googleNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0255d37-8d63-4018-aa5a-34407e26b8f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-07 17:56:06.125998: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications import InceptionV3\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 设置图像的输入尺寸和类别数\n",
    "input_shape = (128, 128, 3)  # 输入图像的尺寸\n",
    "num_classes = 3  # 类别数\n",
    "\n",
    "\n",
    "train_dirs = ['/root/images/ED1', '/root/images/ED2_GaussianNoisy',\n",
    "             '/root/images/ED3_NotBalancedGan', '/root/images/ED4_BalancedGan',\n",
    "             '/root/images/ED5_ColorJittering', '/root/images/ED6_MirrorHorizontally',\n",
    "             '/root/images/ED7_GaussianBlur', '/root/images/ED8_MixUp']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2c602d2-585b-4ec3-8ad1-398031efa67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制混淆矩阵\n",
    "def plot_confusion_matrix(model, test_generator, dataset_name):\n",
    "    # 评估模型在测试集上的准确率\n",
    "    test_loss, test_acc = model.evaluate(test_generator, verbose=2)\n",
    "    print(\"Test Accuracy: {:.2f}%\".format(test_acc * 100))\n",
    "\n",
    "    # 预测测试集的类别\n",
    "    y_true = test_generator.classes\n",
    "    y_pred = model.predict(test_generator)\n",
    "    y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "    # 计算混淆矩阵\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "    # 绘制混淆矩阵图像\n",
    "    class_names = list(test_generator.class_indices.keys())\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "    # plt.title(\"Confusion Matrix\")\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(class_names))\n",
    "    plt.xticks(tick_marks, class_names, rotation=45)\n",
    "    plt.yticks(tick_marks, class_names)\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # 在每个格子中添加对应的数字\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            plt.text(j, i, format(cm[i, j], 'd'),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    save_dir = 'Result_img'\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "\n",
    "    # 保存混淆矩阵图像\n",
    "    plt.savefig(os.path.join(save_dir, dataset_name + 'confusion_matrix.png'), dpi=600)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ab55f3f6-af49-4210-9474-20e79aa2c05f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-07 17:56:08.011317: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2023-06-07 17:56:08.092838: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:ce:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6\n",
      "coreClock: 1.695GHz coreCount: 82 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 871.81GiB/s\n",
      "2023-06-07 17:56:08.092885: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2023-06-07 17:56:08.097224: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2023-06-07 17:56:08.097356: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
      "2023-06-07 17:56:08.098621: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10\n",
      "2023-06-07 17:56:08.099011: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10\n",
      "2023-06-07 17:56:08.100006: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11\n",
      "2023-06-07 17:56:08.100907: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11\n",
      "2023-06-07 17:56:08.101112: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2023-06-07 17:56:08.102037: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2023-06-07 17:56:08.102877: 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:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-06-07 17:56:08.120945: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:ce:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6\n",
      "coreClock: 1.695GHz coreCount: 82 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 871.81GiB/s\n",
      "2023-06-07 17:56:08.122741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2023-06-07 17:56:08.122861: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2023-06-07 17:56:09.092120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2023-06-07 17:56:09.092166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 \n",
      "2023-06-07 17:56:09.092175: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N \n",
      "2023-06-07 17:56:09.094788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22308 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:ce:00.0, compute capability: 8.6)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inception_v3 (Functional)    (None, 2, 2, 2048)        21802784  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               524544    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 3)                 771       \n",
      "=================================================================\n",
      "Total params: 22,328,099\n",
      "Trainable params: 525,315\n",
      "Non-trainable params: 21,802,784\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# 加载预训练的 GoogleNet 模型，不包括顶部的全连接层\n",
    "base_model = InceptionV3(include_top=False, weights='imagenet', input_shape=input_shape)\n",
    "\n",
    "# 冻结预训练模型的权重\n",
    "base_model.trainable = False\n",
    "\n",
    "# 添加自定义的顶部全连接层\n",
    "model = tf.keras.Sequential([\n",
    "    base_model,\n",
    "    tf.keras.layers.GlobalAveragePooling2D(),\n",
    "    tf.keras.layers.Dense(256, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.5),\n",
    "    tf.keras.layers.Dense(num_classes, activation='softmax')\n",
    "])\n",
    "\n",
    "# 编译模型\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ea24b8f-7c52-40d9-bd05-0eb87c6ad896",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2400 images belonging to 3 classes.\n",
      "Found 600 images belonging to 3 classes.\n",
      "Found 750 images belonging to 3 classes.\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-07 17:56:12.215080: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2023-06-07 17:56:12.243454: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2600000000 Hz\n",
      "2023-06-07 17:56:16.024437: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2023-06-07 17:56:16.820505: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8101\n",
      "2023-06-07 17:56:17.732482: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2023-06-07 17:56:18.385221: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 5/75 [=>............................] - ETA: 2s - loss: 2.9463 - accuracy: 0.4437"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-07 17:56:18.732759: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75/75 [==============================] - 11s 56ms/step - loss: 0.9982 - accuracy: 0.6717 - val_loss: 0.5455 - val_accuracy: 0.7900\n",
      "Epoch 2/50\n",
      "75/75 [==============================] - 3s 35ms/step - loss: 0.4948 - accuracy: 0.8129 - val_loss: 0.4449 - val_accuracy: 0.8383\n",
      "Epoch 3/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.4002 - accuracy: 0.8604 - val_loss: 0.4006 - val_accuracy: 0.8583\n",
      "Epoch 4/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.3190 - accuracy: 0.8863 - val_loss: 0.3488 - val_accuracy: 0.8783\n",
      "Epoch 5/50\n",
      "75/75 [==============================] - 3s 37ms/step - loss: 0.2531 - accuracy: 0.9117 - val_loss: 0.3055 - val_accuracy: 0.9133\n",
      "Epoch 6/50\n",
      "75/75 [==============================] - 3s 37ms/step - loss: 0.2264 - accuracy: 0.9217 - val_loss: 0.2986 - val_accuracy: 0.9050\n",
      "Epoch 7/50\n",
      "75/75 [==============================] - 3s 37ms/step - loss: 0.2068 - accuracy: 0.9283 - val_loss: 0.3185 - val_accuracy: 0.9117\n",
      "Epoch 8/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.1731 - accuracy: 0.9367 - val_loss: 0.2708 - val_accuracy: 0.9250\n",
      "Epoch 9/50\n",
      "75/75 [==============================] - 3s 38ms/step - loss: 0.1385 - accuracy: 0.9500 - val_loss: 0.2748 - val_accuracy: 0.9400\n",
      "Epoch 10/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.1336 - accuracy: 0.9554 - val_loss: 0.2504 - val_accuracy: 0.9350\n",
      "Epoch 11/50\n",
      "75/75 [==============================] - 3s 38ms/step - loss: 0.1141 - accuracy: 0.9604 - val_loss: 0.2656 - val_accuracy: 0.9450\n",
      "Epoch 12/50\n",
      "75/75 [==============================] - 3s 44ms/step - loss: 0.1081 - accuracy: 0.9613 - val_loss: 0.2504 - val_accuracy: 0.9550\n",
      "Epoch 13/50\n",
      "75/75 [==============================] - 3s 42ms/step - loss: 0.1069 - accuracy: 0.9617 - val_loss: 0.2412 - val_accuracy: 0.9500\n",
      "Epoch 14/50\n",
      "75/75 [==============================] - 3s 44ms/step - loss: 0.0910 - accuracy: 0.9646 - val_loss: 0.2906 - val_accuracy: 0.9500\n",
      "Epoch 15/50\n",
      "75/75 [==============================] - 3s 43ms/step - loss: 0.0890 - accuracy: 0.9692 - val_loss: 0.2819 - val_accuracy: 0.9383\n",
      "Epoch 16/50\n",
      "75/75 [==============================] - 3s 40ms/step - loss: 0.0871 - accuracy: 0.9696 - val_loss: 0.2643 - val_accuracy: 0.9483\n",
      "Epoch 17/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.0709 - accuracy: 0.9754 - val_loss: 0.3226 - val_accuracy: 0.9433\n",
      "Epoch 18/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.0750 - accuracy: 0.9737 - val_loss: 0.2467 - val_accuracy: 0.9467\n",
      "Epoch 19/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.0581 - accuracy: 0.9800 - val_loss: 0.2855 - val_accuracy: 0.9483\n",
      "Epoch 20/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.0691 - accuracy: 0.9737 - val_loss: 0.2828 - val_accuracy: 0.9517\n",
      "Epoch 21/50\n",
      "75/75 [==============================] - 3s 37ms/step - loss: 0.0484 - accuracy: 0.9829 - val_loss: 0.3152 - val_accuracy: 0.9450\n",
      "Epoch 22/50\n",
      "75/75 [==============================] - 3s 39ms/step - loss: 0.0558 - accuracy: 0.9808 - val_loss: 0.2956 - val_accuracy: 0.9583\n",
      "Epoch 23/50\n",
      "75/75 [==============================] - 3s 45ms/step - loss: 0.0423 - accuracy: 0.9846 - val_loss: 0.3359 - val_accuracy: 0.9417\n",
      "Epoch 24/50\n",
      "75/75 [==============================] - 3s 43ms/step - loss: 0.0446 - accuracy: 0.9862 - val_loss: 0.3380 - val_accuracy: 0.9467\n",
