[Lab] FineTune with Best Hyper parameters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51a86985",
   "metadata": {},
   "source": [
    "# Prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef824979",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-17 14:29:09.717679: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "from tqdm.auto import tqdm\n",
    "import cv2\n",
    "\n",
    "# from tensorflow.keras.applications import Densnet\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Flatten, Dense, Dropout, GlobalAveragePooling2D, Input\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.data import Dataset\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam, RMSprop, Nadam, SGD\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "from itertools import product\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from tensorflow.keras.applications import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a65102e",
   "metadata": {},
   "source": [
    "# Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb1163b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 경로 설정\n",
    "small_train_images_path = './data/PREPROCESSING-TRAIN'\n",
    "small_test_images_path = './data/PREPROCESSING-TEST'\n",
    "data_path = './data'\n",
    "\n",
    "train_images_name = sorted(os.listdir(small_train_images_path))\n",
    "train_images_path = \\\n",
    "[os.path.join(small_train_images_path, name) for name in train_images_name]\n",
    "\n",
    "test_images_name = sorted(os.listdir(small_test_images_path))\n",
    "test_images_path = \\\n",
    "[os.path.join(small_test_images_path, name) for name in test_images_name]\n",
    "\n",
    "label_dfs = []\n",
    "case_names = sorted(os.listdir('./data/train'))\n",
    "\n",
    "for case in case_names :\n",
    "    label_path = data_path+'/train/'+case+'/label-{}.csv'.format(str(int(case[-2:])))\n",
    "    df = pd.read_csv(label_path)\n",
    "    label_dfs.append(df)\n",
    "    \n",
    "label_df = pd.concat(label_dfs, axis=0)\n",
    "label_df.to_csv('./data/labels.csv', index=False)\n",
    "\n",
    "label_df.set_index('img_name', inplace=True)\n",
    "\n",
    "label_df['img_path'] = train_images_path\n",
    "\n",
    "shuffled_df = label_df.sample(frac=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7354ed83",
   "metadata": {},
   "source": [
    "# Data Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a92c65a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train : Validation = 0.8 : 0.2 Split\n",
    "train_len = int(len(shuffled_df)*0.8)\n",
    "\n",
    "train_df = shuffled_df[:train_len]\n",
    "valid_df = shuffled_df[train_len:]\n",
    "\n",
    "train_img_path = train_df['img_path'].to_list()\n",
    "train_label = train_df['leaf_weight'].to_list()\n",
    "\n",
    "valid_img_path = valid_df['img_path'].to_list()\n",
    "valid_label = valid_df['leaf_weight'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d27a8485",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.3 s, sys: 2.61 s, total: 13.9 s\n",
      "Wall time: 15.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_img = np.array(list(map(cv2.imread, train_img_path)))\n",
    "valid_img = np.array(list(map(cv2.imread, valid_img_path)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74aa7cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "                                 rotation_range=10,\n",
    "                                 shear_range=0.1,\n",
    "                                 horizontal_flip=True,\n",
    "                                 vertical_flip=True,\n",
    "                                 fill_mode = 'nearest')\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b833f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 19\n",
    "\n",
    "train_generator = train_datagen.flow(train_img,\n",
    "                                    train_label,\n",
    "                                    batch_size=BATCH_SIZE)\n",
    "\n",
    "valid_generator = test_datagen.flow(valid_img,\n",
    "                                    valid_label,\n",
    "                                   batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec049c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_SIZE = 512\n",
    "MODEL_IMAGE_SIZE = 299"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee73d72d",
   "metadata": {},
   "source": [
    "# Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3c4651b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def NMAE(y_true, y_pred) :\n",
    "    error = y_true - y_pred\n",
    "    absolute_error = tf.abs(error)\n",
    "    return tf.reduce_mean(absolute_error) / tf.reduce_mean(y_true)\n",
    "\n",
    "def nmae(y_true, y_pred):\n",
    "    score = tf.py_function(func=NMAE, inp=[y_true, y_pred], Tout=tf.float32, name='nmae')\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e8d738",
   "metadata": {},
   "source": [
    "<h1> inception_v2_resnet 0.01 adam 0.2 </h1>\n",
    "<img src='./Training_Result/inception_v2_resnet 0.01 adam 0.2.png'/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e678f04",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-17 14:29:32.428954: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2022-05-17 14:29:32.485764: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n",
      "2022-05-17 14:29:32.560058: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-17 14:29:32.562068: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:00:1e.0 name: Tesla T4 computeCapability: 7.5\n",
      "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
      "2022-05-17 14:29:32.562465: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
      "2022-05-17 14:29:32.652222: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
      "2022-05-17 14:29:32.652281: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10\n",
      "2022-05-17 14:29:32.700062: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
      "2022-05-17 14:29:32.709235: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
      "2022-05-17 14:29:32.792047: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
      "2022-05-17 14:29:32.802433: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10\n",
      "2022-05-17 14:29:32.940984: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
      "2022-05-17 14:29:32.941113: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-17 14:29:32.941779: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-17 14:29:32.943009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
      "2022-05-17 14:29:32.945237: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-05-17 14:29:32.945991: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-17 14:29:32.946569: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:00:1e.0 name: Tesla T4 computeCapability: 7.5\n",
      "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
      "2022-05-17 14:29:32.946594: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
      "2022-05-17 14:29:32.946609: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
      "2022-05-17 14:29:32.946620: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10\n",
      "2022-05-17 14:29:32.946631: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
      "2022-05-17 14:29:32.946641: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
      "2022-05-17 14:29:32.946652: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
      "2022-05-17 14:29:32.946662: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10\n",
      "2022-05-17 14:29:32.946674: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
      "2022-05-17 14:29:32.946725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-17 14:29:32.947292: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-17 14:29:32.947819: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
      "2022-05-17 14:29:32.948637: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
      "2022-05-17 14:29:34.671756: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2022-05-17 14:29:34.671787: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
      "2022-05-17 14:29:34.672367: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
      "2022-05-17 14:29:34.673360: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-17 14:29:34.674004: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-17 14:29:34.674579: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-17 14:29:34.675117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5)\n",
      "2022-05-17 14:29:34.678021: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         [(None, 512, 512, 3)]     0         \n",
      "_________________________________________________________________\n",
      "resize (Functional)          (None, 299, 299, 3)       0         \n",
      "_________________________________________________________________\n",
      "inception_resnet_v2 (Functio (None, 8, 8, 1536)        54336736  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1536)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1536)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              1573888   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 1025      \n",
      "=================================================================\n",
      "Total params: 55,911,649\n",
      "Trainable params: 1,574,913\n",
      "Non-trainable params: 54,336,736\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = inception_resnet_v2.InceptionResNetV2(\n",
    "            weights='imagenet',\n",
    "            include_top = False,\n",
    "            input_shape = (MODEL_IMAGE_SIZE, MODEL_IMAGE_SIZE,3))\n",
    "\n",
    "input_data = Input((IMAGE_SIZE, IMAGE_SIZE, 3))\n",
    "output_data = tf.keras.layers.experimental.preprocessing.Resizing(MODEL_IMAGE_SIZE, MODEL_IMAGE_SIZE)(input_data)\n",
    "resizing = Model(inputs=input_data, outputs=output_data, name='resize')\n",
    "\n",
    "# model\n",
    "inputs = Input((IMAGE_SIZE, IMAGE_SIZE, 3))\n",
    "x = resizing(inputs)\n",
    "x = base_model(x, training=False)\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "outputs = Dense(1, activation='relu')(x)\n",
    "model = Model(inputs, outputs)\n",
    "\n",
    "base_model.trainable = False\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "950ff4aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-17 14:29:41.979305: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n",
