03 - Train_CNN_Analog-Readout_Version-Small2.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# CNN Training\n",
    "\n",
    "Target of this code is to train a CNN network to extract the needle position of an analog needle device.\n",
    "\n",
    "### Preparing the training\n",
    "* First all libraries are loaded\n",
    "    * It is assumed, that they are installed during the Python setup\n",
    "* matplotlib is set to print the output inline in the jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "########### Basic Parameters for Running: ################################\n",
    "    \n",
    "TFlite_Version  = \"1400\"   \n",
    "TFlite_MainType = \"ana-cont\"\n",
    "TFlite_Size     = \"s2\"\n",
    "Training_Percentage = 0.05              # 0.0 = Use all Images for Training\n",
    "\n",
    "##########################################################################\n",
    "\n",
    "## 2024-03-30: Code adapted to TF 2.16####################################\n",
    "\n",
    "\n",
    "\n",
    "import os\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.python import keras\n",
    "from tensorflow.python.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, InputLayer, Conv2D, MaxPool2D, Flatten, BatchNormalization\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.callbacks import History \n",
    "import math\n",
    "from PIL import Image \n",
    "\n",
    "loss_ges = np.array([])\n",
    "val_loss_ges = np.array([])\n",
    "\n",
    "%matplotlib inline\n",
    "np.set_printoptions(precision=4)\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load training data\n",
    "* The data is expected in the \"Input_dir\"\n",
    "* Picture size must be 32x32 with 3 color channels (RGB)\n",
    "* The filename contains the informations needed for training in the first 3 digits::\n",
    "* Typical filename: \n",
    "    * x.y-zzzz.jpg \n",
    "    * e.g. \"4.6_Lfd-1406_zeiger3_2019-06-02T050011\"\n",
    "\n",
    "|Place holder | Meaning                     | Usage        |\n",
    "|------------- |-----------------------------|--------------|\n",
    "| **x.y**          | readout value               | **to be learned** |\n",
    "| zzzz        | additional information              | not needed   |\n",
    "\n",
    "* The images are stored in the x_data[]\n",
    "* The expected output for each image in the corresponding y_data[]\n",
    "    * The periodic nature is reflected in a **sin/cos coding**, which allows to restore the angle/counter value with an arctan later on.\n",
    "\n",
    "* The last step is a shuffle (from sklearn.utils) as the filenames are on order due to the encoding of the expected analog readout in the filename "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(594, 32, 32, 3)\n",
      "(594, 2)\n"
     ]
    }
   ],
   "source": [
    "Input_dir='data_resize_all'\n",
    "\n",
    "files = glob.glob(Input_dir + '/*.*')\n",
    "x_data = []\n",
    "y_data = []\n",
    "\n",
    "for aktfile in files:\n",
    "    test_image = Image.open(aktfile)\n",
    "    test_image = np.array(test_image, dtype=\"float32\")\n",
    "    test_image = np.reshape(test_image, (32,32,3))\n",
    "    base = os.path.basename(aktfile)\n",
    "    target_number = (float(base[0:3])) / 10\n",
    "    target_sin = math.sin(target_number * math.pi * 2)\n",
    "    target_cos = math.cos(target_number * math.pi * 2)\n",
    "\n",
    "    x_data.append(test_image)\n",
    "    zw = np.array([target_sin, target_cos])\n",
    "    y_data.append(zw)\n",
    "\n",
    "x_data = np.array(x_data)\n",
    "y_data = np.array(y_data)\n",
    "print(x_data.shape)\n",
    "print(y_data.shape)\n",
    "\n",
    "x_data, y_data = shuffle(x_data, y_data)\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=Training_Percentage)\n",
    "else:\n",
    "    X_train = x_data\n",
    "    y_train = y_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the model\n",
    "\n",
    "The layout of the network ist a typcial CNN network with alternating **Conv2D** and **MaxPool2D** layers. Finished after **flattening** with additional **Dense** layer.\n",
    "\n",
    "#### Important\n",
    "* Shape of the input layer: (32, 32, 3)\n",
    "* Shape of the output layer: (2) - sin and cos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
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       "<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: \"functional\"</span>\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[1mModel: \"functional\"\u001b[0m\n"
      ]
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       "<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",
       "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │            <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d (<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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,432</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<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\">8</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\">12,816</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<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\">4</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_2 (<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\">4</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",
       "│ max_pooling2d_2 (<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\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<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\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<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\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<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\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</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\">2</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">130</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "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",
       "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │            \u001b[38;5;34m12\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m2,432\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │        \u001b[38;5;34m12,816\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │         \u001b[38;5;34m4,640\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m16,512\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │           \u001b[38;5;34m130\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
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     "output_type": "display_data"
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    {
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       "<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\">44,798</span> (174.99 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m44,798\u001b[0m (174.99 KB)\n"
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       "<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\">44,792</span> (174.97 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m44,792\u001b[0m (174.97 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "<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\">6</span> (24.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m6\u001b[0m (24.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inputs = tf.keras.Input(shape=(32, 32, 3))\n",
    "inputs2 = tf.keras.layers.BatchNormalization()(inputs)\n",
    "inputs3 = tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation=\"relu\")(inputs2)\n",
    "inputs4 = tf.keras.layers.MaxPool2D(pool_size=(4,4))(inputs3)\n",
    "inputs5 = tf.keras.layers.Conv2D(16, (5, 5), padding='same', activation=\"relu\")(inputs4)\n",
    "inputs6 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs5)\n",
    "inputs7 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs6)\n",
    "inputs8 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs7)\n",
    "inputs9 = tf.keras.layers.Flatten()(inputs8)\n",
    "inputs10 = tf.keras.layers.Dense(128,activation=\"relu\")(inputs9)\n",
    "inputs11 = tf.keras.layers.Dense(64,activation=\"relu\")(inputs10)\n",
    "output = tf.keras.layers.Dense(2)(inputs11)\n",
    "\n",
    "model = tf.keras.Model(inputs=inputs, outputs=output)\n",
    "\n",
    "model.compile(loss=keras.losses.mean_squared_error, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "The input pictures are randomly scattered for brightness and pixel shift variations. These is implemented with a ImageDataGenerator.\n",
    "\n",
    "\n",
    "The training is splitted into two steps:\n",
    "1. Variation of the brightness only\n",
    "2. Variation of brightness and Pixel Shift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Brigthness scattering only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muell\\anaconda3\\envs\\py311-tf218\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 17ms/step - accuracy: 0.5162 - loss: 0.5314 - val_accuracy: 0.6667 - val_loss: 0.4600\n",
      "Epoch 2/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.6247 - loss: 0.3978 - val_accuracy: 0.8667 - val_loss: 0.3102\n",
      "Epoch 3/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8669 - loss: 0.2036 - val_accuracy: 0.8667 - val_loss: 0.1584\n",
      "Epoch 4/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8748 - loss: 0.1605 - val_accuracy: 0.9333 - val_loss: 0.1095\n",
      "Epoch 5/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9249 - loss: 0.0962 - val_accuracy: 0.9667 - val_loss: 0.0602\n",
      "Epoch 6/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9278 - loss: 0.0764 - val_accuracy: 0.9667 - val_loss: 0.0375\n",
      "Epoch 7/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9553 - loss: 0.0582 - val_accuracy: 0.9667 - val_loss: 0.0325\n",
      "Epoch 8/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9634 - loss: 0.0476 - val_accuracy: 0.9333 - val_loss: 0.0305\n",
      "Epoch 9/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9549 - loss: 0.0364 - val_accuracy: 0.9667 - val_loss: 0.0508\n",
      "Epoch 10/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9618 - loss: 0.0337 - val_accuracy: 0.9667 - val_loss: 0.0154\n",
      "Epoch 11/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9671 - loss: 0.0267 - val_accuracy: 0.9667 - val_loss: 0.0277\n",
      "Epoch 12/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9474 - loss: 0.0220 - val_accuracy: 0.9667 - val_loss: 0.0181\n",
      "Epoch 13/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9607 - loss: 0.0222 - val_accuracy: 0.9667 - val_loss: 0.0449\n",
      "Epoch 14/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9602 - loss: 0.0168 - val_accuracy: 0.9667 - val_loss: 0.0172\n",
      "Epoch 15/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9766 - loss: 0.0182 - val_accuracy: 0.9667 - val_loss: 0.0615\n",
      "Epoch 16/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9664 - loss: 0.0190 - val_accuracy: 1.0000 - val_loss: 0.0108\n",
      "Epoch 17/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9811 - loss: 0.0131 - val_accuracy: 0.9667 - val_loss: 0.0094\n",
      "Epoch 18/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9797 - loss: 0.0168 - val_accuracy: 0.9667 - val_loss: 0.0096\n",
