovoiSf6gMM10dprww8/wMsv+yBL8C/D8+d78L799jmtXtpatvSJxsDzp8+bl8vaiKQn10F6/ETEvc3sajM7FJ8cKWZg1CKOmT1jZiFa+sQd82vc+sZm9riZ7WtmZwDx3/t/ynD9RbLjhRc8Ah461Kc2HDzYI6WaNXNdMxYuhJNO8tbGI4+svIlAateG88/3IOq//y2aXKYqGjwYunf3TBmZ9P77nsRn6VJo3963xVrTzWDEiMxeD+C777z1fpNNYIstMl9+VbLFFjB3Ljz7bPXN5FKWtm3973rlSv8inJ9fNEnXJZfktGrl9n//B+us4x+fU6fmujYiaSipH0xlLcB/SZ6RJQATgTZxxz4Tt69P3HYDPiilnADMADYpqz7qky45tWCBj5KMDQ496ihPc1KFxDImbLON94mubFV1ApV4773nz1Hbtpmr7/z5PuANvE9+vGXLQvjjj8xcJ1GsL/LFF2enfKl6hgzx17xRoxBmz/a++Ycfvnr87ZXkzz8rb6yISDrIZJ70TAshXAschndpmQcsx/ug3wfkhRDK/IE1epCHAOfiWWHm4FlelgAjgf8B24YQyurbLpI7Q4f6fPbPPefN008+Ca+84r8/VxEffACPP+6tty+84LeVrQr8mFCmnj1h441hwgR/zjLhyithyhTv737hhcX31akDHTokP6+i3nnHb6tS33/Jri5d/Jegf/+Fp57yvvlvvLF6/O2VZLPNsjtWRCQbLGSj0+RqLC8vLwwZMiTX1ZA1SQjwv//BFVd4x+utt/bgvIr1LZg1Czp18kGLd91VNPmJJHfPPf4c7btvUf/x8vr6a9h9dx/wNnSov0WSCcGHLdSs6QNsK2rMGA9umjaFf/7xLkeyZvjwQ9h/f9hwQxg/PjdfyEXWBGY2NISQl2xfyi3pZnatmbUs+0gRSdmMGZ6p5ZJLPEA/91wftZWDAD0ETyYzb57P9Dl2LAwfXpTB5fXXPUDfbbfVL8NDLpx8sv8g8skn8Oef5S8nPx/OPNPXr7yy5AAdvLWzZ09vac9EppfYLKP77acAfU3Ts2fRj3hjxuS0KiJrrHS6u9wETDKzd83sUDNbjX/4EqkCPvsMttnG+0M0beo5DR96KONzrq9YAVdf7dk59t8f9tijKG0feJeMxo09CKtb16uy4YY+MHGbbYrSlp11Frz5pg+qW51/9q4szZrB8cf7+sMPl7+cmjWhXz84/HBPJ1eagw/ObKaXddeF7bZTV5c1kZn/AnTAAZ4hRUQqX8rdXcysK3AqcAye5nAG8CzQL4QwOms1rGTq7iJZt2IF3HAD3H67N1/vvrt38G7Vquxzy+H22+Gqq4pvO/DAolbSadOKsjrWrOm5vuvXL1oeegi6dctK1aq9X37xILdlS5g0qXLyMz/+uLe8d+zov4Rk4gtVCEW56kVEJHNK6+6Sdp90M1sLOBIP2HfDM6d8AzwBvB5CqKSsydmhIF2yasIEH4X1/fee4+366715NEtN06NHe/eIZcvgvvu8dbx+fVh//aIeNQUF3qWlfn11aciGl17y7iJNm6Z33sqV8O23/h0uHcuXez/yiRN9aMPRmsJNRKTKykif9JgQwpIQwnMhhG7A5sCdwCZ4esRpZvawmW1bgfqKVE+vvQbbbusB+kYbwaBB3qKexb4jl1ziAXqvXj6hxwEHeNaG+C7vNWpAkyYK0LPluOPSD9DBv1R16+b90NMRPwvpTTeVv2/6O+9UrC+9iIhUTEVTMP4FDAX+wHOVNwROB4aa2fsaaCqCzzN++ulw1FE+bd+hh8Kvv/oIzCx75BGfPfHee7N+KSnD8uU+8DYVY8f6jyzgYwjS1asXtGnjfdNfey3985cvhxNO8LSOkyalf76IiFRcuYJ0M9vSzO4F/sZnB+2AT0q0MdAKuAXoDvTLUD1FVk/Dh0Nenuc8r1sX+vb10ZfNmlXK5Vu18rTrlXQ5KcEXX/iAzvPOK/vYEPw73dKl/gWrZ8/0r1enjs/K2qdP+c7/8kvvAtWpk9dbREQqX8rDmMysIXAs3he9K1AAfAQ8DrwfQiiIO/x6M1sI3JDBuoqsPpYs8Sjprrt8oOgWW3gH4dLy52VICN5V4aCD1typzauaDh1g9mx4+22YPLn0McJPPuk9oZo39y4v5XXCCeU/NzaoWFldRERyJ51/4f8AjwIt8XSM7UIIB4UQ3k0I0GMmAmtloI4iq5ePP4attoJbb/UA/ayz4KefKiVAB+jf33vUHHigB+ySey1begrM/Hx47LGSj5s6tWiSqAcfhHXWycz1ly5NvW967Ese+Bc9ERHJjXSC9E+Bg/Hg/MYQwpTSDg4h9A8hqB1P1hzTpsExx3j/gvHjPVD/5hvvGN6gQaVUYdYsuOACXz/sMKXNq0piXV0ef9yD5mRuuAEWLPDg+KijMnPdF16ATTZJPW/6iBGeGWa99TSNuohILqUcRIcQDg0hJHZrEZH8fJ+tpkMHb8Zeay244w74+WfYeedKrcrFF8PMmT7Y8LTTKvXSUoadd/bkPjNnljyY8557PJh/+OHMfcFassRnkE0100t8K7q6S4mI5E7KH8FmtpeZ3VbK/tvMrHtmqiWymvjlF4++zj3Xm0APOMBTalx+eaXnNPzoI281rVcPnnhCrehVjVlRa/pDDyU/pkkT7+ay0UaZu258ppdUWtOXLoWGDdXVRUQk19JpJ7kC2LSU/e2iY0Sqv4UL4dJLPXPLjz/6lJ2vv+4j7tq2rfTq/PuvzzIJ3mK6aWl/qZIzxx7rOdP//RfmzCna/uqr3uKdDfF502+8sezW9P/+17tN7bdfduojIiKpSSdI3wb4vpT9P0THiFRvb7/t2VruvddH2V14IfzxBxxxxCrN18OHe2N7tj3yiOez7tLFu7xI1VS/PgwZAr//XpQW85NPfFbQHXbwWUazIdaa/scfqeVNr1tXk1uJiORaOkF6E2BRKfuXAOWYV09kNTFpEhxyiI/InDLFI+Iff4T//Q8aN17l8MWLYddd/bBYPJ8tl1wCd9/t6ftqpZxYVXJh442LvsstXAhnnOHrxx2Xvdcu1VlIf/opey36IiKSnnSC9KlAl1L2dwFSnE9PZDWycqWP6OvY0UfVNWoEDzwAP/zgEXgJBg3ybg0FBd4z5pRTYNmy7FSxVi2/xrbbZqd8ybwJE/xL3MSJsN12/vplU6w1PQRP9Zho0SLYfXdo0cInxhURkdxKJ0h/H+hlZnsn7jCzvYBewAeZqphIlfDDD97v/LLLPIr5z3+8z8D550PNmqWe+tFHftu9uyd8eeYZePTRzFavf3/P3CGrl6FDoV07+PVXv//UU9nvXlKnDnz+Ofz2W/JZRAcO9EGjHTv6AFYREcmtdH5cvQU4AvjYzD4Efom2bwvsh7ei35zJyonkTEEBXHGFt6CH4INB+/aF/fdPuYjp0e9K//2vZ1x56CFPApMpw4f7rJKNGsGYMZmb+Eayb7vtfIKjadPgoov8fmXYeOOS92mWURGRqiXlID2E8I+Z7Qw8ggflsWglAB8C54UQpmW+iiKVrKAAzjnHp4aM9SO5/nof9ZeGV1+FGTN8gGCtWtCvX9G+mTPh22+9i3t55Od7HvSVKz1jiAL01UuNGjBgAHz6qY8nqGxTp/r4hWuv9R+ECgoUpIuIVDVpDVMKIUwE9jezphSlYxwbQpib8ZqJ5EJ8gF6vnkdS++5b7uJatFh128qVcOSR8OWXPsPk9denP2nM/ff7IL+NNoLbSpy9QKqyrl1zM6NnCLDXXvDnn7D55j5J7o8/+hfKNm18olwREcm9cs0nF0KYG0L4KVoUoEv1kMEAffLkkrO51KzpLeg1anje6qOO8u7uqRo3zltAwfu4J0ksI1Iis6JBqrFML7FZRg8+WJNgiYhUFeUK0s2soZltZGatE5dMV1CkUmQwQF+2DDp08FbJhQtX3W/muczff98H6L3xBuyyi2f5KEsInrJvyRJP2XfAAeWqoqzhEvOm//GHb1dXFxGRqiOtIN3MjjGz34D5wETgrySLyOolw11cBg/2HOlNm/r06iXp2dOTx7Rv71k+unb1c0vz00+e2nGddTw9u0h5JOZNf/11GD3aUzCKiEjVkHKQbmaHAi/h/dgfAwx4GXgNWAEMBW7KfBVFsijDAToUpV7s2bPsYzff3AP1ffbxwaSxc0uy/fbw/ffw7LPQvHmFqilruMTW9PbtPXgXEZGqIZ2W9MuAP/CUi9dH2/qFEI4B8oDNKUrLKFL1ZSFAB/jwQ79NJUgHb3H/4APPtnFTCl9zu3ZVNxepuPjW9LffzmlVREQkiXSC9K2BZ0MIS4GCaFtNgBDCb8DjwFWZrZ5IlmQpQJ88GX7/3bu57LJL6ufVqgWnnlqU5WX6dDjpJJgbDcv+4APvwy6SSb16wRZbwD//eNYhERGpOtJJwVgTmB2tL4lu4+el+xM4OxOVEsmqLAXoAB9/7Ld77VWxrgNnnukZN777Dp57zgP46dO9O0yPHhmpqgh16nj3qWXL/IuiiIhUHel8LE8B2gCEEJaY2QygC/B6tH9zII1EciI5kMUAHeCLL/w21a4uJXnwQc/28uuvsPPOvm2XXbzvukgmKYWniEjVlE53l2+BvePuvwNcZGbXm1kf4FxgUOaqJpJhWQ7QAZ55Br76Cg4/vGLltG4N33wDRxzh9+vU8T7r6U56JCIiIqundFrSHwYOM7O1QghLgGuA7YE+0f7f8cGlIlVPJQToALVrw267ZaasBg3g1Vfh5Zd9ZtEOHTJTroiIiFR9KQfpIYSfgJ/i7s8EtjWzrYF84I8QQkFJ54vkTCUF6CFkfrbGGjXg+OMzW6aIiIhUfSn9eG5mDaJuLasMWQshDA8h/K4AXaqkSgrQwSeC6dkTJk3KSvEiIiKyBkmpJT2EsMjMrgbOy3J9RDKnEgP0f/7x2ULr1dMkQyIiIlJx6QxDGwesn62KiGRUJQboAJ984rfdusFaa2XtMiIiIrKGSCdIfxg43czWyVZlRDLmkksqLUAHz18OsN9+Wb2MiIiIrCHSye7yLzAH+NPMngXGAIsTDwohPJehuomUz2uvwf33e97CSgjQ8/OLJjGqaH50EREREUgvSH8mbv3iEo4JgIJ0yZ0JE+D003397ruzHqAD/PwzzJ4NbdvCZptl/XIiIiKyBkgnSO+erUqY2cHA+fgMpvWByfhkSbeGEGancH5v4OkULnVjCKFP+WsqVdqKFXDccTB/Phx8MJxXOeOcP/zQb3v2zHwKRhEREVkzpZMn/ctsVMDMbgSuT9i8KXAJcLiZ7R5CmJyhy63IUDlSFfXpA999BxtuCP36VVrEfPLJ0KIFbLttpVxORERE1gAWQsjdxc12A76K7hYA1wJ/AFcAO0bbPwkhrJKfPaGcFkCyjgZbA32j9QB0CCGMLq2svLy8MGTIkNQegFQdn30G++zjgfnnn3uaFREREZEqzMyGhhDyku1LuSXdzBJbu5MJIYSbU64ZXBS33i+EcFt0raHARMCAfc1syxDC76VcdAYwI0mdz4i7+25ZAbqspmbMgBNO8Ck/r79eAbqIiIis9tLpk96nlH0BD6gDkE6QHt/PfXBhYSFMNrNJQJto055AiUF6Mma2IXBM3Ka70zlfVhMFBdC7N0yfDrvtBtdeW6mX79MH/v3Xu7+3a1eplxYREZFqLJ0gPVkIUgvYBM/20gTolWphZtYUaBq3aXrCIdMpCtI3Sb2ahc4HakfrP4YQvi5HGVLV/e9/PnKzaVN48UWolc5bumJC8FTs06f79wQRERGRTEln4OjEEnaNM7NP8b7lJwNXp1hkg4T7y0u53zDFMgEws4bAmXGbSm1Fj7rFnAHQunXrdC4luTR0KFx5pa8//TS0alWplx8+3AP0DTaArbaq1EuLiIhINZfOjKMlCj769HXgpDROW5Rwv24p9xemWaVTgbWj9fHAm6UdHEJ4PISQF0LIa968eZqXkpz491845hhPu3juuXDIIZVehdgso0q9KCIiIpmWkSA9UgdYJ9WDQwhzgblxm9ZPOKRl3Pq4VMs1s5oUH5D6vxBCfqrny2rinHNg7FjYemuftCgH4vOji4iIiGRSRoJ0M8sDLsTTJ6bji7j13eLKawfE9134PI0yDwfaRutzgH5p1kmquueegxdegPr1oX9/qFev0quwYAF88w3UqAF7713plxcREZFqLp0UjONL2NUMaASsBE5L8/oP4EE1QG8zGweMpHi/9oGx9Itm9gxFg1NLmj300rj1R0MIid1qZHU2erS3ogM8+CB06JCTanz+OaxcCbvs4mNWRURERDIpnVQYk/AUi/EC8DMwGng8hDAhnYuHEL40s1uAa/BW/VuSXDPlwN/MdgF2iO4uAx5Mpz5SxS1b5v3QFy3y25NPzllV2rXztIsaMCoiIiLZkE52lz2yUYEQwrVmNgRPmdgZqA9MBt4BbgshzEyjuPhW9BdDCIlpHWV1dsUVMGyYR8iPPprT0ZrbbOMN+SIiIiLZYJ6YRWLy8vLCkCFDcl0NSfTee3DQQZ4H/ZtvYPvtc10jERERkQoxs6EhhLxk+1IeOGpmR5vZc6Xsf9bM/lOeCorEW7QIxsePgJg6tWi2oFtvzXmA/sEH0K8f/PNPTqshIiIi1Vg62V3OAwpK2Z+Pd1kRqZAHH4TNNoPBg4H8fDjhBJg9G3r0gEsvLfP8bHvgATj1VPj001zXRERERKqrdIL0LYBhpewfBnSsWHVEfAbP/Hy45x7gtttg0CBYbz149lnPeZhDixd7dQD23TenVREREZFqLJ2IpwHeWl6SgKdiFEnLyJGw334wMxoivO++ULs2vPNOYOINUZr7557zQD3HvvzSk8x06QItWuS6NiIiIlJdpROk/wXsWsr+XfGUiSIpCcG7tnTpAh99BDfc4NvXXx+OOmQZBQXGwwVnwuWXV5lm648+8tv99sttPURERKR6SydIfws40sxOTdxhZqcARwJvZqpiUr1Nmwb77w8XXABLl8Ipp8Add0Q7Q+D8mdcD8GTNs1hyzX9zV9EEsSC9Z8/c1kNERESqt3SC9NuBP4DHzew3M3shWkYATwB/Ardmo5JSvQwYAFtv7QFvs2bwxhvw1FPQKNZZ6tFH2eHLO+laYyhz8pvw0mu1c1rfmPHjfcLTJk1ghx3KPl5ERESkvFIO0kMI/wK7AI8BLYHjomUD4BFg5xDCgmxUUqqPUaPgsMNg1izYZx8YMQIOPzzugF9/hYsvBuD8s1YA8OGHOahoEvPnQ/fucMABnq5dREREJFvKNZmRmRmwbnR3VqhGMyJpMqPsu+YaaN7cu7oUS9by77+Ql+fN1aefzrIHH+err2DvvXM6uegqQqha9REREZHVU2mTGWnG0QQK0jNr5UrPorjrrt4KXaIQ4MQT4cUXYaut4McfYa21Kq2eIiIiIpUtUzOOnmtmA0vZ/4mZnVmeCkr19Ndf0K0bXH+9Txi6bFkpBz/9tAfo9evDq6+uEqBPmuRdZHJl3Dj4/nvP3y4iIiKSbekMHO0NjCll/2jglArVRqqFEHzeoW22gW+/9cmJnnoK6tYt4YTff4fzzvP1Rx6BLbYotvvee6FdO7j//uzWuzSPPQY77eRfOERERESyLZ0gvT0wopT9v0fHyBps/nw4+mhvOf/3XzjiCBg+3PuVJ7VoERx1FCxZAr16wUknrXJI165QUACPP15Ga3wWxVIv7rVXbq4vIiIia5Z0gvTaQL1S9tcrY7+sAa69Fl57zXutPP20r6+zTiknXHCBTznaoQP07Zv0kF139Vb5GTO8J0xlmzrVs9A0aAC77FL51xcREZE1TzpB+mhgn1L27wuMq1h1ZHW32WbQo4cH5717l5EF5YUXoF8/qFfPo+8GDZIeZlbUG+bBBzNe5TJ9/LHf7rlnKV12RERERDIonSD9ZWBfM7vZzOrENppZbTO7EQ/SX8p0BWX1cv753jVk//3LOPDPP+Gss3z9wQehU6dSDz/uOGjaFH76CX74ITN1TZVmGRUREZHKlk6Qfh/wFXAN8LeZDTazwcA04DpgMHBP5qso1c6SJd4PfdEiOPZYOPXUMk+pXx9OO83XK7M1feVK+PRTX1eQLiIiIpUlnRlHV+Ct5VcCU4DtomUycDmwdwhheTYqKauHceO8lXvRojIOvOQSH0266aaeNiXFmYHOOQfqRL/hVFZ6/wkT/Jrt28PGG1fONUVERETSaUknhLAihHBnCGHbEEKDaNkuhHB3CGGFmanH7hrs6adhxx198qISvfoqPPqoR76vvgqNGqVcftu2MG2ad2WvrBk/N93UrzloUOVcT0RERATSDNJLYmZdzOxh4O9MlCerp+HD/XbrrUs4YNy4oj4r990H222X9jWaNStf3SqiRg3P9S4iIiJSWWqV90QzawacgE9g1AkwPAOMrKFiQXrSMaDLlnk/9H//hf/8B84+u9zXKSjwwZwNGviMptmyeLFXu2nT7F1DREREJJm0W9LNrIeZ9Qem4oNJ6wI3Ap1CCB0yXD9ZTcyfDxMneorC9smmtLr8cvj5Z5869IknKtRf5bnn4IAD4Jpryl/fVLz1FjRvDpdemt3riIiIiCRKKUg3s7ZmdpOZTQQ+APYAXo92XxNCuCmE8HuW6iirgd9+89stt4Raib/PvPUWPPAA1K4N/fvD2mtX6Fr/+Q80bgzffAPDhlWoqFJ9+CHk50OrVtm7hoiIiEgypQbpZna8mX0GjAWuAIYAhwEbAn3wLi4iJXd1mTABTjnF1++8E7p2rfC1GjaEk0/29YceqnBxSQ0eDG++6etKvSgiIiKVrayW9OeBNsBFwAYhhCNCCO+EEFZmvWayWvnjD78tNmh0xQo45hiYNw8OPhguvDBj1zv3XL996SWYPTtjxQIwdKh3p1myBE4/HTqoE5eIiIhUsrKC9GVAW+AQoKeZrZX1Gslq6f774a+/4MQT4zZefbUnTm/VyvMzZjBvYvv2sN9+sHQpPPlkxopl5Ejo0QMWLIAjj4RHHslc2SIiIiKpKitIb4m3oq+Dt6pPN7OnzGx31NVF4ph5HvPmzaMN778Pd98NNWvCK69kJXfi+ef77cMP+8ygmXD33d4yv//+no+9Zs3MlCsiIiKSjlJTMIYQ5gEPAQ+ZWWfgVOBYoDcwEwhAk+xWUVY7U6ZAr16+fuutsPPOWblMjx7QvTvstZf3rFllwGo5PPqozyx66aVFs5uKiIiIVDYLac6vHs0qegQesO8RbR6BZ3t5a3XP8pKXlxeGDBmS62qsVj780Hu2HHcc/N/FKz1yHjzY+6O8957PBlSFzZ7tOdfr1ct1TURERGRNYmZDQwh5yfalHT2FEJaFEF4KIewFbALcAjQFbgJ+rVBNZbX088/wyy8wfTqewWXwYJ+i89lnq3yAPn8+7LMPHHQQLFqU69qIiIiIuApFUCGECSGE6/HBpfsDb2aiUrJ6iaVf3HrzZXDvvX7nmWfiOqhn19y5cPvtcOON6Z23aJFncRk2zDNFLlyYleqJiIiIpC0DvXgheJ+Zj6JF1jCFOdInvud9R7p2hb33rrTrT5sGV10F9et7lsdU5kpatgwOO8wnRGrVCgYOhPXWy3pVRURERFJSYku6me1V3kLNrPIiNMmppUth9GioUSPQ8fWbfOPFF2c03WJZOnb0waOLF3umx7KsXOnp2z/9FFq08AC9TZvs11NEREQkVaV1d/nIzD43swPNrMxEdGZW28wOM7MvgQ8yV0WpykaOhIIC2HzDRdQbPRw22gj+859Kr0csHWPfvl6fkhQU+ASob7/tLe6ffgqbbVYZNRQRERFJXWndXbYD7gXeAWaa2UDgR2AcMAfPk94MaA/sCOwFrA18AmybtRpLlVLY1WV5lBHn/POhdu1Kr8eBB3qe9nHjPNvMAQckP27lSp+oqEED+OijhBlSRURERKqIEoP0EMJvwL5mthNwDj7r6LF4bvR4BizAB40+EkL4KUt1lSqoUye44pQZbNfvEe8UfvrpOalHzZpwzjlw+eXw4IMlB+l16sBrr8GoUV53ERERkaoo5TzpUZeXLkBHoDkerM8EfgOGhRBK6WSw+lCe9HI49VTo1w/OPRceeihn1ZgzBzbcsKiffPv2Rfv69/c0i/Xr56x6IiIiIsWUlic95ewuIYR8vLvLj5mqmFQDM2bAiy/6QNELL8xpVZo1gzvu8BlDN964aPuDD8IFF/gcSwMHVvnU7SIiIiIVy5OeKWZ2sJl9amZzzGypmY0xs3vMbJ1ylLWHmb1uZn+b2TIzm2lmQ8zsfjOr/M7S1djcufDEaT8wdNmW3kwd33SdIxdc4P3Ta0ZDnZ95xrcBHHusAnQRERFZPWQkT3pFmNmNwPUJmzcFLgEON7PdQwiTUyzrTuD/EjavGy1dgGuAFRWrscQM/W45Z7x7ELvQlMEXr8x1dVbxyiveEwfgnnty1l1eREREJG05DdLNbDeKAvQC4FrgD+AKPGNMW+BJoEcKZZ1GUYD+L/AQ8D2wDGgD7A7kZ672MvzZYcAOdFpnGnSr/LSLJZk1C3bbzQeHAtxwA1xySW7rJCIiIpKOXLekXxS33i+EcBt4J3pgIp45Zl8z2zKE8HtJhUTdWOInhT84hDAo4bDHM1JjcSEw4tNpAGx9QKtKnbyoLE2awMKFvn7hhR6ki4iIiKxOct1Dt3vc+uDYStS9ZVLcvj3LKGdHYINofQqwp5mNivq3T4z6ozfNSI3FffYZw+e2AqBT7y45rkxxtWvDe+/B88/DffdVqe8PIiIiIinJWZAeBc3xgfP0hEPi729SRnHxU9JsBFwHbA7UBVoDFwDfmtnaJdTljGhw6ZCZM2emUHtZec/9jKQjAJ06V73xuNtsAyecoABdREREVk8VDtLNrIuZ7WNm9dI8tUHC/eWl3G9YRllrJ9wfCRwKnI5PtATQAbgy2ckhhMdDCHkhhLzmzZuXcSnhjz8Y+9EYlrIWbVrl06RJriskIiIiUr2kHKSb2WVm9m7CtpfwvOkfASPMbL00rr0o4X7dUu4vLKOspQn3Lw0hDAghPAk8Erd9/zTqJyW5/36msiGNai+h0zY1c10bERERkWonnZb0Y4jrJ25me0bbXsFTG7YELk+1sBDCXGBu3Kb1Ew5pGbc+roziJibc/6uEdbX5VtTs2fDcc+zF58z/ZQIvvpjrComIiIhUP+kE6W3x9IgxhwLTgBNCCLcDjwIHpXn9L+LWd4utmFk7oFXcvs/LKGcwxdMrti1hPTGYl3Q99hgsWQL77Yd13ILGjXNdIREREZHqJ50gvQGwJO7+nsDAEEKI7o8ENkzz+g/Erfc2s6vN7FCgf9z2gbH0i2b2jJmFaOkTOyCEMB14Pe6cu83sEDM7BTg7bvtLadZP4i1fDg89BED+BRfnuDIiIiIi1Vc6QfpUoBOAmbUBOgJfxu1vik8clLIQwpfALXF1uQV4C+gabZsEnJZicRcAf0brWwFvA09R1MVlAPBEOvWTBP37w7RpLNhiBxoetjc77QSFX9FEREREJGPSCdLfBc42s4fwVutlwPtx+7cCJqRbgRDCtcBheJeWeXhWl3HAfUBeCCGlLiohhBnADsCteLC+DB+c+gPemn54CEEzjpZXCJ50HPjt0GtZutRYtkwpDkVERESyIZ0ZR2/C85GfgwfAF4UQ/gEws7XwQPup8lQihPA23vJd1nG9gd6l7J+PD2K9pjz1kFJ89RUMGwYtWjC8ZQ8AOnXKcZ1EREREqqmUg/QoG8teZtYYWBJCWJFwSDdgciYrJ1VI1IrO2WczYpRPXrT11qUcLyIiIiLllvZkRiGEBYkBeghhSQjh1xDCnMxVTaqMsWPhnXegTh04+2yGD/fNCtJFREREsiOdyYy2N7PTE7YdYmYjzGyqmd2a+epJlXD//d4n/YQTCC3WU5AuIiIikmXptKTfABwcu2NmrYGX8UmI5gNXmNnJma2e5Ny8efD0075+0UVMmgQLFkDz5rBeOvPLioiIiEjK0hk4ug3wYNz9YwADtg0hTDWzD4EzgKczWD/JtSeegEWLYO+9oVMn1p4PL7zgm0REREQkO9IJ0tcB/om73wP4KoQwNbr/DnBzpiomVcCKFfBg9L3sYp+8qEkTOP74HNZJREREZA2QTneXecB6AGZWF9gR+CpufwDWyljNJPfeeAMmT4bNN4eePXNdGxEREZE1Rjot6b8Ap5nZQDwnej3g47j97Sje0i6rs7jJi7j4Yqjh3+f++1/vi37ccdCgQQ7rJyIiIlKNWUhxXncz2xn4BG8tN+DTEEKPuP2/A8NDCMdmo6KVJS8vLwwZMiTX1ci9b7+FXXaBZs28Nb1+fZYuhYYNPX5fuBDW0u8mIiIiIuVmZkNDCHnJ9qUzmdG3ZtYZ74s+H3gl7gLr4AH8WxWsq1QVsVb0s86C+vUB+OMPyM/33i8K0EVERESyJ53uLoQQRgOjk2yfDVycqUpJjv31F7z5JtSuDeeeW7h5xAi/VX50ERERkexKK0gHMLO2wN74INIXQwgTzKwOni99eghheWarKJXuwQehoMDTuGywQeFmTWIkIiIiUjnSye6Cmd0BjAEeB24CNo521QNGAudktHZS+RYsgCef9PWLi/84oiBdREREpHKkHKSb2ZnA/wF9gX3xwaMAhBAW4HnSD8p0BaWS9esH//4L3brBdtsV2xUL0jt1ykG9RERERNYg6XR3OQd4K4RwUTRQNNFw4LzMVEtyIj8f7r/f1xNa0ZcsgU02gVq1oE2bHNRNREREZA2STpC+GfBIKftnAutWrDqSU2+9BRMmwKabwoEHFtu11lrwzTeeftEs+ekiIiIikhnp9ElfCpQ2fU0bfFZSWR0tXQpXXunrF18MNWsmPUwBuoiIiEj2pROk/4jPNLoKM6sHnAh8k4lKSQ7cdReMGwdbbgmnn77K7unTYeXKHNRLREREZA2UTpB+F7CTmT0PxPJ7rG9mPYBBwEbA3ZmtnlSKv/6CW2/19b59PT96goMO8tlGhw6t5LqJiIiIrIHSmXF0oJmdDdwPHBdtfj66XQ6cHkL4LsP1k8pw4YXe3eX44z2rS4L8fPjtN1i2zAePioiIiEh2pTvj6ONm9g5wJNABT8M4Bng1hDA1C/WTbHv3XV8aN4a7k/8QMnasx/CtWsHaa1du9URERETWRCkF6WbWEHgA+DCE8BrwYFZrJZVj8WK44AJfv/lmWH/9pIeNGOG3msRIREREpHKk1Cc9hLAQOAZonN3qSKW6/XZPubjNNnBOyZPFaqZRERERkcqVzsDRkUDbLNVDKtuYMXDHHb7et6/PUlQCzTQqIiIiUrnSCdLvBM42s82yVRmpJCHA+efD8uXQuzfsskuph6slXURERKRypTNwtAMwGRhhZu/hA0YXJxwTQgg3Z6pykiVvvQUff+yjQGOt6aUYMMAD9c309UxERESkUlgIIbUDzQpSOCyEEJJPVbmayMvLC0OGDMl1NbJn0SLYYguYPBkefhjOPjvXNRIRERFZI5nZ0BBCXrJ96bSkt8tQfSSXbr7ZA/QuXeCMM3JdGxERERFJIp3JjCZmsyJSCf74A+65B8y8Fb1m2T96PPCAjzE97TRPAiMiIiIi2ZfOwFFZnYUA550HK1fC6afD9tundNrrr8NDD8E//2S5fiIiIiJSKOWWdDPrV8YhAVgCTAI+DSEMq0jFJMNefRU+/xzWWQduvTWlU0JQ+kURERGRXEinT3pvPBAHsIR9idtvM7NXgJNCCPnlr55kxL//wiWX+Prtt3ugnoIpU2D+fFh33RInIxURERGRLEinu0tz4GfgNWAHYO1o2RF4HRiCDy7tGt0/Brg8c1WVcuvTB/7+G3bYAU45JeXT4vOjW+LXMhERERHJmnSC9LuBf0IIx4QQfgohLIiWH0MIRwOzgD4hhKHR/W+AE7JRaUnDb7/B/fdDjRo+WLRG6i+5JjESERERyY10gvSDgA9K2f9+dEzMOyhtY26FAOeeC/n5ng+9c+e0Tld/dBEREZHcSCdIrwdsUMr+jaJjYhYBK8tTKcmQF16Ar76C5s09P3qattoKdtkl7dheRERERCoonSD9W+B8M9sxcYeZ7QScFx0T0wmYXLHqSbnNmweXXebrd90FTZumXcQ118DgwbDtthmtmYiIiIiUIZ0gPYr4+MbMvjOzZ6LlO2AwnuHlMgAzqwfsCbybSsFmdrCZfWpmc8xsqZmNMbN7zCy1NCRexiAzC6Usr6fxWFd/118PM2bArrvCSSflujYiIiIikoZ0ZhwdbmZdgFuB/fAML+DdWt4Arg0hjI6OXQp0SKVcM7sRuD5h86bAJcDhZrZ7CEEt8un45Rfo29dnFO3bt1ypWSZMgMWLYbPNoFY6iTpFREREpMLSmnE0hDA2hHAU0ARoGS1NQghHxQL0dJjZbhQF6AXA1cBhwPfRtrbAk+mWC+yWZLm2HOWsfgoK4Jxz/Pb888udmqVvX9hyS7jttgzXT0RERETKVK420hBCAZCJieIvilvvF0K4DcDMhgIT8cmR9jWzLUMIv6dRv8EZqNvq6Zln4LvvfPahG28sdzHK7CIiIiKSO2m1pJtZDTM72czeMbPfouUdM+ttZmmVFeket14YWEfdWybF7dszzXpOMLPlUR/3z8zsyHLUbfUzZw5ccYWv33MPNG5c7qJGjPBb5UgXERERqXwpB9ZmthbwGd79ZH+8y0uTaP0pYGA0YDTV8poC8SlHpiccEn9/k1TLjbQBakfl7wm8amb3pVnG6ueaa2DWLOjeHY49ttzFzJoF06ZBgwbQtm3mqiciIiIiqUmn9ftaoBtwD9A8hNAqhNAKWBefjXQP4Jo0ymuQcH95KfcbplDeDOBxoBewD3AKEN9F5iIz2z7ZiWZ2hpkNMbMhM2fOTOFSVdDQofDYYz7K86GHyjVYNCbWit6pU1oTlIqIiIhIhqTTJ/1o4NUQwuXxG0MI84ArzKwNcCxwXYrlLUq4X7eU+wvLKiwa0FqMmb0HjAMaRZsOAn5Mcu7jeIBPXl5eKOtaVdIdd/gMoxdcAB07VqioWH90dXURERERyY102kk3AgaVsv/L6JiUhBDmAnPjNq2fcEjLuPVxqZabcI2ZQHzWmfXKU06VN3UqvPmmt6JfckmFi4tvSRcRERGRypdOkD4Pz19ekk2jY9LxRdz6brEVM2sHtIrb93lphZjZBmbWOsn25sBmcZumpVm/1cNjj0F+Phx+OGy4YYWLe/hhb00/cs0YbisiIiJS5aQTpH8KnGtmPRJ3mNm+wNnAx2le/4G49d5mdrWZHQr0j9s+MJZ+MZrhNDaDaJ+4YzYDxphZfzPrZWZ7mllv/EtArKtLAVD9Zh1dtsyDdIDzzstIkXXqeCv6etXzdwcRERGRKi+dPunXAj2AD8xsGEWDMrcEtgNmserMoaUKIXxpZrfgA05rALckHDIJOC3F4uoAR0XLKpcCrgohjEinfquF11+HGTO8A/muu+a6NiIiIiKSASm3pIcQJgJ5wCt4y/WJ0dIeeBnoGh2TlhDCtfgso5/j3WWW433Q7wPyUixzCJ7N5Q1gDLAAWAFMAV4Fdg8h3Jlu3VYLDz3kt+edV6GMLjHvvw/dusGjj1a4KBEREREpp5Ra0s2sJrAhsDCEcLyZGdA82j0zhFChjCghhLeBt1M4rjfQO8n2hcDT0bLmGDIEvv8e1l4bjjsuI0X++CN89RXsvHNGihMRERGRcki1Jb02MB44FSC4GdGyeqYsrA769vXbU0/1mYcyIJZ+UZldRERERHInpSA9hLAU73OemNtccmXmTHj5Ze/icvbZGStWOdJFREREci+d7C4fAAdmqyKSpqee8swu++8Pm2ySkSIXLoTx46F2bdh884wUKSIiIiLlkE6QfjnQ0syeNbNOZlYvW5WSMqxcCY884usZSrsI8NtvftuxowfqIiIiIpIb6aRgnIGnMtwGOAHAVs0mEkII6ZQp5fHeezBpErRvD/vum7Fi1dVFREREpGpIJ6B+Dg/SJdcefNBvzz0XaqTzY0jp2rf37u3du2esSBEREREpB1NyluLy8vLCkCFDcl2Nko0cCVtu6dlcpkzx9IsiIiIistoxs6EhhLxk+8psSTezWsAhwKbATOCdEMKszFZRUhZLu3jiiQrQRURERKqpUoN0M2sKDAK2Agzv7nKXme0bQhia/epJMfPnw7PP+vq552a06IICePtt2Hhj2HbbjBYtIiIiImkqqyX9WqAT8B7wMbAZcBbwONAlu1WTVTz7LCxa5J3Gt9oqo0VPnw5HHAHNm8OMGRktWkRERETSVFaQfhDwUQjh4NgGM5sA3G1mG4UQpmSzchKnoKCoq0sG0y7GjBvntxtvnPGiRURERCRNZaUGaYVPYhTvXbzrS5us1EiSGzgQRo+GjTaCgw8u+/g0jR/vtxmaF0lEREREKqCsIL0uMCdh29y4fVJZHnrIb88+G2plPhW9WtJFREREqo6KJNlW7sbKMn68T2BUpw6cdlrWLgFqSRcRERGpClJpkr3UzI6Ju18bD9BvMbPEVIwhhHBIxmq3BgkBli6FtdZKsvORR/yAY46BFi2ycn21pIuIiIhUHakE6dtFS6Idk2xT63o5DBoEhx4KO+8MHySOAFi8GJ56ytezMGA0ZsIEv1VLuoiIiEjulRqkhxAyN+e8lKhFC0+BPnp0kp0vvwxz58L220PXrlmrw8SJvrRsmbVLiIiIiEiKFIRXARtvDGbemr1iRdyOEODBB309i63o4N3d27eHGnpHiIiIiOScQrIqoF49aN0a8vPhr7/idnzzDfz6q88wdNRROaufiIiIiFQuBelVxGab+W2xLi+xtItnnAF1s5fx8oknYLfd4IUXsnYJEREREUmDgvQqon17vx0zJtrw99/wxhtQsyaceWZWrz1sGAweDLNnZ/UyIiIiIpIiBelVRCxIL2xJf+wxWLnS0760apXVa8dypCv9ooiIiEjVkPmpK6Vc9t3X4/LttweWL/c7kPUBo1CUI13pF0VERESqBgXpVUTHjr4A8PIb8M8/sNVW0K1bVq+bn1+UI71du6xeSkRERERSpO4uVVFswOh553luxiyaPNl71WywQQmznYqIiIhIpUu5Jd3MxpdxSACWAJOAT4AnQgiLKlC3Nc6AAfDTu9M4/dsptGnSBI4/PuvXVH90ERERkaonne4uk4ANgE2B+UAso3c7oAkwBg/SdwR6AmeZ2W4hhJmZq2719vjj8MEHLelCZ9qc3BYaNsz6NVu0gPPPV1cXERERkaokne4uFwHNgHOAFiGEziGEzkBz4Lxo36nAusD5QHvgpozWtppr32oJAGNoD+ecUynX3GoreOABuPjiSrmciIiIiKQgnSD9bqB/COHREELh5PUhhJUhhIeB14B7QggFIYS+wMvAAZmtbvXWfvpgAEZvtFdRTkYRERERWeOkE6TvAAwvZf9wvKtLzLfAeuWp1BopP5/NvnsWgDFr51XaZT/5BIYM8ayPIiIiIlI1pBOkLwO6lrJ/++iYmLrAwvJUao303nu0n+Et6WNmN6u0yx59NHTtCnPnVtolRURERKQM6QTp7wAnm9mVZlY/ttHM6pvZVUCv6JiYnYHRSGoeeohWTKZOzZVMm2b8+2/2Lzl3LsybBw0a+ABSEREREaka0snuchmwHXArcJOZ/R1t3yAqZwTwfwBmVg9YCvTNXFWrsT/+gIEDqVm/Pp02g+X5MHs2NGqU3cvGp1/Mcjp2EREREUlDykF6CGGOme0AnAYciKdeBPgMeBd4MoSwPDp2KXBihutafb3xht+ecAI/PVqr0gLmceP8dpNNKud6IiIiIpKadFrSiYLwh6NFMuWaa6BbN2jZslJbtDWRkYiIiEjVlFaQLlliBrvtVng3BFi4MPvdXdSSLiIiIlI1pRWkm1kD4Dh8oqJ1gMR23xBCODVDdVsjDRkC3btDp07w7bfZvdaECX6rlnQRERGRqiXlIN3Mtgfew2cULUnAZx1Ni5kdjM9S2gWoD0zGM8XcGkKYXY7yGgK/APFtxN1DCIPSLauybbCBt6KPGZP9a334IUyaBOspm72IiIhIlZJOCsZ7gTrAUcC6IYQaSZaa6VbAzG4EBgB7A03x/OqbApcAQ8ysVbplAvdRPEBfbbRs6SkRZ83Kfu7yWrW8Fb1Bg+xeR0RERETSk06Q3gW4J4TweghhTiYubma7AddHdwuAq4HDgO+jbW2BJ9Ms8yA8A83STNSxspnBppv6emW0pouIiIhI1ZNOkL4ASLvrSRkuilvvF0K4LYTwNt5aH6Lt+5rZlqkUZmbNgSeiu1dkqpKVbbPN/DabQfqXX8JOO8Ftt2XvGiIiIiJSPukE6W8CPTJ8/e5x64NjKyGEycCkuH17plje48B6wEDgwQrXLkfat/fbbAbpv/8O339flOFFRERERKqOdIL0K4AWZvagmW1iVrGM3mbWFO+DHjM94ZD4+2X2LzezU4BDgblA7xBCKP2MqisWpI8enb1rxHKkK/2iiIiISNWTTgrGeXgXlO2BcwCSxOkhhJBqmYnDFZeXcr9haQWZWTvgf9Hds0MIU1OsQ+z8M4AzAFq3bp3OqVnRrRs8+SR07py9a2giIxEREZGqK50g/TmK+olnwqKE+3VLub+wjLIeBBoBL4UQ+qdbkRDC43hXGfLy8nLeAt+uHZya5WzzmshIREREpOpKOUgPIfTO5IVDCHPNbC5FXV7WTzikZdx6WT2nN4pujzOz40o45ouo5b9pCGFeOnWtbkJQS7qIiIhIVZZOn/Rs+CJufbfYStR9JT4/+ueVVqMq4r334Kqr4I8/Ml/2zJk+YdLaa0OzZpkvX0REREQqJp3uLtnwAHB4tN7bzMYBI/F86TEDQwi/A5jZM0CvaPuNIYQ+0fpdQPMk5d8Xt94XGAssyUjNs+yll+Dllz0d4xZbZLbsEOCiizJbpoiIiIhkTolBupkV4BMM1Q8hLI/ul9VfO52Bo4QQvjSzW4Br8Fb9WxIOmYRPTFRWOS8m225m8UH66yGEQanWLdeymSt9vfXgvvvKPk5EREREcqO0gDo2UDQ/4X5GhRCuNbMhwPlAZ6A+MBl4B7gthDAz09dcHVRGGkYRERERqZpKDNITB4pmeuBoQtlvA2+ncFxvIOV6hBAqlMs9l7I5odH334MZbLUVNEhMhCkiIiIiOZfrgaNSgliQPnas9yHPpMsvhx139GBdRERERKqecg0cNbP6wDpAstmMJlW0UgJNm8K668KsWfD337DhhpkrO5YjXekXRURERKqmlIN0M6sBXI73HU/MaR6vZkUrJa5zZ0+XOG9e5oL0JUs86K9VC1q1Kvt4EREREal86bSk3w5cBvwOvAHMzkqNpNDHH2e+zAkT/LZNGw/URURERKTqSSdMOwH4KISwf7YqI9kX6+qyySa5rYeIiIiIlCydgaNNgQHZqogkV1AAc+Zkrrzx4/1W/dFFREREqq50gvQRQMtsVURWNWoUNGoEu+6auTJjQbpa0kVERESqLgsp5vczswOAp4CuIYTJWa1VDuXl5YUhQ4bkuhoALFzoQXqdOrB4MdTMwJDc/HyYOhXq1/fsMSIiIiKSG2Y2NISQl2xfOn3SuwATgZFm9hbwF0WzkcaEEMLN5aumJGrYEFq2hGnTYPJkaNu24mXWrAmtW1e8HBERERHJnnSC9D5x6yeUcEwAFKRn0GabeZA+enRmgnQRERERqfrS6ZPeLoVFwxEzLDbz6JgxFS9r2jTYfns455yKlyUiIiIi2ZNyS3oIYWI2KyLJbbaZ32YiSB8zBn76CWqk89VMRERERCqdwrUqLpMt6Uq/KCIiIrJ6KLEl3cyux/uY3xJCKIjul0UDRzNsxx3hmWegU6eKl6WJjERERERWD6V1d/n/9u49aq6qzPP490eIAeQWAQW5JWCriLYi0UYFJKIojA2CgizHCw6KLqdFl+MV0EZHLtM2rYhiC4h4RRsvCNqi0uGqggYHsZExiAQI1wABEiAGyJ4/zqnOofJeqt7Um6p6+X7WOqvO2WfXqac2G3jeXfvscyxVkv5/gBU8/sbR0XjjaI9tuSW87W29uZYj6ZIkScNhrCR9NkApZUXzWMPLkXRJkqThMGqS3n6jqDeO9s9Pfwrz5sEhh8CLXjTx6ziSLkmSNBy6WSddfXL++fClL8HTnz7xJH3lSnjrW2Hhwuo6kiRJGlxdJ+lJ5gB/B8xk9dVhvHF0EvRiGcZ11oF//ufexCNJkqTJ1XGSnmR94AfAPkCobhJNfbo0ykzSe6yXyzBKkiRp8HWzTvonqBL044C5VEn524B9gcuA3wLP6XWAWpWkL1gw8Wv88Y/wq1/Bfff1JCRJkiRNom6S9DcA55RSPgH8Z112aynlZ8ArgScBh/U2PAHMng3TpsEtt8Dy5RO7xhe+AC97WbXmuiRJkgZbN0n6tsAl9f5j9euTAEopjwJnA4f2LjS1TJ9eJeqlrFpGsVsuvyhJkjQ8urlxdGmj/lJgJdBcJ+R+YMsexaU2u+4Km24KDz88sfe7/KIkSdLw6CZJvwF4JkAp5bEk11