      "2022-05-17 14:29:42.001648: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2499995000 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-17 14:29:49.688851: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
      "2022-05-17 14:29:50.587797: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67/67 [==============================] - 79s 1s/step - loss: 36.1472 - nmae: 0.4553 - val_loss: 19.2146 - val_nmae: 0.2354\n",
      "\n",
      "Epoch 00001: val_nmae improved from inf to 0.23545, saving model to ./Best_Check_Point/best_params1_top_layers.ckpt\n",
      "Epoch 2/2000\n",
      "67/67 [==============================] - 66s 976ms/step - loss: 15.6074 - nmae: 0.1973 - val_loss: 14.7266 - val_nmae: 0.1769\n",
      "\n",
      "Epoch 00002: val_nmae improved from 0.23545 to 0.17689, saving model to ./Best_Check_Point/best_params1_top_layers.ckpt\n",
      "Epoch 3/2000\n",
      "67/67 [==============================] - 66s 980ms/step - loss: 14.2599 - nmae: 0.1826 - val_loss: 17.1865 - val_nmae: 0.2266\n",
      "\n",
      "Epoch 00003: val_nmae did not improve from 0.17689\n",
      "Epoch 4/2000\n",
      "67/67 [==============================] - 65s 975ms/step - loss: 13.1329 - nmae: 0.1739 - val_loss: 14.6932 - val_nmae: 0.1749\n",
      "\n",
      "Epoch 00004: val_nmae improved from 0.17689 to 0.17490, saving model to ./Best_Check_Point/best_params1_top_layers.ckpt\n",
      "Epoch 5/2000\n",
      "67/67 [==============================] - 65s 973ms/step - loss: 13.5585 - nmae: 0.1790 - val_loss: 12.8179 - val_nmae: 0.1593\n",
      "\n",
      "Epoch 00005: val_nmae improved from 0.17490 to 0.15935, saving model to ./Best_Check_Point/best_params1_top_layers.ckpt\n",
      "Epoch 6/2000\n",
      "67/67 [==============================] - 65s 972ms/step - loss: 13.5646 - nmae: 0.1698 - val_loss: 11.9016 - val_nmae: 0.1486\n",
      "\n",
      "Epoch 00006: val_nmae improved from 0.15935 to 0.14855, saving model to ./Best_Check_Point/best_params1_top_layers.ckpt\n",
      "Epoch 7/2000\n",
      "67/67 [==============================] - 65s 971ms/step - loss: 11.5501 - nmae: 0.1425 - val_loss: 15.2053 - val_nmae: 0.1780\n",
      "\n",
      "Epoch 00007: val_nmae did not improve from 0.14855\n",
      "Epoch 8/2000\n",
      "67/67 [==============================] - 65s 966ms/step - loss: 12.7508 - nmae: 0.1693 - val_loss: 12.2526 - val_nmae: 0.1426\n",
      "\n",
      "Epoch 00008: val_nmae improved from 0.14855 to 0.14264, saving model to ./Best_Check_Point/best_params1_top_layers.ckpt\n",
      "Epoch 9/2000\n",
      "67/67 [==============================] - 65s 973ms/step - loss: 17.7070 - nmae: 0.2184 - val_loss: 11.4209 - val_nmae: 0.1426\n",
      "\n",
      "Epoch 00009: val_nmae improved from 0.14264 to 0.14256, saving model to ./Best_Check_Point/best_params1_top_layers.ckpt\n",
      "Epoch 10/2000\n",
      "67/67 [==============================] - 65s 974ms/step - loss: 11.9215 - nmae: 0.1545 - val_loss: 26.9800 - val_nmae: 0.3107\n",
      "\n",
      "Epoch 00010: val_nmae did not improve from 0.14256\n",
      "Epoch 11/2000\n",
      "67/67 [==============================] - 66s 980ms/step - loss: 13.0294 - nmae: 0.1600 - val_loss: 10.4596 - val_nmae: 0.1255\n",
      "\n",
      "Epoch 00011: val_nmae improved from 0.14256 to 0.12552, saving model to ./Best_Check_Point/best_params1_top_layers.ckpt\n",
      "Epoch 12/2000\n",
      "67/67 [==============================] - 65s 969ms/step - loss: 10.8153 - nmae: 0.1385 - val_loss: 14.1316 - val_nmae: 0.1779\n",
      "\n",
      "Epoch 00012: val_nmae did not improve from 0.12552\n",
      "Epoch 13/2000\n",
      "67/67 [==============================] - 65s 974ms/step - loss: 10.2547 - nmae: 0.1314 - val_loss: 17.4154 - val_nmae: 0.1978\n",
      "\n",
      "Epoch 00013: val_nmae did not improve from 0.12552\n",
      "Epoch 14/2000\n",
      "67/67 [==============================] - 66s 976ms/step - loss: 12.7657 - nmae: 0.1650 - val_loss: 17.0529 - val_nmae: 0.2066\n",
      "\n",
      "Epoch 00014: val_nmae did not improve from 0.12552\n",
      "Epoch 15/2000\n",
      "67/67 [==============================] - 66s 976ms/step - loss: 12.9393 - nmae: 0.1706 - val_loss: 11.0010 - val_nmae: 0.1359\n",
      "\n",
      "Epoch 00015: val_nmae did not improve from 0.12552\n",
      "Epoch 16/2000\n",
      "67/67 [==============================] - 65s 974ms/step - loss: 11.3803 - nmae: 0.1455 - val_loss: 11.4320 - val_nmae: 0.1345\n",
      "\n",
      "Epoch 00016: val_nmae did not improve from 0.12552\n",
      "Epoch 17/2000\n",
      "67/67 [==============================] - 65s 976ms/step - loss: 10.1918 - nmae: 0.1294 - val_loss: 12.4699 - val_nmae: 0.1522\n",
      "\n",
      "Epoch 00017: val_nmae did not improve from 0.12552\n",
      "Epoch 18/2000\n",
      "67/67 [==============================] - 65s 975ms/step - loss: 10.6302 - nmae: 0.1344 - val_loss: 11.4285 - val_nmae: 0.1360\n",
      "\n",
      "Epoch 00018: val_nmae did not improve from 0.12552\n",
      "Epoch 19/2000\n",
      "67/67 [==============================] - 66s 978ms/step - loss: 11.0604 - nmae: 0.1399 - val_loss: 12.2757 - val_nmae: 0.1429\n",
      "\n",
      "Epoch 00019: val_nmae did not improve from 0.12552\n",
      "Epoch 20/2000\n",
      "67/67 [==============================] - 65s 974ms/step - loss: 10.2633 - nmae: 0.1272 - val_loss: 19.9651 - val_nmae: 0.2469\n",
      "\n",
      "Epoch 00020: val_nmae did not improve from 0.12552\n",
      "Epoch 21/2000\n",
      "67/67 [==============================] - 65s 973ms/step - loss: 11.7999 - nmae: 0.1614 - val_loss: 11.2284 - val_nmae: 0.1349\n",
      "\n",
      "Epoch 00021: val_nmae did not improve from 0.12552\n",
      "Epoch 22/2000\n",
      "67/67 [==============================] - 65s 973ms/step - loss: 9.4598 - nmae: 0.1239 - val_loss: 15.6346 - val_nmae: 0.1908\n",
      "\n",
      "Epoch 00022: val_nmae did not improve from 0.12552\n",
      "Epoch 23/2000\n",
      "67/67 [==============================] - 65s 973ms/step - loss: 12.5018 - nmae: 0.1635 - val_loss: 13.8008 - val_nmae: 0.1586\n",
      "\n",
      "Epoch 00023: val_nmae did not improve from 0.12552\n",
      "Epoch 00023: early stopping\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=Adam(0.01),\n",
    "            loss='mae',\n",
    "            metrics=[nmae])\n",
    "\n",
    "es = EarlyStopping(monitor='loss',\n",
    "                mode='auto',\n",
    "                patience=5,\n",
    "                verbose=1)\n",
    "\n",
    "ckpt_path = './Best_Check_Point/best_params1_top_layers.ckpt'\n",
    "checkpointer = ModelCheckpoint(filepath=ckpt_path,\n",
    "                              monitor='val_nmae',\n",
    "                              save_weights_only = True,\n",
    "                              save_best_only= True,\n",
    "                              verbose=1)\n",
    "\n",
    "STEPS_PER_EPOCH = len(train_img) // BATCH_SIZE\n",
    "VAL_STEPS = len(valid_img) // BATCH_SIZE\n",
    "\n",
    "history = model.fit(train_generator, epochs=2000,\n",
    "                  steps_per_epoch=STEPS_PER_EPOCH,\n",
    "                  validation_data=valid_generator,\n",
    "                  validation_steps = VAL_STEPS,\n",
    "                  callbacks=[checkpointer, es],\n",
    "                  verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21bb3f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('./Model_Save/best1_top_layer.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "869a3688",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss = history.history['loss']\n",
    "valid_loss = history.history['val_loss']\n",
    "train_nmae = history.history['nmae']\n",
    "valid_nmae = history.history['val_nmae']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f47d50a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax1.plot(train_loss, label='Training Loss')\n",
    "ax1.plot(valid_loss, label='Validation Loss')\n",
    "ax1.legend(loc='upper right')\n",
    "ax1.set_title('MAE : inception_v2_resnet 0.01 adam 0.2 : top layer')\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2.plot(train_nmae, label='Training NMAE')\n",
    "ax2.plot(valid_nmae, label='Validation NMAE')\n",
    "ax2.legend(loc='upper right')\n",
    "ax2.set_title('NMAE : inception_v2_resnet 0.01 adam 0.2 : top layer')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc3a0e9",
   "metadata": {},
   "source": [
    "### finetune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "89eaea11",
   "metadata": {},
   "outputs": [],
   "source": [
    "history_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "91e29a6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "67/67 [==============================] - 76s 1s/step - loss: 231.3031 - nmae: 3.6799 - val_loss: 16.2300 - val_nmae: 0.1924\n",
      "\n",
      "Epoch 00001: val_nmae improved from inf to 0.19238, saving model to ./Best_Check_Point/best_params1_finetune_0.9.ckpt\n",
      "Epoch 2/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 17.5188 - nmae: 0.2323 - val_loss: 12.6547 - val_nmae: 0.1536\n",
      "\n",
      "Epoch 00002: val_nmae improved from 0.19238 to 0.15361, saving model to ./Best_Check_Point/best_params1_finetune_0.9.ckpt\n",
      "Epoch 3/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 16.3782 - nmae: 0.2010 - val_loss: 10.6496 - val_nmae: 0.1322\n",
      "\n",
      "Epoch 00003: val_nmae improved from 0.15361 to 0.13220, saving model to ./Best_Check_Point/best_params1_finetune_0.9.ckpt\n",
      "Epoch 4/2000\n",
      "67/67 [==============================] - 66s 979ms/step - loss: 20.0623 - nmae: 0.2475 - val_loss: 19.3272 - val_nmae: 0.2338\n",
      "\n",
      "Epoch 00004: val_nmae did not improve from 0.13220\n",
      "Epoch 5/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 17.8314 - nmae: 0.2121 - val_loss: 13.6394 - val_nmae: 0.1646\n",
      "\n",
      "Epoch 00005: val_nmae did not improve from 0.13220\n",
      "Epoch 6/2000\n",
      "67/67 [==============================] - 66s 979ms/step - loss: 14.3168 - nmae: 0.1779 - val_loss: 24.2706 - val_nmae: 0.2912\n",
      "\n",
      "Epoch 00006: val_nmae did not improve from 0.13220\n",
      "Epoch 7/2000\n",
      "67/67 [==============================] - 66s 976ms/step - loss: 12.5786 - nmae: 0.1592 - val_loss: 8.7963 - val_nmae: 0.1075\n",