      "Epoch 19/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9731 - loss: 0.0138 - val_accuracy: 0.9667 - val_loss: 0.0080\n",
      "Epoch 20/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9808 - loss: 0.0118 - val_accuracy: 0.9667 - val_loss: 0.0108\n",
      "Epoch 21/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9543 - loss: 0.0235 - val_accuracy: 1.0000 - val_loss: 0.0098\n",
      "Epoch 22/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9816 - loss: 0.0076 - val_accuracy: 1.0000 - val_loss: 0.0108\n",
      "Epoch 23/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9794 - loss: 0.0083 - val_accuracy: 0.9667 - val_loss: 0.0064\n",
      "Epoch 24/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9743 - loss: 0.0115 - val_accuracy: 0.9667 - val_loss: 0.0052\n",
      "Epoch 25/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9826 - loss: 0.0072 - val_accuracy: 0.9667 - val_loss: 0.0181\n",
      "Epoch 26/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9831 - loss: 0.0092 - val_accuracy: 0.9667 - val_loss: 0.0257\n",
      "Epoch 27/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9869 - loss: 0.0081 - val_accuracy: 0.9667 - val_loss: 0.0082\n",
      "Epoch 28/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9736 - loss: 0.0090 - val_accuracy: 0.9667 - val_loss: 0.0060\n",
      "Epoch 29/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9910 - loss: 0.0059 - val_accuracy: 1.0000 - val_loss: 0.0033\n",
      "Epoch 30/30\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9854 - loss: 0.0089 - val_accuracy: 1.0000 - val_loss: 0.0050\n"
     ]
    }
   ],
   "source": [
    "Batch_Size = 8\n",
    "Epoch_Anz = 30\n",
    "Shift_Range = 0\n",
    "Brightness_Range = 0.3\n",
    "\n",
    "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], height_shift_range=[-Shift_Range,Shift_Range],brightness_range=[1-Brightness_Range,1+Brightness_Range])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n",
    "    history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    history = model.fit(train_iterator, epochs = Epoch_Anz)\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "### Step 1: Learing result\n",
    " \n",
    "* Visualization of the training and validation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(val_loss_ges)\n",
    "\n",
    "plt.semilogy(history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Brigthness and Pixel Shift scattering\n",
    "Here a higher number of epochs is used to reach the minimum loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.5801 - loss: 0.4974 - val_accuracy: 0.7333 - val_loss: 0.3528\n",
      "Epoch 2/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.6910 - loss: 0.3593 - val_accuracy: 0.7000 - val_loss: 0.2878\n",
      "Epoch 3/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.8091 - loss: 0.2388 - val_accuracy: 0.8667 - val_loss: 0.1806\n",
      "Epoch 4/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8704 - loss: 0.1880 - val_accuracy: 0.8000 - val_loss: 0.2632\n",
      "Epoch 5/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.8584 - loss: 0.1839 - val_accuracy: 0.8667 - val_loss: 0.1343\n",
      "Epoch 6/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9232 - loss: 0.0994 - val_accuracy: 0.9333 - val_loss: 0.0905\n",
      "Epoch 7/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9151 - loss: 0.0767 - val_accuracy: 0.9333 - val_loss: 0.0663\n",
      "Epoch 8/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9400 - loss: 0.0807 - val_accuracy: 0.9333 - val_loss: 0.0663\n",
      "Epoch 9/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9621 - loss: 0.0496 - val_accuracy: 0.9333 - val_loss: 0.0881\n",
      "Epoch 10/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9301 - loss: 0.0648 - val_accuracy: 0.9667 - val_loss: 0.0373\n",
      "Epoch 11/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9530 - loss: 0.0440 - val_accuracy: 1.0000 - val_loss: 0.0311\n",
      "Epoch 12/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9462 - loss: 0.0443 - val_accuracy: 0.9667 - val_loss: 0.0255\n",
      "Epoch 13/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9434 - loss: 0.0414 - val_accuracy: 0.9667 - val_loss: 0.0325\n",
      "Epoch 14/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9491 - loss: 0.0398 - val_accuracy: 0.9667 - val_loss: 0.0278\n",
      "Epoch 15/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9580 - loss: 0.0301 - val_accuracy: 0.9667 - val_loss: 0.0306\n",
      "Epoch 16/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9705 - loss: 0.0268 - val_accuracy: 0.9667 - val_loss: 0.0223\n",
      "Epoch 17/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9696 - loss: 0.0386 - val_accuracy: 0.9667 - val_loss: 0.0214\n",
      "Epoch 18/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9437 - loss: 0.0330 - val_accuracy: 0.9000 - val_loss: 0.0587\n",
      "Epoch 19/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9558 - loss: 0.0326 - val_accuracy: 0.9667 - val_loss: 0.0219\n",
      "Epoch 20/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9485 - loss: 0.0293 - val_accuracy: 0.9667 - val_loss: 0.0622\n",
      "Epoch 21/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9644 - loss: 0.0318 - val_accuracy: 0.9667 - val_loss: 0.0391\n",
      "Epoch 22/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9725 - loss: 0.0213 - val_accuracy: 0.9667 - val_loss: 0.0186\n",
      "Epoch 23/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9658 - loss: 0.0252 - val_accuracy: 1.0000 - val_loss: 0.0240\n",
      "Epoch 24/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9661 - loss: 0.0244 - val_accuracy: 0.9333 - val_loss: 0.0322\n",
      "Epoch 25/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9702 - loss: 0.0210 - val_accuracy: 0.9667 - val_loss: 0.0100\n",
      "Epoch 26/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9710 - loss: 0.0166 - val_accuracy: 0.9667 - val_loss: 0.0234\n",
      "Epoch 27/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9670 - loss: 0.0181 - val_accuracy: 0.9667 - val_loss: 0.0245\n",
      "Epoch 28/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9603 - loss: 0.0292 - val_accuracy: 0.9333 - val_loss: 0.0246\n",
      "Epoch 29/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9617 - loss: 0.0174 - val_accuracy: 0.9667 - val_loss: 0.0089\n",
      "Epoch 30/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9671 - loss: 0.0164 - val_accuracy: 0.9667 - val_loss: 0.0179\n",
      "Epoch 31/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9740 - loss: 0.0139 - val_accuracy: 1.0000 - val_loss: 0.0382\n",
      "Epoch 32/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9675 - loss: 0.0180 - val_accuracy: 0.9667 - val_loss: 0.0157\n",
      "Epoch 33/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9618 - loss: 0.0162 - val_accuracy: 0.9667 - val_loss: 0.0113\n",
      "Epoch 34/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9617 - loss: 0.0203 - val_accuracy: 0.9667 - val_loss: 0.0082\n",
      "Epoch 35/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9766 - loss: 0.0117 - val_accuracy: 0.9667 - val_loss: 0.0142\n",
      "Epoch 36/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - accuracy: 0.9727 - loss: 0.0129 - val_accuracy: 0.9667 - val_loss: 0.0077\n",
      "Epoch 37/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9811 - loss: 0.0109 - val_accuracy: 0.9667 - val_loss: 0.0082\n",
      "Epoch 38/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9695 - loss: 0.0159 - val_accuracy: 1.0000 - val_loss: 0.0152\n",
      "Epoch 39/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9817 - loss: 0.0128 - val_accuracy: 1.0000 - val_loss: 0.0198\n",
      "Epoch 40/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9821 - loss: 0.0124 - val_accuracy: 0.9667 - val_loss: 0.0122\n",
      "Epoch 41/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9781 - loss: 0.0099 - val_accuracy: 1.0000 - val_loss: 0.0097\n",
      "Epoch 42/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9725 - loss: 0.0110 - val_accuracy: 0.9333 - val_loss: 0.0180\n",
      "Epoch 43/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9684 - loss: 0.0124 - val_accuracy: 0.9667 - val_loss: 0.0180\n",
      "Epoch 44/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9626 - loss: 0.0141 - val_accuracy: 0.9333 - val_loss: 0.0155\n",
      "Epoch 45/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9813 - loss: 0.0104 - val_accuracy: 0.9667 - val_loss: 0.0060\n",
      "Epoch 46/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9710 - loss: 0.0098 - val_accuracy: 1.0000 - val_loss: 0.0126\n",
      "Epoch 47/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9762 - loss: 0.0103 - val_accuracy: 1.0000 - val_loss: 0.0105\n",
      "Epoch 48/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9746 - loss: 0.0096 - val_accuracy: 0.9667 - val_loss: 0.0123\n",
      "Epoch 49/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9792 - loss: 0.0129 - val_accuracy: 1.0000 - val_loss: 0.0082\n",
      "Epoch 50/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9708 - loss: 0.0072 - val_accuracy: 0.9667 - val_loss: 0.0122\n",
      "Epoch 51/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9854 - loss: 0.0096 - val_accuracy: 0.9667 - val_loss: 0.0063\n",
      "Epoch 52/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9634 - loss: 0.0101 - val_accuracy: 1.0000 - val_loss: 0.0151\n",
      "Epoch 53/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9701 - loss: 0.0096 - val_accuracy: 0.9667 - val_loss: 0.0072\n",
      "Epoch 54/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9832 - loss: 0.0071 - val_accuracy: 0.9667 - val_loss: 0.0065\n",
      "Epoch 55/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9854 - loss: 0.0082 - val_accuracy: 0.9667 - val_loss: 0.0077\n",
      "Epoch 56/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9832 - loss: 0.0064 - val_accuracy: 0.9667 - val_loss: 0.0113\n",
      "Epoch 57/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9821 - loss: 0.0068 - val_accuracy: 1.0000 - val_loss: 0.0066\n",
      "Epoch 58/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9742 - loss: 0.0076 - val_accuracy: 0.9667 - val_loss: 0.0092\n",
      "Epoch 59/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9884 - loss: 0.0063 - val_accuracy: 0.9667 - val_loss: 0.0051\n",
      "Epoch 60/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9870 - loss: 0.0058 - val_accuracy: 1.0000 - val_loss: 0.0042\n",
      "Epoch 61/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9801 - loss: 0.0069 - val_accuracy: 0.9667 - val_loss: 0.0042\n",
      "Epoch 62/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9742 - loss: 0.0060 - val_accuracy: 1.0000 - val_loss: 0.0045\n",
      "Epoch 63/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9799 - loss: 0.0062 - val_accuracy: 1.0000 - val_loss: 0.0092\n",
      "Epoch 64/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9827 - loss: 0.0063 - val_accuracy: 1.0000 - val_loss: 0.0038\n",
      "Epoch 65/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9822 - loss: 0.0060 - val_accuracy: 0.9667 - val_loss: 0.0042\n",
