{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PkguGjcsIOIS",
    "outputId": "0dba5e80-f8c1-46d7-8e51-9adbfb26be7b"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2ItiaVL3IdD9"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout,BatchNormalization,ReLU,ReLU,Softmax\n",
    "from tensorflow.keras.utils import to_categorical, plot_model\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.model_selection import  KFold\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B9qdOC6oIfJ6"
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('/content/drive/MyDrive/1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cq_gODctIg3E",
    "outputId": "8dd565cf-fbfb-4d1e-f706-d0e0dd174cc1"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "   0.102581735489803  0.724556409349005  0.764948186436089  0.757163322363814  \\\n",
      "0           0.602749           0.284292           0.720245           0.741244   \n",
      "1           0.760409           0.134785           0.716614           0.507548   \n",
      "2           0.762393           0.208646           0.642720           0.605443   \n",
      "3           0.815246           0.441437           0.365664           0.809652   \n",
      "4           0.673585           0.625720           0.320228           0.700190   \n",
      "\n",
      "   0.530662468164046  0.371689696046641  0.284461706937353  0.444367899771322  \\\n",
      "0           0.454986           0.633573           0.290452           0.806809   \n",
      "1           0.671592           0.697440           0.538643           0.449177   \n",
      "2           0.899200           0.503576           0.430611           0.180712   \n",
      "3           0.773291           0.481069           0.102943           0.384725   \n",
      "4           0.499120           0.709004           0.181829           0.673383   \n",
      "\n",
      "   0.714094925340514  0.322918052054923  0.801823692806282  0.67721240399929  \\\n",
      "0           0.200687           0.718742           0.308505          0.549963   \n",
      "1           0.083641           0.399047           0.363404          0.826378   \n",
      "2           0.413816           0.493964           0.662314          0.749980   \n",
      "3           0.583345           0.518811           0.512786          0.557656   \n",
      "4           0.144706           0.566671           0.529550          0.797952   \n",
      "\n",
      "   0.0289067950135131  0.786849552072489  0.222068869672817  \\\n",
      "0            0.079399           0.851810           0.169524   \n",
      "1            0.148645           1.000000           0.305487   \n",
      "2            0.171882           0.946018           0.333909   \n",
      "3            0.304696           0.818874           0.385432   \n",
      "4            0.485813           0.877971           0.423867   \n",
      "\n",
      "   0.821860291534149  0.604044343681583  0.498664485712102  0.67747345818933  \\\n",
      "0           0.742309           0.661901           0.603816          0.713771   \n",
      "1           0.674207           0.702632           0.713821          0.684765   \n",
      "2           0.661408           0.728011           0.712760          0.661336   \n",
      "3           0.522193           0.830188           0.778664          0.653913   \n",
      "4           0.490473           0.908601           0.823217          0.609619   \n",
      "\n",
      "   0  \n",
      "0  0  \n",
      "1  0  \n",
      "2  0  \n",
      "3  0  \n",
      "4  0  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156623 entries, 0 to 156622\n",
      "Data columns (total 20 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   0.102581735489803   156623 non-null  float64\n",
      " 1   0.724556409349005   156623 non-null  float64\n",
      " 2   0.764948186436089   156623 non-null  float64\n",
      " 3   0.757163322363814   156623 non-null  float64\n",
      " 4   0.530662468164046   156623 non-null  float64\n",
      " 5   0.371689696046641   156623 non-null  float64\n",
      " 6   0.284461706937353   156623 non-null  float64\n",
      " 7   0.444367899771322   156623 non-null  float64\n",
      " 8   0.714094925340514   156623 non-null  float64\n",
      " 9   0.322918052054923   156623 non-null  float64\n",
      " 10  0.801823692806282   156623 non-null  float64\n",
      " 11  0.67721240399929    156623 non-null  float64\n",
      " 12  0.0289067950135131  156623 non-null  float64\n",
      " 13  0.786849552072489   156623 non-null  float64\n",
      " 14  0.222068869672817   156623 non-null  float64\n",
      " 15  0.821860291534149   156623 non-null  float64\n",
      " 16  0.604044343681583   156623 non-null  float64\n",
      " 17  0.498664485712102   156623 non-null  float64\n",
      " 18  0.67747345818933    156623 non-null  float64\n",
      " 19  0                   156623 non-null  int64  \n",
      "dtypes: float64(19), int64(1)\n",
      "memory usage: 23.9 MB\n",
      "None\n",
      "       0.102581735489803  0.724556409349005  0.764948186436089  \\\n",
      "count      156623.000000      156623.000000      156623.000000   \n",
      "mean            0.518655           0.505645           0.479088   \n",
      "std             0.268414           0.273008           0.272898   \n",
      "min             0.000000           0.000000           0.000000   \n",
      "25%             0.292865           0.271842           0.243490   \n",
      "50%             0.526884           0.501979           0.467729   \n",
      "75%             0.745828           0.741806           0.711057   \n",
      "max             1.000000           1.000000           1.000000   \n",
      "\n",
      "       0.757163322363814  0.530662468164046  0.371689696046641  \\\n",
      "count      156623.000000      156623.000000      156623.000000   \n",
      "mean            0.466694           0.465148           0.467574   \n",
      "std             0.286198           0.285123           0.286729   \n",
      "min             0.000000           0.000000           0.000000   \n",
      "25%             0.214099           0.213816           0.213231   \n",
      "50%             0.437292           0.435744           0.439153   \n",
      "75%             0.711886           0.710079           0.713401   \n",
      "max             1.000000           1.000000           1.000000   \n",
      "\n",
      "       0.284461706937353  0.444367899771322  0.714094925340514  \\\n",
      "count      156623.000000      156623.000000      156623.000000   \n",
      "mean            0.377368           0.382317           0.354512   \n",
      "std             0.279303           0.279027           0.276872   \n",
      "min             0.000000           0.000000           0.000000   \n",
      "25%             0.120759           0.127849           0.100695   \n",
      "50%             0.332090           0.343407           0.299588   \n",
      "75%             0.609650           0.610500           0.571622   \n",
      "max             1.000000           1.000000           1.000000   \n",
      "\n",
      "       0.322918052054923  0.801823692806282  0.67721240399929  \\\n",
      "count      156623.000000      156623.000000     156623.000000   \n",
      "mean            0.541857           0.495443          0.523748   \n",
      "std             0.251077           0.305777          0.302697   \n",
      "min             0.000000           0.000000          0.000000   \n",
      "25%             0.387337           0.228836          0.246810   \n",
      "50%             0.543572           0.451187          0.537918   \n",
      "75%             0.721037           0.799254          0.808471   \n",
      "max             1.000000           1.000000          1.000000   \n",
      "\n",
      "       0.0289067950135131  0.786849552072489  0.222068869672817  \\\n",
      "count       156623.000000      156623.000000      156623.000000   \n",
      "mean             0.273751           0.451913           0.542933   \n",
      "std              0.369948           0.265138           0.259846   \n",
      "min              0.000000           0.000000           0.000000   \n",
      "25%              0.008337           0.228806           0.363180   \n",
      "50%              0.068020           0.443981           0.547189   \n",
      "75%              0.422994           0.647125           0.748064   \n",
      "max              1.000000           1.000000           1.000000   \n",
      "\n",
      "       0.821860291534149  0.604044343681583  0.498664485712102  \\\n",
      "count      156623.000000      156623.000000      156623.000000   \n",
      "mean            0.364475           0.466899           0.481417   \n",
      "std             0.208909           0.249841           0.280186   \n",
      "min             0.000000           0.000000           0.000000   \n",
      "25%             0.205358           0.289576           0.242153   \n",
      "50%             0.335767           0.417450           0.485370   \n",
      "75%             0.494680           0.663056           0.729370   \n",
      "max             1.000000           1.000000           1.000000   \n",
      "\n",
      "       0.67747345818933              0  \n",
      "count     156623.000000  156623.000000  \n",
      "mean           0.510511     183.924251  \n",
      "std            0.286160     204.498835  \n",
      "min            0.000000       0.000000  \n",
      "25%            0.264321      15.000000  \n",
      "50%            0.519612     123.000000  \n",
      "75%            0.752868     345.000000  \n",
      "max            1.000000     614.000000  \n"
     ]
    }
   ],
   "source": [
    "print(data.head())\n",
    "print(data.info())\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MMLMOkLhIjc2"
   },
   "outputs": [],
   "source": [
    "feature_columns = data.columns[:-1]\n",
    "label_column = data.columns[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jnPdibjpImSi"
   },
   "outputs": [],
   "source": [
    "# Extract features and labels\n",
    "X = data[feature_columns]\n",
    "y = data[label_column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MC0E7E2GIo3F"
   },
   "outputs": [],
   "source": [
    "# Convert labels to numeric\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "y_encoded = label_encoder.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TvXzVtc4Ir2v"
   },
   "outputs": [],
   "source": [
    "# Number of samples\n",
    "num_cases_per_sample = 251\n",
    "\n",
    "# Create samples\n",
    "samples = []\n",
    "labels = []\n",
    "\n",
    "num_samples = len(X) // num_cases_per_sample\n",
    "\n",
    "for i in range(num_samples):\n",
    "    start_index = i * num_cases_per_sample\n",
    "    end_index = start_index + num_cases_per_sample\n",
    "\n",
    "    # Extract the sample and the corresponding labels\n",
    "    sample_X = X.iloc[start_index:end_index]\n",
    "    sample_y = y_encoded[start_index:end_index]\n",
    "\n",
    "    # Convert the 2D sample to a 3D vector (num_cases_per_sample, num_features, 1)\n",
    "    sample_X_3d = sample_X.values.reshape(num_cases_per_sample, -1, 1)\n",
    "\n",
    "    # Append the 3D vector to the samples list\n",
    "    samples.append(sample_X_3d)\n",
    "\n",
    "    # Majority voting for the label\n",
    "    label = np.bincount(sample_y).argmax()  # Choosing the most frequent label in the sample\n",
    "    labels.append(label)\n",
    "\n",
    "# Convert the list of samples and labels to NumPy arrays\n",
    "X_samples = np.array(samples)\n",
    "y_samples = np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xEHsTmtWIvED",
    "outputId": "2300015a-e430-41a4-f3ce-d2d746fda658"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38])"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "np.unique(y_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RV_L-9W5Iyrv"
   },
   "outputs": [],
   "source": [
    "num_classes = len(np.unique(y_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zwTRho8qI2lr"
   },
   "outputs": [],
   "source": [
    "def create_cnn_model(input_shape, num_classes):\n",
    "    model = Sequential([\n",
    "\n",
    "    Conv2D(8, (3, 3), activation='relu', input_shape=(num_cases_per_sample, X.shape[1], 1)),\n",
    "\n",
    "    # 3. Batch Normalization\n",
    "    BatchNormalization(),\n",
    "\n",
    "    # 4. ReLU Activation\n",
    "    ReLU(),\n",
    "\n",
    "    # 5. 2-D Max Pooling: 2x2 pool size, stride 2x2, no padding\n",
    "    MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),\n",
    "\n",
    "    # 6. 2-D Convolution: 16 filters, 3x3 kernel size, stride 1x1, 'same' padding\n",
    "    Conv2D(16, (3, 3), strides=(1, 1), padding='same'),\n",
    "\n",
    "    # 7. Batch Normalization\n",
    "    BatchNormalization(),\n",
    "\n",
    "    # 8. ReLU Activation\n",
    "    ReLU(),\n",
    "\n",
    "    # 9. 2-D Max Pooling: 2x2 pool size, stride 2x2, no padding\n",
    "    MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),\n",
    "\n",
    "    # 10. 2-D Convolution: 32 filters, 3x3 kernel size, stride 1x1, 'same' padding\n",
    "    Conv2D(32, (3, 3), strides=(1, 1), padding='same'),\n",
    "\n",
    "    # 11. Batch Normalization\n",
    "    BatchNormalization(),\n",
    "\n",
    "    # 12. ReLU Activation\n",
    "    ReLU(),\n",
    "\n",
    "    # 13. Flatten the output before passing it to the fully connected layer\n",
    "    Flatten(),\n",
    "\n",
    "    # 14. Fully Connected Layer: 39 units\n",
    "    Dense(39),\n",
    "\n",
    "    # 15. Softmax Layer for classification\n",
    "    Softmax()\n",
    "])\n",
    "    model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    model.summary()\n",
    "    plot_model(model, to_file='model_structure.png', show_shapes=True, show_layer_names=True)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "lsYqMeEuI8Fl",
    "outputId": "b3f90a93-93da-478a-c40b-dc88f0a10fd5"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m80\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_6                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m32\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_6 (\u001b[38;5;33mReLU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m1,168\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_7                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │              \u001b[38;5;34m64\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_7 (\u001b[38;5;33mReLU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │           \u001b[38;5;34m4,640\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_8                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_8 (\u001b[38;5;33mReLU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7936\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │         \u001b[38;5;34m309,543\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_2 (\u001b[38;5;33mSoftmax\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_6                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,168</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_7                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_8                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7936</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">309,543</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Softmax</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m315,655\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,655</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m315,543\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,543</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m112\u001b[0m (448.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">112</span> (448.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training for fold 1 ...\n",
      "Epoch 1/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 42ms/step - accuracy: 0.4676 - loss: 3.4398 - val_accuracy: 0.1040 - val_loss: 3.4451\n",
      "Epoch 2/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 31ms/step - accuracy: 0.8902 - loss: 0.4220 - val_accuracy: 0.1040 - val_loss: 3.4715\n",
      "Epoch 3/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 0.9689 - loss: 0.1150 - val_accuracy: 0.0960 - val_loss: 3.2561\n",
      "Epoch 4/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - accuracy: 0.9769 - loss: 0.0440 - val_accuracy: 0.0960 - val_loss: 2.9403\n",
      "Epoch 5/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 52ms/step - accuracy: 1.0000 - loss: 0.0056 - val_accuracy: 0.2240 - val_loss: 2.3676\n",
      "Epoch 6/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 0.0040 - val_accuracy: 0.6560 - val_loss: 1.5547\n",
      "Epoch 7/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 0.9826 - loss: 0.0916 - val_accuracy: 0.8960 - val_loss: 0.4310\n",
      "Epoch 8/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 0.9879 - loss: 0.0174 - val_accuracy: 0.9440 - val_loss: 0.3196\n",
      "Epoch 9/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 0.0021 - val_accuracy: 0.9920 - val_loss: 0.1625\n",
      "Epoch 10/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 0.0015 - val_accuracy: 0.9840 - val_loss: 0.0793\n",
      "Epoch 11/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 9.8352e-04 - val_accuracy: 0.9840 - val_loss: 0.0491\n",
      "Epoch 12/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 5.6786e-04 - val_accuracy: 1.0000 - val_loss: 0.0409\n",
      "Epoch 13/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 4.9745e-04 - val_accuracy: 0.9840 - val_loss: 0.0384\n",
      "Epoch 14/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 5.4464e-04 - val_accuracy: 0.9840 - val_loss: 0.0376\n",
      "Epoch 15/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 4.7351e-04 - val_accuracy: 0.9920 - val_loss: 0.0348\n",
      "Epoch 16/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 52ms/step - accuracy: 1.0000 - loss: 4.1689e-04 - val_accuracy: 0.9840 - val_loss: 0.0353\n",
      "Epoch 17/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 2.9600e-04 - val_accuracy: 0.9920 - val_loss: 0.0337\n",
      "Epoch 18/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 2.9020e-04 - val_accuracy: 0.9920 - val_loss: 0.0328\n",
      "Epoch 19/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 3.5755e-04 - val_accuracy: 0.9920 - val_loss: 0.0326\n",
      "Epoch 20/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 50ms/step - accuracy: 1.0000 - loss: 3.3266e-04 - val_accuracy: 0.9840 - val_loss: 0.0343\n",
      "Epoch 21/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 48ms/step - accuracy: 1.0000 - loss: 2.7205e-04 - val_accuracy: 0.9840 - val_loss: 0.0341\n",
      "Epoch 22/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 2.0372e-04 - val_accuracy: 0.9840 - val_loss: 0.0326\n",
      "Epoch 23/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 1.8080e-04 - val_accuracy: 0.9920 - val_loss: 0.0311\n",
      "Epoch 24/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 1.9741e-04 - val_accuracy: 0.9920 - val_loss: 0.0301\n",
      "Epoch 25/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 1.6468e-04 - val_accuracy: 0.9920 - val_loss: 0.0302\n",
      "Epoch 26/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 44ms/step - accuracy: 1.0000 - loss: 1.5586e-04 - val_accuracy: 0.9920 - val_loss: 0.0294\n",
      "Epoch 27/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 1.5027e-04 - val_accuracy: 0.9920 - val_loss: 0.0292\n",
      "Epoch 28/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 1.3729e-04 - val_accuracy: 0.9920 - val_loss: 0.0290\n",
      "Epoch 29/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 1.4764e-04 - val_accuracy: 0.9920 - val_loss: 0.0286\n",
      "Epoch 30/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 1.1274e-04 - val_accuracy: 0.9920 - val_loss: 0.0276\n",
      "Epoch 31/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 1.0000 - loss: 1.1278e-04 - val_accuracy: 0.9920 - val_loss: 0.0272\n",
      "Epoch 32/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 49ms/step - accuracy: 1.0000 - loss: 1.4238e-04 - val_accuracy: 0.9920 - val_loss: 0.0276\n",
      "Epoch 33/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 9.5131e-05 - val_accuracy: 0.9920 - val_loss: 0.0267\n",
      "Epoch 34/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 8.6366e-05 - val_accuracy: 0.9920 - val_loss: 0.0262\n",
      "Epoch 35/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 9.0263e-05 - val_accuracy: 0.9920 - val_loss: 0.0255\n",
      "Epoch 36/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 8.8529e-05 - val_accuracy: 0.9920 - val_loss: 0.0267\n",
      "Epoch 37/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 8.1303e-05 - val_accuracy: 0.9920 - val_loss: 0.0254\n",
      "Epoch 38/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 8.2978e-05 - val_accuracy: 0.9920 - val_loss: 0.0241\n",
      "Epoch 39/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 43ms/step - accuracy: 1.0000 - loss: 8.0818e-05 - val_accuracy: 0.9920 - val_loss: 0.0237\n",
      "Epoch 40/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 8.1921e-05 - val_accuracy: 0.9920 - val_loss: 0.0241\n",
      "Epoch 41/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 8.0042e-05 - val_accuracy: 0.9920 - val_loss: 0.0246\n",
      "Epoch 42/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 6.5468e-05 - val_accuracy: 0.9920 - val_loss: 0.0243\n",
      "Epoch 43/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 5.8408e-05 - val_accuracy: 0.9920 - val_loss: 0.0240\n",
      "Epoch 44/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 41ms/step - accuracy: 1.0000 - loss: 5.9029e-05 - val_accuracy: 0.9920 - val_loss: 0.0229\n",
      "Epoch 45/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 5.5472e-05 - val_accuracy: 0.9920 - val_loss: 0.0233\n",
      "Epoch 46/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 5.8506e-05 - val_accuracy: 0.9920 - val_loss: 0.0234\n",
      "Epoch 47/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 4.5395e-05 - val_accuracy: 0.9920 - val_loss: 0.0235\n",
      "Epoch 48/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 4.3537e-05 - val_accuracy: 0.9920 - val_loss: 0.0228\n",
      "Epoch 49/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 52ms/step - accuracy: 1.0000 - loss: 4.1755e-05 - val_accuracy: 0.9920 - val_loss: 0.0221\n",
      "Epoch 50/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 41ms/step - accuracy: 1.0000 - loss: 4.6596e-05 - val_accuracy: 0.9920 - val_loss: 0.0218\n",