      "Epoch 25/50\n",
      "75/75 [==============================] - 3s 45ms/step - loss: 0.0764 - accuracy: 0.9762 - val_loss: 0.3848 - val_accuracy: 0.9533\n",
      "Epoch 26/50\n",
      "75/75 [==============================] - 3s 44ms/step - loss: 0.0755 - accuracy: 0.9725 - val_loss: 0.3443 - val_accuracy: 0.9517\n",
      "Epoch 27/50\n",
      "75/75 [==============================] - 3s 44ms/step - loss: 0.0802 - accuracy: 0.9721 - val_loss: 0.3106 - val_accuracy: 0.9517\n",
      "Epoch 28/50\n",
      "75/75 [==============================] - 3s 45ms/step - loss: 0.0947 - accuracy: 0.9683 - val_loss: 0.3287 - val_accuracy: 0.9400\n",
      "Epoch 29/50\n",
      "75/75 [==============================] - 3s 45ms/step - loss: 0.0703 - accuracy: 0.9762 - val_loss: 0.3088 - val_accuracy: 0.9383\n",
      "Epoch 30/50\n",
      "75/75 [==============================] - 3s 46ms/step - loss: 0.0782 - accuracy: 0.9704 - val_loss: 0.2946 - val_accuracy: 0.9517\n",
      "Epoch 31/50\n",
      "75/75 [==============================] - 3s 46ms/step - loss: 0.0522 - accuracy: 0.9800 - val_loss: 0.3165 - val_accuracy: 0.9517\n",
      "Epoch 32/50\n",
      "75/75 [==============================] - 3s 45ms/step - loss: 0.0515 - accuracy: 0.9837 - val_loss: 0.3031 - val_accuracy: 0.9433\n",
      "Epoch 33/50\n",
      "75/75 [==============================] - 3s 44ms/step - loss: 0.0477 - accuracy: 0.9825 - val_loss: 0.4144 - val_accuracy: 0.9433\n",
      "Epoch 34/50\n",
      "75/75 [==============================] - 3s 41ms/step - loss: 0.0507 - accuracy: 0.9821 - val_loss: 0.3973 - val_accuracy: 0.9467\n",
      "Epoch 35/50\n",
      "75/75 [==============================] - 3s 35ms/step - loss: 0.0447 - accuracy: 0.9837 - val_loss: 0.4130 - val_accuracy: 0.9467\n",
      "Epoch 36/50\n",
      "75/75 [==============================] - 3s 40ms/step - loss: 0.0460 - accuracy: 0.9850 - val_loss: 0.3412 - val_accuracy: 0.9450\n",
      "Epoch 37/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.0367 - accuracy: 0.9867 - val_loss: 0.4109 - val_accuracy: 0.9450\n",
      "Epoch 38/50\n",
      "75/75 [==============================] - 3s 40ms/step - loss: 0.0437 - accuracy: 0.9850 - val_loss: 0.3830 - val_accuracy: 0.9433\n",
      "Epoch 39/50\n",
      "75/75 [==============================] - 3s 35ms/step - loss: 0.0649 - accuracy: 0.9783 - val_loss: 0.2765 - val_accuracy: 0.9533\n",
      "Epoch 40/50\n",
      "75/75 [==============================] - 3s 35ms/step - loss: 0.0532 - accuracy: 0.9837 - val_loss: 0.2898 - val_accuracy: 0.9467\n",
      "Epoch 41/50\n",
      "75/75 [==============================] - 3s 35ms/step - loss: 0.0689 - accuracy: 0.9787 - val_loss: 0.3824 - val_accuracy: 0.9483\n",
      "Epoch 42/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.0828 - accuracy: 0.9667 - val_loss: 0.3409 - val_accuracy: 0.9417\n",
      "Epoch 43/50\n",
      "75/75 [==============================] - 3s 37ms/step - loss: 0.0749 - accuracy: 0.9725 - val_loss: 0.3571 - val_accuracy: 0.9417\n",
      "Epoch 44/50\n",
      "75/75 [==============================] - 3s 36ms/step - loss: 0.0564 - accuracy: 0.9783 - val_loss: 0.3619 - val_accuracy: 0.9500\n",
      "Epoch 45/50\n",
      "75/75 [==============================] - 3s 37ms/step - loss: 0.0621 - accuracy: 0.9783 - val_loss: 0.4311 - val_accuracy: 0.9367\n",
      "Epoch 46/50\n",
      "75/75 [==============================] - 3s 38ms/step - loss: 0.0556 - accuracy: 0.9775 - val_loss: 0.3936 - val_accuracy: 0.9433\n",
      "Epoch 47/50\n",
      "75/75 [==============================] - 3s 41ms/step - loss: 0.0612 - accuracy: 0.9771 - val_loss: 0.4004 - val_accuracy: 0.9450\n",
      "Epoch 48/50\n",
      "75/75 [==============================] - 3s 39ms/step - loss: 0.0639 - accuracy: 0.9754 - val_loss: 0.3827 - val_accuracy: 0.9500\n",
      "Epoch 49/50\n",
      "75/75 [==============================] - 3s 37ms/step - loss: 0.0644 - accuracy: 0.9721 - val_loss: 0.3550 - val_accuracy: 0.9433\n",
      "Epoch 50/50\n",
      "75/75 [==============================] - 3s 38ms/step - loss: 0.0524 - accuracy: 0.9800 - val_loss: 0.3633 - val_accuracy: 0.9517\n",
      "24/24 - 1s - loss: 0.6510 - accuracy: 0.9307\n",
      "Test Accuracy: 93.07%\n",
      "-------- ED1 -------- Over\n",
      "Found 4800 images belonging to 3 classes.\n",
      "Found 1200 images belonging to 3 classes.\n",
      "Found 750 images belonging to 3 classes.\n",
      "Epoch 1/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.5639 - accuracy: 0.7831 - val_loss: 0.4584 - val_accuracy: 0.8492\n",
      "Epoch 2/50\n",
      "150/150 [==============================] - 7s 43ms/step - loss: 0.4238 - accuracy: 0.8352 - val_loss: 0.4791 - val_accuracy: 0.8392\n",
      "Epoch 3/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.3755 - accuracy: 0.8562 - val_loss: 0.4340 - val_accuracy: 0.8567\n",
      "Epoch 4/50\n",
      "150/150 [==============================] - 7s 49ms/step - loss: 0.3247 - accuracy: 0.8729 - val_loss: 0.4427 - val_accuracy: 0.8575\n",
      "Epoch 5/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.3026 - accuracy: 0.8765 - val_loss: 0.4146 - val_accuracy: 0.8608\n",
      "Epoch 6/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.2646 - accuracy: 0.8942 - val_loss: 0.4148 - val_accuracy: 0.8658\n",
      "Epoch 7/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.2567 - accuracy: 0.8965 - val_loss: 0.4461 - val_accuracy: 0.8675\n",
      "Epoch 8/50\n",
      "150/150 [==============================] - 7s 47ms/step - loss: 0.2286 - accuracy: 0.9096 - val_loss: 0.4596 - val_accuracy: 0.8675\n",
      "Epoch 9/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.2072 - accuracy: 0.9192 - val_loss: 0.4322 - val_accuracy: 0.8750\n",
      "Epoch 10/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1926 - accuracy: 0.9165 - val_loss: 0.4568 - val_accuracy: 0.8700\n",
      "Epoch 11/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.1857 - accuracy: 0.9229 - val_loss: 0.4638 - val_accuracy: 0.8717\n",
      "Epoch 12/50\n",
      "150/150 [==============================] - 8s 50ms/step - loss: 0.1684 - accuracy: 0.9285 - val_loss: 0.4741 - val_accuracy: 0.8858\n",
      "Epoch 13/50\n",
      "150/150 [==============================] - 8s 51ms/step - loss: 0.1558 - accuracy: 0.9335 - val_loss: 0.4778 - val_accuracy: 0.8808\n",
      "Epoch 14/50\n",
      "150/150 [==============================] - 7s 48ms/step - loss: 0.1581 - accuracy: 0.9319 - val_loss: 0.5275 - val_accuracy: 0.8867\n",
      "Epoch 15/50\n",
      "150/150 [==============================] - 7s 47ms/step - loss: 0.1512 - accuracy: 0.9392 - val_loss: 0.4924 - val_accuracy: 0.8733\n",
      "Epoch 16/50\n",
      "150/150 [==============================] - 8s 50ms/step - loss: 0.1537 - accuracy: 0.9362 - val_loss: 0.4952 - val_accuracy: 0.8742\n",
      "Epoch 17/50\n",
      "150/150 [==============================] - 8s 50ms/step - loss: 0.1330 - accuracy: 0.9442 - val_loss: 0.4682 - val_accuracy: 0.8858\n",
      "Epoch 18/50\n",
      "150/150 [==============================] - 7s 50ms/step - loss: 0.1359 - accuracy: 0.9429 - val_loss: 0.5473 - val_accuracy: 0.8750\n",
      "Epoch 19/50\n",
      "150/150 [==============================] - 7s 50ms/step - loss: 0.1253 - accuracy: 0.9490 - val_loss: 0.5480 - val_accuracy: 0.8725\n",
      "Epoch 20/50\n",
      "150/150 [==============================] - 7s 49ms/step - loss: 0.1293 - accuracy: 0.9460 - val_loss: 0.5284 - val_accuracy: 0.8742\n",
      "Epoch 21/50\n",
      "150/150 [==============================] - 7s 43ms/step - loss: 0.1185 - accuracy: 0.9556 - val_loss: 0.5304 - val_accuracy: 0.8875\n",
      "Epoch 22/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.1293 - accuracy: 0.9465 - val_loss: 0.5468 - val_accuracy: 0.8750\n",
      "Epoch 23/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1276 - accuracy: 0.9506 - val_loss: 0.5846 - val_accuracy: 0.8658\n",
      "Epoch 24/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1168 - accuracy: 0.9515 - val_loss: 0.5781 - val_accuracy: 0.8842\n",
      "Epoch 25/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1505 - accuracy: 0.9410 - val_loss: 0.6259 - val_accuracy: 0.8692\n",
      "Epoch 26/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1101 - accuracy: 0.9550 - val_loss: 0.5777 - val_accuracy: 0.8817\n",
      "Epoch 27/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1210 - accuracy: 0.9515 - val_loss: 0.5603 - val_accuracy: 0.8883\n",
      "Epoch 28/50\n",
      "150/150 [==============================] - 7s 47ms/step - loss: 0.1089 - accuracy: 0.9558 - val_loss: 0.5864 - val_accuracy: 0.8858\n",
      "Epoch 29/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.1043 - accuracy: 0.9608 - val_loss: 0.6175 - val_accuracy: 0.8825\n",
      "Epoch 30/50\n",
      "150/150 [==============================] - 7s 47ms/step - loss: 0.1026 - accuracy: 0.9565 - val_loss: 0.5667 - val_accuracy: 0.8850\n",
      "Epoch 31/50\n",
      "150/150 [==============================] - 7s 49ms/step - loss: 0.1056 - accuracy: 0.9558 - val_loss: 0.7005 - val_accuracy: 0.8842\n",
      "Epoch 32/50\n",
      "150/150 [==============================] - 7s 48ms/step - loss: 0.0943 - accuracy: 0.9629 - val_loss: 0.7088 - val_accuracy: 0.8792\n",
      "Epoch 33/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.1112 - accuracy: 0.9565 - val_loss: 0.5196 - val_accuracy: 0.8917\n",
      "Epoch 34/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.1203 - accuracy: 0.9521 - val_loss: 0.6523 - val_accuracy: 0.8758\n",
      "Epoch 35/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.1022 - accuracy: 0.9577 - val_loss: 0.6758 - val_accuracy: 0.8800\n",
      "Epoch 36/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.1084 - accuracy: 0.9550 - val_loss: 0.6689 - val_accuracy: 0.8767\n",
      "Epoch 37/50\n",
      "150/150 [==============================] - 7s 43ms/step - loss: 0.1032 - accuracy: 0.9556 - val_loss: 0.6291 - val_accuracy: 0.8842\n",
      "Epoch 38/50\n",