      "Epoch 66/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9743 - loss: 0.0051 - val_accuracy: 0.9667 - val_loss: 0.0044\n",
      "Epoch 67/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9698 - loss: 0.0084 - val_accuracy: 0.9667 - val_loss: 0.0053\n",
      "Epoch 68/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9864 - loss: 0.0068 - val_accuracy: 0.9667 - val_loss: 0.0052\n",
      "Epoch 69/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9697 - loss: 0.0080 - val_accuracy: 0.9667 - val_loss: 0.0028\n",
      "Epoch 70/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9699 - loss: 0.0067 - val_accuracy: 1.0000 - val_loss: 0.0045\n",
      "Epoch 71/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9775 - loss: 0.0052 - val_accuracy: 0.9667 - val_loss: 0.0034\n",
      "Epoch 72/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9863 - loss: 0.0055 - val_accuracy: 0.9667 - val_loss: 0.0045\n",
      "Epoch 73/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9771 - loss: 0.0057 - val_accuracy: 0.9667 - val_loss: 0.0048\n",
      "Epoch 74/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9764 - loss: 0.0056 - val_accuracy: 0.9667 - val_loss: 0.0066\n",
      "Epoch 75/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9857 - loss: 0.0062 - val_accuracy: 0.9667 - val_loss: 0.0041\n",
      "Epoch 76/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9805 - loss: 0.0039 - val_accuracy: 0.9667 - val_loss: 0.0047\n",
      "Epoch 77/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9834 - loss: 0.0048 - val_accuracy: 0.9667 - val_loss: 0.0058\n",
      "Epoch 78/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9705 - loss: 0.0071 - val_accuracy: 0.9667 - val_loss: 0.0070\n",
      "Epoch 79/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9802 - loss: 0.0047 - val_accuracy: 1.0000 - val_loss: 0.0068\n",
      "Epoch 80/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9776 - loss: 0.0051 - val_accuracy: 1.0000 - val_loss: 0.0058\n",
      "Epoch 81/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9644 - loss: 0.0058 - val_accuracy: 0.9667 - val_loss: 0.0051\n",
      "Epoch 82/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9877 - loss: 0.0050 - val_accuracy: 0.9667 - val_loss: 0.0070\n",
      "Epoch 83/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9753 - loss: 0.0066 - val_accuracy: 1.0000 - val_loss: 0.0053\n",
      "Epoch 84/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9801 - loss: 0.0057 - val_accuracy: 0.9667 - val_loss: 0.0032\n",
      "Epoch 85/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9856 - loss: 0.0035 - val_accuracy: 1.0000 - val_loss: 0.0045\n",
      "Epoch 86/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9787 - loss: 0.0053 - val_accuracy: 0.9667 - val_loss: 0.0066\n",
      "Epoch 87/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9798 - loss: 0.0055 - val_accuracy: 1.0000 - val_loss: 0.0045\n",
      "Epoch 88/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9765 - loss: 0.0046 - val_accuracy: 0.9667 - val_loss: 0.0032\n",
      "Epoch 89/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9809 - loss: 0.0053 - val_accuracy: 0.9667 - val_loss: 0.0054\n",
      "Epoch 90/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9941 - loss: 0.0041 - val_accuracy: 0.9667 - val_loss: 0.0049\n",
      "Epoch 91/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9896 - loss: 0.0046 - val_accuracy: 0.9667 - val_loss: 0.0050\n",
      "Epoch 92/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9867 - loss: 0.0036 - val_accuracy: 1.0000 - val_loss: 0.0046\n",
      "Epoch 93/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9729 - loss: 0.0077 - val_accuracy: 0.9667 - val_loss: 0.0038\n",
      "Epoch 94/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9804 - loss: 0.0040 - val_accuracy: 1.0000 - val_loss: 0.0056\n",
      "Epoch 95/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9787 - loss: 0.0045 - val_accuracy: 1.0000 - val_loss: 0.0040\n",
      "Epoch 96/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9888 - loss: 0.0041 - val_accuracy: 1.0000 - val_loss: 0.0088\n",
      "Epoch 97/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9854 - loss: 0.0037 - val_accuracy: 1.0000 - val_loss: 0.0026\n",
      "Epoch 98/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9890 - loss: 0.0052 - val_accuracy: 0.9667 - val_loss: 0.0032\n",
      "Epoch 99/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9931 - loss: 0.0036 - val_accuracy: 0.9667 - val_loss: 0.0049\n",
      "Epoch 100/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9822 - loss: 0.0040 - val_accuracy: 0.9667 - val_loss: 0.0041\n",
      "Epoch 101/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9827 - loss: 0.0031 - val_accuracy: 1.0000 - val_loss: 0.0023\n",
      "Epoch 102/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9807 - loss: 0.0053 - val_accuracy: 1.0000 - val_loss: 0.0025\n",
      "Epoch 103/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9797 - loss: 0.0043 - val_accuracy: 1.0000 - val_loss: 0.0025\n",
      "Epoch 104/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9874 - loss: 0.0033 - val_accuracy: 0.9667 - val_loss: 0.0080\n",
      "Epoch 105/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9836 - loss: 0.0038 - val_accuracy: 1.0000 - val_loss: 0.0037\n",
      "Epoch 106/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9680 - loss: 0.0036 - val_accuracy: 0.9667 - val_loss: 0.0115\n",
      "Epoch 107/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9616 - loss: 0.0075 - val_accuracy: 1.0000 - val_loss: 0.0046\n",
      "Epoch 108/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9778 - loss: 0.0039 - val_accuracy: 0.9667 - val_loss: 0.0030\n",
      "Epoch 109/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9915 - loss: 0.0031 - val_accuracy: 0.9667 - val_loss: 0.0044\n",
      "Epoch 110/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9800 - loss: 0.0037 - val_accuracy: 1.0000 - val_loss: 0.0036\n",
      "Epoch 111/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9826 - loss: 0.0038 - val_accuracy: 1.0000 - val_loss: 0.0043\n",
      "Epoch 112/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9877 - loss: 0.0039 - val_accuracy: 1.0000 - val_loss: 0.0030\n",
      "Epoch 113/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9816 - loss: 0.0037 - val_accuracy: 1.0000 - val_loss: 0.0030\n",
      "Epoch 114/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9863 - loss: 0.0041 - val_accuracy: 1.0000 - val_loss: 0.0037\n",
      "Epoch 115/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9834 - loss: 0.0045 - val_accuracy: 1.0000 - val_loss: 0.0018\n",
      "Epoch 116/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9896 - loss: 0.0034 - val_accuracy: 1.0000 - val_loss: 0.0024\n",
      "Epoch 117/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9820 - loss: 0.0038 - val_accuracy: 1.0000 - val_loss: 0.0037\n",
      "Epoch 118/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9741 - loss: 0.0040 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 119/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9847 - loss: 0.0045 - val_accuracy: 0.9667 - val_loss: 0.0024\n",
      "Epoch 120/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9816 - loss: 0.0031 - val_accuracy: 1.0000 - val_loss: 0.0038\n",
      "Epoch 121/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9884 - loss: 0.0034 - val_accuracy: 0.9667 - val_loss: 0.0039\n",
      "Epoch 122/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9712 - loss: 0.0028 - val_accuracy: 0.9667 - val_loss: 0.0051\n",
      "Epoch 123/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9864 - loss: 0.0037 - val_accuracy: 0.9667 - val_loss: 0.0025\n",
      "Epoch 124/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9833 - loss: 0.0037 - val_accuracy: 0.9667 - val_loss: 0.0033\n",
      "Epoch 125/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9886 - loss: 0.0039 - val_accuracy: 1.0000 - val_loss: 0.0063\n",
      "Epoch 126/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9863 - loss: 0.0035 - val_accuracy: 1.0000 - val_loss: 0.0031\n",
      "Epoch 127/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9886 - loss: 0.0041 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 128/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9829 - loss: 0.0026 - val_accuracy: 1.0000 - val_loss: 0.0017\n",
      "Epoch 129/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9783 - loss: 0.0033 - val_accuracy: 0.9667 - val_loss: 0.0026\n",
      "Epoch 130/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9853 - loss: 0.0028 - val_accuracy: 0.9667 - val_loss: 0.0043\n",
      "Epoch 131/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9872 - loss: 0.0046 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 132/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9811 - loss: 0.0022 - val_accuracy: 0.9667 - val_loss: 0.0025\n",
      "Epoch 133/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9920 - loss: 0.0030 - val_accuracy: 1.0000 - val_loss: 0.0028\n",
      "Epoch 134/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9844 - loss: 0.0034 - val_accuracy: 0.9667 - val_loss: 0.0031\n",
      "Epoch 135/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9791 - loss: 0.0028 - val_accuracy: 0.9667 - val_loss: 0.0061\n",
      "Epoch 136/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9821 - loss: 0.0030 - val_accuracy: 1.0000 - val_loss: 0.0019\n",
      "Epoch 137/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9891 - loss: 0.0032 - val_accuracy: 0.9667 - val_loss: 0.0044\n",
      "Epoch 138/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9826 - loss: 0.0029 - val_accuracy: 1.0000 - val_loss: 0.0030\n",
      "Epoch 139/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9816 - loss: 0.0045 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 140/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9922 - loss: 0.0027 - val_accuracy: 0.9667 - val_loss: 0.0045\n",
      "Epoch 141/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9907 - loss: 0.0029 - val_accuracy: 0.9667 - val_loss: 0.0054\n",
      "Epoch 142/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9829 - loss: 0.0027 - val_accuracy: 1.0000 - val_loss: 0.0015\n",
      "Epoch 143/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9839 - loss: 0.0039 - val_accuracy: 0.9667 - val_loss: 0.0037\n",
      "Epoch 144/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9826 - loss: 0.0036 - val_accuracy: 0.9667 - val_loss: 0.0049\n",
      "Epoch 145/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9912 - loss: 0.0027 - val_accuracy: 0.9667 - val_loss: 0.0024\n",
      "Epoch 146/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9890 - loss: 0.0022 - val_accuracy: 1.0000 - val_loss: 0.0017\n",
      "Epoch 147/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9878 - loss: 0.0035 - val_accuracy: 1.0000 - val_loss: 0.0018\n",
      "Epoch 148/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9864 - loss: 0.0042 - val_accuracy: 1.0000 - val_loss: 0.0019\n",
      "Epoch 149/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9945 - loss: 0.0022 - val_accuracy: 0.9667 - val_loss: 0.0022\n",