      "Score for fold 1: loss of 0.02176155336201191; compile_metrics of 99.19999837875366%\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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XK/jr+/PUarc1atTQZZddpo8++kj5+fn673//q8zMzCJznt59910NHTpUDRs21LRp0/TFF19o0aJFuuSSS0r173KunnnmGY0ePVrdunXTu+++q4ULF2rRokVq2bJlhb7uqSr656I0atSooXXr1unTTz91z0Lr06dPkblx3bp109atWzV9+nS1atVKb775pi644AK9+eabHqsTAABvxzko56Cl4cvnoBkZGfrvf/+rbdu2qXHjxu5LixYtdPz4cc2aNcuj57F/XcDOpaT/Ld599916+umndf311+uDDz7Ql19+qUWLFqlatWrn9LkPHjxYWVlZmj9/vgzD0KxZs3TllVcqNja2zM8F+DIWIgNw3pYuXapDhw5p3rx56tatm3v/tm3bLKzqpBo1aig8PFxbtmwpdl9J+/5qw4YN+uOPP/TWW29p8ODB7v1n+sr72dStW1dLlixRVlZWkU6H33//vUzPM2jQIH3xxRf6/PPPNWvWLMXExKhfv37u++fOnasGDRpo3rx5Rb4GVdJXqUpTsyRt3rxZDRo0cO8/ePBgsc6BuXPn6uKLL9a0adOK7D969Kji4uLct8vy1ay6detq8eLFyszMLNLp4Prqo6s+T6hbt65+/vlnOZ3OIt22JdUSFhamfv36qV+/fnI6nbrrrrv0+uuv6/HHH3d32VStWlXDhg3TsGHDlJWVpW7duumJJ57QiBEjPPaeAADwNZyDlh3noCZvPAedN2+ecnJy9NprrxWpVTL/fR577DEtX75cF110kRo2bKiFCxfq8OHDp+22bdiwoZxOpzZu3HjGhd+qVKmio0ePFtmXl5entLS0Utc+d+5cDRkyRJMmTXLvy8nJKfa8DRs21C+//HLW52vVqpXatWun9957T7Vr19bOnTv16quvlroewF/QaQvgvLn+mnzqX37z8vL073//26qSiggODlbPnj01f/587d27171/y5YtxWZQne7xUtH3ZxiGXn755XOuqW/fviooKNBrr73m3udwOMp8MjJgwABFRkbq3//+tz7//HNdffXVCg8PP2PtP/zwg1auXFnmmnv27KnQ0FC9+uqrRZ5v8uTJxY4NDg4u1gnw4Ycfas+ePUX2VapUSZKKndCVpG/fvnI4HJoyZUqR/S+99JJsNlupZ8OVh759+2rfvn2aM2eOe19BQYFeffVVRUVFub+2eOjQoSKPCwoKUps2bSRJubm5JR4TFRWlRo0aue8HAAAl4xy07DgHNXnjOei7776rBg0a6I477tC1115b5DJmzBhFRUW5RyRcc801MgxDEyZMKPY8rvc/YMAABQUFaeLEicW6XU/9jBo2bFhkPrEkvfHGG6fttC1JSZ/7q6++Wuw5rrnmGq1fv14ff/zxaet2ueWWW/Tll19q8uTJqlatmkfP9QFvQactgPPWuXNnValSRUOGDNE999wjm82md955x6Nf3zmbJ554Ql9++aW6dOmiO++8033i1apVK61bt+6Mj23WrJkaNmyoMWPGaM+ePYqJidFHH310XrNR+/Xrpy5duugf//iHtm/frhYtWmjevHllnrUVFRWlAQMGuGeKnfq1NEm68sorNW/ePA0cOFBXXHGFtm3bpqlTp6pFixbKysoq02tVr15dY8aMUWpqqq688kr17dtXP/30kz7//PNi3QBXXnmlJk6cqGHDhqlz587asGGD3nvvvSLdEZJ5kli5cmVNnTpV0dHRqlSpkjp16lTirKx+/frp4osv1qOPPqrt27crOTlZX375pT755BPdd999RRZ8KA9LlixRTk5Osf0DBgzQ7bffrtdff11Dhw7Vjz/+qHr16mnu3Llavny5Jk+e7O7CGDFihA4fPqxLLrlEtWvX1o4dO/Tqq6+qbdu27jloLVq0UI8ePdS+fXtVrVpVa9as0dy5czVq1KhyfT8AAPgbzkHLjnNQk7edg+7du1dff/11scXOXOx2u3r16qUPP/xQr7zyii6++GLdcssteuWVV7R582b17t1bTqdT3377rS6++GKNGjVKjRo10qOPPqonn3xSXbt21dVXXy273a7Vq1erZs2aSk1NlWSer95xxx265pprdNlll2n9+vVauHBhsc/2TK688kq98847io2NVYsWLbRy5UotXrxY1apVK3Lcgw8+qLlz5+q6667T8OHD1b59ex0+fFiffvqppk6dquTkZPexN998sx566CF9/PHHuvPOOxUaGnoOnyzg4wwAKMHIkSONv/4nonv37kbLli1LPH758uXG3/72NyMiIsKoWbOm8dBDDxkLFy40JBlff/21+7ghQ4YYdevWdd/etm2bIcl4/vnniz2nJGP8+PHu2+PHjy9WkyRj5MiRxR5bt25dY8iQIUX2LVmyxGjXrp0RFhZmNGzY0HjzzTeNBx54wAgPDz/Np3DSxo0bjZ49expRUVFGXFyccdtttxnr1683JBkzZswo8v4qVapU7PEl1X7o0CHjlltuMWJiYozY2FjjlltuMX766adiz3k2n332mSHJSExMNBwOR5H7nE6n8cwzzxh169Y17Ha70a5dO+N///tfsX8Hwyj+ec+YMcOQZGzbts29z+FwGBMmTDASExONiIgIo0ePHsYvv/xS7PPOyckxHnjgAfdxXbp0MVauXGl0797d6N69e5HX/eSTT4wWLVoYISEhRd57STVmZmYa999/v1GzZk0jNDTUaNy4sfH8888bTqez2Hsp7c/FX7l+Jk93eeeddwzDMIz9+/cbw4YNM+Li4oywsDCjdevWxf7d5s6da1x++eVGjRo1jLCwMKNOnTrG3//+dyMtLc19zFNPPWV07NjRqFy5shEREWE0a9bMePrpp428vLwz1gkAgD/iHLQozkFN/n4OOmnSJEOSsWTJktMeM3PmTEOS8cknnxiGYRgFBQXG888/bzRr1swICwszqlevbvTp08f48ccfizxu+vTpRrt27Qy73W5UqVLF6N69u7Fo0SL3/Q6Hw3j44YeNuLg4IzIy0ujVq5exZcuWYjW7/l1Wr15drLYjR464z4ujoqKMXr16Gb/99luJ7/vQoUPGqFGjjFq1ahlhYWFG7dq1jSFDhhjp6enFnrdv376GJGPFihWn/VwAf2YzDC/6MyQAeNiAAQP066+/avPmzVaXAgAAgADBOShwdgMHDtSGDRtKNQMa8EfMtAUQME6cOFHk9ubNm7VgwQL16NHDmoIAAADg9zgHBcouLS1Nn332mW655RarSwEsQ6ctgICRmJiooUOHqkGDBtqxY4dee+015ebm6qefflLjxo2tLg8AAAB+iHNQoPS2bdum5cuX680339Tq1au1detWJSQkWF0WYAkWIgMQMHr37q33339f+/btk91uV0pKip555hlOlgEAAFBhOAcFSu+bb77RsGHDVKdOHb311lsEtghodNoCAAAAAAAAgBdhpi0AAAAAAAAAeBFCWwAAAAAAAADwIgE309bpdGrv3r2Kjo6WzWazuhwAAACUgWEYyszMVM2aNRUUFLj9B5zTAgAA+KbSns8GXGi7d+9eJSUlWV0GAAAAzsOuXbtUu3Ztq8uwDOe0AAAAvu1s57MBF9pGR0dLMj+YmJgYi6sBAABAWWRkZCgpKcl9TheoOKcFAADwTaU9nw240Nb19bGYmBhOcAEAAHxUoI8E4JwWAADAt53tfDZwB4EBAAAAAAAAgBcitAUAAAAAAAAAL0JoCwAAAAAAAABeJOBm2gIAgPLhcDiUn59vdRnwM6GhoQoODra6DAAAAMBShLYAAKBMDMPQvn37dPToUatLgZ+qXLmyEhISAn6xMQAAAAQuQlsAAFAmrsC2Ro0aioyMJFhDuTEMQ8ePH9eBAwckSYmJiRZXBAAAAFiD0BYAAJSaw+FwB7bVqlWzuhz4oYiICEnSgQMHVKNGDUYlAAAAICCxEBkAACg11wzbyMhIiyuBP3P9fDEzGQAAAIGK0BYAAJQZIxFQkfj5AgAAQKAjtAUAAAAAAAAAL0JoCwAAcI7q1aunyZMnl/r4pUuXymaz6ejRoxVWEwAAAADfR2gLAAD8ns1mO+PliSeeOKfnXb16tW6//fZSH9+5c2elpaUpNjb2nF6vtAiHAQAAAN9maWj72muvqU2bNoqJiVFMTIxSUlL0+eefn/b4mTNnFvslKzw83IMVAwAAX5SWlua+TJ48WTExMUX2jRkzxn2sYRgqKCgo1fNWr169TIuyhYWFKSEhgZmtAAAAAM7I0tC2du3aevbZZ/Xjjz9qzZo1uuSSS9S/f3/9+uuvp33MX3/J2rFjhwcrBgAAvighIcF9iY2Nlc1mc9/+7bffFB0drc8//1zt27eX3W7Xd999p61bt6p///6Kj49XVFSULrzwQi1evLjI8/51PILNZtObb76pgQMHKjIyUo0bN9ann37qvv+vHbAzZ85U5cqVtXDhQjVv3lxRUVHq3bu30tLS3I8pKCjQPffco8qVK6tatWp6+OGHNWTIEA0YMOCcP48jR45o8ODBqlKliiIjI9WnTx9t3rzZff+OHTvUr18/ValSRZUqVVLLli21YMEC92MHDRqk6tWrKyIiQo0bN9aMGTPOuRYAAAAAxVka2vbr1099+/ZV48aN1aRJEz399NOKiorS999/f9rHnPpLVkJCguLj4z1YMQAA+CvDMHQ8r8DjF8MwyvV9/OMf/9Czzz6rTZs2qU2bNsrKylLfvn21ZMkS/fTTT+rdu7f69eunnTt3nvF5JkyYoOuvv14///yz+vbtq0GDBunw4cOnPf748eN64YUX9M4772jZsmXauXNnkc7f5557Tu+9955mzJih5cuXKyMjQ/Pnzz+v9zp06FCtWbNGn376qVauXCnDMNS3b1/l5+dLkkaOHKnc3FwtW7ZMGzZs0HPPPaeoqChJ0uOPP66NGzfq888/16ZNm/Taa68pLi7uvOoBAAAAUFSI1QW4OBwOffjhh8rOzlZKSsppj8vKylLdunXldDp1wQUX6JlnnlHLli1Pe3xubq5yc3PdtzMyMsq1bgAAAt2JfIdajFvo8dfdOLGXIsPK71Rm4sSJuuyyy9y3q1atquTkZPftJ598Uh9//LE+/fRTjRo16rTPM3ToUN10002SpGeeeUavvPKKVq1apd69e5d4fH5+vqZOnaqGDRtKkkaNGqWJEye673/11Vc1duxYDRw4UJI0ZcoUd9frudi8ebM+/fRTLV++XJ07d5Ykvffee0pKStL8+fN13XXXaefOnbrmmmvUunVrSVKDBg3cj9+5c6fatWunDh06SDK7jQEAAACUL8sXItuwYYOioqJkt9t1xx136OOPP1aLFi1KPLZp06aaPn26PvnkE7377rtyOp3q3Lmzdu/efdrnT01NVWxsrPuSlJRUUW8FAAD4MFcI6ZKVlaUxY8aoefPmqly5sqKiorRp06azdtq2adPGfb1SpUqKiYnRgQMHTnt8ZGSkO7CVpMTERPfxx44d0/79+9WxY0f3/cHBwWrfvn2Z3tupNm3apJCQEHXq1Mm9r1q1amratKk2bdokSbrnnnv01FNPqUuXLho/frx+/vln97F33nmnZs+erbZt2+qhhx7SihUrzrkWf7Fs2TL169dPNWvWlM1mK1Un9NKlS3XBBRfIbrerUaNGmjlzZoXXCQAAAN9headt06ZNtW7dOh07dkxz587VkCFD9M0335QY3KakpBTpwu3cubOaN2+u119/XU8++WSJzz927FiNHj3afTsjI4PgFgCAchQRGqyNE3tZ8rrlqVKlSkVujxkzRosWLdILL7ygRo0aKSIiQtdee63y8vLO+DyhoaFFbttsNjmdzjIdX96jH8pqxIgR6tWrlz777DN9+eWXSk1N1aRJk3T33XerT58+2rFjhxYsWKBFixbp0ksv1ciRI/XCCy9YWrOVsrOzlZycrOHDh+vqq68+6/Hbtm3TFVdcoTvuuEPvvfeelixZohEjRigxMVG9enn+f0sAAADwPpaHtmFhYWrUqJEkqX379lq9erVefvllvf7662d9bGhoqNq1a6ctW7ac9hi73S673V5u9QIAgKJsNlu5jinwFsuXL9fQoUPdYwmysrK0fft2j9YQGxur+Ph4rV69Wt26dZNkjpRau3at2rZte07P2bx5cxUUFOiHH35wj0c4dOiQfv/99yJ/NE9KStIdd9yhO+64Q2PHjtV//vMf3X333ZKk6tWra8iQIRoyZIi6du2qBx98MKBD2z59+qhPnz6lPn7q1KmqX7++Jk2aJMn8N/nuu+/00ksvEdoCAABAkheEtn/ldDqLzKA9E4fDoQ0bNqhv374VXJUHFORK27+T/vhCOrZHuuIFKaam1VUBABCwGjdurHnz5qlfv36y2Wx6/PHHz9gxW1HuvvtupaamqlGjRmrWrJleffVVHTlyRDab7ayP3bBhg6Kjo923bTabkpOT1b9/f9122216/fXXFR0drX/84x+qVauW+vfvL0m677771KdPHzVp0kRHjhzR119/rebNm0uSxo0bp/bt26tly5bKzc3V//73P/d9KJ2VK1eqZ8+eRfb16tVL991332kfwzoNZZfvcGrBhjR9tHaPMk7kl/px1SqF6eE+zdQkPvrsB1eAvUdPaPmWdK3Yekjb0rMtqeFUkc5sDcp+W3bl6p1KQ3UsqLLVJQEAUO76tk7Q7d0anv1AD7I0tB07dqz69OmjOnXqKDMzU7NmzdLSpUu1cKG5mMngwYNVq1YtpaamSjIXCPnb3/6mRo0a6ejRo3r++ee1Y8cOjRgxwsq3ce6y06U/FppB7davpLysk/fFJEpXTLKuNgAAAtyLL76o4cOHq3PnzoqLi9PDDz9sSVD28MMPa9++fRo8eLCCg4N1++23q1evXgoOPvt4CFd3rktwcLAKCgo0Y8YM3XvvvbryyiuVl5enbt26acGCBe5RDQ6HQyNHjtTu3bsVExOj3r1766WXXpJkfktq7Nix2r59uyIiItS1a1fNnj27/N+4H9u3b5/i4+OL7IuPj1dGRoZOnDihiIiIYo9JTU3VhAkTPFWiTzuSnadZq3bqnZU7tC8j55yeY9X2w3r9lvbq3DCunKsr7ujxPK3cekjLt6Zr+RbvCGpdOtk26Z9hr6m2LV2SlHziez2cf7u+cl5gcWUAAJSvNrVjrS6hGJth4dC0W2+9VUuWLFFaWppiY2PVpk0bPfzww+6Vm3v06KF69eq5F2a4//77NW/ePO3bt09VqlRR+/bt9dRTT6ldu3alfs2MjAzFxsbq2LFjiomJqYi3dXqGIR3YJP3xufT7F9Lu1ZJO+fij4qVaHaTfP5PCoqUHfpPsUZ6tEQCAM8jJydG2bdtUv359hYeHW11OQHI6nWrevLmuv/76087093Vn+jmz9FyuFGw2mz7++GMNGDDgtMc0adJEw4YN09ixY937FixYoCuuuELHjx8vMbQtqdM2KSnJaz8HK2w5kKnpy7dr3trdysk3u+Ljouy65W911bJm6T4jQ9Iby7Zq9fYjCg226YXrktW/ba1yrfNEnkOrtx/W8q3pWrHlkH7Ze0yn/kYWZJPa1K6sixrFqVWtWIUEnb2rvrzZHHlq+Mtk1f19mmwydLxSkpwhEYo69ockaXeDG7Q5+R9yhFY6yzMBAOAbaleNULMEz5xTlfZ81tJO22nTpp3x/qVLlxa5/dJLL7m7PHzGqWMP/vhCOvqXFacT2khNektNe0uJ7SSbTZrSQTq0RdrwodRhmDV1AwAAr7Bjxw59+eWX6t69u3JzczVlyhRt27ZNN998s9Wl4RwlJCRo//79Rfbt379fMTExJQa2Eus0nI5hGPrmj4Oavny7lv1x0L2/Zc0Y3XpRfV3RJlH2kLItWti1cZwe+HC9Pvs5TffOXqfdR07orh4NSzWS5ExWbE3XlK+2aM32I8pzFB210rhGlLo0ilOXRnHq1KCqYsJDT/MsHrD/V2ne7dL+X8zbFwxWZK9npKBQ6asnpZVTVPvPOap9dLV09X+k2h2sqxUAAD/mdTNt/c7at6UFY07eDrZLDbqbQW2T3lJsCX+5bz9M+vJRac00qf1QM8gFAAABKSgoSDNnztSYMWNkGIZatWqlxYsXM0fWh6WkpGjBggVF9i1atEgpKSkWVeR7TuQ5NO+n3ZqxfLu2HDBHjNls0uUt4jW8S311rF/1nEPW8NBgvXpjO9WqHKE3lv2p5xf+rt1HTujJ/i0VEhxU5uc7mJmrpz/bqPnr9rr3JcaGF4a01dS5YZziY0r45sLPH5oj1Jr0Mi+hJQf65cbplL7/t7RkguTIkyKrSVe9KjW74uQxvZ6WGl8uzb9TOvynNO1yqduDUrcxUnAFB80FedLWJdJv/5PCoqSWV0tJHcvndyXDkHavkX75SEr//fyf73RCI83Pr8VVUkSVinud8pKfI21ZJG38VDqebnU1AFCxmvaVOt5mdRVFENpWtMaXS1EJUpPLpSZ9zMA27CxfI2p7s7RkorRvg7TnR/56DQBAAEtKStLy5cutLgNnkJWVpS1btrhvb9u2TevWrVPVqlVVp04djR07Vnv27NHbb78tSbrjjjs0ZcoUPfTQQxo+fLi++uorffDBB/rss8+segs+I9/h1OvfbNWb323T0ePm4mJR9hBd3yFJQzvXU51qkeXyOkFBNj3St7lqVY7QE//9Ve+v2ql9x05oys0XqJK9dL9COZyGZv2wQ/9c+Lsycwpks0n/16muhnWpp/pxlc4cKmcdkD4dJRXkSOtnmaPTml8ptbpWatBDCi7nX+OO7TaD2G3LzNuNe0n9p0hRNYof26C7dOdyacGD5jcDv3nWDPYGviHFNSrfupwOaccK83U2fiLlHD153w9Tpdg6UutrzM8lvmXZA9wDm8zn/uUj6cj28qz89H77n/TZA1KjnlLra6Wmfc7++6EnOQqk7cukDXOlTf+Vcln0EECAqOpdi5BJFs+0tYIlc9AMo+wnEB/fIa1/X0q+WRr4WsXUBQBAGTHTFp7gazNtly5dqosvvrjY/iFDhmjmzJkaOnSotm/fXmT019KlS3X//fdr48aNql27th5//HENHTq01K/pjZ9DRfvzYJbun7NO63cfkyQlVY3QsM71dV2H2oquwHECC3/dp3ve/0m5BU61qhWj6UMvVI3oM//3b8PuY3ps/gZ3ra1rxeqpAa2UnFS5dC+6aLy0fLJUuY45aPfYKSPWIuOklgOk1tdJtTtKQWXv/i1a7Fzps9FSzjGzE7TX0+Y3/0rz+8tfH3v5U1KH4efX/WoY0t6fzOf+dZ6UmXbyvqh4qeVA6cRRM/w8dSHn6s1PBrhV65/++Y/skH6ZK234SDrw68n9oZXMruIG3c1REBUhY7f0y7yToyfcr9vXrLvhJVJIWMW89pkYhrneiuszzz45akQxtaRW15ihuPgGKAA/Vq2RVLu9R16qtOdxhLbeatdqaVpPKSRcGr1JiqxqdUUAABDawiN8LbS1QiB9DoZh6P1Vu/Tk/zbqRL5DMeEheuKqlurftpaCPbRI19qdRzTirTU6nJ2nWpUj9NbwC9WoRnSx4zJy8vXil3/o7ZXb5TSkaHuIxvRqqv/7W93S13riiPRSaykvU7ppttn1unu12RH668dFv6YemyS1utoMcONblS0sPXFE+myMGWBKUq3259YtW5Yu3TM5+EdhmPqhOXrBJTxWatHfDDXrXSQFFc4ozj9hrhmyYa60+UtzpINLrQ5mF2vLgVJ0gtm5/Ot887l3rzp5XFCo1Pgy89gmvT3X8Xrgt5Pv9dQO34gq5nttfZ1Up/P5B/Jns/9X8/P7ZW7RtVciqpqfXetrpaS/VXwdABBgCG1Pw2dOcA1DmtpV2r9B6vWMlDLS6ooAACC0hUcQ2p5doHwO6Vm5enjuz1ry2wFJUueG1TTp+mQlxlbwfNcSbE/P1tAZq7T90HHFhIfoP4M7qFODapLMYPm/P6fpyf9t1MHMXEnSVck19dgVzVWjpHm1Z/LN89LXT0k1Wkp3fFc0MHMUSNuWml2im/5rBrsucU2lGmWYdb17tZSxR7IFS90fkrqOOfexC06n9MNr0uIJkiPXnIdbr2vpH394qzkaziUk4mT3aaNLpZCzLMLn6rzdMFfa9o1kFC70ZguSarSQDmw8uU82qX5XMxht3s/a2bKGIe1ZWxjIz5OyTlmgMLqmOSbPVkGBafof5ufiEhZldhq3vq5wBIeFi+EBgJ8jtD0NnzrBXTNd+t/9Zov2qDUsSAYAsByhLTyB0PbsAuFzWLJpvx7+6GelZ+UpLDhID/VuquFd6ivIQ921JTmcnacRb63W2p1HFRYcpBeuT1brWrF6fP4v+m6L2QHbIK6SJvZvpYsax5X9BfKypZdaSScOS9dMMzsdTyf/hNlhumGu9MdCMywtq6oNpavfKL81NPZvlObdbjaelFVQiNTwUjM0bNpHskedWw2Z+6WN8wu7alef3F+rvRkCtxwoxSSe23NXJKdD2v6t+e+58VMp91jFv2ZwmLkGS6trCjuNy2cmNADgzAhtT8OnTnBzM6VJzc2/oA/+1JyvBACAhQht4QmEtmfnz5/D8bwCPfm/TXp/lfl17WYJ0XrphrZqnugd7zMn36F7Z/+khb+aXZFhwUHKczgVFhKkURc30t+7N5A9JPjcnnzlv6SFj0hVG5hNG0GlfJ6cY9LmRebIg9IKi5JaXFX+IwEKcs3Q8dRFw87GHm2Gh+U9Eu7IdnPsXK0LpGret8DMaRXkSlu/MkdPVJTwyuZoiIjKFfcaAIASlfY8rpyXHUW5skdLba6X1kwzL4S2AAAA8GPrdh3V/XPWaVt6tiRpxEX1NaZXU4WHnmMIWgHCQ4P170Ht9dRnGzVj+XblOZzq3qS6JvZvqbrVziMALciVVrxqXu9yX+kDW8mc+3qmrlxPCrFLba6zugpTlXrmxdeE2M1uYwBAQGOiuLfrMNzc/vaZlLnP2loAAAhwPXr00H333ee+Xa9ePU2ePPmMj7HZbJo/f/55v3Z5PQ/gjQocTr28eLOueW2FtqVnKyEmXO+N6KTHrmzhVYGtS3CQTeP7tdQbt7TXjGEXauawC88vsJWk9e9LmWnmLNPkG8unUAAA4LMIbb1dQispqZPkLJDWvmN1NQAA+KR+/fqpd+/eJd737bffymaz6eeffy7z865evVq33377+ZZXxBNPPKG2bdsW25+WlqY+fSq282rmzJmqXLlyhb4G8Ff7M3J03esr9dLiP+RwGrqyTaIW3tdNXRqdw0xYD7u8ZYIublpDtvNde8JRIH032bze+e6zL7wFAAD8HqGtL+hwq7n9caY5oB4AAJTJrbfeqkWLFmn37uLzAWfMmKEOHTqoTZs2ZX7e6tWrKzLSMwu3JCQkyG4nyIH/mfjfjfpp51FF20M0+Ya2evWmdoqNDLCV6zfOl45skyKqSu2HWF0NAADwAoS2vqBFf/MELmO3uUIsAAAokyuvvFLVq1fXzJkzi+zPysrShx9+qFtvvVWHDh3STTfdpFq1aikyMlKtW7fW+++/f8bn/et4hM2bN6tbt24KDw9XixYttGjRomKPefjhh9WkSRNFRkaqQYMGevzxx5Wfny/J7HSdMGGC1q9fL5vNJpvN5q75r+MRNmzYoEsuuUQRERGqVq2abr/9dmVlZbnvHzp0qAYMGKAXXnhBiYmJqlatmkaOHOl+rXOxc+dO9e/fX1FRUYqJidH111+v/fv3u+9fv369Lr74YkVHRysmJkbt27fXmjVrJEk7duxQv379VKVKFVWqVEktW7bUggULzrkW+IfdR47r81/SJEmzbvubBrSrdf5dq77G6ZS+nWRe/9td5b8wGAAA8EksROYLQsOldoPMhQnWTGcoPQDAuxiGlH/c868bGimVMtwJCQnR4MGDNXPmTD366KPuUOjDDz+Uw+HQTTfdpKysLLVv314PP/ywYmJi9Nlnn+mWW25Rw4YN1bFjx7O+htPp1NVXX634+Hj98MMPOnbsWJH5ty7R0dGaOXOmatasqQ0bNui2225TdHS0HnroId1www365Zdf9MUXX2jx4sWSpNjY2GLPkZ2drV69eiklJUWrV6/WgQMHNGLECI0aNapIMP31118rMTFRX3/9tbZs2aIbbrhBbdu21W233Vaqz+2v788V2H7zzTcqKCjQyJEjdcMNN2jp0qWSpEGDBqldu3Z67bXXFBwcrHXr1ik01OyYHDlypPLy8rRs2TJVqlRJGzduVFRUVJnrgH95Z+UOOQ2pc8Nqal27+M96QPjjC+nARiksWupY9v9tAgAA/0Ro6yvaDzND282LpCPbfXMVVACAf8o/Lj1T0/Ov+8jeMnWkDR8+XM8//7y++eYb9ejRQ5I5GuGaa65RbGysYmNjNWbMGPfxd999txYuXKgPPvigVKHt4sWL9dtvv2nhwoWqWdP8PJ555plic2gfe+wx9/V69eppzJgxmj17th566CFFREQoKipKISEhSkhIOO1rzZo1Szk5OXr77bdVqZL5GUyZMkX9+vXTc889p/j4eElSlSpVNGXKFAUHB6tZs2a64oortGTJknMKbZcsWaINGzZo27ZtSkpKkiS9/fbbatmypVavXq0LL7xQO3fu1IMPPqhmzZpJkho3bux+/M6dO3XNNdeodevWkqQGDRqUuQb4l+zcAs1atVOSNLxLfYursYhhSN++YF7vOEKKqGxpOQAAwHswHsFXVGsoNbhYkmHOtgUAAGXSrFkzde7cWdOnT5ckbdmyRd9++61uvdWcHe9wOPTkk0+qdevWqlq1qqKiorRw4ULt3LmzVM+/adMmJSUluQNbSUpJSSl23Jw5c9SlSxclJCQoKipKjz32WKlf49TXSk5Odge2ktSlSxc5nU79/vvv7n0tW7ZUcHCw+3ZiYqIOHDhQptc69TWTkpLcga0ktWjRQpUrV9amTZskSaNHj9aIESPUs2dPPfvss9q6dav72HvuuUdPPfWUunTpovHjx5/Twm/wLx+t3a3MnALVqxapS5rVsLoca2xbJu35UQoJN0cjAAAAFKLT1pdceKv059fS2nekHo9IIWFWVwQAgDmm4JG91rxuGd166626++679a9//UszZsxQw4YN1b17d0nS888/r5dfflmTJ09W69atValSJd13333Ky8srt5JXrlypQYMGacKECerVq5diY2M1e/ZsTZo0qdxe41Su0QQuNptNTqezQl5Lkp544gndfPPN+uyzz/T5559r/Pjxmj17tgYOHKgRI0aoV69e+uyzz/Tll18qNTVVkyZN0t13311h9cB7OZ2GZizfLkka1qW+goICbI6ti2uW7QWDpagADa4BAECJ6LT1JU36SNGJ0vF0adOnVlcDAIDJZjPHFHj6cg6LFV1//fUKCgrSrFmz9Pbbb2v48OHu+bbLly9X//799X//939KTk5WgwYN9Mcff5T6uZs3b65du3YpLS3Nve/7778vcsyKFStUt25dPfroo+rQoYMaN26sHTt2FDkmLCxMDofjrK+1fv16ZWdnu/ctX75cQUFBatq0aalrLgvX+9u1a5d738aNG3X06FG1aNHCva9Jkya6//779eWXX+rqq6/WjBkz3PclJSXpjjvu0Lx58/TAAw/oP//5T4XUCu+39I8D2paerejwEF3bvrbV5Vhj9xpp2zdSUIjUmT9eAACAoghtfUlwiHTBEPP6mhlnPhYAABQTFRWlG264QWPHjlVaWpqGDh3qvq9x48ZatGiRVqxYoU2bNunvf/+79u/fX+rn7tmzp5o0aaIhQ4Zo/fr1+vbbb/Xoo48WOaZx48bauXOnZs+era1bt+qVV17Rxx9/XOSYevXqadu2bVq3bp3S09OVm5tb7LUGDRqk8PBwDRkyRL/88ou+/vpr3X333brlllvc82zPlcPh0Lp164pcNm3apJ49e6p169YaNGiQ1q5dq1WrVmnw4MHq3r27OnTooBMnTmjUqFFaunSpduzYoeXLl2v16tVq3ry5JOm+++7TwoULtW3bNq1du1Zff/21+z4EnmnfbZMk3XhhkirZA/TLf64u2zY3SJXrWFsLAADwOoS2vuaCwZItWNrxnXTgN6urAQDA59x66606cuSIevXqVWT+7GOPPaYLLrhAvXr1Uo8ePZSQkKABAwaU+nmDgoL08ccf68SJE+rYsaNGjBihp59+usgxV111le6//36NGjVKbdu21YoVK/T4448XOeaaa65R7969dfHFF6t69ep6//33i71WZGSkFi5cqMOHD+vCCy/Utddeq0svvVRTpkwp24dRgqysLLVr167IpV+/frLZbPrkk09UpUoVdevWTT179lSDBg00Z84cSVJwcLAOHTqkwYMHq0mTJrr++uvVp08fTZgwQZIZBo8cOVLNmzdX79691aRJE/373/8+73rhe37bl6HlWw4pyCYN6VzP6nKssf9X6fcFkmzSRfdbXQ0AAPBCNsMwDKuL8KSMjAzFxsbq2LFjiomJsbqcczN7kPTb/6ROd0h9nrO6GgBAAMnJydG2bdtUv359hYeHW10O/NSZfs784lyuHPjy5/Dw3J81Z80u9W2doH8Pam91Odb4aIS04UOpxQDp+resrgYAAHhQac/j6LT1RR2Gm9t170t52Wc+FgAAAPAS6Vm5+njdHknS8C71La7GIof/lH75yLzedbS1tQAAAK9FaOuLGlwsVakv5R47ecIHAAAAeLlZP+xUXoFTybVj1b5uFavLscZ3kyXDKTW6TEpMtroaAADgpQhtfVFQkNRhmHl9zXRrawEAAABKIbfAoXe+3yFJGn5RfdlsNosrskDGXmndLPN61wesrQUAAHg1Qltf1XaQFBwm7f1J2rPW6moAAACAM/rf+jQdzMxVfIxdfVolWl2ONVZMkZz5Up3OUt0Uq6sBAABejNDWV1WKMxcukOi2BQAAgFczDEPTl2+TJA1OqaewkAD8NST7kPTjDPM6XbYAAOAsAvBsyY+4FiT75SMp/4S1tQAAAorT6bS6BPgxfr78z6pth/Xr3gzZQ4J0c8c6VpdjjR+mSvnHzTm2jS61uhoAAODlQqwuAOehzt+kmNpSxm5px3KpUU+rKwIA+LmwsDAFBQVp7969ql69usLCwgJzLiUqhGEYysvL08GDBxUUFKSwsDCrS0I5mfad2WV79QW1VaVSAP675mRIq143r180WuK/mwAA4CwIbX2ZzSY1ukRa+7a0ZQmhLQCgwgUFBal+/fpKS0vT3r17rS4HfioyMlJ16tRRUBBfCvMHOw8d16JN+yVJw7vUs7YYq6yZJuUck+KaSM2vsroaAADgAwhtfV3DS0+GtgAAeEBYWJjq1KmjgoICORwOq8uBnwkODlZISAgd3H5k5ortMgypW5PqahwfbXU5npd/Qlr5L/P6RfdL/DECAACUAqGtr2vQQ7IFS+m/S0d3SZWTrK4IABAAbDabQkNDFRoaanUpALxYZk6+PlizS1IAd9n+9K6UfVCKTZJaX2d1NQAAwEfwZ15fF1FZqt3BvL6VblsAAAB4jw/W7FZWboEa1YhS9ybVrS7H8xz50vKXzetd7pWC+UMXAAAoHUJbf9CwcPVZRiQAAADASzichmauMBcgG9alXmCOvNjwoXRsl1SphtTu/6yuBgAA+BBCW3/QqDC0/fMbyVFgbS0AAACApMWb9mvX4ROqHBmqq9vVtrocz3M6pG9fNK+njJRCI6ytBwAA+BRCW39Qs50UUUXKPSbtWWN1NQAAAICmfWd22d7csY4iwoItrsYCm/4rHdoshcdKHYZbXQ0AAPAxhLb+IChYanCxeZ0RCQAAALDYL3uOadW2wwoJsumWlLpWl+N5hiF9O8m83vHvUniMtfUAAACfQ2jrL1wjEliMDAAAABabvtzssu3bOlGJsQE4FmDLEmnfz1JopNTpDqurAQAAPojQ1l+4FiPbs1Y6ftjaWgAAABCwDmfn6b/r90qShl9U3+JqLOLqsm0/TKpUzdpaAACATyK09RcxiVKNlpIMaetXVlcDAACAALX1YJbyHYZqV4lQ26TKVpfjeTtWSDtXSMFhUudRVlcDAAB8FKGtP2l0ibkltAUAAIBFDmbmSpISYsItrsQi375obtveLMXUtLYWAADgswht/YlrRMKWJebiBwAAAICHuULb6tF2iyuxQNp6acsiyRYkdbnX6moAAIAPI7T1J3VSzMUOsvZJ+3+1uhoAAAAEoPSsAA5tXbNsW10jVW1gbS0AAMCnEdr6k9Bwqd5F5vWtS6ytBQAAAAHJ1WkbFxVgoe3BP6SNn5rXL7rf2loAAIDPI7T1N6eOSAAAAAA8LGDHIyyfLMmQmvaV4ltaXQ0AAPBxhLb+plFPc7tzpZSXbW0tAAAACDgHXeMRAqnT9uhO6ec55vWuD1hbCwAA8AuEtv6mWkOpch3JkSdt/87qagAAABBg0gOx03bFq5KzQKrfXardwepqAACAHyC09Tc2GyMSAAAAYAnDMNydtnGBEtpmHZDWvm1ep8sWAACUE0Jbf9TIFdoutrYOAAAABJRjJ/KV7zAkSXFRYRZX4yHf/1sqyJFqdZDqd7O6GgAA4CcIbf1R/W5SUIh0eKt0ZLvV1QAAACBAuBYhi40IlT0k2OJqPODEEWnVm+b1rg+Y33oDAAAoB4S2/ig8Vqrd0bzOiAQAAAB4iHsRskAZjbDqTSkvU6rRQmrS2+pqAACAHyG09VeNLjG3W7+ytg4AAAAEDFenbUCMRijIM0cjSGaXbRC/WgEAgPLDmYW/atTT3P75jeTIt7YWAAAABARXaFs9OtziSjzg2C7pxGEpNFJqMcDqagAAgJ8htPVXCclSZJz5da1dq6yuBgAAAAHAPR4hKgDGI2TtN7fRCVJwiLW1AAAAv0No66+CgqSGF5vXtzLXFgAAABUvPTNPUoDMtM3cZ26j4q2tAwAA+CVCW3/mGpGwZbG1dQAAACAguDptA2KmbdYBc0toCwAAKgChrT9rWLgYWdp6KeugtbUAAADA752caRsAnbZZdNoCAICKY2lo+9prr6lNmzaKiYlRTEyMUlJS9Pnnn5/xMR9++KGaNWum8PBwtW7dWgsWLPBQtT4oqoaU0Nq8/ufX1tYCAAAAvxdYoW1hp200oS0AACh/loa2tWvX1rPPPqsff/xRa9as0SWXXKL+/fvr119/LfH4FStW6KabbtKtt96qn376SQMGDNCAAQP0yy+/eLhyH9LwUnPLiAQAAABUIIfT0OHsAFqIjJm2AACgAlka2vbr1099+/ZV48aN1aRJEz399NOKiorS999/X+LxL7/8snr37q0HH3xQzZs315NPPqkLLrhAU6ZM8XDlPsQ113brV5LTaW0tAAAA8FuHs/PkNCSbTapaiZm2AAAA58NrZto6HA7Nnj1b2dnZSklJKfGYlStXqmfPnkX29erVSytXrvREib4pqZMUFiVlH5T2b7C6GgAAAPgp12iEapXCFBLsNb9mVBxm2gIAgAoUYnUBGzZsUEpKinJychQVFaWPP/5YLVq0KPHYffv2KT6+6ElRfHy89u3bd9rnz83NVW5urvt2RkZG+RTuK0LCpHpdpT8+l7YskRKTra4IAAAAfuhglnnOHRcIoxEcBVJ2unmd0BYAAFQAy/8E3rRpU61bt04//PCD7rzzTg0ZMkQbN24st+dPTU1VbGys+5KUlFRuz+0zGrnm2i6xtg4AAAD4rfRAWoTseLokQ7IFSZXirK4GAAD4IctD27CwMDVq1Ejt27dXamqqkpOT9fLLL5d4bEJCgvbv319k3/79+5WQkHDa5x87dqyOHTvmvuzatatc6/cJrtB21/dSbqa1tQAAAMAvuTptA2oRskrVpaBga2sBAAB+yfLQ9q+cTmeRcQanSklJ0ZIlRbtFFy1adNoZuJJkt9sVExNT5BJwqjaQqtSXnAXStm+trgYAAAB+6GAgddqyCBkAAKhgloa2Y8eO1bJly7R9+3Zt2LBBY8eO1dKlSzVo0CBJ0uDBgzV27Fj38ffee6+++OILTZo0Sb/99pueeOIJrVmzRqNGjbLqLfgO94iExdbWAQAAAL+UnhVIoS2LkAEAgIplaWh74MABDR48WE2bNtWll16q1atXa+HChbrsssskSTt37lRaWpr7+M6dO2vWrFl64403lJycrLlz52r+/Plq1aqVVW/BdzTqaW63MtcWAAAA5c/VaRsQC5FlFY5sI7QFAAAVJMTKF582bdoZ71+6dGmxfdddd52uu+66CqrIj9XrKgWFSke2S4e2StUaWl0RAAAA/EhAjUfILAxtowltAQBAxfC6mbaoIPYoqc7fzOtb6LYFAABA+ToYUOMR6LQFAAAVi9A2kDS82NzuXGFtHQAAAPAreQVOHT2eL0mqHhDjEViIDAAAVCxC20ASXzj7N32LtXUAAADArxzKNrtsQ4Jsio0ItbgaD2AhMgAAUMEIbQNJtUbm9tAWyem0thYAAAD4jVMXIQsKsllcTQUzjJOdtsy0BQAAFYTQNpBUrmsuRlZwQsrYbXU1AAAA8BMBtQhZbqaUf9y8TqctAACoIIS2gSQ4RKrW0LyevtnaWgAAAOA30gNqEbLCLtuwaCmskrW1AAAAv0VoG2hcIxIIbQEAAFBOTo5HCLO4Eg9wz7OtYW0dAADArxHaBpq4xub2EKEtAAAAykdAjUfI2m9uGY0AAAAqEKFtoIlrYm7ptAUAAEA5OegajxAVCKEti5ABAICKR2gbaKoVdtoS2gIAAKCcpGfmSZKqR4dbXIkHZLrGIxDaAgCAikNoG2jiCmfaZu6VcrOsrQUAAAB+wdVpGxgzbQs7bQltAQBABSK0DTQRVaRK1c3rh7ZYWwsAAAD8QmDNtKXTFgAAVDxC20DEiAQAAACUkxN5DmXlFkgKlNCWmbYAAKDiEdoGIteIhEOEtgAAADg/6YWjEcJDgxRlD7G4Gg9gpi0AAPAAQttAFNfE3NJpCwAAgPN0INM1z9Yum81mcTUVzJEvHT9kXie0BQAAFYjQNhAxHgEAAADlJKDm2WYflGRItmApsprV1QAAAD9GaBuI4gpD20NbJKfT2loAAADg0w4WjkeoHhUAoW3WfnNbqboUFGxtLQAAwK8R2gaiynWloFCp4ISUscfqagAAAODD0gOp05ZFyAAAgIcQ2gai4BCpagPzevof1tYCAAAAn+bqtI0LhE5bFiEDAAAeQmgbqE4dkQAAAACco4CaaevqtCW0BQAAFYzQNlBVa2RuWYwMAAAA5yGwQls6bQEAgGcQ2gaquCbmlvEIAAAAOA/pgTQewbUQWXSCtXUAAAC/R2gbqBiPAAAAgPNkGIa707ZGIHTaZhaGtlE1rK0DAAD4PULbQOUaj5CxR8rNsrYWAAAA+KTM3ALlFjglBVinLeMRAABABSO0DVSRVaXIOPM63bYAAAA4B64u22h7iCLCgi2upoIZBguRAQAAjyG0DWSMSAAAAMB5SC8MbeMCYTRCboZUcMK8TmgLAAAqGKFtIHONSEjfbG0dAAAA8EkHCxchqx4QoxEKu2ztMVJYpLW1AAAAv0doG8jimpjb9D+srQMAAAA+yTUeoXogdNpm7jO3LEIGAAA8gNA2kLnHI9BpCwAAcD7+9a9/qV69egoPD1enTp20atWqMx4/efJkNW3aVBEREUpKStL999+vnJwcD1VbftKzAii0dS9ClmBtHQAAICAQ2gayaq7QdqvkdFpbCwAAgI+aM2eORo8erfHjx2vt2rVKTk5Wr169dODAgRKPnzVrlv7xj39o/Pjx2rRpk6ZNm6Y5c+bokUce8XDl58/VaRsXFWZxJR7gDm3ptAUAABWP0DaQVakrBYVK+celjD1WVwMAAOCTXnzxRd12220aNmyYWrRooalTpyoyMlLTp08v8fgVK1aoS5cuuvnmm1WvXj1dfvnluummm87aneuNAmo8gju0ZREyAABQ8QhtA1lwqFS1vnmdEQkAAABllpeXpx9//FE9e/Z07wsKClLPnj21cuXKEh/TuXNn/fjjj+6Q9s8//9SCBQvUt29fj9Rcng4G1HiEws7paEJbAABQ8UKsLgAWq9bYXIgsfYvU8BKrqwEAAPAp6enpcjgcio8vGuTFx8frt99+K/ExN998s9LT03XRRRfJMAwVFBTojjvuOON4hNzcXOXm5rpvZ2RklM8bOE/pmXmSpOpR4RZX4gHuhcgIbQEAQMWj0zbQuRYjS//D2joAAAACxNKlS/XMM8/o3//+t9auXat58+bps88+05NPPnnax6Smpio2NtZ9SUpK8mDFJXM6DfdCZHHRgTDTtrDTltAWAAB4AJ22gc4V2jIeAQAAoMzi4uIUHBys/fv3F9m/f/9+JSQklPiYxx9/XLfccotGjBghSWrdurWys7N1++2369FHH1VQUPG+irFjx2r06NHu2xkZGZYHt0dP5KvAaUiSqlUKhPEIdNoCAADPodM20FVzddpusbYOAAAAHxQWFqb27dtryZIl7n1Op1NLlixRSkpKiY85fvx4sWA2ODhYkmQYRomPsdvtiomJKXKxmmsRsiqRoQoL8fNfKxz50vFD5vXoksN4AACA8kSnbaBzddpm7JbysqWwStbWAwAA4GNGjx6tIUOGqEOHDurYsaMmT56s7OxsDRs2TJI0ePBg1apVS6mpqZKkfv366cUXX1S7du3UqVMnbdmyRY8//rj69evnDm99QXogLkIWFCJFVLW2FgAAEBAIbQNdZFUpsprZOXBoi5SYbHVFAAAAPuWGG27QwYMHNW7cOO3bt09t27bVF1984V6cbOfOnUU6ax977DHZbDY99thj2rNnj6pXr65+/frp6aeftuotnBNXp21cVCCEtoXjLyrVkEoYXwEAAFDeCG1hjkg4fkhK30xoCwAAcA5GjRqlUaNGlXjf0qVLi9wOCQnR+PHjNX78eA9UVnFcoW1AddpG1bC2DgAAEDD4MzFOjkhIZzEyAAAAlM5B13iEgOi0ZREyAADgWYS2OBnaHiK0BQAAQOmkB2KnbTShLQAA8AxCW5jjESQ6bQEAAFBqrk7bgJhpm0mnLQAA8CxCW0hxTcztoS2S02ltLQAAAPAJgTXTtnAhMkJbAADgIYS2kKrUlYJCpPzjUuZeq6sBAACADyC0BQAAqDiEtpCCQ6Uq9c3rjEgAAADAWRQ4nDp8PE9SgIW20QnW1gEAAAIGoS1MrhEJhLYAAAA4i8PZeTIMKcgmVYkMs7qcimUYUqar07aGtbUAAICAQWgLU1wjc3uI0BYAAABndqBwNEK1KLuCg2wWV1PBco5JDvP9Mh4BAAB4CqEtTNUam1s6bQEAAHAWB7MK59lGBcJohAPm1h4rhUZYWwsAAAgYhLYwMR4BAAAApZQeUIuQ7TO3jEYAAAAeRGgLU1xhp23Gbikv29paAAAA4NVcnbZxgdRpyyJkAADAgwhtYYqsKkVUNa8f2mptLQAAAPBqBwOp0zaTTlsAAOB5hLY4yT0i4Q9r6wAAAIBXC6jQNmu/uY2i0xYAAHgOoS1Oimtkbg9tsbYOAAAAeLV093iEMIsr8QB3aEunLQAA8BxCW5xUrXCuLYuRAQAA4AwCstOWmbYAAMCDCG1xEuMRAAAAUAqu0LZGQIS2hQuR0WkLAAA8yNLQNjU1VRdeeKGio6NVo0YNDRgwQL///vsZHzNz5kzZbLYil/DwcA9V7OfiCjttD22VDMPaWgAAAOCVcvIdysgpkCRVjwqA83D3QmTx1tYBAAACiqWh7TfffKORI0fq+++/16JFi5Sfn6/LL79c2dnZZ3xcTEyM0tLS3JcdO3Z4qGI/V6WeFBQi5WdLGXutrgYAAABe6FB2niQpLDhIMREhFldTwQrypBOHzessRAYAADzI0rOsL774osjtmTNnqkaNGvrxxx/VrVu30z7OZrMpIYGTpnIXHGoGt4e2mCMSYmtZXREAAAC8jGs0QlxUmGw2m8XVVLDswtEIQSFSRBVrawEAAAHFq2baHjt2TJJUtWrVMx6XlZWlunXrKikpSf3799evv/7qifICg2uu7aEt1tYBAAAArxSQi5BFxUtBXvWrEwAA8HNec+bhdDp13333qUuXLmrVqtVpj2vatKmmT5+uTz75RO+++66cTqc6d+6s3bt3l3h8bm6uMjIyilxwBtUamdv0zdbWAQAAAK+UnhVAoW2mK7RlETIAAOBZXjOEauTIkfrll1/03XffnfG4lJQUpaSkuG937txZzZs31+uvv64nn3yy2PGpqamaMGFCudfrt1yLkaX/YW0dAAAA8EonxyMEQGjr7rRlNBsAAPAsr+i0HTVqlP73v//p66+/Vu3atcv02NDQULVr105btpT8df6xY8fq2LFj7suuXbvKo2T/xXgEAAAAnEFgjUconGlLpy0AAPAwS0NbwzA0atQoffzxx/rqq69Uv379Mj+Hw+HQhg0blJiYWOL9drtdMTExRS44g2qFnbbHdkl5x62tBQAAAF4nsELbfeY2mk5bAADgWZaGtiNHjtS7776rWbNmKTo6Wvv27dO+fft04sQJ9zGDBw/W2LFj3bcnTpyoL7/8Un/++afWrl2r//u//9OOHTs0YsQIK96C/6lU7eTKuHTbAgAA4C/cM20DYjwCnbYAAMAaloa2r732mo4dO6YePXooMTHRfZkzZ477mJ07dyotLc19+8iRI7rtttvUvHlz9e3bVxkZGVqxYoVatGhhxVvwT+4RCSxGBgAAgKIOFoa2cYHQaZtZ2GkbFW9tHQAAIOBYuhCZYRhnPWbp0qVFbr/00kt66aWXKqgiSDJHJOz6QUqn0xYAAABFuccjBFSnLeMRAACAZ3nFQmTwMnGNzG36H9bWAQAAAK+SnVug43kOSQEw09YwTs60ZTwCAADwMEJbFMd4BAAAAJTANc82MixYleyWfmmv4uUclRx55nXGIwAAAA8jtEVx1Rqb2/QtZocBAAAAoJOjEeICYTRC5n5zGx4rhYZbWwsAAAg4hLYorko9yRYs5WdLGXutrgYAAABewj3P1t9HI0hSVmFoyzxbAABgAUJbFBcSJlWtb15nRAIAAAAKHcwKxEXImGcLAAA8j9AWJXOPSCC0BQAAgCk9oDptXYuQMc8WAAB4HqEtShbXyNwS2gIAAKCQq9M2IGbausYjRDMeAQAAeB6hLUoW18TcMh4BAAAAhQJqpq1rITLGIwAAAAsQ2qJk7vEIW6ytAwAAAF4joEJbFiIDAAAWIrRFyeIKQ9tjO6W849bWAgAAAK+QnpUnKdBCWzptAQCA5xHaomSR1aSIKub1w1utrQUAAACWMwzD3WkbFxVmcTUewExbAABgIUJblMxmk6o2MK8f3WltLQAAALBcxokC5TmckgJgIbKCXOnEEfN6VLy1tQAAgIBEaIvTi040txl7ra0DAAAAljuYlSNJigkPUXhosMXVVLCsA+Y2KPTkt88AAAA8iNAWpxdTy9xm7LG2DgAAAFjuYGYgzbMtDG2j4s1voAEAAHgYoS1OL6amuaXTFgAAIOAdzHLNsw2E0HafuWURMgAAYBFCW5weoS0AAAAKuRYhC4xOWxYhAwAA1iK0xekR2gIAAKBQQIW2mYWhLZ22AADAIoS2OL1TQ1vDsLYWAAAAWCo9oMYjuEJbOm0BAIA1CG1xetGFoW3BCSnnqKWlAAAAwFoB1WnrXoiMTlsAAGANQlucXmi4FFHVvM6IBAAAgIAWWKFt4UJkzLQFAAAWIbTFmcXUMreEtgAAAAHtYOF4hOoBMR7B1Wkbb20dAAAgYBHa4sxYjAwAACDgOZyGDmfnSQqATlvDOGWmLeMRAACANQhtcWYxieaW0BYAACBgHTmeJ4fTkM0mVa0UZnU5FevEEclhBtR02gIAAKsQ2uLM3OMR9lhbBwAAACzjmmdbNTJMocF+/iuEq8s2vLIU4uddxQAAwGv5+RkXzptrPEJmmrV1AAAAwDLpWYG0CFlhaMsiZAAAwEKEtjizaMYjAAAABDpXp21cICxClsk8WwAAYD1CW5wZ4xEAAAACniu0DahO2yg6bQEAgHUIbXFmrvEIOcekvGxrawEAAIAlAjO0pdMWAABYh9AWZxYeI4VFmdczmGsLAAAQiNwzbQNhPAIzbQEAgBcgtMXZubptGZEAAAAQkA4WhrZx0WEWV+IB7k7beGvrAAAAAY3QFmfnDm1ZjAwAACAQuccjRIVbXIkHsBAZAADwAiFWFwAf4FqMLJPQFgAAIBA9e00bpR3NUYuaMVaXUvFYiAwAAHgBQlucXXSiuaXTFgAAICBdUKeKVMfqKjwgP0fKOWpep9MWAABYiPEIODvGIwAAACAQZB8wt8FhUkQVa2sBAAABjdAWZ+caj0BoCwAAAH+WVRjaRsVLNpu1tQAAgIBGaIuzi2E8AgAAAAJA5j5zGxVvbR0AACDgEdri7FydttkHpII8a2sBAAAAKop7ETJCWwAAYC1CW5xdZDVzrpckZaZZWwsAAABQUdyhLYuQAQAAaxHa4uxsNim6cEQCoS0AAAD8lSu0jU6wtg4AABDwCG1ROu7FyPZYWwcAAABQUdwLkdFpCwAArEVoi9KJqWluWYwMAAAA/irnmLkNr2xpGQAAAIS2KB13aMt4BAAAAPip3Axza4+xtg4AABDwCG1ROu7QlvEIAAAA8FO5WebWHm1tHQAAIOAR2qJ0GI8AAAAAf5ebaW7tUdbWAQAAAh6hLUrHtRBZJuMRAAAA4Kfy6LQFAADegdAWpROdaG4z0ySnw9paAAAAgPLmyJcKcszrYXTaAgAAaxHaonSi4iVbkOQskLIPWl0NAAAAUL5coxEkOm0BAIDlCG1ROsEhUlSCeZ25tgAAAPA3rtA2JFwKDrW2FgAAEPAIbVF6MYUjEghtAQAA4G/ci5DRZQsAAKxHaIvSi6lpbgltAQAA4G9YhAwAAHgRQluUXkwtc5uxx9o6AAAAgPLm6rRlETIAAOAFCG1RetGF4xEy06ytAwAAAChv7vEIMdbWAQAAIEJblIW705bxCAAAAPAz7tCWTlsAAGA9QluUnnumLeMRAAAA4GeYaQsAALwIoS1Kzx3apkmGYW0tAAAAQHlipi0AAPAiloa2qampuvDCCxUdHa0aNWpowIAB+v3338/6uA8//FDNmjVTeHi4WrdurQULFnigWrhn2hackE4csbYWAAAAoDy5xyPQaQsAAKxnaWj7zTffaOTIkfr++++1aNEi5efn6/LLL1d2dvZpH7NixQrddNNNuvXWW/XTTz9pwIABGjBggH755RcPVh6gQsOlyGrmdebaAgAAwJ+wEBkAAPAiIVa++BdffFHk9syZM1WjRg39+OOP6tatW4mPefnll9W7d289+OCDkqQnn3xSixYt0pQpUzR16tQKrzngxdSUjh+SMtOkhFZWVwMAAACUDxYiAwAAXsSrZtoeO3ZMklS1atXTHrNy5Ur17NmzyL5evXpp5cqVFVobCkWzGBkAAAD8EAuRAQAAL2Jpp+2pnE6n7rvvPnXp0kWtWp2+g3Pfvn2Kj48vsi8+Pl779u0r8fjc3Fzl5ua6b2dkZJRPwYHKvRgZ4xEAAADgR1iIDAAAeBGv6bQdOXKkfvnlF82ePbtcnzc1NVWxsbHuS1JSUrk+f8CJqWVuCW0BAADgT3LptAUAAN7DK0LbUaNG6X//+5++/vpr1a5d+4zHJiQkaP/+/UX27d+/XwkJCSUeP3bsWB07dsx92bVrV7nVHZBiEs0toS0AAAD8SW7hN/JYiAwAAHgBS0NbwzA0atQoffzxx/rqq69Uv379sz4mJSVFS5YsKbJv0aJFSklJKfF4u92umJiYIhecB8YjAAAAwB+xEBkAAPAils60HTlypGbNmqVPPvlE0dHR7rm0sbGxioiIkCQNHjxYtWrVUmpqqiTp3nvvVffu3TVp0iRdccUVmj17ttasWaM33njDsvcRUBiPAAAAAH/EQmQAAMCLWNpp+9prr+nYsWPq0aOHEhMT3Zc5c+a4j9m5c6fS0tLctzt37qxZs2bpjTfeUHJysubOnav58+efcfEylCNXp23usZNzvwAAAALcv/71L9WrV0/h4eHq1KmTVq1adcbjjx49qpEjRyoxMVF2u11NmjTRggULPFQtiinIlRx55nUWIgMAAF7A0k5bwzDOeszSpUuL7bvuuut03XXXVUBFOCt7tBQWLeVlSplpkr2x1RUBAACUWb169TR8+HANHTpUderUOa/nmjNnjkaPHq2pU6eqU6dOmjx5snr16qXff/9dNWrUKHZ8Xl6eLrvsMtWoUUNz585VrVq1tGPHDlWuXPm86sB5OLUZgU5bAADgBbxiITL4GPdc2z3W1gEAAHCO7rvvPs2bN08NGjTQZZddptmzZys3N/ecnuvFF1/UbbfdpmHDhqlFixaaOnWqIiMjNX369BKPnz59ug4fPqz58+erS5cuqlevnrp3767k5OTzeUs4H65FyEIjpaBga2sBAAAQoS3OhTu0TTvzcQAAAF7qvvvu07p167Rq1So1b95cd999txITEzVq1CitXbu21M+Tl5enH3/8UT179nTvCwoKUs+ePbVy5coSH/Ppp58qJSVFI0eOVHx8vFq1aqVnnnlGDofjtK+Tm5urjIyMIheUI+bZAgAAL0Noi7Kj0xYAAPiJCy64QK+88or27t2r8ePH680339SFF16otm3bavr06Wcd55Weni6Hw6H4+Pgi++Pj492L7P7Vn3/+qblz58rhcGjBggV6/PHHNWnSJD311FOnfZ3U1FTFxsa6L0lJSWV/szi93ExzS2gLAAC8BKEtys4d2u61tg4AAIDzlJ+frw8++EBXXXWVHnjgAXXo0EFvvvmmrrnmGj3yyCMaNGhQub+m0+lUjRo19MYbb6h9+/a64YYb9Oijj2rq1KmnfczYsWN17Ngx92XXrl3lXldAc4W2LEIGAAC8hKULkcFHuULbTMYjAAAA37R27VrNmDFD77//voKCgjR48GC99NJLatasmfuYgQMH6sILLzzj88TFxSk4OFj79+8vsn///v1KSEgo8TGJiYkKDQ1VcPDJ2anNmzfXvn37lJeXp7CwsGKPsdvtstvtZXmLKAs6bQEAgJeh0xZlF814BAAA4NsuvPBCbd68Wa+99pr27NmjF154oUhgK0n169fXjTfeeMbnCQsLU/v27bVkyRL3PqfTqSVLliglJaXEx3Tp0kVbtmyR0+l07/vjjz+UmJhYYmALDyC0BQAAXoZOW5Qd4xEAAICP+/PPP1W3bt0zHlOpUiXNmDHjrM81evRoDRkyRB06dFDHjh01efJkZWdna9iwYZKkwYMHq1atWkpNTZUk3XnnnZoyZYruvfde3X333dq8ebOeeeYZ3XPPPef/xnBuWIgMAAB4GUJblF1MLXObfVAqyJVC+KoeAADwLQcOHNC+ffvUqVOnIvt/+OEHBQcHq0OHDqV+rhtuuEEHDx7UuHHjtG/fPrVt21ZffPGFe3GynTt3Kijo5BfckpKStHDhQt1///1q06aNatWqpXvvvVcPP/xw+bw5lB0zbQEAgJchtEXZRVaVgu2SI1fK3CdVOXOXCgAAgLcZOXKkHnrooWKh7Z49e/Tcc8/phx9+KNPzjRo1SqNGjSrxvqVLlxbbl5KSou+//75Mr4EKlEunLQAA8C7MtEXZ2WxSTKJ5nREJAADAB23cuFEXXHBBsf3t2rXTxo0bLagIlsrNMLeEtgAAwEsQ2uLcuEYksBgZAADwQXa7Xfv37y+2Py0tTSEhfBkt4LAQGQAA8DKEtjg3rsXIMtOsrQMAAOAcXH755Ro7dqyOHTvm3nf06FE98sgjuuyyyyysDJZgITIAAOBlaCOoYPszcrRm+xFVsgerR9MaVpdTfqIZjwAAAHzXCy+8oG7duqlu3bpq166dJGndunWKj4/XO++8Y3F18DgWIgMAAF6G0LaCrdt1VCNnrdUFdSr7V2jLeAQAAODDatWqpZ9//lnvvfee1q9fr4iICA0bNkw33XSTQkNDrS4PnsZCZAAAwMsQ2lawaLv5EWflFlhcSTlzjUfIYDwCAADwTZUqVdLtt99udRnwBsy0BQAAXobQtoJFh5udGpk5/hraMh4BAAD4ro0bN2rnzp3Ky8srsv+qq66yqCJYIo/QFgAAeBdC2woWFW5+xH4b2mamSU6HFBRsbT0AAABl8Oeff2rgwIHasGGDbDabDMOQJNlsNkmSw+Gwsjx4kmHQaQsAALxO0Lk8aNeuXdq9e7f79qpVq3TffffpjTfeKLfC/EV0+MnxCE6nYXE15SgqXrIFS4ZDyj5odTUAAABlcu+996p+/fo6cOCAIiMj9euvv2rZsmXq0KGDli5danV58KSCHMlZ2GDBQmQAAMBLnFNoe/PNN+vrr7+WJO3bt0+XXXaZVq1apUcffVQTJ04s1wJ9XZT9ZDNzVp4fddsGBZvBrcRiZAAAwOesXLlSEydOVFxcnIKCghQUFKSLLrpIqampuueee6wuD57kWoRMIrQFAABe45xC219++UUdO3aUJH3wwQdq1aqVVqxYoffee08zZ84sz/p8XnhosMKCzY85y19HJDDXFgAA+BiHw6HoaPOr8HFxcdq71zyfqVu3rn7//XcrS4On5WaY27AoKeicfj0CAAAod+c00zY/P192u12StHjxYvdCDc2aNVNaWlr5VecnosJDdDg7zz/n2u4RoS0AAPA5rVq10vr161W/fn116tRJ//znPxUWFqY33nhDDRo0sLo8eFJeYact82wBAIAXOac/Jbds2VJTp07Vt99+q0WLFql3796SpL1796patWrlWqA/ODnXNt/iSspZTC1zS2gLAAB8zGOPPSan0ylJmjhxorZt26auXbtqwYIFeuWVVyyuDh7lWoSM0QgAAMCLnFOn7XPPPaeBAwfq+eef15AhQ5ScnCxJ+vTTT91jE3CSK7TN8LtO20RzS2gLAAB8TK9evdzXGzVqpN9++02HDx9WlSpVZLPZLKwMHpdLpy0AAPA+5xTa9ujRQ+np6crIyFCVKlXc+2+//XZFRkaWW3H+wrUYmf/NtKXTFgAA+J78/HxFRERo3bp1atWqlXt/1apVLawKlnF12hLaAgAAL3JO4xFOnDih3Nxcd2C7Y8cOTZ48Wb///rtq1KhRrgX6g+jwUEnyz5m2kpRJaAsAAHxHaGio6tSpI4fDYXUp8AauhcgIbQEAgBc5p9C2f//+evvttyVJR48eVadOnTRp0iQNGDBAr732WrkW6A+iCzttM3P8bKZt9CnjEQzD2loAAADK4NFHH9Ujjzyiw4cPW10KrMZCZAAAwAudU2i7du1ade3aVZI0d+5cxcfHa8eOHXr77bdZuKEEJxci87NOW1doW5AjnThibS0AAABlMGXKFC1btkw1a9ZU06ZNdcEFFxS5IICwEBkAAPBC5zTT9vjx44qONv8S/eWXX+rqq69WUFCQ/va3v2nHjh3lWqA/iAp3ddr6WWgbGi5FxknH081u20jmwAEAAN8wYMAAq0uAt2AhMgAA4IXOKbRt1KiR5s+fr4EDB2rhwoW6//77JUkHDhxQTExMuRboD/x2pq0kxSSeDG0TWp39eAAAAC8wfvx4q0uAt3AvREanLQAA8B7nNB5h3LhxGjNmjOrVq6eOHTsqJSVFktl1265du3It0B9E+etMW0mKqWVuM/ZYWwcAAABwLvJcoS3NJwAAwHucU6fttddeq4suukhpaWlKTk5277/00ks1cODAcivOX/jtTFtJiqlpbjPTrK0DAACgDIKCgmSz2U57v8Ph8GA1sJS705bxCAAAwHucU2grSQkJCUpISNDu3bslSbVr11bHjh3LrTB/EuPP4xGiC0NbOm0BAIAP+fjjj4vczs/P108//aS33npLEyZMsKgqWIKFyAAAgBc6p9DW6XTqqaee0qRJk5SVZQ7uj46O1gMPPKBHH31UQUHnNHXBb51ciMwfxyO4Qtu91tYBAABQBv379y+279prr1XLli01Z84c3XrrrRZUBUuwEBkAAPBC5xTaPvroo5o2bZqeffZZdenSRZL03Xff6YknnlBOTo6efvrpci3S1wXEeARCWwAA4Af+9re/6fbbb7e6DHgSC5EBAAAvdE6h7VtvvaU333xTV111lXtfmzZtVKtWLd11112Etn/hWogswx/HI7gXImOmLQAA8G0nTpzQK6+8olq1alldCjyJhcgAAIAXOqfQ9vDhw2rWrFmx/c2aNdPhw4fPuyh/E1040zavwKncAofsIcEWV1SOYhLNbe4xs0uBr5UBAAAfUKVKlSILkRmGoczMTEVGRurdd9+1sDJ4lGEw0xYAAHilcwptk5OTNWXKFL3yyitF9k+ZMkVt2rQpl8L8iavTVpKycgpkj/Kj0NYebXYl5GaY3bbVCW0BAID3e+mll4qEtkFBQapevbo6deqkKlWqWFgZPCr/hGQ4zes0HwAAAC9yTqHtP//5T11xxRVavHixUlJSJEkrV67Url27tGDBgnIt0B8EB9lUKSxY2XkOZeUWqFqU3eqSyldMTelghpS5V6rexOpqAAAAzmro0KFWlwBv4OqylU0Kq2RpKQAAAKcKOpcHde/eXX/88YcGDhyoo0eP6ujRo7r66qv166+/6p133invGv2Ca0RCpj/OtY0uHJHAYmQAAMBHzJgxQx9++GGx/R9++KHeeustCyqCJdyLkEVLp3ReAwAAWO2cQltJqlmzpp5++ml99NFH+uijj/TUU0/pyJEjmjZtWnnW5zeiwl2LkeVbXEkFcC9GtsfaOgAAAEopNTVVcXFxxfbXqFFDzzzzjAUVwRJ5p4S2AAAAXuScQ1uUTXRhaJvlj522MTXNbUaatXUAAACU0s6dO1W/fv1i++vWraudO3daUBEswSJkAADASxHaeohrMTK/HI8Qw3gEAADgW2rUqKGff/652P7169erWrVqFlQES+RmmVs6bQEAgJchtPWQmMKZtlm5/hjaMh4BAAD4lptuukn33HOPvv76azkcDjkcDn311Ve69957deONN1pdHjzFPdOWTlsAAOBdQspy8NVXX33G+48ePXo+tfi1k522/jjT1jUegU5bAADgG5588klt375dl156qUJCzPM0p9OpwYMHM9M2kDDTFgAAeKkyhbaxsbFnvX/w4MHnVZC/cs20zfTHTtvowtD2eLpUkCuF2K2tBwAA4CzCwsI0Z84cPfXUU1q3bp0iIiLUunVr1a1b1+rS4EnuTtsYa+sAAAD4izKFtjNmzKioOvxeVLgfz7SNrCoF2yVHrpSZJlWpZ3VFAAAApdK4cWM1btzY6jJgFddMWxYiAwAAXoaZth4S7Zpp64+hrc3GiAQAAOBTrrnmGj333HPF9v/zn//UddddZ0FFsEQu4xEAAIB3IrT1EPd4BH+caSudshgZoS0AAPB+y5YtU9++fYvt79Onj5YtW2ZBRbAEC5EBAAAvRWjrIdF2Px6PIEkxieaW0BYAAPiArKwshYWFFdsfGhqqjIwMCyqCJViIDAAAeClCWw9xj0fwx4XIJMYjAAAAn9K6dWvNmTOn2P7Zs2erRYsWFlQES7g6bcMIbQEAgHcp00JkOHd+vRCZdHI8QiahLQAA8H6PP/64rr76am3dulWXXHKJJGnJkiWaNWuW5s6da3F18BjXQmR02gIAAC9DaOshfj/TNprxCAAAwHf069dP8+fP1zPPPKO5c+cqIiJCycnJ+uqrr1S1alWry4OnsBAZAADwUoS2HuKaaZuVWyDDMGSz2SyuqJy5xyOkWVsHAABAKV1xxRW64oorJEkZGRl6//33NWbMGP34449yOBwWVwePyHN12rIQGQAA8C7MtPUQ10xbpyEdz/PDXwIqxZnb44esrQMAAKAMli1bpiFDhqhmzZqaNGmSLrnkEn3//fdWlwVPcXfaxlhbBwAAwF/Qaesh4aFBCg6yyeE0lJlToEp2P/voI6uZ24ITUt5xKSzS2noAAABOY9++fZo5c6amTZumjIwMXX/99crNzdX8+fNZhCyQGMYpC5HRaQsAALyLpZ22y5YtU79+/VSzZk3ZbDbNnz//jMcvXbpUNput2GXfvn2eKfg82Gw2/55rGxYlBYeZ1+m2BQAAXqpfv35q2rSpfv75Z02ePFl79+7Vq6++anVZsEJetiTDvM5MWwAA4GUsbffMzs5WcnKyhg8frquvvrrUj/v9998VE3PyK0w1atSoiPLKXXR4iI4ez1dmboHVpZQ/m83sts1MM0PbyklWVwQAAFDM559/rnvuuUd33nmnGjdubHU5sJKry9YWJIVGWFsLAADAX1ga2vbp00d9+vQp8+Nq1KihypUrl39BFSzKHirphDJz/DC0lYqGtgAAAF7ou+++07Rp09S+fXs1b95ct9xyi2688Uary4IV3IuQRZsNCAAAAF7EJxcia9u2rRITE3XZZZdp+fLlVpdTaq7xCFl+G9pWNbfHD1tbBwAAwGn87W9/03/+8x+lpaXp73//u2bPnq2aNWvK6XRq0aJFyszMtLpEeEpuhrllETIAAOCFfCq0TUxM1NSpU/XRRx/po48+UlJSknr06KG1a9ee9jG5ubnKyMgocrFKtN2PZ9pKJxcjo9MWAAB4uUqVKmn48OH67rvvtGHDBj3wwAN69tlnVaNGDV111VVWlwdPyC3stGURMgAA4IV8KrRt2rSp/v73v6t9+/bq3Lmzpk+frs6dO+ull1467WNSU1MVGxvrviQlWTdr1d1p648zbSVCWwAA4JOaNm2qf/7zn9q9e7fef/99q8uBp7hm2rIIGQAA8EI+FdqWpGPHjtqyZctp7x87dqyOHTvmvuzatcuD1RUVVRjaZvjteARCWwAA4LuCg4M1YMAAffrpp1aXAk9wh7Z02gIAAO9j6UJk5WHdunVKTEw87f12u112u92DFZ1edHioJH+eaUtoCwAAAB9x6kJkAAAAXsbS0DYrK6tIl+y2bdu0bt06Va1aVXXq1NHYsWO1Z88evf3225KkyZMnq379+mrZsqVycnL05ptv6quvvtKXX35p1Vsokyhm2gIAAADewb0QGaEtAADwPpaGtmvWrNHFF1/svj169GhJ0pAhQzRz5kylpaVp586d7vvz8vL0wAMPaM+ePYqMjFSbNm20ePHiIs/hzWLCXaGtv3baVjW3xw9bWwcAAABwNu6FyAhtAQCA97E0tO3Ro4cMwzjt/TNnzixy+6GHHtJDDz1UwVVVHPd4BBYiAwAAAKzFQmQAAMCL+fxCZL4koMYjnCGMBwAAACznnmnLQmQAAMD7ENp6ULRrPIK/dtpGFI5HcOaf7FwAAAAAvBGdtgAAwIsR2npQlL/PtA2LlEIjzeuMSAAAAIA3cy1ExkxbAADghQhtPSjGNdPWX0Nb6ZQRCSxGBgAAAC/mWoiMTlsAAOCFCG09yDXT9kS+Q/kOp8XVVJDIwhEJdNoCAADAmzEeAQAAeDFCWw9yjUeQ/Ljb9tTFyAAAAABvxUJkAADAixHaelBocJAiQoMlSVn+uhgZoS0AAAB8AZ22AADAixHaepir2zYjJ9/iSioIoS0AAAC8ndN5stOWhcgAAIAXIrT1sOjC0JbxCAAAAIBFXIGtRKctAADwSoS2HhZduBhZpt+GtixEBgAAAC/nGo0QFCKF2K2tBQAAoASEth4WHR4qyZ9n2saZ2+OHra0DAAAAOB33ImTRks1mbS0AAAAlILT1sCh3py0zbQEAAABLsAgZAADwcoS2HuaaaZvpt522hLYAAADwcq7QlkXIAACAlyK09bCocH+faVsY2p44bK7KCwAAAHgbOm0BAICXI7T1MNdMW/8dj1C4EJnhlHKOWloKAAAAUCL3TNsoa+sAAAA4DUJbD4sp7LTN8tdO2+BQyR5rXmdEAgAAALwRnbYAAMDLEdp62MmFyPw0tJVOdtsS2gIAAMAb5WaY2zA6bQEAgHcitPUw93gEf12ITGIxMgAAAHi3XNd4hBhr6wAAADgNQlsP8/uFyCRCWwAAAHg3xiMAAAAvR2jrYdGumba5froQmURoCwAAAO/GQmQAAMDLEdp6WDQzbQEAAABr0WkLAAC8HKGth7ln2uYUyDAMi6upIO5O28PW1gEAAACUxBXashAZAADwUoS2HuaaaetwGsrJd1pcTQVhPAIAAAC8mbvTloXIAACAdyK09bBKYcEKspnXM3P8dK4toS0AAAC8mTu0pdMWAAB4J0JbD7PZbIpyzbXN9dO5toS2AAAA8GbuhciYaQsAALwToa0FTp1r65cIbQEAAODNWIgMAAB4OUJbC0QXzrXN8vfQNueY5PDTERAAAADwTU6HlH/cvB5GaAsAALwToa0F3OMR/HWmbURlSYWDe08csbISAAAAoChXl63ETFsAAOC1CG0t4Oq09duZtkHBUkQV8zojEgAAAOBNXPNsg8OkELu1tQAAAJwGoa0Fovx9pq3EXFsAAAB4J+bZAgAAH0BoawF3p62/jkeQCG0BAADgnVyhbRijEQAAgPcitLWA3y9EJhHaAgAAwDu5O21jrK0DAADgDAhtLRDtXojMn0PbquaW0BYAAADehPEIAADABxDaWiC6cKZtlr8uRCad0ml72No6AAAAPOBf//qX6tWrp/DwcHXq1EmrVq0q1eNmz54tm82mAQMGVGyBOMm1EJmd8QgAAMB7EdpaIKqw0zaDmbYAAAA+b86cORo9erTGjx+vtWvXKjk5Wb169dKBAwfO+Ljt27drzJgx6tq1q4cqhSQ6bQEAgE8gtLWAe6atP3faVoozt4S2AADAz7344ou67bbbNGzYMLVo0UJTp05VZGSkpk+fftrHOBwODRo0SBMmTFCDBg08WC2UW9hpy0JkAADAixHaWiAqPBBm2tJpCwAA/F9eXp5+/PFH9ezZ070vKChIPXv21MqVK0/7uIkTJ6pGjRq69dZbPVEmTpWbYW7ptAUAAF4sxOoCAlFM4UzbTMYjAAAA+LT09HQ5HA7Fx8cX2R8fH6/ffvutxMd89913mjZtmtatW1fq18nNzVVubq77dkZGxjnVCzEeAQAA+AQ6bS3gmmmb5dedtlXNLQuRAQAAuGVmZuqWW27Rf/7zH8XFxZX6campqYqNjXVfkpKSKrBKP+deiIzQFgAAeC86bS3gmmmbneeQw2koOMhmcUUVwNVpm5cl5edIoeHW1gMAAFAB4uLiFBwcrP379xfZv3//fiUkJBQ7fuvWrdq+fbv69evn3ud0OiVJISEh+v3339WwYcNijxs7dqxGjx7tvp2RkUFwe67otAUAAD6ATlsLuGbaSn68GJk9RgoqfJ+MSAAAAH4qLCxM7du315IlS9z7nE6nlixZopSUlGLHN2vWTBs2bNC6devcl6uuukoXX3yx1q1bd9og1m63KyYmpsgF54iFyAAAgA+g09YC9pBghYUEKa/AqcycfMVGhFpdUvmz2cxu26z9ZmgbW8vqigAAACrE6NGjNWTIEHXo0EEdO3bU5MmTlZ2drWHDhkmSBg8erFq1aik1NVXh4eFq1apVkcdXrlxZkortRwVxL0RG8A0AALwXoa1FYsJDlJ6V57+dtlLR0BYAAMBP3XDDDTp48KDGjRunffv2qW3btvriiy/ci5Pt3LlTQUF8wc1ruGfa0mkLAAC8F6GtRaLsZmib6deLkRXOtSW0BQAAfm7UqFEaNWpUifctXbr0jI+dOXNm+ReE02OmLQAA8AH8yd8i0eHmSIQsvw5tq5rb44etrQMAAABwcYW2zLQFAABejNDWIlF2s8k5Iyff4koqEJ22AAAA8CaOfKkgx7xOpy0AAPBihLYWiQ43Q1vGIwAAAAAe4uqylQhtAQCAVyO0tUhUYWjr9wuRSYS2AAAA8A6uRchCwqXgUGtrAQAAOANCW4vEFM60zWQ8AgAAAOAZLEIGAAB8BKGtRVzjEViIDAAAAPCQ3MJOWxYhAwAAXo7Q1iKuhciYaQsAAAB4CJ22AADARxDaWiTaNR4hUGbaGoa1tQAAAAC5GeaW0BYAAHg5QluLuBYiC4iZto5cKS/b2loAAAAA10JkhLYAAMDLEdpaJDo8AMYjhEaaK/NKjEgAAACA9RiPAAAAfAShrUWiC2faZvnzeASbjbm2AAAA8B4sRAYAAHyEpaHtsmXL1K9fP9WsWVM2m03z588/62OWLl2qCy64QHa7XY0aNdLMmTMrvM6K4J5p68+dtpIUWdXcHj9sbR0AAAAAM20BAICPsDS0zc7OVnJysv71r3+V6vht27bpiiuu0MUXX6x169bpvvvu04gRI7Rw4cIKrrT8uWbaZvl9aEunLQAAALwEM20BAICPCLHyxfv06aM+ffqU+vipU6eqfv36mjRpkiSpefPm+u677/TSSy+pV69eFVVmhXDNtM1zOJWT71B4aLDFFVUQQlsAAAB4C2baAgAAH+FTM21Xrlypnj17FtnXq1cvrVy50qKKzl1U2Mm83K/n2hLaAgAAwFsQ2gIAAB9haadtWe3bt0/x8fFF9sXHxysjI0MnTpxQREREscfk5uYqNzfXfTsjI6PC6yyNoCCbouwhysotUGZOgeKi7FaXVDEIbQEAAOAtWIgMAAD4CJ/qtD0Xqampio2NdV+SkpKsLsnNNSIhMyff4koqEKEtAAAAvAWdtgAAwEf4VGibkJCg/fv3F9m3f/9+xcTElNhlK0ljx47VsWPH3Jddu3Z5otRSibIHwGJk7tD2sLV1AAAAAHmEtgAAwDf41HiElJQULViwoMi+RYsWKSUl5bSPsdvtstu9c/SAq9M2IyBCWzptAQAAYDE6bQEAgI+wtNM2KytL69at07p16yRJ27Zt07p167Rz505JZpfs4MGD3cffcccd+vPPP/XQQw/pt99+07///W998MEHuv/++60o/7xFhYdKYiEyAAAAwCOYaQsAAHyEpaHtmjVr1K5dO7Vr106SNHr0aLVr107jxo2TJKWlpbkDXEmqX7++PvvsMy1atEjJycmaNGmS3nzzTfXq1cuS+s9XwM20NQxrawEAAEDgKsiVHIULFNNpCwAAvJyl4xF69Ogh4wxB3syZM0t8zE8//VSBVXlOTHggzLStam4Nh5RzTIqobGk5AAAACFCuLluJ0BYAAHg9n1qIzN+4FiLL9OfxCCF2KazwpJgRCQAAALCKaxGy0EgpKNjaWgAAAM6C0NZC0YUzbTP9udNWOtltS2gLAAAAq7AIGQAA8CGEthZyd9r680xbicXIAAAAYD0WIQMAAD6E0NZCJxci8/dOW0JbAAAAWIxOWwAA4EMIbS3kCm2z/HmmrURoCwAAAOvlEdoCAADfQWhroZMzbRmPAAAAAFQoOm0BAIAPIbS1kGumbZbfj0dgITIAAABYjNAWAAD4EEJbCwXeTNvD1tYBAACAwMVCZAAAwIcQ2lrINR4hK69ATqdhcTUViPEIAAAAsBqdtgAAwIcQ2lrI1WlrGFJ2nh932xLaAgAAwGruhcjotAUAAN6P0NZC9pAghQbbJPn5iARCWwAAAFjN3WkbY20dAAAApUBoayGbzXZyMbLcAAhtTxyVHH78PgEAAOC9mGkLAAB8CKGtxVxzbTNz8i2upAJFVCm8Ykg5R62sBAAAAIGKmbYAAMCHENpazNVp69fjEYJDpPDK5nVGJAAAAMAKhLYAAMCHENpazLUYmV+HthJzbQEAAGCtPEJbAADgOwhtLeYKbf16pq1EaAsAAABr0WkLAAB8CKGtxQJipq1EaAsAAADrGAYLkQEAAJ9CaGsxd6ct4xEAAACAilGQKzkLmyTotAUAAD6A0NZiroXIMvw+tK1qbo8ftrYOAAAABJ68rJPX6bQFAAA+gNDWYifHI/h5aFspztzSaQsAAABPy80wt2FRUhC/AgEAAO/HGYvFotwLkTHTFgAAAKgQLEIGAAB8DKGtxWIKQ1u/77QltAUAAIBVWIQMAAD4GEJbi7lm2mblEtoCAAAAFYJOWwAA4GMIbS0WMDNt3aEtC5EBAADAw1wLkdnptAUAAL6B0NZirk5b/w9tq5rb3AypIM/aWgAAABBYXAuR2WOsrQMAAKCUCG0tFu2eaevnC5HZYyVbsHn9BN22AAAA8CBm2gIAAB9DaGuxmMLxCLkFTuUVOC2upgIFBZ3stmWuLQAAADyJmbYAAMDHENparJI92H09YBYjy063tg4AAAAEFkJbAADgYwhtLRYSHKTIMDO49fsRCe7FyOi0BQAAgAfluUJbxiMAAADfQGjrBQJuMTJCWwAAAHiSu9OWhcgAAIBvILT1AicXI/P30NbVactCZAAAAPAgFiIDAAA+htDWC0QVLkYWMDNt6bQFAACAJzHTFgAA+BhCWy8Q4+60ZaYtAAAAUO7yCjttmWkLAAB8BKGtF3CNR6DTFgAAAKgAdNoCAAAfQ2jrBQJnITJCWwAAAFiAhcgAAICPIbT1AtGFM20z/H48QlVzy0JkAAAA8BTDOBnashAZAADwEYS2XsDVaZtFpy0AAABQvvJPSIbDvM54BAAA4CMIbb1AdHiAjUcoOCHlHbe2FgAAAAQG1yJksklhlSwtBQAAoLQIbb1AwCxEFhYlBYeZ1+m2BQAAgCecugiZzWZtLQAAAKVEaOsFXDNtM/19pq3NxogEAAAAeBbzbAEAgA8itPUCrpm2fj8eQSK0BQAAgGed2mkLAADgIwhtvUDAzLSVpMiq5vb4YWvrAAAAQGBwzbQltAUAAD6E0NYLBMx4BIlOWwAAAHiWu9OW8QgAAMB3ENp6gVMXIjMMw+JqKhihLQAAADwpN8Pc0mkLAAB8CKGtF3CFtk5DOp7nsLiaChYZZ24JbQEAAOAJuYXjEcIIbQEAgO8gtPUCEaHBCg6ySTK7bf0anbYAAADwJBYiAwAAPojQ1gvYbDZF2V2Lkfn5XFv3QmSEtgAAAPAA90JkzLQFAAC+g9DWS5wMbQOl0/awtXUAAAAgMNBpCwAAfBChrZdwzbUNnNCWTlsAAAB4AKEtAADwQYS2XsIV2gbUTFvDsLYWAAAA+D9XaMtCZAAAwIcQ2nqJ6PBQSQE009aZf/IEGgAAAKgodNoCAAAfRGjrJQJmPEJohBRaybzOiAQAAABUNBYiAwAAPojQ1ksEzEJkEouRAQAAwHPotAUAAD6I0NZLnByPEAihbeGIhOPp1tYBAAAA/5db2GnLTFsAAOBDCG29xMmFyPx8pq1UdDEyAAAAoKIYhpRHpy0AAPA9XhHa/utf/1K9evUUHh6uTp06adWqVac9dubMmbLZbEUu4eHhHqy2YgTMTFuJ0BYAAACekX9cMpzmdUJbAADgQywPbefMmaPRo0dr/PjxWrt2rZKTk9WrVy8dOHDgtI+JiYlRWlqa+7Jjxw4PVlwxXDNts3IJbQEAAIBy4ZpnawsyF8QFAADwEZaHti+++KJuu+02DRs2TC1atNDUqVMVGRmp6dOnn/YxNptNCQkJ7kt8fLwHK64Yrpm2GXTaAgAAAOXj1EXIbDZrawEAACgDS0PbvLw8/fjjj+rZs6d7X1BQkHr27KmVK1ee9nFZWVmqW7eukpKS1L9/f/3666+eKLdCnRyPEAgzbV0LkR22tg4AAAD4N1doyyJkAADAx1ga2qanp8vhcBTrlI2Pj9e+fftKfEzTpk01ffp0ffLJJ3r33XfldDrVuXNn7d69u8Tjc3NzlZGRUeTijdzjEei0BQAAAMpHLouQAQAA32T5eISySklJ0eDBg9W2bVt1795d8+bNU/Xq1fX666+XeHxqaqpiY2Pdl6SkJA9XXDoxheMRWIgMAAAAKCfu0DbK2joAAADKyNLQNi4uTsHBwdq/f3+R/fv371dCQkKpniM0NFTt2rXTli1bSrx/7NixOnbsmPuya9eu8667IkQVjkc4ke9QgcNpcTUVjNAWAAAAnnB4q7mNqWVtHQAAAGVkaWgbFham9u3ba8mSJe59TqdTS5YsUUpKSqmew+FwaMOGDUpMTCzxfrvdrpiYmP9v777DoyrT/4+/J72HhJKEDhLpRUIRFEFAAygrCIJIL7oq4QsiPxGl2RZUVFRcWJGiq4CigrgqiCyi0gXDglKVJpAEpIQEUpg5vz+GDJkQQgJJzszweV3X2VPnnHvOM1kf7nnmPk6TK8opjwCQlunho21zkrbnT4HNam4sIiIiIuK5ki8++yK6gblxiIiIiBSR6eURRo8ezezZs3n//ffZuXMnjz32GOnp6QwePBiAAQMGMG7cOMfxzz//PN9++y1//PEHW7dupV+/fhw8eJBhw4aZ9RaKhZ+PF/4+9ubw+BIJOQ8iM2yQccbcWERERETEc+UkbSvUNzcOERERkSLyufohJat3794cP36ciRMnkpSURJMmTVi+fLnj4WSHDh3Cy+tSbvnUqVM8/PDDJCUlERERQVxcHOvWraNevXpmvYViExrgS2Zapucnbb19wT8cMs/YSyTkJHFFRERERIqLNRuO77YvRylpKyIiIu7F9KQtQEJCAgkJCfnu+/77753W33jjDd54441SiKr0hQb4cCItk7MZ2WaHUvKCIi8lbYk1OxoRERER8TQn9oItG/xCoUxVs6MRERERKRLTyyPIJaEXH0bm8TVtQQ8jExEREZGSlVMaIao+WCzmxiIiIiJSREraupCcpK3Hl0cAJW1FREREpGQl77DPVRpBRERE3JCSti4kxP9i0vZGGmmbMwJCRERERKQ45R5pKyIiIuJmlLR1IaEBvgA3Rk3bm+Pt843/gr0rzY1FRERE5Dq98847VK9enYCAAFq2bMmmTZuueOzs2bNp06YNERERRERE0LFjxwKPl2uU8pt9rqStiIiIuCElbV1IzkjbtBuhPEL9bhA3GDDgs6Fw8g+zIxIRERG5Jh9//DGjR49m0qRJbN26lcaNGxMfH09KSkq+x3///ff06dOH1atXs379eqpUqcLdd9/NkSNHSjlyD3buJKRevJ8V6pobi4iIiMg1UNLWhYTdSDVtATq/DJWbQ8YZ+Lg/ZJ0zOyIRERGRInv99dd5+OGHGTx4MPXq1WPWrFkEBQUxd+7cfI//6KOPePzxx2nSpAl16tThvffew2azsWrVqlKO3IPljLItUxUCws2NRUREROQaKGnrQkIuJm3TboSatgA+/tDrAwgub39QxJf/B4ZhdlQiIiIihZaVlcWWLVvo2LGjY5uXlxcdO3Zk/fr1hTrHuXPnyM7OJjIy8orHZGZmkpqa6jRJARz1bBuYG4eIiIjINVLS1oXcUDVtc4RVhAfeB4s3bF8MG2eZHZGIiIhIoZ04cQKr1UpUVJTT9qioKJKSkgp1jrFjx1KxYkWnxG9eU6ZMITw83DFVqVLluuL2eMk77HPVsxURERE3paStC8mpaZt6o5RHyFH9Noh/yb684lk48JO58YiIiIiUkqlTp7Jo0SKWLFlCQEDAFY8bN24cZ86ccUyHDx8uxSjdUPLF8ggV6pkbh4iIiMg1UtLWhYQG3EAPIsur5aPQ8AEwrLB4EKQeNTsiERERkasqV64c3t7eJCcnO21PTk4mOjq6wNdOmzaNqVOn8u2339KoUaMCj/X39ycsLMxpkiuw2S7VtFV5BBEREXFTStq6EEd5hMwbqDxCDosFur5p71inH4dPBsCFTLOjEhERESmQn58fcXFxTg8Ry3moWKtWra74uldeeYUXXniB5cuX06xZs9II9cZxaj9knwOfAIisaXY0IiIiItdESVsXckOPtAXwC4be/7Y/4ffPzbD8abMjEhEREbmq0aNHM3v2bN5//3127tzJY489Rnp6OoMHDwZgwIABjBs3znH8yy+/zIQJE5g7dy7Vq1cnKSmJpKQk0tLSzHoLniXnIWTl64C3j7mxiIiIiFwjJW1dSE7S9mzGBQzDMDkak0TWhB5zAAv8PBe2/tvsiEREREQK1Lt3b6ZNm8bEiRNp0qQJiYmJLF++3PFwskOHDnHs2DHH8TNnziQrK4uePXsSExPjmKZNm2bWW/AsOUlblUYQERERN6avnl1IzoPILtgMMi/YCPD1Njkik8TeBXc+C6tfhK+etD/1t1JTs6MSERERuaKEhAQSEhLy3ff99987rR84cKDkA7qRJe+wz6PqmxuHiIiIyHXQSFsXEuzng8ViX07NuAHr2ubW5kmo3QWsmfBxf0g/YXZEIiIiIuIOHA8hq2duHCIiIiLXQUlbF+LlZSHE71KJhBualxd0nwWRN0Hqn/DpELDe4PdERERERAqWmQYn99uXVR5BRERE3JiSti7mhn8YWW4B4fDgR+AbDPvXwKrnzI5IRERERFzZ8V2AASFREFzO7GhERERErpmSti4mJEAjbZ1UqAvd3rEvr3sLDm8yNx4RERERcV2qZysiIiIeQklbFxMa4AtAWuYNXtM2t/rdoUk/+/KKZ8AwzI1HRERERFxT8q/2uZK2IiIi4uaUtHUxOeURTqYraeuk/XjwDYI/N8OvS8yORkRERERcUXLOQ8hUz1ZERETcm5K2LqZuTBgASxOPmByJiwmLgdtG2pe/mwzZGaaGIyIiIiIuxjAulUeoUM/cWERERESuk5K2LmZAq2r4eFnYtP8kiYdPmx2Oa2k9AkJj4PRB2PQvs6MREREREVeSehQyToPFG8rXNjsaERERkeuipK2LiQkP5L4mlQB494ffTY7GxfgFQ/sJ9uUfXoP0v8yNR0RERERcR04923I3g4+/ubGIiIiIXCclbV3QI3fUBGD5jiQOnEg3ORoX07gPRDeEzDOwZqrZ0YiIiIiIq8gpjaCHkImIiIgHUNLWBdWODqVd7fLYDHjvpz/MDse1eHnB3S/ZlzfPgeN7zI1HRERERFxDSs5DyJS0FREREfenpK2Lyhltu/jnP/krLdPkaFxMzbZwc2cwrLByotnRiIiIiIgryCmPoKStiIiIeAAlbV1Uq5plaVgpnMwLNj5Yf9DscFzPXc/bHzKx5xvY/4PZ0YiIiIiImS5kwomLv8BS0lZEREQ8gJK2LspisfD3tvbRth+sP8D5LKvJEbmY8jdD86H25RXPgE33R0REROSGdWIP2C5AQDiEVTI7GhEREZHrpqStC+tUP5oqkYGcOpfNp1sOmx2O62n7NPiHQ9J22LbI7GhERERExCyO0ggNwGIxNxYRERGRYuBjdgByZT7eXgy7vSaTlv3K7B/381DLanh7qRPqEFwW7njSXtf2vy9A/W7gF2x2VCIiIiJS2lTPVkTE7VitVrKzs80OQ6TY+fr64u3tfd3nUdLWxT3QrDJvfLeHQyfPsXxHEvc0ijE7JNfS4u+weQ6cPgjrZkC7sWZHJCIiIiKlTUlbERG3YRgGSUlJnD592uxQREpMmTJliI6OxnIdvwBS0tbFBfn5MKBVdd5atZd3f/idLg2vr8E9jm8AdJwMnw6GtdOh6QAIU2JbRERE5IaSk7StoKStiIiry0nYVqhQgaCgIOU4xKMYhsG5c+dISUkBICbm2nNUStq6gYGtqvGvNb+z7c8zbNx/kltrljU7JNdSvztsmAl/boLVL8J975gdkYiIiIiUlvQTkJZkX65Q19xYRESkQFar1ZGwLVtWuQ3xTIGBgQCkpKRQoUKFay6VoAeRuYGyIf70jKsMwL/W/G5yNC7IYoH4f9iXf/nI/mAyEREREbkx5IyyjagB/iHmxiIiIgXKqWEbFBRkciQiJSvnM349dZuVtHUTw9rUxGKB1buPsyf5rNnhuJ4qzaH+/YABK54FwzA7IhEREREpDapnKyLidlQSQTxdcXzGlbR1EzXKBdOpfjQA7/7wh8nRuKiOk8DbD/avgb3fmh2NiIiIiJSGlJykbQNz4xARESmi6tWrM3369EIf//3332OxWPQQtxuEkrZu5JE7agLwReIRks5kmByNC4qoDrc+Zl/+dgJYL5gajoiIiIiUAsdI23rmxiEiIh7LYrEUOE2ePPmazrt582YeeeSRQh/funVrjh07Rnh4+DVd71rUqVMHf39/kpKSSu2aYqekrRu5pWoELapHkm01mLduv9nhuKY2T0JQWTixGza9a3Y0IiIiIlKSbFZI2Wlf1khbEREpIceOHXNM06dPJywszGnbmDFjHMcahsGFC4UbRFa+fPki1ff18/MjOjq61MpL/PTTT5w/f56ePXvy/vvvl8o1C3I99WHdkZK2biZntO2CDYc4m3FjfVgLJSAc2o2zL694BrZ+YG48IiIiIlJyTv4BFzLAN8j+qysREZESEB0d7ZjCw8OxWCyO9V27dhEaGso333xDXFwc/v7+/PTTT/z+++/cd999REVFERISQvPmzfnuu++czpu3PILFYuG9996je/fuBAUFERsby7Jlyxz785ZHmD9/PmXKlGHFihXUrVuXkJAQOnXqxLFjxxyvuXDhAv/3f/9HmTJlKFu2LGPHjmXgwIF069btqu97zpw5PPTQQ/Tv35+5c+detv/PP/+kT58+REZGEhwcTLNmzdi4caNj/5dffknz5s0JCAigXLlydO/e3em9Ll261Ol8ZcqUYf78+QAcOHAAi8XCxx9/TNu2bQkICOCjjz7ir7/+ok+fPlSqVImgoCAaNmzIwoULnc5js9l45ZVXqFWrFv7+/lStWpWXXnoJgPbt25OQkOB0/PHjx/Hz82PVqlVXvSelSUlbN9O+TgVqVQjhbOYFFm46ZHY4rqnZUGg2BDBg2QhY/0+zIxIRERGRkpC8wz6vUBe8vM2NRURErolhGJzLumDKZBTjQ8yffvpppk6dys6dO2nUqBFpaWl06dKFVatW8csvv9CpUye6du3KoUMF53Kee+45evXqxf/+9z+6dOlC3759OXny5BWPP3fuHNOmTePf//43P/zwA4cOHXIa+fvyyy/z0UcfMW/ePNauXUtqauplydL8nD17lsWLF9OvXz/uuusuzpw5w48//ujYn5aWRtu2bTly5AjLli1j27ZtPPXUU9hsNgC++uorunfvTpcuXfjll19YtWoVLVq0uOp183r66acZOXIkO3fuJD4+noyMDOLi4vjqq6/YsWMHjzzyCP3792fTpk2O14wbN46pU6cyYcIEfvvtNxYsWEBUVBQAw4YNY8GCBWRmZjqO//DDD6lUqRLt27cvcnwlycfsAKRovLwsPNKmJk999j/m/nSAQa1r4Oej3LsTLy+453XwC4Z1b8OKcZCVBnf8P9ATKkVEREQ8R/Jv9nlUfXPjEBGRa3Y+20q9iStMufZvz8cT5Fc8qbHnn3+eu+66y7EeGRlJ48aNHesvvPACS5YsYdmyZZeN9Mxt0KBB9OnTB4B//OMfvPXWW2zatIlOnTrle3x2djazZs3ipptuAiAhIYHnn3/esf/tt99m3LhxjlGuM2bM4Ouvv77q+1m0aBGxsbHUr2//b+yDDz7InDlzaNOmDQALFizg+PHjbN68mcjISABq1arleP1LL73Egw8+yHPPPefYlvt+FNaoUaO4//77nbblTkqPGDGCFStW8Mknn9CiRQvOnj3Lm2++yYwZMxg4cCAAN910E7fffjsA999/PwkJCXzxxRf06tULsI9YHjRoUKmVnSgsZfvc0H23VKRCqD9JqRl8ue2o2eG4JosF7noB7hxvX1/9EqycAMX4LZqIiIiImMzxEDLVsxUREXM1a9bMaT0tLY0xY8ZQt25dypQpQ0hICDt37rzqSNtGjRo5loODgwkLCyMlJeWKxwcFBTkStgAxMTGO48+cOUNycrLTCFdvb2/i4uKu+n7mzp1Lv379HOv9+vVj8eLFnD17FoDExERuueUWR8I2r8TERDp06HDV61xN3vtqtVp54YUXaNiwIZGRkYSEhLBixQrHfd25cyeZmZlXvHZAQIBTuYetW7eyY8cOBg0adN2xFjeNtHVD/j7eDLqtOq8s3827P/zB/U0rudy3AS7BYoG2/88+4nbFOPuo28yz9lG4+vmciIiIiPtzlEeoZ24cIiJyzQJ9vfnt+XjTrl1cgoODndbHjBnDypUrmTZtGrVq1SIwMJCePXuSlZVV4Hl8fX2d1i0Wi6PkQGGPv96yD7/99hsbNmxg06ZNjB071rHdarWyaNEiHn74YQIDAws8x9X25xdnfg8ay3tfX331Vd58802mT59Ow4YNCQ4OZtSoUY77erXrgr1EQpMmTfjzzz+ZN28e7du3p1q1ald9XWnTSFs31bdlNYL9vNmdfJbv9xw3O5wiO/hXOm+s3ENyakbJX6zV4/C3twELbJkPS/4OVj3ETURERMStZaTC6YP2ZZVHEBFxWxaLhSA/H1OmkhwAt3btWgYNGkT37t1p2LAh0dHRHDhwoMSul5/w8HCioqLYvHmzY5vVamXr1q0Fvm7OnDnccccdbNu2jcTERMc0evRo5syZA9hHBCcmJl6x3m6jRo0KfLBX+fLlnR6YtnfvXs6dO3fV97R27Vruu+8++vXrR+PGjalZsyZ79uxx7I+NjSUwMLDAazds2JBmzZoxe/ZsFixYwJAhQ656XTMoaeumwgN96dOiKgAzV/+OzeY+P/s/fS6Lvu9t5M1Vexk4dxPpmRdK/qJNB0DPOeDlA9sXwycDIbsUEsYiIiIiUjJSdtrnoRUhKP+fZoqIiJglNjaWzz//nMTERLZt28ZDDz1U4IjZkjJixAimTJnCF198we7duxk5ciSnTp26YsI6Ozubf//73/Tp04cGDRo4TcOGDWPjxo38+uuv9OnTh+joaLp168batWv5448/+Oyzz1i/fj0AkyZNYuHChUyaNImdO3eyfft2Xn75Zcd12rdvz4wZM/jll1/4+eefefTRRy8bNZyf2NhYVq5cybp169i5cyd///vfSU5OduwPCAhg7NixPPXUU3zwwQf8/vvvbNiwwZFszjFs2DCmTp2KYRiOer+uRklbNzbk9hr4eXux6cBJxny6DasbJG6tNoP/W5TIn6fOA7Ar6SyjP0ksnaRzgx7w4ALw9ofdX8HC3pCVXvLXFREREZHil5JTz1ajbEVExPW8/vrrRERE0Lp1a7p27Up8fDxNmzYt9TjGjh1Lnz59GDBgAK1atSIkJIT4+HgCAgLyPX7ZsmX89ddf+SYy69atS926dZkzZw5+fn58++23VKhQgS5dutCwYUOmTp2Kt7e95ES7du1YvHgxy5Yto0mTJrRv355NmzY5zvXaa69RpUoV2rRpw0MPPcSYMWMICgq66vsZP348TZs2JT4+nnbt2jkSx7lNmDCBJ598kokTJ1K3bl169+59WV3gPn364OPjQ58+fa54L8xmMa630IWbSU1NJTw8nDNnzhAWFmZ2ONftP/87yshFiVhtBl0bV+SNXo3x8XbdXPy0FbuZsXofAb5eTLy3PpOX/UqW1cbIDrE8cdfNpRPE/h9gYR/ISoMqLeGhTyCwTOlcW0RERK6Lp/XlrpXuA/DVk7D5PbhtFNz13FUPFxER82VkZLB//35q1KjhsokyT2ez2ahbty69evXihRdeMDsc0xw4cICbbrqJzZs3l0gyvaDPemH7ca6b3ZNCubdRRd55qCm+3ha+3HaUEQt/IetC6Q+3L4xvf01ixup9AEy9vxEPtazKi93tT/p9c9Vevt5+rKCXF58ad8CALyAgHA5vhPfvhfQTpXNtERERESkeyTkjbRuYG4eIiIgLO3jwILNnz2bPnj1s376dxx57jP379/PQQw+ZHZopsrOzSUpKYvz48dx6662mjH4uLCVtPUCnBtHM6heHn7cX3+xI4vGPtpB5wWp2WE7+OJ7Gk59sA2BQ6+p0u6USAL2aVWHo7TUAePKTbfx69EzpBFS5GQz6GoLLQ9J2mBsP2xbZH2ghIiIiIq7NMHIlbeuZG4uIiIgL8/LyYv78+TRv3pzbbruN7du3891331G3bl2zQzPF2rVriYmJYfPmzcyaNcvscAqkpK2H6FA3itkDm+Hv48V3O1N45IMtZGS7RuI2PfMCf//3Fs5mXqBF9Uievcf5/xjGda5Dm9hynM+28sgHWziRllk6gUU3gMHLIawy/LUPlvwdXq0Fi/rCjs8h6+pPLRQRERERE5w5DJmp4OULZWPNjkZERMRlValShbVr13LmzBlSU1NZt24dd9xxh9lhmaZdu3YYhsHu3btp2LCh2eEUSElbD9L25vLMHdScAF8v1uw5zrD3f+Z8lrmJW8MweOrT/7E3JY0Kof7M6HsLvnlq7vp4ezGjT1NqlAvmyOnzPPbhltIr8VCuFjyyGto9A+VuBmsm7PoPfDrYnsD9dCjs+houlFIiWURERESuLvk3+7x8bfDxMzcWERERkRKgpK2Hua1WOd4f3IJgP29+2neCQfM2kZ55wbR4Zv/4B19tP4avt4WZ/ZpSITT/QuPhQb7MHtCMUH8fNh84xcQvdlBqz8gLqQDtxsLwTfDoWrh9NJSpBtnpsONTWNQHXo2FpY/Dvu/Aml06cYmIiIhI/pJ32OdR9c2NQ0RERKSE+JgdgBS/ljXL8sHQFgyau5mN+08ycO4m5g1uTmiAb6nGsW7fCaZ+swuAiffWI65aZIHH16oQwlsP3cKQ+ZtZtPkwdWPCGNi6eilEepHFYi+ZEN0AOkyEI1thx2fw6xI4exQSP7JPQWWhaisIibo4lc+1XAGCK4CvnoIpIiIiUmIc9WyVtBURERHPpKSth4qrFsmHw1rSf85Gfj54in5zNvHBkBaEB5ZO4vbo6fMkLPwFmwE9mlam363VCvW6O2tXYFznOvzj6108/5/fqFUhhNtqlSvhaPNhsUDlOPt094tweMPFBO5SOHfCXkKhIP7h9gRuSBQElwO/YPD2A58A+0/4fAIurvvnWr64z9sPLF5g8b44t9jnXjnruSdv8Pa1v9Y34OI5/MEn0L7dYimV2yUiIiJSqnKSthWUtBURERHPpKStB2tcpQwLHr6V/nM2su3wafq+t4F/D2lJRHDJ1v3KyLby2IdbOJmeRf2KYbzUvQGWIiQPH25Tk13HzvL5L0d4/KOtLEu4jWplg0sw4qvw8oJqre1Tp5fh4Fo4sQfSj0NaMqSlOM+tWZB5xj79tde8uLFcSuL6Btrn3hfb3rBdnIxLc4zLt1ss9gd8ePuAl4992csnz/rFxLGXr3Ny2cvbnlTOmVu87PfSsc3LHiPkSi7nXs+zz2YFW7a9PIXtwsV5fusX7Ot5z1vQssVy8f3kek8569551vNOjv2+ubblPVeee+GYe+VZ97HH4igNcnF+xXWck/heuZL5jm25k/353NeC7nnefZcdk/te5tmW0755X+uY58SmLxZERNzCgZ8gI7VorylfG8reVPyxZGdc6mNppK2IiIh4KJdI2r7zzju8+uqrJCUl0bhxY95++21atGhxxeMXL17MhAkTOHDgALGxsbz88st06dKlFCN2Hw0qhbPwkVvpO3sjO46k0mf2Bl7s1oD6FcMJ9PMukWs+9+WvbPvzDGWCfJnVL44A36Jdx2Kx8I/7G/L7iXS2HT7NsPd/5vPHW5d6eYd8eftAzbb2KT+GARmnIS0noZsM6Sfgwnn7w8wuZNqTuhcycq1nwoWL26xZ9smRPLWBLdeyYc21/eKy0/kycgdz8brn7TGJuCRLIZLZuZL8louvuVpiOOeLB/L7YgLn7RbLxUR33li8nOPJnQR3ioE88eRKkOebVM9nxHzOawzD/qWDYbXPbdaLf+sX5459F//+c2J1fJGQa92Se7vXpWWn13hfeVvu93KlhHzuZad7nLOcz9xp2ZannXJ9aeR0PLli4MrtTkH78jk2p+3z+2LlSp/Lyz5HV3vfOfLUab+sbnsR98MVPk+5vqQpcL/3VY4p4IsZp/a4OA+raL9X4rlWPAPHthX9dTFNoOED0OB+++ekOBzfZf+bC4yE0OjiOaeIiIiIizE9afvxxx8zevRoZs2aRcuWLZk+fTrx8fHs3r2bChUqXHb8unXr6NOnD1OmTOHee+9lwYIFdOvWja1bt9KgQQMT3oHrqxMdxqJHbuWh9zayK+ksPWetx8tiryHbsFIZGlYKo2HlcOrFXH8id9GmQyzcdBiLBd568BaqRAZd03kCfL15t38cXd/+ib0paTzxcSLv9m+Gl5eLj8qzWCAwwj6Vv7n0r28Y9iRu9vlLSdzcc2smzomY3EkmLk86GYZ99KrNemlEq9N6nuXcyeScuZEr6eRIQltzJTjAeQRpPqNLDePSaFbvnNG+frmWfZ33eeX+v7b8EiiG87VstovvLfd0cWRv7vXc7znvfmve1+dsz31f8iTg8m635XpoYL6jXfOs504W2QpI7DtNRbjn+S0Xu4tJSi6AtQROLyKl4//9AcFlzY5CSlKF+pd+sVMY1mxI2g7HEu3Tt+Oh+u3QoAfUuw+CCn7WQYFSfrPPo+o7f7EgIiLi4tq1a0eTJk2YPn06ANWrV2fUqFGMGjXqiq+xWCwsWbKEbt26Xde1i+s8UnoshnHZUI5S1bJlS5o3b86MGTMAsNlsVKlShREjRvD0009fdnzv3r1JT0/nP/+5VFP01ltvpUmTJsyaNeuq10tNTSU8PJwzZ84QFhZWfG/EDew/kc7Ub3ay9dBpjp/NvGx/TiK3QaVwGl6cbiofgp+PFz7eFny8vPCycMVSB4mHT9Nr1nqyrDb+X3xtht9Z67pjTjx8ml7/Wk/WBRsPxFXm9thyRAT5ERnsR5kgXyKC/Ajy8y5S+QURKQaGkU/CN9foyctGV+azrTCJ7Jw5Rj4jG69wXowCRr3mHjGaM2owd+I7VxxOy7niumwkqC3XPckzuveypHne7XnOn7tURt7Rnk4jYi+OznUaiZv7C4fco3IvfqmQ+8sB24WL18z9mgvO+/O9x+RK/ucZXZrfPb5sVGueUbpO7ZR3VG+ubVccuYtzbIX9fOQd+es0gtma57OQZ3vOl1x5y33k975zRmI75Plv1WX/7bra/jwu+4zl/aIm57Od8zdUwGcx75c8ue9vzrWueC+BJ3ddXxKuCG7kvlxubnEf0k/Ab0th+6dwaP2l7V4+UKsjNOgJtTuDf0jRzrviWVg/A1o+Bp2nFmvIIiJSsjIyMti/fz81atQgIMB9HuDdtWtXsrOzWb58+WX7fvzxR+644w62bdtGo0aNCjxP3qTt8ePHCQ4OJijoygPeippsnTx5MkuXLiUxMdFpe1JSEhEREfj7+xfqPNfj/PnzVKpUCS8vL44cOVIq13Q1BX3WC9uPM3WkbVZWFlu2bGHcuHGObV5eXnTs2JH169fn+5r169czevRop23x8fEsXbq0JEP1CDXKBfOv/s0ASE7N4H9/nmH7kTPsOGKfHz+byZ7kNPYkp/H51iNXPI+Pl8WRxPX2suDrbcHby0Lq+QtkWW3cXS+Kx9oWT/2yJlXK8HKPhjzx8TYWb/mTxVv+vOwYPx8vIi4mcCOC/IgI9qVMkB9+3vb4vL0seFkseHuBt8WCxWK5bLuXxXJxAi8v+zFeFhzbLLn3WyyX/TvaYrE4/qntyE9gybWcvyv/e/zyHVc69srnzn9PUdPbufMtl87htHLN5y6s6/1mKXdcue+L8/brvEiB1y+5k7vS9xVFD8WC/T9Dpv/oo2A5uV2vXOtuqCQ/hzcSV/qbcyVx/mVwgSJG4mqCy0HzYfbp9GH49XPYvtg+AnfPcvvkG2RP3Na7D4IKOVr70Ab7XPVsRUSklAwdOpQePXrw559/UrlyZad98+bNo1mzZldN2OanfPnyxRXiVUVHl15Joc8++4z69etjGAZLly6ld+/epXbtvAzDwGq14uPj4v/uzIepEZ84cQKr1UpUVJTT9qioKHbt2pXva5KSkvI9PikpKd/jMzMzycy8NKo0NbWID1DwUFFhAdxVL4C76l26l8mpGWzPk8hNyWdE7gWbwQWbAdgu21c7KpTXejUu1jIG3W+pjAUL3/6WxKn0bE6dy7JP6dlkWW1kXbCRnJpJcurlsYqIiEjJ2zrhLiJL+EGn4ubKVIHbRtqn47vto293fAon/4Adn9mnooqqV/xxioiI5OPee++lfPnyzJ8/n/Hjxzu2p6WlsXjxYl599VX++usvEhIS+OGHHzh16hQ33XQTzzzzDH369LniefOWR9i7dy9Dhw5l06ZN1KxZkzfffPOy14wdO5YlS5bw559/Eh0dTd++fZk4cSK+vr7Mnz+f5557Drg0YGnevHkMGjToshG727dvZ+TIkaxfv56goCB69OjB66+/TkiI/RcwgwYN4vTp09x+++289tprZGVl8eCDDzJ9+nR8fQv+un7OnDn069cPwzCYM2fOZUnbX3/9lbFjx/LDDz9gGAZNmjRh/vz53HSTfQDg3Llzee2119i3bx+RkZH06NGDGTNmcODAAWrUqMEvv/xCkyZNADh9+jQRERGsXr2adu3a8f3333PnnXfy9ddfM378eLZv3863335LlSpVGD16NBs2bCA9PZ26desyZcoUOnbs6IgrMzOTiRMnsmDBAlJSUqhSpQrjxo1jyJAhxMbG8uijjzJmzBjH8YmJidxyyy3s3buXWrWu/9fmeblfmrmIpkyZ4vjASsGiwgKIqhdAx1yJ3KwLNqw2g2ybDavVnqy12gyyrfbt9gSujQtWA5thUDcmDF/vvD8HvX7dbqlEt1sqOW0zDINzWVZOncvi9LlsTqbnJHOzOH0++2KMYDPsMVtthv0bFsOwb7fZl3PmhmE/Nmdun+zXsRlgtRmO7fbr45gb5Nnm+J9L+worv4IlVzrDlaqbXPn4IoVy6Ty5Xpj7FLnPV9T3WaQ4jIJGGhf8BUHuuJzidYq95JhcgcZJQfexOM5dUkrys1VULtScLuVicQSXoCYyh7eGIEtRlK8N7Z+FO5+Bo1th+2ewf429Dm5hRTewP+RMRETcn2FA9jlzru0bVKh/JPn4+DBgwADmz5/Ps88+60iILl68GKvVSp8+fUhLSyMuLo6xY8cSFhbGV199Rf/+/bnpppto0aLFVa9hs9m4//77iYqKYuPGjZw5cybfWrehoaHMnz+fihUrsn37dh5++GFCQ0N56qmn6N27Nzt27GD58uV89913AISHh192jvT0dOLj42nVqhWbN28mJSWFYcOGkZCQwPz58x3HrV69mpiYGFavXs2+ffvo3bs3TZo04eGHH77i+/j9999Zv349n3/+OYZh8MQTT3Dw4EGqVasGwJEjR7jjjjto164d//3vfwkLC2Pt2rVcuGB/xsvMmTMZPXo0U6dOpXPnzpw5c4a1a9de9f7l9fTTTzNt2jRq1qxJREQEhw8fpkuXLrz00kv4+/vzwQcf0LVrV3bv3k3VqlUBGDBgAOvXr+ett96icePG7N+/nxMnTmCxWBgyZAjz5s1zStrOmzePO+64o0QStmBy0rZcuXJ4e3uTnJzstD05OfmKw7ajo6OLdPy4ceOcyimkpqZSpUqV64z8xuHnY0/ABuJ6T4S2WCwE+/sQ7O9D5QizoxERERGRIrFYoFKcfRIRkRtX9jn4R0Vzrv3MUfALLtShQ4YM4dVXX2XNmjW0a9cOsCftevToQXh4OOHh4U4JvREjRrBixQo++eSTQiVtv/vuO3bt2sWKFSuoWNF+P/7xj3/QuXNnp+Nyj/StXr06Y8aMYdGiRTz11FMEBgYSEhKCj49PgeUQFixYQEZGBh988AHBwfb3P2PGDLp27crLL7/s+IV7REQEM2bMwNvbmzp16nDPPfewatWqApO2c+fOpXPnzkRE2BM18fHxzJs3j8mTJwPwzjvvEB4ezqJFixwjdm+++dKD3F988UWefPJJRo4c6djWvHnzq96/vJ5//nnuuusux3pkZCSNGzd2rL/wwgssWbKEZcuWkZCQwJ49e/jkk09YuXKlY/RtzZo1HccPGjSIiRMnsmnTJlq0aEF2djYLFixg2rRpRY6tsIp/SGQR+Pn5ERcXx6pVqxzbbDYbq1atolWrVvm+plWrVk7HA6xcufKKx/v7+xMWFuY0iYiIiIiIiIiIFFadOnVo3bo1c+fOBWDfvn38+OOPDB06FACr1coLL7xAw4YNiYyMJCQkhBUrVnDo0KFCnX/nzp1UqVLFkbAF8s11ffzxx9x2221ER0cTEhLC+PHjC32N3Ndq3LixI2ELcNttt2Gz2di9e7djW/369fH2vjSILyYmhpSUlCue12q18v7779OvXz/Htn79+jF//nxsNnuJzcTERNq0aZNviYWUlBSOHj1Khw4divR+8tOsWTOn9bS0NMaMGUPdunUpU6YMISEh7Ny503HvEhMT8fb2pm3btvmer2LFitxzzz2O9v/yyy/JzMzkgQceuO5Yr8T08gijR49m4MCBNGvWjBYtWjB9+nTS09MZPHgwYB+aXKlSJaZMmQLAyJEjadu2La+99hr33HMPixYt4ueff+bdd981822IiIiIiIiIiEhR+QbZR7yade0iGDp0KCNGjOCdd95h3rx53HTTTY4k36uvvsqbb77J9OnTadiwIcHBwYwaNYqsrKxiC3f9+vX07duX5557jvj4eMeI1ddee63YrpFb3sSqxWJxJF/zs2LFCo4cOXJZDVur1cqqVau46667CAwMvOLrC9oH4OVlH3uauwxhdnb+5ZVyJ6QBxowZw8qVK5k2bRq1atUiMDCQnj17OtrnatcGGDZsGP379+eNN95g3rx59O7dm6Cgon2GisL0pG3v3r05fvw4EydOJCkpiSZNmrB8+XLHUOxDhw45GgWgdevWLFiwgPHjx/PMM88QGxvL0qVLadCggVlvQUREREREREREroXFUugSBWbr1asXI0eOZMGCBXzwwQc89thjjvq2a9eu5b777nOMMrXZbOzZs4d69Qr34My6dety+PBhjh07RkxMDAAbNmxwOmbdunVUq1aNZ5991rHt4MGDTsf4+flhtVqveq358+eTnp7uSG6uXbsWLy8vateuXah48zNnzhwefPBBp/gAXnrpJebMmcNdd91Fo0aNeP/998nOzr4sKRwaGkr16tVZtWoVd95552XnL1++PADHjh3jlltuAewjZAtj7dq1DBo0iO7duwP2kbcHDhxw7G/YsCE2m401a9Y4PZwsty5duhAcHMzMmTNZvnw5P/zwQ6Gufa1MT9oCJCQkkJCQkO++77///rJtDzzwQIkOPxYREREREREREcktJCSE3r17M27cOFJTUxk0aJBjX2xsLJ9++inr1q0jIiKC119/neTk5EInbTt27MjNN9/MwIEDefXVV0lNTb0s+RkbG8uhQ4dYtGgRzZs356uvvmLJkiVOx1SvXp39+/eTmJhI5cqVCQ0Nxd/f3+mYvn37MmnSJAYOHMjkyZM5fvw4I0aMoH///o5BlEV1/PhxvvzyS5