      "\n",
      "Epoch 00007: val_nmae improved from 0.13220 to 0.10754, saving model to ./Best_Check_Point/best_params1_finetune_0.9.ckpt\n",
      "Epoch 8/2000\n",
      "67/67 [==============================] - 66s 974ms/step - loss: 11.1968 - nmae: 0.1470 - val_loss: 18.3891 - val_nmae: 0.2222\n",
      "\n",
      "Epoch 00008: val_nmae did not improve from 0.10754\n",
      "Epoch 9/2000\n",
      "67/67 [==============================] - 65s 975ms/step - loss: 12.9719 - nmae: 0.1613 - val_loss: 8.7146 - val_nmae: 0.1016\n",
      "\n",
      "Epoch 00009: val_nmae improved from 0.10754 to 0.10158, saving model to ./Best_Check_Point/best_params1_finetune_0.9.ckpt\n",
      "Epoch 10/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 11.5389 - nmae: 0.1390 - val_loss: 16.0303 - val_nmae: 0.1893\n",
      "\n",
      "Epoch 00010: val_nmae did not improve from 0.10158\n",
      "Epoch 11/2000\n",
      "67/67 [==============================] - 66s 982ms/step - loss: 16.2156 - nmae: 0.1857 - val_loss: 17.1837 - val_nmae: 0.2032\n",
      "\n",
      "Epoch 00011: val_nmae did not improve from 0.10158\n",
      "Epoch 12/2000\n",
      "67/67 [==============================] - 66s 982ms/step - loss: 13.7513 - nmae: 0.1784 - val_loss: 9.7112 - val_nmae: 0.1153\n",
      "\n",
      "Epoch 00012: val_nmae did not improve from 0.10158\n",
      "Epoch 13/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 11.1572 - nmae: 0.1395 - val_loss: 8.3421 - val_nmae: 0.1005\n",
      "\n",
      "Epoch 00013: val_nmae improved from 0.10158 to 0.10051, saving model to ./Best_Check_Point/best_params1_finetune_0.9.ckpt\n",
      "Epoch 14/2000\n",
      "67/67 [==============================] - 66s 975ms/step - loss: 9.4451 - nmae: 0.1200 - val_loss: 8.4176 - val_nmae: 0.0998\n",
      "\n",
      "Epoch 00014: val_nmae improved from 0.10051 to 0.09984, saving model to ./Best_Check_Point/best_params1_finetune_0.9.ckpt\n",
      "Epoch 15/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 9.4532 - nmae: 0.1273 - val_loss: 7.8129 - val_nmae: 0.0909\n",
      "\n",
      "Epoch 00015: val_nmae improved from 0.09984 to 0.09095, saving model to ./Best_Check_Point/best_params1_finetune_0.9.ckpt\n",
      "Epoch 16/2000\n",
      "67/67 [==============================] - 66s 980ms/step - loss: 9.4547 - nmae: 0.1130 - val_loss: 7.6005 - val_nmae: 0.0925\n",
      "\n",
      "Epoch 00016: val_nmae did not improve from 0.09095\n",
      "Epoch 17/2000\n",
      "67/67 [==============================] - 66s 979ms/step - loss: 10.8823 - nmae: 0.1349 - val_loss: 9.4674 - val_nmae: 0.1130\n",
      "\n",
      "Epoch 00017: val_nmae did not improve from 0.09095\n",
      "Epoch 18/2000\n",
      "67/67 [==============================] - 66s 976ms/step - loss: 8.1106 - nmae: 0.1041 - val_loss: 7.1462 - val_nmae: 0.0864\n",
      "\n",
      "Epoch 00018: val_nmae improved from 0.09095 to 0.08641, saving model to ./Best_Check_Point/best_params1_finetune_0.9.ckpt\n",
      "Epoch 19/2000\n",
      "67/67 [==============================] - 66s 978ms/step - loss: 8.2453 - nmae: 0.1085 - val_loss: 7.3532 - val_nmae: 0.0900\n",
      "\n",
      "Epoch 00019: val_nmae did not improve from 0.08641\n",
      "Epoch 20/2000\n",
      "67/67 [==============================] - 66s 978ms/step - loss: 8.5160 - nmae: 0.1149 - val_loss: 11.2696 - val_nmae: 0.1391\n",
      "\n",
      "Epoch 00020: val_nmae did not improve from 0.08641\n",
      "Epoch 21/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 10.2067 - nmae: 0.1314 - val_loss: 8.1783 - val_nmae: 0.1009\n",
      "\n",
      "Epoch 00021: val_nmae did not improve from 0.08641\n",
      "Epoch 22/2000\n",
      "67/67 [==============================] - 65s 974ms/step - loss: 10.8301 - nmae: 0.1467 - val_loss: 9.0007 - val_nmae: 0.1042\n",
      "\n",
      "Epoch 00022: val_nmae did not improve from 0.08641\n",
      "Epoch 23/2000\n",
      "67/67 [==============================] - 66s 981ms/step - loss: 12.6939 - nmae: 0.1616 - val_loss: 7.3368 - val_nmae: 0.0895\n",
      "\n",
      "Epoch 00023: val_nmae did not improve from 0.08641\n",
      "Epoch 00023: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "67/67 [==============================] - 78s 1s/step - loss: 934.8070 - nmae: 9.6820 - val_loss: 83.9411 - val_nmae: 0.9839\n",
      "\n",
      "Epoch 00001: val_nmae improved from inf to 0.98387, saving model to ./Best_Check_Point/best_params1_finetune_0.7.ckpt\n",
      "Epoch 2/2000\n",
      "67/67 [==============================] - 66s 983ms/step - loss: 78.8818 - nmae: 0.9913 - val_loss: 57.7569 - val_nmae: 0.6936\n",
      "\n",
      "Epoch 00002: val_nmae improved from 0.98387 to 0.69357, saving model to ./Best_Check_Point/best_params1_finetune_0.7.ckpt\n",
      "Epoch 3/2000\n",
      "67/67 [==============================] - 66s 984ms/step - loss: 64.4702 - nmae: 0.8437 - val_loss: 84.1463 - val_nmae: 0.9945\n",
      "\n",
      "Epoch 00003: val_nmae did not improve from 0.69357\n",
      "Epoch 4/2000\n",
      "67/67 [==============================] - 66s 984ms/step - loss: 76.2498 - nmae: 0.9862 - val_loss: 85.0526 - val_nmae: 0.9935\n",
      "\n",
      "Epoch 00004: val_nmae did not improve from 0.69357\n",
      "Epoch 5/2000\n",
      "67/67 [==============================] - 66s 987ms/step - loss: 77.1728 - nmae: 1.0035 - val_loss: 83.3124 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00005: val_nmae did not improve from 0.69357\n",
      "Epoch 6/2000\n",
      "67/67 [==============================] - 66s 986ms/step - loss: 74.0092 - nmae: 0.9994 - val_loss: 86.3585 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00006: val_nmae did not improve from 0.69357\n",
      "Epoch 7/2000\n",
      "67/67 [==============================] - 67s 990ms/step - loss: 1296.5375 - nmae: 28.9906 - val_loss: 85.0016 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00007: val_nmae did not improve from 0.69357\n",
      "Epoch 8/2000\n",
      "67/67 [==============================] - 66s 986ms/step - loss: 84.8694 - nmae: 1.0000 - val_loss: 85.7384 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00008: val_nmae did not improve from 0.69357\n",
      "Epoch 00008: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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amAKhDXiul/378jVCTe/h7j6CUFsF4cO7ktLnSN7HCX3J3ktoZphQ9NyBWgjsFdUs5/V1TDCzTxN1iHf3ZTUoQylLCf3Cis+f4e6er5Eu9bfeRAiOeW+p8Pe9P/+7CP16TnT35YTPyRZCM9vLRedR3h8INV45Qv+67s4iNLOuJPTn+SwhYXp/BeXbFnqLaf19zgDGdWsOLjemvUqoBeke05aX3r1s/wuMsTC69Qx6xrRqz9nuMW1Wt3NyB3efDeDuP48qMPaMnvcvZbx+Ned5seeAVLdm97JjWnR/lbu/VkZZu7s8Kt+UKKZ9gq6YtJLS50heX/FwoBYCU7r97in0HdO+Tfg8Hu/ub9SgDKUsBe7v9rce5u7nRY+X+lu/SXUxrc+8xN3f712DUW6i8vMo7yzgz17mqO9K51H8DPCebjUomFm+j8ZJhOraqVFh/4XSo5+3p5uA91qYEiVlYf6kqe6eIyQ5P4iusDCzcRZGlxX7tpm1mtnRhPeXHyG0ivAl0pvZwCVR374xhD4GtZgW5OeEflnvKioLZvYewnv9sLs/OIDXvxH4oJm9z8ySZtZuZtPNbLyZ7WJmH4oSow7CFWC/UxS5+0rCF8K/mdkIM0uY2d5mlm+yWAWMt95Ht0Go8bnEzEab2f6EL+3retn3pug9HB2V9TuEZoUNEGr6zKydcFWYiN5jSy+vNZzwwV1nYYqdQv+a6OJpAV3nyFGEUXjFz+0gdBrfgdCEUBMe+qQ8Clwalf9UQlC9udT+ZnZm9PuPKzc4VOlB4A0zu9DMhkTHepKZHRo9vgqYYKGPV96jwAwzazGzaVTWpPPfwCwzyycwCULfWdx9EaH5/ktF++drDkcQvvAyhFrxrYK9mb2DkIwdFpXvh4Rai59T/5j2E+BTZnZs9FkaZ2b7l/E5g9DH6kvRsT6d0EctXzvZa0yLaovmEo718Oh4f5UBxrSoVuVXwL8SWh7uzj9Ww3P2GuDzZna4BUPN7APR+9jPzN5jYaqZLYTPejnTrlVznhdE36G3AN+JynMk4aKyt1qqG4DPmNkBFvqyXUJR/Iu+29oJNU/5uN1bi+FwooGD0Xd38bRDDxA+E1+KXvM0wmeg+Lkl42EN3Ec49l+y0H8030/2/0rtbGYXES7Gj6syYS7XHcC+ZvbJ6HPTYmaHWlc/6VKfm0eBj0fnxQn0bJ4vqYK8JL9/pedR3ln0/v3ZQ0WJood+BwtKPPRJ4FF3/193fyV/I1RvTzGzSdF+77Ce8ygeWuL1MLO7zOxblZSvlzK/TKgF/RphxNKjdF2ZXUjotPoXM3sDuIet+wm+QqiSX0FIPj5f1Mz6E0K/lnVmdluJX/1dQhLxOPAEYYBCLeZamk0YNfV/3ZqW/5FQa3Vn0bG9q9IXd/elhBPtW4SOrksJgSQR3b5GOB6vE07+vy/zpc8iNPc8RTimvyLUSkIIBAuBV8yst+bySwldCF4ijDL+Vw+dxoHCxMBHR+9hIWGAzE2E5sfh3cr5SUKwu4pQjb+Z3mtGf0iYCupVwqCu33Z7/OOETvOvR2UsbsK8ISrv8uh9/6WX31GtGYS+qGsJ3Q0+4lE3jyhJLm5q/S6hI/9DRefHf9e4PPmE4oOEi8XFhOP2P4RzE7oubl4zs0ei+/9ISMrWEvoWVtKP7z8Ifdr+18J8YLsS+rnlfYdQG5iX/2K7P7pd7qFDekF0Hp1N6D/3OmEgzVVRTPsP4CTrmpfzYyVi2s6lCmph0vIzK3hvJUUXgp8iNAGuj95HPlHu63MGYTTnPoS/yyzCOZP/kv0P4CNmttbMrizxq79IqP19kVAD+3NqMx3Lzwm17r/s1hxXk3M2+s76LGHQxVpCzD8neriN8Nl5lRDvdybEvv5es5rzvLu/J8SW1YS4fl4Uu7CuRSX2iH7fbwkDhH5PiCkvsXWSdgkhjn2TUEO4ma0HTxb7NqFv7npCv/tCC6G7pwndms4hHKuPsXUL4g/pOx5WLfrdpxDO4XXAp4FTou2Y2ZlmVlxT9s+E2s7ni86PAecMJcq1gTAobAbhu+8VQiVYfh7LUrnAlwnnxzrCeIXbKF9/eUl3ZZ9H0bZ3AOMpZ1qc/HMG1qQ/eJnZdMLUF+PrXBQRkQEzs3MIMw1sq37iIjIINeQSfiIiIiKy7SlRjJmoeap7U9bGapusiqqeS9326P8VRESq10f8Obr/Z5d8vaN7e81al11E1PQsIiIiIr1QjaKIiIiIlKREUURERERKqmRlllgYM2aMT5gwod7FEJHt6OGHH37V3cf2v2e8KX6JNJ9Gj18NlyhOmDCBBQtKTeUoIoOVmb3U/17xp/gl0nwaPX6p6VlERERESqo6UYyWpvmbmd0R/X9HM7vbzJ6Pfo4u2vciM1tkZs8WL0VjZm83syeix640s1qsFykiIiIiNTCQGsUvA08X/f+bwL3uvg9wb/R/zOwAwtI3BwInAD82s2T0nKuAcwlLSu0TPS4iIiIiMVBVH0UzGw98gLBW6FejzScT1iAGuJ6wwPeF0fY57t4BLDazRcBhZrYEGOHuD0SveQNhnceK1ycW6a6zs5Nly5axZcuWehdFKtDe3s748eNpaWmpd1FEtinFqMFnsMavagez/BD4B2B40bZd3H0lgLuvNLOdo+3jCIuH5y2LtnVG97tv78HMziXUPLLHHlpMRPq3bNkyhg8fzoQJE1CPhsbg7rz22mssW7aMiRMn1rs4ItuUYtTgMpjjV8VNz2Z2ErDa3R8u9ykltnkf23tudL/a3ae5+7SxYxt2hLlsR1u2bGGnnXZSAG4gZsZOO+2kGhZpCopRg8tgjl/V1CgeCXzIzE4E2oERZnYjsMrMdo1qE3cFVkf7LwN2L3r+eGBFtH18ie0iNaEA3Hj0N5NmovN9cBmsf8+KaxTd/SJ3H+/uEwiDVP7P3T8BzAPOjnY7G7g9uj8PmGFmbWY2kTBo5cGomXqDmR0RjXY+q+g5Ig3ttddeY+rUqUydOpW3vOUtjBs3rvD/dDrd53MXLFjAl770pX5/xzvf+c6alPW+++7jpJNOqslriUhjaLQYZWb8+te/Lmw76aSTuO+++wCYPn06e+yxB+5djZKnnHIKw4YN2+p1fvCDH9De3s769eu3eu2RI0cW3vvUqVO55557alLuwaKWE25fAcw1s88ALwOnA7j7QjObCzwFZIDz3T0bPec84DpgCGEQiwayyKCw00478eijjwIwc+ZMhg0bxte//vXC45lMhlSq9Mdv2rRpTJs2rd/f8ec//7kmZRWR5tNoMWr8+PHMmjWLD37wgyUfHzVqFH/605846qijWLduHStXruyxz+zZszn00EO59dZbOeeccwrbjz76aO64446alXWwGdCE2+5+n7ufFN1/zd2Pdfd9op+vF+03y933dvf93P2uou0L3H1S9NgXvPhyQGSQOeecc/jqV7/KMcccw4UXXsiDDz7IO9/5Tg4++GDe+c538uyzzwJb1/DNnDmTT3/600yfPp299tqLK6+8svB6+avl++67j+nTp/ORj3yE/fffnzPPPLNwZX3nnXey//77c9RRR/GlL32poprD2bNnM3nyZCZNmsSFF14IQDab5ZxzzmHSpElMnjyZH/zgBwBceeWVHHDAAUyZMoUZM2YM/GCJyHYX5xh10EEHMXLkSO6+++6Sj8+YMYM5c+YAcMstt3Daaadt9fgLL7zAxo0b+e53v8vs2bMHcJSaT8Mt4SdSqW//eiFPrXijpq95wG4juPSDB1b8vOeee4577rmHZDLJG2+8wfz580mlUtxzzz1861vf4uabb+7xnGeeeYbf//73bNiwgf3224/zzjuvx/QLf/vb31i4cCG77bYbRx55JH/605+YNm0an/vc55g/fz4TJ07kjDPOKLucK1as4MILL+Thhx9m9OjRHH/88dx2223svvvuLF++nCeffBKAdevWAXDFFVewePFi2traCttEpDyKUeXFqEsuuYRLLrmE4447rsdjxx57LJ/97GfJZrPMmTOHq6++mssuu6zw+OzZsznjjDM4+uijefbZZ1m9ejU77xwmZ/nDH/7A1KlTC/vefPPN7L333pUctkFNS/iJbEenn346