      "Epoch 150/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9870 - loss: 0.0027 - val_accuracy: 1.0000 - val_loss: 0.0038\n",
      "Epoch 151/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9781 - loss: 0.0043 - val_accuracy: 1.0000 - val_loss: 0.0022\n",
      "Epoch 152/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9850 - loss: 0.0027 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 153/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9945 - loss: 0.0027 - val_accuracy: 0.9667 - val_loss: 0.0028\n",
      "Epoch 154/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9810 - loss: 0.0027 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 155/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9892 - loss: 0.0022 - val_accuracy: 1.0000 - val_loss: 0.0023\n",
      "Epoch 156/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9878 - loss: 0.0026 - val_accuracy: 0.9667 - val_loss: 0.0033\n",
      "Epoch 157/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9823 - loss: 0.0027 - val_accuracy: 0.9667 - val_loss: 0.0014\n",
      "Epoch 158/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9749 - loss: 0.0020 - val_accuracy: 0.9667 - val_loss: 0.0014\n",
      "Epoch 159/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9797 - loss: 0.0020 - val_accuracy: 1.0000 - val_loss: 0.0018\n",
      "Epoch 160/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9924 - loss: 0.0038 - val_accuracy: 1.0000 - val_loss: 0.0035\n",
      "Epoch 161/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9892 - loss: 0.0028 - val_accuracy: 0.9667 - val_loss: 0.0028\n",
      "Epoch 162/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9933 - loss: 0.0025 - val_accuracy: 1.0000 - val_loss: 0.0017\n",
      "Epoch 163/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9918 - loss: 0.0016 - val_accuracy: 0.9667 - val_loss: 0.0021\n",
      "Epoch 164/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9830 - loss: 0.0024 - val_accuracy: 0.9667 - val_loss: 0.0056\n",
      "Epoch 165/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9795 - loss: 0.0039 - val_accuracy: 1.0000 - val_loss: 0.0027\n",
      "Epoch 166/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9873 - loss: 0.0025 - val_accuracy: 1.0000 - val_loss: 0.0018\n",
      "Epoch 167/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9960 - loss: 0.0021 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 168/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 0.0024 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 169/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9794 - loss: 0.0022 - val_accuracy: 1.0000 - val_loss: 0.0019\n",
      "Epoch 170/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9865 - loss: 0.0020 - val_accuracy: 1.0000 - val_loss: 0.0042\n",
      "Epoch 171/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9891 - loss: 0.0028 - val_accuracy: 1.0000 - val_loss: 0.0027\n",
      "Epoch 172/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9887 - loss: 0.0019 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 173/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9855 - loss: 0.0024 - val_accuracy: 1.0000 - val_loss: 0.0026\n",
      "Epoch 174/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 0.0020 - val_accuracy: 0.9667 - val_loss: 0.0025\n",
      "Epoch 175/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9923 - loss: 0.0060 - val_accuracy: 1.0000 - val_loss: 0.0015\n",
      "Epoch 176/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9802 - loss: 0.0025 - val_accuracy: 1.0000 - val_loss: 0.0019\n",
      "Epoch 177/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9858 - loss: 0.0022 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 178/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9988 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 0.0030\n",
      "Epoch 179/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9860 - loss: 0.0024 - val_accuracy: 0.9667 - val_loss: 0.0019\n",
      "Epoch 180/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9978 - loss: 0.0019 - val_accuracy: 0.9667 - val_loss: 0.0017\n",
      "Epoch 181/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9829 - loss: 0.0029 - val_accuracy: 0.9667 - val_loss: 0.0024\n",
      "Epoch 182/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9945 - loss: 0.0035 - val_accuracy: 1.0000 - val_loss: 9.9074e-04\n",
      "Epoch 183/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9899 - loss: 0.0019 - val_accuracy: 0.9667 - val_loss: 0.0017\n",
      "Epoch 184/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9830 - loss: 0.0024 - val_accuracy: 0.9667 - val_loss: 0.0020\n",
      "Epoch 185/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9770 - loss: 0.0024 - val_accuracy: 1.0000 - val_loss: 0.0028\n",
      "Epoch 186/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9913 - loss: 0.0020 - val_accuracy: 1.0000 - val_loss: 0.0017\n",
      "Epoch 187/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9886 - loss: 0.0043 - val_accuracy: 1.0000 - val_loss: 0.0026\n",
      "Epoch 188/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9800 - loss: 0.0018 - val_accuracy: 0.9667 - val_loss: 0.0017\n",
      "Epoch 189/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9824 - loss: 0.0022 - val_accuracy: 0.9667 - val_loss: 0.0038\n",
      "Epoch 190/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9876 - loss: 0.0021 - val_accuracy: 0.9667 - val_loss: 0.0018\n",
      "Epoch 191/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9931 - loss: 0.0020 - val_accuracy: 0.9667 - val_loss: 0.0021\n",
      "Epoch 192/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 0.0027 - val_accuracy: 0.9667 - val_loss: 0.0022\n",
      "Epoch 193/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9852 - loss: 0.0020 - val_accuracy: 0.9667 - val_loss: 0.0028\n",
      "Epoch 194/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9832 - loss: 0.0019 - val_accuracy: 0.9667 - val_loss: 0.0020\n",
      "Epoch 195/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9909 - loss: 0.0017 - val_accuracy: 0.9667 - val_loss: 0.0043\n",
      "Epoch 196/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9773 - loss: 0.0024 - val_accuracy: 0.9667 - val_loss: 0.0018\n",
      "Epoch 197/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9916 - loss: 0.0016 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 198/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9920 - loss: 0.0026 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 199/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9990 - loss: 0.0019 - val_accuracy: 1.0000 - val_loss: 0.0019\n",
      "Epoch 200/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9906 - loss: 0.0019 - val_accuracy: 1.0000 - val_loss: 0.0019\n",
      "Epoch 201/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9861 - loss: 0.0020 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 202/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9840 - loss: 0.0017 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 203/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9859 - loss: 0.0021 - val_accuracy: 0.9667 - val_loss: 0.0010\n",
      "Epoch 204/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9721 - loss: 0.0018 - val_accuracy: 0.9667 - val_loss: 0.0016\n",
      "Epoch 205/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9855 - loss: 0.0017 - val_accuracy: 1.0000 - val_loss: 0.0017\n",
      "Epoch 206/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9863 - loss: 0.0017 - val_accuracy: 0.9667 - val_loss: 0.0025\n",
      "Epoch 207/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9810 - loss: 0.0019 - val_accuracy: 0.9667 - val_loss: 0.0016\n",
      "Epoch 208/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9900 - loss: 0.0026 - val_accuracy: 1.0000 - val_loss: 0.0015\n",
      "Epoch 209/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9940 - loss: 0.0030 - val_accuracy: 1.0000 - val_loss: 0.0018\n",
      "Epoch 210/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9964 - loss: 0.0027 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 211/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9931 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 212/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9712 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 0.0019\n",
      "Epoch 213/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9796 - loss: 0.0021 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 214/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9895 - loss: 0.0021 - val_accuracy: 0.9667 - val_loss: 0.0016\n",
      "Epoch 215/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9868 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 0.0015\n",
      "Epoch 216/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9922 - loss: 0.0017 - val_accuracy: 1.0000 - val_loss: 0.0014\n",
      "Epoch 217/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9943 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 0.0015\n",
      "Epoch 218/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9920 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 219/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9810 - loss: 0.0019 - val_accuracy: 1.0000 - val_loss: 0.0019\n",
      "Epoch 220/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9893 - loss: 0.0019 - val_accuracy: 1.0000 - val_loss: 0.0018\n",
      "Epoch 221/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9931 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 222/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9943 - loss: 0.0017 - val_accuracy: 1.0000 - val_loss: 0.0018\n",
      "Epoch 223/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9858 - loss: 0.0017 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 224/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9889 - loss: 0.0016 - val_accuracy: 1.0000 - val_loss: 0.0027\n",
      "Epoch 225/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9895 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 226/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9970 - loss: 0.0017 - val_accuracy: 1.0000 - val_loss: 0.0014\n",
      "Epoch 227/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9956 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0016\n",
      "Epoch 228/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9927 - loss: 0.0015 - val_accuracy: 0.9667 - val_loss: 0.0022\n",
      "Epoch 229/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9876 - loss: 0.0021 - val_accuracy: 1.0000 - val_loss: 0.0029\n",
      "Epoch 230/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9867 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 0.0014\n",
      "Epoch 231/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9888 - loss: 0.0020 - val_accuracy: 0.9667 - val_loss: 0.0010\n",
      "Epoch 232/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9811 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 233/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9903 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 234/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9815 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0028\n",