YtW3bZwMoBAwbQvXt3Tp48SUJCAm+//TYPPvgg48aNIzw8nA0bNtCiRQtq167N5MmTefTRR6lQoQKdO3fm7NmzrF27lhEjRhAYGMitt97K1KlTqVGjBikpKU41fgsSGxvL559/TteuXbFYLEyYMMFp1HD16tUZOHAgQ4YMcTyI7ODBg6SkpNCrVy8AvL29GTRoEOPGjSM2NvaKpVqLi6k1bUVERERERERERNzF0KFDOXXqFPHx8U71Z8ePH0/Tpk2Jj4+nXbt2REdH061bt0Kf18vLiyVLlnD+/HlatGjBsGHDeOmll5yO+dvf/sYTTzxBQkICTZo0Yd26dUyYMMHpmB49etCpUyfuvPNOypcvz8KFCy+7VlBQECtWrODkyZM0b96cnj170qFDB2bMmFG0m5FLzkPN8qtH26FDBwIDA/nwww8pW7Ys//3vf0lLS6Nt27bExcUxe/Zsx6jbgQMHMn36dP75z39Sv3597r33Xvbu3es419y5c7lw4QJxcXGMGjWKF198sVDxvf7660RERNC6dWu6du1KfHw8TZs2dTpm5syZ9OzZk8cff5w6derw8MMPk56e7nTM0KFDycrKcpR1LUkWI3chiBtAamoq4eHhnDlzRg8lExEREXEz6svZ6T6IiIg7ysjIYP/+/dSoUYOAgACzwxEpsh9//JEOHTpw+PDhAkclF/RZL2w/ziXKI4iIiIiIiIiIiIi4oszMTI4fP87kyZN54IEHrrmMRFGoPIKIiIiIiIiIiIjIFSxcuJBq1apx+vRpXnnllVK5ppK2IiIiIiIiIiIiIlcwaNAgrFYrW7ZsoVKlSqVyTSVtRURERERERERERFyIkrYiIiIiIiIiIiIiLkRJWxERERERERERKTWGYZgdgkiJKo7PuJK2IiIiIiIiIiJS4nx9fQE4d+6cyZGIlKycz3jOZ/5a+BRXMCIiIiIiIiIiIlfi7e1NmTJlSElJASAoKAiLxWJyVCLFxzAMzp07R0pKCmXKlMHb2/uaz6WkrYiIiIiIiIiIlIro6GgAR+JWxBOVKVPG8Vm/VkraioiIiIiIiIhIqbBYLMTExFChQgWys7PNDkek2Pn6+l7XCNscStqKiIiIiIiIiEip8vb2LpbEloin0oPIRERERERERERERFyIkrYiIiIiIiIiIiIiLkRJWxEREREREREREREXcsPVtDUMA4DU1FSTIxERERGRosrpw+X06W5U6tOKiIiIuKfC9mdvuKTt2bNnAahSpYrJkYiIiIjItTp79izh4eFmh2Ea9WlFRERE3NvV+rMW4wYbpmCz2Th69CihoaFYLJZSuWZqaipVqlTh8OHDhIWFlco1peSoPT2L2tOzqD09i9rT8xRHmxqGwdmzZ6lYsSJeXjdupa/S7tPq79GzqD09j9rUs6g9PYva07OUZn/2hhtp6+XlReXKlU25dlhYmP5APYja07OoPT2L2tOzqD09z/W26Y08wjaHWX1a/T16FrWn51Gbeha1p2dRe3qW0ujP3rjDE0RERERERERERERckJK2IiIiIiIiIiIiIi5ESdtS4O/vz6RJk/D39zc7FCkGak/Povb0LGpPz6L29DxqU/eltvMsak/Pozb1LGpPz6L29Cyl2Z433IPIRERERERERERERFyZRtqKiIiIiIiIiIiIuBAlbUVERERERERERERciJK2IiIiIiIiIiIiIi5ESdsS9s4771C9enUCAgJo2bIlmzZtMjskKaQffviBrl27UrFiRSwWC0uXLnXabxgGEydOJCYmhsDAQDp27MjevXvNCVYKNGXKFJo3b05oaCgVKlSgW7du7N692+mYjIwMhg8fTtmyZQkJCaFHjx4kJyebFLFczcyZM2nUqBFhYWGEhYXRqlUrvvnmG8d+taf7mjp1KhaLhVGjRjm2qT3dy+TJk7FYLE5TnTp1HPvVnu5JfVr3pP6sZ1Gf1rOoP+vZ1Kd1b67Sn1XStgR9/PHHjB49mkmTJrF161YaN25MfHw8KSkpZocmhZCenk7jxo1555138t3/yiuv8NZbbzFr1iw2btxIcHAw8fHxZGRklHKkcjVr1qxh+PDhbNiwgZUrV5Kdnc3dd99Nenq645gnnniCL7/8ksWLF7NmzRqOHj3K/fffb2LUUpDKlSszdepUtmzZws8//0z79u257777+PXXXwG1p7vavHkz//rXv2jUqJHTdrWn+6lfvz7Hjh1zTD/99JNjn9rT/ahP677Un/Us6tN6FvVnPZf6tJ7BJfqzhpSYFi1aGMOHD3esW61Wo2LFisaUKVNMjEquBWAsWbLEsW6z2Yzo6Gjj1VdfdWw7ffq04e/vbyxcuNCECKUoUlJSDMBYs2aNYRj2tvP19TUWL17sOGbnzp0GYKxfv96sMKWIIiIijPfee0/t6abOnj1rxMbGGitXrjTatm1rjBw50jAM/X26o0mTJhmNGzfOd5/a0z2pT+sZ1J/1POrTeh71Z92f+rSewVX6sxppW0KysrLYsmULHTt2dGzz8vKiY8eOrF+/3sTIpDjs37+fpKQkp/YNDw+nZcuWal83cObMGQAiIyMB2LJlC9nZ2U7tWadOHapWrar2dANWq5VFixaRnp5Oq1at1J5uavjw4dxzzz1O7Qb6+3RXe/fupWLFitSsWZO+ffty6NAhQO3pjtSn9Vzqz7o/9Wk9h/qznkN9Ws/hCv1Zn2I9mzicOHECq9VKVFSU0/aoqCh27dplUlRSXJKSkgDybd+cfeKabDYbo0aN4rbbbqNBgwaAvT39/PwoU6aM07FqT9e2fft2WrVqRUZGBiEhISxZsoR69eqRmJio9nQzixYtYuvWrWzevPmyffr7dD8tW7Zk/vz51K5dm2PHjvHcc8/Rpk0bduzYofZ0Q+rTei71Z92b+rSeQf1Zz6I+redwlf6skrYickMZPnw4O3bscKpHI+6pdu3aJCYmcubMGT799FMGDhzImjVrzA5Liujw4cOMHDmSlStXEhAQYHY4Ugw6d+7sWG7UqBEtW7akWrVqfPLJJwQGBpoYmYiI51Cf1jOoP+s51Kf1LK7Sn1V5hBJSrlw5vL29L3t6XHJyMtHR0SZFJcUlpw3Vvu4lISGB//znP6xevZrKlSs7tkdHR5OVlcXp06edjld7ujY/Pz9q1apFXFwcU6ZMoXHjxrz55ptqTzezZcsWUlJSaNq0KT4+Pvj4+LBmzRreeustfHx8iIqKUnu6uTJlynDzzTezb98+/X26IfVpPZf6s+5LfVrPof6s51Cf1rOZ1Z9V0raE+Pn5ERcXx6pVqxzbbDYbq1atolWrViZGJsWhRo0aREdHO7VvamoqGzduVPu6IMMwSEhIYMmSJfz3v/+lRo0aTvvj4uLw9fV1as/du3dz6NAhtacbsdlsZGZmqj3dTIcOHdi+fTuJiYmOqVmzZvTt29exrPZ0b2lpafz+++/ExMTo79MNqU/rudSfdT/q03o+9Wfdl/q0ns2s/qzKI5Sg0aNHM3DgQJo1a0aLFi2YPn066enpDB482OzQpBDS0tLYt2+fY33//v0kJiYSGRlJ1apVGTVqFC+++CKxsbHUqFGDCRMmULFiRbp162Ze0JKv4cOHs2DBAr744gtCQ0MddWbCw8MJDAwkPDycoUOHMnr0aCIjIwkLC2PEiBG0atWKW2+91eToJT/jxo2jc+fOVK1albNnz7JgwQK+//57VqxYofZ0M6GhoY5afDmCg4MpW7asY7va072MGTOGrl27Uq1aNY4ePcqkSZPw9vamT58++vt0U+rTui/1Zz2L+rSeRf1Zz6I+rWdxmf6sISXq7bffNqpWrWr4+fkZLVq0MDZs2GB2SFJIq1evNoDLpoEDBxqGYRg2m82YMGGCERUVZfj7+xsdOnQwdu/ebW7Qkq/82hEw5s2b5zjm/PnzxuOPP25EREQYQUFBRvfu3Y1jx46ZF7QUaMiQIUa1atUMPz8/o3z58kaHDh2Mb7/91rFf7ene2rZta4wcOdKxrvZ0L7179zZiYmIMPz8/o1KlSkbv3r2Nffv2OfarPd2T+rTuSf1Zz6I+rWdRf9bzqU/rvlylP2sxDMMo3jSwiIiIiIiIiIiIiFwr1bQVERERERERERERcSFK2oqIiIiIiIiIiIi4ECVtRURERERERERERFyIkrYiIiIiIiIiIiIiLkRJWxEREREREREREREXoqStiIiIiIiIiIiIiAtR0lZERERERERERETEhShpKyIiIiIiIiIiIuJClLQVEblBWSwWli5danYYIiIiIiLXRP1ZEfFkStqKiJhg0KBBWCyWy6ZOnTqZHZqIiIiIyFWpPysiUrJ8zA5ARORG1alTJ+bNm+e0zd/f36RoRERERESKRv1ZEZGSo5G2IiIm8ff3Jzo62mmKiIgA7D/1mjlzJp07dyYwMJCaNWvy6aefOr1++/bttG/fnsDAQMqWLcsjjzxCWlqa0zFz586lfv36+Pv7ExMTQ0JCgtP+EydO0L17d4KCgoiNjWXZsmWOfadOnaJv376UL1+ewMBAYmNjL+uUi4iIiMiNS/1ZEZGSo6StiIiLmjBhAj169GDbtm307duXBx98kJ07dwKQnp5OfHw8ERERbN68mcWLF/Pdd985dWJnzpzJ8OHDeeSRR9i+fTvLli2jVq1aTtd47rnn6NWrF//73//o0qULffv25eTJk47r//bbb3zzzTfs3LmTmTNnUq5cudK7ASIiIiLi1tSfFRG5dhbDMAyzgxARudEMGjSIDz/8kICAAKftzzzzDM888wwWi4VHH32UmTNnOvbdeuutNG3alH/+85/Mnj2bsWPHcvjwYYKDgwH4+uuv6dq1K0ePHiUqKopKlSoxePBgXnzxxXxjsFgsjB8/nhdeeAGwd5xDQkL45ptv6NSpE3/7298oV64cc+fOLaG7ICIiIiLuSv1ZEZGSpZq2IiImufPOO506sQCRkZGO5VatWjnta9WqFYmJiQDs3LmTxo0bOzq4ALfddhs2m43du3djsVg4evQoHTp0KDCGRo0aOZaDg4MJCwsjJSUFgMcee4wePXqwdetW7r77brp160br1q2v6b2KiIiIiOdRf1ZEpOQoaSsiYpLg4ODLft5VXAIDAwt1nK+vr9O6xWLBZrMB0LlzZw4ePMjXX3/NypUr6dChA8OHD2fatGnFHq+IiIiIuB/1Z0VESo5q2oqIuKgNGzZctl63bl0A6taty7Zt20hPT3fsX7t2LV5eXtSuXZvQ0FCqV6/OqlWrriuG8uXLM3DgQD788EOmT5/Ou+++e13nExEREZEbh/qzIiLXTiNtRURMkpmZSVJSktM2Hx8fx8MRFi9eTLNmzbj99tv56KOP2LRpE3PmzAGgb9++TJo0iYEDBzJ58mSOHz/OiBEj6N+/P1FRUQBMnjyZRx99lAoVKtC5c2fOnj3L2rVrGTFiRKHimzhxInFxcdSvX5/MzEz+85//ODrZIiIiIiLqz4qIlBwlbUVETLJ8+XJiYmKcttWuXZtdu3YB9ifhLlq0iMcff5yYmBgWLlxIvXr1AAgKCmLFihWMHDmS5s2bExQURI8ePXj99dcd5xo4cCAZGRm88cYbjBkzhnLlytGzZ89Cx+fn58e4ceM4cOAAgYGBtGnThkWLFhXDOxcRERERT6D+rIhIybEYhmGYHYSIiDizWCwsWbKEbt26mR2KiIiIiEiRqT8rInJ9VNNWRERERERERERExIUoaSsiIiIiIiIiIiLiQlQeQURERERERERERMSFaKStiIiIiIiIiIiIiAtR0lZERERERERERETEhShpKyIiIiIiIiIiIuJClLQVERERERERERERcSFK2oqIiIiIiIiIiIi4ECVtRURERERERERERFyIkrYiIiIiIiIiIiIiLkRJWxEREREREREREREXoqStiIiIiIiIiIiIiAv5/5CyMPc3pr8OAAAAAElFTkSuQmCC\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         6\n",
      "           1       1.00      1.00      1.00         4\n",
      "           2       1.00      1.00      1.00         4\n",
      "           3       1.00      1.00      1.00         2\n",
      "           4       1.00      1.00      1.00         5\n",
      "           5       1.00      1.00      1.00         2\n",
      "           6       1.00      1.00      1.00         2\n",
      "           7       0.75      1.00      0.86         3\n",
      "           8       1.00      1.00      1.00         1\n",
      "           9       1.00      1.00      1.00         5\n",
      "          10       1.00      1.00      1.00         5\n",
      "          11       1.00      1.00      1.00         2\n",
      "          12       1.00      1.00      1.00         2\n",
      "          13       1.00      1.00      1.00         1\n",
      "          14       1.00      1.00      1.00         4\n",
      "          15       1.00      1.00      1.00         4\n",
      "          16       1.00      1.00      1.00         4\n",
      "          17       1.00      1.00      1.00         2\n",
      "          19       1.00      1.00      1.00         3\n",
      "          20       1.00      1.00      1.00         4\n",
      "          21       1.00      1.00      1.00         3\n",
      "          22       1.00      1.00      1.00         6\n",
      "          23       1.00      1.00      1.00         5\n",
      "          24       1.00      1.00      1.00         6\n",
      "          25       1.00      1.00      1.00         1\n",
      "          26       1.00      1.00      1.00         5\n",
      "          27       1.00      1.00      1.00         4\n",
      "          28       1.00      1.00      1.00         3\n",
      "          29       1.00      1.00      1.00         2\n",
      "          30       1.00      1.00      1.00         1\n",
      "          31       1.00      0.67      0.80         3\n",
      "          32       1.00      1.00      1.00         4\n",
      "          33       1.00      1.00      1.00         2\n",
      "          34       1.00      1.00      1.00         4\n",
      "          35       1.00      1.00      1.00         3\n",
      "          36       1.00      1.00      1.00         3\n",
      "          37       1.00      1.00      1.00         3\n",
      "          38       1.00      1.00      1.00         2\n",
      "\n",
      "    accuracy                           0.99       125\n",
      "   macro avg       0.99      0.99      0.99       125\n",
      "weighted avg       0.99      0.99      0.99       125\n",
      "\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m80\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_9                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m32\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_9 (\u001b[38;5;33mReLU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m1,168\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_10               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │              \u001b[38;5;34m64\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_10 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │           \u001b[38;5;34m4,640\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_11               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_11 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_3 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7936\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │         \u001b[38;5;34m309,543\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_3 (\u001b[38;5;33mSoftmax\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_9                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,168</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_10               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_11               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7936</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">309,543</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Softmax</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m315,655\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,655</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m315,543\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,543</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m112\u001b[0m (448.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">112</span> (448.00 B)\n",
       "</pre>\n"
      ]
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     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training for fold 2 ...\n",
      "Epoch 1/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 40ms/step - accuracy: 0.4209 - loss: 3.3895 - val_accuracy: 0.0400 - val_loss: 3.7208\n",
      "Epoch 2/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 51ms/step - accuracy: 0.9404 - loss: 0.2183 - val_accuracy: 0.0400 - val_loss: 4.3026\n",
      "Epoch 3/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 32ms/step - accuracy: 0.9657 - loss: 0.0952 - val_accuracy: 0.0400 - val_loss: 4.1264\n",
      "Epoch 4/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 0.9945 - loss: 0.0259 - val_accuracy: 0.1520 - val_loss: 3.2722\n",
      "Epoch 5/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 0.9953 - loss: 0.0375 - val_accuracy: 0.4480 - val_loss: 1.9054\n",
      "Epoch 6/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - accuracy: 1.0000 - loss: 0.0090 - val_accuracy: 0.8240 - val_loss: 0.6963\n",
      "Epoch 7/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 50ms/step - accuracy: 1.0000 - loss: 0.0063 - val_accuracy: 0.9440 - val_loss: 0.2257\n",
      "Epoch 8/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - accuracy: 0.9944 - loss: 0.0182 - val_accuracy: 0.9520 - val_loss: 0.1295\n",
      "Epoch 9/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9903 - loss: 0.0114 - val_accuracy: 0.9600 - val_loss: 0.1538\n",
      "Epoch 10/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 0.9907 - loss: 0.0188 - val_accuracy: 0.9760 - val_loss: 0.0531\n",
      "Epoch 11/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9840 - val_loss: 0.0421\n",
      "Epoch 12/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9920 - val_loss: 0.0320\n",
      "Epoch 13/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 51ms/step - accuracy: 1.0000 - loss: 5.8117e-04 - val_accuracy: 0.9840 - val_loss: 0.0392\n",
      "Epoch 14/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - accuracy: 1.0000 - loss: 4.4194e-04 - val_accuracy: 0.9840 - val_loss: 0.0361\n",
      "Epoch 15/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 3.4060e-04 - val_accuracy: 0.9840 - val_loss: 0.0354\n",
      "Epoch 16/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 3.1442e-04 - val_accuracy: 0.9840 - val_loss: 0.0345\n",
      "Epoch 17/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 2.8814e-04 - val_accuracy: 0.9840 - val_loss: 0.0329\n",
      "Epoch 18/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 2.8557e-04 - val_accuracy: 0.9840 - val_loss: 0.0334\n",
      "Epoch 19/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 55ms/step - accuracy: 1.0000 - loss: 1.8490e-04 - val_accuracy: 0.9840 - val_loss: 0.0312\n",
      "Epoch 20/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 43ms/step - accuracy: 1.0000 - loss: 1.8667e-04 - val_accuracy: 0.9840 - val_loss: 0.0338\n",
      "Epoch 21/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 2.0069e-04 - val_accuracy: 0.9840 - val_loss: 0.0326\n",
      "Epoch 22/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 1.7396e-04 - val_accuracy: 0.9760 - val_loss: 0.0314\n",
      "Epoch 23/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 1.6629e-04 - val_accuracy: 0.9840 - val_loss: 0.0299\n",
      "Epoch 24/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 51ms/step - accuracy: 1.0000 - loss: 1.7946e-04 - val_accuracy: 0.9840 - val_loss: 0.0286\n",
      "Epoch 25/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 1.4161e-04 - val_accuracy: 0.9840 - val_loss: 0.0283\n",
      "Epoch 26/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 1.3030e-04 - val_accuracy: 0.9760 - val_loss: 0.0308\n",
      "Epoch 27/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 1.1279e-04 - val_accuracy: 0.9760 - val_loss: 0.0293\n",
      "Epoch 28/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 1.3300e-04 - val_accuracy: 0.9760 - val_loss: 0.0274\n",
      "Epoch 29/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 40ms/step - accuracy: 1.0000 - loss: 1.0426e-04 - val_accuracy: 0.9760 - val_loss: 0.0289\n",
      "Epoch 30/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 50ms/step - accuracy: 1.0000 - loss: 1.0335e-04 - val_accuracy: 0.9760 - val_loss: 0.0287\n",
      "Epoch 31/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 1.1044e-04 - val_accuracy: 0.9760 - val_loss: 0.0278\n",
      "Epoch 32/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 9.3176e-05 - val_accuracy: 0.9760 - val_loss: 0.0275\n",
      "Epoch 33/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 8.8685e-05 - val_accuracy: 0.9760 - val_loss: 0.0286\n",
      "Epoch 34/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 8.1745e-05 - val_accuracy: 0.9840 - val_loss: 0.0294\n",
      "Epoch 35/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 6.6623e-05 - val_accuracy: 0.9760 - val_loss: 0.0285\n",
      "Epoch 36/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 54ms/step - accuracy: 1.0000 - loss: 7.0481e-05 - val_accuracy: 0.9760 - val_loss: 0.0287\n",
      "Epoch 37/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 6.1155e-05 - val_accuracy: 0.9760 - val_loss: 0.0287\n",
      "Epoch 38/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 7.2850e-05 - val_accuracy: 0.9840 - val_loss: 0.0294\n",
      "Epoch 39/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 7.5677e-05 - val_accuracy: 0.9840 - val_loss: 0.0270\n",
      "Epoch 40/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 6.1501e-05 - val_accuracy: 0.9840 - val_loss: 0.0270\n",
      "Epoch 41/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 50ms/step - accuracy: 1.0000 - loss: 5.2495e-05 - val_accuracy: 0.9840 - val_loss: 0.0281\n",
      "Epoch 42/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 5.1860e-05 - val_accuracy: 0.9840 - val_loss: 0.0293\n",
      "Epoch 43/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 5.0879e-05 - val_accuracy: 0.9840 - val_loss: 0.0285\n",
      "Epoch 44/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 5.4614e-05 - val_accuracy: 0.9840 - val_loss: 0.0287\n",
      "Epoch 45/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 1.0000 - loss: 4.0779e-05 - val_accuracy: 0.9840 - val_loss: 0.0289\n",
      "Epoch 46/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - accuracy: 1.0000 - loss: 3.7597e-05 - val_accuracy: 0.9840 - val_loss: 0.0287\n",
      "Epoch 47/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 51ms/step - accuracy: 1.0000 - loss: 4.0832e-05 - val_accuracy: 0.9840 - val_loss: 0.0300\n",
      "Epoch 48/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 3.9935e-05 - val_accuracy: 0.9840 - val_loss: 0.0299\n",
      "Epoch 49/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 3.4414e-05 - val_accuracy: 0.9840 - val_loss: 0.0306\n",
      "Epoch 50/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 4.2737e-05 - val_accuracy: 0.9840 - val_loss: 0.0307\n",
      "Score for fold 2: loss of 0.03070802614092827; compile_metrics of 98.4000027179718%\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         5\n",
      "           1       1.00      1.00      1.00         4\n",
      "           2       1.00      1.00      1.00         3\n",
      "           3       1.00      1.00      1.00         4\n",
      "           4       1.00      1.00      1.00         1\n",
      "           5       1.00      1.00      1.00         3\n",
      "           6       1.00      1.00      1.00         4\n",
      "           7       1.00      0.67      0.80         3\n",
      "           8       1.00      1.00      1.00         4\n",
      "           9       1.00      1.00      1.00         4\n",
      "          10       1.00      1.00      1.00         4\n",
      "          11       1.00      1.00      1.00         5\n",
      "          12       1.00      1.00      1.00         5\n",
      "          13       1.00      1.00      1.00         3\n",
      "          14       1.00      1.00      1.00         4\n",
      "          15       0.75      1.00      0.86         3\n",
      "          16       1.00      1.00      1.00         4\n",
      "          17       1.00      1.00      1.00         2\n",
      "          18       1.00      1.00      1.00         1\n",
      "          19       1.00      1.00      1.00         4\n",
      "          21       1.00      1.00      1.00         5\n",
      "          22       1.00      1.00      1.00         4\n",
      "          23       1.00      1.00      1.00         1\n",
      "          24       1.00      1.00      1.00         2\n",
      "          25       1.00      1.00      1.00         3\n",
      "          26       1.00      1.00      1.00         4\n",
      "          27       1.00      1.00      1.00         2\n",
      "          28       1.00      1.00      1.00         2\n",
      "          29       1.00      1.00      1.00         3\n",
      "          30       1.00      1.00      1.00         5\n",
      "          31       0.67      1.00      0.80         2\n",
      "          32       1.00      1.00      1.00         2\n",
      "          33       1.00      0.67      0.80         3\n",
      "          34       1.00      1.00      1.00         3\n",
      "          35       1.00      1.00      1.00         3\n",
      "          36       1.00      1.00      1.00         3\n",
      "          37       1.00      1.00      1.00         4\n",
      "          38       1.00      1.00      1.00         4\n",
      "\n",
      "    accuracy                           0.98       125\n",
      "   macro avg       0.98      0.98      0.98       125\n",
      "weighted avg       0.99      0.98      0.98       125\n",
      "\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m80\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_12               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m32\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_12 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m1,168\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_13               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │              \u001b[38;5;34m64\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_13 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │           \u001b[38;5;34m4,640\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_14               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_14 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_4 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7936\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │         \u001b[38;5;34m309,543\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_4 (\u001b[38;5;33mSoftmax\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_12               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,168</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_13               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_14               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7936</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">309,543</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Softmax</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m315,655\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,655</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m315,543\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,543</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m112\u001b[0m (448.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">112</span> (448.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training for fold 3 ...\n",
      "Epoch 1/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 36ms/step - accuracy: 0.5106 - loss: 3.0776 - val_accuracy: 0.0720 - val_loss: 3.4453\n",
      "Epoch 2/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9395 - loss: 0.1764 - val_accuracy: 0.0400 - val_loss: 3.5356\n",
      "Epoch 3/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 0.9731 - loss: 0.1132 - val_accuracy: 0.0480 - val_loss: 3.1872\n",
      "Epoch 4/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9942 - loss: 0.0284 - val_accuracy: 0.2640 - val_loss: 2.7435\n",
      "Epoch 5/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 48ms/step - accuracy: 1.0000 - loss: 0.0045 - val_accuracy: 0.5840 - val_loss: 2.1425\n",
      "Epoch 6/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 48ms/step - accuracy: 1.0000 - loss: 0.0022 - val_accuracy: 0.7360 - val_loss: 1.3656\n",
      "Epoch 7/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.8400 - val_loss: 0.7119\n",
      "Epoch 8/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 9.6487e-04 - val_accuracy: 0.9280 - val_loss: 0.3402\n",
      "Epoch 9/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 0.0011 - val_accuracy: 0.9520 - val_loss: 0.1975\n",
      "Epoch 10/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 46ms/step - accuracy: 1.0000 - loss: 8.8769e-04 - val_accuracy: 0.9520 - val_loss: 0.1578\n",
      "Epoch 11/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 6.1894e-04 - val_accuracy: 0.9520 - val_loss: 0.1410\n",
      "Epoch 12/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 6.4759e-04 - val_accuracy: 0.9440 - val_loss: 0.1273\n",
      "Epoch 13/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 4.3738e-04 - val_accuracy: 0.9520 - val_loss: 0.1454\n",
      "Epoch 14/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 5.9053e-04 - val_accuracy: 0.9440 - val_loss: 0.1373\n",
      "Epoch 15/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 2.8230e-04 - val_accuracy: 0.9520 - val_loss: 0.1444\n",
      "Epoch 16/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 49ms/step - accuracy: 1.0000 - loss: 3.0532e-04 - val_accuracy: 0.9440 - val_loss: 0.1450\n",
      "Epoch 17/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 49ms/step - accuracy: 1.0000 - loss: 2.4364e-04 - val_accuracy: 0.9440 - val_loss: 0.1442\n",
      "Epoch 18/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 46ms/step - accuracy: 1.0000 - loss: 2.3465e-04 - val_accuracy: 0.9520 - val_loss: 0.1539\n",
      "Epoch 19/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 2.3801e-04 - val_accuracy: 0.9520 - val_loss: 0.1567\n",
      "Epoch 20/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 2.1823e-04 - val_accuracy: 0.9440 - val_loss: 0.1441\n",
      "Epoch 21/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 1.8118e-04 - val_accuracy: 0.9440 - val_loss: 0.1372\n",
      "Epoch 22/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 1.5595e-04 - val_accuracy: 0.9440 - val_loss: 0.1450\n",
      "Epoch 23/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 1.3367e-04 - val_accuracy: 0.9440 - val_loss: 0.1446\n",
      "Epoch 24/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 42ms/step - accuracy: 1.0000 - loss: 1.6598e-04 - val_accuracy: 0.9520 - val_loss: 0.1545\n",
      "Epoch 25/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 1.1016e-04 - val_accuracy: 0.9520 - val_loss: 0.1614\n",
      "Epoch 26/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 9.1597e-05 - val_accuracy: 0.9440 - val_loss: 0.1550\n",
      "Epoch 27/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 9.5464e-05 - val_accuracy: 0.9440 - val_loss: 0.1527\n",
      "Epoch 28/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 9.0283e-05 - val_accuracy: 0.9440 - val_loss: 0.1582\n",