yWSYb379+vWcfvrpTJo0iQsuuICFCxeWfM4HPvAB2traGDNmDDvvvDOrVq3qsc9hhx3G+PHjSSQSTJ06lSVLlvDMM8+w1157FaZqqCRRfOihh5g+fTpjx44llUpx5plnMn/+fPbaay9efPFFvvjFL/Lb3/6WESNGADBlyhTOPPNMbrzxRpLJJB2d2X5+g4jEUZxj1NFHHw2ExK67ZDLJUUcdxS9+8Qs2b97MhAkTtnp8zpw5zJgxg0QiwWmnncYvf/nLrV730UcfLdzG7zmBXE4NnHmqUZRBr5qr6m1l6NChhfv/+I//yDHHHMOtt97KkiVLmD59esnntLW1Fe4nk0kymUxZ+wykJ0dvzx09ejSPPfYYv/vd7/jRj37E3Llzufbaa/nNb37D/PnzmTdvHt/+zneY+78PMHn30aSSuhYV6Y9iVPkuvvhiZs2aVbL/5IwZMzj11FOZOXPmVtsff/xxnn/++UJNZDqdZq+99uL888/v8RruzvOrNvKWke2MGdbW4/FmpCguUifr169n3Lgwx/x1111X89fff//9efHFF1myZAkAv/jFL8p+7uGHH87999/Pq6++SjabZfbs2bz73e/m1VdfJZfL8eEPf5jLLruMRx55hFwux9KlSznmmGP43ve+x/p169m0aSNZXZGLNLQ4xqjjjz+etWvX8thjj/V47Oijj+aiiy7qUTM5e/ZsZs6cyZIlS1iyZAkrVqxg+fLlvPTSSz1eI+eQcyeTVfzKU6IoUif/8A//wEUXXcSRRx5JNlv7ptohQ4bw4x//mBNOOIGjjjqKXXbZhZEjR5bc995772X8+PGF25IlS7j88ss55phjOOiggzjkkEM4+eSTWb58OdOnT2fq1Kmcc845XH755WSzWT7xiU8wefJkDj74YD53/hcZMXJk6dnzRaRhxClGFbv44otZtmxZj+1mxte//nXGjBmz1fY5c+Zw6qmnbrXt1FNPLQx+yfdRnDp1KocccjB3/+Z2XBGswBptoPG0adN8wYIF9S6GxNzTTz/N2972tnoXo+42btzIsGHDcHfOP/989tlnHy644IJt+jtXb9jCK+u38Nadh7FDa+W9W0r97czsYXfvfz6OmFP8kjzFqKAeMaovndkcT698gzHD2tht1JCKnz8Y45dqFEUGsWuuuYapU6dy4IEHsn79ej73uc9t89+Zv/ZssGtQEamDesSovuQrzxqtEm1b0mAWkUHsggsu2O5X512Bdrv+WhFpQPWIUX3J6UK3B9UoikhNFWoU1cdHRBpMV/ySPCWKIlJTueinrshFpNHkL3BzCmAFShRFpKbUx0dEGpX6WPekRFFEaiofYHN97yYiEjuFC906lyNOlCiKbAPTp0/nd7/73VbbfvjDH/L3f//3fT4nP3XKiSeeWHLN5JkzZ/L973+/z99922238dRTTxX+/0//9E/cc889FZS+tPvuu4+TTjqp3/10RS4Sf4M1RpkZv/71rwvbTjrpJO67775C+ffYY4+tWjtOOeUUhg0bVvh/DvjZNT9m//E7sX79+q1ee+TIkYX5FqdOnVqTMjcCJYoi28AZZ5xRmMw1b86cOWWvt3znnXcyatSoqn539yD8ne98h/e+971VvVY18n181PQsEl+DNUaNHz+eWbNm9fr4qFGj+NOf/gTAunXrWLly5VaPu8Ndt9/MlKmHcOutt271WPc1obdnXK0nJYoi28BHPvIR7rjjDjo6OgAKy0YdddRRnHfeeUybNo0DDzyQSy+9tOTzJ0yYwKuvvgrArFmz2G+//Xjve9/Ls88+W9jnmmuu4dBDD+Wggw7iwx/+MG+++SZ//vOfmTdvHt/4xjeYOnUqL7zwAueccw6/+tWvgLACy8EHH8zkyZP59Kc/XSjfhAkTuPTSSznkkEOYPHkyzzzzTNnvdfbs2UyePJlJkyZx4YUX4g7ZbJbzP/d3TJo0icmTJ/ODH/wAgCuvvJIDDjiAKVOmMGPGjMoPrIjUxGCNUQcddBAjR47k7rvvLvn4jBkzCgnyLbfcwmmnnbbV4y++sIjNb27iKxf+I7Nnzy73cA5qmkdRBr+7vgmvPFHb13zLZHj/Fb0+vNNOO3HYYYfx29/+lpNPPpk5c+bwsY99DDNj1qxZ7LjjjmSzWY499lgef/xxpkyZUvJ1Hn74YebMmcPf/vY3MpkMhxxyCG9/+9sBOO200/jsZz8LwCWXXMJPfvITvvjFL/KhD32Ik046iY985CNbvdaWLVs455xzuPfee9l3330566yzuOqqq/jKV74CwJgxY3jkkUf48Y9/zPe//33+53/+p9/DsGLFCi688EIefvhhRo8ezfHHH8/E38xj1NhdWblyBU8++SRAoYnqiiuuYPHixbS1tZVsthJpSopRQO1i1CWXXMIll1zCcccd1+OxY489ls9+9rNks1nmzJnD1VdfzWWXXVZ4/Fdz53LChz7M2494JxdfcB6rV69m5513BrqW+su7+eab2XvvvXs9xoOFahRFtpHipp3iJp25c+dyyCGHcPDBB7Nw4cKtmmC6+8Mf/sCpp57KDjvswIgRI/jQhz5UeOzJJ5/k6KOPZvLkydx0000sXLiwz/I8++yzTJw4kX333ReAs88+m/nz5xcez19Zv/3tb2fJkiVlvceHHnqI6dOnM3bsWFKpFGeeeSYPPvAnxu8xgZcWL+aLX/wiv/3tbxkxYgQAU6ZM4cwzz+TGG28kldJ1qkg9DdYYdfTRRxfK1l0ymeSoo47iF7/4BZs3b2bChAlbPX7Lr+ZywsmnYZbgtNNO45e//OVWr1vc9NwMSSKoRlGaQR9X1dvSKaecwle/+lUeeeQRNm/ezCGHHMLixYv5/ve/z0MPPcTo0aM555xz2LJlS5+vY2Ylt59zzjncdtttHHTQQVx33XWFDtu96a/PYFtbGxACaSaT6XPf/l5zxKhR3PvHB3n0L/fzox/9iLlz53Lttdfym9/8hvnz5zNv3jwuu+wyFi5cqIRRRDEKqG2Muvjii5k1a1bJ+DJjxgxOPfVUZs6cudX2xx9/nBdfWMTnP34qYHi2k7322ovzzz+/z9812KlGUWQbGTZsGNOnT+fTn/504Ur9jTfeYOjQoYwcOZJVq1Zx11139fka73rXu7j11lvZvHkzGzZs2Go034YNG9h1113p7OzkpptuKmwfPnw4GzZs6PFa+++/P0uWLGHRokUA/OxnP+Pd7373gN7j4Ycfzv3338+rr75KNptl9uzZHPqOo1j7+mtkc1k+/OEPc9lll/HII4+Qy+VYunQpxxxzDN/73vdYt24dGzduHNDvF5HqDeYYdfzxx7N27Voee+yxHo8dffTRXHTRRT0G7syePZtvXHQJdz3wOPc8+AQrVqxg+fLlvPTSS1WVYbDQpbzINnTGGWdw2mmnFZp3DjroIA4++GAOPPBA9tprL4488sg+n3/IIYfwsY99jKlTp7LnnnsWmlQALrvsMg4//HD23HNPJk+eXAi8M2bM4LOf/SxXXnlloYM4QHt7Oz/96U85/fTTyWQyHHrooXz+85+v6P3ce++9jB8/vvD/X/7yl1x++eUcc8wxuDsnnngi73nfB3j88Uf5wie/SDKqaLj88svJZrN84hOfYP369bg7F1xwQdWjJkWkNgZbjCp28cUXc/LJJ/fYbmZ8/etf77F9zpw53Pir24CulVlOPfVU5syZw+GHH96jj+Ill1zSo5/lYGSNNoXFtGnTPD+Pk0hvnn76ad72trfVuxhN6ZlX3iCdybHj0FbGj96h4ueX+tuZ2cPuPq1WZawXxS/JU4yKp1VvbGHVG1tImDFp3MiKnz8Y45eankWkpjThtog0qq4lSOtckBhRoigiNVVIFOtbDBGRinXFL9eiAREliiJSU11X5AqyItJYiteoVwgLlCjKoKVEpT7ygbaaw6+/mTQTne/xU/w38QrbRQbr31OJogxK7e3tvPbaa4P2gxtX7l3NNbkKj72789prr9He3r4tiiYSK4pR8VT856jkTzOY45emx5FBafz48Sxbtow1a9bUuyhNxd1ZtS5MzrsulaDj1baKnt/e3r7V9Dsig5ViVDy9vinNm+ksAIn17SQTpScTL2Wwxi8lijIotbS0MHHixHoXo+ls6shw4qW/A+DgPUZx699PrW+BasTMLgD+jjBG5wngU8AOwC+ACcAS4KPuvrZORZQGoxgVT5/72QJ+t3AVAPd/Yzp77jS0ziWqPzU9i0jNpDO5kvcbmZmNA74ETHP3SUASmAF8E7jX3fcB7o3+LyINrDhudWYHRwwbqIoTRTNrN7MHzewxM1toZt+Ots80s+Vm9mh0O7HoOReZ2SIze9bM3le0/e1m9kT02JXW24KRItIQ0tnBlyhGUsAQM0sRahJXACcD10ePXw+cUp+iiUitdGa7OiZ2DK4YVrVqahQ7gPe4+0HAVOAEMzsieuwH7j41ut0JYGYHEK6+DwROAH5sZslo/6uAc4F9otsJVb8TEam7wXg17u7Lge8DLwMrgfXu/r/ALu6+MtpnJbBz/UopIrUwGFtFBqriRNGDjdF/W6JbX2ODTgbmuHuHuy8GFgGHmdmuwAh3f8DDsK8b0BW5SEPL1yi2phKDJsia2WhCHJsI7AYMNbNPVPD8c81sgZkt0MAFkXjryOYKA1gGSwwbqKr6KJpZ0sweBVYDd7v7X6OHvmBmj5vZtVFwBRgHLC16+rJo27jofvftpX6fAq1IA8gH1uFtqa2aoRvce4HF7r7G3TuBW4B3AquiC16in6tLPdndr3b3ae4+bezYsdut0CJSuc5MjmFtYZxvcTN0M6sqUXT3rLtPBcYTagcnEZqR9yY0R68E/i3avVS/Q+9je6nfp0Ar0gDyieKw9tRg6t/zMnCEme0Q9aM+FngamAecHe1zNnB7nconIjWSznYliulsts6liYcBjXp293XAfcAJ7r4qSiBzwDXAYdFuy4Ddi542ntARfFl0v/t2EWlQ+VrEoa2pwdRH8a/Ar4BHCFPjJICrgSuA48zseeC46P8i0sDSRTWKanoOqhn1PNbMRkX3hxCaZZ7JN8FETgWejO7PA2aYWZuZTSQMWnkw6vy9wcyOiK7Sz0JX5CINrTNfo9iWGlRB1t0vdff93X2Su38y6nP9mrsf6+77RD9fr3c5RWRgOrM5hraF8bZpNT0D1U24vStwfTRyOQHMdfc7zOxnZjaV0Hy8BPgcgLsvNLO5wFNABjjf3fP1uecB1wFDgLuim4g0qI5sV9NzziGTzZFKarpWEWkM6UyOYe0thftSRaLo7o8DB5fY/sk+njMLmFVi+wJgUqVlEJF4ShfVKEJoilaiKCKNIp3JMVxNz1tRBBeRmskH1qH5UYMZNd2ISOPoKGp6Hiz9rAdKiaKI1Ew+sA6LAm2HRg2KSINw96iPomoUiylRFJGa6Wp6Vh8fEWksmZzjTlfTs2oUASWKIlJD6aLBLKBEUUQaRz5e7RAlioNoLtgBUaIoIjXTVaOY7+OjPooi0hjyXWdakwlakqY+ihEliiJSM4UJt9XHR0QaTD5etaYStCYHz3r1A6VEUURqpvuoZy2BJSKNoqM4UUwpUcxToigiNZPO5EgljPZUNOpZgVZEGkS+qbktlaAlmVDTc0SJoojUTDqTK1yNg/ooikjjyHedaUmqRrGYEkURqZnObC4E2Wg1FgVaEWkUhT6KUaLYoRpFQImiiNRQOrt1jaISRRFpFN0Hs3QqfgFKFEWkhjoyucLVOGgwi4g0jh5Nz6pRBJQoikgNpTM52or7KGqtZxFpEJoepzQliiJSM/k+ii1JA1AfHxFpGPnEsE3T42xFiaKI1Ex+1HNbMln4v4hII8jP0hAudjU9Tp4SRRGpGQ1mEZFGle9TnY9hmgc2UKIoIjWT7j6YRYFWRBpEuvvKLKpRBJQoikgNpbNOSypBMmEkDDXdiEjDSEdNz63RXLCKX4ESRRGpmXyNIqArchFpKFtNuK1RzwVKFEWkZtKZLG1Rs7MCrYg0kh5Nz4pfgBJFEamh/GAWgNZUUp3BRaRhdE8UtVZ9oERRRGqmM+NdTc9JUx8fEWkYndkcCYNkwmhRi0iBEkURqZl0NkdLKky2raYbEWkkW7eIhD7W7qpVVKIoIjUTBrOEybaVKIpIIykejNdWWK9eMUyJoojUTH5lFtCoZxFpLMU1ivllSNVPUYmiiNSIu3cLtJqHTEQax1bTeyW1aECeEkURqYnOwmS1UR/FpJbAEpHGsXWLiNarz1OiKCI1kW9m3qrpWUFWRBpEcaLY1fSsGKZEUURqonhVAwidwZUoikij6MzmaClaWQpQqwhVJIpm1m5mD5rZY2a20My+HW3f0czuNrPno5+ji55zkZktMrNnzex9RdvfbmZPRI9daWZWm7clIttb12S1oclGfRRFpJEU97EujHpWolhVjWIH8B53PwiYCpxgZkcA3wTudfd9gHuj/2NmBwAzgAOBE4Afm1kyeq2rgHOBfaLbCdW/FRGpp3xSmG+y0ahnEWkkHd3Wqgc1PUMViaIHG6P/tkQ3B04Gro+2Xw+cEt0/GZjj7h3uvhhYBBxmZrsCI9z9AQ8zWt5Q9BwRaTAdmW59FLWygYg0kM5uszaA5lGEKvsomlnSzB4FVgN3u/tfgV3cfSVA9HPnaPdxwNKipy+Lto2L7nffLiINKJ8Utmkwi4g0IE2PU1pViaK7Z919KjCeUDs4qY/dS/U79D6293wBs3PNbIGZLVizZk3F5RWRba/7qOeWpJqeRaRxdF8wIL+t2Q1o1LO7rwPuI/QtXBU1JxP9XB3ttgzYvehp44EV0fbxJbaX+j1Xu/s0d582duzYgRRZRLaRrj6KGvUsIo1HTc+lVTPqeayZjYruDwHeCzwDzAPOjnY7G7g9uj8PmGFmbWY2kTBo5cGoeXqDmR0RjXY+q+g5ItJguk+Pkx/MErogi4jEWzqT2+pCN7+t2aWqeM6uwPXRyOUEMNfd7zCzB4C5ZvYZ4GXgdAB3X2hmc4GngAxwvrtno9c6D7gOGALcFd1EpAGlSwxmcYdMzgsjoUVE4qp4ehw1PXepOFF098eBg0tsfw04tpfnzAJmldi+AOirf6OINIjuo55biqaXyF+li4jEVfH0OPmYpelxtDKLiNRIPqBq1KCINKLOUjWKShSVKIpIbfRoelbTjYg0kHSJCbcVv5QoikiNdJ8eR2ulikijyGRz5HzrPtagGkVQoigiNdJj1LP6+IhIg+jMhtkZWtR1pgcliiJSE4V5FNXHR0QaTPeuM4mEkUqYEkWUKIpIjXT0UqOoQCsicdeRDbP25RPF/H3FLyWKIlIjpSbcLt4uIhJX+abntqKpvFqSCXWdQYmiiNRIOpujJWkkEmFybTU9i0ijyF/QtqS6FgfIry7V7JQoikhNdGa2nli7RU3PItIgulpEkoVtrcmEZm1AiaKI1Ejx8legtVJFpHF0H8ySv59vkm5mShRFpCaKJ6sFNT2LSOPIx6nidelbkwnSmWy9ihQbShRFpCbSma1rFDWPoog0it5qFNUiokRRRGqke9Nzi5qeRaRB5GsU29T03IMSRRGpiR5NzxrMIiINojM/6nmrAXmacBuUKIpIjXSvUdRazyLSKLqvVR/uJ+lQ1xkliiJSG91rFPNNOGq6EZG4675gQP6+ahSVKIpIjXQfzKJ5FEWkUZQezGIajIcSRRGpkc7s1hNuJxNGMmGks5peQkTirdD0rBrFHpQoikhNdHSrUYTBE2jNbJSZ/crMnjGzp83sHWa2o5ndbWbPRz9H17ucIlIdTY/TOyWKIlIT3QezwKCaXuI/gN+6+/7AQcDTwDeBe919H+De6P8i0oBKDWZpSSbU9IwSRRGpkXQmR1ty65DSMgjWSjWzEcC7gJ8AuHva3dcBJwPXR7tdD5xSj/KJyMCVmh5HNYqBEkURqYnufRQhjHweBIF2L2AN8FMz+5uZ/Y+ZDQV2cfeVANHPnetZSBGpXjqbwwxSiaIl/FIJTY+DEkURqZHuo54huiJv/ECbAg4BrnL3g4FNVNDMbGbnmtkCM1uwZs2abVVGERmA/PReZl2JYlvU9Ow+KLrPVE2JoojURMlEMZkgnWn4Uc/LgGXu/tfo/78iJI6rzGxXgOjn6lJPdver3X2au08bO3bsdimwiFSmVB/rlmQCd8jklCiKiAxYyUCbsoYfzOLurwBLzWy/aNOxwFPAPODsaNvZwO11KJ6I1ED3BQOga2DLIOg+MyCpehdARBqfu9OZ9R59FAfL9DjAF4GbzKwVeBH4FOFCe66ZfQZ4GTi9juUTkQHoretM/rGhbfUoVTwoURSRAcv3Q2wr1UdxECSK7v4oMK3EQ8du56KIyDbQW9Mz0PRT5KjpWUQGrNQ6qQCtqaRGDYpI7JWatSGfODb6FF8DpURRRAas1KoGAK1JK8xPJiISV6X6KOZbSAbBzA0DUnGiaGa7m9nvo2WsFprZl6PtM81suZk9Gt1OLHrORWa2yMyeNbP3FW1/u5k9ET12pRWPSxeRhpEfsFLqirzZg6yIxF+pJUjV9BxU00cxA3zN3R8xs+HAw2Z2d/TYD9z9+8U7m9kBwAzgQGA34B4z29fds8BVwLnAX4A7gROAu6p7KyJSL73XKA6OPooiMrh1ZkuMek5q1DNUUaPo7ivd/ZHo/gbCmqfj+njKycAcd+9w98XAIuCwaN6xEe7+gIfZLG9AS2CJNKR0NsyVWHLC7SYPsiISf/2Nem5mA+qjaGYTgIOB/ES0XzCzx83sWjMbHW0bBywtetqyaNu46H737SLSYDp6GczSEq1sICISZ6VGPbeqjyIwgETRzIYBNwNfcfc3CM3IewNTgZXAv+V3LfF072N7qd+lJbBEYizfR7E1tfXHWjWKItIIOjNe8kIXVKNYVaJoZi2EJPEmd78FwN1XuXvW3XPANcBh0e7LgN2Lnj4eWBFtH19iew9aAksk3rqmx0lutb01ldD0OCISe+lsjpZUL6OelShWJhqZ/BPgaXf/96LtuxbtdirwZHR/HjDDzNrMbCKwD/Cgu68ENpjZEdFrnoWWwBJpSL0NZmmLBrOEbsgiIvHU5xJ+TX6xW82o5yOBTwJPmNmj0bZvAWeY2VRC8/ES4HMA7r7QzOYS1kbNAOdHI54BzgOuA4YQRjtrxLNIA+ptMEu+6SaTc1qSmv1KROJJ0+P0ruJE0d3/SOn+hXf28ZxZwKwS2xcAkyotg4jESzqTn0exZx/F8HjPVQ9EROIiTI/Te/xqZorcIjJgfa31DAq0IhJvJafH0WAWQImiiNRAX4NZQH18RCTeSk6Pk08Us83dx1qJoogMWG+DWTS9hIjEXTbnZHNecglSUPxSoigiA5bv7N1j1LNqFEUk5nqLX0oUAyWKIjJg+UDaYzCLahRFJOZ6W1kqmTCSCWv6Uc9KFEVkwNK6IheRBpWPT90H40G4+G32FhEliiIyYH2t9Qyah0xE4isfn0pN4dWa1DKkShRFZMDyqxqERZa6qEZRROKut8F4YVuycCHcrJQoisiAdWZzJVdeyQderfcsInHVW9cZgNak+igqURSRASs1WS1oMIuIxF/XYLxSNYpqelaiKCID1muimFIfRRGJtz5rFJUoKlEUkYErtaoBqEZRROKvMOq5RI1iSzLR9Be6ShRFZMDS2VyvzTagRFFE4qu3Cbfz2zQ9jojIAOVHPXentZ5FJO767KOYTGjUc70LICKNL53J9TJZrWoURSTe+p4eR03PShRFZMB6G8yitZ5FJO76nh5Hg1mUKIrIgHX21kdRNYoiEnPpXlaWAo16BiWKIlIDvY16TiSMVMKaPtCKSHz1Oz1Ok7eIKFEUkQHrbTALaHoJEYm3vmoUW5IJOpv8QleJoogMWG99FEFNNyISb/kL2RbVKJakRFFEBiyd7b1GUYFWROKszz6Kmh5HiaKIDFyfNYoKtCISY13zKFqPxzQ9jhJFEamB3gazQJgipzPr27lEIiLlSWed1lQCsxKJoqbHUaIoIgPX32CWdCa7nUskIlKevuJXaypBziHTxLWKShRFZMA6s7mSHcFBg1lEJN7S2Wyfg/GApm4VUaIoIgOSyzmdWddgFhFpSP21iOT3aVZKFEVkQPqarBZCH5/OTPNejYtIvHVmnZZUz/6J0BXXOrLN231GiaKIDEg+UWzrJVFsSSXoUI2iiMRUXzWKbapRVKIoIgPTWZhaovcaxWYOsiISbx2ZHK2pZMnH8jWN6qNYATPb3cx+b2ZPm9lCM/tytH1HM7vbzJ6Pfo4ues5FZrbIzJ41s/cVbX+7mT0RPXallRqbLiKx1l/Tc1tKo55FJL46szlaS8yhCNCaDAlkM1/sVlOjmAG+5u5vA44AzjezA4BvAve6+z7AvdH/iR6bARwInAD82MzyqftVwLnAPtHthAG8FxGpg75WNYD8hLXNezUuIvHW3xKk+X2aVcWJoruvdPdHovsbgKeBccDJwPXRbtcDp0T3TwbmuHuHuy8GFgGHmdmuwAh3f8DdHbih6Dki0iAKiWJvfRST1tRBVkTira8FA/KrtTTzzA0D6qNoZhOAg4G/Aru4+0oIySSwc7TbOGBp0dOWRdvGRfe7bxeRBtLvqGdNjyMiMdbZz1r1oBrFqpjZMOBm4Cvu/kZfu5bY5n1sL/W7zjWzBWa2YM2aNZUXVkS2mX6bnpPJpg6yIhJv6Uyu18F4+dkcmvlit6pE0cxaCEniTe5+S7R5VdScTPRzdbR9GbB70dPHAyui7eNLbO/B3a9292nuPm3s2LHVFFlEtpH+mp61MouIxFmffRSjwSydTRzDqhn1bMBPgKfd/d+LHpoHnB3dPxu4vWj7DDNrM7OJhEErD0bN0xvM7IjoNc8qeo6INIj+J9w20tkcoSuyiEi8dPSRKOanx2nmGsVUFc85Evgk8ISZPRpt+xZwBTDXzD4DvAycDuDuC81sLvAUYcT0+e6enyvjPOA6YAhwV3QTkQZSzqhnCPOQtfay+oGISL302UdRE25Xnii6+x8p3b8Q4NhenjMLmFVi+wJgUqVlEJH46Mz2M+F2UR+f3q7aRUTqpa/YpMEsWplFRAaoo78+iroiF5EY62sJv0L8auKmZyWKIjIg+QSwr7WeoavmUUQkTjpVo9gnJYoiMiD9D2ZRoBWReMrlnM6sl9V1plkpURSRAclPG9FfoO1QoigiMdPfhW4+rml6HBGRKvUXaNvUdCMiMZWPX711nUklDDPVKIqIVK2/6XEKV+RNHGhFJJ76axExM1qTzb1ogBJFERmQdCHQlp41S318RCSu+msRyT/WzPFLiaKIDEg667QmE4QFlnrSYBYRiav+WkTyjzVz/FKiKCID0tc6qaDpJUQkvgoLBvRXo9jE8UuJoogMSDqb7TNRbNGEtSISUx3l1Ciq6VlEpHp9rWoAg2fUs5klzexvZnZH9P8dzexuM3s++jm63mUUkcr0t2AAhIvdZh6Mp0RRRAakM+u0pHpb/n1QNT1/GXi66P/fBO51932Ae6P/i0gD6cw60M9gFvVRFBGpXn81ioNh1LOZjQc+APxP0eaTgeuj+9cDp2znYonIAKX7mR4HQgxr5gUDlCiKyIB0ZHK0ppK9Pj5I5lH8IfAPQPGb2MXdVwJEP3cu9UQzO9fMFpjZgjVr1mzzgopI+dLZLNB/jWKDx68BUaIoIgOSzg7uUc9mdhKw2t0frub57n61u09z92ljx46tcelEZCDKmh6nyUc9p+pdABFpbJ2ZHK29TLYNXQG4gZtujgQ+ZGYnAu3ACDO7EVhlZru6+0oz2xVYXddSikjF0oU+in33s163uWHj14CpRlFEBqTfGsUGn3Db3S9y9/HuPgGYAfyfu38CmAecHe12NnB7nYooIlXqqlHsvftMazJBZ8a3V5FiR4miiAxIf4NZEgkjlbDB2MfnCuA4M3seOC76v4g0kEKi2Nf0OE0+j6KankVkQPpbmQUGTx8fd78PuC+6/xpwbD3LIyIDU1iZpZ/uM4MhflVLNYoiMiCd2VyfU0uAVjYQkXgqp0ZR0+OIiAxARzk1ik1+RS4i8ZS/gO27n/Wg7DpTNiWKIjIg6Wyuz+WvIMylqBpFEYkbTY/TPyWKIjIg/Q1mgbCOajMHWhGJp3Q2R0vSMOt7epxmvtBVoigiA9JMg1lEZHAp50K3JZkgm3OyueacIkeJoogMiAaziEijKvdCFxp+GdKqKVEUkarlck4m5/0G2pYmXytVROKprAvdxl9dakCUKIpI1coZMQga9Swi8VROjWKbahRFRKrTUcaIQVAfRRGJp45+liAFCjWOzRrDlCiKSNU6y61RbPIJa0UknjrLGMySj29KFEVEKlTOHGT5x5u12UZE4itdRo1iIVFs0hhWVaJoZtea2Woze7Jo20wzW25mj0a3E4seu8jMFpnZs2b2vqLtbzezJ6LHrrS+JjISkdgpZ/mr/OPNGmRFJL7KnR4nv28zqrZG8TrghBLbf+DuU6PbnQBmdgAwAzgwes6PzSwZ7X8VcC6wT3Qr9ZoiElMazCIijayS6XGa9WK3qkTR3ecDr5e5+8nAHHfvcPfFwCLgMDPbFRjh7g+4uwM3AKdUUx4RqY988lfWPIpKFEUkZsqZHqdNNYo19QUzezxqmh4dbRsHLC3aZ1m0bVx0v/v2HszsXDNbYGYL1qxZU+Mii0i1yq1RDPMoNueqBiISXx1l1Ci2aHqcmrkK2BuYCqwE/i3aXqrfofexvedG96vdfZq7Txs7dmwNiioitZC/wm5TjaKINKCyBrOoRrE23H2Vu2fdPQdcAxwWPbQM2L1o1/HAimj7+BLbRaRBVDqYJfQyERGJh86spsfpT80SxajPYd6pQH5E9Dxghpm1mdlEwqCVB919JbDBzI6IRjufBdxeq/KIyLaXb4rpt49Pk3cGF5F4KmfUc7MPZklV8yQzmw1MB8aY2TLgUmC6mU0lNB8vAT4H4O4LzWwu8BSQAc5392z0UucRRlAPAe6KbiLSIMquUUzm+/g4bVVFHRGR2itr1HOTNz1XFbLd/YwSm3/Sx/6zgFklti8AJlVTBhGpv/IHs4QuyelMDtq2ebFERMrSmXVNj9MPrcwiIlUrf63nMHVqs16Ri0g8pTP9T4/T7DWKShRFpGqVrPUMzRtoRSR+3L2sUc+aHkdEpEplr/Xc5E03IhI/+XjUpj6KfVKiKCJVK38wS1EfRRGRGMgvApDvQ92bliaPX0oURaRqlcyjCKpRFJH4KLdFxMzCevVNurqUEkURqVq+z04q0fcVeWtSg1lEJF66LnST/e7bzKtLKVEUkap1RB3Bw5z5vdNgFhGJm64FA/qOX5BfXSrb736DkRJFEalaOpPrd51n6ArEzTpqUETip6PMrjMQmqc7M2p6FhGpSDmrGkBXIO5QjaKIxES+haO/Uc8ALSlr2j7WShRFpGqd2f4nqwWt9Swi8VPuPLAQahSbteuMEkURqVrZNYoazCIiMZMu9FEsp1Uk2bQtIkoURaRq5axqAKHZBtRHUUTio9zpccI+1rTxS4miiFQtncmVGWQ16llE4qXceWDz+zRr/FKiKCJV66hwMEuzBloRiZ/Kmp4TTdvHWomiiFStM1tmjaIGs4hIzFQ06jmZUNOziEilyh3M0pJQjaKIxEtFTc8a9SwiUrlyB7MkEkZLsnnnIROR+OmstOlZiaKISGXKHcwCzX1FLiLxk65kHkX1URQRqVxn1mkpI8hCc1+Ri0j8qOm5PEoURaRqldQoNnNncBGJn45K5lFUjaKISOXKnR4HVKMoIvFSWMKvzLlgmzV+KVEUkaqlM9myppaAkCh2NOkVuYjETzqTI5UwEgnrd9+WVPO2iChRFJGqdWadlmT/QRaa+4pcROKn3Om9IMSvzqyTy/k2LlX8KFEUkaqVOz0OhBrFZr0iF5H46czmypoaB5p70QAliiJSlWzOyeac1mSyrP1VoygicVLRhW6UUDbjxa4SRRGpSiVTS+T3U6IoInHRUck8sE28Xr0SRRGpSrqwqkGZfRSbeHoJEYmfzqxXNBgP1PQsIlK2/JV1uYG2RU3PIhIj6Uy2/D6K+abnjAaziIiUpZLlr/L7NePVuIjEUyWjnlsKNYrZbVmkWKoqUTSza81stZk9WbRtRzO728yej36OLnrsIjNbZGbPmtn7ira/3cyeiB670szKa8MSkbqrtI9im2oURSRGqhnM0tGEMazaGsXrgBO6bfsmcK+77wPcG/0fMzsAmAEcGD3nx2aWHyZ5FXAusE906/6aIhJTnYU+ihrMIiKNpzNT/jywbRrMUhl3nw+83m3zycD10f3rgVOKts9x9w53XwwsAg4zs12BEe7+gLs7cEPRc0Qk5tIVrJMKWutZROKlI5ujNVXe9F4thelx1EdxIHZx95UA0c+do+3jgKVF+y2Lto2L7nff3oOZnWtmC8xswZo1a2pYZBGpVoemxxGRBpbW9Dhl2R6DWUrV63of23tudL/a3ae5+7SxY8fWtHAiUp2q5lFUjaKIxERnNkdrqvzpvUCDWQZqVdScTPRzdbR9GbB70X7jgRXR9vEltotIA8g3I5d9Rd7Ea6WKSPxUUqOY78uY1vQ4AzIPODu6fzZwe9H2GWbWZmYTCYNWHoyapzeY2RHRaOezip4jIjFXTY0iQGdOtYoiUn+VTI/T1sQTbqeqeZKZzQamA2PMbBlwKXAFMNfMPgO8DJwO4O4LzWwu8BSQAc5393zd7XmEEdRDgLuim4g0gIrnUUx29fFpK7MDuYjIttJZ0fQ4IWY1Yx/FqhJFdz+jl4eO7WX/WcCsEtsXAJOqKYOI1Felo56buTO4iMRPOpOraHovoClnbtDKLCJSlXQV8ygWP69RmNnuZvZ7M3vazBaa2Zej7b0uMiAi8ddRQY1iVx/FxopftaBEUUSqUs1az9CQa6VmgK+5+9uAI4Dzo4UESi4yICLx5+6hG4xaRPqlRFFEqlLtYJZGm17C3Ve6+yPR/Q3A04Q5X3tbZEBEYi4Tzb4w2FtEakGJoohUpdrBLI28VqqZTQAOBv5K74sMdH+OFgwQiZlKL3RbEqpRFBGpSKWDWRp9rVQzGwbcDHzF3d8o93laMEAkfipNFBMJoyVpqlEUESlXZzaHGSQT5a1s0MhrpZpZCyFJvMndb4k297bIgIjEXGeFg/EgXBQ36oXuQChRFJGq5Fc1CPPl969RO4NHCwL8BHja3f+96KHeFhkQkZirdK16gJZUoimnx6lqHkURkY4KVjWAxh3MAhwJfBJ4wswejbZ9i14WGRCR+Ms3IZc7awM0b42iEkURqUo6m6s4yELj1Si6+x+B3qpNSy4yICLxVula9RAudhstftWCmp5FpCqdFaxqANCaiiasbcA+iiIyuOQTvspiWEKDWUREypWuYFUDaO61UkUkXiod9QzN2/SsRFFEqpIfzFKuRh3MIiKDT1WJomoURUTKl652MEum4QaziMggU+la9aAaRRGRiqSzlfZRbN4lsEQkXipdqx5CUtmM0+MoURSRqlRao9iSDINZGnHCbREZXCpdgjS/r2oURUTKVO30OI281rOIDA7VTo/TjPFLiaKIVKXSwSxm1rR9fEQkXgrT41R4saumZxGRMnVW2EcRmrfpRkTipTDqWfMo9kuJoohUpdI+ihD6KTbjFbmIxEs1az03a4uIEkURqUo1iaJqFEUkDvKD6iqtUWzGwXhKFEWkKpWuzALN23QjIvFSzYTbLapRFBEpX6WDWaB5m25EJF7S2SzJhJFMWNnPadYWESWKIlKVamoUW5KqURSR+uvMemFu13LlW0Tcm6v5WYmiiFSlmhrFtia9IheReKmuRaQ5Fw1QoigiFctkc+S8sv490LxNNyISLx2ZHK2pZEXPadZlSJUoikjF8lfUVc2j2GRBVkTip7PClaWga4R0s13sKlEUkYpVM2IQQmKpeRRFpN7SmVzFfRTzq7g0WwxToigiFevIZoEqmp416llEYqCqeWBVoygiUp58oGzTEn4i0oCqnQcWulZ1aRY1TxTNbImZPWFmj5rZgmjbjmZ2t5k9H/0cXbT/RWa2yMyeNbP31bo8IlJ7hT6Kqcqnl2i2ICsi8VPNWvVtanquqWPcfaq7T4v+/03gXnffB7g3+j9mdgAwAzgQOAH4sZlVNgxJRLa7Qh/FZIWjBtVHUURioKOK6XFa1PS8TZ0MXB/dvx44pWj7HHfvcPfFwCLgsO1UJhGpUrWDWTTqWUTioNq16kHT49SCA/9rZg+b2bnRtl3cfSVA9HPnaPs4YGnRc5dF20QkxtIazCIiDawzW90SpNB8NYqpbfCaR7r7CjPbGbjbzJ7pY99SHZx6THkeJZznAuyxxx61KaWIVK2j0PSswSwi0niqqVFsUY1ibbj7iujnauBWQlPyKjPbFSD6uTrafRmwe9HTxwMrSrzm1e4+zd2njR07ttZFFpEK5QeztFY4mKUlmSCTc3K55loCS0TipapRz01ao1jTRNHMhprZ8Px94HjgSWAecHa029nA7dH9ecAMM2szs4nAPsCDtSyTiNRe1YNZmvSKXETipbPKteqh+RLFWjc97wLcamb51/65u//WzB4C5prZZ4CXgdMB3H2hmc0FngIywPnunq1xmUSkxqodzNJWlCi2t2iCAxGpj3Q2V2hKLld+1HOzzdxQ00TR3V8EDiqx/TXg2F6eMwuYVctyiMi2VfVglia9IheReKlmepxmjV9amUVEKtaZiSbcrnSt1Ca9IheReElncoUWjnI1a9cZJYoiUrGObJXzKDZpZ3ARiZdqVmZRjaKISJm61nqucjBLkwVaEYmPTDZHzgdwoasaRRGJgxXrNvObx1fWuxglDWRlFkDrPYtI3aTVIlIRJYoiMfWf//c85//8EZa+/ma9i9JDvo9hpX0UW9VHUUTqLN/HutLBLImEkUqYEkURqT93Z/5zrwLwmyfiV6uYzuRIGKTUx0dEGkxHNGtDpdPjQBiQ12wXukoURWLohTUbWb5uMwmDOx7vsVhR3VWzqgE076hBEYmPrj7W1cWwZrvQVaIoEkP3R7WJZ71jAk8uf4Mlr26qc4m2lq5iDjJo3j4+IhIf1faxzj+n2S50lSiKxND859aw15ihnPuuvYD4NT9XW6OoeRRFpN7ya9VXOj0OhIvddKa51qpXoigSM1s6s/x18Wu8a9+x7DZqCG/fczR3xGz0c9U1ihr1LCJ1phrFyihRFImZBxe/zpbOHO/edywAH5i8K0+vfIMX1mysc8m6pDPV1Si2aTCLiNRZtUuQQr5GMVvrIsWaEkWRmJn/3BpakwkO32tHAE6cvCsAd8aoVrHaRFGDWUSk3tJVLkEKIYblm66bhRJFkZiZ//waDpu4Izu0pgB4y8h2Dp0wOlb9FKtZ/gqK+iiqRlFE6iR/oVrpWs8QkstmaxFRoigSIyvXb+a5VRt5175jttp+0pTdeOaVDSxavaFOJduapscRkUZV6KNY4RKkoOlxRKTO5j+3BoB3Rf0T894/6S2YEZtBLR2aHkdEGlRnlUv4heck6WiyC10liiIxMv+5V9llRBv77TJ8q+07j2jnsAk7xmbt52r7KOb7BClRFJF6ycefqvooJq3pus4oURSJiWzO+eOiV3nXPmMx6xnATjpoN55fvZFnX6l/83NntroaRTMLowabrDO4iMSHpsepjBJFkZh4bNk61m/u7NHsnHfCgW8hYfCbGCzpV22NIjRnHx+RZpSJaULVMZCm52TzxS8liiIxMf+5NZjBUW8dU/LxscPbOGKvnbjjiZW417dGrtrBLJC/Im+uechEms3TK9/gkMvu5sa/vFTvovTQWRjMUt3MDc22spQSRZGYuP+5NUwZP4rRQ1t73ecDU3blxTWbeKbOzc/VrswCzXlFLtJM3J1v/3ohb2zJ8J07nuKpFW/Uu0hbSQ9oMEvzxS8liiIxsP7NTh5buq6wGktvTjjwLSQTxh11bn7uzOZoqbJGsSVlTTdhrUgzuevJV/jLi6/z1eP2ZdSQFr4w+xHeTGfqXayC9ABqFJUoikhd/HHRq+Qc3r1v6WbnvJ2GtfHOvXfiN4/Xt/m52ulxQDWKIoPZls4ss37zNPu/ZTjnH/NWfvixqSx+dRMz5y2sd9EKOrM5zCCZqG5lFg1mEZHtbv5zaxjenuKg8aP63fcDk3dlyWtvsrCOzTnpTK6qVQ0gmodMiaLIoPT/7n+R5es2c+kHDySZMN751jH8/fS9mbtgGfMeq/9APOjqOlNqdon+hFkbcnXvJ749KVEUqTN35/7n1nDUW8eQKqOW7n0HvoVUwuo2+ba712AwixJFkcFmxbrNXHX/Ik6c/BbesfdOhe1fee++HLLHKC6+5QmWvv5mHUsYdAxk1oZkAnfI5JQoish28vzqjbzyxpZ++yfmjR7aypFvHcNvnlhRl6vaTM5xr65/D4QJa9MZjXoWGWwuv+sZ3OFbJ75tq+0tyQT/MeNgMPji7L/VfdRwZ3YgLSLNt7pUqt4FEGl2vS3bx/plsPSvMP5QGLXHVg99YMqu/MOvHueJ5euZ0r252h3WPAsvPwDpTeC5HrdMNkNnxsklkjhJcokkOUuRI0UukSJLEjxHMteBZdMksh0ksh1YtoNcZwf/nFrMQS+Mhi07QiIJiRRYIvxMJMGSXb8P3+p3f2rjclazE/CObXZMRWT7enDx6/z6sRV86dh9GD96hx6P777jDlxx2hTO//kj/Pvdz3HhCfvXoZRBOpOjpcoL3fzz6p3sbk9KFEXq7P7n1vDWnYex26ghYYM7PDYH7voH6Ij6IY6eCHu9Gya+Cya+m/cd8BYuTj7BN375OHvstAM7ZVZxwJa/sf/mv7Hfm39jZPa1Pn9niuo+/Fu8hQ5aOC7ZwrBXk7AWyGXBs5DLhPu5DOCAgVlIIItux2ScF5N7VfHbRSSOsjln5ryF7Dqync+/u/fP9gem7MofF+3Of9//AkfuPYaj9ul78N62MtCuM6AaRRHZTjans/x18et84vA9w4Y3X4c7vgJP3Q57vAPecwm88gQsng9P3gIPXwfAyF0mMXuPSax5fR1TljzGuFzor7jWRrIgeRAPt07mkcQk0m2jGdrWwtD2NnZoa2FYeytDh7QyrL2NHVoTpCxHqEfM0kKOpGVIkSXpWTAjm2wjY61kEq1kacEJeWwyYRx/4C7Q3lL6jbmHJLGEh55bw5bOLAfW9lCKSJ3MXbCUp1a+wZVnHMwOrX2nFf900oEsWLKWC+Y+yl1fPpoxw9q2Uym7DGge2ChRbKYBeUoUReror4tfI53J8e79xsKie+C28+HN1+C9M+GdXwrNuBOOgiPOg2wGVj4Ki++HF+9n2tLbINECbz0S9voyTHw3o3d+G8eacWyd31dvSSKUaGIXkYa1fnMn//q7Zzlswo58cMquYWMmHeLZc3dB6/DQdWb0njBqD4aM2oP//PjBnPxff+Lvrl/A4RN3JGU5RmZeY3R6OTt2rGBkx0pGbllOS2YjOXdyDrmcR/edbPR/h3BR6kX3CX2ou1o1wMK9wv0zt2RCH8Ubd6z4/b7rjS38tOUNcuveCjvuV4MjGH91TxTN7ATgP4Ak8D/ufkWdizS4dW6GVHufX+Sy/cx/7lVGpjo58rl/gQXXwNj94cy5sOtBPXdOpmD8tHA7+mshGFsibJe6UQyTZvYf9zzP2jfT/NNJb8OWPRS6zSy8BTavhbaRkE1DZvNWz9l/yI48sNOuPLsmwVtWr2Eca2i1rgFuWTdWshPrfSiOhUTPwMxIGqTMSOAh/uWZYRgeZYQGFPLFSH7s35AUjBzSEi7KKzQ0m2Fc2xZSqEZxuzCzJPAj4DhgGfCQmc1z96cG+tpPLnych359DdY6lET7MFLtw2gbMoy2HUbQPnQYOwwbyQ7t7bRZlhbL0EqGlqJbik7MDE+04YkWcslWcokWcokWPNmKJ1og2UIimSKRbMWSrVgqSSLZSiKZwiwRndjhlLX8VY0ZieiEB8KZm9kS3TpCIpdNh0EBqXY82Uo20Uo22UqOFrKEhdbT2RzpTHQrvp8JAwZ22PIKwza8wNANi2lf/wLt61+kZd0LpDatItc2gtxO+8HY/bCd9ye589tg5/1hxLjSCaR7KFtmM3QWlTWzOfq5pWt7cd+08FeO7lt4T23DoX0EtI+EthHhlojB4PtcFrasD4Hjzdejn6/B5tdhyxswZBQMHdvtNgaSLT1fJ70JOt8MP9ObAA81f4lUSOqic4dEijVP/4E72/+T1IKlcMTfw7GXQkt7eWVO9b7Un2wf2zKG3funB8AStLW109rWTlv7EIa0D6G9fQhD2loY0poklQhfnElyJDwTYkc2+uk5SLaGcy7ZGm6JVOwuEt2dTM7J5sLPnDsJM5I4ifR6kpvXkuxYi21eG5IPgB3GwA47hs/gDmOgtefgiQoKEN2KB195iZ+Ez22yNXbHcCv575T0m5DeGGJQIgWtQ6FtGLQOCy0VNbBo9Qbuf+AB/mf3J5h087dg7ZJQEbH/B2DKDNj7mPC7N70K616GdUuiny+z49qXeMew9TDyCBg9AUZPIDdyTzIj9yQ3YhwjSbJjwmhNJsqaNmx7GR7dmkm9qyIOAxa5+4sAZjYHOBkYcJAd9sYLfGrLDbAFqMO8xBlPkMPIkSBL1/3w00iRo41O2qyzz9cxugYe5NxIRz3KWkmQwmgngUevmX/9nXiD9qLXXe878LTvxgu5/XjZj2bnzDr22bycty6fxxi7sbDfJm9nhY2lhSztdNBOmrbolii+LKuxDT6EDexAB60YkDCP3pVHCbaTgMI7TODhp+f/n8Pw/FbcuvbMWbJwfPLPNfdur5Wl3bf0+h7Dc0s/tp5hbLF22ryDIWyhjb7/nt39J7CpbWf4+O2w1/SKniuxsM1i2MF3f5QdewleaU+SIYURLnQrkfYknaSKPgFW+IzkPxnhbDdyZoXHu27h4jdBjiQ5klGES0U/E+QAIxM9kiVJpvAzSTaKjV1NhFtLkWW0bWQHNpGw8uLOFlpZywjW2wjASG110Z+hxcPPFJlCxExG77ia2NZBKx20kKaFdHS/01qIGjgL8vlk12WzF35//sjn4xfRv/kt5P8mlshffhdeI/+a+ftJz9DuW2hnC+2+hWQ/tV1baGWzDeHNKGolyNFKZ+G4pTxDC53R8ctGf8tk9H0W/q4ZkowE7m1dh6+2MNDu3RfC/ieFyoBiw8aG2/i391muBJC//C3zclm2g3oniuOApUX/XwYcXosXnnDEKTBtVVSzsxHSb+LpTby5aT2bNmxg86Y32LJlC53WQicp0p4MH3pPkvYkW3JJwEl5hmQuTYpOkp4hlesk6eFGLoPlMliuE3JZLNdJIpfBPPo/ucKVqnkW3DHPgWfJWZJMIgwU6Ey0krG2ws+MtUTBrTN8WL2TFk+TKvxMkzQjaU7SnJQ5CXOS5GjBeb1tFJtGTGTD0L14Y+gENqVGk3HHss5u2RyZbI6FWedvmRypza8xfOMLjNr0IqM3vcjw9Go2WwuvWRudiTbS0c/Ows9W0tZKmqL71kqaVtLWEmpMiQKihZrUJE7CIEmWIf4mQ3IbGZLbxJDsJobkNtKe3Uh7bhOpXAc5D6Ev50Yu+ulA1imEd7cotFoySgZDyDXPFW75Y250bctF+7sVh+fw/y2JHdiUHMWm5Ag2JUfyZnIEm1Kj2JwaRUeinSG5TYzIrWd4Zi0jcmsZnlnL8Ow6hmfX0prbQjrRTocNoSPRToeF25bEEDqsDUiQJEuKLCky4TwiF742kymOPvVzDB2zSy1Oe9n++o1hZnYucC7AHntsPc1RX/zEf+OVzRvpTG8hk+4g27mFTGeYnijb2UEukyZrSTKeImNhSqNOy6dDISFLkiHlIXZ13UL8SkTxyTwH5Ag5WQ7Lx6wobTF3IAceUpd8W14+ach/rnIkyVlIFQ2K0sQsSY8SSg/pYqGVJWpxSeQbHTA8kWRFagQvpEaxOTWCzfnPZGoEm5IjyHmO9o61tHeuZ0jnWoZ0vs6QznXskFnPDpl15DDSliJjLeFTlz8m1kLWQqkK6VohFnQlzIWCkW/KjO5bSMhaPEOLd5DydCEet+Q6SXkayPeV62rxLMx16h7Fqq1jz1apqhel5FHtZiK671HW6YX0EChK7rckhpDOx6DEELYkdiCdaCdt7STJ0e6baffNDMltps030557k/bcZlp9MzmSdFpr4Vh1WgsZS5GhlWxUu5sspIg5kt51f+PEyUw85mwYsVvZ57Y0lnoniqXq73tc3lUVaM1CE15Le2iiiH7Z0OgmeXsTKkVEpAr9xjB3vxq4GmDatGllV1/tdNhHB1YyEZEaqHfD/zJg96L/jwd6LAbp7le7+zR3nzZ2rEZMikhslBXDREQaVb0TxYeAfcxsopm1AjOAeXUuk4hIuRTDRGRQq2vTs7tnzOwLwO8IU0tc6+4L61kmEZFyKYaJyGBX7z6KuPudwJ31LoeISDUUw0RkMKt307OIiIiIxJQSRREREREpSYmiiIiIiJSkRFFERERESlKiKCIiIiIlKVEUERERkZKUKIqIiIhISUoURURERKQkJYoiIiIiUpK5e73LUBEzWwO8VObuY4BXt2FxBiKuZYtruSC+ZYtruSC+Zau0XHu6+9htVZjtpcL4BYPn77c9xbVscS0XxLdsg6VcDR2/Gi5RrISZLXD3afUuRylxLVtcywXxLVtcywXxLVtcyxU3cT1OcS0XxLdscS0XxLdsKlc8qOlZREREREpSoigiIiIiJQ32RPHqehegD3EtW1zLBfEtW1zLBfEtW1zLFTdxPU5xLRfEt2xxLRfEt2wqVwwM6j6KIiIiIlK9wV6jKCIiIiJVGrSJopmdYGbPmtkiM/tmvctTzMyWmNkTZvaomS2oYzmuNbPVZvZk0bYdzexuM3s++jk6RmWbaWbLo+P2qJmdWIdy7W5mvzezp81soZl9Odpe1+PWR7nqeszMrN3MHjSzx6JyfTvaHovzLK4Uv8oqh+JX5eVS/Kq8bE0fwwZl07OZJYHngOOAZcBDwBnu/lRdCxYxsyXANHev6/xQZvYuYCNwg7tPirZ9D3jd3a+IvqBGu/uFMSnbTGCju39/e5enqFy7Aru6+yNmNhx4GDgFOIc6Hrc+yvVR6njMzMyAoe6+0cxagD8CXwZOIwbnWRwpfpVdDsWvysul+FV52Zo+hg3WGsXDgEXu/qK7p4E5wMl1LlPsuPt84PVum08Gro/uX0/4sG53vZSt7tx9pbs/Et3fADwNjKPOx62PctWVBxuj/7ZENycm51lMKX6VQfGrcopflVMMG7yJ4jhgadH/lxGTky7iwP+a2cNmdm69C9PNLu6+EsKHF9i5zuXp7gtm9njUtFPXqn4zmwAcDPyVGB23buWCOh8zM0ua2aPAauBud4/V8Yohxa/qxf28UvyqrFwQg2PW7DFssCaKVmJbnNrYj3T3Q4D3A+dHzRTSv6uAvYGpwErg3+pVEDMbBtwMfMXd36hXOborUa66HzN3z7r7VGA8cJiZTdreZWgwil+DU90/i3mKX5Vp9hg2WBPFZcDuRf8fD6yoU1l6cPcV0c/VwK2Epqa4WBX1F8n3G1ld5/IUuPuq6AObA66hTsct6qdyM3CTu98Sba77cStVrrgcs6gs64D7gBOIwfGKMcWv6sX2vIrLZ1Hxq3rNGsMGa6L4ELCPmU00s1ZgBjCvzmUCwMyGRp11MbOhwPHAk30/a7uaB5wd3T8buL2OZdlK/kMZOZU6HLeoY/NPgKfd/d+LHqrrceutXPU+ZmY21sxGRfeHAO8FniHG51kMKH5VL7bnVb0/i1EZFL8qL1vTx7BBOeoZIBpG/0MgCVzr7rPqW6LAzPYiXIUDpICf16tsZjYbmA6MAVYBlwK3AXOBPYCXgdPdfbt3yu6lbNMJTRAOLAE+l+8jsh3LdRTwB+AJIBdt/hahP03djlsf5TqDOh4zM5tC6OidJFyYznX375jZTsTgPIsrxa+yyqL4VXm5FL8qL1vTx7BBmyiKiIiIyMAM1qZnERERERkgJYoiIiIiUpISRREREREpSYmiiIiIiJSkRFFERERESlKiKCIiIiIlKVEUERERkZKUKIqIiIhISf8fuVUmvJqGDCIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "67/67 [==============================] - 79s 1s/step - loss: 216.3431 - nmae: 2.5655 - val_loss: 83.6147 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00001: val_nmae improved from inf to 1.00000, saving model to ./Best_Check_Point/best_params1_finetune_0.5.ckpt\n",
      "Epoch 2/2000\n",
      "67/67 [==============================] - 66s 986ms/step - loss: 78.7906 - nmae: 1.0000 - val_loss: 84.2922 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00002: val_nmae did not improve from 1.00000\n",
      "Epoch 3/2000\n",
      "67/67 [==============================] - 66s 984ms/step - loss: 78.1245 - nmae: 1.0000 - val_loss: 84.3750 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00003: val_nmae did not improve from 1.00000\n",
      "Epoch 4/2000\n",
      "67/67 [==============================] - 67s 1s/step - loss: 80.8178 - nmae: 1.0000 - val_loss: 86.0423 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00004: val_nmae did not improve from 1.00000\n",
      "Epoch 5/2000\n",
      "67/67 [==============================] - 66s 987ms/step - loss: 79.2745 - nmae: 1.0000 - val_loss: 83.5477 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00005: val_nmae did not improve from 1.00000\n",
      "Epoch 6/2000\n",
      "67/67 [==============================] - 66s 986ms/step - loss: 76.4578 - nmae: 1.0000 - val_loss: 84.0141 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00006: val_nmae did not improve from 1.00000\n",
      "Epoch 7/2000\n",
      "67/67 [==============================] - 66s 987ms/step - loss: 79.2220 - nmae: 1.0000 - val_loss: 83.7418 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00007: val_nmae did not improve from 1.00000\n",
      "Epoch 8/2000\n",
      "67/67 [==============================] - 66s 986ms/step - loss: 81.8454 - nmae: 1.0000 - val_loss: 83.0796 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00008: val_nmae did not improve from 1.00000\n",
      "Epoch 9/2000\n",
      "67/67 [==============================] - 66s 981ms/step - loss: 81.4006 - nmae: 1.0000 - val_loss: 85.8801 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00009: val_nmae did not improve from 1.00000\n",
      "Epoch 10/2000\n",
      "67/67 [==============================] - 67s 991ms/step - loss: 79.3743 - nmae: 1.0000 - val_loss: 83.3592 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00010: val_nmae did not improve from 1.00000\n",
      "Epoch 00010: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "67/67 [==============================] - 82s 1s/step - loss: 24179.9328 - nmae: 236.4196 - val_loss: 94.5878 - val_nmae: 1.2112\n",
      "\n",
      "Epoch 00001: val_nmae improved from inf to 1.21116, saving model to ./Best_Check_Point/best_params1_finetune_0.3.ckpt\n",
      "Epoch 2/2000\n",
      "67/67 [==============================] - 66s 985ms/step - loss: 104.9681 - nmae: 1.4989 - val_loss: 85.4963 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00002: val_nmae improved from 1.21116 to 1.00000, saving model to ./Best_Check_Point/best_params1_finetune_0.3.ckpt\n",
      "Epoch 3/2000\n",
      "67/67 [==============================] - 66s 985ms/step - loss: 77.6283 - nmae: 1.0000 - val_loss: 84.8209 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00003: val_nmae did not improve from 1.00000\n",
      "Epoch 4/2000\n",
      "67/67 [==============================] - 67s 987ms/step - loss: 79.7925 - nmae: 0.9999 - val_loss: 84.7748 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00004: val_nmae did not improve from 1.00000\n",
      "Epoch 5/2000\n",
      "67/67 [==============================] - 66s 985ms/step - loss: 79.1467 - nmae: 0.9999 - val_loss: 85.3970 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00005: val_nmae did not improve from 1.00000\n",
      "Epoch 6/2000\n",
      "67/67 [==============================] - 67s 990ms/step - loss: 78.8026 - nmae: 0.9999 - val_loss: 83.7372 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00006: val_nmae did not improve from 1.00000\n",
      "Epoch 7/2000\n",
      "67/67 [==============================] - 69s 1s/step - loss: 77.8626 - nmae: 0.9999 - val_loss: 84.9497 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00007: val_nmae did not improve from 1.00000\n",
      "Epoch 8/2000\n",
      "67/67 [==============================] - 67s 990ms/step - loss: 71.8933 - nmae: 0.9998 - val_loss: 84.7560 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00008: val_nmae did not improve from 1.00000\n",
      "Epoch 9/2000\n",
      "67/67 [==============================] - 66s 986ms/step - loss: 79.8706 - nmae: 0.9999 - val_loss: 82.5418 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00009: val_nmae did not improve from 1.00000\n",
      "Epoch 10/2000\n",
      "67/67 [==============================] - 67s 987ms/step - loss: 80.6590 - nmae: 0.9999 - val_loss: 86.0387 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00010: val_nmae did not improve from 1.00000\n",
      "Epoch 11/2000\n",
      "67/67 [==============================] - 67s 987ms/step - loss: 78.8807 - nmae: 0.9998 - val_loss: 83.5333 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00011: val_nmae did not improve from 1.00000\n",
      "Epoch 12/2000\n",
      "67/67 [==============================] - 67s 990ms/step - loss: 86.7416 - nmae: 0.9999 - val_loss: 83.1126 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00012: val_nmae did not improve from 1.00000\n",
      "Epoch 13/2000\n",
      "67/67 [==============================] - 66s 984ms/step - loss: 80.4341 - nmae: 0.9999 - val_loss: 82.9195 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00013: val_nmae did not improve from 1.00000\n",
      "Epoch 14/2000\n",
      "67/67 [==============================] - 66s 985ms/step - loss: 79.1857 - nmae: 0.9999 - val_loss: 84.3562 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00014: val_nmae did not improve from 1.00000\n",
      "Epoch 15/2000\n",
      "67/67 [==============================] - 66s 982ms/step - loss: 76.7348 - nmae: 0.9999 - val_loss: 83.2787 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00015: val_nmae did not improve from 1.00000\n",
      "Epoch 16/2000\n",
      "67/67 [==============================] - 66s 985ms/step - loss: 79.1166 - nmae: 0.9998 - val_loss: 82.8126 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00016: val_nmae did not improve from 1.00000\n",
      "Epoch 17/2000\n",
      "67/67 [==============================] - 66s 985ms/step - loss: 80.9837 - nmae: 0.9999 - val_loss: 84.5550 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00017: val_nmae did not improve from 1.00000\n",
      "Epoch 18/2000\n",
      "67/67 [==============================] - 67s 987ms/step - loss: 80.7131 - nmae: 0.9999 - val_loss: 83.0706 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00018: val_nmae did not improve from 1.00000\n",
      "Epoch 19/2000\n",
      "67/67 [==============================] - 67s 988ms/step - loss: 83.9381 - nmae: 0.9999 - val_loss: 85.6384 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00019: val_nmae did not improve from 1.00000\n",
      "Epoch 20/2000\n",