      "Epoch 235/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9813 - loss: 0.0019 - val_accuracy: 1.0000 - val_loss: 8.2100e-04\n",
      "Epoch 236/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9894 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 6.6820e-04\n",
      "Epoch 237/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9930 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0016\n",
      "Epoch 238/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9911 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 239/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9933 - loss: 0.0015 - val_accuracy: 1.0000 - val_loss: 0.0043\n",
      "Epoch 240/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9953 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 241/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9814 - loss: 0.0018 - val_accuracy: 0.9667 - val_loss: 0.0029\n",
      "Epoch 242/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9943 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 9.5904e-04\n",
      "Epoch 243/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9966 - loss: 9.8914e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 244/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9888 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 245/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9934 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 0.0025\n",
      "Epoch 246/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9974 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 247/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9853 - loss: 0.0017 - val_accuracy: 0.9667 - val_loss: 0.0016\n",
      "Epoch 248/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9978 - loss: 0.0017 - val_accuracy: 1.0000 - val_loss: 0.0015\n",
      "Epoch 249/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9990 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0017\n",
      "Epoch 250/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9885 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 0.0077\n"
     ]
    }
   ],
   "source": [
    "Batch_Size = 8\n",
    "Epoch_Anz = 250\n",
    "Shift_Range = 3\n",
    "Brightness_Range = 0.3\n",
    "\n",
    "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], height_shift_range=[-Shift_Range,Shift_Range],brightness_range=[1-Brightness_Range,1+Brightness_Range])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n",
    "    history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    history = model.fit(train_iterator, epochs = Epoch_Anz)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall Learing results (Step 1 & Step 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(val_loss_ges)\n",
    "\n",
    "\n",
    "plt.semilogy(loss_ges)\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9869 - loss: 0.0029 - val_accuracy: 1.0000 - val_loss: 7.7409e-04\n",
      "Epoch 2/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9883 - loss: 0.0014 - val_accuracy: 0.9667 - val_loss: 0.0022\n",
      "Epoch 3/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9927 - loss: 0.0012 - val_accuracy: 0.9667 - val_loss: 0.0013\n",
      "Epoch 4/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9780 - loss: 0.0019 - val_accuracy: 0.9667 - val_loss: 0.0014\n",
      "Epoch 5/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9886 - loss: 0.0013 - val_accuracy: 0.9667 - val_loss: 0.0010\n",
      "Epoch 6/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9916 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 7/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9927 - loss: 0.0019 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 8/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9910 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 7.5403e-04\n",
      "Epoch 9/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9818 - loss: 9.6807e-04 - val_accuracy: 0.9667 - val_loss: 6.2045e-04\n",
      "Epoch 10/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 0.0015 - val_accuracy: 0.9667 - val_loss: 0.0041\n",
      "Epoch 11/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9871 - loss: 0.0028 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 12/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9824 - loss: 0.0013 - val_accuracy: 0.9667 - val_loss: 0.0013\n",
      "Epoch 13/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9927 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 6.8015e-04\n",
      "Epoch 14/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9924 - loss: 0.0012 - val_accuracy: 0.9667 - val_loss: 9.3785e-04\n",
      "Epoch 15/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9905 - loss: 0.0020 - val_accuracy: 1.0000 - val_loss: 5.7963e-04\n",
      "Epoch 16/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9877 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 0.0018\n",
      "Epoch 17/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9932 - loss: 0.0012 - val_accuracy: 0.9667 - val_loss: 0.0014\n",
      "Epoch 18/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9961 - loss: 0.0016 - val_accuracy: 1.0000 - val_loss: 0.0035\n",
      "Epoch 19/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9825 - loss: 0.0018 - val_accuracy: 1.0000 - val_loss: 9.0721e-04\n",
      "Epoch 20/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9881 - loss: 0.0016 - val_accuracy: 0.9667 - val_loss: 0.0010\n",
      "Epoch 21/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9890 - loss: 0.0015 - val_accuracy: 1.0000 - val_loss: 8.5000e-04\n",
      "Epoch 22/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9843 - loss: 0.0011 - val_accuracy: 1.0000 - val_loss: 8.7728e-04\n",
      "Epoch 23/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 0.0011 - val_accuracy: 1.0000 - val_loss: 7.0553e-04\n",
      "Epoch 24/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9986 - loss: 0.0011 - val_accuracy: 1.0000 - val_loss: 5.7140e-04\n",
      "Epoch 25/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9871 - loss: 9.6128e-04 - val_accuracy: 1.0000 - val_loss: 0.0018\n",
      "Epoch 26/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9828 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 27/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9840 - loss: 0.0014 - val_accuracy: 0.9667 - val_loss: 0.0033\n",
      "Epoch 28/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9912 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 7.2457e-04\n",
      "Epoch 29/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9882 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 30/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9945 - loss: 0.0011 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 31/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9915 - loss: 0.0020 - val_accuracy: 0.9667 - val_loss: 0.0021\n",
      "Epoch 32/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9961 - loss: 0.0012 - val_accuracy: 0.9667 - val_loss: 0.0012\n",
      "Epoch 33/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9897 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 0.0017\n",
      "Epoch 34/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9970 - loss: 0.0026 - val_accuracy: 1.0000 - val_loss: 9.4030e-04\n",
      "Epoch 35/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9947 - loss: 0.0011 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 36/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9986 - loss: 9.5454e-04 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 37/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9774 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 38/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9907 - loss: 0.0011 - val_accuracy: 0.9667 - val_loss: 0.0010\n",
      "Epoch 39/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9911 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 8.2999e-04\n",
      "Epoch 40/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9864 - loss: 0.0011 - val_accuracy: 1.0000 - val_loss: 9.8667e-04\n",
      "Epoch 41/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9896 - loss: 0.0014 - val_accuracy: 0.9667 - val_loss: 0.0013\n",
      "Epoch 42/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9963 - loss: 0.0010 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 43/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9941 - loss: 0.0012 - val_accuracy: 0.9667 - val_loss: 0.0011\n",
      "Epoch 44/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9916 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 6.0415e-04\n",
      "Epoch 45/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9852 - loss: 0.0012 - val_accuracy: 0.9667 - val_loss: 0.0011\n",
      "Epoch 46/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9885 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 8.3697e-04\n",
      "Epoch 47/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9939 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 48/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9939 - loss: 9.8182e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 49/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9942 - loss: 9.8244e-04 - val_accuracy: 1.0000 - val_loss: 8.6827e-04\n",
      "Epoch 50/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9960 - loss: 0.0010 - val_accuracy: 1.0000 - val_loss: 6.5262e-04\n",
      "Epoch 51/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9960 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 0.0020\n",
      "Epoch 52/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9913 - loss: 0.0016 - val_accuracy: 1.0000 - val_loss: 9.0619e-04\n",
      "Epoch 53/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9939 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 8.2366e-04\n",
      "Epoch 54/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9863 - loss: 9.2057e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 55/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9942 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 9.9061e-04\n",
      "Epoch 56/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9937 - loss: 9.5902e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 57/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9969 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 7.3141e-04\n",
      "Epoch 58/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9920 - loss: 8.8918e-04 - val_accuracy: 1.0000 - val_loss: 0.0014\n",
      "Epoch 59/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9988 - loss: 9.5478e-04 - val_accuracy: 1.0000 - val_loss: 7.1588e-04\n",
      "Epoch 60/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9944 - loss: 0.0011 - val_accuracy: 1.0000 - val_loss: 8.0292e-04\n",
      "Epoch 61/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9847 - loss: 9.3757e-04 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 62/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9891 - loss: 0.0013 - val_accuracy: 0.9667 - val_loss: 6.4569e-04\n",
      "Epoch 63/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9909 - loss: 8.5611e-04 - val_accuracy: 1.0000 - val_loss: 7.4743e-04\n",