JNgTkzSYCDgFt6H6IAvvOdib/30UerpRehGpGXJEnSYOtmusuFwOuTTKuPvwy8JskNwPVU89K/0uP41AOLFlWJ+lZbwQYb9DsaSZIkjaebJP1EVq3qQinlVOCDVNNclgBHAf/U6wC1ymOPwZ13dv8+56NLkiQNl46nu5RSlgF/aiv7F+Bfeh2UVnfrrVWS/ZSnwG23dffe1nx0k3RJkqTh0FGSnmRD4PfAKaWUz01qRBrRlltWq7vcfjssWwYbbtj5ew8/HPbbrxqJlyRJ0uDraLpLPYq+GbBscsPRaKZNWzUS/uc/d/feddaBrbeG7bbrfVySJEnqvW7mpF8BzJmsQDS+1pNHr7++v3FIkiRpcnWTpH8UOCTJ2+slF7WWPfOZ1euCBd29b6+94JBDJr7GuiRJktauMeekJ9kOWFxKeZjqBtElwBnAP9VLLz7U9pZSStl7UiLVhEbSlyyBSy6p5rCvt97kxCVJkqTeGu/G0RuBNwNnAzsABbi5Pve0SYxLI2iNpHeTpLeWX9xhB/D3D0mSpOEwXpIeVq2LPmvSo9GYnv98+MY3YOedO39Pa/nFHXaYnJgkSZLUex2vk67+mzkT3vzm7t7jg4wkSZKGTzc3jmoIOZIuSZI0fDoZSd8jSTdPJv16t0Ek2R94L7ArsAFwC3AecHwp5Z4Or/Ex4BXAs6jWdJ8O3AtcA3wL+HoppXQb26D5+c/hggvggAPg5S8fv35zTrokSZKGQyfJ9xH1Np5Q3VjaVZKe5JPAJ9qKnwF8ADgoyZ6llFs6uNS7gO3byp4GvKredgWO7Ca2QXTxxfDZz8LGG3eWpO+7L2y2Gey006SHJkmSpB7pJEk/jepBRj2XZA9WJegrgWOA64CPALsBs6iWfHx1B5e7HPgysIBqqcjtgY8B9cKFvDvJR0sp7ctGDpVul2H80IcmLxZJkiRNjk6S9MtKKd+epM9/f2P/zFLKCQBJrgJuohqd3yfJzqWUa8e6UClltVsqk9wH/KA+nA6sz+pruw+ViT7QSJIkScOj3zeOzm3sX97aqae33Nw494puLppkepJnAoc3iq/qdH77IGuOpI83w/7WW+Gyy+CuuyY/LkmSJPVO35L0JDOBmY2iO9qqNI87WkAwyQuSFGAF8CfgvwGPAT8GDpx4tINjiy2q+ej33w933z123R/9CPbcE44+eu3EJkmSpN7o50j6k9uOV4xxvOEaflaAGaOeTI5IMj/J/MWLF6/hR02upPMpL63lF10jXZIkabiMOSe9lDKZSfyDbcftSXTzeFmH17we2KN+72zgPcAuVCPqL0ry7FLKkvY3lVJOo7pBljlz5gz8Mo0vehGsuy6sXDl2PZdflCRJGk59e+JoKWVJkiWsmvKyZVuVrRr7N3R4zQdpzG1P8l1gMVXS/lTgIOArE415UJx6amf1fJCRJEnScOr3jaMXNfb3aO0kmQ1s2zg3b6yLJNlglFOl3lqe0m2Aw6qUVSPpTneRJEkaLv1O0j/f2D8syVFJXgd8t1F+YWv5xSRnJSn1dmyjzhFJrklyTJJDkuyd5O3AxcB6jXpXTs7XWPsefRQWLRr9/OLF8OCDsOmmMHPm6PUkSZI0ePo23QWglHJJkuOAo6n+YDiurcrNwDs6vNzz6m00p5ZSLu0+ysHzwAOw+eYwfTosW1bdTNrO+eiSJEnDq69JOkAp5Zgk84H3Ai8ENgBuAc4DTiildLLcykVUTxvdDXg61Tz3R4Dbgd8AXyulXDAJ4ffFxhtX2z33wG23wdZbr15nt93g9turpRolSZI0XFLGeyLOE8ycOXPK/Pnz+x3GuF76Uvj1r+Gii2CvvfodjSRJkrqV5KpSypyRzvV7TromqPnkUUmSJE0tJulDarwHGh1+OBx00Kq56ZIkSRoeJulDaryR9AsugB/+EKZNW3sxSZIkqTdM0odUayR9pCT94YerG0rXXRe22WbtxiVJkqQ11/fVXTQxz3oWnH02PPvZq5+78cbqddasKlGXJEnScDGFG1Lrrw+HHjryub/8pXp1jXRJkqTh5HSXKah1s+iOO/Y3DkmSJE2MSfoQmzcPjjwSfvzjx5c7ki5JkjTcTNKH2Pz5cMopcOGFjy+fMwfe+EbYddf+xCVJkqQ145z0ITbaMoxveUu1SZIkaTg5kj7ExlqGUZIkScPLJH2I7bgjJNUc9EceqcqWLoWLL4ZFi/oamiRJktaASfoQW2892HZbeOwxWLiwKrv6apg7Fw4+uJ+RSZIkaU2YpA+51rz0BQuq19byi67sIkmSNLy8cXTIvfjFsHw5TJ9eHbeWX3SNdEmSpOFlkj7kjj/+8ceOpEuSJA0/p7tMMT7ISJIkafiZpE8BjzyyKjlvjaQ73UWSJGl4Od1lyD36KGy0EaxYAXfdBYsXw4wZsNVW/Y5MkiRJE+VI+pBbd13YZhsoBe68s0rUr7gC1vGfrCRJ0tAylZsCmk8e3WILeMEL+hqOJEmS1pBJ+hTQWiv9+uv7G4ckSZJ6wyR9Cmgl6R/+MBxwAFx6aX/jkSRJ0poxSZ8CWtNdAM47Dx54oH+xSJIkac2ZpE8BrZH0FtdIlyRJGm4m6VPAdtvBt7+96nj27P7FIkmSpDVnkj4FTJsGL3lJtf/0p8P66/c3HkmSJK0Zk/QpwieNSpIkTR0m6VPE979fvS5Y0N84JEmStOZM0qeIgw+GTTeFU07pdySSJElaU+v2OwD1xty5cO+9kPQ7EkmSJK0pR9KnEBN0SZKkqcEkXZIkSRowJumSJEnSgDFJlyRJkgaMSbokSZI0YEzSJUmSpAEzEEl6kv2T/CLJvUmWJ7k+yUlJNuvw/ZskOTLJD5IsSHJfkhVJbknyrSQvmOSvIEmSJPVM35P0JJ8EfgS8EpgJzACeAXwAmJ9k2w4usxNwMnAg8DfAJsB0YBvgTcBvkuzb++glSZKk3utrkp5kD+AT9eFK4CiqRPuKumwWcEaHl1sJ/Bj4H8CrgI8CD9bnpgNfWPOIJUmSpMnX7yeOvr+xf2Yp5QSAJFcBNwEB9kmycynl2jGuswjYpZRyTaPswiSPACfVxzskeWop5a7ehS9JkiT1Xr+nu8xt7F/e2iml3ALc3Dj3irEuUkpZ1Jagt/yp7fjBEepIkiRJA6VvSXqSmVRz0FvuaKvSPN5xgh/zxsb+f5RSTNIlSZI08Po5kv7ktuMVYxxv2O3Fk3wQeEt9eD/w3jHqHpFkfpL5ixcv7vajJEmSpJ7qZ5LePqo9Y4zjZZ1eNJWTgM/URfcB+5ZSrhvtPaWU00opc0opc7bYYotOP0qSJEmaFH27cbSUsiTJElZNedmyrcpWjf0bOrlmkhnA14FD6qJFwH6llD+sSaySJEnS2tTvG0cvauzv0dpJMhtoro8+b7wLJdkU+DmrEvRrgN1M0CVJkjRs+p2kf76xf1iSo5K8Dvhuo/zC1vKLSc5KUurt2FaFJE8FfgnsWRfdAnwMmJ1k98a2yWR+GUmSJKkX+rpOeinlkiTHAUdT/cFwXFuVm4F3dHCp59Rby7bAT0aoNxe4uPtIJUmSpLWn3w8zopRyTJL5VKuvvBDYgGok/DzghFLKWl1u5aqrrro7yU0TeOvmwN29jucJzPbsPdu0t2zP3rNNe8v27D3btPee6G26/WgnUkpZm4FMWUnml1Lm9DuOqcL27D3btLdsz96zTXvL9uw927T3bNPR9XtOuiRJkqQ2JumSJEnSgDFJ753T+h3AFGN79p5t2lu2Z+/Zpr1le/aebdp7tukonJMuSZIkDRhH0iVJkqQBY5IuSZIkDRiT9DWQZP8kv0hyb5LlSa5PclKSzfod2zBJMqvxJNnRttf2O85BlOT9Sc5JcmNbex02Sv3N6z56fd1n7637sO1b66ZN256CPNI2vw9fYaAk2SXJ8UkuS3JzkoeTPJjk90n+McmGI7zHfjqKbtvTPjq+JNslOT3J75LcmeSRJA8lWZDkq0n+doT32EdH0W172kfHUEpxm8AGfBIoo2w3Atv2O8Zh2YBZY7Rla3ttv+McxA24b5T2OmyEutsDN43Rxh/v9/cZhK3LNj1rnH47v9/fp98b8K/jtNG1wCaN+vbT3ranfXT8Nt1rnDZaDrykUd8+2tv2tI+OsvX9iaPDKMkewCfqw5XAMcB1wEeA3aiSzjOAV/cjviH3U+D4EcqvXduBDIk/AAuA+cCxwFPHqPsVYLt6/0rgRGAn4NNUv6p9Msl/lFJ+NWnRDodu2rTpYOCOtrKlvQtrqN0LfAO4CHgUeBtVewE8B3gf8Kn62H46vm7as8k+OrJlwNlU7XkrVZvuDnyM6snsM4B/AH5d17ePjq3b9myyjzb1+6+EYdyA77PqL7zTG+XbUiXtrXM79zvWYdh4/Ej6Wf2OZ1g3YGGjHQ9rO/e8xrmVwDaNc6c3zp3T7+8xSNtYbVqfP6txfla/4x3EDdgT2KitbB3g9422+/e63H7aw/asz9lHJ97WP2q03fl1mX20h+1Zl9tHR9mckz4xcxv7l7d2Sim3ADc3zr1irUU0deyfZEmSvyZZmOTMJM/sd1BTQLMv3lRKWdQ4/mVjv9m31Z1L6357f5JfJjkiyRP+v7GllEtLKUvbylZS/VrRsqx+tZ+Oo8v2bGcf7UCSDZO8GnhZo/hn9at9tEvjtGc7+2jDE/aLT1SSmcDMRlH7zzLN4x0nP6IpZyawKfAkqnl/bwd+l+Sl/QxqCtihsT9Wn90syaaTH86UtC1Vv90YeCnwZeCcJOlrVAMo1c31ezeKzqtf7acTMEZ7trOPjiHJ55IUqukVFwCbAXcD/wicWlezj3aow/ZsZx9tMEnv3pPbjleMcbzaqgUaUQGuBj4OvB54DdV8yofq80+mmuOviWv227H6LNhvu3E/8C3gcGAf4E1Acx7qQayaKywgySZUP3u3BjsuAL5d79tPuzROe4J9tBdmANPqffvommu2J9hHR+WNo917sO14xhjHo/3kqIZSyk3ALm3FP0tyG9VKBgA7JdmxlHLD2o1uymj227H6LNhvO1ZKeV97WZIfUt1IPqsu+nvg39ZiWAMryTZUN4c/ty6aB7y+nq4B9tOudNCe9tHunAx8j+rX3DnA/wI2B46iuoH8ndhHu9FJe9pHx+BIepdKKUuAJY2iLduqbNXYN6FcM79sO35aX6KYGv7S2B+rz95TSrlv8sOZukopy4GrGkX2WyDJ86hWc2gllP8G7FdKeahRzX7aoQ7bc0T20ZGVUm4spVxeSvlxKeVYqqSy5e1JZmAf7ViH7Tnae+2jmKRP1EWN/T1aO0lmU82napm31iIaYkl2TfKkEU7t3nZ8+9qIZ4pq9sXtkmzXON6zsd/s2xpDko2TPGeE8vWBXRtFT/h+m2QucBmwTV10EnBoKeWvbVXtpx3otD3to51JssEop1Y29qdRzZO2j46jm/a0j47N6S4T83mqOVIAhyW5Afgj1U84LReWUlzbuzPvBV6Z5FtUo+fLqe4C/2CjzvxSyo39CG6QJdkHaP0Hsfkfxhcmua/ev7yU8ockF1GtOBCqG3FOoFpT+a11vUL18+QTWqdtSjXf9A9JLqCaE3wDsAXV+r+zGu97wv1E25TkQOA7VDeDQbV+8rnAyxr3gi0vpcy3n46vm/YEnoJ9tBMXJ1kEXMiqZVfnAB9q1PlLKWUxsNg+Oq6O2zPJLOyjo+v3GpDDulE9tGC0p2PdBGzf7xiHZWP8p43diWvOj9Z2C8dpuwLsVdedDdwyRr1j+/19BmHrtE3p7Em5X+z39+n31sG/3wVY2KhvP+1Re9pHO27Tq8dpo6XA3EZ9+2iP2tM+OvbmdJcJKqUcAxxI9dPXfVR3dd8AfBaYU6qbIdWZE6me4Ho5sIiqLR+kevLjicBzi79KrLFS/RKxK/A5qr66gqrvzgMOKNWcQXXuVuBQ4JtUNzgtoXqy3h3A+cD+pZT/2b/whpP9tKfso505Gfgh1XzzpcBjVCuOXAV8hmqQ6KJWZfvouLppT/voGFL/JSNJkiRpQDiSLkmSJA0Yk3RJkiRpwJikS5IkSQPGJF2SJEkaMCbpkiRJ0oAxSZckSZIGjEm6JEmSNGBM0iVpQCWZlaQkOXYNrnFWEh+I0QXbTNIgWLffAUjSsOgycZtdSlk4WbFIkqY2k3RJ6txb2o73AI4ATgMuazu3uAefdxOwPtVjsifqncC7exCLJGktMkmXpA6VUr7ZPE6yLlWS/uv2c+2SbFRKWdrl5xVgedeBPv4ajwCPrMk1JElrn3PSJanHkixMcnGSXZL8LMn9wDX1uY2SfDrJlUnuTvLXJH9OcmKSDdqus9qc9GZZktcm+W2S5UluT/KZ+g+H5jVWm1/dKkuySZIvJbmrvsYvk/zdCN9nsyRnJrknybIk8+rvdnGShV20yxuTXJ5kaZKH6jZ4wwj1Sh3jK5NcUde9I8nJSTYcof6sJN9IcmfdnjckOb69Peu6Gyc5Lsl19Xe+p47p0BHqjts+SdZJ8v4k19Tf64Ekf0rylSTTO20bSWrnSLokTY7tgHnAOcD3gVZyuTXwjrrs21RTWV4OfBjYBXh1h9ffD3gP8K/AmcABwAeBJcDxHV7jZ1TTcj4FbAZ8APhJktmtUf8kM4ALgRcAZwG/Af62Lru3w88hyaeBo4ELgI8DK4EDgXOS/EMp5Yttb3kh8AbgdODrwFzgSOC5SV5VSllZX3f7OqZNgFOB64G9gI8BL0uydynl0brupsDlwM7A94AvAdOo2v21wHe6bZ/6O30KOJ/qn8VjwGxgf2AG/oohaaJKKW5ubm5uE9iAw4ACHNZWvrAuf8cI73kSMH2E8v9dv+fFjbJZddmxI5Q9CMxqlAf4T+D2tuueRT1zpr0MOLWt/OC6/F2NsvfUZUe31W2VL+ygnV5Y1z1+hHPnAg8AGzXKSr29rq3uyXX5oY2yb9Vl+7XV/Uxdfnij7NS67IgR4lhngu3zO+CP/e6Lbm5uU29zuoskTY57ga+2F5ZSVpRqnjhJ1k0yM8nmVCPTAKtNNxnFuaWxekwppQAXAVuONCVkFJ9tO55Xv/5No+zvqUaHT26rewZwf4ef89+pktuvJdm8uQHnARsBL2l7z59KKee2lZ1Yvx4I1VQTqhHr/1tK+fe2uiewarS+VfdQ4LpSymntAZZ6ZL5NJ+1zP7B1kt1HeL8kTZjTXSRpctxQSnlspBNJ3kO14srOrH5v0MwOr/+XEcruqV83A5Z1e41Syj1JWu9vmQ3cVkpZ1lZ3RZIbO4x3J6qR/v83Rp2ntR1f116hlHJ7kvuAHeqiLaimEV07Qt17k9zeqLt5HesFHcTb0kn7HEX1a8BlSW4DLgZ+AnyvlLKii8+SpMcxSZekyfHQSIVJPgCcBPwc+DxwG7CCaq76WXR+Q/+IfwC0PqaTC4z2R0Sn7+9CqEbS92X0uFdLtPutk/Yppfw6yY5U9xLMrbc3Acck2b2U0vG8fUlqMkmXpLXrLVRz1vdtTrFI8pq+RTS2hcArk2zYHE2vVy6ZDdzXwTWuB14D3FxKWW2EfBQ7tRck2QrYlFUj3IuBpVS/SLTXnQlsBVxdF91NdVPt8zv8/I7V7fL9emv9UvJF4HCqufGS1DXnpEvS2vUY1ajyf43G1ssmfrRvEY3tfKoVUN7XVv5OqhVVOvGN+vX4JNPaTyZpn+oC8Kwkr2sr+0j9ei781zzy84FdRvgj56NU/4/7YaPu2cBzkhw+QgwT+vWgnlff7nf161Mmck1JAkfSJWlt+x7VTY0/TfIDYGOq6RGDulTfGcC7gE8neQarlmA8BPgzHfx/pJTy23qt92OBq5OcQzXNZytgV6rlJJ/U9rY/AN9McjrVSPxcqiUZLwG+26h3FPAq4Nwkp9Yx7Qm8EbgU+Fqj7jHAK4AzkuxDtRxjqJZgXJfVnyjbieuSXAFc2fhOR1BNYWpf0lGSOmaSLklr12eoEsPDqVZMuYMq6fwq8Mc+xjWiUspfk+xNFfcBVMn5lcDeVAn8ag8MGuU6n0wyn2qt8/cDTwbuolo28sgR3vI7qnXJj6O6yfYB4AvAUc1pQqWUm+oHDH0KeDPVdJhFVH8IfbrUa6TXdZckeQlVYn8Q1covS6na/ZROvscITqL6I+NIql8W7gKuAE4opfx+gteUJFKt2iVJUufqaSt3A1eWUno6n75+QurXSimH9fK6kjRMnJMuSRpTkvVHKH431aj1L9ZuNJL0xOB0F0nSeE5Psh7wK+CvVA8eehPV/O/VHgwkSVpzjqRLksbzc2Bb4OPA54C9qOaj715KWdq/sCRp6nJOuiRJkjRgHEmXJEmSBoxJuiRJkjRgTNIlSZKkAWOSLkmSJA0Yk3RJkiRpwJikS5IkSQPm/wOlV2lpoeaQEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########画图\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "acc = history.history['acc']     #获取训练集准确性数据\n",
    "val_acc = history.history['val_acc']    #获取验证集准确性数据\n",
    "loss = history.history['loss']          #获取训练集错误值数据\n",
    "val_loss = history.history['val_loss']  #获取验证集错误值数据\n",
    "font = {\n",
    "    'family' : 'Bitstream Vera Sans',\n",
    "    'weight' : 'bold',\n",
    "    'size'   : 18\n",
    "}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "width = 12\n",
    "height = 8\n",
    "plt.figure(figsize=(width, height))\n",
    "epochs = range(1,len(acc)+1)\n",
    "plt.plot(epochs,acc,'r',label='Trainning acc',linewidth=2)     #以epochs为横坐标，以训练集准确性为纵坐标\n",
    "plt.plot(epochs,val_acc,'b--',label='Vaildation acc',linewidth=2) #以epochs为横坐标，以验证集准确性为纵坐标\n",
    "plt.legend(loc='best', shadow=True)   #绘制图例，即标明图中的线段代表何种含义\n",
    "plt.title(\"Training session's progress over epochs\")\n",
    "plt.ylabel('Training Progress (Accuracy values)')\n",
    "plt.xlabel('Training epochs')\n",
    "\n",
    "plt.figure(figsize=(width, height))\n",
    "plt.plot(epochs,loss,'r',label='Trainning loss',linewidth=2)\n",
    "plt.plot(epochs,val_loss,'b--',label='Vaildation loss',linewidth=2)\n",
    "plt.legend(loc='best', shadow=True)   #绘制图例，即标明图中的线段代表何种含义\n",
    "plt.title(\"Training session's progress over epochs\")\n",
    "plt.ylabel('Training Progress (Loss values)')\n",
    "plt.xlabel('Training epochs') \n",
    "plt.show()    #显示所有图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "plt.rcParams[\"font.family\"] = 'DejaVu Sans'\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 86.50%\n",
      "WARNING:tensorflow:From <ipython-input-14-d7dabfabcd32>:9: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "(1718,)\n"
     ]
    }
   ],
   "source": [
    "#model2.load_weights(\"M2-weights-improvement-45-0.97.hdf5\")\n",
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer='Adam',\n",
    "              metrics=['accuracy'])\n",
    "scores = model.evaluate(X_test, Y_test, verbose=0)\n",
    "print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "\n",
    "# confusion matrix\n",
    "Y_pred=model.predict_classes(X_test)\n",
    "print(Y_pred.shape)\n",
    "# Y_pred=np.transpose([Y_pred])\n",
    "# print(Y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Confusion Matrix |\n",
      "--------------------\n",
      "\n",
      " [[584 102   3]\n",
      " [ 51 563  39]\n",
      " [  1  36 339]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Classifiction Report |\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.85      0.88       689\n",
      "           1       0.80      0.86      0.83       653\n",
      "           2       0.89      0.90      0.90       376\n",
      "\n",
      "    accuracy                           0.86      1718\n",
      "   macro avg       0.87      0.87      0.87      1718\n",
      "weighted avg       0.87      0.86      0.87      1718\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm_cmap=plt.cm.Greens\n",
    "cm = confusion_matrix(Y_test,Y_pred)\n",
    "\n",
    "\n",
    "print('| Confusion Matrix |')\n",
    "print('--------------------')\n",
    "print('\\n {}'.format(cm))\n",
    "class_labels=['Pre','Mid','Post']\n",
    "# plot confusin matrix\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.grid(b=False)\n",
    "plot_confusion_matrix(cm, classes=class_labels, normalize=True, title='Normalized confusion matrix', cmap = cm_cmap)\n",
    "plt.show()\n",
    "class_labels=[0,1,2]\n",
    "# get classification report\n",
    "print('| Classifiction Report |')\n",
    "print('-------------------------')\n",
    "classification_report = metrics.classification_report(Y_test,Y_pred,labels =class_labels)\n",
    "# store report in results\n",
    "\n",
    "\n",
    "print(classification_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}