      "67/67 [==============================] - 67s 988ms/step - loss: 83.6778 - nmae: 0.9998 - val_loss: 82.2291 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00020: val_nmae did not improve from 1.00000\n",
      "Epoch 21/2000\n",
      "67/67 [==============================] - 67s 989ms/step - loss: 76.9022 - nmae: 0.9998 - val_loss: 84.2188 - val_nmae: 1.0000\n",
      "\n",
      "Epoch 00021: val_nmae did not improve from 1.00000\n",
      "Epoch 00021: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for freeze_rate in [0.9, 0.7, 0.5, 0.3] :\n",
    "    model.load_weights('./Best_Check_Point/best_params1_top_layers.ckpt')\n",
    "    \n",
    "    base_model.trainable=True\n",
    "    fine_tune_at = int(len(base_model.layers) * freeze_rate)\n",
    "    for layer in base_model.layers[:fine_tune_at] :\n",
    "        layer.trainable = False    \n",
    "        \n",
    "    model.compile(optimizer=Adam(0.001),\n",
    "            loss='mae',\n",
    "            metrics=[nmae])\n",
    "\n",
    "    es = EarlyStopping(monitor='loss',\n",
    "                    mode='auto',\n",
    "                    patience=5,\n",
    "                    verbose=1)\n",
    "\n",
    "    ckpt_path = './Best_Check_Point/best_params1_finetune_{}.ckpt'.format(freeze_rate)\n",
    "    checkpointer = ModelCheckpoint(filepath=ckpt_path,\n",
    "                                  monitor='val_nmae',\n",
    "                                  save_weights_only = True,\n",
    "                                  save_best_only= True,\n",
    "                                  verbose=1)\n",
    "\n",
    "    STEPS_PER_EPOCH = len(train_img) // BATCH_SIZE\n",
    "    VAL_STEPS = len(valid_img) // BATCH_SIZE\n",
    "\n",
    "    history_fine = model.fit(train_generator, epochs=2000,\n",
    "                      steps_per_epoch=STEPS_PER_EPOCH,\n",
    "                      validation_data=valid_generator,\n",
    "                      validation_steps = VAL_STEPS,\n",
    "                      callbacks=[checkpointer, es],\n",
    "                      verbose=1)\n",
    "    \n",
    "    model.save('./Model_Save/best_params1_finetune_{}.h5'.format(freeze_rate))\n",
    "    model.load_weights(ckpt_path)\n",
    "    model.save('./Model_Save/best_params1_finetune_{}_2.h5'.format(freeze_rate))\n",
    "    \n",
    "    history_dict[str(freeze_rate)] = history_fine\n",
    "    \n",
    "    fig = plt.figure(figsize=(10,6))\n",
    "    ax1 = fig.add_subplot(1,2,1)\n",
    "    ax1.plot(train_loss + history_fine.history['loss'], label='Training Loss')\n",
    "    ax1.plot(valid_loss + history_fine.history['val_loss'], label='Validation Loss')\n",
    "    ax1.legend(loc='upper right')\n",
    "    ax1.set_title('MAE : inception_v2_resnet 0.01 adam 0.2 finetune-{}'.format(freeze_rate))\n",
    "\n",
    "    ax2 = fig.add_subplot(1,2,2)\n",
    "    ax2.plot(train_nmae + history_fine.history['nmae'], label='Training NMAE')\n",
    "    ax2.plot(valid_nmae + history_fine.history['val_nmae'], label='Validation NMAE')\n",
    "    ax2.legend(loc='upper right')\n",
    "    ax2.set_title('NMAE : inception_v2_resnet 0.01 adam 0.2 finetune-{}'.format(freeze_rate))\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39d191c",
   "metadata": {},
   "source": [
    "<h1> inception_v2_resnet 0.001 adam 0.2 </h1>\n",
    "<img src='./Training_Result/inception_v2_resnet 0.001 adam 0.2.png'/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "03982a23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         [(None, 512, 512, 3)]     0         \n",
      "_________________________________________________________________\n",
      "resize (Functional)          (None, 299, 299, 3)       0         \n",
      "_________________________________________________________________\n",
      "inception_resnet_v2 (Functio (None, 8, 8, 1536)        54336736  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1536)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1536)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              1573888   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 1025      \n",
      "=================================================================\n",
      "Total params: 55,911,649\n",
      "Trainable params: 1,574,913\n",
      "Non-trainable params: 54,336,736\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "\n",
    "base_model = inception_resnet_v2.InceptionResNetV2(\n",
    "            weights='imagenet',\n",
    "            include_top = False,\n",
    "            input_shape = (MODEL_IMAGE_SIZE, MODEL_IMAGE_SIZE,3))\n",
    "\n",
    "input_data = Input((IMAGE_SIZE, IMAGE_SIZE, 3))\n",
    "output_data = tf.keras.layers.experimental.preprocessing.Resizing(MODEL_IMAGE_SIZE, MODEL_IMAGE_SIZE)(input_data)\n",
    "resizing = Model(inputs=input_data, outputs=output_data, name='resize')\n",
    "\n",
    "# model\n",
    "inputs = Input((IMAGE_SIZE, IMAGE_SIZE, 3))\n",
    "x = resizing(inputs)\n",
    "x = base_model(x, training=False)\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "outputs = Dense(1, activation='relu')(x)\n",
    "model = Model(inputs, outputs)\n",
    "\n",
    "base_model.trainable = False\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ffe376de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "67/67 [==============================] - 76s 1s/step - loss: 49.3046 - nmae: 0.6622 - val_loss: 17.7064 - val_nmae: 0.2246\n",
      "\n",
      "Epoch 00001: val_nmae improved from inf to 0.22463, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 2/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 18.5583 - nmae: 0.2330 - val_loss: 16.0626 - val_nmae: 0.1896\n",
      "\n",
      "Epoch 00002: val_nmae improved from 0.22463 to 0.18961, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 3/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 15.9873 - nmae: 0.1949 - val_loss: 15.6835 - val_nmae: 0.1858\n",
      "\n",
      "Epoch 00003: val_nmae improved from 0.18961 to 0.18580, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 4/2000\n",
      "67/67 [==============================] - 65s 975ms/step - loss: 15.7078 - nmae: 0.2023 - val_loss: 15.2492 - val_nmae: 0.1820\n",
      "\n",
      "Epoch 00004: val_nmae improved from 0.18580 to 0.18200, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 5/2000\n",
      "67/67 [==============================] - 65s 973ms/step - loss: 14.0614 - nmae: 0.1891 - val_loss: 13.6535 - val_nmae: 0.1686\n",
      "\n",
      "Epoch 00005: val_nmae improved from 0.18200 to 0.16856, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 6/2000\n",
      "67/67 [==============================] - 65s 973ms/step - loss: 13.6979 - nmae: 0.1739 - val_loss: 15.6799 - val_nmae: 0.1964\n",
      "\n",
      "Epoch 00006: val_nmae did not improve from 0.16856\n",
      "Epoch 7/2000\n",
      "67/67 [==============================] - 65s 975ms/step - loss: 13.1737 - nmae: 0.1683 - val_loss: 15.5467 - val_nmae: 0.1908\n",
      "\n",
      "Epoch 00007: val_nmae did not improve from 0.16856\n",
      "Epoch 8/2000\n",
      "67/67 [==============================] - 66s 976ms/step - loss: 13.0262 - nmae: 0.1698 - val_loss: 12.6839 - val_nmae: 0.1543\n",
      "\n",
      "Epoch 00008: val_nmae improved from 0.16856 to 0.15430, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 9/2000\n",
      "67/67 [==============================] - 65s 974ms/step - loss: 13.6215 - nmae: 0.1835 - val_loss: 12.6186 - val_nmae: 0.1562\n",
      "\n",
      "Epoch 00009: val_nmae did not improve from 0.15430\n",
      "Epoch 10/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 12.9818 - nmae: 0.1585 - val_loss: 14.7666 - val_nmae: 0.1747\n",
      "\n",
      "Epoch 00010: val_nmae did not improve from 0.15430\n",
      "Epoch 11/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 13.5668 - nmae: 0.1757 - val_loss: 15.9329 - val_nmae: 0.2011\n",
      "\n",
      "Epoch 00011: val_nmae did not improve from 0.15430\n",
      "Epoch 12/2000\n",
      "67/67 [==============================] - 65s 975ms/step - loss: 13.5050 - nmae: 0.1737 - val_loss: 12.1583 - val_nmae: 0.1480\n",
      "\n",
      "Epoch 00012: val_nmae improved from 0.15430 to 0.14802, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 13/2000\n",
      "67/67 [==============================] - 65s 975ms/step - loss: 12.9014 - nmae: 0.1747 - val_loss: 12.9110 - val_nmae: 0.1634\n",
      "\n",
      "Epoch 00013: val_nmae did not improve from 0.14802\n",
      "Epoch 14/2000\n",
      "67/67 [==============================] - 65s 975ms/step - loss: 11.2804 - nmae: 0.1497 - val_loss: 12.1103 - val_nmae: 0.1495\n",
      "\n",
      "Epoch 00014: val_nmae did not improve from 0.14802\n",
      "Epoch 15/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 11.0923 - nmae: 0.1464 - val_loss: 15.7364 - val_nmae: 0.1992\n",
      "\n",
      "Epoch 00015: val_nmae did not improve from 0.14802\n",
      "Epoch 16/2000\n",
      "67/67 [==============================] - 66s 977ms/step - loss: 13.9431 - nmae: 0.1733 - val_loss: 11.6451 - val_nmae: 0.1400\n",
      "\n",
      "Epoch 00016: val_nmae improved from 0.14802 to 0.14003, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 17/2000\n",
      "67/67 [==============================] - 65s 973ms/step - loss: 11.2402 - nmae: 0.1497 - val_loss: 11.1528 - val_nmae: 0.1360\n",
      "\n",
      "Epoch 00017: val_nmae improved from 0.14003 to 0.13603, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 18/2000\n",
      "67/67 [==============================] - 66s 976ms/step - loss: 12.0245 - nmae: 0.1547 - val_loss: 11.1800 - val_nmae: 0.1337\n",
      "\n",
      "Epoch 00018: val_nmae improved from 0.13603 to 0.13373, saving model to ./Best_Check_Point/best_params2_top_layers.ckpt\n",
      "Epoch 19/2000\n",
      "67/67 [==============================] - 66s 976ms/step - loss: 11.3387 - nmae: 0.1498 - val_loss: 12.2161 - val_nmae: 0.1437\n",
      "\n",
      "Epoch 00019: val_nmae did not improve from 0.13373\n",
      "Epoch 00019: early stopping\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=Adam(0.001),\n",
    "            loss='mae',\n",
    "            metrics=[nmae])\n",
    "\n",
    "es = EarlyStopping(monitor='loss',\n",
    "                mode='auto',\n",
    "                patience=5,\n",
    "                verbose=1)\n",
    "\n",