      "Epoch 64/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9906 - loss: 0.0013 - val_accuracy: 0.9667 - val_loss: 8.1137e-04\n",
      "Epoch 65/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9887 - loss: 8.9361e-04 - val_accuracy: 1.0000 - val_loss: 8.8759e-04\n",
      "Epoch 66/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9960 - loss: 0.0015 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 67/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9972 - loss: 8.4150e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 68/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9985 - loss: 0.0010 - val_accuracy: 1.0000 - val_loss: 5.3750e-04\n",
      "Epoch 69/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9968 - loss: 0.0011 - val_accuracy: 0.9667 - val_loss: 9.1386e-04\n",
      "Epoch 70/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9890 - loss: 0.0028 - val_accuracy: 1.0000 - val_loss: 6.4527e-04\n",
      "Epoch 71/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9964 - loss: 7.2471e-04 - val_accuracy: 1.0000 - val_loss: 3.6377e-04\n",
      "Epoch 72/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 0.0011 - val_accuracy: 0.9667 - val_loss: 7.9372e-04\n",
      "Epoch 73/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9968 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 8.1119e-04\n",
      "Epoch 74/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9920 - loss: 0.0014 - val_accuracy: 1.0000 - val_loss: 7.2836e-04\n",
      "Epoch 75/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9967 - loss: 8.8387e-04 - val_accuracy: 1.0000 - val_loss: 8.6370e-04\n",
      "Epoch 76/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9972 - loss: 9.3821e-04 - val_accuracy: 1.0000 - val_loss: 7.0743e-04\n",
      "Epoch 77/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9931 - loss: 8.5459e-04 - val_accuracy: 1.0000 - val_loss: 7.8377e-04\n",
      "Epoch 78/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9982 - loss: 0.0010 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 79/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9927 - loss: 8.3768e-04 - val_accuracy: 1.0000 - val_loss: 8.8135e-04\n",
      "Epoch 80/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9927 - loss: 8.9791e-04 - val_accuracy: 1.0000 - val_loss: 5.1811e-04\n",
      "Epoch 81/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9902 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 0.0014\n",
      "Epoch 82/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9897 - loss: 0.0010 - val_accuracy: 1.0000 - val_loss: 9.9758e-04\n",
      "Epoch 83/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9923 - loss: 0.0013 - val_accuracy: 0.9667 - val_loss: 0.0011\n",
      "Epoch 84/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9964 - loss: 9.2814e-04 - val_accuracy: 1.0000 - val_loss: 7.1936e-04\n",
      "Epoch 85/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9958 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 7.2442e-04\n",
      "Epoch 86/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 8.8609e-04 - val_accuracy: 1.0000 - val_loss: 7.9283e-04\n",
      "Epoch 87/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 8.9022e-04 - val_accuracy: 1.0000 - val_loss: 9.8282e-04\n",
      "Epoch 88/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9909 - loss: 9.4547e-04 - val_accuracy: 0.9667 - val_loss: 7.3659e-04\n",
      "Epoch 89/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9964 - loss: 0.0011 - val_accuracy: 1.0000 - val_loss: 6.6085e-04\n",
      "Epoch 90/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9946 - loss: 9.3043e-04 - val_accuracy: 1.0000 - val_loss: 0.0021\n",
      "Epoch 91/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9988 - loss: 0.0013 - val_accuracy: 1.0000 - val_loss: 5.7805e-04\n",
      "Epoch 92/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 8.4700e-04 - val_accuracy: 1.0000 - val_loss: 9.3045e-04\n",
      "Epoch 93/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9909 - loss: 8.0010e-04 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 94/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9962 - loss: 0.0011 - val_accuracy: 0.9667 - val_loss: 0.0010\n",
      "Epoch 95/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9883 - loss: 8.6504e-04 - val_accuracy: 1.0000 - val_loss: 0.0019\n",
      "Epoch 96/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9898 - loss: 9.3827e-04 - val_accuracy: 1.0000 - val_loss: 5.2981e-04\n",
      "Epoch 97/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9910 - loss: 8.3225e-04 - val_accuracy: 1.0000 - val_loss: 4.6496e-04\n",
      "Epoch 98/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9812 - loss: 8.1114e-04 - val_accuracy: 1.0000 - val_loss: 0.0017\n",
      "Epoch 99/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 9.4139e-04 - val_accuracy: 1.0000 - val_loss: 6.4237e-04\n",
      "Epoch 100/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9884 - loss: 8.8952e-04 - val_accuracy: 0.9667 - val_loss: 0.0014\n",
      "Epoch 101/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9896 - loss: 9.2984e-04 - val_accuracy: 1.0000 - val_loss: 6.4121e-04\n",
      "Epoch 102/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9835 - loss: 8.9372e-04 - val_accuracy: 1.0000 - val_loss: 6.9823e-04\n",
      "Epoch 103/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9972 - loss: 9.6590e-04 - val_accuracy: 0.9667 - val_loss: 0.0012\n",
      "Epoch 104/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9953 - loss: 9.4482e-04 - val_accuracy: 1.0000 - val_loss: 0.0015\n",
      "Epoch 105/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9953 - loss: 9.7127e-04 - val_accuracy: 1.0000 - val_loss: 8.5385e-04\n",
      "Epoch 106/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 8.2187e-04 - val_accuracy: 0.9667 - val_loss: 5.4639e-04\n",
      "Epoch 107/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9897 - loss: 6.9752e-04 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 108/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9850 - loss: 8.8068e-04 - val_accuracy: 0.9667 - val_loss: 7.8101e-04\n",
      "Epoch 109/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9972 - loss: 8.0802e-04 - val_accuracy: 0.9667 - val_loss: 5.1896e-04\n",
      "Epoch 110/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9943 - loss: 6.8156e-04 - val_accuracy: 0.9667 - val_loss: 0.0018\n",
      "Epoch 111/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9902 - loss: 9.7119e-04 - val_accuracy: 1.0000 - val_loss: 5.6657e-04\n",
      "Epoch 112/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9992 - loss: 7.8401e-04 - val_accuracy: 1.0000 - val_loss: 8.9474e-04\n",
      "Epoch 113/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9969 - loss: 7.4126e-04 - val_accuracy: 1.0000 - val_loss: 5.7969e-04\n",
      "Epoch 114/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9945 - loss: 7.5604e-04 - val_accuracy: 1.0000 - val_loss: 8.0974e-04\n",
      "Epoch 115/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9968 - loss: 0.0011 - val_accuracy: 0.9667 - val_loss: 7.4893e-04\n",
      "Epoch 116/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9918 - loss: 8.0127e-04 - val_accuracy: 1.0000 - val_loss: 7.2413e-04\n",
      "Epoch 117/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9935 - loss: 8.6527e-04 - val_accuracy: 1.0000 - val_loss: 5.8617e-04\n",
      "Epoch 118/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9921 - loss: 9.5052e-04 - val_accuracy: 1.0000 - val_loss: 3.2377e-04\n",
      "Epoch 119/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9947 - loss: 8.7729e-04 - val_accuracy: 1.0000 - val_loss: 7.0263e-04\n",
      "Epoch 120/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9916 - loss: 0.0011 - val_accuracy: 1.0000 - val_loss: 7.3909e-04\n",
      "Epoch 121/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9974 - loss: 9.2037e-04 - val_accuracy: 1.0000 - val_loss: 7.3799e-04\n",
      "Epoch 122/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9951 - loss: 7.1596e-04 - val_accuracy: 1.0000 - val_loss: 5.0634e-04\n",
      "Epoch 123/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9907 - loss: 9.1013e-04 - val_accuracy: 1.0000 - val_loss: 8.7499e-04\n",
      "Epoch 124/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9968 - loss: 9.2760e-04 - val_accuracy: 1.0000 - val_loss: 8.1922e-04\n",
      "Epoch 125/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9928 - loss: 8.1368e-04 - val_accuracy: 1.0000 - val_loss: 0.0029\n",
      "Epoch 126/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9983 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 7.2689e-04\n",
      "Epoch 127/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9974 - loss: 6.6122e-04 - val_accuracy: 0.9667 - val_loss: 7.0254e-04\n",
      "Epoch 128/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9959 - loss: 6.2685e-04 - val_accuracy: 1.0000 - val_loss: 4.2965e-04\n",
      "Epoch 129/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9883 - loss: 7.5969e-04 - val_accuracy: 1.0000 - val_loss: 5.8184e-04\n",
      "Epoch 130/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9941 - loss: 8.1043e-04 - val_accuracy: 1.0000 - val_loss: 9.6443e-04\n",
      "Epoch 131/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9973 - loss: 7.1910e-04 - val_accuracy: 1.0000 - val_loss: 8.3554e-04\n",
      "Epoch 132/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9884 - loss: 8.4082e-04 - val_accuracy: 1.0000 - val_loss: 5.1276e-04\n",
      "Epoch 133/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9950 - loss: 8.2700e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 134/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9953 - loss: 9.6718e-04 - val_accuracy: 1.0000 - val_loss: 4.1077e-04\n",
      "Epoch 135/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9930 - loss: 7.1781e-04 - val_accuracy: 1.0000 - val_loss: 5.6292e-04\n",
      "Epoch 136/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9896 - loss: 7.1681e-04 - val_accuracy: 1.0000 - val_loss: 8.8776e-04\n",
      "Epoch 137/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9955 - loss: 7.0089e-04 - val_accuracy: 1.0000 - val_loss: 5.3627e-04\n",
      "Epoch 138/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9916 - loss: 8.2961e-04 - val_accuracy: 1.0000 - val_loss: 8.1056e-04\n",
      "Epoch 139/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9932 - loss: 7.3435e-04 - val_accuracy: 1.0000 - val_loss: 5.7745e-04\n",
      "Epoch 140/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9912 - loss: 9.0096e-04 - val_accuracy: 1.0000 - val_loss: 6.6325e-04\n",
      "Epoch 141/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9976 - loss: 8.7092e-04 - val_accuracy: 1.0000 - val_loss: 5.8439e-04\n",
      "Epoch 142/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9998 - loss: 5.8643e-04 - val_accuracy: 0.9667 - val_loss: 8.7387e-04\n",
      "Epoch 143/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9974 - loss: 7.2393e-04 - val_accuracy: 1.0000 - val_loss: 6.4778e-04\n",
      "Epoch 144/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9941 - loss: 8.2158e-04 - val_accuracy: 1.0000 - val_loss: 0.0016\n",