      "Epoch 29/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 52ms/step - accuracy: 1.0000 - loss: 9.4892e-05 - val_accuracy: 0.9440 - val_loss: 0.1509\n",
      "Epoch 30/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 38ms/step - accuracy: 1.0000 - loss: 7.7958e-05 - val_accuracy: 0.9520 - val_loss: 0.1651\n",
      "Epoch 31/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 8.1870e-05 - val_accuracy: 0.9440 - val_loss: 0.1453\n",
      "Epoch 32/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 47ms/step - accuracy: 1.0000 - loss: 7.9289e-05 - val_accuracy: 0.9440 - val_loss: 0.1573\n",
      "Epoch 33/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 52ms/step - accuracy: 1.0000 - loss: 6.7201e-05 - val_accuracy: 0.9520 - val_loss: 0.1645\n",
      "Epoch 34/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 6.7924e-05 - val_accuracy: 0.9520 - val_loss: 0.1665\n",
      "Epoch 35/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 6.5481e-05 - val_accuracy: 0.9440 - val_loss: 0.1580\n",
      "Epoch 36/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 5.8973e-05 - val_accuracy: 0.9440 - val_loss: 0.1495\n",
      "Epoch 37/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 4.9017e-05 - val_accuracy: 0.9440 - val_loss: 0.1612\n",
      "Epoch 38/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 54ms/step - accuracy: 1.0000 - loss: 7.1485e-05 - val_accuracy: 0.9440 - val_loss: 0.1409\n",
      "Epoch 39/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 4.8183e-05 - val_accuracy: 0.9440 - val_loss: 0.1564\n",
      "Epoch 40/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 4.4375e-05 - val_accuracy: 0.9440 - val_loss: 0.1549\n",
      "Epoch 41/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 3.7075e-05 - val_accuracy: 0.9440 - val_loss: 0.1622\n",
      "Epoch 42/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 4.0040e-05 - val_accuracy: 0.9440 - val_loss: 0.1659\n",
      "Epoch 43/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 51ms/step - accuracy: 1.0000 - loss: 2.8969e-05 - val_accuracy: 0.9520 - val_loss: 0.1689\n",
      "Epoch 44/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 44ms/step - accuracy: 1.0000 - loss: 3.7466e-05 - val_accuracy: 0.9520 - val_loss: 0.1697\n",
      "Epoch 45/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 4.2354e-05 - val_accuracy: 0.9440 - val_loss: 0.1597\n",
      "Epoch 46/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 2.9229e-05 - val_accuracy: 0.9440 - val_loss: 0.1638\n",
      "Epoch 47/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 36ms/step - accuracy: 1.0000 - loss: 3.3072e-05 - val_accuracy: 0.9440 - val_loss: 0.1669\n",
      "Epoch 48/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 54ms/step - accuracy: 1.0000 - loss: 2.9160e-05 - val_accuracy: 0.9520 - val_loss: 0.1770\n",
      "Epoch 49/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 3.0710e-05 - val_accuracy: 0.9440 - val_loss: 0.1545\n",
      "Epoch 50/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 2.4438e-05 - val_accuracy: 0.9440 - val_loss: 0.1669\n",
      "Score for fold 3: loss of 0.16690057516098022; compile_metrics of 94.40000057220459%\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:tensorflow:5 out of the last 9 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7ebc1c420c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[1m3/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step "
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:tensorflow:6 out of the last 12 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7ebc1c420c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         2\n",
      "           1       1.00      1.00      1.00         2\n",
      "           2       1.00      1.00      1.00         3\n",
      "           3       1.00      1.00      1.00         2\n",
      "           4       1.00      1.00      1.00         3\n",
      "           5       1.00      1.00      1.00         3\n",
      "           6       1.00      1.00      1.00         3\n",
      "           7       0.38      0.75      0.50         4\n",
      "           8       1.00      1.00      1.00         4\n",
      "           9       1.00      1.00      1.00         2\n",
      "          10       1.00      1.00      1.00         2\n",
      "          12       1.00      1.00      1.00         4\n",
      "          13       1.00      1.00      1.00         7\n",
      "          14       1.00      1.00      1.00         3\n",
      "          15       1.00      1.00      1.00         2\n",
      "          16       1.00      1.00      1.00         2\n",
      "          17       1.00      1.00      1.00         4\n",
      "          18       1.00      1.00      1.00         3\n",
      "          19       1.00      1.00      1.00         3\n",
      "          20       1.00      1.00      1.00         7\n",
      "          21       1.00      1.00      1.00         2\n",
      "          22       1.00      1.00      1.00         2\n",
      "          23       0.80      1.00      0.89         4\n",
      "          24       1.00      1.00      1.00         3\n",
      "          25       1.00      1.00      1.00         5\n",
      "          26       1.00      0.50      0.67         2\n",
      "          27       1.00      1.00      1.00         1\n",
      "          28       1.00      1.00      1.00         5\n",
      "          29       1.00      1.00      1.00         4\n",
      "          30       1.00      1.00      1.00         4\n",
      "          31       0.67      0.29      0.40         7\n",
      "          32       1.00      1.00      1.00         2\n",
      "          33       1.00      1.00      1.00         3\n",
      "          34       1.00      1.00      1.00         4\n",
      "          35       1.00      1.00      1.00         4\n",
      "          36       1.00      1.00      1.00         2\n",
      "          37       1.00      1.00      1.00         3\n",
      "          38       1.00      1.00      1.00         3\n",
      "\n",
      "    accuracy                           0.94       125\n",
      "   macro avg       0.97      0.96      0.96       125\n",
      "weighted avg       0.95      0.94      0.94       125\n",
      "\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m80\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_15               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m32\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_15 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_10 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m1,168\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_16               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │              \u001b[38;5;34m64\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_16 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_11 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │           \u001b[38;5;34m4,640\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_17               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_17 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7936\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │         \u001b[38;5;34m309,543\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_5 (\u001b[38;5;33mSoftmax\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_15               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,168</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_16               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_17               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7936</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">309,543</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Softmax</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m315,655\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,655</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m315,543\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,543</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m112\u001b[0m (448.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">112</span> (448.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training for fold 4 ...\n",
      "Epoch 1/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 48ms/step - accuracy: 0.4748 - loss: 3.3451 - val_accuracy: 0.0565 - val_loss: 3.3633\n",
      "Epoch 2/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step - accuracy: 0.9155 - loss: 0.2850 - val_accuracy: 0.0161 - val_loss: 3.3739\n",
      "Epoch 3/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 32ms/step - accuracy: 0.9464 - loss: 0.2496 - val_accuracy: 0.0726 - val_loss: 3.2949\n",
      "Epoch 4/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 0.9766 - loss: 0.1165 - val_accuracy: 0.0726 - val_loss: 2.9941\n",
      "Epoch 5/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9727 - loss: 0.0636 - val_accuracy: 0.3306 - val_loss: 2.2801\n",
      "Epoch 6/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 41ms/step - accuracy: 0.9931 - loss: 0.0147 - val_accuracy: 0.7581 - val_loss: 1.2960\n",
      "Epoch 7/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 52ms/step - accuracy: 0.9904 - loss: 0.0281 - val_accuracy: 0.9032 - val_loss: 0.6895\n",
      "Epoch 8/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 39ms/step - accuracy: 0.9972 - loss: 0.0093 - val_accuracy: 0.9597 - val_loss: 0.2491\n",
      "Epoch 9/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 0.0028 - val_accuracy: 0.9758 - val_loss: 0.1471\n",
      "Epoch 10/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 0.0016 - val_accuracy: 0.9758 - val_loss: 0.0953\n",
      "Epoch 11/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9919 - val_loss: 0.0221\n",
      "Epoch 12/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 36ms/step - accuracy: 1.0000 - loss: 8.4784e-04 - val_accuracy: 0.9919 - val_loss: 0.0180\n",
      "Epoch 13/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 4.7433e-04 - val_accuracy: 0.9919 - val_loss: 0.0138\n",
      "Epoch 14/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 5.3310e-04 - val_accuracy: 0.9919 - val_loss: 0.0129\n",
      "Epoch 15/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 3.6722e-04 - val_accuracy: 0.9919 - val_loss: 0.0133\n",
      "Epoch 16/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 3.6220e-04 - val_accuracy: 0.9919 - val_loss: 0.0122\n",
      "Epoch 17/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 35ms/step - accuracy: 1.0000 - loss: 3.6884e-04 - val_accuracy: 0.9919 - val_loss: 0.0133\n",
      "Epoch 18/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 51ms/step - accuracy: 1.0000 - loss: 3.2286e-04 - val_accuracy: 0.9919 - val_loss: 0.0131\n",
      "Epoch 19/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 35ms/step - accuracy: 1.0000 - loss: 2.5849e-04 - val_accuracy: 0.9919 - val_loss: 0.0119\n",
      "Epoch 20/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 35ms/step - accuracy: 1.0000 - loss: 2.8189e-04 - val_accuracy: 0.9919 - val_loss: 0.0128\n",
      "Epoch 21/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 2.2852e-04 - val_accuracy: 0.9919 - val_loss: 0.0126\n",
      "Epoch 22/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 2.3105e-04 - val_accuracy: 0.9919 - val_loss: 0.0130\n",
      "Epoch 23/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 50ms/step - accuracy: 1.0000 - loss: 2.1504e-04 - val_accuracy: 0.9919 - val_loss: 0.0145\n",
      "Epoch 24/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 1.4585e-04 - val_accuracy: 0.9919 - val_loss: 0.0137\n",
      "Epoch 25/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 1.6324e-04 - val_accuracy: 0.9919 - val_loss: 0.0117\n",
      "Epoch 26/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 1.2329e-04 - val_accuracy: 0.9919 - val_loss: 0.0121\n",
      "Epoch 27/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - accuracy: 1.0000 - loss: 1.2664e-04 - val_accuracy: 0.9919 - val_loss: 0.0126\n",
      "Epoch 28/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 1.3211e-04 - val_accuracy: 0.9919 - val_loss: 0.0125\n",
      "Epoch 29/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 1.2006e-04 - val_accuracy: 0.9919 - val_loss: 0.0124\n",
      "Epoch 30/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 1.2619e-04 - val_accuracy: 0.9919 - val_loss: 0.0128\n",
      "Epoch 31/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 1.4222e-04 - val_accuracy: 0.9919 - val_loss: 0.0135\n",
      "Epoch 32/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 1.1987e-04 - val_accuracy: 0.9919 - val_loss: 0.0140\n",
      "Epoch 33/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 52ms/step - accuracy: 1.0000 - loss: 8.2864e-05 - val_accuracy: 0.9919 - val_loss: 0.0136\n",
      "Epoch 34/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 7.6923e-05 - val_accuracy: 0.9919 - val_loss: 0.0136\n",
      "Epoch 35/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 1.0165e-04 - val_accuracy: 0.9919 - val_loss: 0.0142\n",
      "Epoch 36/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 7.9442e-05 - val_accuracy: 0.9919 - val_loss: 0.0146\n",
      "Epoch 37/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 5.7515e-05 - val_accuracy: 0.9919 - val_loss: 0.0140\n",
      "Epoch 38/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 52ms/step - accuracy: 1.0000 - loss: 7.1371e-05 - val_accuracy: 0.9919 - val_loss: 0.0142\n",
      "Epoch 39/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 5.9428e-05 - val_accuracy: 0.9919 - val_loss: 0.0142\n",
      "Epoch 40/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 7.4212e-05 - val_accuracy: 0.9919 - val_loss: 0.0139\n",
      "Epoch 41/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 5.2710e-05 - val_accuracy: 0.9919 - val_loss: 0.0141\n",
      "Epoch 42/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 6.7043e-05 - val_accuracy: 0.9919 - val_loss: 0.0142\n",
      "Epoch 43/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 4.7899e-05 - val_accuracy: 0.9919 - val_loss: 0.0142\n",
      "Epoch 44/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 4.8858e-05 - val_accuracy: 0.9919 - val_loss: 0.0148\n",
      "Epoch 45/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 4.0269e-05 - val_accuracy: 0.9919 - val_loss: 0.0143\n",
      "Epoch 46/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 3.4771e-05 - val_accuracy: 0.9919 - val_loss: 0.0140\n",
      "Epoch 47/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 40ms/step - accuracy: 1.0000 - loss: 3.5354e-05 - val_accuracy: 0.9919 - val_loss: 0.0143\n",
      "Epoch 48/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 51ms/step - accuracy: 1.0000 - loss: 3.7419e-05 - val_accuracy: 0.9919 - val_loss: 0.0139\n",
      "Epoch 49/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 3.6129e-05 - val_accuracy: 0.9919 - val_loss: 0.0151\n",
      "Epoch 50/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 3.7070e-05 - val_accuracy: 0.9919 - val_loss: 0.0138\n",
      "Score for fold 4: loss of 0.013824766501784325; compile_metrics of 99.19354915618896%\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         1\n",
      "           2       1.00      1.00      1.00         3\n",
      "           3       1.00      1.00      1.00         4\n",
      "           4       1.00      1.00      1.00         4\n",
      "           5       1.00      1.00      1.00         5\n",
      "           6       1.00      1.00      1.00         4\n",
      "           7       1.00      0.67      0.80         3\n",
      "           8       1.00      1.00      1.00         4\n",
      "           9       1.00      1.00      1.00         3\n",
      "          10       1.00      1.00      1.00         2\n",
      "          11       1.00      1.00      1.00         4\n",
      "          12       1.00      1.00      1.00         1\n",
      "          13       1.00      1.00      1.00         2\n",
      "          14       1.00      1.00      1.00         3\n",
      "          15       1.00      1.00      1.00         3\n",
      "          16       1.00      1.00      1.00         4\n",
      "          17       1.00      1.00      1.00         7\n",
      "          18       1.00      1.00      1.00         6\n",
      "          19       1.00      1.00      1.00         3\n",
      "          20       1.00      1.00      1.00         3\n",
      "          21       1.00      1.00      1.00         3\n",
      "          22       1.00      1.00      1.00         3\n",
      "          23       1.00      1.00      1.00         4\n",
      "          24       1.00      1.00      1.00         2\n",
      "          25       1.00      1.00      1.00         4\n",
      "          26       1.00      1.00      1.00         2\n",
      "          27       1.00      1.00      1.00         4\n",
      "          28       1.00      1.00      1.00         3\n",
      "          29       1.00      1.00      1.00         4\n",
      "          30       1.00      1.00      1.00         4\n",
      "          31       0.67      1.00      0.80         2\n",
      "          32       1.00      1.00      1.00         4\n",
      "          33       1.00      1.00      1.00         2\n",
      "          34       1.00      1.00      1.00         2\n",
      "          35       1.00      1.00      1.00         2\n",
      "          36       1.00      1.00      1.00         4\n",
      "          37       1.00      1.00      1.00         3\n",
      "          38       1.00      1.00      1.00         3\n",
      "\n",
      "    accuracy                           0.99       124\n",
      "   macro avg       0.99      0.99      0.99       124\n",
      "weighted avg       0.99      0.99      0.99       124\n",
      "\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1mModel: \"sequential_6\"\u001b[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_6\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m80\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_18               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │              \u001b[38;5;34m32\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_18 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m249\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m8\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_12 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m1,168\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_19               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │              \u001b[38;5;34m64\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_19 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │           \u001b[38;5;34m4,640\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_20               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_20 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_6 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7936\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │         \u001b[38;5;34m309,543\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_6 (\u001b[38;5;33mSoftmax\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_18               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">249</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,168</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_19               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_20               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ re_lu_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7936</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">309,543</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ softmax_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Softmax</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m315,655\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,655</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m315,543\u001b[0m (1.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">315,543</span> (1.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m112\u001b[0m (448.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">112</span> (448.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training for fold 5 ...\n",
      "Epoch 1/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 47ms/step - accuracy: 0.4133 - loss: 3.4730 - val_accuracy: 0.0161 - val_loss: 4.5280\n",
      "Epoch 2/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 0.9153 - loss: 0.3908 - val_accuracy: 0.0161 - val_loss: 5.2056\n",
      "Epoch 3/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 0.9756 - loss: 0.0629 - val_accuracy: 0.0161 - val_loss: 4.7363\n",
      "Epoch 4/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 0.9860 - loss: 0.0283 - val_accuracy: 0.0242 - val_loss: 3.3908\n",
      "Epoch 5/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 0.9963 - loss: 0.0143 - val_accuracy: 0.2661 - val_loss: 2.2668\n",
      "Epoch 6/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 61ms/step - accuracy: 0.9924 - loss: 0.0134 - val_accuracy: 0.7339 - val_loss: 1.1729\n",
      "Epoch 7/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 62ms/step - accuracy: 0.9943 - loss: 0.0072 - val_accuracy: 0.9113 - val_loss: 0.5000\n",
      "Epoch 8/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 0.0027 - val_accuracy: 0.9839 - val_loss: 0.2014\n",
      "Epoch 9/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9919 - val_loss: 0.0924\n",
      "Epoch 10/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 1.0000 - loss: 8.2008e-04 - val_accuracy: 0.9919 - val_loss: 0.0615\n",
      "Epoch 11/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 0.0016 - val_accuracy: 0.9919 - val_loss: 0.0476\n",
      "Epoch 12/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 6.4418e-04 - val_accuracy: 0.9919 - val_loss: 0.0467\n",
      "Epoch 13/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 35ms/step - accuracy: 1.0000 - loss: 6.7058e-04 - val_accuracy: 0.9919 - val_loss: 0.0379\n",
      "Epoch 14/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 5.4719e-04 - val_accuracy: 0.9919 - val_loss: 0.0381\n",
      "Epoch 15/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 5.2670e-04 - val_accuracy: 0.9919 - val_loss: 0.0383\n",
      "Epoch 16/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 42ms/step - accuracy: 1.0000 - loss: 5.7931e-04 - val_accuracy: 0.9919 - val_loss: 0.0444\n",
      "Epoch 17/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 36ms/step - accuracy: 1.0000 - loss: 2.8278e-04 - val_accuracy: 0.9919 - val_loss: 0.0389\n",
      "Epoch 18/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 1.0000 - loss: 2.8534e-04 - val_accuracy: 0.9919 - val_loss: 0.0415\n",
      "Epoch 19/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 3.4766e-04 - val_accuracy: 0.9919 - val_loss: 0.0387\n",
      "Epoch 20/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 1.0000 - loss: 2.5223e-04 - val_accuracy: 0.9919 - val_loss: 0.0425\n",
      "Epoch 21/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 55ms/step - accuracy: 1.0000 - loss: 2.4983e-04 - val_accuracy: 0.9919 - val_loss: 0.0397\n",
      "Epoch 22/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 2.5529e-04 - val_accuracy: 0.9919 - val_loss: 0.0424\n",
      "Epoch 23/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 2.1909e-04 - val_accuracy: 0.9919 - val_loss: 0.0417\n",
      "Epoch 24/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 2.3735e-04 - val_accuracy: 0.9919 - val_loss: 0.0410\n",
      "Epoch 25/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 1.0000 - loss: 1.8880e-04 - val_accuracy: 0.9919 - val_loss: 0.0493\n",
      "Epoch 26/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 55ms/step - accuracy: 1.0000 - loss: 2.4206e-04 - val_accuracy: 0.9839 - val_loss: 0.0407\n",
      "Epoch 27/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 41ms/step - accuracy: 1.0000 - loss: 1.4462e-04 - val_accuracy: 0.9919 - val_loss: 0.0448\n",
      "Epoch 28/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 1.1833e-04 - val_accuracy: 0.9919 - val_loss: 0.0421\n",
      "Epoch 29/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 1.5678e-04 - val_accuracy: 0.9919 - val_loss: 0.0444\n",
      "Epoch 30/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 43ms/step - accuracy: 1.0000 - loss: 1.0429e-04 - val_accuracy: 0.9919 - val_loss: 0.0448\n",
      "Epoch 31/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 9.8947e-05 - val_accuracy: 0.9919 - val_loss: 0.0446\n",
      "Epoch 32/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 8.9187e-05 - val_accuracy: 0.9919 - val_loss: 0.0452\n",
      "Epoch 33/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 1.0011e-04 - val_accuracy: 0.9919 - val_loss: 0.0494\n",
      "Epoch 34/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 9.3700e-05 - val_accuracy: 0.9919 - val_loss: 0.0451\n",
      "Epoch 35/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 50ms/step - accuracy: 1.0000 - loss: 8.3455e-05 - val_accuracy: 0.9919 - val_loss: 0.0473\n",
      "Epoch 36/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 52ms/step - accuracy: 1.0000 - loss: 5.9729e-05 - val_accuracy: 0.9919 - val_loss: 0.0483\n",
      "Epoch 37/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 7.3359e-05 - val_accuracy: 0.9919 - val_loss: 0.0515\n",
      "Epoch 38/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 5.4466e-05 - val_accuracy: 0.9919 - val_loss: 0.0494\n",
      "Epoch 39/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 4.8897e-05 - val_accuracy: 0.9919 - val_loss: 0.0504\n",
      "Epoch 40/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - accuracy: 1.0000 - loss: 8.1585e-05 - val_accuracy: 0.9919 - val_loss: 0.0509\n",