      "150/150 [==============================] - 7s 43ms/step - loss: 0.0905 - accuracy: 0.9646 - val_loss: 0.6542 - val_accuracy: 0.8808\n",
      "Epoch 39/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.0865 - accuracy: 0.9669 - val_loss: 0.6446 - val_accuracy: 0.8808\n",
      "Epoch 40/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.0856 - accuracy: 0.9665 - val_loss: 0.6530 - val_accuracy: 0.8908\n",
      "Epoch 41/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.0822 - accuracy: 0.9671 - val_loss: 0.6681 - val_accuracy: 0.8817\n",
      "Epoch 42/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.1011 - accuracy: 0.9579 - val_loss: 0.6859 - val_accuracy: 0.8750\n",
      "Epoch 43/50\n",
      "150/150 [==============================] - 7s 50ms/step - loss: 0.0842 - accuracy: 0.9658 - val_loss: 0.6854 - val_accuracy: 0.8858\n",
      "Epoch 44/50\n",
      "150/150 [==============================] - 7s 49ms/step - loss: 0.0866 - accuracy: 0.9598 - val_loss: 0.6941 - val_accuracy: 0.8808\n",
      "Epoch 45/50\n",
      "150/150 [==============================] - 8s 50ms/step - loss: 0.0999 - accuracy: 0.9604 - val_loss: 0.6936 - val_accuracy: 0.8925\n",
      "Epoch 46/50\n",
      "150/150 [==============================] - 8s 50ms/step - loss: 0.0914 - accuracy: 0.9635 - val_loss: 0.7700 - val_accuracy: 0.8833\n",
      "Epoch 47/50\n",
      "150/150 [==============================] - 7s 49ms/step - loss: 0.0906 - accuracy: 0.9602 - val_loss: 0.6456 - val_accuracy: 0.8858\n",
      "Epoch 48/50\n",
      "150/150 [==============================] - 7s 49ms/step - loss: 0.0901 - accuracy: 0.9663 - val_loss: 0.6927 - val_accuracy: 0.8867\n",
      "Epoch 49/50\n",
      "150/150 [==============================] - 7s 48ms/step - loss: 0.0708 - accuracy: 0.9700 - val_loss: 0.7941 - val_accuracy: 0.8817\n",
      "Epoch 50/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.0824 - accuracy: 0.9675 - val_loss: 0.6736 - val_accuracy: 0.8875\n",
      "24/24 - 1s - loss: 0.8896 - accuracy: 0.9467\n",
      "Test Accuracy: 94.67%\n",
      "-------- ED2_GaussianNoisy -------- Over\n",
      "Found 4800 images belonging to 3 classes.\n",
      "Found 1200 images belonging to 3 classes.\n",
      "Found 750 images belonging to 3 classes.\n",
      "Epoch 1/50\n",
      "150/150 [==============================] - 5s 36ms/step - loss: 0.7666 - accuracy: 0.6596 - val_loss: 0.8538 - val_accuracy: 0.5533\n",
      "Epoch 2/50\n",
      "150/150 [==============================] - 6s 40ms/step - loss: 0.5969 - accuracy: 0.7221 - val_loss: 0.7532 - val_accuracy: 0.6542\n",
      "Epoch 3/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.5366 - accuracy: 0.7556 - val_loss: 0.6309 - val_accuracy: 0.7100\n",
      "Epoch 4/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.4585 - accuracy: 0.7902 - val_loss: 0.5968 - val_accuracy: 0.7383\n",
      "Epoch 5/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.4013 - accuracy: 0.8167 - val_loss: 0.5653 - val_accuracy: 0.7692\n",
      "Epoch 6/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.3856 - accuracy: 0.8242 - val_loss: 0.6434 - val_accuracy: 0.7267\n",
      "Epoch 7/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.3464 - accuracy: 0.8398 - val_loss: 0.5602 - val_accuracy: 0.7967\n",
      "Epoch 8/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.3197 - accuracy: 0.8535 - val_loss: 0.4569 - val_accuracy: 0.8283\n",
      "Epoch 9/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.3087 - accuracy: 0.8673 - val_loss: 0.4505 - val_accuracy: 0.8283\n",
      "Epoch 10/50\n",
      "150/150 [==============================] - 6s 40ms/step - loss: 0.2797 - accuracy: 0.8752 - val_loss: 0.4778 - val_accuracy: 0.8242\n",
      "Epoch 11/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.2556 - accuracy: 0.8885 - val_loss: 0.5201 - val_accuracy: 0.8025\n",
      "Epoch 12/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.2538 - accuracy: 0.8821 - val_loss: 0.5014 - val_accuracy: 0.8333\n",
      "Epoch 13/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.2490 - accuracy: 0.8881 - val_loss: 0.4706 - val_accuracy: 0.83250.245\n",
      "Epoch 14/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.2235 - accuracy: 0.8971 - val_loss: 0.5093 - val_accuracy: 0.8250\n",
      "Epoch 15/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.2154 - accuracy: 0.9010 - val_loss: 0.4714 - val_accuracy: 0.8417\n",
      "Epoch 16/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.2018 - accuracy: 0.9085 - val_loss: 0.5684 - val_accuracy: 0.8233\n",
      "Epoch 17/50\n",
      "150/150 [==============================] - 6s 37ms/step - loss: 0.1959 - accuracy: 0.9133 - val_loss: 0.3997 - val_accuracy: 0.8775\n",
      "Epoch 18/50\n",
      "150/150 [==============================] - 5s 36ms/step - loss: 0.1970 - accuracy: 0.9069 - val_loss: 0.5691 - val_accuracy: 0.8217\n",
      "Epoch 19/50\n",
      "150/150 [==============================] - 5s 35ms/step - loss: 0.1947 - accuracy: 0.9123 - val_loss: 0.4456 - val_accuracy: 0.8533\n",
      "Epoch 20/50\n",
      "150/150 [==============================] - 5s 35ms/step - loss: 0.1818 - accuracy: 0.9165 - val_loss: 0.4313 - val_accuracy: 0.8608\n",
      "Epoch 21/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.1805 - accuracy: 0.9196 - val_loss: 0.4489 - val_accuracy: 0.8608\n",
      "Epoch 22/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.1723 - accuracy: 0.9219 - val_loss: 0.4510 - val_accuracy: 0.8642\n",
      "Epoch 23/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1762 - accuracy: 0.9198 - val_loss: 0.4494 - val_accuracy: 0.8650\n",
      "Epoch 24/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.1686 - accuracy: 0.9244 - val_loss: 0.4952 - val_accuracy: 0.8592\n",
      "Epoch 25/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.1506 - accuracy: 0.9323 - val_loss: 0.4765 - val_accuracy: 0.8608\n",
      "Epoch 26/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.1556 - accuracy: 0.9298 - val_loss: 0.4606 - val_accuracy: 0.8658\n",
      "Epoch 27/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.1441 - accuracy: 0.9375 - val_loss: 0.5319 - val_accuracy: 0.8550\n",
      "Epoch 28/50\n",
      "150/150 [==============================] - 6s 37ms/step - loss: 0.1399 - accuracy: 0.9400 - val_loss: 0.5647 - val_accuracy: 0.8417\n",
      "Epoch 29/50\n",
      "150/150 [==============================] - 5s 36ms/step - loss: 0.1643 - accuracy: 0.9242 - val_loss: 0.5956 - val_accuracy: 0.8483\n",
      "Epoch 30/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.1305 - accuracy: 0.9404 - val_loss: 0.5633 - val_accuracy: 0.8492\n",
      "Epoch 31/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.1371 - accuracy: 0.9402 - val_loss: 0.5729 - val_accuracy: 0.8483\n",
      "Epoch 32/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1554 - accuracy: 0.9302 - val_loss: 0.4661 - val_accuracy: 0.8625\n",
      "Epoch 33/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.1347 - accuracy: 0.9406 - val_loss: 0.4575 - val_accuracy: 0.8775\n",
      "Epoch 34/50\n",
      "150/150 [==============================] - 7s 43ms/step - loss: 0.1232 - accuracy: 0.9435 - val_loss: 0.5112 - val_accuracy: 0.8542\n",
      "Epoch 35/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1125 - accuracy: 0.9506 - val_loss: 0.5689 - val_accuracy: 0.8708\n",
      "Epoch 36/50\n",
      "150/150 [==============================] - 6s 37ms/step - loss: 0.1121 - accuracy: 0.9485 - val_loss: 0.5101 - val_accuracy: 0.8775\n",
      "Epoch 37/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.1142 - accuracy: 0.9460 - val_loss: 0.5080 - val_accuracy: 0.8883\n",
      "Epoch 38/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1213 - accuracy: 0.9479 - val_loss: 0.6064 - val_accuracy: 0.8567\n",
      "Epoch 39/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1112 - accuracy: 0.9450 - val_loss: 0.5308 - val_accuracy: 0.8758\n",
      "Epoch 40/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.1220 - accuracy: 0.9450 - val_loss: 0.5146 - val_accuracy: 0.8808\n",
      "Epoch 41/50\n",
      "150/150 [==============================] - 7s 43ms/step - loss: 0.1138 - accuracy: 0.9498 - val_loss: 0.7300 - val_accuracy: 0.8442\n",
      "Epoch 42/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1192 - accuracy: 0.9452 - val_loss: 0.5082 - val_accuracy: 0.8742\n",
      "Epoch 43/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1179 - accuracy: 0.9458 - val_loss: 0.5058 - val_accuracy: 0.8683\n",
      "Epoch 44/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1068 - accuracy: 0.9560 - val_loss: 0.4574 - val_accuracy: 0.8867\n",
      "Epoch 45/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1326 - accuracy: 0.9448 - val_loss: 0.5682 - val_accuracy: 0.8642\n",
      "Epoch 46/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.1184 - accuracy: 0.9485 - val_loss: 0.5762 - val_accuracy: 0.8842\n",
      "Epoch 47/50\n",
      "150/150 [==============================] - 5s 36ms/step - loss: 0.1227 - accuracy: 0.9390 - val_loss: 0.5701 - val_accuracy: 0.8792\n",
      "Epoch 48/50\n",
      "150/150 [==============================] - 6s 38ms/step - loss: 0.1210 - accuracy: 0.9465 - val_loss: 0.7426 - val_accuracy: 0.8475\n",
      "Epoch 49/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1081 - accuracy: 0.9494 - val_loss: 0.7356 - val_accuracy: 0.8517\n",
      "Epoch 50/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1053 - accuracy: 0.9510 - val_loss: 0.6601 - val_accuracy: 0.8700\n",
      "24/24 - 1s - loss: 0.6985 - accuracy: 0.9560\n",
      "Test Accuracy: 95.60%\n",
      "-------- ED3_NotBalancedGan -------- Over\n",
      "Found 4800 images belonging to 3 classes.\n",
      "Found 1200 images belonging to 3 classes.\n",
      "Found 750 images belonging to 3 classes.\n",
      "Epoch 1/50\n",
      "150/150 [==============================] - 6s 38ms/step - loss: 0.4074 - accuracy: 0.8546 - val_loss: 0.4298 - val_accuracy: 0.8108\n",
      "Epoch 2/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.3461 - accuracy: 0.8619 - val_loss: 0.4147 - val_accuracy: 0.8142\n",