      "Epoch 145/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9953 - loss: 8.8013e-04 - val_accuracy: 1.0000 - val_loss: 4.3355e-04\n",
      "Epoch 146/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9974 - loss: 6.6925e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 147/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 6.7722e-04 - val_accuracy: 1.0000 - val_loss: 5.1965e-04\n",
      "Epoch 148/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9878 - loss: 6.8659e-04 - val_accuracy: 1.0000 - val_loss: 5.8634e-04\n",
      "Epoch 149/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9961 - loss: 6.1229e-04 - val_accuracy: 1.0000 - val_loss: 8.1967e-04\n",
      "Epoch 150/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9974 - loss: 5.8872e-04 - val_accuracy: 1.0000 - val_loss: 5.8115e-04\n",
      "Epoch 151/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9995 - loss: 5.6742e-04 - val_accuracy: 1.0000 - val_loss: 5.0859e-04\n",
      "Epoch 152/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9913 - loss: 5.4465e-04 - val_accuracy: 1.0000 - val_loss: 7.2912e-04\n",
      "Epoch 153/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9978 - loss: 8.0726e-04 - val_accuracy: 0.9667 - val_loss: 6.3985e-04\n",
      "Epoch 154/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9947 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 7.2926e-04\n",
      "Epoch 155/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9968 - loss: 6.6394e-04 - val_accuracy: 1.0000 - val_loss: 6.2178e-04\n",
      "Epoch 156/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9887 - loss: 5.4768e-04 - val_accuracy: 1.0000 - val_loss: 6.4878e-04\n",
      "Epoch 157/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9982 - loss: 5.9524e-04 - val_accuracy: 1.0000 - val_loss: 5.3309e-04\n",
      "Epoch 158/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9991 - loss: 8.0084e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 159/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9903 - loss: 7.8434e-04 - val_accuracy: 1.0000 - val_loss: 7.6706e-04\n",
      "Epoch 160/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9993 - loss: 7.7197e-04 - val_accuracy: 1.0000 - val_loss: 4.5892e-04\n",
      "Epoch 161/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9957 - loss: 6.9708e-04 - val_accuracy: 1.0000 - val_loss: 7.9162e-04\n",
      "Epoch 162/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9967 - loss: 6.9068e-04 - val_accuracy: 1.0000 - val_loss: 6.7115e-04\n",
      "Epoch 163/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9839 - loss: 6.5127e-04 - val_accuracy: 1.0000 - val_loss: 7.2305e-04\n",
      "Epoch 164/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9882 - loss: 6.5388e-04 - val_accuracy: 1.0000 - val_loss: 0.0016\n",
      "Epoch 165/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9931 - loss: 8.2740e-04 - val_accuracy: 1.0000 - val_loss: 6.3786e-04\n",
      "Epoch 166/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9904 - loss: 6.8830e-04 - val_accuracy: 1.0000 - val_loss: 7.0835e-04\n",
      "Epoch 167/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9962 - loss: 6.7405e-04 - val_accuracy: 1.0000 - val_loss: 4.9874e-04\n",
      "Epoch 168/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9981 - loss: 7.3102e-04 - val_accuracy: 1.0000 - val_loss: 9.4580e-04\n",
      "Epoch 169/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9958 - loss: 6.1201e-04 - val_accuracy: 1.0000 - val_loss: 7.0961e-04\n",
      "Epoch 170/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9986 - loss: 6.4974e-04 - val_accuracy: 1.0000 - val_loss: 8.5578e-04\n",
      "Epoch 171/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9989 - loss: 8.9280e-04 - val_accuracy: 0.9667 - val_loss: 4.0725e-04\n",
      "Epoch 172/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9957 - loss: 5.7302e-04 - val_accuracy: 1.0000 - val_loss: 3.9158e-04\n",
      "Epoch 173/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9952 - loss: 6.3615e-04 - val_accuracy: 1.0000 - val_loss: 5.7086e-04\n",
      "Epoch 174/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9954 - loss: 6.1927e-04 - val_accuracy: 1.0000 - val_loss: 8.4190e-04\n",
      "Epoch 175/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9978 - loss: 7.5559e-04 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 176/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9934 - loss: 6.6941e-04 - val_accuracy: 1.0000 - val_loss: 5.1034e-04\n",
      "Epoch 177/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9980 - loss: 6.2880e-04 - val_accuracy: 1.0000 - val_loss: 0.0017\n",
      "Epoch 178/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9914 - loss: 6.0902e-04 - val_accuracy: 1.0000 - val_loss: 3.1120e-04\n",
      "Epoch 179/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9958 - loss: 7.2467e-04 - val_accuracy: 1.0000 - val_loss: 3.2254e-04\n",
      "Epoch 180/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9971 - loss: 7.4693e-04 - val_accuracy: 1.0000 - val_loss: 9.1875e-04\n",
      "Epoch 181/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 8.4158e-04 - val_accuracy: 1.0000 - val_loss: 6.0156e-04\n",
      "Epoch 182/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9884 - loss: 6.0289e-04 - val_accuracy: 1.0000 - val_loss: 7.2742e-04\n",
      "Epoch 183/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9949 - loss: 6.1889e-04 - val_accuracy: 0.9667 - val_loss: 7.1577e-04\n",
      "Epoch 184/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9975 - loss: 8.0995e-04 - val_accuracy: 1.0000 - val_loss: 6.6125e-04\n",
      "Epoch 185/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9940 - loss: 5.4528e-04 - val_accuracy: 1.0000 - val_loss: 7.1419e-04\n",
      "Epoch 186/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 7.3318e-04 - val_accuracy: 1.0000 - val_loss: 4.8962e-04\n",
      "Epoch 187/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9930 - loss: 8.0933e-04 - val_accuracy: 1.0000 - val_loss: 9.1561e-04\n",
      "Epoch 188/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9987 - loss: 6.4772e-04 - val_accuracy: 1.0000 - val_loss: 7.3386e-04\n",
      "Epoch 189/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9950 - loss: 5.3448e-04 - val_accuracy: 1.0000 - val_loss: 9.2539e-04\n",
      "Epoch 190/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9936 - loss: 7.4105e-04 - val_accuracy: 1.0000 - val_loss: 7.0573e-04\n",
      "Epoch 191/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9954 - loss: 6.3174e-04 - val_accuracy: 1.0000 - val_loss: 5.6386e-04\n",
      "Epoch 192/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9935 - loss: 5.2592e-04 - val_accuracy: 1.0000 - val_loss: 4.3371e-04\n",
      "Epoch 193/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9983 - loss: 4.8513e-04 - val_accuracy: 1.0000 - val_loss: 7.8981e-04\n",
      "Epoch 194/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9958 - loss: 6.3426e-04 - val_accuracy: 1.0000 - val_loss: 5.3358e-04\n",
      "Epoch 195/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9836 - loss: 5.0651e-04 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 196/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9905 - loss: 6.2935e-04 - val_accuracy: 1.0000 - val_loss: 6.1231e-04\n",
      "Epoch 197/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 6.7948e-04 - val_accuracy: 1.0000 - val_loss: 3.5082e-04\n",
      "Epoch 198/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9926 - loss: 5.7074e-04 - val_accuracy: 1.0000 - val_loss: 5.1503e-04\n",
      "Epoch 199/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9999 - loss: 7.0253e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 200/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 6.7636e-04 - val_accuracy: 1.0000 - val_loss: 4.9209e-04\n",
      "Epoch 201/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 5.1731e-04 - val_accuracy: 1.0000 - val_loss: 4.8560e-04\n",
      "Epoch 202/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9927 - loss: 6.8141e-04 - val_accuracy: 1.0000 - val_loss: 3.5492e-04\n",
      "Epoch 203/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9953 - loss: 8.8353e-04 - val_accuracy: 1.0000 - val_loss: 2.6286e-04\n",
      "Epoch 204/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9983 - loss: 5.9878e-04 - val_accuracy: 1.0000 - val_loss: 2.7275e-04\n",
      "Epoch 205/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 6.1585e-04 - val_accuracy: 1.0000 - val_loss: 0.0011\n",
      "Epoch 206/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9926 - loss: 5.8450e-04 - val_accuracy: 1.0000 - val_loss: 4.4711e-04\n",
      "Epoch 207/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9929 - loss: 7.0998e-04 - val_accuracy: 1.0000 - val_loss: 6.6807e-04\n",
      "Epoch 208/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9958 - loss: 6.9847e-04 - val_accuracy: 1.0000 - val_loss: 6.1351e-04\n",
      "Epoch 209/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9882 - loss: 6.6550e-04 - val_accuracy: 0.9667 - val_loss: 3.0212e-04\n",
      "Epoch 210/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9994 - loss: 7.3355e-04 - val_accuracy: 1.0000 - val_loss: 6.0150e-04\n",
      "Epoch 211/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9937 - loss: 6.2038e-04 - val_accuracy: 1.0000 - val_loss: 5.7398e-04\n",
      "Epoch 212/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9923 - loss: 4.4908e-04 - val_accuracy: 0.9667 - val_loss: 5.6235e-04\n",
      "Epoch 213/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9892 - loss: 6.9457e-04 - val_accuracy: 1.0000 - val_loss: 6.3450e-04\n",
      "Epoch 214/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9961 - loss: 6.0992e-04 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 215/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9963 - loss: 7.4903e-04 - val_accuracy: 0.9667 - val_loss: 7.1724e-04\n",
      "Epoch 216/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9961 - loss: 5.3977e-04 - val_accuracy: 1.0000 - val_loss: 3.6615e-04\n",
      "Epoch 217/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9925 - loss: 6.4122e-04 - val_accuracy: 1.0000 - val_loss: 5.0651e-04\n",
      "Epoch 218/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9911 - loss: 6.2133e-04 - val_accuracy: 1.0000 - val_loss: 6.9483e-04\n",
      "Epoch 219/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9961 - loss: 5.4029e-04 - val_accuracy: 1.0000 - val_loss: 3.3090e-04\n",
      "Epoch 220/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9937 - loss: 6.3718e-04 - val_accuracy: 1.0000 - val_loss: 6.0138e-04\n",
      "Epoch 221/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9965 - loss: 5.9567e-04 - val_accuracy: 1.0000 - val_loss: 4.5882e-04\n",
      "Epoch 222/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9909 - loss: 5.1514e-04 - val_accuracy: 1.0000 - val_loss: 6.3252e-04\n",
      "Epoch 223/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9896 - loss: 5.2901e-04 - val_accuracy: 0.9667 - val_loss: 6.7377e-04\n",
      "Epoch 224/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9933 - loss: 6.1003e-04 - val_accuracy: 1.0000 - val_loss: 7.8288e-04\n",