    "ckpt_path = './Best_Check_Point/best_params2_top_layers.ckpt'\n",
    "checkpointer = ModelCheckpoint(filepath=ckpt_path,\n",
    "                              monitor='val_nmae',\n",
    "                              save_weights_only = True,\n",
    "                              save_best_only= True,\n",
    "                              verbose=1)\n",
    "\n",
    "STEPS_PER_EPOCH = len(train_img) // BATCH_SIZE\n",
    "VAL_STEPS = len(valid_img) // BATCH_SIZE\n",
    "\n",
    "history = model.fit(train_generator, epochs=2000,\n",
    "                  steps_per_epoch=STEPS_PER_EPOCH,\n",
    "                  validation_data=valid_generator,\n",
    "                  validation_steps = VAL_STEPS,\n",
    "                  callbacks=[checkpointer, es],\n",
    "                  verbose=1)\n",
    "\n",
    "model.save('./best2_top_layer.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "475c8603",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss = history.history['loss']\n",
    "valid_loss = history.history['val_loss']\n",
    "train_nmae = history.history['nmae']\n",
    "valid_nmae = history.history['val_nmae']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "248eb926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax1.plot(train_loss, label='Training Loss')\n",
    "ax1.plot(valid_loss, label='Validation Loss')\n",
    "ax1.legend(loc='upper right')\n",
    "ax1.set_title('MAE : inception_v2_resnet 0.001 adam 0.2 : top layer')\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2.plot(train_nmae, label='Training NMAE')\n",
    "ax2.plot(valid_nmae, label='Validation NMAE')\n",
    "ax2.legend(loc='upper right')\n",
    "ax2.set_title('NMAE : inception_v2_resnet 0.001 adam 0.2 : top layer')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7be1381",
   "metadata": {},
   "source": [
    "### finetune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "21774523",
   "metadata": {},
   "outputs": [],
   "source": [
    "history_dict2 = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c334322d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "67/67 [==============================] - 77s 1s/step - loss: 16.2273 - nmae: 0.1960 - val_loss: 14.9480 - val_nmae: 0.2007\n",
      "\n",
      "Epoch 00001: val_nmae improved from inf to 0.20068, saving model to ./Best_Check_Point/best_params2_finetune_0.9.ckpt\n",
      "Epoch 2/2000\n",
      "67/67 [==============================] - 68s 1s/step - loss: 11.7200 - nmae: 0.1577 - val_loss: 8.1477 - val_nmae: 0.0972\n",
      "\n",
      "Epoch 00002: val_nmae improved from 0.20068 to 0.09716, saving model to ./Best_Check_Point/best_params2_finetune_0.9.ckpt\n",
      "Epoch 3/2000\n",
      "67/67 [==============================] - 67s 1s/step - loss: 8.1970 - nmae: 0.1091 - val_loss: 8.1985 - val_nmae: 0.0952\n",
      "\n",
      "Epoch 00003: val_nmae improved from 0.09716 to 0.09518, saving model to ./Best_Check_Point/best_params2_finetune_0.9.ckpt\n",
      "Epoch 4/2000\n",
      "67/67 [==============================] - 67s 994ms/step - loss: 9.1687 - nmae: 0.1152 - val_loss: 9.3730 - val_nmae: 0.1151\n",
      "\n",
      "Epoch 00004: val_nmae did not improve from 0.09518\n",
      "Epoch 5/2000\n",
      "67/67 [==============================] - 67s 994ms/step - loss: 7.6989 - nmae: 0.1006 - val_loss: 9.2352 - val_nmae: 0.1158\n",
      "\n",
      "Epoch 00005: val_nmae did not improve from 0.09518\n",
      "Epoch 6/2000\n",
      "67/67 [==============================] - 67s 996ms/step - loss: 7.4049 - nmae: 0.0927 - val_loss: 8.1988 - val_nmae: 0.1012\n",
      "\n",
      "Epoch 00006: val_nmae did not improve from 0.09518\n",
      "Epoch 7/2000\n",
      "67/67 [==============================] - 66s 989ms/step - loss: 7.4964 - nmae: 0.0989 - val_loss: 12.2837 - val_nmae: 0.1528\n",
      "\n",
      "Epoch 00007: val_nmae did not improve from 0.09518\n",
      "Epoch 8/2000\n",
      "67/67 [==============================] - 66s 989ms/step - loss: 6.9610 - nmae: 0.0868 - val_loss: 9.7766 - val_nmae: 0.1153\n",
      "\n",
      "Epoch 00008: val_nmae did not improve from 0.09518\n",
      "Epoch 9/2000\n",
      "67/67 [==============================] - 66s 987ms/step - loss: 7.4447 - nmae: 0.0945 - val_loss: 7.4822 - val_nmae: 0.0877\n",
      "\n",
      "Epoch 00009: val_nmae improved from 0.09518 to 0.08767, saving model to ./Best_Check_Point/best_params2_finetune_0.9.ckpt\n",
      "Epoch 10/2000\n",
      "67/67 [==============================] - 67s 990ms/step - loss: 7.4901 - nmae: 0.0959 - val_loss: 7.1904 - val_nmae: 0.0866\n",
      "\n",
      "Epoch 00010: val_nmae improved from 0.08767 to 0.08658, saving model to ./Best_Check_Point/best_params2_finetune_0.9.ckpt\n",
      "Epoch 11/2000\n",
      "67/67 [==============================] - 66s 987ms/step - loss: 6.0569 - nmae: 0.0772 - val_loss: 5.8789 - val_nmae: 0.0707\n",
      "\n",
      "Epoch 00011: val_nmae improved from 0.08658 to 0.07067, saving model to ./Best_Check_Point/best_params2_finetune_0.9.ckpt\n",
      "Epoch 12/2000\n",
      "67/67 [==============================] - 66s 986ms/step - loss: 5.9249 - nmae: 0.0693 - val_loss: 6.5452 - val_nmae: 0.0756\n",
      "\n",
      "Epoch 00012: val_nmae did not improve from 0.07067\n",
      "Epoch 13/2000\n",
      "67/67 [==============================] - 66s 989ms/step - loss: 6.2338 - nmae: 0.0760 - val_loss: 8.0608 - val_nmae: 0.0945\n",
      "\n",
      "Epoch 00013: val_nmae did not improve from 0.07067\n",
      "Epoch 14/2000\n",
      "67/67 [==============================] - 66s 990ms/step - loss: 6.3076 - nmae: 0.0778 - val_loss: 7.3525 - val_nmae: 0.0835\n",
      "\n",
      "Epoch 00014: val_nmae did not improve from 0.07067\n",
      "Epoch 15/2000\n",
      "67/67 [==============================] - 66s 987ms/step - loss: 6.0460 - nmae: 0.0769 - val_loss: 8.0067 - val_nmae: 0.0946\n",
      "\n",
      "Epoch 00015: val_nmae did not improve from 0.07067\n",
      "Epoch 16/2000\n",
      "67/67 [==============================] - 66s 988ms/step - loss: 4.8289 - nmae: 0.0690 - val_loss: 7.8901 - val_nmae: 0.0943\n",
      "\n",
      "Epoch 00016: val_nmae did not improve from 0.07067\n",
      "Epoch 17/2000\n",
      "67/67 [==============================] - 66s 987ms/step - loss: 5.8763 - nmae: 0.0765 - val_loss: 6.2597 - val_nmae: 0.0760\n",
      "\n",
      "Epoch 00017: val_nmae did not improve from 0.07067\n",
      "Epoch 00017: early stopping\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'history_dict2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [25]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     36\u001b[0m model\u001b[38;5;241m.\u001b[39mload_weights(ckpt_path)\n\u001b[1;32m     37\u001b[0m model\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./Model_Save/best_params2_finetune_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m_2.h5\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(freeze_rate))\n\u001b[0;32m---> 39\u001b[0m \u001b[43mhistory_dict2\u001b[49m[\u001b[38;5;28mstr\u001b[39m(freeze_rate)] \u001b[38;5;241m=\u001b[39m history_fine\n\u001b[1;32m     41\u001b[0m fig \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m     42\u001b[0m ax1 \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39madd_subplot(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'history_dict2' is not defined"
     ]
    }
   ],
   "source": [
    "for freeze_rate in [0.9, 0.7, 0.5, 0.3] :\n",
    "    model.load_weights('./Best_Check_Point/best_params2_top_layers.ckpt')\n",
    "    \n",
    "    base_model.trainable=True\n",
    "    fine_tune_at = int(len(base_model.layers) * freeze_rate)\n",
    "    for layer in base_model.layers[:fine_tune_at] :\n",
    "        layer.trainable = False    \n",
    "        \n",
    "    model.compile(optimizer=Adam(0.0001),\n",
    "            loss='mae',\n",
    "            metrics=[nmae])\n",
    "\n",
    "    es = EarlyStopping(monitor='loss',\n",
    "                    mode='auto',\n",
    "                    patience=5,\n",
    "                    verbose=1)\n",
    "\n",
    "    ckpt_path = './Best_Check_Point/best_params2_finetune_{}.ckpt'.format(freeze_rate)\n",
    "    checkpointer = ModelCheckpoint(filepath=ckpt_path,\n",
    "                                  monitor='val_nmae',\n",
    "                                  save_weights_only = True,\n",
    "                                  save_best_only= True,\n",
    "                                  verbose=1)\n",
    "\n",
    "    STEPS_PER_EPOCH = len(train_img) // BATCH_SIZE\n",
    "    VAL_STEPS = len(valid_img) // BATCH_SIZE\n",
    "\n",
    "    history_fine = model.fit(train_generator, epochs=2000,\n",
    "                      steps_per_epoch=STEPS_PER_EPOCH,\n",
    "                      validation_data=valid_generator,\n",
    "                      validation_steps = VAL_STEPS,\n",
    "                      callbacks=[checkpointer, es],\n",
    "                      verbose=1)\n",
    "                             \n",
    "    model.save('./Model_Save/best_params2_finetune_{}.h5'.format(freeze_rate))\n",
    "    model.load_weights(ckpt_path)\n",
    "    model.save('./Model_Save/best_params2_finetune_{}_2.h5'.format(freeze_rate))\n",
    "    \n",
    "    history_dict2[str(freeze_rate)] = history_fine\n",
    "    \n",
    "    fig = plt.figure(figsize=(10,6))\n",
    "    ax1 = fig.add_subplot(1,2,1)\n",
    "    ax1.plot(train_loss + history_fine.history['loss'], label='Training Loss')\n",
    "    ax1.plot(valid_loss + history_fine.history['val_loss'], label='Validation Loss')\n",
    "    ax1.legend(loc='upper right')\n",
    "    ax1.set_title('MAE : inception_v2_resnet 0.01 adam 0.2 finetune-{}'.format(freeze_rate))\n",
    "\n",
    "    ax2 = fig.add_subplot(1,2,2)\n",
    "    ax2.plot(train_nmae + history_fine.history['nmae'], label='Training NMAE')\n",
    "    ax2.plot(valid_nmae + history_fine.history['val_nmae'], label='Validation NMAE')\n",
    "    ax2.legend(loc='upper right')\n",
    "    ax2.set_title('NMAE : inception_v2_resnet 0.01 adam 0.2 finetune-{}'.format(freeze_rate))\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d4fdbd8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f77c8b3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e8fff0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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