      "Epoch 3/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.2897 - accuracy: 0.8852 - val_loss: 0.4431 - val_accuracy: 0.8175\n",
      "Epoch 4/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.2695 - accuracy: 0.8875 - val_loss: 0.4705 - val_accuracy: 0.7950\n",
      "Epoch 5/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.2454 - accuracy: 0.8919 - val_loss: 0.3950 - val_accuracy: 0.8408\n",
      "Epoch 6/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.2264 - accuracy: 0.9062 - val_loss: 0.4829 - val_accuracy: 0.7958\n",
      "Epoch 7/50\n",
      "150/150 [==============================] - 7s 43ms/step - loss: 0.2047 - accuracy: 0.9150 - val_loss: 0.4583 - val_accuracy: 0.8283\n",
      "Epoch 8/50\n",
      "150/150 [==============================] - 5s 36ms/step - loss: 0.1884 - accuracy: 0.9165 - val_loss: 0.4195 - val_accuracy: 0.8367\n",
      "Epoch 9/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1846 - accuracy: 0.9254 - val_loss: 0.4053 - val_accuracy: 0.8417\n",
      "Epoch 10/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1723 - accuracy: 0.9231 - val_loss: 0.3767 - val_accuracy: 0.8708\n",
      "Epoch 11/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1607 - accuracy: 0.9290 - val_loss: 0.4125 - val_accuracy: 0.8600\n",
      "Epoch 12/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1625 - accuracy: 0.9304 - val_loss: 0.3841 - val_accuracy: 0.8558\n",
      "Epoch 13/50\n",
      "150/150 [==============================] - 6s 43ms/step - loss: 0.1521 - accuracy: 0.9350 - val_loss: 0.3931 - val_accuracy: 0.8783\n",
      "Epoch 14/50\n",
      "150/150 [==============================] - 6s 37ms/step - loss: 0.1500 - accuracy: 0.9350 - val_loss: 0.3750 - val_accuracy: 0.9017\n",
      "Epoch 15/50\n",
      "150/150 [==============================] - 6s 40ms/step - loss: 0.1535 - accuracy: 0.9333 - val_loss: 0.3711 - val_accuracy: 0.8825\n",
      "Epoch 16/50\n",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.1146 - accuracy: 0.956324/24 - 1s - loss: 0.8462 - accuracy: 0.9467\n",
      "Test Accuracy: 94.67%\n",
      "-------- ED4_BalancedGan -------- Over\n",
      "Found 4800 images belonging to 3 classes.\n",
      "Found 1200 images belonging to 3 classes.\n",
      "Found 750 images belonging to 3 classes.\n",
      "Epoch 1/50\n",
      "150/150 [==============================] - 6s 38ms/step - loss: 0.3287 - accuracy: 0.9006 - val_loss: 0.1929 - val_accuracy: 0.9542\n",
      "Epoch 2/50\n",
      "150/150 [==============================] - 6s 38ms/step - loss: 0.2026 - accuracy: 0.9279 - val_loss: 0.1369 - val_accuracy: 0.9700\n",
      "Epoch 3/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.1453 - accuracy: 0.9454 - val_loss: 0.1492 - val_accuracy: 0.9808\n",
      "Epoch 4/50\n",
      "150/150 [==============================] - 6s 42ms/step - loss: 0.1214 - accuracy: 0.9527 - val_loss: 0.1322 - val_accuracy: 0.9742\n",
      "Epoch 5/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.0941 - accuracy: 0.9631 - val_loss: 0.1574 - val_accuracy: 0.9758\n",
      "Epoch 6/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.0824 - accuracy: 0.9683 - val_loss: 0.1346 - val_accuracy: 0.9792\n",
      "Epoch 7/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.0912 - accuracy: 0.9648 - val_loss: 0.1353 - val_accuracy: 0.9792\n",
      "Epoch 8/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.0700 - accuracy: 0.9717 - val_loss: 0.1442 - val_accuracy: 0.9825\n",
      "Epoch 9/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0659 - accuracy: 0.9744 - val_loss: 0.1479 - val_accuracy: 0.9833\n",
      "Epoch 10/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0690 - accuracy: 0.9706 - val_loss: 0.1425 - val_accuracy: 0.9833\n",
      "Epoch 11/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0699 - accuracy: 0.9694 - val_loss: 0.1503 - val_accuracy: 0.9850\n",
      "Epoch 12/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0551 - accuracy: 0.9758 - val_loss: 0.1600 - val_accuracy: 0.9825\n",
      "Epoch 13/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0586 - accuracy: 0.9771 - val_loss: 0.1552 - val_accuracy: 0.9808\n",
      "Epoch 14/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0519 - accuracy: 0.9800 - val_loss: 0.1355 - val_accuracy: 0.9858\n",
      "Epoch 15/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0531 - accuracy: 0.9775 - val_loss: 0.1561 - val_accuracy: 0.9842\n",
      "Epoch 16/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0530 - accuracy: 0.9794 - val_loss: 0.1855 - val_accuracy: 0.9775\n",
      "Epoch 17/50\n",
      "150/150 [==============================] - 7s 47ms/step - loss: 0.0657 - accuracy: 0.9756 - val_loss: 0.1811 - val_accuracy: 0.9817\n",
      "Epoch 18/50\n",
      "150/150 [==============================] - 7s 47ms/step - loss: 0.0625 - accuracy: 0.9744 - val_loss: 0.1719 - val_accuracy: 0.9833\n",
      "Epoch 19/50\n",
      "150/150 [==============================] - 7s 48ms/step - loss: 0.0543 - accuracy: 0.9798 - val_loss: 0.2145 - val_accuracy: 0.9758\n",
      "Epoch 20/50\n",
      "150/150 [==============================] - 7s 47ms/step - loss: 0.0481 - accuracy: 0.9810 - val_loss: 0.1883 - val_accuracy: 0.9808\n",
      "Epoch 21/50\n",
      "150/150 [==============================] - 7s 47ms/step - loss: 0.0485 - accuracy: 0.9796 - val_loss: 0.1897 - val_accuracy: 0.9825\n",
      "Epoch 22/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0521 - accuracy: 0.9810 - val_loss: 0.1986 - val_accuracy: 0.9733\n",
      "Epoch 23/50\n",
      "150/150 [==============================] - 7s 46ms/step - loss: 0.0514 - accuracy: 0.9802 - val_loss: 0.1851 - val_accuracy: 0.9783\n",
      "Epoch 24/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.0567 - accuracy: 0.9752 - val_loss: 0.1782 - val_accuracy: 0.9808\n",
      "Epoch 25/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.0416 - accuracy: 0.9817 - val_loss: 0.1819 - val_accuracy: 0.9775\n",
      "Epoch 26/50\n",
      "150/150 [==============================] - 6s 40ms/step - loss: 0.0415 - accuracy: 0.9829 - val_loss: 0.1759 - val_accuracy: 0.9800\n",
      "Epoch 27/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.0455 - accuracy: 0.9840 - val_loss: 0.2441 - val_accuracy: 0.9792\n",
      "Epoch 28/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.0386 - accuracy: 0.9812 - val_loss: 0.2355 - val_accuracy: 0.9775\n",
      "Epoch 29/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.0379 - accuracy: 0.9854 - val_loss: 0.2392 - val_accuracy: 0.9750\n",
      "Epoch 30/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.0415 - accuracy: 0.9808 - val_loss: 0.2211 - val_accuracy: 0.9783\n",
      "Epoch 31/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.0428 - accuracy: 0.9819 - val_loss: 0.2185 - val_accuracy: 0.9792\n",
      "Epoch 32/50\n",
      "150/150 [==============================] - 7s 44ms/step - loss: 0.0419 - accuracy: 0.9837 - val_loss: 0.2034 - val_accuracy: 0.9825\n",
      "Epoch 33/50\n",
      "150/150 [==============================] - 6s 39ms/step - loss: 0.0312 - accuracy: 0.9869 - val_loss: 0.2287 - val_accuracy: 0.9792\n",
      "Epoch 34/50\n",
      "150/150 [==============================] - 6s 40ms/step - loss: 0.0382 - accuracy: 0.9850 - val_loss: 0.2293 - val_accuracy: 0.9742\n",
      "Epoch 35/50\n",
      "150/150 [==============================] - 6s 41ms/step - loss: 0.0470 - accuracy: 0.9812 - val_loss: 0.2198 - val_accuracy: 0.9775\n",
      "Epoch 36/50\n",
      "150/150 [==============================] - 6s 38ms/step - loss: 0.0418 - accuracy: 0.9835 - val_loss: 0.2397 - val_accuracy: 0.9775\n",
      "Epoch 37/50\n",
      "150/150 [==============================] - 6s 40ms/step - loss: 0.0386 - accuracy: 0.9854 - val_loss: 0.2424 - val_accuracy: 0.9783\n",
      "Epoch 38/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.0410 - accuracy: 0.9835 - val_loss: 0.3282 - val_accuracy: 0.9733\n",
      "Epoch 39/50\n",
      "150/150 [==============================] - 7s 45ms/step - loss: 0.0458 - accuracy: 0.9817 - val_loss: 0.2379 - val_accuracy: 0.9725\n",
      "Epoch 40/50\n",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.0432 - accuracy: 0.9806"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_177/1907692104.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;31m# 训练模型并绘制准确率图像\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m     history = model.fit(\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0mtrain_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1181\u001b[0m                 _r=1):\n\u001b[1;32m   1182\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1184\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    915\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3021\u001b[0m       (graph_function,\n\u001b[1;32m   3022\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3023\u001b[0;31m     return graph_function._call_flat(\n\u001b[0m\u001b[1;32m   3024\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m   3025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1958\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1959\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1960\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1961\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1962\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    589\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    592\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     