      "Epoch 41/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 55ms/step - accuracy: 1.0000 - loss: 5.9050e-05 - val_accuracy: 0.9919 - val_loss: 0.0514\n",
      "Epoch 42/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 4.7900e-05 - val_accuracy: 0.9919 - val_loss: 0.0528\n",
      "Epoch 43/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 4.6089e-05 - val_accuracy: 0.9919 - val_loss: 0.0533\n",
      "Epoch 44/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 4.7589e-05 - val_accuracy: 0.9919 - val_loss: 0.0540\n",
      "Epoch 45/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 5.5118e-05 - val_accuracy: 0.9919 - val_loss: 0.0553\n",
      "Epoch 46/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 49ms/step - accuracy: 1.0000 - loss: 4.2041e-05 - val_accuracy: 0.9919 - val_loss: 0.0551\n",
      "Epoch 47/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 3.8343e-05 - val_accuracy: 0.9919 - val_loss: 0.0532\n",
      "Epoch 48/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 3.7521e-05 - val_accuracy: 0.9919 - val_loss: 0.0564\n",
      "Epoch 49/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 3.9537e-05 - val_accuracy: 0.9919 - val_loss: 0.0563\n",
      "Epoch 50/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 3.5723e-05 - val_accuracy: 0.9919 - val_loss: 0.0574\n",
      "Score for fold 5: loss of 0.05738368257880211; compile_metrics of 99.19354915618896%\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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LFngXKfj9+6uvlW4bNGigc845R++++64qKir03//+V0VFRdVmO73++usaM2aMWrdurRdffFEff/yxFixYoLPOOuu4/lxq64EHHtCECRN0+umn6/XXX9f8+fO1YMECde7c2a+veyh//14cjwYNGmjlypX64IMPvPPPBg8eXG1W3Omnn66NGzfqpZdeUpcuXfTCCy/opJNO0gsvvFBvdQIAEMy4PuX69HgE8/VpYWGh/vvf/2rTpk1q27at99apUyeVlpZq1qxZ9XqN+/tF7TwO99/iTTfdpL///e+69NJL9dZbb+mTTz7RggULlJaWVqvPfdSoUSouLtbcuXNlGIZmzZql888/X0lJSSf8XICdsDgZgCNavHix9uzZo/fee0+nn3669/imTZssrOqgBg0aKDo6Whs2bKhx3+GO/d6aNWv066+/6pVXXtGoUaO8x4/29fdjad68uRYtWqTi4uJqXQ2//PLLCT3PyJEj9fHHH+ujjz7SrFmzlJiYqKFDh3rvf+edd9SqVSu999571b72dLivTh1PzZK0fv16tWrVynt89+7dNboE3nnnHZ155pl68cUXqx0vKChQenq69+cT+SpW8+bNtXDhQhUVFVXravB81dFTX31o3ry5Vq9eLbfbXa3r9nC1OJ1ODR06VEOHDpXb7daNN96o5557Tvfcc4+3oyY1NVVjx47V2LFjVVxcrNNPP1333nuvrrnmmnp7TwAA2AnXpyeO61NTIF6fvvfeezpw4ICeeeaZarVK5p/P3XffraVLl+qPf/yjWrdurfnz5ys/P/+IXbetW7eW2+3W2rVrj7oYXEpKigoKCqodKy8vV05OznHX/s4772j06NF67LHHvMcOHDhQ43lbt26tH3/88ZjP16VLF/Xs2VNvvPGGmjRpouzsbD311FPHXQ9gV3TcAjgiz78cH/qvvOXl5frXv/5lVUnVhIeHa8CAAZo7d6527NjhPb5hw4Yac6eO9Hip+vszDENPPPFErWsaMmSIKisr9cwzz3iPuVyuE77oGDZsmGJjY/Wvf/1LH330kS688EJFR0cftfZvvvlGy5YtO+GaBwwYoMjISD311FPVnm/GjBk1zg0PD6/xr/5vv/22tm/fXu1YXFycJNW4cDucIUOGyOVy6emnn652/B//+IccDsdxz4PzhSFDhig3N1dz5szxHqusrNRTTz2l+Ph479cU9+zZU+1xYWFh6tatmySprKzssOfEx8erTZs23vsBAMCJ4/r0xHF9agrE69PXX39drVq10vXXX6+LL7642m3ixImKj4/3jku46KKLZBiGpk6dWuN5PO9/2LBhCgsL07Rp02p0vR76GbVu3bravGJJ+ve//33EjtvDOdzn/tRTT9V4josuukirVq3S+++/f8S6Pa688kp98sknmjFjhtLS0ur17wFAoKLjFsARnXrqqUpJSdHo0aN18803y+Fw6LXXXqvXr+scy7333qtPPvlEp512mm644QbvBVaXLl20cuXKoz62Q4cOat26tSZOnKjt27crMTFR7777bp1mpQ4dOlSnnXaa7rzzTm3evFmdOnXSe++9d8LzteLj4zVs2DDvHLFDv4YmSeeff77ee+89DR8+XOedd542bdqkZ599Vp06dVJxcfEJvVZGRoYmTpyo6dOn6/zzz9eQIUP0ww8/6KOPPqrxL//nn3++pk2bprFjx+rUU0/VmjVr9MYbb1TrhJDMi8Hk5GQ9++yzSkhIUFxcnPr06XPY+VhDhw7VmWeeqbvuukubN29W9+7d9cknn+g///mPbr311moLPfjCokWLdODAgRrHhw0bpuuuu07PPfecxowZo++//14tWrTQO++8o6VLl2rGjBnejotrrrlG+fn5Ouuss9SkSRNt2bJFTz31lHr06OGdfdapUyedccYZ6tWrl1JTU/Xdd9/pnXfe0fjx4336fgAACCVcn544rk9NgXZ9umPHDn322Wc1FkDziIqK0sCBA/X222/rySef1Jlnnqkrr7xSTz75pNavX69BgwbJ7Xbriy++0Jlnnqnx48erTZs2uuuuu3TfffepX79+uvDCCxUVFaXly5erUaNGmj59uiTzWvb666/XRRddpHPOOUerVq3S/Pnza3y2R3P++efrtddeU1JSkjp16qRly5Zp4cKFSktLq3beX/7yF73zzju65JJLdNVVV6lXr17Kz8/XBx98oGeffVbdu3f3nnvFFVfor3/9q95//33dcMMNioyMrMUnC9iMASCkjBs3zvj9f/r9+/c3OnfufNjzly5davzhD38wYmJijEaNGhl//etfjfnz5xuSjM8++8x73ujRo43mzZt7f960aZMhyXjkkUdqPKckY8qUKd6fp0yZUqMmSca4ceNqPLZ58+bG6NGjqx1btGiR0bNnT8PpdBqtW7c2XnjhBeP22283oqOjj/ApHLR27VpjwIABRnx8vJGenm5ce+21xqpVqwxJxssvv1zt/cXFxdV4/OFq37Nnj3HllVcaiYmJRlJSknHllVcaP/zwQ43nPJYPP/zQkGRkZWUZLper2n1ut9t44IEHjObNmxtRUVFGz549jf/97381/hwMo+bn/fLLLxuSjE2bNnmPuVwuY+rUqUZWVpYRExNjnHHGGcaPP/5Y4/M+cOCAcfvtt3vPO+2004xly5YZ/fv3N/r371/tdf/zn/8YnTp1MiIiIqq998PVWFRUZNx2221Go0aNjMjISKNt27bGI488Yrjd7hrv5Xh/L37P8zt5pNtrr71mGIZh7Ny50xg7dqyRnp5uOJ1Oo2vXrjX+3N555x3j3HPPNRo0aGA4nU6jWbNmxp///GcjJyfHe879999vnHLKKUZycrIRExNjdOjQwfj73/9ulJeXH7VOAABCDden1XF9arL79eljjz1mSDIWLVp0xHNmzpxpSDL+85//GIZhGJWVlcYjjzxidOjQwXA6nUZGRoYxePBg4/vvv6/2uJdeesno2bOnERUVZaSkpBj9+/c3FixY4L3f5XIZd9xxh5Genm7ExsYaAwcONDZs2FCjZs+fy/Lly2vUtnfvXu81c3x8vDFw4EDj559/Puz73rNnjzF+/HijcePGhtPpNJo0aWKMHj3ayMvLq/G8Q4YMMSQZX3311RE/FyCUOAwjgP5pEgB8ZNiwYfrpp5+0fv16q0sBAAAAuD4FjsPw4cO1Zs2a45oJDYQCZtwCCHr79++v9vP69es1b948nXHGGdYUBAAAgJDG9Slw4nJycvThhx/qyiuvtLoUIGDQcQsg6GVlZWnMmDFq1aqVtmzZomeeeUZlZWX64Ycf1LZtW6vLAwAAQIjh+hQ4fps2bdLSpUv1wgsvaPny5dq4caMyMzOtLgsICCxOBiDoDRo0SG+++aZyc3MVFRWlvn376oEHHuCiGAAAAJbg+hQ4fp9//rnGjh2rZs2a6ZVXXiG0BQ5Bxy0AAAAAAAAABBhm3AIAAAAAAABAgCG4BQAAAAAAAIAAE9Qzbt1ut3bs2KGEhAQ5HA6rywEAAIAPGIahoqIiNWrUSGFhodtnwLUuAACA/ZzItW5QB7c7duxQ06ZNrS4DAAAAfrB161Y1adLE6jIsw7UuAACAfR3PtW5QB7cJCQmSzDeamJhocTUAAADwhcLCQjVt2tR7rRequNYFAACwnxO51g3q4NbzlbHExEQuZgEAAGwm1McDcK0LAABgX8dzrRu6Q8MAAAAAAAAAIEAR3AIAAAAAAABAgCG4BQAAAAAAAIAAE9QzbgEAgH+5XC5VVFRYXQZsJjIyUuHh4VaXAQAAAAQ0glsAAFCDYRjKzc1VQUGB1aXAppKTk5WZmRnyC5ABAAAAR0JwCwAAavCEtg0aNFBsbCzhGnzGMAyVlpZq165dkqSsrCyLKwIAAAACE8EtAACoxuVyeUPbtLQ0q8uBDcXExEiSdu3apQYNGjA2AQAAADgMFicDAADVeGbaxsbGWlwJ7Mzz+8UMZQAAAODwCG4BAMBhMR4B/sTvFwAAAHB0BLcAAAAAAAAAEGAIbgEAAI6iRYsWmjFjxnGfv3jxYjkcDhUUFPitJgAAAAD2R3ALAABsweFwHPV277331up5ly9fruuuu+64zz/11FOVk5OjpKSkWr3e8SIgBgAAAOwtwuoCAAAAfCEnJ8e7P2fOHE2ePFm//PKL91h8fLx33zAMuVwuRUQc+1IoIyPjhOpwOp3KzMw8occAAAAAwO/RcQsAAGwhMzPTe0tKSpLD4fD+/PPPPyshIUEfffSRevXqpaioKH355ZfauHGjLrjgAjVs2FDx8fHq3bu3Fi5cWO15fz8qweFw6IUXXtDw4cMVGxurtm3b6oMPPvDe//tO2JkzZyo5OVnz589Xx44dFR8fr0GDBlULmisrK3XzzTcrOTlZaWlpuuOOOzR69GgNGzas1p/H3r17NWrUKKWkpCg2NlaDBw/W+vXrvfdv2bJFQ4cOVUpKiuLi4tS5c2fNmzfP+9iRI0cqIyNDMTExatu2rV5++eVa1wIAAADgxBHcAgCAYzIMQ6XllZbcDMPw2fu488479eCDD2rdunXq1q2biouLNWTIEC1atEg//PCDBg0apKFDhyo7O/uozzN16lRdeumlWr16tYYMGaKRI0cqPz//iOeXlpbq0Ucf1WuvvaYlS5YoOztbEydO9N7/0EMP6Y033tDLL7+spUuXqrCwUHPnzq3Tex0zZoy+++47ffDBB1q2bJkMw9CQIUNUUVEhSRo3bpzKysq0ZMkSrVmzRg899JC3K/mee+7R2rVr9dFHH2ndunV65plnlJ6eXqd6AAAAAJwYRiUAAIBj2l/hUqfJ8y157bXTBirW6ZtLlmnTpumcc87x/pyamqru3bt7f77vvvv0/vvv64MPPtD48eOP+DxjxozRiBEjJEkPPPCAnnzySX377bcaNGjQYc+vqKjQs88+q9atW0uSxo8fr2nTpnnvf+qppzRp0iQNHz5ckvT00097u19rY/369frggw+0dOlSnXrqqZKkN954Q02bNtXcuXN1ySWXKDs7WxdddJG6du0qSWrVqpX38dnZ2erZs6dOPvlkSWbXMQAAAID6RcctAAAIGZ4g0qO4uFgTJ05Ux44dlZycrPj4eK1bt+6YHbfdunXz7sfFxSkxMVG7du064vmxsbHe0FaSsrKyvOfv27dPO3fu1CmnnOK9Pzw8XL169Tqh93aodevWKSIiQn369PEeS0tLU/v27bVu3TpJ0s0336z7779fp512mqZMmaLVq1d7z73hhhs0e/Zs9ejRQ3/961/11Vdf1boWu1iyZImGDh2qRo0ayeFwHFdH9OLFi3XSSScpKipKbdq00cyZM/1eJwAAAOyDjlsAAHBMMZHhWjttoGWv7StxcXHVfp44caIWLFigRx99VG3atFFMTIwuvvhilZeXH/V5IiMjq/3scDjkdrtP6HxfjoCojWuuuUYDBw7Uhx9+qE8++UTTp0/XY489pptuukmDBw/Wli1bNG/ePC1YsEBnn322xo0bp0cffdTSmq1UUlKi7t2766qrrtKFF154zPM3bdqk8847T9dff73eeOMNLVq0SNdcc42ysrI0cKA1/y0BAAAguBDcAgCAY3I4HD4bVxBIli5dqjFjxnhHFBQXF2vz5s31WkNSUpIaNmyo5cuX6/TTT5ckuVwurVixQj169KjVc3bs2FGVlZX65ptvvKMS9uzZo19++UWdOnXynte0aVNdf/31uv766zVp0iQ9//zzuummmyRJGRkZGj16tEaPHq1+/frpL3/5S0gHt4MHD9bgwYOP+/xnn31WLVu21GOPPSbJ/DP58ssv9Y9//IPgFgAAAMfFfn8DCySl+dKHE6ReY6RWZ1hdDQAA+J22bdvqvffe09ChQ+VwOHTPPfcctXPWX2666SZNnz5dbdq0UYcOHfTUU09p7969cjgcx3zsmjVrlJCQ4P3Z4XCoe/fuuuCCC3TttdfqueeeU0JCgu688041btxYF1xwgSTp1ltv1eDBg9WuXTvt3btXn332mTp27ChJmjx5snr16qXOnTurrKxM//vf/7z34fgsW7ZMAwYMqHZs4MCBuvXWW4/4mLKyMpWVlXl/Liws9Fd5qIPySrcKSsu1p6Rce0vKlV9arvwS87a3pOp4ablKylxWlwoAAI7T5KGddFKzFKvLqIHg1p9+eE366X1pz0bp+i+srgYAAPzO448/rquuukqnnnqq0tPTdccdd1gSlt1xxx3Kzc3VqFGjFB4eruuuu04DBw5UePixx0R4unQ9wsPDVVlZqZdfflm33HKLzj//fJWXl+v000/XvHnzvGMbXC6Xxo0bp23btikxMVGDBg3SP/7xD0mS0+nUpEmTtHnzZsXExKhfv36aPXu279+4jeXm5qphw4bVjjVs2FCFhYXav3+/YmJiajxm+vTpmjp1an2VCEmGYajwQKU3eM0/RhibX1KuogOVVpcNAAB8LFD//91hWD1grQ4KCwuVlJSkffv2KTEx0epyanrnKunHd83923+VEhoe/XwAAALAgQMHtGnTJrVs2VLR0dFWlxOS3G63OnbsqEsvvVT33Xef1eX4xdF+zwL9Gs/hcOj999/XsGHDjnhOu3btNHbsWE2aNMl7bN68eTrvvPNUWlp62OD2cB23TZs2DdjPIRAdqHBpb2m59hQfDFoPH8ZWaE9JuQpKy1XpPvG/DoU5pJRYp1LinEqNcyo11qnU+Kpt1bG4qAgdu2ceAAAEgh7NkpUeH1Uvr3Ui17p03PrTjpUH9zcuknpcYVkpAAAgcG3ZskWffPKJ+vfvr7KyMj399NPatGmTrriCa4dglZmZqZ07d1Y7tnPnTiUmJh42tJWkqKgoRUXVz18Y7MDlNrQup1DfbsrX8s35+m7LXu0uKjv2Aw8jzhmulDin0uKqh7E1jlUdT4qJVFiYD2NZt1tyB2anD+BzDocUHnns82qr8ugLjALAYYUFZkQamFXZwYFCKX/jwZ/XLyC4BQAAhxUWFqaZM2dq4sSJMgxDXbp00cKFC5krG8T69u2refPmVTu2YMEC9e3b16KKgt+BCpdWb9un5Zvz9e2mfH2/Za+Ky2qGnRFhDjNoPaT7NSUuUqlxUUqNjawKY6OUHBuptHinUmKdio489lgSn6vYL238VFr3X+mXj6QDBfVfA2CVjA5Sx6HmLbObGebWlmFIO1aY/y2t+6+0Z4Pv6gQQOv7vXanNgGOfV88Ibv0ld425dYRLhsu8KHNVSuF85AAAoLqmTZtq6dKlVpeBoyguLtaGDQfDgE2bNmnlypVKTU1Vs2bNNGnSJG3fvl2vvvqqJOn666/X008/rb/+9a+66qqr9Omnn+qtt97Shx9+aNVbCDolZZX6dnO+lld11K7auk/lruqLByZEReik5ik6pWWqerdIVfvMBCVGRxzXwn6WOFAorf9EWveB2dhRUWp1RYA1dv9s3pY8IiU3kzr+yQxxm5wihYUd+/Ful5S9rCqs/Z9UuM3/NQOABUgR/SVnpblte46U/bX5L+jbv5ea9bGyKgAAANTCd999pzPPPNP784QJEyRJo0eP1syZM5WTk6Ps7Gzv/S1bttSHH36o2267TU888YSaNGmiF154QQMHDqz32oPJ3pJyLVy3U/N/ytWS9Xkqr6we1KbHR+mUlinq3cIMajtmJSrclyML/KEkT/plnhkw/bZYch3yNe6kZlLH883AqkGnunUdAsGiskz67XNp3X+k9Qulgmxp2dPmLb6h1OE887+JFv2qj1SoLJM2LTH/4ePneVJp3sH7nPFS23PN/55a9vfvKAYA9hQZa3UFh0Vw6y85q8xt415SZIz00/vShoUEtwAAAEHojDPO0NHW9J05c+ZhH/PDDz/4sSp7yNm3X5/8ZIa132zKl+uQxcKapMToD63SdEqLVPVumaoWabGB2017qP17pVVzzLA2+yvJOCSATm9/8CviWd0JaxGaul1i3spLzfVg1v1X+uVjqXin9N1L5i06SWo3WGr2B2nLUunX+VJZ4cHniEmR2leFvK3OkCJZUBWA/RDc+osnuM3qLiU2rgpuF0hn3WVtXQAAAIDFfttdrPk/7dTHP+Vq1daCavd1zErUwM4NNahLpto3TAiOoPZQhiG9fpH5bTuPrB4Hw9qM9paVBgQcZ+zB/zYqy6XNS8wQ9+cPpZLd0urZ5s0jPvPg+c1PYxQhANvjf+X8obxEyvvV3M/qIamqa2DHD1Lxbik+w6rKAAAAAEuUlFXquc836qMfc7V+V7H3uMMhndQsRYM6Z2pg50w1SwvMryoet5yVZmgbES2dPcX82ndKc6urAgJfhNNcGKjNAOm8x6Wt35gh7o4fpCYnm3NwG598fDNwAcAmCG79IfdH8+tQ8ZlSQkPzWGZXc8GyjZ9K3S+ztj4AAACgnt353hr9d9UOSVJEmEN9W6dpYOdMndupoRok2ugrzmveMbftBkl9b7S2FiBYhYVLzU81bwAQwghu/eHQMQkebc4xg9sNCwhuAQAAEFJ+yS3S/1aboe30C7tqSJcsJcXacPEgt9sckSZJXS+2thYAABD0+I6BP+SsNLeNehw81vYcc7thkeR21XdFAADgOJ1xxhm69dZbvT+3aNFCM2bMOOpjHA6H5s6dW+fX9tXzAIHmiUW/yjCkIV0zNeKUZvYMbSVp69dS4XYpKtFs3AAAAKgDglt/OFzHbZNTpKgkaX++OaMHAAD41NChQzVo0KDD3vfFF1/I4XBo9erVJ/y8y5cv13XXXVfX8qq599571aNHjxrHc3JyNHjwYJ++1u/NnDlTycnJfn0N4FA/5xZq3ppcSdItZ7ezuBo/84xJ6DiUFe4BAECdEdz6WsUBadc6c//Q4DY8QmrV39zfsLD+6wIAwOauvvpqLViwQNu2batx38svv6yTTz5Z3bp1O+HnzcjIUGxs/SyWlJmZqaioqHp5LaC+PLFwvSTpvK5Zap+ZYHE1fuSqkNbONfe7XGRpKQAAwB4sDW7vvfdeORyOarcOHTpYWVLd7fxJMlxSbLqU2Lj6fZ5xCesX1H9dAADY3Pnnn6+MjAzNnDmz2vHi4mK9/fbbuvrqq7Vnzx6NGDFCjRs3VmxsrLp27ao333zzqM/7+1EJ69ev1+mnn67o6Gh16tRJCxbU/P/1O+64Q+3atVNsbKxatWqle+65RxUVFZLMjtepU6dq1apV3usfT82/H5WwZs0anXXWWYqJiVFaWpquu+46FRcXe+8fM2aMhg0bpkcffVRZWVlKS0vTuHHjvK9VG9nZ2brgggsUHx+vxMREXXrppdq5c6f3/lWrVunMM89UQkKCEhMT1atXL3333XeSpC1btmjo0KFKSUlRXFycOnfurHnz5tW6FgS/tTsK9dGPuXI4pFsGtLW6HP/67XOpdI/594CW/a2uBgAA2IDli5N17txZCxce7ECNiLC8pLrxzLfN6i45HNXvazPA3G7/XirZI8Wl1WtpAADUmmFIFaXWvHZkbM3/Tz2MiIgIjRo1SjNnztRdd90lR9Vj3n77bblcLo0YMULFxcXq1auX7rjjDiUmJurDDz/UlVdeqdatW+uUU0455mu43W5deOGFatiwob755hvt27ev2jxcj4SEBM2cOVONGjXSmjVrdO211yohIUF//etfddlll+nHH3/Uxx9/7L0GSkpKqvEcJSUlGjhwoPr27avly5dr165duuaaazR+/Phq4fRnn32mrKwsffbZZ9qwYYMuu+wy9ejRQ9dee+0x38/h3p8ntP38889VWVmpcePG6bLLLtPixYslSSNHjlTPnj31zDPPKDw8XCtXrlRkpDmvdNy4cSovL9eSJUsUFxentWvXKj4+/oTrgH08uehgt227hjbutpWkH6vGJHQeZn7bDgAAoI4sv6KIiIhQZmam1WX4zqHB7e8lNpIadJZ2/SRt/FTqdkm9lgYAQK1VlEoPNLLmtf+2Q3LGHdepV111lR555BF9/vnnOuOMMySZYxIuuugiJSUlKSkpSRMnTvSef9NNN2n+/Pl66623jiu4XbhwoX7++WfNnz9fjRqZn8cDDzxQYy7t3Xff7d1v0aKFJk6cqNmzZ+uvf/2rYmJiFB8ff8xroFmzZunAgQN69dVXFRdnvv+nn35aQ4cO1UMPPaSGDRtKklJSUvT0008rPDxcHTp00HnnnadFixbVKrhdtGiR1qxZo02bNqlp06aSpFdffVWdO3fW8uXL1bt3b2VnZ+svf/mL91tSbdse7KLMzs7WRRddpK5du0qSWrVqdcI1wD5+2rFPH/9U1W17ts27bSv2S+v+Z+53udjaWgAAgG1YPuN2/fr1atSokVq1aqWRI0cqOzv7iOeWlZWpsLCw2i3geBYma9Tj8Pe3req6Zc4tAAA+16FDB5166ql66aWXJEkbNmzQF198oauvvlqS5HK5dN9996lr165KTU1VfHy85s+ff9Trj0OtW7dOTZs29Ya2ktS3b98a582ZM0ennXaaMjMzFR8fr7vvvvu4X+PQ1+revbs3tJWk0047TW63W7/88ov3WOfOnRUeHu79OSsrS7t27Tqh1zr0NZs2beoNbSWpU6dOSk5O1rp15gz/CRMm6JprrtGAAQP04IMPauPGjd5zb775Zt1///067bTTNGXKlFotBgf78My2Pb9bI7W1e7ft+gVSeZGU2ERq2sfqagAAgE1Y2nHbp08fzZw5U+3bt1dOTo6mTp2qfv366ccff1RCQs2Lu+nTp2vq1KkWVHqcKsulnWvN/cN13EpSm3OkpU+Ywa3bLYVZnp0DAHBskbFm56tVr30Crr76at1000365z//qZdfflmtW7dW//7mvMlHHnlETzzxhGbMmKGuXbsqLi5Ot956q8rLy31W7rJlyzRy5EhNnTpVAwcOVFJSkmbPnq3HHnvMZ69xKM+YAg+HwyG32+2X15LMNQquuOIKffjhh/roo480ZcoUzZ49W8OHD9c111yjgQMH6sMPP9Qnn3yi6dOn67HHHtNNN93kt3oQmH7cvk+frN1Z1W3bxupy/M8zJqHLhVzfAwAAn7H0qmLw4MG65JJL1K1bNw0cOFDz5s1TQUGB3nrrrcOeP2nSJO3bt89727p1az1XfAy71kruCik6WUpufvhzmvaRnAlSad7BsQoAAAQ6h8McV2DF7Tjm2x7q0ksvVVhYmGbNmqVXX31VV111lXfe7dKlS3XBBRfo//7v/9S9e3e1atVKv/7663E/d8eOHbV161bl5OR4j3399dfVzvnqq6/UvHlz3XXXXTr55JPVtm1bbdmypdo5TqdTLpfrmK+1atUqlZSUeI8tXbpUYWFhat++/XHXfCI87+/Qa6y1a9eqoKBAnTp18h5r166dbrvtNn3yySe68MIL9fLLL3vva9q0qa6//nq99957uv322/X888/7pVYEtieqZtv+qXsjtWlg827bA4XSr/PN/a6MSQAAAL4TUP8cnJycrHbt2mnDhg2HvT8qKkqJiYnVbgHFMybhcAuTeUQ4pVZVq8xuWFQ/dQEAEELi4+N12WWXadKkScrJydGYMWO897Vt21YLFizQV199pXXr1unPf/6zdu7cedzPPWDAALVr106jR4/WqlWr9MUXX+iuu+6qdk7btm2VnZ2t2bNna+PGjXryySf1/vvvVzunRYsW2rRpk1auXKm8vDyVlZXVeK2RI0cqOjpao0eP1o8//qjPPvtMN910k6688krvfNvacrlcWrlyZbXbunXrNGDAAHXt2lUjR47UihUr9O2332rUqFHq37+/Tj75ZO3fv1/jx4/X4sWLtWXLFi1dulTLly9Xx44dJUm33nqr5s+fr02bNmnFihX67LPPvPchdPy4fZ8WrN2pMId001k2n20rSb/MkyoPSGltpcxuVlcDAABsJKCC2+LiYm3cuFFZWVlWl1I7hwa3R9PGM+d2gX/rAQAgRF199dXau3evBg4cWG0e7d13362TTjpJAwcO1BlnnKHMzEwNGzbsuJ83LCxM77//vvbv369TTjlF11xzjf7+979XO+dPf/qTbrvtNo0fP149evTQV199pXvuuafaORdddJEGDRqkM888UxkZGXrzzTdrvFZsbKzmz5+v/Px89e7dWxdffLHOPvtsPf300yf2YRxGcXGxevbsWe02dOhQORwO/ec//1FKSopOP/10DRgwQK1atdKcOXMkSeHh4dqzZ49GjRqldu3a6dJLL9XgwYO9o6xcLpfGjRunjh07atCgQWrXrp3+9a9/1bleBJcZC80udrPbNt7iaurBmqoxCV0vPuFvCAAAAByNwzAMw6oXnzhxooYOHarmzZtrx44dmjJlilauXKm1a9cqIyPjmI8vLCxUUlKS9u3bFxjdt8+fJW3/XrroxaN/TapgqzSji+QIk/6yUYpNrb8aAQA4hgMHDmjTpk1q2bKloqOjrS4HNnW037OAu8azSDB+Dmu27dPQp79UmENaOKG/WmXYPLgt2SM91k5yV0rjv5PSQ6DDGAAA1MmJXONZ2nG7bds2jRgxQu3bt9ell16qtLQ0ff3118cV2gYcV4WU+6O536jn0c9NbipldJAMt/TbZ/6vDQAAAKgHnm7bYT0a2z+0laS1c83QNrMboS0AAPC5CCtffPbs2Va+vG/l/Sq5ysyFx1JaHvv8NgOk3T+bc267XOT/+gAAAAA/WrW1QIt+3qUwhzT+rDZWl1M/fnzP3LIoGQAA8IOAmnEb1HasNLdZ3aSw4/hY255jbjcslNxuv5UFAAAA1IcnFq2XJA3rGSLdtoU7pC1Lzf3OF1pbCwAAsCWCW1/xLkzW4/jOb9ZXioyTindKO9f4rSwAAADA31ZuLdCnP+9SeJhDN58VIiMDfnxPkmFe1yc3tboaAABgQwS3vuINbrsf3/kRUVLL08399Qv8UxMAAABQDw6dbdsiPc7iaurJj++YW8aeAQAAPyG49QW3S8pdbe436nH8j2s7wNxuWOTzkgAAqCs3o3zgR/x+2ceK7L1a/Mtus9v27BCZbbtno7TjB8kRLnUaZnU1AADApixdnMw29myQKkqlyFgp7QQuVttUBbdbv5H2F0gxyf6oDgCAE+J0OhUWFqYdO3YoIyNDTqdTDofD6rJgE4ZhqLy8XLt371ZYWJicTqfVJaGOnlhozra9sGdjNU8LlW7bd81tq/5SfIa1tQAAANsiuPUFz8JkmV2lsPDjf1xKCymtrbRnvfTbYqnzMN/XBgDACQoLC1PLli2Vk5OjHTt2WF0ObCo2NlbNmjVT2PEs6oqA9f2Wvfr8V7Pb9qZQmW1rGNIaz5iEi62tBQAA2BrBrS+c6MJkh2p7jhncblhAcAsACBhOp1PNmjVTZWWlXC6X1eXAZsLDwxUREUEntw14ZttedFJjNUuLtbiaerLzJynvFyk8Sup4vtXVAAAAGyO49YUTXZjsUG0GSF//y5xzaxgSf4EBAAQIh8OhyMhIRUZGWl0KgAD04/Z9+mJ9niJCqdtWOrgoWdtzpOgka2sBAAC2xnfT6srtrltw2/w0KSJGKsox//UeAAAACAJrdxRKkvq2TlPT1BDptjWMg/NtuzImAQAA+BfBbV3t3SSVF0kR0VJGhxN/fGS01LKfub9hgW9rAwAAAPxkd3GZJKlBQrTFldSjbculgmzJGS+1G2R1NQAAwOYIbusqZ6W5bdhZCq/l5Ik255jbDYt8UhIAAADgb3lVwW16gtPiSuqRZ1GyDudJkTHW1gIAAGyP4Laudqw0t7UZk+DRdoC5zV4mHSisc0kAAACAv+UVl0uSMuKjLK6knrgqpZ/eN/e7MCYBAAD4H8FtXXnn2/ao/XOktjJv7kpp0+c+KQsAAADwp7wis+M2IyFEgtvNX0glu6SYFKnVGVZXAwAAQgDBbV0YRt0WJjuUZ1zCeubcAgAAIPB5Ztymh0rH7Y9VYxI6XSBFhNB4CAAAYBmC27oo2CIdKJDCIqUGHev2XG0PmXNrGHUuDQAAAPCnvFAKbivLpHX/NfcZkwAAAOoJwW1deLptG3aSIup4wdr8NCk8SircJu3+ue61AQAAAH5S4XKroLRCkpQeHwLdpxsWSQf2SQlZUvNTra4GAACECILbuvDVmARJcsZKLU4z9zd/WffnAwAAAPxkT9XCZOFhDqXEhkBw+8s8c9t5uBQWbm0tAAAgZBDc1sWOlebWF8GtJKW3M7f7tvnm+QAAAAA/8IxJSI1zKizMYXE19SBvvbltcrK1dQAAgJBCcFtb1RYm6+mb50zINLdFub55PgAAAMAPPAuTZYTCfFtJyv/N3Ka2srYOAAAQUghua6twh1SaJznCzRm3vhBfFdwWE9wCAAAgcOUVVS1MlhACwW1ZkVSyy9wnuAUAAPWI4La2claa24wOUmSMb54zoaG5Ldrpm+cDAAAA/MDTcRsSC5N5um1j06XoJGtrAQAAIYXgtrY8YxIa9fDdcyZkmduiHN89JwAAAOBjeUXm4mQhMSqBMQkAAMAiBLe15euFySQpvqrj9kCBVHHAd88LAAAA+FCet+M2hILbtNbW1gEAAEIOwW1teRcm82FwG5MihVdd/DLnFgAAAAHKG9wmhMCohD103AIAAGsQ3NZGUW5VsOqQMrv67nkdDubcAgAAIOB5gtuM+GiLK6kHjEoAAAAWIbitDU+3bXo7yRnn2+eOzzS3dNwCAAAgQOUVmzNuQ6LjluAWAABYhOC2NvyxMJmHt+OW4BYAAACBp8Ll1t7SquDW7jNuy4oPNlSktrS2FgAAEHIIbmvDH/NtPRKyzC3BLQAAAAJQfkm5DEMKc0gpsTbvuN27ydzGpJrrUQAAANQjgtva2LHS3PojuI2v6rgtZsYtAAAAAs/uInO+bWpclMLDHBZX42eeMQlpra2tAwAAhCSC2xNVkicVbjP3M7v5/vm9Hbc5vn9uAAAAoI48C5Olx9u821aS9mw0t8y3BQAAFiC4PVGeMQmpraXoRN8/v3fGLR23AAAACDyehckyEmw+31ZiYTIAAGApgtsTlbPS3PpjTIIkxWea22Jm3AIAACDweDpuM+y+MJkk5VfNuE1lVAIAAKh/BLcnytNx26iHf57fMyqhdI9UWe6f1wAAAABqKa9qxm16SHTcMioBAABYh+D2RHmCW3913MamSmGR5j4LlAEAACDA7A6VGbflJQfXnUhtaW0tAAAgJBHcnoj9e6W9m819fyxMJkkOhxRfNeeW4BYAAAAB5uDiZDbvuPVc98ekmM0VAAAA9Yzg9kQUZJsXbsnN/XvxllA159bzL/wAAABAgMgrMsd52T643cOYBAAAYK0IqwsIKlndpb9uMjtv/ckb3LJAGQAAAAJLyHTc5v9mbgluAQCARei4PVEOh/+/KuUZlUBwCwAAgABS6XIrv9TsuM2w++Jk3oXJWltbBwAACFkEt4EoIcvcFhPcAgAAIHDkl5bLMKQwh5QaZ/PFyfI3mVs6bgEAgEUIbgNRgqfjlsXJAAAAEDg8821T45wKD3NYXI2feUYlpNFxCwAArEFwG4g8HbeMSgAAAEAA2R0q823LS6XC7eY+HbcAAMAiBLeByDPjllEJAAAACCB5RSES3O7dbG6jk6SYFEtLAQAAoYvgNhAlZJrbkjzJVWFtLQAAAECVPG/Hrd3n21aNSUhtbS5ODAAAYAGC20AUmy45wiUZUvEuq6sBAAAAJB0a3Nq84zZ/o7llTAIAALAQwW0gCgtjXAIAAAACTl6xuThZRoLdg1tPxy3BLQAAsA7BbaBKqApui3ZaWwcAAABQJXQ6bquC27TW1tYBAABCGsFtoErIMrdFOdbWAQAAAFTZ7VmczO4dt3vouAUAANYjuA1U3lEJdNwCAAAgMITE4mQV+6XCbeY+wS0AALAQwW2gSsg0t0XMuAUAAID1XG5D+SVVM27tPCph7xZzG5UkxaZZWwsAAAhpBLeBiuAWAAAAASS/pFxuQ3I4pNQ4G3fc5m80t6ktzTcLAABgEYLbQBVfFdwWE9wCAADAep4xCSmxTkWE2/ivEfnMtwUAAIHBxldcQY6OWwAAAAQQT3Br6zEJ0sHgNq21tXUAAICQR3AbqDzBbcluye2ythYAAACEPO/CZAk2HpMgSXs8oxLouAUAANYiuA1UcRmSI0wy3GZ4CwAAAFgor8hcmCzd9h23m8wtwS0AALAYwW2gCguX4hqY+0U51tYCAACAkLfb03Fr5+C2skzat9XcT2VUAgAAsBbBbSBLaGhui3ZaWwcAAABCXl5RCAS3ezdLMiRnghSXbnU1AAAgxBHcBrL4qjm3xSxQBgAAAGsd7Li18Yxbz8JkqS0lh8PaWgAAQMgjuA1kngXKighuAQAAYK28YnPGbUaCjTtuPcFtGmMSAACA9QhuAxnBLQAAAAJEXijMuN2z0dyyMBkAAAgABLeBLL5qxm0xM24BAABgHbfbUH5JCHXcEtwCAIAAQHAbyBKyzG1RjrV1AAAAIKTtLS2Xy21IklLj7Dzj1tNxy6gEAABgPYLbQJZQ1XFbRMctAAAArONZmCwlNlKR4Tb9K0RlmbRvm7lPxy0AAAgANr3qsglPx23xTsnttrYWAAAAhKy8InNMgq3n2xZkS4ZbcsZL8Q2srgYAAIDgNqDFNZDkkAyXVJpndTUAAAAIUaG1MFlLyeGwthYAAAAR3Aa28AgpLt3cL8q1thYAAIAQ989//lMtWrRQdHS0+vTpo2+//fao58+YMUPt27dXTEyMmjZtqttuu00HDhyop2p9yxPcsjAZAABA/SG4DXQJmeaW4BYAAMAyc+bM0YQJEzRlyhStWLFC3bt318CBA7Vr167Dnj9r1izdeeedmjJlitatW6cXX3xRc+bM0d/+9rd6rtw3dodCx603uGVhMgAAEBgIbgNdfFVwW0xwCwAAYJXHH39c1157rcaOHatOnTrp2WefVWxsrF566aXDnv/VV1/ptNNO0xVXXKEWLVro3HPP1YgRI47ZpRuovDNuE5wWV+JH+Z5RCXTcAgCAwEBwG+gSGprbop3W1gEAABCiysvL9f3332vAgAHeY2FhYRowYICWLVt22Meceuqp+v77771B7W+//aZ58+ZpyJAhR3ydsrIyFRYWVrsFitDquCW4BQAAgSHC6gJwDAlZ5rYox9o6AAAAQlReXp5cLpcaNmxY7XjDhg31888/H/YxV1xxhfLy8vTHP/5RhmGosrJS119//VFHJUyfPl1Tp071ae2+kldUNePWrsFtZblUkG3upzEqAQAABAY6bgNdfNVfEIrpuAUAAAgWixcv1gMPPKB//etfWrFihd577z19+OGHuu+++474mEmTJmnfvn3e29atW+ux4qPLs3vHbUG2ZLilyNiD198AAAAWo+M20LE4GQAAgKXS09MVHh6unTur/0P6zp07lZmZedjH3HPPPbryyit1zTXXSJK6du2qkpISXXfddbrrrrsUFlazfyIqKkpRUYEXjLrdhvaU2HzG7aFjEhwOa2sBAACoQsdtoPOOSiC4BQAAsILT6VSvXr20aNEi7zG3261Fixapb9++h31MaWlpjXA2PDxckmQYhv+K9YOC/RVyuc2a0+ICL1j2CebbAgCAAETHbaA7dFSCYdABAAAAYIEJEyZo9OjROvnkk3XKKadoxowZKikp0dixYyVJo0aNUuPGjTV9+nRJ0tChQ/X444+rZ8+e6tOnjzZs2KB77rlHQ4cO9Qa4wcIzJiE5NlLOCJv2feRvNLcEtwAAIIAQ3AY6T3DrrpBK86W4NGvrAQAACEGXXXaZdu/ercmTJys3N1c9evTQxx9/7F2wLDs7u1qH7d133y2Hw6G7775b27dvV0ZGhoYOHaq///3vVr2FWvMsTGbb+bYSHbcAACAgEdwGuginFJsmle6RinIIbgEAACwyfvx4jR8//rD3LV68uNrPERERmjJliqZMmVIPlfnXbu/CZDadbysdDG7TWltbBwAAwCFs+l0nm4mvWvSimDm3AAAAqF+77d5x66qQ9m4x9+m4BQAAAYTgNhgkVAW3RTuPfh4AAADgY3nF5ZJsHNwWZEuGS4qIOdgwAQAAEAACJrh98MEH5XA4dOutt1pdSuDxBrc51tYBAACAkONZnCwjwabBbf4mc5vaSgoLmL8eAQAABEZwu3z5cj333HPq1q2b1aUEJs8CZcV03AIAAKB+eYNbu3bc5m80t6ktra0DAADgdywPbouLizVy5Eg9//zzSklJsbqcwJSQZW7puAUAAEA98wS36Qk2XZzMszAZ820BAECAsTy4HTdunM477zwNGDDgmOeWlZWpsLCw2i0kJFR13DLjFgAAAPUsr8jmM249wW1aa2vrAAAA+J0IK1989uzZWrFihZYvX35c50+fPl1Tp071c1UByLNIQnGutXUAAAAgpLjdxsGOW7sGt3s8oxLouAUAAIHFso7brVu36pZbbtEbb7yh6Ojo43rMpEmTtG/fPu9t69atfq4yQHgXJ8uVDMPaWgAAABAy9u2vUKXbvP5Mi7fhqARXpVSwxdwnuAUAAAHGso7b77//Xrt27dJJJ53kPeZyubRkyRI9/fTTKisrU3h4eLXHREVFKSrKpv/SfzSexclc5dL+vVJsqrX1AAAAICR4um0ToyMUFRF+jLOD0L6tkrtSioiWEhpZXQ0AAEA1lgW3Z599ttasWVPt2NixY9WhQwfdcccdNULbkBYZLcWkmKFt8U6CWwAAANSL3VXBbUaCTZsn8qvGJKS0lMIsX/4DAACgGsuC24SEBHXp0qXasbi4OKWlpdU4DplzbvfvlYpypAYdra4GAAAAISCv2O4Lk20yt4xJAAAAAYh/Vg4WCVXjEop2WlsHAAAAQkZeUdXCZHbtuPUsTJZGcAsAAAKPZR23h7N48WKrSwhcCVnmtjjX2joAAAAQMjwzbjNs23H7m7ml4xYAAAQgOm6DhWeBsiKCWwAAANSP3Z6O23inxZX4CcEtAAAIYAS3wSIh09wS3AIAAKCeeDpubTnj1lUp7d1s7qe2trQUAACAwyG4DRae4LaYGbcAAACoH57FyTLsOOO2cJvkrpDCo6TExlZXAwAAUAPBbbCI93Tc5lhbBwAAAEKGrTtuvWMSWkph/LUIAAAEHq5QgkWCZ8btTskwrK0FAAAAtmcYhvZUddym27Hjds9Gc8t8WwAAEKAIboOFp+O2cr90YJ+1tQAAAMD2CvdXqtzlliSlxdlwcbL8TeaW4BYAAAQogttg4YyVopLMfebcAgAAwM92V41JSIiOUHRkuMXV+IF3VALBLQAACEwEt8HEs0BZUa61dQAAAMD2dheZwW2GHefbSlI+oxIAAEBgI7gNJt45twS3AAAA8C9bL0zmdkl7N5v7BLcAACBAEdwGE8+c22KCWwAAAPiXJ7jNsOPCZIXbJVe5FO6UkppYXQ0AAMBhEdwGE++oBGbcAgAAwL8OdtzacGGyPVVjElJaSGE2nN8LAABsgeA2mHiD2xxr6wAAAIDt5RWVS7LpqITC7eY2uZm1dQAAABwFwW0wia+acVtMxy0AAAD8y9txa8dRCWVF5jY6ydo6AAAAjoLgNpgkZJlbFicDAACAn+228+JkZcXm1hlvbR0AAABHQXAbTLyjEghuAQAA4F95RTaecVtWaG6jEqytAwAA4CgIboOJZ1RCRcnBr3cBAAAAPmYYhvKKbTzj1nMtHZVobR0AAABHQXAbTKLiJWdVV0ARc24BAADgH4UHKlXuckuSMuw845aOWwAAEMAIboNNQlXXbVGOtXUAAADAtjwLkyVERSg6MtziavyA4BYAAAQBgttg41mgrJiOWwAAAPiHd76tHbttJam8anGyKBYnAwAAgYvgNtjE03ELAAAA/zo439aGC5NJLE4GAACCAsFtsEnINLdFudbWAQAAANvaXXRAkk0XJpNYnAwAAAQFgttg4wluGZUAAAAAPznYcWv34JaOWwAAELgIboNNPB23AAAA8C/P4mQZdp1xS3ALAACCAMFtsEnwzLgluAUAAIB/eIJbW3bcVpZJLrOjWE4WJwMAAIGL4DbYJGSZW0YlAAAAwE9223lxsrLig/t03AIAgABGcBts4qs6bssKpfISa2sBAACALeUVVXXc2nFUQlmhuY2Mk8LCra0FAADgKAhug01UghQZa+4zLgEAAAA+ZhjGwRm3dhyVwHxbAAAQJAhug43DISVULVDGuAQAAAD4WFFZpcoq3ZJsOuPWG9wy3xYAAAQ2gttgFF8V3BblWFsHAAAAbMczJiHOGa4Ypw1HCZRXzbil4xYAAAQ4gttg5Om4LaLjFgAAAL6VV7UwWYYd59tKjEoAAABBg+A2GHlHJTDjFgAAAL7lmW9ryzEJ0sHFyaISra0DAADgGAhug1F8Q3PL4mQAAADwMfsHt3TcAgCA4EBwG4wSsswtwS0AAAB8zDPjNj3BaXElflJWNePWyeJkAAAgsBHcBqMEOm4BAADgH7urZtzScQsAAGAtgttgFM+MWwAAAPjH7iJGJQAAAAQCgttg5Fmc7MA+qWK/tbUAAADAVuw/49azOBnBLQAACGwEt8EoOkmKiDb3GZcAAAAAH/IEtxkJdg1uPR23idbWAQAAcAwEt8HI4ZDiq+bcFu+0thYAAADYhmEYB4Nbu3bcllctThbF4mQAACCwEdwGq4Qsc0vHLQAAAHykpNylAxVuSVJ6gtPiavyEGbcAACBIENwGq4SqjluCWwAAAPhIXtXCZLHOcMU6Iyyuxk8IbgEAQJAguA1Wno7bYoJbAAAA+IbtFyaTmHELAACCBsFtsPLMuC1ixi0AAAB8Y3eRJ7i16ZgEwzgY3DqZcQsAAAIbwW2wSsg0t0U51tYBAAAA2/AuTJZg047b8hJJhrnPqAQAABDgCG6DlSe4LabjFgAAAL6xu7hcko1HJXi6bR3hUmSMtbUAAAAcA8FtsIr3dNwy4xYAAAC+YfsZt4cuTOZwWFsLAADAMRDcBitPx+3+fKmyzNpaAAAAYAt5nhm3dh2VwMJkAAAgiBDcBquYFCm8atEIxiUAAADAB7wzbu26OFlZobmNYmEyAAAQ+Ahug5XDISVkmfuFO6ytBQAAALaw2+6jEsqLzS0LkwEAgCBAcBvMkpqa233brK0DAAAAtpBXFCKLkxHcAgCAIEBwG8ySmpjbgmxr6wAAAEDQKymr1P4KlyQpw/YzbgluAQBA4IuwugDUQTIdtwAAAPCN0nKXTmqWrOKySsVF2fSvCd4ZtwS3AAAg8Nn0iixEeDpuCW4BAABQRxkJUXrvxtOsLsO/yqpm3DoJbgEAQOBjVEIw8wa3W62tAwAAAAgGjEoAAABBhOA2mCU1M7d03AIAAADHRnALAACCCMFtMEtqbG7LCqUD+6ytBQAAAAh0BLcAACCIENwGM2ecFJNq7hcwLgEAAAA4Km9wG29tHQAAAMeB4DbYJTc1t4xLAAAAAI6u3BPcJlpbBwAAwHEguA12SZ7glo5bAAAA4KgYlQAAAIIIwW2wS2pibgluAQAAgKMjuAUAAEGE4DbYJTEqAQAAADguBLcAACCIENwGO2/HLcEtAAAAcESuCqnygLnvZHEyAAAQ+Ahug52n47aAUQkAAADAEXm6bSU6bgEAQFAguA12yVXBbVGO2UUAAAAAoCZPcBsRI4VHWlsLAADAcSC4DXax6VJ4lCRDKtxhdTUAAABAYGK+LQAACDIEt8EuLExKamzu72NcAgAAAHBYBLcAACDIENzagWfOLQuUAQAAAIdXXmxuo1iYDAAABAeCWztggTIAAADg6MoKzW1UorV1AAAAHCeCWzvwLFDGqAQAAADg8BiVAAAAggzBrR0kNTG3jEoAAAAADo/gFgAABBmCWzvwBrd03AIAAPjLP//5T7Vo0ULR0dHq06ePvv3226OeX1BQoHHjxikrK0tRUVFq166d5s2bV0/VogZPcOtkxi0AAAgOEVYXAB84dHEyw5AcDmvrAQAAsJk5c+ZowoQJevbZZ9WnTx/NmDFDAwcO1C+//KIGDRrUOL+8vFznnHOOGjRooHfeeUeNGzfWli1blJycXP/Fw1TmWZyMjlsAABAcCG7tILGxua0olfbvlWJTra0HAADAZh5//HFde+21Gjt2rCTp2Wef1YcffqiXXnpJd955Z43zX3rpJeXn5+urr75SZGSkJKlFixb1WTJ+z7s4GcEtAAAIDoxKsIPIaCmuqtOjINvaWgAAAGymvLxc33//vQYMGOA9FhYWpgEDBmjZsmWHfcwHH3ygvn37aty4cWrYsKG6dOmiBx54QC6Xq77Kxu95Z9wmWlsHAADAcaLj1i6Sm0olu8xxCY16WF0NAACAbeTl5cnlcqlhw4bVjjds2FA///zzYR/z22+/6dNPP9XIkSM1b948bdiwQTfeeKMqKio0ZcqUwz6mrKxMZWVl3p8LCwt99ybA4mQAACDo0HFrF94FyrZZWwcAAADkdrvVoEED/fvf/1avXr102WWX6a677tKzzz57xMdMnz5dSUlJ3lvTpk3rseIQ4A1uWZwMAAAEB4Jbu/AuULbV2joAAABsJj09XeHh4dq5c2e14zt37lRmZuZhH5OVlaV27dopPDzce6xjx47Kzc1VeXn5YR8zadIk7du3z3vbupXrOp8qZ3EyAAAQXAhu7YLgFgAAwC+cTqd69eqlRYsWeY+53W4tWrRIffv2PexjTjvtNG3YsEFut9t77Ndff1VWVpacTudhHxMVFaXExMRqN/gQoxIAAECQsTS4feaZZ9StWzfvhWnfvn310UcfWVlS8GJUAgAAgN9MmDBBzz//vF555RWtW7dON9xwg0pKSjR27FhJ0qhRozRp0iTv+TfccIPy8/N1yy236Ndff9WHH36oBx54QOPGjbPqLaCsamYwi5MBAIAgYeniZE2aNNGDDz6otm3byjAMvfLKK7rgggv0ww8/qHPnzlaWFnw8wW0BHbcAAAC+dtlll2n37t2aPHmycnNz1aNHD3388cfeBcuys7MVFnawJ6Jp06aaP3++brvtNnXr1k2NGzfWLbfcojvuuMOqtxDaDIOOWwAAEHQchmEYVhdxqNTUVD3yyCO6+uqrj3luYWGhkpKStG/fPr5KVpovPdzS3L9rpxQZbW09AAAAtcQ1nonPwYfKS6QHGpn7k7azQBkAALDMiVzjBcyMW5fLpdmzZ6ukpOSIs8JwFDEpUmSsuV+43dpaAAAAgEBSVrUwmRySM87SUgAAAI6XpaMSJGnNmjXq27evDhw4oPj4eL3//vvq1KnTYc8tKytTWVmZ9+fCwsL6KjPwORzmuIS8X80FytJaW10RAAAAEBi8YxISzetmAACAIGB5x2379u21cuVKffPNN7rhhhs0evRorV279rDnTp8+XUlJSd5b06ZN67naAJdU9XmwQBkAAABwkHdhMubbAgCA4GF5cOt0OtWmTRv16tVL06dPV/fu3fXEE08c9txJkyZp37593tvWrSzEVQ0LlAEAAAA1sTAZAAAIQpaPSvg9t9tdbRzCoaKiohQVFVXPFQUROm4BAACAmsqrZtyyKBkAAAgilga3kyZN0uDBg9WsWTMVFRVp1qxZWrx4sebPn29lWcEr2RPc0nELAAAAeNFxCwAAgpClwe2uXbs0atQo5eTkKCkpSd26ddP8+fN1zjnnWFlW8PKMSiC4BQAAAA4iuAUAAEHI0uD2xRdftPLl7ccb3G6X3G4pzPIRxgAAAJZp0aKFrrrqKo0ZM0bNmjWzuhxYicXJAABAECLZs5PExpIckqtMKs2zuhoAAABL3XrrrXrvvffUqlUrnXPOOZo9e/YR11KAzXk6bp0EtwAAIHgQ3NpJeKSUkGXuFzAuAQAAhLZbb71VK1eu1LfffquOHTvqpptuUlZWlsaPH68VK1ZYXR7qU5lncTKCWwAAEDwIbu2GObcAAADVnHTSSXryySe1Y8cOTZkyRS+88IJ69+6tHj166KWXXpJhGFaXCH9jxi0AAAhCBLd2k9zU3O7bZm0dAAAAAaKiokJvvfWW/vSnP+n222/XySefrBdeeEEXXXSR/va3v2nkyJFWlwh/I7gFAABByNLFyeAHdNwCAABIklasWKGXX35Zb775psLCwjRq1Cj94x//UIcOHbznDB8+XL1797awStQLFicDAABBiODWbpLouAUAAJCk3r1765xzztEzzzyjYcOGKTIyssY5LVu21OWXX25BdahXdNwCAIAgRHBrN97glo5bAAAQ2n777Tc1b978qOfExcXp5ZdfrqeKYJlyFicDAADBhxm3duMZlVBAcAsAAELbrl279M0339Q4/s033+i7776zoCJYho5bAAAQhAhu7cazONn+fKm8xNpaAAAALDRu3Dht3VrzH7O3b9+ucePGWVARLENwCwAAghDBrd1EJ0lRieb+vu3W1gIAAGChtWvX6qSTTqpxvGfPnlq7dq0FFcESrkqpotTc91wnAwAABAGCWzvyjEvYl21tHQAAABaKiorSzp07axzPyclRRARLPYQMz3xbSXLGW1cHAADACSK4tSPvAmXbrK0DAADAQueee64mTZqkffv2eY8VFBTob3/7m8455xwLK0O98oxJCI+SIpzW1gIAAHACaDWwIxYoAwAA0KOPPqrTTz9dzZs3V8+ePSVJK1euVMOGDfXaa69ZXB3qDfNtAQBAkCK4tSPvqAQ6bgEAQOhq3LixVq9erTfeeEOrVq1STEyMxo4dqxEjRigyMtLq8lBfCG4BAECQIri1o+Rm5pbgFgAAhLi4uDhdd911VpcBK3mDW+bbAgCA4EJwa0csTgYAAOC1du1aZWdnq7y8vNrxP/3pTxZVhHpV7gluE62tAwAA4AQR3NqRJ7gt3CG5XVJYuLX1AAAAWOC3337T8OHDtWbNGjkcDhmGIUlyOBySJJfLZWV5qC+MSgAAAEEqrDYP2rp1q7ZtO/g1/G+//Va33nqr/v3vf/usMNRBQpbkCJfclVLxTqurAQAAsMQtt9yili1bateuXYqNjdVPP/2kJUuW6OSTT9bixYutLg/1heAWAAAEqVoFt1dccYU+++wzSVJubq7OOeccffvtt7rrrrs0bdo0nxaIWggLlxIbm/sFW62tBQAAwCLLli3TtGnTlJ6errCwMIWFhemPf/yjpk+frptvvtnq8lBfCG4BAECQqlVw++OPP+qUU06RJL311lvq0qWLvvrqK73xxhuaOXOmL+tDbXnn3BLcAgCA0ORyuZSQYIZ16enp2rFjhySpefPm+uWXX6wsDfXJE9w6WZwMAAAEl1rNuK2oqFBUVJQkaeHChd6FHTp06KCcnBzfVYfaS24qZUvat+2YpwIAANhRly5dtGrVKrVs2VJ9+vTRww8/LKfTqX//+99q1aqV1eWhvpSxOBkAAAhOteq47dy5s5599ll98cUXWrBggQYNGiRJ2rFjh9LS0nxaIGqJjlsAABDi7r77brndbknStGnTtGnTJvXr10/z5s3Tk08+aXF1qDeMSgAAAEGqVh23Dz30kIYPH65HHnlEo0ePVvfu3SVJH3zwgXeEAizmDW7puAUAAKFp4MCB3v02bdro559/Vn5+vlJSUuRwOCysDPWK4BYAAASpWgW3Z5xxhvLy8lRYWKiUlBTv8euuu06xsbE+Kw51kNTM3BLcAgCAEFRRUaGYmBitXLlSXbp08R5PTU21sCpYguAWAAAEqVqNSti/f7/Kysq8oe2WLVs0Y8YM/fLLL2rQoIFPC0QteTpuCxiVAAAAQk9kZKSaNWsml8tldSmwmje4ZXEyAAAQXGoV3F5wwQV69dVXJUkFBQXq06ePHnvsMQ0bNkzPPPOMTwtELXmC27J90oF91tYCAABggbvuukt/+9vflJ+fb3UpsFI5i5MBAIDgVKvgdsWKFerXr58k6Z133lHDhg21ZcsWvfrqqyz0ECii4qWYqjEW+7ZbWwsAAIAFnn76aS1ZskSNGjVS+/btddJJJ1W7IUQwKgEAAASpWs24LS0tVUKCeeHzySef6MILL1RYWJj+8Ic/aMuWLT4tEHWQ1ETav1fat1Vq2MnqagAAAOrVsGHDrC4BVjMMglsAABC0ahXctmnTRnPnztXw4cM1f/583XbbbZKkXbt2KTGRryAFjKSmUu4aM7gFAAAIMVOmTLG6BFit8oDkrjT3ncy4BQAAwaVWoxImT56siRMnqkWLFjrllFPUt29fSWb3bc+ePX1aIOogqam5ZYEyAAAAhKKy4oP7BLcAACDI1Krj9uKLL9Yf//hH5eTkqHv37t7jZ599toYPH+6z4lBHngXK9m2ztg4AAAALhIWFyeFwHPF+l8tVj9XAEmWF5taZIIXVqmcFAADAMrUKbiUpMzNTmZmZ2rbNDAWbNGmiU045xWeFwQcIbgEAQAh7//33q/1cUVGhH374Qa+88oqmTp1qUVWoV8y3BQAAQaxWwa3b7db999+vxx57TMXF5tePEhISdPvtt+uuu+5SGP+aHRiSm5lbZtwCAIAQdMEFF9Q4dvHFF6tz586aM2eOrr76aguqQr0iuAUAAEGsVsHtXXfdpRdffFEPPvigTjvtNEnSl19+qXvvvVcHDhzQ3//+d58WiVrydNwW5UiuCik80tp6AAAAAsAf/vAHXXfddVaXgfrgDW6ZbwsAAIJPrYLbV155RS+88IL+9Kc/eY9169ZNjRs31o033khwGyjiGkjhTslVboa3ng5cAACAELV//349+eSTaty4sdWloD6UVy1ORsctAAAIQrUKbvPz89WhQ4caxzt06KD8/Pw6FwUfCQuTEhtLezdJBVsJbgEAQEhJSUmptjiZYRgqKipSbGysXn/9dQsrQ73xLE5GcAsAAIJQrYLb7t276+mnn9aTTz5Z7fjTTz+tbt26+aQw+EhSEzO4ZYEyAAAQYv7xj39UC27DwsKUkZGhPn36KCUlxcLKUG+8oxISra0DAACgFmoV3D788MM677zztHDhQvXt21eStGzZMm3dulXz5s3zaYGoIxYoAwAAIWrMmDFWlwCrsTgZAAAIYmG1eVD//v3166+/avjw4SooKFBBQYEuvPBC/fTTT3rttdd8XSPqwrNAGcEtAAAIMS+//LLefvvtGsfffvttvfLKKxZUhHrnCW6dLE4GAACCT62CW0lq1KiR/v73v+vdd9/Vu+++q/vvv1979+7Viy++6Mv6UFfe4JZRCQAAILRMnz5d6enpNY43aNBADzzwgAUVod6VsTgZAAAIXrUObhEkkpqaW4JbAAAQYrKzs9WyZcsax5s3b67s7GwLKkK9Y3EyAAAQxAhu7c4T3BZslQzD2loAAADqUYMGDbR69eoax1etWqW0tDQLKkK9Y3EyAAAQxAhu7S6psbmtKJH277W2FgAAgHo0YsQI3Xzzzfrss8/kcrnkcrn06aef6pZbbtHll19udXmoD97glhm3AAAg+EScyMkXXnjhUe8vKCioSy3wh8gYKS5DKtltjkuITbW6IgAAgHpx3333afPmzTr77LMVEWFe9rrdbo0aNYoZt6HCG9wyKgEAAASfEwpuk5KSjnn/qFGj6lQQ/CCpSVVwu1XK6mZ1NQAAAPXC6XRqzpw5uv/++7Vy5UrFxMSoa9euat68udWlob6UszgZAAAIXicU3L788sv+qgP+lNRE2vEDC5QBAICQ1LZtW7Vt29bqMmAFOm4BAEAQY8ZtKEhqZm4LWD0ZAACEjosuukgPPfRQjeMPP/ywLrnkEgsqQr1yuw7puGVxMgAAEHwIbkNBUhNzS8ctAAAIIUuWLNGQIUNqHB88eLCWLFliQUWoV57QVpKcLE4GAACCD8FtKCC4BQAAIai4uFhOp7PG8cjISBUWFlpQEepVWVVwGxYpRURZWwsAAEAtENyGguSm5nbfVmvrAAAAqEddu3bVnDlzahyfPXu2OnXqZEFFqFeHzrd1OKytBQAAoBZOaHEyBKmkquC2eKdUWUbHAQAACAn33HOPLrzwQm3cuFFnnXWWJGnRokWaNWuW3nnnHYurg9+xMBkAAAhyBLehIDZNioiRKvdLhdul1FZWVwQAAOB3Q4cO1dy5c/XAAw/onXfeUUxMjLp3765PP/1UqampVpcHfyurGofBwmQAACBIMSohFDgcB+fcFjAuAQAAhI7zzjtPS5cuVUlJiX777Tddeumlmjhxorp37251afA3b8ctC5MBAIDgRHAbKligDAAAhKglS5Zo9OjRatSokR577DGdddZZ+vrrr60uC/5WXrU4GaMSAABAkGJUQqgguAUAACEkNzdXM2fO1IsvvqjCwkJdeumlKisr09y5c1mYLFQw4xYAAAQ5Om5DRXIzc7sv29o6AAAA/Gzo0KFq3769Vq9erRkzZmjHjh166qmnrC4L9Y3gFgAABDk6bkMFHbcAACBEfPTRR7r55pt1ww03qG3btlaXA6t4FidzMuMWAAAEJzpuQwXBLQAACBFffvmlioqK1KtXL/Xp00dPP/208vLyrC4L9c3bcZtobR0AAAC1RHAbKpKamtuCrZLbbW0tAAAAfvSHP/xBzz//vHJycvTnP/9Zs2fPVqNGjeR2u7VgwQIVFRVZXSLqQxmLkwEAgOBGcBsqEhtLckiuMqlkt9XVAAAA+F1cXJyuuuoqffnll1qzZo1uv/12Pfjgg2rQoIH+9Kc/WV0e/I0ZtwAAIMgR3IaKCKeUkGXu79tqbS0AAAD1rH379nr44Ye1bds2vfnmm1aXg/pAcAsAAIIcwW0oSfaMS8i2tg4AAACLhIeHa9iwYfrggw+sLgX+5g1uWZwMAAAEJ4LbUJLczNzScQsAAAC7K2dxMgAAENwIbkPJoQuUAQAAAHbGqAQAABDkCG5DCaMSAAAAECoIbgEAQJAjuA0lSYxKAAAAQAioLJNc5eY+wS0AAAhSBLehJPmQUQmGYW0tAAAAgL94um0lycniZAAAIDgR3IYSz4zb8iLpQIGlpQAAAAB+4wluI+OksHBrawEAAKglgttQ4oyVYtPNfRYoAwAAgF0x3xYAANgAwW2o8YxLYM4tAAAA7IrgFgAA2ADBbajxjEsoyLa2DgAAAMBfvMEt820BAEDwIrgNNcnNzC2jEgAAAGBXdNwCAAAbILgNNZ7gdh8dtwAAALCpck9wm2htHQAAAHVAcBtqvKMS6LgFAACATdFxCwAAbIDgNtSwOBkAAECt/POf/1SLFi0UHR2tPn366Ntvvz2ux82ePVsOh0PDhg3zb4E4iOAWAADYAMFtqPF03JbukcpLrK0FAAAgSMyZM0cTJkzQlClTtGLFCnXv3l0DBw7Url27jvq4zZs3a+LEierXr189VQpJB4NbJ4uTAQCA4EVwG2pikg/O+mJcAgAAwHF5/PHHde2112rs2LHq1KmTnn32WcXGxuqll1464mNcLpdGjhypqVOnqlWrVvVYLei4BQAAdmBpcDt9+nT17t1bCQkJatCggYYNG6ZffvnFypJCQxLjEgAAAI5XeXm5vv/+ew0YMMB7LCwsTAMGDNCyZcuO+Lhp06apQYMGuvrqq+ujTByK4BYAANiApcHt559/rnHjxunrr7/WggULVFFRoXPPPVclJXyF36+Sm5nbgmxr6wAAAAgCeXl5crlcatiwYbXjDRs2VG5u7mEf8+WXX+rFF1/U888/f9yvU1ZWpsLCwmo31JI3uE20tg4AAIA6iLDyxT/++ONqP8+cOVMNGjTQ999/r9NPP92iqkIAC5QBAAD4TVFRka688ko9//zzSk9PP+7HTZ8+XVOnTvVjZSGEjlsAAGADlga3v7dv3z5JUmpqqsWV2JxnVAIdtwAAAMeUnp6u8PBw7dy5s9rxnTt3KjMzs8b5Gzdu1ObNmzV06FDvMbfbLUmKiIjQL7/8otatW9d43KRJkzRhwgTvz4WFhWratKmv3kZo8Qa3LE4GAACCV8AEt263W7feeqtOO+00denS5bDnlJWVqayszPszXx+rJU/HLYuTAQAAHJPT6VSvXr20aNEiDRs2TJJ57bpo0SKNHz++xvkdOnTQmjVrqh27++67VVRUpCeeeOKIYWxUVJSioqJ8Xn9IKi82t3TcAgCAIBYwwe24ceP0448/6ssvvzziOXx9zEeSqmbcMioBAADguEyYMEGjR4/WySefrFNOOUUzZsxQSUmJxo4dK0kaNWqUGjdurOnTpys6OrpGI0JycrIkHbFBAT7GjFsAAGADARHcjh8/Xv/73/+0ZMkSNWnS5Ijn8fUxH/EsTlaUK1WWSxFOa+sBAAAIcJdddpl2796tyZMnKzc3Vz169NDHH3/sXbAsOztbYWGWrvsLD7ebGbcAAMAWLA1uDcPQTTfdpPfff1+LFy9Wy5Ytj3o+Xx/zkbh0KSJGqtwvFW6TUltZXREAAEDAGz9+/GFHI0jS4sWLj/rYmTNn+r4gHF5FiSTD3Hcy4xYAAAQvS9sCxo0bp9dff12zZs1SQkKCcnNzlZubq/3791tZlv05HFJSVWczc24BAABgJ55uW0e4FBljbS0AAAB1YGlw+8wzz2jfvn0644wzlJWV5b3NmTPHyrJCg3eBsmxr6wAAAAB8qeyQhckcDmtrAQAAqAPLRyXAIklVwS0LlAEAAMBOWJgMAADYBCsohCrPAmWMSgAAAICdlBWaWxYmAwAAQY7gNlR5gls6bgEAAGAn3o5bFiYDAADBjeA2VCUx4xYAAAA25A1u6bgFAADBjeD2BGzbW6rrXv1O1776ndWl1J1ncbLC7ZLbZW0tAAAAgK+UH7I4GQAAQBCzdHGyYONwOPTJ2p1yhofJMAw5gnmV2oQsKSxCcldKRTlSUhOrKwIAAADqjhm3AADAJui4PQFpcU5JUrnLraKySourqaOwcCmxsbnPAmUAAACwC++ohERr6wAAAKgjgtsTEB0Zrvgos0k5r6jM4mp8gAXKAAAAYDee4NbJ4mQAACC4EdyeoPR4s+t2T0m5xZX4AAuUAQAAwG7KmHELAADsgeD2BKXFR0mS9hTboeOW4BYAAAA24x2VQHALAACCG8HtCfLMuc0rtlHHLaMSAAAAYBcEtwAAwCYIbk/QwY5bGwS3nhm3LE4GAAAAuygrNLcEtwAAIMgR3J6ggzNubTQqYd82yTCsrQUAAADwBTpuAQCATRDcniDPqARbdNwmNpHkkCr3SyV5VlcDAAAA1F05i5MBAAB7ILg9QZ5RCbvtsDhZhFNKyDT397FAGQAAAGyAjlsAAGATBLcnKM0zKsEOwa10cIGyAoJbAAAABLnKcqnygLlPcAsAAIIcwe0JyvAsTlZig1EJEguUAQAAwD48YxIkyUlwCwAAghvB7QnyjEooKK1QhcttcTU+4F2gjOAWAAAAQa6s0NxGxEjhEdbWAgAAUEcEtycoOSZSYQ5zf68dum69oxIIbgEAABDkyliYDAAA2AfB7QkKC3MoNc7sus0rtkFw6xmVQMctAAAAgh0LkwEAABshuK2FdM8CZSU2WKCMxckAAABgFwS3AADARghuayHNE9zaouO2KrgtK5T2F1haCgAAAFAnnhm3BLcAAMAGCG5rIc07KsEGHbfOOCk2zdxnXAIAAACCGR23AADARghua8HTcWuLGbcSC5QBAADAHspZnAwAANgHwW0tpMebHbd77NBxKx0cl0DHLQAAAIIZHbcAAMBGCG5r4eDiZHbpuG1mblmgDAAAAMHME9w6462tAwAAwAcIbmvBM+PWdh23BLcAAAAIZixOBgAAbITgthZsN+M2uarjllEJAAAACGZlnhm3idbWAQAA4AMEt7XgnXFbUibDMCyuxgdYnAwAAAB2wIxbAABgIwS3teDpuD1Q4VZJucvianzAMyqhNE8qL7W2FgAAAKC2CG4BAICNENzWQqwzQjGR4ZJsMuc2OllyVl3cMi4BAAAAwcob3LI4GQAACH4Et7Vkqzm3DschC5QR3AIAACBI0XELAABshOC2lrxzbu3QcSsdskBZtrV1AAAAALVV7gluWZwMAAAEP4LbWkqv6rjdU2KDjluJBcoAAAAQ3AyDjlsAAGArBLe1lBZnt47bquCWGbcAAAAIRhWlkuE29wluAQCADRDc1pKtZtxKdNwCAAAguHm6bR1hUmSstbUAAAD4AMFtLaV5ZtzaZVSCZ8ZtATNuAQAAEIQ8wa0zwVx8FwAAIMgR3NaSZ8ZtXpFdRiVUBbdFOVKlTcJoAAAAhA7m2wIAAJshuK0l74zbEpsEt3EZUkS0JEMq3G51NQAAAMCJIbgFAAA2Q3BbS54Zt3vsMuPW4ZCSmpj7LFAGAACAYOMNbuOtrQMAAMBHCG5rKb1qxm1+ablcbsPianyEBcoAAAAQrOi4BQAANkNwW0spsZFyOCTDkPaW2qTrNtkT3LJAGQAAAIIMwS0AALAZgttaiggPU0qszcYleBYoY1QCAAAAgk05wS0AALAXgts6SIvzBLc2WaAsqSq4peMWAAAAwcbbcZtobR0AAAA+QnBbB54FynbbJbj1jEqg4xYAAADBxhPcOlmcDAAA2APBbR2kVS1QZptRCZ7FyfZtl9wua2sBAAAATgQzbgEAgM0Q3NZBumdUQolNOm4TsiRHuOSukIpyra4GAAAAOH5lxeaW4BYAANgEwW0d2K7jNjxCSmps7jMuAQAAAMGkrNDcEtwCAACbILitg/Sq4DbPLsGtdMgCZQS3AAAACCIsTgYAAGyG4LYOPIuT2WZUgnTIAmXZ1tYBAAAAnAhvcMviZAAAwB4Ibusg3RPc2qrjtiq4peMWAAAAwYTFyQAAgM0Q3NZBWpxnxq0dO24JbgEAABBEylmcDAAA2AvBbR14RiWUlLu0v9xlcTU+kuyZccuoBAAAAAQJV6VUUWruM+MWAADYBMFtHcRHRcgZYX6EeXbpuj10VIJhWFsLAAAAcDzKiw7uO5lxCwAA7IHgtg4cDofS4zwLlNlkzm1SE3NbuV8q3WNtLQAAAMDx2L/X3EbESBFOa2sBAADwEYLbOkqLt9mc24goKT7T3GdcAgAAAIJBUa65Tci0tg4AAAAfIrito/SqObd7im3ScSuxQBkAAACCS+EOc5vYyNo6AAAAfIjgto48Hbd5JTbpuJVYoAwAAADBpSjH3CZkWVsHAACADxHc1lGaHTtuD12gDAAAAAh0jEoAAAA2RHBbR+lxNptxKzEqAQAAAMGFUQkAAMCGCG7ryNNxm2erjlvPqASCWwAAAAQBRiUAAAAbIritI++MW1t23DLjFgAAAEGA4BYAANgQwW0dpcVVzbgtsVPHbVVwe2CfeQMAAAAClWFIhVXBbSLBLQAAsA+C2zpKr+q4zS8pl9ttWFyNj0TFSzGp5j7jEgAAABDI9u+VXFXffqPjFgAA2AjBbR2lVnXcutyG9u2vsLgaH2KBMgAAAAQDz5iEmFQpIsraWgAAAHyI4LaOnBFhSoqJlCTtKbHRnFvPuAQ6bgEAABDIvGMSGllbBwAAgI8R3PpAWrzZdZtXbKM5t8nNzC0LlAEAACCQFe0wt4xJAAAANkNw6wPpceZXsvKKbdRxm9zc3OZvsrYOAAAA4GiKcs1tQqa1dQAAAPgYwa0PeDpu99ip47ZhZ3Obs8raOgAAAICjKazquGVUAgAAsBmCWx84GNzaqOM2q5u53bdVKtljbS0AAADAkXgWJ2NUAgAAsBmCWx9I84xKKLFRx210kpTa2tzPWWlpKQAAAMAREdwCAACbIrj1gXQ7dtxKUlZ3c0twCwAAgEBVWBXcJhLcAgAAeyG49YH0eLPj1lYzbiWpUQ9zu2OllVUAAAAAh+eqkEp2m/sJzLgFAAD2QnDrA2me4NZOoxIkKauHuWWBMgAAAASi4p2SDCksUopNs7oaAAAAnyK49QHP4mR5dh2VULBFKs23thYAAADg9zxjEhIypTD+agMAAOyFqxsfSK9anKzoQKUOVLgsrsaHYpKllBbmPl23AAAACDQsTAYAAGyM4NYHEmMiFBHmkCTlMy4BAAAAqB9Fh3TcAgAA2AzBrQ84HA7vuATbLlCWs9LKKgAAAICaCneY20QWJgMAAPZjaXC7ZMkSDR06VI0aNZLD4dDcuXOtLKdO0qrGJeSV2HTO7Y6VlpYBAAAA1FCUa27puAUAADZkaXBbUlKi7t2765///KeVZfiEbTtuPaMS9m6S9hdYWQkAAABQXVFVx20CHbcAAMB+Iqx88cGDB2vw4MFWluAzGfFmx+2eYpt13MamSsnNpIJsKXe11PJ0qysCAAAATIVVM24TWZwMAADYDzNufcTbcWu3xckkxiUAAABI+uc//6kWLVooOjpaffr00bfffnvEc59//nn169dPKSkpSklJ0YABA456PmrJOyqB4BYAANhPUAW3ZWVlKiwsrHYLFGlVHbd5RTbruJUOjktggTIAABCi5syZowkTJmjKlClasWKFunfvroEDB2rXrl2HPX/x4sUaMWKEPvvsMy1btkxNmzbVueeeq+3bt9dz5TZWViSVF5n7BLcAAMCGgiq4nT59upKSkry3pk2bWl2SV1qc2XGbZ8eO20Y9zG3OKkvLAAAAsMrjjz+ua6+9VmPHjlWnTp307LPPKjY2Vi+99NJhz3/jjTd04403qkePHurQoYNeeOEFud1uLVq0qJ4rtzHPmISoRCkq3tpaAAAA/CCogttJkyZp37593tvWrVutLskr3a4zbqWDHbd7NkgHAqfLGQAAoD6Ul5fr+++/14ABA7zHwsLCNGDAAC1btuy4nqO0tFQVFRVKTU094jmB/O2ygFRUFdwmZFpbBwAAgJ8EVXAbFRWlxMTEardA4Z1xW2zDjtu4dCmxibmfu9raWgAAAOpZXl6eXC6XGjZsWO14w4YNlZube1zPcccdd6hRo0bVwt/fC+RvlwUkb3DLmAQAAGBPlga3xcXFWrlypVauXClJ2rRpk1auXKns7Gwry6oVz4zbPSVlMgzD4mr8gHEJAAAAtfLggw9q9uzZev/99xUdHX3E8wL522UBqXCHuU1sZG0dAAAAfhJh5Yt/9913OvPMM70/T5gwQZI0evRozZw506Kqascz47bCZajwQKWSYiItrsjHsnpIP/9P2rHS6koAAADqVXp6usLDw7Vz585qx3fu3KnMzKN/Tf/RRx/Vgw8+qIULF6pbt25HPTcqKkpRUVF1rjdkFFV1OzMqAQAA2JSlHbdnnHGGDMOocQu20FaSoiPDlRBl5uD2nHPb3dzmrLS0DAAAgPrmdDrVq1evaguLeRYa69u37xEf9/DDD+u+++7Txx9/rJNPPrk+Sg0tRVUdtwl03AIAAHsKqhm3gc4757bEhnNuPaMS8tZLZUWWlgIAAFDfJkyYoOeff16vvPKK1q1bpxtuuEElJSUaO3asJGnUqFGaNGmS9/yHHnpI99xzj1566SW1aNFCubm5ys3NVXFxsVVvwX4Kq2bcJjLjFgAA2JOloxLsJi0+Spv3lCqvyIYdt/ENzG6Goh1S7o9S8yN3lwAAANjNZZddpt27d2vy5MnKzc1Vjx499PHHH3sXLMvOzlZY2MGeiGeeeUbl5eW6+OKLqz3PlClTdO+999Zn6fblHZVAcAsAAOyJ4NaHPHNu8+zYcSuZ4xKKdpjjEghuAQBAiBk/frzGjx9/2PsWL15c7efNmzf7v6BQ5nZLxQS3AADA3hiV4ENp8eZiEraccSsdHJfAAmUAAACwUmme5K6U5JDiG1pdDQAAgF8Q3PpQumfGbbFdO257mNucVZaWAQAAgBBXWLUwWXwDKZwvEQIAAHsiuPUhz6iEPSU27bjN6m5u836RykusrQUAAAChq6hqYTLGJAAAABsjuPUhz6iEPLt23CZmmV9FM9zmAmUAAACAFTzBbWIja+sAAADwI4JbH0q3+4xb6ZBxCSutrAIAAAChrNDTcZtpbR0AAAB+RHDrQ54Zt7btuJUOLlDGnFsAAABYpahqxm0CHbcAAMC+CG59yDMqYd/+CpVXui2uxk88c253rLS0DAAAAISwolxzm8iMWwAAYF8Etz6UHBOpMIe5v7fUpl23nlEJu3+WKvZbWgoAAABCFKMSAABACCC49aGwMIdS4zwLlNl0zm1iIykuQzJc0s6frK4GAAAAoYhRCQAAIAQQ3PqYZ87tHrvOuXU4DhmX8IO1tQAAACD0VByQ9u819xmVAAAAbIzg1sfSPMFtiU07bqWD4xJyVlpZBQAAAEJRUdWYhIhoKTrZ0lIAAAD8ieDWx9KqRiXYtuNWkhr1MLc7VllaBgAAAEKQJ7hNyDK/DQYAAGBTBLc+lh7vmXFr4+DWMyph9zrzq2oAAABAfTk0uAUAALAxglsf84xKsO3iZJKU1FSKSZXcldIuFigDAABAPSqsCm6ZbwsAAGyO4NbHDi5OZuPg1uE4ZFzCSisrAQAAQKih4xYAAIQIglsf8864LbHxqATpkAXKmHMLAACAekRwCwAAQgTBrY+leTtu7R7cVs25zVlpaRkAAAAIMYxKAAAAIYLg1scOLk5WJsMwLK7GjzyjEnaulSptPBYCAAAAgcXbcdvI2joAAAD8jODWxzwdt2WVbpWUuyyuxo+Sm0vRyZK7Qtq1zupqAAAAEAoM45DgNtPaWgAAAPyM4NbHYp0RiokMlxQCC5QxLgEAAAD1af9eqfKAuc+MWwAAYHMEt36QnmB23ebZfc6tZ1zCjpVWVgEAAIBQUZRrbmNSpchoa2sBAADwM4JbP0iLOzjn1tayephbOm4BAABQH4p2mFu6bQEAQAgguPWD9Ko5t3vs3nHrGZWw8yfJVWFtLQAAALC/wqr5tokEtwAAwP4Ibv3A03Fr6xm3kpTaSopKklzlLFAGAAAA//OMSqDjFgAAhACCWz9I83Tclti849bhkLK6mfuMSwAAAIC/MSoBAACEEIJbP0iLD5EZt9LBBcpyVllaBgAAAEIAoxIAAEAIIbj1g5CZcSsdXKBsx0orqwAAAEAoKKoKbhMaWVsHAABAPSC49QPvjNuSEOi49QS3O3+UXJWWlgIAAACb8wa3mdbWAQAAUA8Ibv0gPcHsuM0LhY7b1FaSM0GqPCDt/tnqagAAAGBXrgqpeJe5n0jHLQAAsD+CWz/wdNzuLS1XpcttcTV+FhZ2yAJlzLkFAACAnxTvkmRIYRFSbLrV1QAAAPgdwa0fpMRGyuGQDEPaW1phdTn+5xmXkLPSyioAAABgZ54xCfGZZvMAAACAzXHF4wcR4WFKia1aoCwU5tw26mFuWaAMAAAA/lK4w9wmZllbBwAAQD0huPWTtLiq4DYU5txmdTe3uWskt8vaWgAAAGBPRbnmNoHgFgAAhAaCWz9Ji/csUBYCHbdpbaTIOKlyv5T3q9XVAAAAwI6KqjpuCW4BAECIILj1k7R4c4GykOi4DQs/uEDZlq+srQUAAAD25Om4ZVQCAAAIEQS3fpIeF0IzbiWp/RBzu/wFc1U2AAAAwJc8M24TGllbBwAAQD0huPWT9KqO27yiEOi4laSTRknOeGnXWmnjp1ZXAwAAALspyjG3CZnW1gEAAFBPCG79xDsqIVQ6bmOSpZ5XmvvL/mlpKQAAALAh76gEOm4BAEBoILj1k4OLk4VIx60k/eF6yREmbVwk7VxrdTUAAACwi7JiqazQ3KfjFgAAhAiCWz9Jjw+xGbeSlNJC6jjU3P+arlsAAAD4iGdMgjNBikqwthYAAIB6QnDrJ2lxVaMSQqnjVpL6jje3q9+SindZWwsAAADswRPcJmZZWwcAAEA9Irj1E8+ohNJyl0rLKy2uph41PUVq0ltylUvLX7C6GgAAANhBIQuTAQCA0ENw6yfxURFyRpgfb+h13Y4zt8tfkCr2W1sLAAAAgl/RDnObwMJkAAAgdBDc+onD4VB6nGeBshCacytJHYZKyc2k0j3SqtlWVwMAAIBgV5RrbhmVAAAAQgjBrR+lJ4TonNvwCKnPDeb+1/+S3G5r6wEAAEBwK/R03BLcAgCA0EFw60dpVR23e0pCrONWknr+nxSVKOX9Km1YaHU1AAAACGaexckIbgEAQAghuPWjtHiz4zYv1DpuJSk6Ueo12txf9pS1tQAAACC4eUclMOMWAACEDoJbP0qLr+q4DcXgVpJO+bPkCJc2LZFyVltdDQAAAIKR231Ix22mtbUAAADUowirC7Cz9Diz43Z3qC1O5pHcVOo8TPrxXXPW7fBnra4IAAAAwaY0T3JXSnJI8Q2trgYA4CMul0sVFRVWlwH4XGRkpMLDw33yXAS3ftS6QZwk6Yv1u1VaXqlYZwh+3H3HmcHtmneks6ewEjAAAABOjKfbNr6BFB5pbS0AgDozDEO5ubkqKCiwuhTAb5KTk5WZmSmHw1Gn5wnBJLH+9G/XQM1SY5WdX6q3lm/VmNNaWl1S/WvcS2p2qpT9lfTtv6UBU6yuCAAAAMGkkDEJAGAnntC2QYMGio2NrXOwBQQSwzBUWlqqXbt2SZKysurWwEhw60fhYQ5de3or3TP3Rz3/xSaN/ENzRYaH4FjhvuPM4Pa7l6TTJ0rOOKsrAgAAQLDwzrdlYTIACHYul8sb2qalpVldDuAXMTExkqRdu3apQYMGdRqbEIIpYv26pFcTpcU5tb1gv+atybG6HGu0HyyltJQOFEgrZ1ldDQAAAIKJJ7hl5BYABD3PTNvY2FiLKwH8y/M7Xtc5zgS3fhYdGa4xp7aQJD37+W8yDMPagqwQFi794UZz/+t/mSsDAwAAAMejcIe5TSC4BQC7YDwC7M5Xv+MEt/Xgyr7NFesM17qcQi1Zn2d1OdboOVKKTpbyf5N+/cjqagAAABAsinLNLcEtAMBmWrRooRkzZhz3+YsXL5bD4WBhtxBCcFsPkmOdurx3M0nSc59vtLgaizjjpJPHmvvL/mltLQAAAAgejEoAAFjM4XAc9XbvvffW6nmXL1+u66677rjPP/XUU5WTk6OkpKRavV5tdOjQQVFRUcrNza2318RBBLf15Op+LRUe5tBXG/do9bYCq8uxxinXSWER0pal0vYVVlcDAACAYMCoBACAxXJycry3GTNmKDExsdqxiRMnes81DEOVlZXH9bwZGRknNO/X6XQqMzOz3kZNfPnll9q/f78uvvhivfLKK/XymkdT13mxwYjgtp40To7Rn7qbK+E+t+Q3i6uxSGIjqcvF5j5dtwAAADiWyjJpf765T3ALALBIZmam95aUlCSHw+H9+eeff1ZCQoI++ugj9erVS1FRUfryyy+1ceNGXXDBBWrYsKHi4+PVu3dvLVy4sNrz/n5UgsPh0AsvvKDhw4crNjZWbdu21QcffOC9//ejEmbOnKnk5GTNnz9fHTt2VHx8vAYNGqScnBzvYyorK3XzzTcrOTlZaWlpuuOOOzR69GgNGzbsmO/7xRdf1BVXXKErr7xSL730Uo37t23bphEjRig1NVVxcXE6+eST9c0333jv/+9//6vevXsrOjpa6enpGj58eLX3Onfu3GrPl5ycrJkzZ0qSNm/eLIfDoTlz5qh///6Kjo7WG2+8oT179mjEiBFq3LixYmNj1bVrV7355pvVnsftduvhhx9WmzZtFBUVpWbNmunvf/+7JOmss87S+PHjq52/e/duOZ1OLVq06JifSX0juK1Hf+7fSpL00ZocbdlTYnE1FulbtUjZT+9L+7ZZWwsAAAACm2dMQkS0FJNibS0AAL8wDEOl5ZWW3Hy5gPydd96pBx98UOvWrVO3bt1UXFysIUOGaNGiRfrhhx80aNAgDR06VNnZ2Ud9nqlTp+rSSy/V6tWrNWTIEI0cOVL5+flHPL+0tFSPPvqoXnvtNS1ZskTZ2dnVOoAfeugh/X97dx4XVbn/AfxzZtiGHRJZ3BdSMoQEMTSXxELtWphekVzArbqJL43rT6Ncs9Kuy8UtvXkV63aR5N4wu5VGZORFVNLwYuFWuC9kKZuyzZzfHwcOMzAgKHJmxs/79Xpe55znnDnnO+cZ8PHLM8/55z//icTERGRmZqKoqKhewtSY4uJipKSkYOLEiXjqqadQWFiI/fv3y/tLSkowePBgXLp0Cbt378axY8cwb9486KofSP/5559j9OjRGDlyJH744Qekp6cjJCTkjtet67XXXsPs2bORl5eH8PBwlJWVISgoCJ9//jmOHz+OF198EZMmTcLhw4fl18THx2PFihVYuHAhfvrpJyQlJcHT0xMAMH36dCQlJaG8vFw+/qOPPkK7du0wdOjQZsd3v1kpHcCDpKeXM4b08MC3J3/Flv2/4K0If6VDan3eAUDngcDZ/cChvwFPL1M6IiIiIiIyVUXViVsnL4BPICciski3K7V4ZNFeRa7905vhsLdpmdTYm2++iaeeekrednd3R0BAgLy9bNkypKamYvfu3fVGfOqLiYlBVFQUAOCdd97BunXrcPjwYQwfPtzo8ZWVldi8eTO6desGAIiNjcWbb74p71+/fj3i4+Pl0a4bNmzAF198ccf3k5ycDF9fX/Tq1QsAMH78eGzduhUDBw4EACQlJeHXX39FdnY23N3dAQDdu3eXX//2229j/PjxWLp0qVynfz+aas6cOXj++ecN6vQT07NmzcLevXuxc+dOhISEoLi4GGvXrsWGDRsQHR0NAOjWrRueeOIJAMDzzz+P2NhYfPrppxg3bhwAaeRyTExMq01B0RwccdvKXhok/SClfH8R10vK73C0hQqt/gV1ZDtw5X+KhkJEREREJqxmxK2Tj7JxEBER3UFwcLDBdklJCebOnQs/Pz+4urrC0dEReXl5dxxx27t3b3ndwcEBzs7OKCgoaPB4e3t7OWkLAN7e3vLxhYWFuHbtmsFIV7VajaCgoDu+n23btmHixIny9sSJE5GSkoLi4mIAQE5ODh577DE5aVtXTk4OwsLC7nidO6l7X7VaLZYtWwZ/f3+4u7vD0dERe/fule9rXl4eysvLG7y2nZ2dwdQPR48exfHjxxETE3PPsd4PHHHbyh7v6o6A9i44drEQHx44i7ineygdUuvzfRrwDgSu5ADbwoGITUCvCIWDIiIiIiKTU5O4deb8tkRElkpjrcZPb4Yrdu2W4uDgYLA9d+5cpKWlYdWqVejevTs0Gg3Gjh2LioqKRs9jbW1tsC0Igjz9QFOPv9cpIH766SccPHgQhw8fxvz58+V6rVaL5ORkzJgxAxqNptFz3Gm/sTiNPXys7n1duXIl1q5di4SEBPj7+8PBwQFz5syR7+udrgtI0yUEBgbi4sWLSExMxNChQ9GpU6c7vk4JHHHbygRBwMuDpb+EfJB1DqXlTXvSoEVRqYDJu4BuQ4HKW0BKNLDvHaCRX0RERERE9AAquiwt+WAyIiKLJQgC7G2sFCn386vxmZmZiImJwejRo+Hv7w8vLy+cPXv2vl3PGBcXF3h6eiI7O1uu02q1OHr0aKOv27p1KwYNGoRjx44hJydHLnFxcdi6dSsAaWRwTk5Og/Pv9u7du9GHfXl4eBg8RO306dO4devWHd9TZmYmnnvuOUycOBEBAQHo2rUrTp06Je/39fWFRqNp9Nr+/v4IDg7Gli1bkJSUhKlTp97xukph4lYBT/fyQueH7FF4uxIfZ19QOhxlaNyAF1Jqp03IeBfYOQkoL1E2LiIiIiIyHcVXpSUTt0REZGZ8fX3xySefICcnB8eOHcMLL7zQ6MjZ+2XWrFlYvnw5Pv30U5w8eRKzZ8/GjRs3GkxaV1ZW4h//+AeioqLw6KOPGpTp06fj0KFD+PHHHxEVFQUvLy9EREQgMzMTv/zyC/79738jKysLALB48WLs2LEDixcvRl5eHnJzc/Huu+/K1xk6dCg2bNiAH374Ad9//z1efvnleqOHjfH19UVaWhoOHDiAvLw8vPTSS7h27Zq8387ODvPnz8e8efPw4Ycf4ueff8bBgwflhHON6dOnY8WKFRBFUZ7/1xQxcasAtUrAjEFdAQBb/5uPSu0DOtJUbQWEvy1NlaC2AU78B9j6NHDjrNKREREREZEpKNZ7OBkREZEZWbNmDdzc3NC/f3+MGjUK4eHh6NOnT6vHMX/+fERFRWHy5MkIDQ2Fo6MjwsPDYWdnZ/T43bt347fffjOazPTz84Ofnx+2bt0KGxsbfPXVV2jbti1GjhwJf39/rFixAmq1NP3EkCFDkJKSgt27dyMwMBBDhw7F4cOH5XOtXr0aHTp0wMCBA/HCCy9g7ty5sLe3v+P7WbBgAfr06YPw8HAMGTJETh7rW7hwIf785z9j0aJF8PPzQ2RkZL15gqOiomBlZYWoqKgG74UpEMR7nfhCQUVFRXBxcUFhYSGcnZ2VDqdZyiq1eOLdb3C9pAJ/jQzA6MfaKx2Ssi5kAx9PAEquARp3YNwHQJdBSkdFRERECjDnPl5L4n0AsDYQuJEPTPkS6NRf6WiIiOgelZWVIT8/H126dDHpZJkl0+l08PPzw7hx47Bs2TKlw1HM2bNn0a1bN2RnZ9+XhHpjn/Xm9PE44lYhdtZqTBnQBQDwt4xf7nniaLPXoS/w4reAz2PA7d+BDyOAw1uAB/2+EBERET2oRJFTJRAREd2jc+fOYcuWLTh16hRyc3Pxpz/9Cfn5+XjhhReUDk0RlZWVuHr1KhYsWIDHH39ckVHQzcHErYIm9usEBxs1TlwtRsapX5UOR3nOPtJoCv9xgKgFvpgLfDYbqGr8iYtEREREZIHKbgJVt6V1TpVARER0V1QqFbZv346+fftiwIAByM3Nxddffw0/Pz+lQ1NEZmYmvL29kZ2djc2bNysdzh1ZKR3Ag8zF3hpRIR3x9//m428Zv2BIj7ZKh6Q8aw3w/PuAZy/g6yXA0Q+A66eAcf8AHD2Ujo6IiIiIWkvNaFuNm9RHJCIiombr0KEDMjMzlQ7DZAwZMsSsvvXOEbcKm/pEF1ipBGT98huOXbipdDimQRCAJ+YAL+wEbJ2B81nAlieB02lARanS0RERERFRayi6LC2dfJSNg4iIiEghTNwqzMdVg2cDpc7o3777WeFoTMzDTwPT0wH3bkDhBeCfY4HlHYD3hwBfvgb8uKt2JAYRERERWZbiK9KS0yQQERHRA4pTJZiAlwZ1wydHL+HL41dx9nopOrdxUDok0+HxMDAjXZo24Uy6lMC9/INUDm2SjnHtBHQMBTr2k5ZtegAq/k2CiIiIyKzVJG6d+WAyIiIiejAxcWsCeng5YWjPtvjmRAHe3/8L3hntr3RIpkXjBoxaK60XXgTOH5TKhYPAtR+Bm+ek8r9k6Rg7V6B9X8C9q9TRd/KRRmo4+0hPJLZ1VOytEBGRCRNFQNRJRaetXtfqrdfdpzN8vSDobxipFwCVFaBSVy/1yx3+4KjTAlXlgLYC0FZWL+uso6G5ugTj1frvt16peY96x3R8XPo3mai1FNWMuOVUCURERPRgYuLWRLw0qCu+OVGAfx25iEG+Hni0nTPauWogCA38Z+tB5dIe8B8rFQAoKwIuZtcmci9+Lz2B+Exaw+ewcapO6FYXZ2/AwQMQ1ICgkv6DLQjSOgS9OlVtnZUtoHGVksQ1SzsX6T/jpkYUgcpb0vzAFSXSto0DYG0vFTV/DbSqqgqgvAgoK5Q+q2WF9Ut5MWBlJ32m5M+Xq962i7RtZdPwdUQR0FUBVWVSskdelkv1amtAbSMljNTWgMq6uk5vve7vH1GUkke6Sukc2krj2xClY2uSSDXr8gTw+ut6jCW99BNechw6w2vUJJYgVl9Sr05XaXgPKsuM3BO9OrWN9PNtrZGWVnZ6pU692kZ63zVFq7euq74X2sraugYTZDVFL+6a2GsSc1XlRpJ1+km8Sun3k9q6TlKwznbNfkFd57pNiK3uPW/ysu5nwNiyJhmqrf2c1SQO69UbaX+jS9Se2+hnRay/X2kGiVy1FFdNm9dNEithejrQPljpKOhBwqkSiIiI6AFnEhmbjRs3YuXKlbh69SoCAgKwfv16hISEKB1Wqwrp4o7HOrrih/M38fJHRwAAznZW8PN2hp+3Mx7xdsYjPs7o3tYRdtYmmBxUip0z0D1MKoCUvLiaC1w6Ij3QoviK3vIKUFEslevFwPVTLR+PrXN1MtfFMKmrtpb2GySr9NabXa9XrasCKkurE7NGSuUtNJqQUNvUJnFt7KXElLVD9bp9dbKksjYBpa1sYLtKSq7oJ4j0E4Pyuk118shKSjRpK2oTitry2hFlVWVSklNbXrsUxYaTjPJ69TVU1lI8+vHJibAqI++hqjpJWDdxX3e95hhVw8fL+1G7XlUmJWUrbzX2CWoeK430GbNxqE3u1SQgteX3nuipSfwBtclZIqr++VbX/rwDaNLv7po/bDSkJsnfFKrq36tWNrW/8wRjo3YbuJ4oSqN8hWYUa03TYiO6F6IIXP0fkPcZcO6AVOfMEbdERET0YFI8cfvxxx8jLi4OmzdvRr9+/ZCQkIDw8HCcPHkSbdu2VTq8ViMIAv4ypjc2ZfyMny4X4UxBCYrKqnAo/3ccyv9dPk6tEtDdwxF+3k7w83ZGVw9HONlZwdHWSl462lnB1uoBTe6qrYF2faRiTHmx9ECzosvSsviylNC99ZveKCz9EWj6I7L06qvKgNs3pRGTt29KiVNAGklZXgQUtsq7bT5rBynRUFEK+T/zNaO5ym4qGVnT6SqVjqBl2DpXJ/pd6hdbRyn5WvMZqxmde7tmRG71B6zqNlB8u2nXU9vUjhoV1LXJbG1Fw0nZpiaR5JGd1aMEa5JH+kkt/SS3VGE4wtZYokt/hK7+cXdMmuudX22rN3K27lJv3bp6BK22Um8EbkMjdW/X/oGh7nuv+aNE3aK20kv21RnFb/B+9LZr/sihttFbtzWst7Ktvaaoq24zbZ2RwJX160SdkRiMFWP3t86yXvvWXd5pv1A7ClhQVScTa9ar61XVSVL9+9fY+Yxdv8E/xqiMxKLSu37NvdCL717II9f1R2hXb4t69dqq2s+U/BnQ+yzwGzlkSXRa4MJhKVmb9xlQeL52n50L4B2oWGhEREQtaciQIQgMDERCQgIAoHPnzpgzZw7mzJnT4GsEQUBqaioiIiLu6dotdR5qXYonbtesWYMZM2ZgypQpAIDNmzfj888/x7Zt2/Daa68pHF3r8vV0wppxgQCA8iotzhSUIO9KMX66XIS8K0XIu1qEm7cqcfJaMU5eK8aunMsNnstGrYJjTSLXtjahW5PgdbKzrl5WF1vrevWOtlawUlvYQ75snaTSxrdlz1tVoZdcu2m4LLtZPcqqRiNfARfqrdT5z7mRekEtjY61caydAqFmXb9YaWoTDqIoJZ5qplCovF09avdWnfVb0nVqRrXW/Vq9wXZ1gqomUaQ/MrehdVGnl1C0qU6y6a9Xl5oEFYT6CUeD9QrDkbWCXtKjbqw1I9T0E22A8QR+zT2ru8/Y17DlOtTWiTopMSgnZp3vbVoNnbZ2uoXbN6UpMBpMUNpK++6UbKqZWqHmq/dyO1ZUfwasDNvZIFHLBBJRkwlC9e8gxbtgRMqqqgDO7pcStSc+B0oLavdZ2wPdhwF+zwIPPy3920lERKSgUaNGobKyEnv27Km3b//+/Rg0aBCOHTuG3r17N+u82dnZcHBo2QfUL1myBLt27UJOTo5B/ZUrV+Dm1jrPK7h9+zbatWsHlUqFS5cuwdbWtlWua4kU/V9DRUUFjhw5gvj4eLlOpVJh2LBhyMrKUjAy5dlaqdHLxwW9fFyAIKlOFEVcKSyTkrhXivDTlSJcunEbJeVVUimrQmmFlCCs0Orwe2kFfi+tuKc4rNUCbK3UsLVSwdZKBRsrlbRtraquq95nrYKNWgW1SgUrlQC1WpCWKgFqQX9bVVuvEqASAJUgQBBq11UCqrf190Oe71dAnUFU1clMwSCnKeBuU0nyueucV6izH0auIAhOAJwAdADUABykYpB61QvUsN74usH5m/KuqqqLwTfyS6uLsZPaArAF4FobkxqARioNzrMsAtBWF4MYjVBXF+s7Ri+dt7K61A1VAIBG5nUFAFV1qfvbTVddGtHcT02z8pUVqG4CLYAbzbpOwxyrC6Q2L6+7v+bDUL/tmxa6Neo3Wk2j17vY3buPed/mtKnYCnOc3v1vJtLHvxUoo5ePM5zsmvKLnMyWtkp6ZsD9VHodOPklcOpL6Q+QNWxdgB4jAL9RQLeh0h+liYiITMS0adMwZswYXLx4Ee3btzfYl5iYiODg4GYnbQHAw8OjpUK8Iy+v1psz/t///jd69eoFURSxa9cuREZGttq16xJFEVqtFlZW5jlwQtGor1+/Dq1WC09PT4N6T09PnDhxot7x5eXlKC+vTRYUFRXd9xhNiSAI8HHVwMdVgzA/T6PHaHUiSiukJG5JeRWKy2qTusVllXJdcb3tShTrrZdVShmuSq2ISm0VSlowR0NERETmJ/WV/nisY+uM0iCFVJQA259pves5eAA9/yAlazsPbPyhm0RERAr6wx/+AA8PD2zfvh0LFiyQ60tKSpCSkoKVK1fit99+Q2xsLL777jvcuHED3bp1w+uvv46oqKgGz1t3qoTTp09j2rRpOHz4MLp27Yq1a9fWe838+fORmpqKixcvwsvLCxMmTMCiRYtgbW2N7du3Y+nSpQBqB2ElJiYiJiam3lQJubm5mD17NrKysmBvb48xY8ZgzZo1cHSUBgfFxMTg5s2beOKJJ7B69WpUVFRg/PjxSEhIgLV143/M37p1KyZOnAhRFLF169Z6idsff/wR8+fPx3fffQdRFBEYGIjt27ejW7duAIBt27Zh9erVOHPmDNzd3TFmzBhs2LABZ8+eRZcuXfDDDz8gMDAQAHDz5k24ublh3759GDJkCL799ls8+eST+OKLL7BgwQLk5ubiq6++QocOHRAXF4eDBw+itLQUfn5+WL58OYYNGybHVV5ejkWLFiEpKQkFBQXo0KED4uPjMXXqVPj6+uLll1/G3Llz5eNzcnLw2GOP4fTp0+jevXuj9+RumVW6efny5fIHkIxTqwQ421nD+R5HxFRqdSgpq0J5lQ7lVVppWSmtV1TpjNaXV+mg1Ymo0ol6S5201BqvhwjoRBG66qUob0t1YvVSq5NGwskzXurNhyk/tByi4QPM70LNiLvac8Jgpe5+w9c2cE79WA3qG3htA8E3fP67G31meP3697ORUO47Y5cVRbHh0b8tcU2l3qwR9zuU1hhZ2lR3+/lt6rnNlSmGfr9++kzxvZJxfDjqA0BQAW163N9rqG2ALoOkZG2HkHubOoiIiCyDKLbsQ5ybw9q+Sf8hsbKywuTJk7F9+3a88cYb8v9NU1JSoNVqERUVhZKSEgQFBWH+/PlwdnbG559/jkmTJqFbt24ICQm54zV0Oh2ef/55eHp64tChQygsLDQ6962TkxO2b98OHx8f5ObmYsaMGXBycsK8efMQGRmJ48ePY8+ePfj6668BAC4u9accKi0tRXh4OEJDQ5GdnY2CggJMnz4dsbGx2L59u3zcvn374O3tjX379uHMmTOIjIxEYGAgZsyY0eD7+Pnnn5GVlYVPPvkEoiji1Vdfxblz59CpUycAwKVLlzBo0CAMGTIE33zzDZydnZGZmYmqKun5Kps2bUJcXBxWrFiBESNGoLCwEJmZmXe8f3W99tprWLVqFbp27Qo3NzdcuHABI0eOxNtvvw1bW1t8+OGHGDVqFE6ePImOHTsCACZPnoysrCysW7cOAQEByM/Px/Xr1yEIAqZOnYrExESDxG1iYiIGDRp035K2gMKJ2zZt2kCtVuPatWsG9deuXTM6hDs+Ph5xcXHydlFRETp06HDf43wQWatVcHPgqAciIiKiB4adMxB7WOkoiIjoQVN5C3jHR5lrv35ZeiZME0ydOhUrV65ERkYGhgwZAkBK3I0ZMwYuLi5wcXExSOrNmjULe/fuxc6dO5uUuP36669x4sQJ7N27Fz4+0v145513MGLECIPj9Ef8du7cGXPnzkVycjLmzZsHjUYDR0dHWFlZNTo1QlJSEsrKyvDhhx/Kc+xu2LABo0aNwrvvvit/M97NzQ0bNmyAWq1Gz5498cwzzyA9Pb3RxO22bdswYsQIeT7d8PBwJCYmYsmSJQCAjRs3wsXFBcnJyfLI3Ycfflh+/VtvvYU///nPmD17tlzXt2/fO96/ut5880089dRT8ra7uzsCAgLk7WXLliE1NRW7d+9GbGwsTp06hZ07dyItLU0ehdu1a1f5+JiYGCxatAiHDx9GSEgIKisrkZSUhFWrVjU7tuZQ9MlTNjY2CAoKQnp6ulyn0+mQnp6O0NDQesfb2trC2dnZoBAREREREREREd1PPXv2RP/+/bFt2zYAwJkzZ7B//35MmzYNAKDVarFs2TL4+/vD3d0djo6O2Lt3L86fP9+k8+fl5aFDhw5y0haA0dzYxx9/jAEDBsDLywuOjo5YsGBBk6+hf62AgACDB6MNGDAAOp0OJ0+elOt69eoFtbr22zHe3t4oKChAQ7RaLT744ANMnDhRrps4cSK2b98OnU6akjMnJwcDBw40Ot1CQUEBLl++jLCwsGa9H2OCg4MNtktKSjB37lz4+fnB1dUVjo6OyMvLk+9dTk4O1Go1Bg8ebPR8Pj4+eOaZZ+T2/+yzz1BeXo4//vGP9xxrYxSfKiEuLg7R0dEIDg5GSEgIEhISUFpaiilTpigdGhERERGRbOPGjVi5ciWuXr2KgIAArF+/vtERNCkpKVi4cCHOnj0LX19fvPvuuxg5cmQrRkxERGQGrO2lka9KXbsZpk2bhlmzZmHjxo1ITExEt27d5ETfypUrsXbtWiQkJMDf3x8ODg6YM2cOKiru7aHx+rKysjBhwgQsXboU4eHh8sjV1atXt9g19NVNrgqCICdgjdm7dy8uXbpUb05brVaL9PR0PPXUU9BoNA2+vrF9AKBSSeNP9ac6rKw08mRzwCApDQBz585FWloaVq1ahe7du0Oj0WDs2LFy+9zp2gAwffp0TJo0CX/961+RmJiIyMhI2Nvf3weqKjriFgAiIyOxatUqLFq0CIGBgcjJycGePXvqPbCMiIiIiEgpH3/8MeLi4rB48WIcPXoUAQEBCA8Pb3DUyYEDBxAVFYVp06bhhx9+QEREBCIiInD8+PFWjpyIiMjECYI0XYESpZkP3Bg3bhxUKhWSkpLw4YcfYurUqfJ8t5mZmXjuuecwceJEBAQEoGvXrjh16lSTz+3n54cLFy7gypUrct3BgwcNjjlw4AA6deqEN954A8HBwfD19cW5c+cMjrGxsYFWq73jtY4dO4bS0lK5LjMzEyqVCj163P1891u3bsX48eORk5NjUMaPH4+tW7cCAHr37o39+/cbTbg6OTmhc+fOBt/M1+fh4QEABvcoJyenSbFlZmYiJiYGo0ePhr+/P7y8vHD27Fl5v7+/P3Q6HTIyMho8x8iRI+Hg4IBNmzZhz549mDp1apOufS8UT9wCQGxsLM6dO4fy8nIcOnQI/fr1UzokIiIiIiLZmjVrMGPGDEyZMgWPPPIINm/eDHt7e/nrcnWtXbsWw4cPx//93//Bz88Py5YtQ58+fbBhw4ZWjpyIiIhaiqOjIyIjIxEfH48rV64gJiZG3ufr64u0tDQcOHAAeXl5eOmll+o906kxw4YNw8MPP4zo6GgcO3YM+/fvxxtvvGFwjK+vL86fP4/k5GT8/PPPWLduHVJTUw2O6dy5M/Lz85GTk4Pr16+jvLy83rUmTJgAOzs7REdH4/jx49i3bx9mzZqFSZMm3fVAyl9//RWfffYZoqOj8eijjxqUyZMnY9euXfj9998RGxuLoqIijB8/Ht9//z1Onz6Nf/zjH/IUDUuWLMHq1auxbt06nD59GkePHsX69esBSKNiH3/8caxYsQJ5eXnIyMgwmPO3Mb6+vvjkk0+Qk5ODY8eO4YUXXjAYPdy5c2dER0dj6tSp2LVrF/Lz8/Htt99i586d8jFqtRoxMTGIj4+Hr6+v0aksWppJJG6JiIiIiExVRUUFjhw5Ij+oApC+qjds2DBkZWUZfU1WVpbB8YD0cI6GjgeA8vJyFBUVGRQiIiIyLdOmTcONGzcQHh5uMB/tggUL0KdPH4SHh2PIkCHw8vJCREREk8+rUqmQmpqK27dvIyQkBNOnT8fbb79tcMyzzz6LV199FbGxsQgMDMSBAwewcOFCg2PGjBmD4cOH48knn4SHhwd27NhR71r29vbYu3cvfv/9d/Tt2xdjx45FWFjYPf2BueZBZ8bmpw0LC4NGo8FHH32Ehx56CN988w1KSkowePBgBAUFYcuWLfK0DNHR0UhISMB7772HXr164Q9/+ANOnz4tn2vbtm2oqqpCUFAQ5syZg7feeqtJ8a1ZswZubm7o378/Ro0ahfDwcPTp08fgmE2bNmHs2LF45ZVX0LNnT8yYMcNgVDIgtX9FRUWrTfEqiPoTQ5iZoqIiuLi4oLCwkA8qIyIiIrIQptbHu3z5Mtq1a4cDBw4YjKyYN28eMjIycOjQoXqvsbGxwQcffICoqCi57r333sPSpUsbHH2zZMkSLF26tF69qdwHIiKie1VWVob8/Hx06dIFdnZ2SodD1Gz79+9HWFgYLly40Ojo5MY+683p63LELRERERGRCYiPj0dhYaFcLly4oHRIRERERATpm1EXL17EkiVL8Mc//rHVns3FxC0RERERUSPatGkDtVpdb6TstWvX4OXlZfQ1Xl5ezToeAGxtbeHs7GxQiIiIiEh5O3bsQKdOnXDz5k385S9/abXrMnFLRERERNQIGxsbBAUFGTzhWKfTIT09vcGHUoSGhtZ7InJaWlqrPMSCiIiIiFpWTEwMtFotjhw5gnbt2rXada1a7UpERERERGYqLi4O0dHRCA4ORkhICBISElBaWio/mGLy5Mlo164dli9fDgCYPXs2Bg8ejNWrV+OZZ55BcnIyvv/+e7z//vtKvg0iIiIiMiNM3BIRERER3UFkZCR+/fVXLFq0CFevXkVgYCD27Nkjz292/vx5qFS1X2br378/kpKSsGDBArz++uvw9fXFrl278Oijjyr1FoiIiIjIzAiiKIpKB3G3TO2Jw0RERER079jHk/A+EBGRpSkrK0N+fj46d+4MjUajdDhE983t27dx9uxZdOnSBXZ2dgb7mtPH4xy3RERERERERER031lbWwMAbt26pXAkRPdXzWe85jN/tzhVAhERERERERER3XdqtRqurq4oKCgAANjb20MQBIWjImo5oiji1q1bKCgogKurK9Rq9T2dj4lbIiIiIiIiIiJqFV5eXgAgJ2+JLJGrq6v8Wb8XTNwSEREREREREVGrEAQB3t7eaNu2LSorK5UOh6jFWVtb3/NI2xpM3BIRERERERERUatSq9UtltwislR8OBkRERERERERERGRiWHiloiIiIiIiIiIiMjEMHFLREREREREREREZGLMeo5bURQBAEVFRQpHQkREREQtpaZvV9PXe1Cxr0tERERkeZrT1zXrxG1xcTEAoEOHDgpHQkREREQtrbi4GC4uLkqHoRj2dYmIiIgsV1P6uoJoxkMZdDodLl++DCcnJwiC0CrXLCoqQocOHXDhwgU4Ozu3yjXp/mKbWia2q+Vhm1omtqvlaYk2FUURxcXF8PHxgUr14M7sxb4utQS2qWViu1oetqllYrtantbu65r1iFuVSoX27dsrcm1nZ2f+0FkYtqllYrtaHrapZWK7Wp57bdMHeaRtDfZ1qSWxTS0T29XysE0tE9vV8rRWX/fBHcJAREREREREREREZKKYuCUiIiIiIiIiIiIyMUzcNpOtrS0WL14MW1tbpUOhFsI2tUxsV8vDNrVMbFfLwzY1b2w/y8M2tUxsV8vDNrVMbFfL09ptatYPJyMiIiIiIiIiIiKyRBxxS0RERERERERERGRimLglIiIiIiIiIiIiMjFM3BIRERERERERERGZGCZum2Hjxo3o3Lkz7Ozs0K9fPxw+fFjpkKgZvvvuO4waNQo+Pj4QBAG7du0y2C+KIhYtWgRvb29oNBoMGzYMp0+fViZYapLly5ejb9++cHJyQtu2bREREYGTJ08aHFNWVoaZM2fioYcegqOjI8aMGYNr164pFDHdyaZNm9C7d284OzvD2dkZoaGh+PLLL+X9bE/zt2LFCgiCgDlz5sh1bFfzs2TJEgiCYFB69uwp72ebmif2dc0b+7qWh31dy8O+7oOB/V3zZ0p9XSZum+jjjz9GXFwcFi9ejKNHjyIgIADh4eEoKChQOjRqotLSUgQEBGDjxo1G9//lL3/BunXrsHnzZhw6dAgODg4IDw9HWVlZK0dKTZWRkYGZM2fi4MGDSEtLQ2VlJZ5++mmUlpbKx7z66qv47LPPkJKSgoyMDFy+fBnPP/+8glFTY9q3b48VK1bgyJEj+P777zF06FA899xz+PHHHwGwPc1ddnY2/va3v6F3794G9WxX89SrVy9cuXJFLv/973/lfWxT88O+rvljX9fysK9redjXtXzs71oOk+nritQkISEh4syZM+VtrVYr+vj4iMuXL1cwKrpbAMTU1FR5W6fTiV5eXuLKlSvlups3b4q2trbijh07FIiQ7kZBQYEIQMzIyBBFUWpDa2trMSUlRT4mLy9PBCBmZWUpFSY1k5ubm/j3v/+d7WnmiouLRV9fXzEtLU0cPHiwOHv2bFEU+XNqrhYvXiwGBAQY3cc2NU/s61oW9nUtE/u6lol9XcvB/q7lMKW+LkfcNkFFRQWOHDmCYcOGyXUqlQrDhg1DVlaWgpFRS8nPz8fVq1cN2tjFxQX9+vVjG5uRwsJCAIC7uzsA4MiRI6isrDRo1549e6Jjx45sVzOg1WqRnJyM0tJShIaGsj3N3MyZM/HMM88YtB/An1Nzdvr0afj4+KBr166YMGECzp8/D4Btao7Y17V87OtaBvZ1LQv7upaH/V3LYip9XasWP6MFun79OrRaLTw9PQ3qPT09ceLECYWiopZ09epVADDaxjX7yLTpdDrMmTMHAwYMwKOPPgpAalcbGxu4uroaHMt2NW25ubkIDQ1FWVkZHB0dkZqaikceeQQ5OTlsTzOVnJyMo0ePIjs7u94+/pyap379+mH79u3o0aMHrly5gqVLl2LgwIE4fvw429QMsa9r+djXNX/s61oO9nUtE/u7lsWU+rpM3BKRRZg5cyaOHz9uMO8MmacePXogJycHhYWF+Ne//oXo6GhkZGQoHRbdpQsXLmD27NlIS0uDnZ2d0uFQCxkxYoS83rt3b/Tr1w+dOnXCzp07odFoFIyMiMgysa9rOdjXtTzs71oeU+rrcqqEJmjTpg3UanW9J8Rdu3YNXl5eCkVFLammHdnG5ik2Nhb/+c9/sG/fPrRv316u9/LyQkVFBW7evGlwPNvVtNnY2KB79+4ICgrC8uXLERAQgLVr17I9zdSRI0dQUFCAPn36wMrKClZWVsjIyMC6detgZWUFT09PtqsFcHV1xcMPP4wzZ87wZ9UMsa9r+djXNW/s61oW9nUtD/u7lk/Jvi4Tt01gY2ODoKAgpKeny3U6nQ7p6ekIDQ1VMDJqKV26dIGXl5dBGxcVFeHQoUNsYxMmiiJiY2ORmpqKb775Bl26dDHYHxQUBGtra4N2PXnyJM6fP892NSM6nQ7l5eVsTzMVFhaG3Nxc5OTkyCU4OBgTJkyQ19mu5q+kpAQ///wzvL29+bNqhtjXtXzs65on9nUfDOzrmj/2dy2fkn1dTpXQRHFxcYiOjkZwcDBCQkKQkJCA0tJSTJkyRenQqIlKSkpw5swZeTs/Px85OTlwd3dHx44dMWfOHLz11lvw9fVFly5dsHDhQvj4+CAiIkK5oKlRM2fORFJSEj799FM4OTnJ88m4uLhAo9HAxcUF06ZNQ1xcHNzd3eHs7IxZs2YhNDQUjz/+uMLRkzHx8fEYMWIEOnbsiOLiYiQlJeHbb7/F3r172Z5mysnJSZ6Lr4aDgwMeeughuZ7tan7mzp2LUaNGoVOnTrh8+TIWL14MtVqNqKgo/qyaKfZ1zR/7upaHfV3Lw76uZWJ/1/KYVF9XpCZbv3692LFjR9HGxkYMCQkRDx48qHRI1Az79u0TAdQr0dHRoiiKok6nExcuXCh6enqKtra2YlhYmHjy5Ellg6ZGGWtPAGJiYqJ8zO3bt8VXXnlFdHNzE+3t7cXRo0eLV65cUS5oatTUqVPFTp06iTY2NqKHh4cYFhYmfvXVV/J+tqdlGDx4sDh79mx5m+1qfiIjI0Vvb2/RxsZGbNeunRgZGSmeOXNG3s82NU/s65o39nUtD/u6lod93QcH+7vmzZT6uoIoimLLp4OJiIiIiIiIiIiI6G5xjlsiIiIiIiIiIiIiE8PELREREREREREREZGJYeKWiIiIiIiIiIiIyMQwcUtERERERERERERkYpi4JSIiIiIiIiIiIjIxTNwSERERERERERERmRgmbomIiIiIiIiIiIhMDBO3RERERERERERERCaGiVsiIgsjCAJ27dqldBhERERERC2OfV0iepAwcUtE1IJiYmIgCEK9Mnz4cKVDIyIiIiK6J+zrEhG1LiulAyAisjTDhw9HYmKiQZ2tra1C0RARERERtRz2dYmIWg9H3BIRtTBbW1t4eXkZFDc3NwDSV7s2bdqEESNGQKPRoGvXrvjXv/5l8Prc3FwMHToUGo0GDz30EF588UWUlJQYHLNt2zb06tULtra28Pb2RmxsrMH+69evY/To0bC3t4evry92794t77tx4wYmTJgADw8PaDQa+Pr61ut8ExEREREZw74uEVHrYeKWiKiVLVy4EGPGjMGxY8cwYcIEjB8/Hnl5eQCA0tJShIeHw83NDdnZ2UhJScHXX39t0FndtGkTZs6ciRdffBG5ubnYvXs3unfvbnCNpUuXYty4cfjf//6HkSNHYsKECfj999/l6//000/48ssvkZeXh02bNqFNmzatdwOIiIiIyGKxr0tE1HIEURRFpYMgIrIUMTEx+Oijj2BnZ2dQ//rrr+P111+HIAh4+eWXsWnTJnnf448/jj59+uC9997Dli1bMH/+fFy4cAEODg4AgC+++AKjRo3C5cuX4enpiXbt2mHKlCl46623jMYgCAIWLFiAZcuWAZA6yI6Ojvjyyy8xfPhwPPvss2jTpg22bdt2n+4CEREREVki9nWJiFoX57glImphTz75pEFnFQDc3d3l9dDQUIN9oaGhyMnJAQDk5eUhICBA7sgCwIABA6DT6XDy5EkIgoDLly8jLCys0Rh69+4trzs4OMDZ2RkFBQUAgD/96U8YM2YMjh49iqeffhoRERHo37//Xb1XIiIiInqwsK9LRNR6mLglImphDg4O9b7O1VI0Gk2TjrO2tjbYFgQBOp0OADBixAicO3cOX3zxBdLS0hAWFoaZM2di1apVLR4vEREREVkW9nWJiFoP57glImplBw8erLft5+cHAPDz88OxY8dQWloq78/MzIRKpUKPHj3g5OSEzp07Iz09/Z5i8PDwQHR0ND766CMkJCTg/fffv6fzEREREREB7OsSEbUkjrglImph5eXluHr1qkGdlZWV/FCElJQUBAcH44knnsA///lPHD58GFu3bgUATJgwAYsXL0Z0dDSWLFmCX3/9FbNmzcKkSZPg6ekJAFiyZAlefvlltG3bFiNGjEBxcTEyMzMxa9asJsW3aNEiBAUFoVevXigvL8d//vMfuTNNRERERNQY9nWJiFoPE7dERC1sz5498Pb2Nqjr0aMHTpw4AUB6Cm5ycjJeeeUVeHt7Y8eOHXjkkUcAAPb29ti7dy9mz56Nvn37wt7eHmPGjMGaNWvkc0VHR6OsrAx//etfMXfuXLRp0wZjx45tcnw2NjaIj4/H2bNnodFoMHDgQCQnJ7fAOyciIiIiS8e+LhFR6xFEURSVDoKI6EEhCAJSU1MRERGhdChERERERC2KfV0iopbFOW6JiIiIiIiIiIiITAwTt0REREREREREREQmhlMlEBEREREREREREZkYjrglIiIiIiIiIiIiMjFM3BIRERERERERERGZGCZuiYiIiIiIiIiIiEwME7dEREREREREREREJoaJWyIiIiIiIiIiIiITw8QtERERERERERERkYlh4paIiIiIiIiIiIjIxDBxS0RERERERERERGRimLglIiIiIiIiIiIiMjH/DzKqBGlSv3GoAAAAAElFTkSuQmCC\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 110ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         3\n",
      "           1       1.00      1.00      1.00         5\n",
      "           2       1.00      1.00      1.00         3\n",
      "           3       1.00      1.00      1.00         4\n",
      "           4       1.00      1.00      1.00         3\n",
      "           5       1.00      1.00      1.00         3\n",
      "           6       1.00      1.00      1.00         3\n",
      "           7       1.00      0.67      0.80         3\n",
      "           8       1.00      1.00      1.00         3\n",
      "           9       1.00      1.00      1.00         2\n",
      "          10       1.00      1.00      1.00         3\n",
      "          11       1.00      1.00      1.00         5\n",
      "          12       1.00      1.00      1.00         4\n",
      "          13       1.00      1.00      1.00         3\n",
      "          14       1.00      1.00      1.00         2\n",
      "          15       1.00      1.00      1.00         4\n",
      "          16       1.00      1.00      1.00         2\n",
      "          17       1.00      1.00      1.00         1\n",
      "          18       1.00      1.00      1.00         6\n",
      "          19       1.00      1.00      1.00         3\n",
      "          20       1.00      1.00      1.00         2\n",
      "          21       1.00      1.00      1.00         3\n",
      "          22       1.00      1.00      1.00         1\n",
      "          23       1.00      1.00      1.00         2\n",
      "          24       1.00      1.00      1.00         3\n",
      "          25       1.00      1.00      1.00         3\n",
      "          26       1.00      1.00      1.00         3\n",
      "          27       1.00      1.00      1.00         5\n",
      "          28       1.00      1.00      1.00         3\n",
      "          29       1.00      1.00      1.00         3\n",
      "          30       1.00      1.00      1.00         2\n",
      "          31       0.50      1.00      0.67         1\n",
      "          32       1.00      1.00      1.00         4\n",
      "          33       1.00      1.00      1.00         6\n",
      "          34       1.00      1.00      1.00         3\n",
      "          35       1.00      1.00      1.00         4\n",
      "          36       1.00      1.00      1.00         4\n",
      "          37       1.00      1.00      1.00         3\n",
      "          38       1.00      1.00      1.00         4\n",
      "\n",
      "    accuracy                           0.99       124\n",
      "   macro avg       0.99      0.99      0.99       124\n",
      "weighted avg       1.00      0.99      0.99       124\n",
      "\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "fold_no = 1\n",
    "acc_per_fold = []\n",
    "loss_per_fold = []\n",
    "confusion_matrices = []\n",
    "\n",
    "all_unique_classes = np.unique(y_samples)\n",
    "\n",
    "for train_index, test_index in kf.split(X_samples):\n",
    "\n",
    "    X_train, X_test = X_samples[train_index],X_samples[test_index]\n",
    "    y_train, y_test = y_samples[train_index], y_samples[test_index]\n",
    "\n",
    "    y_train = to_categorical(y_train, num_classes=len(all_unique_classes))\n",
    "    y_test = to_categorical(y_test, num_classes=len(all_unique_classes))\n",
    "\n",
    "    model = create_cnn_model((num_cases_per_sample, X.shape[1]), len(all_unique_classes))\n",
    "\n",
    "    print(f'Training for fold {fold_no} ...')\n",
    "    history = model.fit(X_train, y_train, epochs=50, batch_size=8, validation_data=(X_test, y_test))\n",
    "\n",
    "    # Evaluate the model\n",
    "    scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "    print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]*100}%')\n",
    "    acc_per_fold.append(scores[1] * 100)\n",
    "    loss_per_fold.append(scores[0])\n",
    "\n",
    "    plt.figure(figsize=(14, 6))\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['loss'], label='Training Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "    plt.title('Training and Validation Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # model.save(f'tcn_model_fold_{fold_no}.h5')\n",
    "    # print(f'Model for fold {fold_no} saved.')\n",
    "\n",
    "\n",
    "\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_classes = np.argmax(y_pred, axis=1)\n",
    "\n",
    "    #     # Print the classification report\n",
    "    print(classification_report(np.argmax(y_test, axis=1), y_pred_classes))\n",
    "\n",
    "        # Plot the confusion matrix\n",
    "    cm = confusion_matrix(np.argmax(y_test, axis=1), y_pred_classes,labels=all_unique_classes)\n",
    "    confusion_matrices.append(cm)\n",
    "    # plt.figure(figsize=(20, 14))\n",
    "    # sns.heatmap(data=cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "    #     # disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "    #     # disp.plot()\n",
    "    # plt.xlabel('Predicted')\n",
    "    # plt.ylabel('Actual')\n",
    "    # plt.title('Confusion Matrix')\n",
    "    # plt.show()\n",
    "\n",
    "\n",
    "    fold_no += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6o8g4X0O1kjc",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "outputId": "d0b60185-7f35-4291-9a15-ac8f71716ef0"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 2000x1400 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "avg_conf_matrix = np.zeros_like(confusion_matrices[0], dtype=np.float64) # Initialize an empty average matrix\n",
    "for cm in confusion_matrices:\n",
    "    avg_conf_matrix += cm  # Sum the matrices\n",
    "avg_conf_matrix /= len(confusion_matrices)\n",
    "plt.figure(figsize=(20, 14))\n",
    "sns.heatmap(data=avg_conf_matrix, annot=True, fmt='.2f', cmap='Blues', cbar=False)\n",
    "# disp = ConfusionMatrixDisplay(confusion_matrix=avg_conf_matrix)\n",
    "# disp.plot()\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "igrBQL6TI_e1",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 659
    },
    "outputId": "dfbddb0a-42e2-4127-a4fc-a0dcc80d3d38"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Average scores for all folds:\n",
      "> Accuracy: 98.0774199962616 (+- 1.864357479717695)\n",
      "> Loss: 0.05811572074890137\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Plotting the metrics for each fold\n",
    "print('Average scores for all folds:')\n",
    "print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')\n",
    "print(f'> Loss: {np.mean(loss_per_fold)}')\n",
    "\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "plot_model(model, to_file='model_structure.png', show_shapes=True, show_layer_names=True)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "c8td-WYe1PC7",
    "outputId": "633f3e3d-7527-4c24-d202-a90e84f63437"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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jUlKSysrKUseOHXM4j+XDDz+se78bnaNNmzaafRz1LrMttkNzmzVr5tFr+9///ldz/Jw9bW2LGcm3YsWK6XrXNm/e3GEdi8Wi+UxPT093abVeCoVCodzeheQbhUKhUHyq+Pn5aebUyWnKlCmGdRz1IDMapikias+ePYb779mzx25vr+LFi9sdCrZ79267rynnBPO3ZGVlZf/jvGfPHtWhQwdVrFgxVaxYMdWhQwd18OBBu383169frzuHv7+/bgW/W5zNq7Zu3TrDeu6stmmvREREqNOnTxse394cW5UrV7abRJs3b55hndDQULtDZ5W6vZNv9pLVcXFxTusuWLDAsK4ZvXkmT56sOUe/fv2c1hk9erSmzs8//5yv96ZYsWIqLS1N04ZHH33UpbqrVq3S1Js6dardfW0/v6xWq6pXr57Tc2zcuNHpNfXz89P8DmZmZtrthZmz3HXXXZrfw8OHD3v02pYrV06TdExOTnbY09aM5JuIPsHoSs9A29WFGzZsmK/vSwqFQqF4XzEj+eYnAACYpEuXLlKtWjXd9gsXLsjbb79tWCc+Pl6mTJli+Nwzzzyj29a6dWtp0KCBbntmZqb06dNHLl++bHisy5cvy8CBA0UppXuuYcOG0qxZM8N6RiwWi1gsFtm0aZO0aNFCVq9eLSkpKZKSkiKrV6+Whx56SE6ePGlYt2HDhrptN27ckKVLlxru36lTJ7vtKFq0qLRq1Uq3/dKlS7J69WrXXowdISEh8u2330q5cuV0z129elU+/vhjw3rdu3cXi8Wi256WliavvPKKYZ3U1FQZPHhwntrrq3bt2mW4vXbt2tKkSRO79YoUKSJt2rQxfC4sLMwjbcupadOmmsc7d+50WqdixYqax7GxsR5tkzO9e/eW4ODg7MebN2+WjRs3ulT3gw8+0Dzu27ev+Pm5FkYvWrRI9u3b53S/n376SfO4Ro0aun2ysrJkyZIl2Y/9/f2la9euTo/do0cPze/hvHnznNZxx+zZs6VIkSLZjydPnmz3s9dMx44d0zyuWrWq0zq27113PvsBAHAVyTcAgGl69uxpuH3p0qVy9epVu/VWrlwpVqtVLl68KMePH5e4uDjZuXOnZGZm6vYdMGCA4THWrFkjf/75p8P27d27V7Zt22b4XO/evR3WtZWeni79+/eXa9eu6Z67cOGCfPjhh4b1SpUqJSVLltRtX7hwoeH+HTt2tNuG9u3bS2BgoG770qVLxWq12q3nTNGiRWXVqlXSsmVLw+ffe+89+fvvvw2fa9u2reH277//Xi5cuGD3nHv37nUpoXO7OX36tMTFxRk+N2nSJAkICDB8bsqUKRIeHm74XM6Ek6fUrFkz++fr169LfHy80zq2SUBH998MjzzyiOZxziSWM9u3b5ezZ89mPy5TpoxhcszIggULXNrvr7/+0jwuXry4S8fr3r2702PbfhZ7Mvk2adIkiYqKyn68Z88emTRpkseO747z589rHpcqVcppncOHD2seu3pfAQBwB8k3AIBp7CVrfv75Z4f1fvvtNwkKCpKwsDCpUqWK1K1bV1q0aGH4T2ZkZKThMVzt6bVu3TrD7e72fliyZInd3m0iIqtWrbL7nNE/2du3b5cTJ07otj/wwAOGyTqRmz0NjeTlH+1y5crJli1bDHvUidxMlNr2Csqpbt26hts3bdrk9Nxr1qxxqY23m+nTpxtub9Wqlaxfv14efvhhCQkJkZCQEGnVqpWsWbNGXnjhBbvHK1SokEfbV7hwYSlTpkz245MnT0pWVpbTeraJkEuXLnm0Xc40atRI89heL0MjSildT7369eu7VPfXX391ab/U1FTNY3tJ0127dsnRo0ezH7du3druZ4KISIUKFaRFixbZj7dv3+5SstQVEyZMkNdffz37cWJiojz55JNy/fp1jxzfXbYJXVcSz7ZJz8qVK3u0TQAAiJB8AwCYpEyZMrphZrccOXLEI+cIDw+XKlWqGD534MABl45x8OBBw+0NGjQwHC5pz9q1ax0+n5CQYDdBERQUpNumlJLFixfrtgcEBEi7du0Mt7dv3163/fjx47J161aHbbOnSZMm8ttvvxkO6xW5mUTt06eP4dBdkZtDVcuXL2/4nG1vEyP5PSzRW3z11Vfy22+/GT7XqlUr2bx5s6Smpkpqaqps2rQpu9dRSkqKYZ309HSPtq98+fKa342EhASX6t24cUPz2NNJQUcCAgJ0Q+Cd9Yy1dejQIc3ju+++22kdq9XqcpLRtneqo8+fnD1jCxUqJJ07d7a7r+2Q07lz57rUHkcKFSoks2bN0kwfcPHiRXnsscfk+PHjeT5+btkm21xJAtq2197fLQAA8oLkGwDAFDl7xtjKOXwrL8qWLWv3uVOnTrl0jNOnTxtuDwoKkqJFi7rclj/++MPh81lZWbohUbfY+yfb3tBTo3nfIiMjDXu/LFiwwG5yzJEePXrIzz//LHfddZfh82vWrJH27dvreuvk5Kg3jivvAU+9T3zN9evXpWfPni4ntURuznE4atQow+fsJeVyq1ixYrk6vm2vJFeGBHpK8eLFNb9nVqtV0tLS3DqG7Rxm9oaF5nTlyhW3zuGq+fPnax47Gnqac8ip1Wp1a7itkfDwcNmwYYNmXsaTJ09KZGSkS3Pbmcl2aLMr1992H3c+9wEAcBXJNwCAKRwlXhzN9+YOR/+8u/qPtaP9HL0GW65MLu7uP+J79+7V9bYREYmKihJ/f3/NNntDTm3/SXfFW2+9JYsXL5bChQsbPv/xxx/L448/7rRHlaN/Yl3pjWVW4sIX/PXXX9KsWTNZv369032PHTsmrVu3tjt/oaevo23vIld71tkm3xwlzz3NNmHoKGlsj20d22Pmp0OHDsnevXuzH7dt29bw961ixYqaIfSrV6+Wixcv5vq89evXl99++00z3P/AgQPSvHlzu3MV5qeIiAjNY1cS2LZ/A8yYIxEAAJJvAABT2JsYXkR0iaPcctSjy9Uho45WLHRlHqtbbIfUecqiRYt028LCwqR58+aabY8//rhuv9jYWLf+Ifb395fZs2fLBx98YHeF0j59+sjIkSNder2O7oErvfE89T7xVWfOnJHHHntMWrduLV988YX88ccfcvnyZcnIyJD4+Hj5/vvv5amnnpL77rtPtm7dKpUqVTI8zrlz5zzaLtth0kaLjBixHWps+x42k+37zZ0h5bfYfla48/lghpwLLwQFBRn2iO3Zs6fHVjnt3r27bNu2TfM+W7t2rTz00EMu9zQ22wMPPKB5bPTlha2srCzNYj5G0wAAAJBXJN8AAKZwNM+Rp3oWOOrBERoa6tIxHO2X36sxGnFl1dP7779fqlatqtvHnV5vAQEBsmjRIhk4cKDh84cPH5ZmzZrZbY8RRz2uXHkPMPzrps2bN8uQIUOkdu3aUqJECSlSpIhUr15dunTpIvPmzcvuSWpvlUZX5z90lW2yzdVkxS+//KJ5XKtWLSldurTH2uWIbc9UVz8fHNVxpbermRYuXKhJABoNPe3Ro0f2z8nJyQ4XfnFk/PjxsmTJEs3v7UcffSQdO3Ys8OtwS61atXS9KXfs2OG0np+fn+bLIleTyQAAuIPkGwDAFI4SV/Ym4XeXoznB7PUCcnW/K1eueHyi+tw4dOiQ4cIDOXu5GA05zcrKcjlR5ufnJ/Pnz7c7b9TKlSulSZMmbg8rS05OtvtcuXLlnNZn4nP32Fv519PJN9vfC1eT6fv379ckaiwWi/Tv399j7Ro7dqx8+eWXhr/Tly9f1vR+K1SokNvJXdth6AWddDp16pQmoRkVFaW5F5UqVdIMOV26dKnbiSWLxSIzZsyQf/3rX9k96KxWq/Tv319ef/31Au/9l1Pv3r01j3fv3i1nzpxxWi8kJETz2Bs+9wEAtx+SbwAAUyQkJNhNwN17770eOcf58+clPj7e8Ll69eq5dIz777/fcPvOnTtz3S5PM0qi3XfffdlJBqOVDjdv3uzyULCpU6dqJmXPacqUKdKlS5dcTdp/5coVuwnSmjVrOq1vb5VV6BUtWlQeeeQR3Xar1erxVWNzO0F9VlaWbhj1sGHDPDK8uFy5cvLaa6/JM888I0eOHJHp06drholmZWXpVlmuU6eOW+eoXbu25rG7q6WaIefQ0+DgYM2Kx7a/07lZ5XTWrFkyZMiQ7McXLlyQNm3aeGTFVE8KDQ2Vl156SbPtq6++cqmu7fv3Tp5rEgBgHpJvAADT2EtgGSUJcgoJCZHU1FRJTk6WkydPyqFDh2TPnj3y/9i77/AoqvWB4+8mIUCAQBJC6CBVBRTBSwdRo1QvVXoTlCIg9YpgAWmKIOAVBEVFqqAiUiQgWJB6iSBd6UonJCGkQBo5vz/4sWaysy3Zye6S7+d55nnY2XPOnNnZ7L68O+ecHTt2WCyy8Ouvv+q2oTf/kZ7Mwzcz2759u0P1c8OqVat050hr06aNlClTRurWrWvxnKNzO/Xu3VtGjBih+9wbb7whY8eOzdZqqfdYu1vO3ntARH8eu7ygWbNmMmjQIJk5c6asXbtWjhw5In/99ZfN4Z0vv/yy7lDKzZs3u/wOrYsXL2reE47eZSpyd7GOzHUrVqxodZVWZ8yfP998/v7+/lKuXDmLu7L27duneZz5rjB7/Pz8pHbt2pp9kZGR2eyt63z99deSmppqfpz5LtiuXbua//3333/Lzp07nWp7+vTpMmDAAPPj8+fPS8OGDS2GD3uCyZMna+5MvHjxoixatMihuhUqVNA8dmaVYQAAHKZc7OjRo0pE2NjY2NjY1AsvvKD7XZGUlKRCQkKs1uvYsaNuvQsXLliUbd68uW7ZO3fuqEceecRm/8LDw3XrpqWlqfLly+vWiY6O1q1TtmxZu6/H6dOndes++OCDduvu2rXLot7GjRvVkCFDLPbfvn1bBQYG2m2zSpUqKiEhQbdPH3/8sUveA+PHj9dt/9atWyo0NNTpa6OUUgcPHnT7e9vatm3bNt0+t2/f3uE2Nm/erNvG+PH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J1H79+ulO3RX5e9plxYoVdT/LO9pET2FPS3SHgp4zV+45T6lYsaKmnQkJCYbK5U0O2ks0msHPz89mCryzkbF5HTlyRLNcs2ZNp2UyMjIMJxnzjm50dC/kHhnr7+8vjz76qN1t8045XbhwoaH2OOLv7y+zZ8/WPD4gJSVF2rdvL6dPny5w/fmVN9lmJAmYt732vrcAACgIkm8AAFPkHhmTV+7pWwVRvnx5u5+dO3fOUB3nz5/XXR8YGCh33HGH4bb85z//cfh5Tk6OzZSoW+z9J9ve1FO95761bNlSd/TLV199ZTfR40jPnj1l06ZNUqFCBd3P4+PjJTY21ma0Tm6ORuMYuQfcdZ8UFnecM3vTS42MXtNL4jqqM79CQkI0y/aeNZdX3lFJRqYEukvp0qU1P2cZGRly7do1l+rI+wwze9NCc7ty5YpL+zBq0aJFmmVHU09zTznNyMhwabqtnoiICPnpp580z2U8e/astGzZ0tCz7cyUd2qzkfOfdxtXfu8DAGAUyTcAgCkcJV4cPe/NFY7+8270P9aOtnN0DHkZebi4q/8R37t3r81oGxGRmJgY8fX11ayzN+U073/SjXjjjTckLi5OSpQoofv55MmTpXPnzpKenu6wHkf/iXVWVsS8xIUZ3HXO7CWyCpJ8M5ocMyrv6CIj11LENvnmKHnubnkTho4SoPbkLZO3zsJ05MgR2bt3r3W5Xbt2uj9vlSpV0kyhX716taSkpOR7vw0bNpTdu3drpvsfPHhQmjdvLocOHcp3ve4SGRmpWTYyKjPvd4CRqaoAALiK5BsAwBR+fn52P8ubOMovRyO6jE7fc/TGQiPPsbrF3vO2Cmrx4sU268LDw6V58+aadZ07d7bZbt++fS79h9jX11c+//xz+fDDD+2+obRfv37yyiuvGDpeR9fAyGg8d90nZnL3ObM3OtLIKDF7bzVNSkpyWtYVeZN8ei8Z0ZN3qnHee9hMee+3/Ezvzfu7wpXfD2bI/eKFwMBA3RGxvXr1cttbTnv06CFbt26VypUrW9etWbNGHnzwQcMjjc12//33a5b1/niRV05OjuZlPvaS2AAAFATJNwCAKRw958hdIwscjeAw+oZHR9sV9tsY9Rh562n9+vWlWrVqNtu4MurNz89PFi9eLE8//bTu50ePHpVmzZrZbY8eRyPXjNwD3j79y4xzlpiYqDtNNDw83GnCyN5Ub3c/gytvss1osmLz5s2a5Tp16kjZsmXd1i5H8o5Mzc8bYPOWMTLa1Uxff/21JgGoN/W0Z8+e1n+npqY6fPGLI++9954sWbJE83M7YcIE6dSpk8fPwy116tSxGU25fft2p+V8fHw0fywymkwGAMAVJN8AAKZwlLiy9xB+Vzl6Jlju0RmO2NvuypUrhqfTmenIkSO6Lx7IPcpFb8ppTk6O4aSPj4+PLFq0yO5zo1auXClNmjRxeVpZamqq3c+ioqKclvfmB5+bdc5ycnLkxIkTNuv9/Px0E6y53X333brrnT2P0FV5fy6MJtMPHDigSdRYLBZ54okn3NauUaNGyZw5c3R/ptPS0jSj3/z9/V1O7uadhu7ppNO5c+c0Cc2YmBjNtahcubJmyunSpUtdTixZLBaZMWOGvPvuu9bkb0ZGhjzxxBPy6quvenz0X259+/bVLP/222/y559/Oi0XHBysWfaG3/sAgNsPyTcAgCkSEhLsJuDsJQlclZycLCdPntT9rEGDBobqqF+/vu76HTt25Ltd7qaXRKtXr541yaD3psMNGzYYngo2adIkzUPZc5s4caJ06dIlX88Nu3Llit0Eae3atZ2Wt/fGUG9g1jkTEdm1a5fuekfnw9/fX/devnnzptvfGpvfB9Tn5OTYTKMePny4W6YXR0VFyciRI+WZZ56RY8eOydSpUzXTRHNycmzesvyPf/zDpX3cc889mmVX35ZqhtxTT4OCgjRvPM57f+bnLaezZ8+WIUOGWJcvXrwobdu2dcsbU92pVKlSMmzYMM26L774wlDZvPdvUXrWJACg6CD5BgAwjb0EVps2bRyWCw4OlqtXr0pqaqqcPXtWjhw5Inv27JHNmzfbPPtq06ZNunXoPf9IT+7pm7lt3LjRUPnCsHjxYt1npHXq1EkqVqwojRs3tvnM6LOdHn/8cRkxYoTuZ++8846MHDkyX29LvcXeyC9n94CI/nPsvIHZ5+znn3/WXd+1a1e7Zdq1a6c7lXLTpk1un0Z39uxZzfEZHWUq8veLJ3KXrVq1qrz55psFbtP06dOtxx8QECCVKlWyGZW1c+dOzXLuUWHO+Pn5ScOGDTXr7CVJC9PSpUs105Rzj4Lt3bu39d+nT5+WLVu2uFT3Bx98IM8884x1+cyZMxIdHW0zfdgbjBkzRjMy8ezZszJ79mxDZatUqaJZNvKSBgAAXKY8KDU1VYkIQRAEcZvG008/rfv7/9q1ayo8PNxuuW7duumWS0hIsNm2VatWuttmZ2er+vXrO2xf27ZtdctmZmaqypUr65ZJTk7WLXPnnXc6PR/Hjx/XLXv33Xc7Lbt161abcqtWrVLPP/+8zfrr16+rkJAQp3XWrFlTXblyRbdNs2bNcss98Oabb+rWn56eriIiIly+NkoptW/fPo/d04VxzsLCwtSNGzds6r9586buveLr66t+/fVX3TYNGjTIlPOQmJho3UdGRoby8fExXDYuLk7TxoyMDBUdHZ3vtrzyyiua+q5fv65q1apls91TTz2l2W7nzp2G9xEbG6spe/ToUbvbXr161bpdcnKy4X3ExMRo9jFnzhxD5VasWGEtc+HCBWWxWFTlypU1db3//vsundN+/fppyp8/f17VrFnTbffPoEGDNPWvWbMm33U99thjNvf9c889Z7h83759NWUnT57stuMkCIIgvCfi4+Ntvi8KE8k3giAIwrQoUaKESkpK0v0OWLRokW6Z0NBQderUKd0y7733nm6ZXbt26W6/d+9eu0moChUqqJMnT+qW++qrr+wek6eSb8OGDbMpl56erjZs2GCzfsmSJYauz6pVq3Tbc/78eVWqVCm33AO1a9fW3YdSSi1evFhZLBabMhEREerYsWN2y3ky+VYY50xE1IIFC3T3k5CQoLp3767KlCmjSpYsqZo2barWrFmju21KSooKDg425Txs2rRJsy9XEjPh4eHq7NmzmvKXL19Wbdq0cbkdgwcPVtnZ2Zq6hg8frrttyZIlVUpKimbbjh07Ot2HxWJR27Zt05QbOXKk3e0LO/nWp08fTblGjRqpF154QbPOyO+YW1G1alWVlpamuY/uuecet94/7kq+Pf744zaJ6lWrVrmUDB49erSm/PPPP+/WYyUIgiC8I0i+ecFFIAiCIMyLZ5991u73wIoVK1SzZs1UUFCQqlixourTp486ceKE7raJiYl2R8s1btxY3bx5U7fc8ePHVb9+/VRERIQKDAxUNWrUUCNGjFAXLlzQ3f7ChQuqQoUKdo/HU8m3cuXKqaysLLvnMrcuXbo4ra9ly5aG6nLFqlWrdPe1evVqh2WaN2+ugoKCVHh4uOrfv781+ao3+ksppfbv3++Re7kwz9ldd92lMjIyClS3owRRQWPChAmaffXv39/lc5n3ZzYjI0ONHz/e0KjNMmXKqDlz5tgcs7OE1bvvvqvZ/vz5805//iZOnKgpc+HCBRUWFmZ3+8JOvgUFBWlGY7711luan7ldu3a5dG3Wrl2raUe3bt3cfv8UNPlWpUoVNW/ePJvrf/jwYUP3T+7I+5+xRo0auf14CYIgCM8HyTcvuAgEQRCEubFs2bICfV9kZmaq2NhYh/sYOnRogb+Xbt68qdq3b+9wP55KvomIWrdundNjuHjxogoICHBa1xtvvFGgc6XHXiLp3nvvzVciKe+IlFt+//13j9zHhXnOCrq/rVu3Kj8/P9PORY8ePTT7++STT1yu46GHHlIXL160aXtycrKaO3eu6tq1q6pdu7YqXbq0CggIUFFRUSo2NlZ98sknulN/P//8c+Xr6+twn/7+/mr37t2acpcvX1ZjxoxRDRs2VKVKlVKBgYGqcuXKqk+fPrrTvZ0ltws7+SYi6ssvv7SW2759u0pPT7cujxgxwnA9eafx//jjj6bcP64k33x8fFS5cuVUvXr11ODBg9WyZct0/9iyfft2VbFiRZfaYbFYNPdgenq6qT83BEEQhOeC5JsXXASCIAjC3AgICFCLFi3K13fF1atX1WOPPWZoP48//rjdEXDOJCUlqRYtWjjdhyeTbwMHDnR6HDNnzjRUV2EnkgYPHuxSXfPnz1dVq1bV/ez48eMeuY8L+5yJ2I66MuLXX39VoaGhpp6LsLAwlZmZad2no2egOYpatWqpHTt2FOgcXr16VQ0dOtTwPitWrKgOHDjg8n6ysrIMTUn0RPKtY8eOum3OzMxUkZGRhutxNEo1P9q2bau7n7zJt4LIzs5W06dPN/RHh7zRtGlTTV0rV6409eeGIAiC8Fx4OvnG204BAKbLyMiQAQMGyFNPPSVnzpwxVEYpJStWrJB69erJd999Z6jMwoULpVGjRrJ8+XKX2jZ9+nSpV6+ey28DLGzffvut5s2Geoy+5bSwffbZZ/Lkk0/KtWvXHG6nlJKpU6fKwIEDJTk5WXeboKAgM5rolf7v//5PhgwZYvdc5JaVlSXTpk2Tdu3aSWpqqqntSklJkQ0bNliX77rrLvnHP/7hcj1Hjx6V5s2bS7du3ey+Gdee9PR0mT17ttSqVUumT59uuNy5c+fkwQcflM8++0yysrIMldm/f7+0b99eZsyY4VIbC8uPP/6oe4+sW7dO/vrrL8P15H2btDfLzs6WRYsWSd26dWXo0KFOfzfq6datm2b5m2++cVfzAADQ8PN0AwAAxYNSSubPny+LFy+WmJgYiY2NlWbNmkm5cuWkbNmykpGRISkpKfLHH3/I5s2bJS4uTo4dO+byfg4dOiRdu3aVOnXqyCOPPCJt27aVqlWrSkREhJQqVUpSUlIkOTlZDh8+LGv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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradio 接口 — CNN-LSTM Fault Classification\n",
    "\n",
    "将原 notebook 内容保留并在末尾添加 Gradio UI，用于加载 `lstm_cnn_model.h5` 并进行预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import gradio as gr\n",
    "except Exception:\n",
    "    !pip install -q gradio\n",
    "    import gradio as gr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "MODEL_FILE = \"lstm_cnn_model.h5\"\n",
    "model = None\n",
    "if os.path.exists(MODEL_FILE):\n",
    "    try:\n",
    "        model = load_model(MODEL_FILE)\n",
    "        print(\"Loaded model:\", MODEL_FILE)\n",
    "    except Exception as e:\n",
    "        print(\"Found model file but failed to load:\", e)\n",
    "        model = None\n",
    "else:\n",
    "    print(\"Model file not found: {}\".format(MODEL_FILE))\n",
    "\n",
    "def prepare_input_array(arr, n_timesteps=1, n_features=None):\n",
    "    if n_features is None:\n",
    "        return arr.reshape(1, n_timesteps, -1)\n",
    "    else:\n",
    "        return arr.reshape(1, n_timesteps, n_features)\n",
    "\n",
    "def predict_text(text, n_timesteps=1, n_features=None):\n",
    "    if model is None:\n",
    "        return \"模型未加载，请先训练并保存 'lstm_cnn_model.h5'。\"\n",
    "    arr = np.fromstring(text, sep=',')\n",
    "    x = prepare_input_array(arr, n_timesteps=n_timesteps, n_features=n_features)\n",
    "    probs = model.predict(x)\n",
    "    label = int(np.argmax(probs, axis=1)[0])\n",
    "    return f\"预测类别: {label} (概率: {float(np.max(probs)):.4f})\"\n",
    "\n",
    "def predict_csv(file, n_timesteps=1, n_features=None):\n",
    "    if model is None:\n",
    "        return {\"error\": \"模型未加载，请先训练并保存 'lstm_cnn_model.h5'。\"}\n",
    "    df = pd.read_csv(file.name)\n",
    "    X = df.values\n",
    "    if n_features is not None:\n",
    "        X = X.reshape(X.shape[0], n_timesteps, n_features)\n",
    "    preds = model.predict(X)\n",
    "    labels = preds.argmax(axis=1).tolist()\n",
    "    return {\"labels\": labels, \"probs\": preds.tolist()}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "with gr.Blocks() as demo:\n",
    "    gr.Markdown(\"# CNN-LSTM Fault Classification — Gradio Demo\")\n",
    "    gr.Markdown(\"优先尝试加载本地 'lstm_cnn_model.h5'，如果没有请先训练并保存模型。\\n\\n上传 CSV 文件或粘贴逗号分隔特征进行预测。\")\n",
    "    with gr.Row():\n",
    "        file_in = gr.File(label=\"上传 CSV（每行一个样本）\")\n",
    "        text_in = gr.Textbox(lines=2, placeholder=\"或粘贴逗号分隔的一行特征...\")\n",
    "    n_ts = gr.Number(value=1, label=\"timesteps (整型)\")\n",
    "    n_feat = gr.Number(value=None, label=\"features (可选，留空尝试自动推断)\")\n",
    "    btn = gr.Button(\"预测\")\n",
    "    out_text = gr.Textbox(label=\"单样本预测输出\")\n",
    "    out_json = gr.JSON(label=\"批量预测结果(JSON)\")\n",
    "\n",
    "    def run_predict(file, text, n_timesteps, n_features):\n",
    "        if file is not None:\n",
    "            return \"CSV 预测完成\", predict_csv(file, n_timesteps, int(n_features) if n_features else None)\n",
    "        if text:\n",
    "            return predict_text(text, n_timesteps, int(n_features) if n_features else None), {}\n",
    "        return \"请提供 CSV 或特征文本\", {}\n",
    "\n",
    "    btn.click(run_predict, inputs=[file_in, text_in, n_ts, n_feat], outputs=[out_text, out_json])\n",
    "\n",
    "demo.launch(share=False)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  },
  "converted_at": "2025-09-12T01:17:03.233325Z"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}