57\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4ogUNU3BZicPkW6puhtVx4+DPP+G888Kdo5Nz4ICrJtu1y7XXZBx0aEx+YyUOU+B89pkb3f3BBwU/aICbb2rwYNi40YKGKdgscJg8pwoPPOAG4PXt6wbVZbRwITz9NFxzDfTpk+dZDJlLLw13Dow5eeGecsQUQW+9BV995abrSGv0njHj2P79++Gmm9y0HYMG2UI+xuQ3FjhMnho3zo1nuOUWNw33hx/CqlXQsaN7TZvmSiMbN7oV8apWze6Mxpi8Zo3jJs+sWePGXTRs6EobZcq47YcPw8CBbv2J7dvdtv794YUXwpdXY0zmjeMWOEyeSEhwA/b27IHoaNc9NaPDh91iRVu3um6rWS2gZIwJPetVZcImNRVuu82tjDdliv+gAa4E8uijeZs3Y0zO2Xc6c9ImTnQjuq+8Ej755NhcUGlef92tS/HRR9ChQ1iyaIwJIquqMidlwABXSjjzTNfNNi1onHmmWwujdm3X5fauu9xKedZDypiCw6qqTFClpLhxFh995BZNGjrUDXBbvx4mTHCv776DQ4dcg/h//2tBw5jCwkocJscOHnSzvf72m1vl7v33/S99euSIm0uqeXOoXDnv82mMOTlW4jBBERcH117rJhj87DN4+OHM05Yu7Qb4GWMKFwscJmBLlsBVV7nJ+saNc20YxpiixwKHCci8eXDFFVC+vFu17pxzwp0jY0y4hLQ7roh0FpE1IhIjIs/72V9PRKaIyFIRmS4itb3tLUVkjois8Pbd5HPMIBHZKCKLvVfLUN6DgVmz3Ap7Vaq49xY0jCnaQhY4RCQC+By4EmgK9BKRphmSvQf8oKotgH7AW972Q8DtqtoM6Ax8JCKVfI57RlVbeq/FoboHA5MmuZJGrVowcybUrx/uHBljwi2UJY42QIyqblDVo8AwoGuGNE2Bqd77aWn7VXWtqq7z3m8DdgLVQ5hX48fYsW5a87POcrPX1qoV7hwZY/KDUAaOWkCsz+c4b5uvJUA37/31QAUROW4+VBFpA5QE1vts7u9VYX0oIn4X4BSR+0UkWkSi4+PjT+Y+iqQRI9zqe5GRMHWqm+LcGGMg/FOOPA10EJFFQAdgK5CStlNEagA/Anepaqq3uS/QGDgPqAI85+/EqjpQVaNUNap6dSus5MTgwW6cRvv2MHmya9swxpg0oexVtRWo4/O5trctnVcN1Q1ARMoD3VV1n/f5FGAc8KKqzvU5xpt4m0QR+Q4XfEyQjB8Pd9zh5pQaOxbKlQt3jowx+U0oSxzzgUYi0kBESgI9gTG+CUSkmoik5aEv8K23vSQwCtdw/lOGY2p4PwW4DlgewnsoUubMgR49oGVL+PVXCxrGGP9CFjhUNRl4BJgIrAJGqOoKEeknIl28ZB2BNSKyFjgN6O9tvxG4GLjTT7fbwSKyDFgGVAPeDNU9FCUrV7o5p2rVcqWOChXCnSNjTH5lc1UVcsnJbvbaBg3ckqwVK56YJjYWzj/fpZ0926U1xpjM5qoKd+O4CbFPPoEvvoDnnoM6deDZZ90Ke2l273bjNPbvdzPaWtAwxmTHAkchFhcHr77qqqAWLnQ/33/fBYd77oEFC9w4jQ0bXEN4ZGS4c2yMKQgscBRiffq4dTM+/RRatXJrZsTEuCqroUMhKsrNQTVsmM1ia4wJnAWOQur33+Hnn+Gll46vfmrQwAWSLVugf38YPhyuuy5s2TTGFEDWOF4IHT7sJiIsWdJNhV6yZLhzZIwpiGwhpyLk3/927RZTpljQMMYEn1VVFTJr1sA778Att8All4Q7N8aYwsgCRyGiCg89BGXLut5TxhgTClZVVYgMHepmsv38czjttHDnxhhTWFmJo5DYvx+efNJ1sX3ggXDnxhhTmFmJo5D48kvYsQPGjIGIiHDnxhhTmFmJoxBISnJTi1xyCbRpE+7cGGMKOytxFAIjR7rpRb74Itw5McYUBVbiKOBUXQ+qxo3hyivDnRtjTFFgJY4CbuZMN4Hhl19CMfsaYIzJA/aoKeA++ACqVYPbbgt3TowxRYUFjgJs7Vo3HfpDD0GZMuHOjTGmqAhp4BCRziKyRkRiROR5P/vricgUEVkqItNFpLbPvjtEZJ33usNne2sRWead8xNv7fEi6aOP3FxUDz0U7pwYY4qSkAUOEYkAPgeuBJoCvUSkaYZk7wE/qGoLoB/wlndsFeBVoC3QBnhVRCp7xwwA7gMaea/OobqH/Gz3bhg0CG691UaJG2PyVihLHG2AGFXdoKpHgWFA1wxpmgJTvffTfPZfAUxS1T2quheYBHQWkRrAKao6V9188D8A14XwHvKtAQPc9OlPPBHunBhjippQBo5aQKzP5zhvm68lQDfv/fVABRGpmsWxtbz3WZ0TABG5X0SiRSQ6Pj4+1zeRHyUmwmefQefO0KxZuHNjjClqwt04/jTQQUQWAR2ArUBKME6sqgNVNUpVo6pXrx6MU+YbQ4a46UWeeircOTHGFEWhHMexFajj87m2ty2dqm7DK3GISHmgu6ruE5GtQMcMx073jq+dYftx5yzsVF0X3BYt4NJLw50bY0xRFMoSx3ygkYg0EJGSQE9gjG8CEakmIml56At8672fCPxLRCp7jeL/Aiaq6nZgv4i083pT3Q78GsJ7yFd27oRXX4Xly91MuEW3P5kxJpxCVuJQ1WQReQQXBCKAb1V1hYj0A6JVdQyuVPGWiCgwE3jYO3aPiLyBCz4A/VR1j/f+IWAQUAb43XsVWikp8Mcf8PXXbubb5GS44gro2TPcOTPGFFXiOidlkUDkWmCcqqbmTZaCLyoqSqOjo8OdjRzZvdvNePvtt24Cw+rV4fbb4Z57oEmTcOfOGFMUiMgCVY3KuD2QEsdNwEci8jOu1LA66LkzJ7jnHlfCuOIKN9Dv2mvdYD9jjAm3bNs4VPVWoBWwHhgkInO8rq4VQp67ImrfPhg/3rVj/P47dO9uQcOYAunILtgwCApuhY1fATWOq+p+4CfcIL4auDEXC0Xk0RDmrcgaM8YtznTDDeHOiTGZWPU+xM8Jdy7yv4VPwty7YN2AcOckqLINHCLSRURG4brDlgDaqOqVQCRgIwlCYORIqFvXVvMz+dTu+bDoaVj8XLhzEpj42fDX/ZByJG+vu38NbB4MEWVg8fPwT2z2xxQQgZQ4ugMfqmpzVX1XVXcCqOoh4J6Q5q4ISkhwvah69LDutiafWvm2+xk/Cw5uCG9espOSCHNuh/VfwfL+eXvt5W9AsdJw6TRXVTX/ITcQqxAIJHC8BsxL+yAiZUSkPoCqTglNtoquMWPg6FGrpjI+kvaHOwfHJKyG2FHQ4A5AYMMP4c5R1lZ/CAfXQ5UoF/D2Lcub6yashs1D4axHoFpbaPEGbPsNtozIm+uHWCCBYyTg27KT4m0zITByJNSpA23bhjsnRVDKUfhnc7hzcbxNQ+CnyjCxHWwc7PIYTqvehYjS0OpdOP1S2PhD/m34PbQVVrwJta+DThOgZGX4615IDcqsRllb3s9VUTV52n0++zEXvKIfhcTdob8+uPvcOi4kpw4kcBT3ZrcFwHtvfXxCICEBJk4sZNVUexbmr2/MWVn6Mow9O/8Ejx3TYO6dUKklHN0Lc26FX+vC0lfh0La8z8+hONj0I5x5D5Su7kod/2yE+P/lfV4CsehZSE2Gc9+HUlWh9cewex6s/Sy0101YCZuHudJGaW+evGLFoe3XcHSPax/KiZRESFgFW3+DA+sDOyY1BebeATOuCUknhkACR7yIdEn7ICJdgV1Bz4lh7FhXTdWjR7hzkomkA7Dx/yA1KbD0MV/BhNbw131BuPZB160xNfnkz+X3/Acg5gtITYRVH4TmGjmxbznMvA4qnAWXToFrVkHHCe5b6/I34Nd68L+esHdp3uVp1QeudJH2LbrO9VC8PGz4Pu/yEKid/4PNQ6Dps1D+DLetXk+oeRUsfREObsrleWfBngVZp1n+BhQvB40zBIjKkdDkWfd3/Pdk/8cm7Yd1X8K83jD1cvi1PgwvA+OawoxrYXwLiB2d9fVTk127zqbB0OJNqN4+wJsLXCCB40HgBRHZIiKxwHPAA0HPiWHkSKhVC9q1C3dOMrH8DZhzG0y7Eo7uyzptzECYdz+UqASxP8M/W07u2iv6u26N6785ufNkZsMg95+2SpRrSD0Sxu9Gh7bC9CvdQ7njeChZCaQY1LwCOv4G166Dsx6F7RNgQivXY+jwjqzPmXLEPeAXPg1r/wvb/3AN24EG4sTdsH4g1LsZytVz24qXg7o3wJaRkHzopG45qFJTYMGjULYONPVZeFQEzvO6xc5/MOcN1Ye3w7TO8Ed7WP+d/zT7VsDm4e7fp3S1E/ef8zJUaOT+zXx/ZwmrYf4jMKqWy9uW4e7vsdoF7pj2P7pG9krnwKxurju0v/ynJrv/o5uHQOS/4ZwXc3aPgVLVgF5AeaB8oOnz06t169aa3yUkqJYqpfr44+HOSSaSDqmOrKI6tonq0BKqYxurHljvP+3aL1QHozrtKtWEtapDIlQXPp37ax/eqTq8nDvnz6erJh3M/bn8SUlW/fVM1QntVPetdNdZ8vLJnXPVh6qbf8r5cUcTVMe1UB1eXnXPoqzTJu5Rje6jOqS46vAKqiveVk0+fHyag1tUF/VV/amau6+hJdzPtNeQ4qpjGqnOukH1n9jMr7X0dZd+7/Ljt/89zW3f8H85v9esHD2gOvc+1Y2DVVNTc3bs2gEuT5uG+9+/+pPc5fmvB9zv648L3fGL+qqmphyfZtaN7t/uyK7Mz/P3dHd89BOqsWNUp1zu/duUVP3zNtVd8zI/NumQ+7cajOpf96umHD22LyVJddZNbt+Kt3N2b5nAzSt4Yjzwt/GERHA18CzwStorkOPyy6sgBI7Bg92/xv/+F+6cZGL99+4PcvsU94c/srJ7GO388/h0a//rBY2rVZOPuG2zblQdUdE9DHJj4bOqg0U15ht37mVvnNStnCB29PEPmhnXufs7uj9350vc5x4Co+q4oBSo5ETVKZe5h9O2iYEfl7BGdfq17h5GN1DdPNL9G83s7oL2kGLunrZPcQ+6Q9tUd8xwv89Fz6vO7OEedr/UVN0dfeL5kw6q/lRVdXqXE/elpqiOrucefsGSmur+ZtKC24R2qvFzAjv2yG73BWdSx8wDTkqyO+dPVd2XkkAkrHa/y/mPuIf13Ptc3mb2cA9zVdW9y9zf6eIXsj9f2vGDcb/3ZW+oHt4RWF5SU9w1BuP+XhL3ekHD+52t+E9g5wlArgMH8AVuidZY3Drgy4BvsjsuP70KQuC47jrVWrVUU1KyTxsWE9qqjj372H/GhDWqvzZUHVpKdeMQt23NZ+4Pd/q1x4KGqurO2W776k9zft3DO1SHlVX9383u84zr3bfrQP/DB2JSB+8hn+Q+x891+V35fu7Ot3HIsYfC1vGBHZOaqjr7dnfM+u9yd93tk1R/O+fYtUdWUV30nOrBTdkfu3ep6qi67ne9ZdTx+1Z95M63c7b/Y5e87B6YWZVYcmLl++56y/+tGvOtK2UOxv0NHNyc9bHzHnYP+L1Ls063d5krff15a2B5mnG9C65pD/fUVNWV77n7ntBG9dB2LwBXcMErO4l7XV43DT++1JAT67/zSv9NVGd09f5m38vduTJxMoFjaYaf5YFZ2R2Xn175PXDs3++qqR57LNw5ycTuaO/B//Hx24/sUp10sduX9oc7vYv75pzRhLYu0GQs2mdn4dPuG3PCavc5/Zvfo7m6lRPsXuj/W9rkTu6boG8ADNTMHu5h91N11ZndAjtm3ZcuH0tfz/n1fKUkqW74UXX9oGPfhAN1aLt7CA4W9wBKTXX/lqPquH/nzOxf5z3o3zq5vKu6ktKQCPd7S/uScvSA6uIXVYeVdq/FL7l/tz2Ljn/F/eb+VgL921jycmBVVjv/9P5t+p24b8soF2x/qenSLH4p4FsNir+nudLxyXzRycLJBI553s+5QE2gFBCT3XH56ZXfA8eQIe5fYtascOckE3Pudv85EveduC/5iOrsO44FD39BQ1V10zCXJnZM4Nc99LfqsDInfiv86wH3TWt/TODnyszs2137SeLe47dvm+jyG/N1zs6XdMj9ruY9pLrgKVftdOjvrI9JSVIdXd9Vn+S0Pj/Ykv5xgS+tDn3dV4GVnP64wLV7ZZb/xL3+q8F8/ROn+vOprmR7NOHE/Qc3q/6v1/FtNBlfP1VzbT+BSD7iSptDS6j+PdV/mtRUd28/n5Z529ruBS5wjKgYWGkj2A5syFnVZg6cTOB4GaiEm3rkb2A7bmGlsAeEQF/5PXBcf71qzZr5tJoqcY/7lvfX/ZmnSU111TtZFblTktw318mdAr/2gie90saa47cf2uZVX/UM/Fz+HNrmHhrzHzlxX2qq6vhWqmPOylk7RVp7yfbJqvtWeaWZd7I+ZsP/eUH115zlP1RSU1zD72Bc6WNcZPYBbd1Al95fw+72Kaq/1DrW9rVv1YlpkhNVJ7Z3QXzfiqyvtWeJ+6bv73VwSwA36CNxj+pvTd1Df++yE/fH/uryvXZA1uc5sivzziIFWK4CB6677vk+n0sBFbM6Jj++8nPgOHBAtXRp1UeDVPMSdKs+cP9xsuvhE4gV7wR+rkPbvdLGbf73L37Je1DNz31+Fr/kHowJa/3v3zTcXSMnvaNm3+7aFtKC6B8Xul5LmT14U1Ndu8RvTXNejRdqMd+6ev3Y0dmnTdznvmDMe/jYtuRE1YXPuN/x2LNd1dDwCq4UNv+x47+dz3/E+12PCP59ZOfgZtVfaqiOqu1KPWlSklz7wZizct8OUcBlFjiyHMehbtW/z30+J6pqQs46/Jqs/PYbHDmST+em0lQ3HXS186Fyy5M/X8P7IKIsrP4o+7Qr/wOpR10fdn+aPgOlqrkZWjUXE8clH3YD/mpdA6c08p+mTnco39DNcRTINVKTIG4M1O4CxUq4bWfeCwfWuQkB/dk2HhKWu/EGEtAqB3nnzLugxz6o3TX7tCUruqk9Ng89NtL5j3ZuipKGD0DnBdCiH3SJcSPP130GYxvCmk/cmIi1n0Hjp9y4kLxWrq4bL3N0H0y/6thMBxsGwf5V0PKtY/+eBghsAOAUEekukvNJMESks4isEZEYEXnez/66IjJNRBaJyFIRucrbfouILPZ5pYpIS2/fdO+caftOzWm+8oukJBg0CE4/Hc4/P9y58ePvKe6h1+ih4JyvZGU44y73cDn8d+bpDm+HmAFQ/9bMH+olToFzXoEdU2H7xJznZdNgSNwFjZ/IPE2xCDfyeE807AhgPs8d0yFpH9Tudmxb3R4urzFf+z9m5dtQtq4b1ZwfFYsIPG2DO9yUGn/d62YMOBQLF/8KbQa4wYIApU+FNl/AlYuhSmtY8Dj8dTec2gFavh2SWwhI5ZZw0c9uupBZ3eFoAix7Faq1h9rXhy9f+ZW/YojvCziAm+TwKLDf+7w/gOMicKsGnoGb22oJ0DRDmoFAb+99U2CTn/M0B9b7fJ4ORGV3fd9XfqyqmjZNtWlTV1nYz09njaDKbYPrjOtcY2NuehZlJmGNN8DulczTRD/uetZk1/idnKj66xluwFxOqnlSU1V/axZY3X3yEVeNMfnS7M/714Oujj7jILx5vV01TsZG2x2zvN5qnwSe9/wsJdn9rgajOvUK14aUldRU1bixrnNFdh0I8sr677zxMPXdzx0zw52jsCI3VVVeYKmgqsVUtaSqnuJ9PiWAmNQG1/tqg7qJEYcBGcu8CqSdqyLgb+a2Xt6xhcL27XDLLdCpExw65KZRfzmT2pig+GczjDkD5j0Y+BxT4Bad2TrGVbVElApefk45C2pe46rA/C2s888WWPcFNLgdKpyZ9bkiSkKL/rBvKUy7ws0EGshMrX9PgoQV0LhP9rNJRpSCs59wJY6dMzNPl5oCcaOg5tVu9lhfZ97r7nXTkOO3r3zbVbedWUiWtSkWAW2/g3bfuaqfMjWyTi/iqgrbD4Iyp+VJFrN1xp3Q/HX4ZxPUuhZOvSjcOcqXimeXQEQu9rddVbP4XwRALdygwTRxQMbJwl8D/vCWoC0HXObnPDdxYsD5TkRSgJ+BN73ImDHf9wP3A9StWzebrIZecjJ8+im8+iokJrpg0bcvlCkTwoumHIFZPbyqny/hQAxc9JOb+yg7MQNdvX7DEExL1vgJmHqpWxWt9KmuOuxAjHsd+RukOJzzUmDnqncjHNoCaz5yM4GWPxPOethViaXdpyrsXw3bfnfzO+2cCaVPg3q9ArvGWQ/Bus9h3gNw5aITAwPA7rlwZAfU6XbivirnQuVWbtLHRg+5B+bepbBtnFunoXjZwPJRENS8Itw5OHnnvAynNIbTOoY7J/lWtoEDeMbnfWlcSWIBcEkQrt8LGKSq74tIe+BHETlHXaM8ItIWOKSqy32OuUVVt4pIBVzguA03sv04qjoQVxVGVFRUWJfdOnIELrwQFiyAzp1dAGnYMA8uHP2Yq5+/eLRr+Jt3n5ugreO4YzOG+pNy1E30V/NqKF8/+Pk6rZN7kK752H0uU8NN/FbzKqjQEE67NOv8+ZJirh2i8RMQ+wus/dSt87z0ZddGAi5YpE2VfkoTF1jOvCfwklTxcnDeF27iwRX/do28GcX+AsVKunvwp+F9bgW4vQtd3f6q/7hJDM96OLA8mLwj4r6QmExlGzhU9VrfzyJSB/gogHNvBer4fK7tbfN1D9DZu84cESkNVAN2evt7AkMz5Ger9/OAiAzBBbJ8vQzZ+PEuaHzxBdx/fx6ttbH+G/fwb/bCsV4x5erDrOthYlvXaFndT4v80b1u9tQjO9w37VAQgU5/wOFtrjoqreH0ZBQrAfVucq89C10vnQ2D3PbTL4WmfaFm52Mzu+ZUzc4uEK14y/X8qdT82D5VFzhO/xeUqOD/+Hq9YOFTrpG8aRW3XsPZfVyHAWMKmEBKHBnFAU0CSDcfaCQiDXABoydwc4Y0W4BLgUEi0gRXookHEJFiwI1AeiWjiBQHKqnqLhEpAVwDZDKxff4xZAiceircc08eBY3d0TD/YTj9cmju8+34tA7wr7muSmfKJa4uOu1Bu32Cq8rZPde1E1SJghohrHYoXc3/tNPBUOVcaPctRH0KUsK1hQTDuR+639Nf98Hlfx7rcbR3sasTP+eVzI8tWckFnE2DIeUwSAQ0fjI4+TImjwXSxvEprhEbXPfdlsDC7I5T1WQReQSYiOth9a2qrhCRfriW+jHAU8BXIvKEd407fdorLgZiVXWDz2lLARO9oBGBCxpfZX+b4ZOQ4MZq3H8/FM9NmM6pI7tcd8LSp8H5Q07sTnnKWfCvOW5O/9k3u3ULEncD4oJFsxehxpVQ9bz8N64gp4JRkvFVuhqc+5FbiW/d5245UHClDYlwjalZOfNet9Tqxu/d+7I1g5s/Y/KI+GlXPj6ByB0+H5NxXWb/DGmugiwqKkqjo6PDcu1Bg+Cuu2DOnDxYoCk1xdXD75wJl/8PqkZlnjYlEZa+Aoe3Qo3OUONfrqHaZE0Vpl8N8TPh6pVu8Ni4ZlD6dLdSX3bHjmsC+9fCNatdEDcmHxORBap6woMkkO/APwFHVDXFO1GEiJRV1Xy05Ff+NXgwnHEGtM3Ynyy3VL0Sgp+Av/oD19W07ddZBw1wDcOt3glSpooQETegbVwzmN8bWr3vBo0FMkhSBKI+c4HDgoYpwAIJHFNw3WQPep/LAH8A+XGsc76yfTtMnQovvHCSbRtJ+90axdsmuDr2Q7GZpz3z3sIzLiC/KlfPLcu54HFI3OO21b4usGNPv8y9jCnAAgkcpVU1LWigqgdFpBB1PA+dESMgNRVuztglIBCHtsLGH12giP8TNBmKV4Aal7veOP66kpY4BepaN8I80ehhN6Bv91yo2g7K1gp3jozJM4EEjn9E5FxVXQggIq2Bw6HNVuEwZAi0bAlNAumD5it+Nszs6uZSqtwSmjwNNa908+bYZGv5Q7EIVyU4ISr/zjNlTIgEEjj6ACNFZBsgwOm40dwmCzExMG8e/Oc/OTxw01CYe5drdL1sJlTMadQxeabSOXBdnI3FMEVOIAMA54tIY+Bsb9MaVc3BpEdF05Ahrl2jZ6BfRlVheT9Y9hqcejFc9AuUqhrKLJpgCNVYFGPysWw76ovIw0A5VV3uTf1RXkRCNKS4cFB1gePii6FOnezTk5IIc25zQaPBHdBpkgUNY0y+FcgIr/tUdV/aB1XdC9wXshwVAosWwZo1ATaKH9kFUy9zI4oj+7vR3MEa6WyMMSEQSBtHhIhI2ohuEYnAra9hMjFkCJQoAd27Z5FIU12vnMXPw9HdcMFwm1jNGFMgBBI4JgDDReRL7/MDwO+hy1LBlpICQ4e6WXCrZlbbFP8nLHgC9sx3M6VePDr7AXvGGJNPBBI4nsOta/Gg93kprmeV8WPWLNi2Dd5/38/Og5vcGtlbRkCZmtDue2hwa8GfE8oYU6QE0qsqVUT+As7EzVZbDbcOhvFj8GAoVw66dMFNUX4gxi1UtHueW9lOisE5r0LTZ4I/CZ8xxuSBTAOHiJyFW2ipF7ALGA6gqp3yJmsFT+L+3XQs/TTP/WcVZcevg6N7fPYK1L8FWr4FZWuHLY/GGHOysipxrAZmAdeoagyAN/25ycSGyd9xS/tB7Cp+iVt7oUJDKN/QrW5X/gwoHsp1Yo0xJm9kFTi64RZfmiYiE4BhuJHjJhNl4kewcHdrmj45xS1JZYwxhVCmrbKqOlpVewKNgWm4qUdOFZEBIvKvPMpfwXFwI/UrzGfx3hspbUHDGFOIZdudR1X/UdUh3trjtYFFuJ5WxsfuRSMBiKh/Q5hzYowxoZWjfqCquldVB6rqpYGkF5HOIrJGRGJE5Hk/++uKyDQRWSQiS0XkKm97fRE5LCKLvdcXPse0FpFl3jk/EcmTVbyzlbJxBPPWn0f7yxuEOyvGGBNSIRtA4I0w/xy4EmgK9BKRphmSvQSMUNVWuPaU//rsW6+qLb3Xgz7bB+CmPGnkvTqH6h4CdmA9pxZfwOS1N9KoUbgzY4wxoRXKkWdtgBhV3aCqR3GN610zpFHgFO99RWBbVicUkRrAKao615sC5QfguqDmOheSN7pqqn+q3nByK/0ZY0wBEMrAUQvwXeM0ztvm6zXgVhGJA8YDj/rsa+BVYc0QkYt8zhmXzTkBEJH7RSRaRKLj4+NzdwfL+8PCp7NNdnjNCObGtKVNp3q5u44xxhQg4Z7rohcwSFVrA1cBP4pIMWA7UNerwnoSGCIip2RxnhN4bTFRqhpVvXr13OXuyA5Y8yHsXZJ5mgMxVEhaxM/zbqSTDY00xhQBoQwcWwHf1Shqe9t83QOMAFDVObjRD9VUNVFVd3vbFwDrgbO8432HXfs7Z/C0eN2t7hb9qFtkw58trppqs/bglByFNmOMKZhCGTjmA41EpIGIlMQ1fo/JkGYLcCmAiDTBBY54EanuNa4jImfgGsE3qOp2YL+ItPN6U90O/BqyOyhZGSL/DfGzYPNwv0mS1o9g9tr2tLqwbsiyYYwx+UnIAoeqJgOPABOBVbjeUytEpJ+IdPGSPQXcJyJLgKHAnV6j98XAUhFZDPwEPKiqaRM/PQR8DcTgSiKhneL9jHug8rmw6GlI/uf4ffvXUuLgYkb8dSOdw9+3yxhj8oRoZlUwhUhUVJRGR0fn/gTxs2HSBdDsBbdKX5rl/WHpS7R8LZaFq2tTLNwtRsYYE0QiskBVT1gsyB51gah+PtS/DVa9BwfWp2/WzSOYu/4Cmre1oGGMKTrscReoVu9AsZKw8En3OWE1krCUoX/eYNVUxpgixQJHoMrUgHNega1jYNuE9N5UP83rweWXhzlvxhiThwJZOtakOftxWP81LHgcJIIl2y+kxhm1OPXUcGfMGGPyjpU4ciKiJLT+CA6shf2r+HbyjVxxRbgzZYwxecsCR07VvBJqXYtSjJFzu1v7hjGmyLHAkRvtv+fdRdM5mFKTdu3CnRljjMlbFjhyQUtU5vMRF3HZZVCiRLhzY4wxecsCRy6sXg1btmDtG8aYIskCRy5MmuR+WuAwxhRFFjhyYc0aqFQJ6tcPd06MMSbvWeDIhdhYqFMn+3TGGFMYWeDIBQscxpiizAJHLsTFWeAwxhRdFjhy6PBh2LXLAocxpuiywJFDcXHupwUOY0xRZYEjh2Jj3U8LHMaYoiqkgUNEOovIGhGJEZHn/eyvKyLTRGSRiCwVkau87ZeLyAIRWeb9vMTnmOneORd7rzydmzYtcNSunZdXNcaY/CNk06qLSATwOXA5EAfMF5ExqrrSJ9lLuLXIB4hIU2A8UB/YBVyrqttE5BzcuuW1fI67RVVPYi3Y3LPAYYwp6kJZ4mgDxKjqBlU9CgwDumZIo8Ap3vuKwDYAVV2kqtu87SuAMiJSKoR5DVhsLFSrBmXKhDsnxhgTHqEMHLWAWJ/PcRxfagB4DbhVROJwpY1H/ZynO7BQVRN9tn3nVVO9LCISxDxny7riGmOKunA3jvcCBqlqbeAq4EcRSc+TiDQD3gEe8DnmFlVtDlzkvW7zd2IRuV9EokUkOj4+PmgZtsF/xpiiLpSBYyvg+4it7W3zdQ8wAkBV5wClgWoAIlIbGAXcrqrr0w5Q1a3ezwPAEFyV2AlUdaCqRqlqVPXq1YNyQ2CBwxhjQhk45gONRKSBiJQEegJjMqTZAlwKICJNcIEjXkQqAeOA51X1z7TEIlJcRNICSwngGmB5CO/hOAcPwr59FjiMMUVbyAKHqiYDj+B6RK3C9Z5aISL9RKSLl+wp4D4RWQIMBe5UVfWOawi8kqHbbSlgoogsBRbjSjBfheoeMrIxHMYYE8LuuACqOh7X6O277RWf9yuBC/wc9ybwZianbR3MPOaEdcU1xpjwN44XKFbiMMYYCxw5EhcHIlArY6diY4wpQixw5EBsLJx2GpQsGe6cGGNM+FjgyAHrimuMMRY4csQChzHGWOAImKoFDmOMAQscAUtIcAMArSuuMaaos8ARIFv5zxhjHAscAbIxHMYY41jgCJAFDmOMcSxwBCg2FooVgxo1wp0TY4wJr5DOVVWYxMZCzZpQ3H5jpgBLSkoiLi6OI0eOhDsrJh8pXbo0tWvXpkSJEgGlt8dggKwrrikM4uLiqFChAvXr1yePF880+ZSqsnv3buLi4mjQoEFAx1hVVYBiY60rrin4jhw5QtWqVS1omHQiQtWqVXNUCrXAEQBVW2vcFB4WNExGOf2bsMARgD174PBhCxzGGAMWOAJiXXGNCY7du3fTsmVLWrZsyemnn06tWrXSPx89ejTLY6Ojo3nssceyvcb5558frOwC0KdPH2rVqkVqampQz1uQWeN4ACxwGBMcVatWZfHixQC89tprlC9fnqeffjp9f3JyMsUz6boYFRVFVFRUtteYPXt2UPIKkJqayqhRo6hTpw4zZsygU6dOQTu3r6zuOz8KaYlDRDqLyBoRiRGR5/3srysi00RkkYgsFZGrfPb19Y5bIyJXBHrOULDAYQqjPn2gY8fgvvr0yXk+7rzzTh588EHatm3Ls88+y7x582jfvj2tWrXi/PPPZ82aNQBMnz6da665BnBB5+6776Zjx46cccYZfPLJJ+nnK1++fHr6jh070qNHDxo3bswtt9yCqgIwfvx4GjduTOvWrXnsscfSz5vR9OnTadasGb1792bo0KHp23fs2MH1119PZGQkkZGR6cHqhx9+oEWLFkRGRnLbbbel399PP/3kN38XXXQRXbp0oWnTpgBcd911tG7dmmbNmjFw4MD0YyZMmMC5555LZGQkl156KampqTRq1Ij4+HjABbiGDRumfw61kIU4EYkAPgcuB+KA+SIyxltnPM1LwAhVHSAiTXHrk9f33vcEmgE1gckicpZ3THbnDLrYWChRwi3iZIwJvri4OGbPnk1ERAT79+9n1qxZFC9enMmTJ/PCCy/w888/n3DM6tWrmTZtGgcOHODss8+md+/eJ4xDWLRoEStWrKBmzZpccMEF/Pnnn0RFRfHAAw8wc+ZMGjRoQK9evTLN19ChQ+nVqxddu3blhRdeICkpiRIlSvDYY4/RoUMHRo0aRUpKCgcPHmTFihW8+eabzJ49m2rVqrFnz55s73vhwoUsX748vRvst99+S5UqVTh8+DDnnXce3bt3JzU1lfvuuy89v3v27KFYsWLceuutDB48mD59+jB58mQiIyOpXr16Dn/zuRPKslEbIEZVNwCIyDCgK+D7kFfgFO99RWCb974rMExVE4GNIhLjnY8Azhl0sbFuudhi1iJkCpGPPgp3Do654YYbiIiIACAhIYE77riDdevWISIkJSX5Pebqq6+mVKlSlCpVilNPPZUdO3ZQO0Of+TZt2qRva9myJZs2baJ8+fKcccYZ6Q/rXr16HfftPs3Ro0cZP348H3zwARUqVKBt27ZMnDiRa665hqlTp/LDDz8AEBERQcWKFfnhhx+44YYbqFatGgBVqlTJ9r7btGlz3NiJTz75hFGjRgEQGxvLunXriI+P5+KLL05Pl3beu+++m65du9KnTx++/fZb7rrrrmyvFyyhDBy1gFifz3FA2wxpXgP+EJFHgXLAZT7Hzs1wbNpK39mdEwARuR+4H6Bu3bo5z73vReJsDIcxoVSuXLn09y+//DKdOnVi1KhRbNq0iY4dO/o9plSpUunvIyIiSE5OzlWazEycOJF9+/bRvHlzAA4dOkSZMmUyrdbKTPHixdMb1lNTU4/rBOB739OnT2fy5MnMmTOHsmXL0rFjxyzHVtSpU4fTTjuNqVOnMm/ePAYPHpyjfJ2McH+H7gUMUtXawFXAjyISlDyp6kBVjVLVqJMtvtmocWPyTkJCArVque+JgwYNCvr5zz77bDZs2MCmTZsAGD58uN90Q4cO5euvv2bTpk1s2rSJjRs3MmnSJA4dOsSll17KgAEDAEhJSSEhIYFLLrmEkSNHsnv3boD0qqr69euzYMECAMaMGZNpCSohIYHKlStTtmxZVq9ezdy57rtzu3btmDlzJhs3bjzuvAD33nsvt95663EltrwQysCxFfB93Nb2tvm6BxgBoKpzgNJAtSyODeScQZWaaoP/jMlLzz77LH379qVVq1Y5KiEEqkyZMvz3v/+lc+fOtG7dmgoVKlCxYsXj0hw6dIgJEyZw9dVXp28rV64cF154IWPHjuXjjz9m2rRpNG/enNatW7Ny5UqaNWvGiy++SIcOHYiMjOTJJ58E4L777mPGjBlERkYyZ86c40oZvjp37kxycjJNmjTh+eefp127dgBUr16dgQMH0q1bNyIjI7npppvSj+nSpQsHDx7M02oqwM1TEooXrhpsA9AAKAksAZplSPM7cKf3vgmujUNwjeJLgFLe8RuAiEDO6e/VunVrza2//1YF1U8/zfUpjMk3Vq5cGe4s5AsHDhxQVdXU1FTt3bu3fvDBB2HOUe7Mnz9fL7zwwqCcy9/fBhCtfp6pIStxqGoy8AgwEViF6z21QkT6iUgXL9lTwH0isgQY6gURVdUVuJLISmAC8LCqpmR2zlDdA1hXXGMKo6+++oqWLVvSrFkzEhISeOCBB8KdpRx7++236d69O2+99VaeX1vU69dcmEVFRWl0dHSujh01Crp1gwUL4Nxzg5wxY/LYqlWraNKkSbizYfIhf38bIrJAVU8YdRnuxvF8z9YaN8aY41ngyEZsLJQqBV7XbGOMKfIscGQjbR0Om4naGGMcCxzZsDEcxhhzPAsc2bDAYUzwdOrUiYkTJx637aOPPqJ3796ZHtOxY0fSOrdcddVV7Nu374Q0r732Gu+9916W1x49ejQrVx6bneiVV15h8uTJOch91orS9OsWOLKQkgJbt1rgMCZYevXqxbBhw47bNmzYsCwnGvQ1fvx4KlWqlKtrZwwc/fr147LLLsviiMBlnH49VEIxIDI3LHBk4e+/XfCwwGEKpQV9YHLH4L4W9Mnykj169GDcuHHp8zVt2rSJbdu2cdFFF9G7d2+ioqJo1qwZr776qt/j69evz65duwDo378/Z511FhdeeGH61Ovgxmicd955REZG0r17dw4dOsTs2bMZM2YMzzzzDC1btmT9+vXHTXc+ZcoUWrVqRfPmzbn77rtJTExMv96rr77KueeeS/PmzVm9erXffBW16dctcGTBuuIaE1xVqlShTZs2/P7774Arbdx4442ICP379yc6OpqlS5cyY8YMli5dmul5FixYwLBhw1i8eDHjx49n/vz56fu6devG/PnzWbJkCU2aNOGbb77h/PPPp0uXLrz77rssXryYM888Mz39kSNHuPPOOxk+fDjLli0jOTk5fR4qgGrVqrFw4UJ69+6daXVY2vTr119/PePGjUufjypt+vUlS5awcOFCmjVrlj79+tSpU1myZAkff/xxtr+3hQsX8vHHH7N27VrATb++YMECoqOj+eSTT9i9ezfx8fHcd999/PzzzyxZsoSRI0ceN/06ELTp1wvOklNhYKPGTaHW+qOwXDatuqpr164MGzaMb775BoARI0YwcOBAkpOT2b59OytXrqRFixZ+zzFr1iyuv/56ypYtC7g5m9IsX76cl156iX379nHw4EGuuOIKv+dIs2bNGho0aMBZZ7klf+644w4+//xz+nirUnXr1g2A1q1b88svv5xwfFGcft0CRxbSAodNqW5M8HTt2pUnnniChQsXcujQIVq3bs3GjRt57733mD9/PpUrV+bOO+/MckrxrNx5552MHj2ayMhIBg0axPTp008qv2lTs2c2LXtRnH7dqqqyEBsLZctC5crhzokxhUf58uXp1KkTd999d3qj+P79+ylXrhwVK1Zkx44d6VVZmbn44osZPXo0hw8f5sCBA4wdOzZ934EDB6hRowZJSUnHPSQrVKjAgQMHTjjX2WefzaZNm4iJiQHgxx9/pEOHDgHfT1Gcft0CRxbSuuLa4D9jgqtXr14sWbIkPXBERkbSqlUrGjduzM0338wFF1yQ5fHnnnsuN910E5GRkVx55ZWcd9556fveeOMN2rZtywUXXEDjxo3Tt/fs2ZN3332XVq1asX79+vTtpUuX5rvvvuOGG26gefPmFCtWjAcffDCg+yiq06/bJIdZeOst2L/f/TSmMLBJDoum6OhonnjiCWbNmpVpmpxMcmhtHFno2zfcOTDGmJPz9ttvM2DAgKAuLWtVVcYYU4g9//zzbN68mQsvvDBo57TAYUwRUxSqp03O5PRvwgKHMUVI6dKl2b17twUPk05V2b17N6VLlw74mJC2cYhIZ+Bj3HrhX6vq2xn2fwh08j6WBU5V1Uoi0gn40CdpY6Cnqo4WkUFAByDB23enqi4O3V0YU3jUrl2buLi4k55ywhQupUuXpnYOBqyFLHCISATwOXA5EAfMF5Exqpo+y5iqPuGT/lGglbd9GtDS214FiAH+8Dn9M6r6E8aYHClRosRxI5CNyY1QVlW1AWJUdYOqHgWGAV2zSN8LGOpnew/gd1U9FII8GmOMyaFQBo5aQKzP5zhv2wlEpB7QAJjqZ3dPTgwo/UVkqYh8KCKlgpFZY4wxgckvjeM9gZ9UNcV3o4jUAJoDviu/9MW1eZwHVAGe83dCEblfRKJFJNrqc40xJnhC2Ti+FfCdV7a2t82fnsDDfrbfCIxS1fRJW1R1u/c2UUS+A572d0JVHQgMBBCReBHZnE1+qwG7sklTGNl9Fy1230XLyd53PX8bQxk45gONRKQBLmD0BG7OmEhEGgOVgTl+ztELV8LwTV9DVbeLiADXAcuzy4iqZjv5vIhE+xtaX9jZfRctdt9FS6juO2SBQ1WTReQRXDVTBPCtqq4QkX5AtKqO8ZL2BIZpho7lIlIfV2LJuA7jYBGpDgiwGAhsNjJjjDFBEdJxHKo6HhifYdsrGT6/lsmxm/DTmK6qlwQvh8YYY3IqvzSO5wcDs09SKNl9Fy1230VLSO67SEyrbowxJnisxGGMMSZHLHAYY4zJkSIfOESks4isEZEYEXk+3PkJJRH5VkR2ishyn21VRGSSiKzzfhaqFdZFpI6ITBORlSKyQkQe97YX6vsGEJHSIjJPRJZ49/66t72BiPzl/c0PF5GS4c5rsIlIhIgsEpHfvM+F/p4BRGSTiCwTkcUiEu1tC/rfepEOHD4TMV4JNAV6iUjT8OYqpAYBnTNsex6YoqqNgCne58IkGXhKVZsC7YCHvX/jwn7fAInAJaoaiZs0tLOItAPeAT5U1YbAXuCe8GUxZB4HVvl8Lgr3nKaTqrb0Gb8R9L/1Ih04yPlEjAWaqs4E9mTY3BX43nv/PW5QZaGhqttVdaH3/gDuYVKLQn7fAOoc9D6W8F4KXAKkzS5d6O5dRGoDVwNfe5+FQn7P2Qj633pRDxwBT8RYiJ3mM43L38Bp4cxMKHmDSlsBf1FE7turslkM7AQmAeuBfaqa7CUpjH/zHwHPAqne56oU/ntOo8AfIrJARO73tgX9bz2kAwBNwaKqKiKFsn+2iJQHfgb6qOp+9yXUKcz37U0c2lJEKgGjcBOEFloicg2wU1UXiEjHMGcnHC5U1a0iciowSURW++4M1t96US9x5GQixsJqhzcLcdpsxDvDnJ+gE5ESuKAxWFV/8TYX+vv2par7gGlAe6CSiKR9aSxsf/MXAF1EZBOu6vkS3Cqkhfme06nqVu/nTtwXhTaE4G+9qAeO9IkYvV4WPYEx2RxT2IwB7vDe3wH8Gsa8BJ1Xv/0NsEpVP/DZVajvG0BEqnslDUSkDG41zlW4ANLDS1ao7l1V+6pqbVWtj/v/PFVVb6EQ33MaESknIhXS3gP/wk0CG/S/9SI/clxErsLViaZNxNg/vDkKHREZCnTETbW8A3gVGA2MAOoCm4EbVTVjA3qBJSIXArOAZRyr834B185RaO8bQERa4BpDI3BfEkeoaj8ROQP3bbwKsAi4VVUTw5fT0PCqqp5W1WuKwj179zjK+1gcGKKq/UWkKkH+Wy/ygcMYY0zOFPWqKmOMMTlkgcMYY0yOWOAwxhiTIxY4jDHG5IgFDmOMMTligcOYXBKRFG8W0rRX0CZKFJH6vrMYG5Of2JQjxuTeYVVtGe5MGJPXrMRhTJB5ayL8x1sXYZ6INPS21xeRqSKyVESmiEhdb/tpIjLKWzdjiYic750qQkS+8tbS+MMb/Y2IPOatL7JURIaF6TZNEWaBw5jcK5Ohquomn30Jqtoc+Aw3MwHAp8D3qtoCGAx84m3/BJjhrZtxLrDC294I+FxVmwH7gO7e9ueBVt55HgzNrRmTORs5bkwuichBVS3vZ/sm3AJKG7wJFv9W1aoisguooapJ3vbtqlpNROKB2r5TYHhTwE/yFt9BRJ4DSqjqmyIyATiImy5mtM+aG8bkCStxGBMamsn7nPCdSymFY22SV+NWrjwXmO8z66sxecIChzGhcZPPzzne+9m4GVsBbsFNvghuOc/ekL7wUsXMTioixYA6qjoNeA6oCJxQ6jEmlOybijG5V8ZbXS/NBFVN65JbWUSW4koNvbxtjwLficgzQDxwl7f9cWCgiNyDK1n0BrbjXwTwf15wEeATb60NY/KMtXEYE2ReG0eUqu4Kd16MCQWrqjLGGJMjVuIwxhiTI1biMMYYkyMWOIwxxuSIBQ5jjDE5YoHDGGNMjljgMMYYkyP/DyyDX7ESsrBhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1f4adH2i2YGKfyn1mynDYu6ziVcalO4+WxhswBv/1A77EJaj9JgSVJyEhgaysLBODWoRzjqysLBISEsLzAbsWwJrHYEfwTC2jhrHuKS286n6LPk/sDX3/CBmzYcNzJY/d/QWs/C20n6QB4cqSPEz7Au1ZUv6xvhzaqfME6jby/3qjdtpCom4TdUGVR9IgdSW5worZUQVERVJ327Zt2b59OzU1tdTwT0JCQomspColZ6NuD6wPz/mNqifvAPz4N2g3saR/v9sNete//BZoeRo0O1FdQV9cDI2Oh0HPa2ZOZUk5Wbe7vyjZEK48DvupKi5N1/+nq9M6IVxqkwbp9z+wHpp1D92OKiAqhKBevXp07Ngx0mYYNYlsjxBk1zAhyN6oroRAd5GxzIbnIW8/nHhbyf1SB4a8DHN6wxeT4RdfwZdTNeXzrMVQv9mxfW58EjQ9seIBY++s4mCcdHfo5/MNGFezEESFa8gwypBTA4XAFcIHafD9A5G2pOZRcFT7/3iHupemQSsY8hLsWwnzBsOO96DfY6G5XEIhZbimklbELXNoZ/FAmqqgSTeo2zgicQITAiM68XUN1ZTY0eFdOt/254XlHhpzbJkJudvLrgZ8aTMWulyr7Z3bng9dr6u6z08Zpr+b/YF7gZXhcAgrgopQJ07bVJsQGEYV4AohZ7O6X/L2aQVoTSBns273fhvx0YQ1CudgzZ+gWU9oPTr4sf0fg0HPwZB/HFtcoDQpw3UbahppXg7k55QfI6goSYNg3wooOFK15y0HEwIj+sjN0JGAqafr8wPrgh9fXRxM121hHuxZHlFTahQ75sL+1dDj9vIv7nEJcMKVxx4XKE3jTpCQGnqcoPSs4qoiaVBE/j5MCIzow+sWOu5s3daUOIFXCAAyv4qYGWGlMm64NX/WHv7HR3B4kYi6h0JdEZSeVVxVeLOWfl5QtectBxMCI/rI2aTb1NO110tNEYKcdG1s1qgDZEWhEOTnwrvtYeOLob8n8xv4+VPofrP+riJJynA4uBlyd5R/rL9ZxVVBQoq2tf7pk6o9bzmYEBjRR85GLddv3FH7z9eUWoKDm1UEkodE54pg3yoN+K64C/LKtkb3y6oHoV5z6PzrsJoWEsme7sCZIawKDpfTZ+hYSB2lNlRjHMmEwIg+sjdqoVGdutrnvSatCBp1gKQhkLtNYxnlkZsBOz8Kt2VVw76Vuj2yG9aE0DF0xwew433Nta/XJLy2hUKLfhDXIDT30KGderMRn1T1dqSOUhGoxpsFEwIj+sjZqCsBgKZdIXsDFBZE1iZXCLlbdJVSNLQkhP/oK+6CT8/WKVs1nX3faR58u4mw9nGdzhWIwjxYfrPOC+h2Y/XZGIw69SBpcGgB40M7NT4gYbiEtjxVz7ur+txDJgRG9JGzEZp4hKBJV80gyt0W/D3h5tAOvfg16gCJfbX1cXlxgsJ8vWN2BbWj9mDvSvVv9/k9FByC1Q8HPnb9M3BgLfR/AuLqV5+N5ZEyHPau0PTQYPibVVxV1G+m9QQmBIZRSY7u1UdjHyGAyLuHctJ126iDXvha9C9/RZC5WDtvQkSnV4WEc7oiaN5bV2Gdf61N4ryBe18O79bq6ta/gOPGVLupQUkZpsKb9XXw4w6F0GfoWEg9XSeulSdIVYQJgRFdeHsM+bqGIPIBY2/qaGNPT6ykIdppsjAv8Hu2zyp2V/xUw4Ugd6v2CfK2gu55n/rQV95b9tjv7oX8g9B/etUWhVUFyUMBKT9OEM4VAehYS5cPuxeF7zN8MCEwogtvDYHXNZTQSv3WkV4ReIWgYXvdJg/RgODelYHfkzFbB563PQ/2rwruc4803u/RvLduGx4H3W6CLa+pq6XouBWwYYa2h2hWA0eU1m8GzXsFF4LCfF3VhHNFkHwy1Klfbe4hEwIjuvAKQeNOuhWpGZlDOZs9otRAnycP0W0g99CBdWpz23HQylMhvat6i4wqxL7vdNu8V/G+HrdD/UQNeIO6j5bdqJk2ve6vfhtDJWUYZH4ZOMHg8C7AhVcI6jbU1Uk11ROYEBjRRfZGzwXXp81z0641wzXUqEPx84btdCZvoIDx9lm6bXOuDj+v1+zY7w7X/iV8Qee9K9Ud55sGWr+5pobu/EBFbNtb+vl9Hqn4SMnqJOVUyM9W150/wllD4EvqKO1LdWRPeD8HEwIj2vDNGPLSpJteiKu5kVcJDqYXxwdAVyrBCssyZkPzPjqusU4cpI44toDx/jWw/CYdBh8OvIHi0nS9Dhq21bGhy2/VjKlOl4fHhqqi9Vkgcfo78Ee4qopLkzoKcPDzZ+H9HEwIjGjDt4bAS9OugCt2G1U3hQVwcGvJFQFowDhnY9nB7IcztbK07bjifamjNAPHm31UUdY9qdtgMYnKkn8Qsn/0PzM4LgF6/Q72LNOA8oC/qLDVZOJbaBppQCEIU5+h0iQNgriG1RInMCEwooeCw1qJW1oImkQ4c+hQhmaANO5Qcn+gOMGOOVqA1sZXCLxxgkpcFI5kweZ/6kXlYHrVF6ftWw04/ysCgI6/0uBn5ysqNgoykrQ5V1c5B7eUfa2o82iYhSCuvv57VUPGmAmBET3kbAZccaDYS5Muuo1UwNibMdSo1DjVFgM0xbJ0nCBjtrodWvQv3tesh7ZJrsxFYcNzKpK9H9Tn3sBuVeFtLeFvRQDa6uPMRTB4RtV+bjjxivB2P6uCwzs1CB4XH347UkfBgTXF4hMmTAiM6CGnVA2Bl/rN9CIaKSHwDqQp7Rqq21Avnr4rgoIjGlxtc27J9gUielHY9UnFWj0XHIX1T0Ors+D4ybrPN52zKvC2lij9/XwJRyuGcNK0CzTtBhmzyr526Kfwxwe8tBql2zBnjNWy345hBCG7VA2BL00imDl0MB0QDfyWJmmIjib0piru+lQnX/m6hby0Ol0zVg6sCf2zt76hd5Pdb9KLV3xy8R18VeFtLVHbLvbl0WactsjOO1Byf1XPKg5G877anTXMcYKw/uZEZLSIrBORDSJyp5/Xp4rIbhFZ4XnUgF60Rq0lZ6PemcanlH2taQRrCQ6ma6qoP1dC8hC98O9frc8zZqsvP3VU2WO9+0J1DzkHa6dD0+7azkFEs3aqMmDsbS0RyC1Um2lzrlZ+7/yw5P6qnlUcDG/GWJjrCcImBCISBzwDnA30ACaLSA8/h77unOvrebwQLnuMGMCbMeSvbUGTrloIFIkunjnpZQPFXrwB46yv9KKaMQtan1lceOZL444aZwj17nD3Iti7XCt8vXfrzftolXJhfgW/RAC8rSUCBYprM8lDtfhtu497yLnw9xkqTeoonWXhdTGGgXCuCAYBG5xzm5xzR4GZwPgwfp4R6/irIfBS1Hzux+qzx4t3II0/GnfWi03mV+qyyd3m3y3kpdUodR+F0lZ77XSo3wI6/l/xvsQ+GjiuqtVRUWuJKFwR1KkLrc+BnXOKhTNvn3azDXfGkC/VUFkeTiFoA/j2/t3u2VeaiSLynYi8JSLtwmiPEc0UFugdU+lAsZemEepCWpivU7sCCYGIxgkyv/LceUrwjpypp+vFaG85w82zN8L2d6HL1RqU9uK9YFeVe6iotUTPqjlfTaPtOE2/zfxSnxfVEFTjiqDpiZXPGAuRSEd3ZgMdnHO9gY+AV/wdJCJXishSEVm6e/duf4cY0cKiC+D731X8fYcyoPBoYCFo3BmQ6g8Y527XtsaNOwY+JnmoBoC3vKadRhukBj7WGycozz207im9o+1ybcn9TbtrM7OqChj7ay0RTbQ+SzvAeovLqquq2JfKZoxVgHAKQQbge4ff1rOvCOdclnPOW/f/AjDA34mcczOcc2nOubSUFD+BQCM6cA52zNWhJRX1YXv73gdyDcXF6115da8IDgZIHfXFGyc4sK5kNbE/GqRCs5OC3x0e3Q+bXoL2F2kXUF/i6mtNQigrgqP7ym/HHKi1RLRQrym0HFGcRlpdfYZKkzrKkzG2NiynD6cQLAG6iEhHEakPXAyUSMoVEV9ZHQdUIC/OiDqOZEFBrs68rWh/lUA1BL5UNnPIucqPuvQdSBOIpIGAJ8AdLD7gJfV0HacYqHfSxhc1E6n7Tf5fb94ntFqC1Y/AR8MDi0aw1hLRRJtxKtIH1kdmRQA+9QThyR4KmxA45/KB64B56AX+DefcahF5UES8f+03iMhqEVkJ3ABMDZc9Ri0g16ecf8vrFXtv9kat0m0YJMzkrSWoyPLaOfjyUpjbt3LL8oPpmrETzK56TfUuv1FHvVsvj1ajdBSkv4Z1OZth3V8g5RStXPZHYh+9uzz8c/DPyXhPt6se8v96UWuJKBeCtufqNmO2CkGdeO0GW5006gjD34B2F4Tl9HXDclYPzrk5wJxS++7z+fku4K5w2lAjKDiqd07NT4q0JTUbb1+XpifC9v/BwGfUPxsKORv1rrtOkD/pJl21vfDhClSGrn8a0v9V/BlNTgjtfV4OpkODNuXP5R38gvYXCmViV8vTPMPN50Pqabpv/xpY/QeNM0gcDHkp8Pt9A8atz/R/TM4mdUM06gDb/gv7vi85awB8WktEsWsIoNHx6v7KmK2dVBu0rv7JaiLQflLYTh/pYHFssOkfMKcnbHsn0pbUbA5u1e2Jt6qbqCLpcsFSR71UdGzl7i9h+S3aBRIqtyzPCZI66kvyYEgZGto56zcvHm6+51tYNAneP0n7/Xe9HsZtKk459IfXlRMsYJzhuX8b9jrUbQLfP1j2mH3f6WuhfL/aTptz1R23f3X1u4WqAROC6sAbcPt6WvHFzijLwS1aVdvhl+ouqYh7KNtP++nSVGSQ/eGf4fNJ2hZi5AdaGVyZ6s7SA2mqitRR+nf1QX/46UM46S4YvwUGTIeG/rK0fYhP0jvbYHGCHe9rs77kQdDtBhWZfatKHrN3pa4Soq21hD/ajNPsr70rqj9QXA3EwG+wBrBniU6ZKsyHLyYHH1geyxzcosvwuARoMx62v61utfI4skdz68sTgobt1L9bnhAUFujv6WgWnPJf7TRZ2YZvhzKCp45WluMnQ7Oe0PshFYA+j0BCBTLqmvcJEgTO1dXYcefo8+43a+sO31hBNLeW8EdSWrEA2IrAAPRCEeoFIe+AJy3wPBg0AzIXw3f3lfu2mMQrBADHXwhH94Y2lav0wPpA1IlTH395rqHv79OL/sC/aW8eUFfLkd3aniFUcrep3z8cK4LE3jDme+h5T+XGPib20RiAv8yjXQu0etYrBPFJuirY+qYnQEx0t5bwh9SBNmP1Z1sRGBQcgXmDYMk1oR2/ZxngNEWww8U6nOOHR2HHvLCaWSvJ3VosBK3O1MyMUNxD2SGkjnopb5D99tmw+vf6e+o0tXh/6kjdVsQ95J1DEKjPUCRJ7KvDcvb/UPa1He+ri67lacX7ut+ic6C9q4Jobi0RiDae7CFbERisekjL+zNmh7YqyFqi2xZpuh3wpC7pv/y/sA+bqFXkH4QjmcWtmuPiod35sP2d8mcNF9UQdAp+HGjAOGej/4K1nE36e0nsD2lPlXyt0fEqNBUJGAcaSFMTKMocWlFyv3M6Ia3VGSW7pcYn6fzhrW+oeBS1liiVSRTNtB4NPe8tv+ivFmJCUBH2LNO7+YRWcGhHcTVrMLKW6IUgIVmf120Iw1/Xgp/Fl1S+UCna8AbRGx5fvK/9hep++Omj4O/N2ah3ab49dQLRpKvGaLypqs7B7sWw5Dpd6UkdOOUtjVOUJnWU9qcPteo5J11TORu2De346qRxZ73rL505dGCN/tu08dPvqPtv9N941cM+rSUaV4+9NYG4+jrlLaFlpC2pckwIQqXgKHw5VZs/nfKW7tu9qPz37VniqRz1oVkPSHta7y5X/77KTa2VeC/MjXyEIPV0DdSW5x7K2RjaagCKM4cyZsOKu2FWJ/hoGGx6US/0Iz8MHNxNHaUxn73fhvZZBzerCASrbYgUdeL0br50wDjjfd22PrvsexKSdVWwZab+7cZKoDgGMCEIldUPa6Bw0IziPuU/Lwz+nsO79QJXWggAOl0GHabAqgeK3UexTK5nReArBHH1od0E7aJZcDjwe0NJHfXirSVYfjOs+ZM2YRvyCkzYpZWbSWmB3+uNE4TqHjqYXjPdQl4S++qKwNfFuWOOCkSjAJXQ3X8DcQ3g6J7YCRTHACYEobDnW71z73ipLpmljpbwlycERfEBP0IgAgOf1TF0qx6ucpNrHQe3qBuldCCu/YVaDbzjA//vyz/kSdEMUQjiU6DP73VFdv4OGDkXOv1K6xbKo6jhW4hCEGwgTU0gsY9mZuV6usUf3a9FU8HaYCekQFdPR9NYChRHOSYE5VFwFL66TP2CA6YX7085RV0SuTsCv3fPEkCgRX//r9drqpWgGbOK0/JilYNb/LtRUkfq6mvrGwHe5+nuGaoQiGjxVddrK+frTR2lLsHy6hsKjmgcqSZX3ZaeTfDTR5pJ5E0bDcRJd8NJ92iLZiMqMCEojx/+oMvngc+pv9pLy1N1GyxOkLUEmp0YvFd71+s0aLfmT1Vjb23Ft4bAlzr1oN1EFcv8Q2VfDzawPhykehq+ZX0d/LiDWwFXw4XAk/HjDRjvmKMr1ORyWl3Ubw59HgotOG/UCkwIgrF3pbptOlxS3IHQS2JfrbYM5B5yTlcE/txCviQkwwlXQPprxQHTWOTg1pIZQ760v1DTS3fM0SyrrCXww59gwWj44mJAtB1CdZDqbfhWjnuoqIagBscI6jWBxifo37kr1H/f1r+omcFtI6yYEASiMA++mqpuiQF/Kft6nbqQfHLgFUHuNu1X4y9QXJrut+h2zROVNrdWU5ivfn5vDUFpWp6mvv1lN8J/kzTNc8Ud+m/ceZr2AopPqh5b6ydqnUGoQlCTVwSgcYK9KzQT6vCu8t1CRlRiQhCINY/rf5CBf4f4Fv6PaXmqtuc9sqfsa8ECxaVp1B46XgIbn4fDmZU2udZyKEMbevlzDYGKbrcbNWe9/YVw8n/g/J0wZjWk/bX6fdWpo3SGbf7BwMfkbNb5CA3KaQAXaZr30VjXltfRecmjI22REQFMCPxxYD18/4AOgWh3XuDjiuIEn5d9bc8S9W+Hmmt94u3qe17/14paW/vxV0NQmp6/hbFrYfAMbdXRIIL9XlJH6Yox2BjHg+kq8HXiqs2sSpHYB3Dw49919RqFxVJG+ZgQlMYVwjdXaq50WjkX5aSBOgjcn3so6xvNs/Yt0w9GsxO1Md36v0JeToXNrtUc9FNDUJNpOVzv9oO5h8LVfrqq8TbVy88OnjZqRDWxIwQFRyCznEwP0HmvP38G/R8v/64zLgGSBpcNGLtCbUcRilvIlx53aF73xucr9r7ajndFEGycY02ibiMdOB+sniDUgTSRpmE7zRQCiw/EMLEjBKse1kHcPz4X+JjcHfDtbbr073RZaOdteape9H3v4g+s11YEoQSKfUkeAi1HaHyivEZr0cTBLRoMrk3piKmjYO8yOLqv7Gv5h3QcZm0QAhFdFSS0DFzvYkQ9sSMEPW7ToOKSq2Hp9f4bhy29TvuwD3ou9JmkKadooDPLZ5D4Hk+guKJCANDjTg2epr9a8ffWVgLVENRkUkfpyq/0ajBnU3GL8uqqbThWBjwJw9+KjUljhl9i5zdfrymcOkvn4a5/Gj49W90wXrb9Tydi9fpdxQaUp5ys/4F8LwhZS7RIrOmJFbez9VmQ2E8LzGKlM6nvHILaQvIQdQ164wQH1mtTwtldtSlb1+s02aA2kNgHWp4SaSuMCBI7QgCawdHvzzDkHxoHmDcY9q9VQVhyrV6AvTn9oVKvieaVlxaCFv0rV5gjoquCA+tg8ysVf39twzlPe4kANQQ1lbh4SBkOGe9pO/H3T9Q2GN7h8Wl/1aZ5hlELiC0h8NJpKpy+QP27Hw6BRZN0DOHgFyp38U45RVsOFBzRtMJ9KyoeKPal3UQt8//6clh5b3SvDI5katpsbVsRgLbJztmow3O6/wbGbfYMjz8u0pYZRoWITSEASBkGo5foBWjXfP2PXNlgWctTtU3ynqWwb5X+XJn4gJc6cXD6J9Bpmra//vQcOJJVuXM5V9yPJxLkZQd/PZQagppKl2u04HBcOvT7k3YnNYxaSOwKAejF58wvYOi/dPJQZUkZrtufFx5boNiXuAQY8iIMel6nYn0wALKWVvw8a6fD7BNg29vHZk9l2PEBvJUIe78LfIy/OQS1hfrNoMtVxdPnDKOWEjNCsGgR3HKLnzHD9Rpre4dQC7/8kZCsfep/XqTxgfqJobdFLo8Tfg1nfq6GfzQMNrwQ+nvzcrR7KsDSa/2nOoaTHx7VjKqMWYGPKVoR1LIYgWFEEWEVAhEZLSLrRGSDiNwZ5LiJIuJEJMh4qGNj1SqYPh3WrQvTB6Scoq0mMr/SQfWhpp+GQtJAGL1Mm699cwWsCPhPWZL1T6sPPu0ZbSi24o6qs6k89izTgDwSfObwwS1aoFU/QD8nwzDCTtiEQETigGeAs4EewGQR6eHnuCbAjUAIZb+VZ+xY3b73Xpg+oOWpWqa/f9Wxu4X8kZAMI+ZC51/DD3/U1Ucw8g7Amj9rtWjX/6fZUBtmlD9VrapYO13bdHe5WofDB4oVeGsIqlI4DcOoEOFcEQwCNjjnNjnnjgIzgfF+jnsI+CMQZCjtsdOuHfTpE04h8MnDPpaMoWDUidPin0YdtB9SsOrjdU/pXNlev9PnvX6nA96/viL4/N+qIDdDu1l2vlxz6V2+Z3Xgh2BzCAzDqBbCKQRtgG0+z7d79hUhIv2Bds6594OdSESuFJGlIrJ09+7dlTZo7Fj4/HPYu7f8YytMw7bFg8rDsSLwUreRZqocWKtzlP1xdJ+2qWgzrngYe92GWjGdvT78M5LXPw0UQrcbNDsrrgHs/ND/sblbLD5gGBEmYsFiEakDPAH8prxjnXMznHNpzrm0lJSUSn/m2LFQUADz5lX6FME5brROfGoY5h70x/0COkzxjNH0M+t47XTI2we9f1dyf6szoOOl6loKlMlzcBt8eztkzKmcbfkHYcNz0PZ8XYHExWts4yc/QpB/UNNia2PGkGFEESEJgYg08ly4EZGuIjJOROqV87YMwLedZFvPPi9NgJ7ApyKSDgwBZoUzYDxwIKSkhNE91O9x+EVYQx3F9J+ubTO+uUJ73ng5skeFoN3E4hbDJd73uGY1ff3rkoVqB7fAN1fD7M4aW1g4Drb+t+J2bXpFK7W731y8r/VZWintbTft+5lgQmAYESbUFcFCIEFE2gAfAv8HvFzOe5YAXUSko4jUBy4GivIInXP7nXPJzrkOzrkOwFfAOOdcJZLlQyMuDs45B+bOhXw/PeeOmboNAk8zq2oSUlQMMr+EH/9WvH/t45CfA70e8P+++CQY8JTWO6z/K+Skw9dXwuwusOkl6HQ5nPO9ttf+4iLY+mboNrlCFaGkQTrG00urM3VbOnvIhMAwagShCoE453KBCcCzzrlJwEnB3uCcyweuA+YBa4A3nHOrReRBERl3LEYfC2PHwp498NVX5R9b4+lwiV5kV9ypLp3DmbDuLzrOsXnPwO87/iIdQrLiThWAza9A5yvh3I0w6G/63pEfaGO1LybDljdCsyfjPcjZoBlKvllAzU6CBq1hZ2kh8KwQalufIcOIMkJtrCMiMhSYAlzu2VfuDD7n3BxgTql99wU4dkSIthwTZ50Fdeuqe2j48Or4xDAiAoP+Du/31IKxpt21b0+g1YDv+wY+C5+dq/77HneUjWvUa6Lpqp+eA4t/CTgVkGCsna4X9XYTy35eqzNhx/u6avC2Oz64xTPX13rzGEYkCXVFcBNwF/C2566+E7AgbFaFkaZN4bTTwhgnqG4ad9L2GBmzYe0TcPwvoVn38t/XqD2csxLSngoc3PaKQfLJsHgKpM8MfL49y7UVRrfr/Tfua3WWBob3flu87+AWzbaq6XN9DSPKCUkInHOfOefGOef+6AkaZzrnbgizbWFj7FhYvRo2b460JVVEt5u0FTZAT78LrspTrzGMmKNpoF9OgdWPwt4VZQf7eAvIOv/a/3lanaFb3zTS3Fo4kMYwopBQs4ZeE5GmItIIWAX8ICK3hde08OGtMn4/aPVCLaJOXb1Yn/EZNO1S9ef3ikHqKFh5F8ztB282hY9OgeW3wqaXdRhL58uhfnP/52iQCs37lAwYH9xq8QHDqAGE6hrq4Zw7AJwHzAU6oplDtZITToBu3aLIPQR6oU0ZFr7z120EIz+EcRvh5NfghCu1odz6p+GryygqIAtG6zO1H1P+QZ3bcCjDVgSGUQMINVhcz1M3cB7wtHMuT0RK9/GsVYwdC3/9K+TkQOPGkbamliCiMYnGnaDDZN1XcBT2faei0LhT8Pe3OgvWPKb9jpqeqIFjEwLDiDihrgieA9KBRsBCETkeOBAuo6qDsWPh6FH4+ONIW1LLiauvbSySB5d/bMpwqBOvcQKrITCMGkOoweKnnHNtnHPnOGULMDLMtoWVYcOgWTOYPTvSlsQQdRtol9afPvIZSGMxAsOINKEGi5uJyBPexm8i8ji6Oqi11KsHo0drwLiwsPzjjSqi1Zmwf7VWRIMFiw2jBhCqa+glIBu40PM4APwjXEZVF2PHwq5dsGxZpC2JIVqfpdv01yChpa4SDMOIKKEKQWfn3P2e2QKbnHO/A8qJDNZ8Ro+GOnWiLHuoptO8lwpA3n6bQ2AYNYRQheCQiBQ1ZBCRYcCh8JhUfSQnw9ChJgTVitQpbkJn8QHDqBGEKgRXA8+ISLqnZfTTwFVhs6oaGTsWli+HjIzyjzWqiFYe95BlDBlGjSDUrKGVzrk+QG+gt3OuHzAqrJZVE+eeq9tZs4IfZ1Qhrc/UNNJmQRvYGoZRTVRoQplz7oCnwhjgljDYU+306AFdu8J/KzGDxagkDVrDuA3Q8VeRtsQwDI5tVKWUf0jNRwQmToRPP4WsrEhbE0M0bOu/S6lhGNXOsQhBrW4x4cvEiTrL2NxDhmHEIkGFQESyReSAn0c2EDXTRPr3h+OPN/eQYRixSdC1uXOuSXUZEklEYMIEeOYZOHBAh9cYhmHECsfiGooqJk7UJnRRM6PAMAwjREwIPAwdCq1bm3vIMIzYw4TAQ506cP75MHcu5OZG2hrDMIzqw4TAhwkTVATmzYu0JYZhGNWHCYEPp50GSUnmHjIMI7YwIfChbl0YP16H1Rw9GmlrDMMwqgcTglJMmKAppPPnR9oSwzCM6iGsQiAio0VknYhsEJE7/bx+tYh8LyIrRORzEekRTntC4YwztI7A3EOGYcQKYRMCEYkDngHOBnoAk/1c6F9zzvVyzvUF/gQ8ES57QiU+XltTv/su5OdH2hrDMIzwE84VwSBgg2ei2VFgJjDe9wCfTqagM5BrRP+iiRMhMxMWLYq0JYZhGOEnnELQBtjm83y7Z18JRORaEdmIrghu8HciEblSRJaKyNLdu3eHxVhffvELaNDA3EOGYcQGEQ8WO+eecc51Bu4A7glwzAznXJpzLi0lJSXsNjVqBGefDW+/DYWFYf84wzCMiBJOIcgA2vk8b+vZF4iZwHlhtKdCTJwIO3bA119H2hLDMIzwEk4hWAJ0EZGOIlIfuBgo0fFfRLr4PB0D/BhGeyrEmDHQuDFcfz1kZ0faGsMwjPARNiFwzuUD1wHzgDXAG8651SLyoIiM8xx2nYisFpEV6OjLS8NlT0Vp1gxefx1WrCjuTGoYhhGNiHM1IlEnZNLS0tzSpUur7fNefhkuuwymTIF//lOb0xmGYdQ2RGSZcy7N32s2NLYcpk6FnTvh7ruhVSt47LFIW2QYhlG1mBCEwJ13auD48cd1ZsFvfhNpiwzDMKoOE4IQEIEnn4Rdu+DWW3VlMGVKpK0yDMOoGkwIQiQuTmMEu3eru6hlSzjzzEhbZRiGcexY6LMCJCTAO+/AiSfCRRfBli2RtsgwDOPYMSGoIM2awf/+pw3pLrrI0koNw6j9mBBUghNOgBdf1KrjO8s01zYMw6hdmBBUkkmT4LrrYPp07UlkGIZRWzEhOAYeewzS0rTgbNOmSFtjGIZROUwIjoH4eHjjDU0vvfBCOHIk0hYZhmFUHBOCY6RjR21DsWyZFZoZhlE7MSGoAsaPh1tugWee0UZ1hmEYtQkTgiri0Udh6FCYNs1GXBqGUbswIagi6tXTYrP27XWWwTffRNoiwzCM0DAhqEJatoSPP4aUFJ17vGJFpC0yDMMoHxOCKqZNG5g/H5o00V5EP/wQaYsMwzCCY0IQBjp0UDGoWxfOOAM2bIi0RYZhGIExIQgTXbqoGOTlwemnW4M6wzBqLiYEYaRHD/jwQzhwQMXg558jbZFhGEZZTAjCTL9+MHcuZGTAhAlWfWwYRs3DhKAaGDIEXnkFvvgCrrwSnIu0RYZhGMXYhLJq4sILYe1auP9+dRndcUekLTIMw1BMCKqRe++FNWvgrrugWzc477xIW2QYhmGuoWpFBF56CQYNgilT4NtvI22RYRiGCUG106CBtqJISoJx42DnzkhbZBhGrBNWIRCR0SKyTkQ2iEiZoY4icouI/CAi34nIfBE5Ppz21BRatYLZs2HvXnUP5eZG2iLDMGKZsAmBiMQBzwBnAz2AySLSo9Rh3wJpzrnewFvAn8JlT02jTx949VVYskSrj7OyIm2RYRixSjhXBIOADc65Tc65o8BMYLzvAc65Bc457/3wV0DbMNpT4xg/Ht58E5Yvh2HDID090hYZhhGLhFMI2gDbfJ5v9+wLxOXAXH8viMiVIrJURJbu3r27Ck2MPBMnwkcfwa5dcPLJsHJlpC0yDCPWqBHBYhG5BEgD/uzvdefcDOdcmnMuLSUlpXqNqwZOOQU+/xzi4vTnTz6JtEWGYcQS4RSCDKCdz/O2nn0lEJEzgN8C45xzMduA4aSTYPFiHWwzejTMnBlpiwzDiBXCKQRLgC4i0lFE6gMXA7N8DxCRfsBzqAjEfEu2du10zOWQITB5sk46e/BB+OADCyYbhhE+wlZZ7JzLF5HrgHlAHPCSc261iDwILHXOzUJdQY2BN0UEYKtzbly4bKoNJCZqx9K774Z587Rhnbc3UadOMHCgtqs4/3wtUDMMwzhWxNWyDmhpaWlu6dKlkTaj2sjOhmXLdAbykiXw5ZfayfSCC+DZZ3UspmEYRnmIyDLnXJq/12pEsNgITJMmMGIE3H67ppqmp8Mf/gCzZmlc4e23I22hYRi1HROCWkbdunDnnbpKaNtWZxxccgns2RNpywzDqK2YENRSevaEr7+GBx6A11/X5++9F2mrDMOojZgQ1GLq1dP5Bl9/rU3szj1Xs41sJKZhGBXBhCAK6N8fli7V1cH//gfdu8M//mGT0AzDCA0TgighPl5XBytW6AS0adO0md2GDZG2zDCMmo5NKIsyTjwRFi6EGTN0HGavXnDjjToHYedOffz0k2737VNX0kMPQcuWkbbcMIxIYXUEUUxGBlx/fXGKaXIytG6tj1atdN9rr0HDhnDPPXDDDbqyMAwj+ghWR2BCEANkZWk9Qv36ZV9btw5uvVUzjjp1gj//2aqWDSMasYKyGCcpyb8IAHTrptPSPvxQVwYTJ8LIkRpsnj8f1q+HQ4dKvic3Vyudn3sOrrlGeyMNHKgdVA3DqH3YisAoIj8fXngB7rsPSo99SE7WzqiHD8PatVBYqPubN4e+fWHzZti6FX7zG405JCRUt/WGYQTDXENGhcjLg23b9LF1a/F261atXejbVx/9+sHxx6sbKTsbbrtNVwk9esA//wkDBkT6mxiG4cWEwKg2PvgALr9cJ67dcw/89rcqHoZhRBaLERjVxujRsGqVpqX+7ncweLCO4qxl9xuGEVOYEBhVTmIi/OtfWuX8889w1lkwdCi8/74JgmHUREwIjLBx/vmwcSP87W9axDZ2LKSlaV2DN9hsGEbksRiBUS3k5ekq4fe/V3E46STtiXT0aNnHccfBoEH6SEvTzCTDMI4NCxYbNYb8fJg5E55+GnJytL7B+4iP13kLmzdroZuXbt1UFE4/HS66yFJTDaMymBAYtY59+7Sj6jff6OPrr9W9lJKiRWz/7/9BamqkrTSM2oMJgVHrcQ4WLIDp07UdRv36MGUK3HyzNtYzDCM4JgRGVLF+PfzlL/Dyy9ru4rTTYPx4TV3t3j14n6S8PK2MTk+HHTu0MZ/vA3TVkZxcctu5s7beqIhbKj9f6ykyMoo/a+dOjXl07gwnnKD9nRo1OoZ/DMMIERMCIyrZs0fbbf/zn7Bmje5r314FYfRoGDYMtmyB5cuLH99/D0eOFJ+jTh11MbVpow8RyMzUFhuZmfoZ3v8ijRvD2WdrNtSYMdC0afF5Cgu1fmLhQvjsM/jqK73wl/7vJVJ2X+vWKgoDBsC4cXDKKRorCcS2bdof6rvvNGYyYkT5TQKdg9WrNRDfokXwY43oxITAiHq2bIF587Sy+eOPteWFL82b6yS3/v21NcYJJ+hFsVWr4Bfd/HzYuxeWLdO013ff1bv8evV08M/gwfraokUa1wAVo+HDiz+jTRvdHnecri4OHNDMqY0bdXDQxo3w44+wZImKVGKiCs1558EvfqErhhUrYNYs/fxvv9XPSUjQ3k/9+mkH2UmTylZxZ2VpttYLL6gQpKSocI4eXTX/7kbtwYTAiCny8uDLLzXA3KmTXvw7dKia1toFBXq3//bb+ti0Cbp2hVNPLX4cf3zlzp2To11g331X4yB79mgmVVKSupZE4OSTddUwfrwKzr//DU88oe6utm3hppu0xcfy5fD881rUd/SoZl398pcqCKtWaXPA3/8+cFdaUBHMzlZhMmo/JgSGEQac04t3kyZVf+78fPjiCxWFjAy9gx8zxv8kucJCmDMHHn8cPv1U3V2FhboK+r//g1//Gnr31mMPHdLVw7PPqitq5kxdufiyfr22IX/lFc3UOu88DcoPH25zKmozERMCERkN/AWIA15wzj1a6vVTgSeB3sDFzrm3yjunCYFhBGbZMp06178/TJigI0r98fbbunLIy9PK7/POgzffhJde0rkScXEqPF276r49e7S475Zb4IILAjcSPHLEptzVVCIiBCISB6wHzgS2A0uAyc65H3yO6QA0BW4FZpkQGEb1sXUrXHKJxjfi4/Ui3q0bTJumK4nWrfW43FyNK0yfrquFtm31GOdg+3ZdsXi3+/ereIwbp4+TT1ZRMSJPpIRgKPCAc+4Xnud3ATjn/uDn2JeB90wIDKN6yc/XC/zmzSoKQ4cGdv94XVDTp8Mnn6gLqlUrFQZv1lVyssZnPvlEVxtJSdpjatw46NlTVxL16+vW+3ODBnouI7wEE4Ig+RLHTBtgm8/z7cDgypxIRK4ErgRo3779sVtmGAagGVO33RbasXXq6EV97FjNpGrSJHDG1YEDmsX17rv6eOWVwOdNSVERmjZNxaI8Cgois8rYvVuD9j17Rt8qJ5xCUGU452YAM0BXBBE2xzBinvIyiZo21XTWSZN0ZbB4sbqOjh7V597tkSOaNvv007rSGDhQBeHiizXYXVioNSKLFxc/NmyAX/1Ks5687quq4uBBtXPrVv3cH34ofmRm6jFjx2qQPZoKAcMpBBlAO5/nbT37DMOIIerV0+rvYOzeDa++qoHpa67RLKWBA7UA0FufkZSkMYdTT9WYxVtvwb33wo03VixA7ZzWbcybp8F138rv/ftLHtu8uXbKPf98HcF68KDO9D7tNE3xbdUq8Ods3qw9sXJzNaMrza9TJjA5OVo8uH178XbMmPCMgA1njKAuGiw+HRWAJcAvnXOr/Rz7MhYjMIyYxzmtgXjpJV0p9OunF/+hQ6FLl+L4xYYNWgsxa5a263jiCTj33MDxjf37Yf58vfh/+KG2GAFdUbRrVxzj8Bb/tWsHJ56oVeelz/nee1rRnZKiMZMePUq+Xlio6bl33qnutAYNVOimTdNVjL8UYFAhevFFrf3YsqVYAH159lkVysoQyfTRc9D00DjgJefcIyLyILDUOTdLRAYCbwOJwGHgJ+fcScHOaUJgGIaXDz/UIro1a+DMM7XSe+9evYju3Vv8+PFHjS00bqztzM86S6u2O3eu3OcuXaouoiNHNBV3xAjdv2GDpuUuXKi1HzNmqJvsoYe0P1ajRvDAA3DttbpSKizU7/D3v6vAFBTouXr21CB8u3bF2+OOO7bUXCsoMwwjasnL0wvp/ferADRvrjGMxMTin7t21Qv/0KGBayAqSno6nHOOXvxfeEHbefz2t5oJ9eSTcOmlJVcTa9eqaM2bp6uNCRPUHZaerquLadPgiisqL07lYUJgGEbUU1CgF97qTEXdu1cv6J9+qs/HjoXnntO7d384p3f+N92k7UlGjoSrrtIYRLB2H1WBCYFhGEaYOHJEXT89esDkyaG14Th6VEWkOocrRaqOwDAMI+qJj4eHH67Ye+rXr1kT9qyezzAMI8YxITAMw4hxTAgMwzBiHBMCwzCMGMeEwDAMI8YxITAMw4hxTAgMwzBiHBMCwzCMGKfWVRaLyG5gSzmHJQOZ1WBOTcO+d2wRq98bYve7H8v3Pt45l+LvhVonBKEgIksDlVJHM/a9Y4tY/d4Qu989XN/bXEOGYRgxjgmBYRhGjBOtQjAj0gZECPvesUWsfm+I3e8elu8dlTECwzAMI3SidUVgGIZhhIgJgWEYRowTdUIgIqNFZJ2IbBCROyNtT7gQkZdE5GcRWeWzr4WIfCQiP3q2iZG0MRyISDsRWSAiP4jIahG50bM/qr+7iCSIyDcistLzvX/n2d9RRL72/L2/LiJhHngYGUQkTkS+FZH3PM+j/nuLSLqIfC8iK0RkqWdfWP7Oo0oIRCQOeAY4G+gBTBaRHpG1Kmy8DIwute9OYL5zrgsw3/M82sgHfuOc6wEMAa71/I6j/bsfAUY55/oAfYHRIjIE+CMw3Tl3ArAXuDxyJoaVG4E1Ps9j5XuPdM719akdCMvfeVQJATAI2OCc2+ScOwrMBMZH2Kaw4JxbCOwptXs88Irn51eA86rTpurAObfTObfc83M2enFoQ5R/d6fkeJ7W8zwcMAp4y7M/6r43gIi0BcYAL3ieCzHwvQMQlr/zaBOCNsA2n+fbPftihVTn3E7Pzz8BNWgqatUjIh2AfsDXxMB397hHVgA/Ax8BG4F9zrl8zyHR+vf+JHA7UOh5nkRsfG8HfCgiy0TkSs++sPyd2/D6KMU550QkanODRaQx8F/gJufcAb1JVKL1uzvnCoC+ItIceBvoHlmLwo+IjAV+ds4tE5ERETanuhnunMsQkZbARyKy1vfFqvw7j7YVQQbQzud5W8++WGGXiLQG8Gx/jrA9YUFE6qEi8Kpz7n+e3THx3QGcc/uABcBQoLmIeG/oovHvfRgwTkTSUVfvKOAvRP/3xjmX4dn+jAr/IML0dx5tQrAE6OLJKKgPXAzMirBN1cks4FLPz5cC70bQlrDg8Q+/CKxxzj3h81JUf3cRSfGsBBCRBsCZaHxkAXCB57Co+97Oubucc22dcx3Q/8+fOOemEOXfW0QaiUgT78/AWcAqwvR3HnWVxSJyDupTjANecs49ElmLwoOI/AcYgbal3QXcD7wDvAG0R1t1X+icKx1QrtWIyHBgEfA9xT7ju9E4QdR+dxHpjQYH49AbuDeccw+KSCf0TrkF8C1wiXPuSOQsDR8e19Ctzrmx0f69Pd/vbc/TusBrzrlHRCSJMPydR50QGIZhGBUj2lxDhmEYRgUxITAMw4hxTAgMwzBiHBMCwzCMGMeEwDAMI8YxITAMDyJS4On06H1UWeM6Eeng2ynWMGoS1mLCMIo55JzrG2kjDKO6sRWBYZSDpy/8nzy94b8RkRM8+zuIyCci8p2IzBeR9p79qSLytmd2wEoROdlzqjgRed4zT+BDT4UwInKDZ77CdyIyM0Jf04hhTAgMo5gGpVxDF/m8tt851wt4Gq1cB/gr8IpzrjfwKvCUZ/9TwGee2QH9gdWe/V2AZ5xzJwH7gIme/XcC/TznuTo8X80wAmOVxYbhQURynHON/exPR4fCbPI0vPvJOZckIplAa+dcnmf/TudcsojsBtr6tjzwtMz+yDNQBBG5A6jnnHtYRD4ActAWIe/4zB0wjGrBVgSGERouwM8VwbcXTgHFMbox6GS9/sASn66ahlEtmBAYRmhc5LP90vPzYrQjJsAUtBke6AjBa6BomEyzQCcVkTpAO+fcAuAOoBlQZlViGOHE7jwMo5gGnglgXj5wznlTSBNF5Dv0rn6yZ9/1wD9E5DZgN3CZZ/+NwAwRuRy9878G2Il/4oB/e8RCgKc88wYMo9qwGIFhlIMnRpDmnMuMtC2GEQ7MNWQYhhHj2IrAMAwjxrEVgWEYRoxjQmAYhhHjmBAYhmHEOCYEhmEYMY4JgWEYRozz/wGjYfPOvFJyzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, train_dir_NP in enumerate(train_dirs):\n",
    "    \n",
    "    # 当前数据集名字\n",
    "    dataset_name = train_dir_NP.split('/')[-1]\n",
    "    \n",
    "    # 定义数据生成器\n",
    "    datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)\n",
    "    \n",
    "    train_dir = train_dir_NP\n",
    "    val_dir = train_dir_NP\n",
    "    test_dir = '/root/images/test'\n",
    "\n",
    "    # 加载训练集和验证集\n",
    "    train_generator = datagen.flow_from_directory(\n",
    "        train_dir,\n",
    "        target_size=(128, 128),\n",
    "        batch_size=32,\n",
    "        class_mode='categorical',\n",
    "        subset='training'\n",
    "    )\n",
    "\n",
    "    validation_generator = datagen.flow_from_directory(\n",
    "        val_dir,\n",
    "        target_size=(128, 128),\n",
    "        batch_size=32,\n",
    "        class_mode='categorical',\n",
    "        subset='validation'\n",
    "    )\n",
    "\n",
    "    # 加载测试集\n",
    "    test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "    test_generator = test_datagen.flow_from_directory(\n",
    "        test_dir,\n",
    "        target_size=(128, 128),\n",
    "        batch_size=32,\n",
    "        class_mode='categorical',\n",
    "        shuffle=False\n",
    "    )\n",
    "\n",
    "    # 训练模型并绘制准确率图像\n",
    "    history = model.fit(\n",
    "        train_generator,\n",
    "        epochs=50,\n",
    "        validation_data=validation_generator\n",
    "    )\n",
    "    \n",
    "    \n",
    "        \n",
    "    # 保存模型参数\n",
    "    save_dir = '/root/Result_img'\n",
    "    model_dir = os.path.join(save_dir, dataset_name)\n",
    "    if not os.path.exists(model_dir):\n",
    "        os.makedirs(model_dir)\n",
    "    model.save_weights(os.path.join(model_dir, 'model_weights.h5'))\n",
    "\n",
    "    \n",
    "    # 创建保存图像的文件夹\n",
    "    save_dir = 'Result_img'\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "        \n",
    "    # 提取训练集和验证集的准确率\n",
    "    train_acc = history.history['accuracy']\n",
    "    val_acc = history.history['val_accuracy']\n",
    "    \n",
    "    # 提取训练集和验证集的损失\n",
    "    train_loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "\n",
    "    # 绘制准确率图像\n",
    "    plt.figure()\n",
    "    epochs = range(1, len(train_acc) + 1)\n",
    "    plt.plot(epochs, train_acc, 'b', label='Training Accuracy')\n",
    "    plt.plot(epochs, val_acc, 'orange', label='Validation Accuracy')\n",
    "    # plt.title('Training and Validation Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "    plt.savefig(os.path.join(save_dir, dataset_name+'accuracy_plot.png'), dpi=1200)\n",
    "\n",
    "    \n",
    "    # 绘制损失图像\n",
    "    plt.figure()\n",
    "    plt.plot(epochs, train_loss, 'b', label='Training Loss')\n",
    "    plt.plot(epochs, val_loss, 'orange', label='Validation Loss')\n",
    "    # plt.title('Training and Validation Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.savefig(os.path.join(save_dir, dataset_name+'loss_plot.png'), dpi=1200)\n",
    "    \n",
    "    # 绘制混淆矩阵\n",
    "    plot_confusion_matrix(model, test_generator, dataset_name)\n",
    "    \n",
    "    print(\"--------\",dataset_name, \"-------- Over\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea7ef6c2-6dbb-4c54-b931-656c18c6993d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}