      "Epoch 225/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9952 - loss: 5.7875e-04 - val_accuracy: 1.0000 - val_loss: 6.4377e-04\n",
      "Epoch 226/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 4.8045e-04 - val_accuracy: 1.0000 - val_loss: 3.6899e-04\n",
      "Epoch 227/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9903 - loss: 5.9833e-04 - val_accuracy: 1.0000 - val_loss: 5.8822e-04\n",
      "Epoch 228/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9933 - loss: 6.9817e-04 - val_accuracy: 1.0000 - val_loss: 5.3858e-04\n",
      "Epoch 229/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9930 - loss: 4.9540e-04 - val_accuracy: 1.0000 - val_loss: 3.9728e-04\n",
      "Epoch 230/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9932 - loss: 4.9480e-04 - val_accuracy: 1.0000 - val_loss: 3.8222e-04\n",
      "Epoch 231/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9932 - loss: 5.7428e-04 - val_accuracy: 0.9667 - val_loss: 5.2071e-04\n",
      "Epoch 232/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9967 - loss: 6.2812e-04 - val_accuracy: 1.0000 - val_loss: 3.8466e-04\n",
      "Epoch 233/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9981 - loss: 5.1120e-04 - val_accuracy: 1.0000 - val_loss: 4.5810e-04\n",
      "Epoch 234/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9983 - loss: 5.8169e-04 - val_accuracy: 0.9667 - val_loss: 6.5922e-04\n",
      "Epoch 235/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9953 - loss: 5.7564e-04 - val_accuracy: 1.0000 - val_loss: 4.5170e-04\n",
      "Epoch 236/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9938 - loss: 5.4659e-04 - val_accuracy: 0.9667 - val_loss: 5.4703e-04\n",
      "Epoch 237/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9969 - loss: 5.0013e-04 - val_accuracy: 1.0000 - val_loss: 5.8445e-04\n",
      "Epoch 238/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9981 - loss: 6.6626e-04 - val_accuracy: 1.0000 - val_loss: 3.6755e-04\n",
      "Epoch 239/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9929 - loss: 6.0389e-04 - val_accuracy: 1.0000 - val_loss: 4.3183e-04\n",
      "Epoch 240/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9938 - loss: 5.2571e-04 - val_accuracy: 1.0000 - val_loss: 3.9276e-04\n",
      "Epoch 241/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9963 - loss: 5.1044e-04 - val_accuracy: 1.0000 - val_loss: 4.0120e-04\n",
      "Epoch 242/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9947 - loss: 5.9640e-04 - val_accuracy: 1.0000 - val_loss: 5.9256e-04\n",
      "Epoch 243/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 5.5646e-04 - val_accuracy: 1.0000 - val_loss: 7.3008e-04\n",
      "Epoch 244/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9993 - loss: 5.1396e-04 - val_accuracy: 1.0000 - val_loss: 7.5343e-04\n",
      "Epoch 245/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9898 - loss: 6.8051e-04 - val_accuracy: 1.0000 - val_loss: 6.7552e-04\n",
      "Epoch 246/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9968 - loss: 5.7281e-04 - val_accuracy: 1.0000 - val_loss: 3.6427e-04\n",
      "Epoch 247/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9972 - loss: 6.9405e-04 - val_accuracy: 1.0000 - val_loss: 4.4396e-04\n",
      "Epoch 248/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9995 - loss: 4.5139e-04 - val_accuracy: 0.9667 - val_loss: 3.1618e-04\n",
      "Epoch 249/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9937 - loss: 5.6284e-04 - val_accuracy: 1.0000 - val_loss: 3.5981e-04\n",
      "Epoch 250/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9955 - loss: 5.3402e-04 - val_accuracy: 1.0000 - val_loss: 6.8671e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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v1msKgiAIQmsRIdRF6LqGYcaFSieFxnRFB5kyLFUQBEEQUhAh1EXomoZJQgh1jkjRFSXUqVPuBUEQBKGHIkKoi9A1DSMhhOgcIaQYQimOkGEY3HzzzYwYMYKcnBzGjx/PCy+8gGEYDB48mPvvv9+1/uuvv0bXdZYtWwZYk+m333578vLyGDJkCL/73e+oq6vrlM8hCIIgCJ2Ft6tvoFdhmhBpyGqpHjMwIk2gRTGa6tDbGx7z5YKaE5S4H/ulWwjdfPPNPPXUUzzwwAOMHj2aGTNmcNppp/HOO+9w8skn8/TTT3PBBRfY6//zn/+w1157MWzYMOv+dZ27776bESNG8PPPP/O73/2OP/7xj9x3333t+xyCIAiCsBkRIdSRRBrgpoFZLVW/+A6x5f60Gvx5mfcrjlAoFOKmm25i2rRp7LHHHgCMHDmSTz75hAcffJA//vGP3HbbbSxfvpyhQ4diGAbPPPMMf/nLX+xzXHrppfbr4cOHc8MNN3D++eeLEBIEQRB6FBIay0CnD13d3CiO0OLFi2loaOCggw4iPz/f/vPEE0/w008/MWHCBMaOHcvTTz8NwEcffcT69es54YQT7HNMmzaNAw88kEGDBlFQUMDpp5/Oxo0baWjIzhETBEEQhO6AOEIZmDJlClOmTKGmpoaioqLsDvLlWs5MltSvXkCeFiJaOBRvXkkb71S5djNoSh5SIpfnjTfeYNCgQa51gUAAgFNPPZWnn36aK6+8kqeffppDDz2UPn36ALB06VKOPPJILrjgAm688UZKS0v55JNPOOeccwiHw+TmNn8vgiAIgtBdECHUkWha8+GpJExfHmg6pi+nVcdlj5MjpM40GzduHIFAgOXLl7PffvulPfKUU07hL3/5C3PmzOGFF17ggQcesPfNmTMHwzC47bbb7H5Izz33XCfcvyAIgiB0LiKEuhBT69zyeRUNw0qe1jQKCgq44ooruOyyyzAMg7333pvq6mo+/fRTCgsLOfPMMxk+fDh77rkn55xzDrFYjKOPPto+11ZbbUUkEuGee+7hqKOO4tNPP3UJJUEQBEHoKUiOUBeS6CO0OZodakA46vQS+tvf/sbVV1/NzTffzNixYzn00EN54403GDFihL3m1FNPZd68eRx77LHk5OTY28ePH8/tt9/O3//+d7bbbjv+85//cPPNN3f6ZxAEQRCEjkYzzU5qa9xLSOQIVVdXU1hY6NrX1NTEkiVLGDFiBMFgsNXnrl69mCJqCef2x188oKNu2aZ+42ryQuvs998bwxjet5C8QPcyAtv7PQqCIAhCMs09v1XEEepCEqGx5B4/HXgF1zsdg6qGcCddSxAEQRB6HiKEupTOnTWWjI6Jltx0URAEQRC2YEQIdSGmFv/6O8kRSpY8OkZK82lBEARB2JIRIdSFdPbQ1WSfSXeuKAiCIAgCIoQ6hDbnm9uOUOeExrQ0OULdMTQm+fqCIAhCVyFCqB14PB4AwuG2JSCLI2SRGMvh8/m6+E4EQRCELY3uVUfdw/B6veTm5lJRUYHP57O7LGdLOGrQFDMJm1HMpqYOv79QOIon6sghw4wQDofohEu1CdM0aWhoYP369RQXF9vCUhAEQRA2FyKE2oGmaQwYMIAlS5awbNmyVh9fX1tNVayaqF6Dt6bjXaFwXSX+aK39vopGtEANtTndy3kpLi6mf//+XX0bgiAIwhaICKF24vf7GT16dJvCY6+9+ARHr7mb9Xlj6Xf2kx1+b9+//DxjVjkzwB6JHEpowllcsP+IZo7avPh8PnGCBEEQhC5DhFAHoOt6mzoih2MQrFuB38zvlI7KWqiGYN0K+31edClrw0j3ZkEQBEGII8nSXYnPEiQeI9RJF7DygwzTSpHup1URjnb+XDNBEARB6CmIEOpCtIQQinVO9rIWr0ZbRwkA5VqlCCFBEARBUBAhlIGpU6cybtw4Jk6c2GnX0Hx5APiMzhFCif4868xiwHKEQiKEBEEQBMFGhFAGpkyZwvz585k9e3anXcMM5APgjzV02jUA1pmlAPSlilAk1qnXEgRBEISehAihLkTzW0IoYDaB0QkCJREaM63QWECL4otUd/x1BEEQBKGHIkKoC9GCBc6bcF3HXyAeGgvho8q0wnC5oYqOv44gCIIg9FBECHUhXl+QiBnvoRPqeCGUmDVmoNmuUEF0Q4dfRxAEQRB6KiKEuhC/z0M98Z4+neEIxYXQwOJcGoJW5+aSyPpOuI4gCIIg9ExECHUhAa+HOnKsN53gCCVyhApzfPQfvg0AfSNrOv46giAIgtBDESHUhQS8OvVmwhGqbX5xW4jnCJloRIuGAtAvtrbjryMIgiAIPRQRQl2I36s7obHOcITioTE0HaNoOAADDBFCgiAIgpBAhFAXEvDq1Jnx0FgnVo0BUDIMgAHmuo6/jiAIgiD0UEQIdSEBr5IsHeqE0JjtCGlQbAmhUmo66VqCIAiC0PMQIdSF5AU81NN5jpBmOqExf34xNWau9b5GEqYFQRAEAUQIdSn5AS918WRpszNzhNDwe3Tq4u6TEa7vhGsJgiAIQs9DhFAXkhfw2qGxaGNNx1/ATAxY1fB7dRrNAACRJhFCgiAIggAihLqUXL8TGuscIRQvn9d0/F6dJvzWtUQICYIgCAIgQqhL0TSNqMeaARZr6gQhpCRL+z06jViOUDQkQkgQBEEQQIRQlxP1WkLIbOrM8nkNTdNoiguhWLih468lCIIgCD0QEUJdTMxnCaHOmTXm5AgBhPW4EJLQmCAIgiAAIoS6HDOQD4DWKeXziReWEIro8cRsCY0JgiAIAiBCqMvR/JYQ0jvTEdKsH3PUFkISGhMEQRAEECHU9QQKAPBEO8GlUUdsAIbXqlCT0JggCIIgWIgQ6mI8QUsIeTtDCOGUzwPEPJYQkoaKgiAIgmAhQqiL8eYUAuAxoxANdezJTWXWGGD6EkJIQmOCIAiCACKEuhxvboHzpoPHbGjxHCEtXjWGLzHXrLFDryMIgiAIPRURQl1MXjBIo2l1fCbcwVPhk6rGNF986GpEHCFBEARBgC1ECB177LGUlJRw/PHHd/WtpJDn99jDUDveEXLnCGl+SwhpUXGEBEEQBAG2ECF0ySWX8MQTT3T1baQlP+ij3kyErDpWCJlKZ2kAPS6E9GhTh15HEARBEHoqW4QQmjRpEgUFBS0v7AJyfB57An2n5QjFQ2PegNXF2hMTR0gQBEEQoBsIoRkzZnDUUUcxcOBANE3jlVdeSVkzdepUhg8fTjAYZLfddmPWrFmb/0Y7iYBXp46EI9TBOUJJoTFPMCGExBESBEEQBOgGQqi+vp7x48czderUtPufffZZLr/8cv7617/y1VdfMX78eA455BDWr19vr5kwYQLbbbddyp/Vq1dvro/RZgI+nXqzcxyh5IaK/rgQ8hviCAmCIAgCgLerb+Cwww7jsMMOy7j/9ttv59xzz+Xss88G4IEHHuCNN97gkUce4corrwRg7ty5HXY/oVCIUMjp51NTU9Nh505HwOuhKhEa6+AcoUSytBZ3hHw5lhDyGR3cr0gQBEEQeihd7gg1RzgcZs6cOUyePNnepus6kydP5vPPP++Ua958880UFRXZf4YMGdIp10kQ9OnUJZKlO8kRMuM5QsEca66Z3xQhJAiCIAjQzYXQhg0biMVilJeXu7aXl5ezdu3arM8zefJkTjjhBN58800GDx7crIi66qqrqK6utv+sWLGizfefDQGvmizdse6ThrtqzB8XQgFCKWEzQRAEQdgS6fLQ2OZg2rRpWa8NBAIEAoFOvJuk66nJ0qGObqiYCI3FHaHCMmKmhkczoG4dFPTv2OsJgiAIQg+jWztCZWVleDwe1q1b59q+bt06+vfvHQ/xgE+nyrScGhorO/TcifL5RGfp3NxcVpp9rW0bF3fotQRBEAShJ9KthZDf72fnnXdm+vTp9jbDMJg+fTp77LFHp1576tSpjBs3jokTJ3bqdQJeD5VxIWQ2bOrYk5uJ/1g/5ny/lyXmAABqlnzJ1Pd/ZF2NlNILgiAIWy5dLoTq6uqYO3euXfm1ZMkS5s6dy/LlywG4/PLLeeihh3j88cf54YcfuOCCC6ivr7eryDqLKVOmMH/+fGbPnt2p1wn6dKqwmj12uBDCHRrLD3pZYlpOWuFHf8V8/2/89sk5HXxNQRAEQeg5dHmO0Jdffsn+++9vv7/88ssBOPPMM3nsscc46aSTqKio4JprrmHt2rVMmDCBt99+OyWBuqfi9+i2I0Rjxwqh5NCYR9dY4xlk77/Q+yr/XHFSh15TEARBEHoSXS6EJk2apMzESs+FF17IhRdeuJnuaPPi9ejUapYjpHV4aCzxvTrG3wb/IIhYr9eYpR17PUEQBEHoYXR5aEyABm8RAFqsCcINHX+BuCMEsCB3Z+YZIwFoNP3qLkEQBEHY4hAh1A2IePOJmB7rTQeGxzTTSLyyt+XnBLkyci4ABVojef4uNwUFQRAEocsQIZSBzVU1BhD0eajCGn9BB4bH7IaKuvNjLszxUhvvW5RPI7l+T4ddTxAEQRB6GiKEMrC5qsYAAj4PVaaVJ9SxCdNxIaTEvwqDPnukR44WpsDXgZcTBEEQhB6GCKFuQMCrU0m8cqzDS+hBU0JjBUEv9YlO1kAfX6TDrycIgiAIPQURQt2AgFenshMcITtHSHWEcnxE8BIyLSuo1CcNFQVBEIQtFxFC3QC1uzQNHTlmwz10FazQGGDnCeWZnVClJgiCIAg9BBFC3YCA0l26Qx0hO0fInSwN2HlC3lh9h11PEARBEHoaIoQysDmrxtyOUEeGxlKTpQvijlA9QQD8UXGEBEEQhC0XEUIZ2LxVY0qy9GaoGgOoi4fG/OIICYIgCFswIoS6AQGvTlUnOELO0FXnxzymfwEFAS+18dBY0BAhJAiCIGy5iBDqBlihsc6oGkt1hPoWBJj55wPZdoQ1fDXQgiMUisYwjOZnwQmCIAhCT0WEUDcg6Gu5j9C736/ludkrWnVeLU3VGECu30sgz5pvFjQaMx7fEI4y8YZpHP/AZ626riAIgiD0FGTQVDcg4FU6SzdVgxED3T364rwn5wCwx6g+DCnNzfLMqVVjzkULASg2qzIePWvJJmqaony1PPMaQRAEQejJiCPUDQh4dWfWGCY0Vrn2m6YTmqpsCGd/4vhxGqkj5iMDdwFgkjELYtGU/V8tr+SZWY4DJeExQRAEoTciQigDm7N8PtfvIYqXRj0uhpLyhNqqQfQ0VWMJIiMns8nMp59WCQteT9n/y/s+4+3v19rvGyOxtt2EIAiCIHRjRAhlYHOWz+fEJ8DX61a4KjlPKKYooXTuTmZSp88n8PuDPBM7wHrz+qUtVquJEBIEQRB6IyKEugFBnyWEqvRia0PNStd+lxBqjQ7KkCwN4PPo3Bk9jhVGX2ishJXNC77GsAghQRAEofchQqgbkBt3hJZ4R1gb1sxz7Y8ahv26NUIoUT6vpTnI69EI42OZ2c/a0FTT7LnEERIEQRB6IyKEugE5cUdooT7K2rB6rmt/W0Nj6WaNJfB5rG01iSTtpqpmz9UgjpAgCILQCxEh1A1ICKHvzZEAmGvmElYcmGibK7YyJ0vbQsiMl+I3VTd7poZwamWZIAiCIPR0RAh1AxLJ0j/EBmHqPrSmak649Xl7v+oIGWb2oqi50JhH19A11RFqXgg1SWhMEARB6IWIEOoGJIRQXVQnGiwBIFK30e4f1GYhhJVblE4IgeUKZe8IiRASBEEQeh8ihDKwOfsIJUJjDeEYUZ/VYbqARlsAqUIo1oYwmZnhx+z36NSSKoTMNGJLqsYEQRCE3ogIoQx0RR+hxkiMmM+aOVagNdi5QdE2O0Lx0JiewRHypneEIrE0QkhCY4IgCEIvRIRQNyDhCJkmhDxxIUSD4gg55fMxI/X4zGTuIwTg1TVqEo5QyCmfV8v1E0hoTBAEQeiNiBDqBiSEEECTx0peztcaicZMFq+v5Y73frT3tyY0lkiWTjt0lUSOUDxZetUcu2w/XZWahMYEQRCE3ogIoW6A16Pjj5ez12uWMCmggahhMPn2Gbzx7Rp7bZtCYxmEkN+rO44QwL/2AyNGVEJjgiAIwhaCCKFuQtBn/SjqzBwACrXGtO5PW4RQph6MXl1zHKEElUuJpom/iSMkCIIg9EZECHUTEgnT1YYlhCxHKFX0tCo0FhdCenPl86ojBFCxkEiaa0iOkCAIgtAbESHUTcj1ewHYFAsCVo5Qex0h4mtNPGl3+7w69QTdGysWpHeEItJZWhAEQeh9iBDqJiQm0G+MBoDmHKHsz9li+byuARrLRp3qbKxYmL58XhwhQRAEoRciQqibkJhAXxGOCyGtwVU2n6B1DRWbX5uYNzZvh7/ASU9ZGysWSPm8IAiCsMUgQigDm7OzNDgl9OvCfgDyaUzrCLUmNKbbydKZQ2MAkagBfbexNm5YRDSaKnqaqxpbtrGel75a2aau14IgCILQlXi7+ga6K1OmTGHKlCnU1NRQVFTU6ddLhMZWN/rBCwVaI1VpQlStyhGyy+fTh8b8Hmt7JGZAyQjQfRBpQK9ZmbI2HM0ck9vvHx/a5zlp4tBW3J8gCIIgdC3iCHUT8gOWENoUs0JjhR1SNRb/b6YcoXhoLBQ1wOOFstHW9k2LUtaGs0hOmrlkU9b3JgiCIAjdARFC3YRBJVbZfHW8r08+jcSi4ZR1reojZCbES/ofc2meFYbbWBeyNvQdA4Bv048payNZCKFMZfqCIAiC0F0RIdRNGFpq9fPZRAEh04uumei1a1PWtaVqjAwCpbzQKp1fV5MQQlaeULAqVQg1FxpzricIgiAIPQsRQt2EoaWWE2Sis8bsA4C3blXKOqMNCcmZcoTKC60w3LraJmtDn60ACNYuS1mbrqQ+9TqtvjVBEARB6FJECHUThvZxOjyvjgshX20aIdSqqrG4i5Nh1li/uCO0PuEIFVuJzoH61Otm5wiJEhIEQRB6FiKEugn9C4P44lVcq8wyAHz1q1PWxTqwaqy8IC6EapuobojQmDcYgGDDWrxEOVr/jBM8HwLZJUvr8tskCIIg9DDk0dVN8Ogag0ssV2g1lhAKtDM0ZleNZXCEEqGxDXVhJvztXfa773vwBtEwOFj/krv99/IP37/oRyXhqMHb363h7EdnOcnVGa8oCIIgCD0DEULdiB0GW/2KVsVDY/50jlAbhq5mcoRKcv1446X1pgnr68LECi1X6E7fVHvdKN26j3/N+JkPFlbw6U8b019PdJAgCILQwxAh1I04cGw54ITG8moWkzwmI4ucZRstEUbL4Ajpuka/goBr28KmEgD8mtNJeqS2BnCqyyJKvpDqUIkOEgRBEHoaIoS6Eftt3ReAr43R1JlB8hpXs6u2wLWmdaGxhBDKvCaRMJ3gq5rClDUJIVQRD4mprpSaOyR9hARBEISehgihDGzuWWMARTk+bj1uBxoI8npsdwB+6fnYtaZNIzaa+TEn8oQS/Dd2ADHTEjSLjEEAnON9i621FXblWEQZyqoKIdFBgiAIQk9DhFAGpkyZwvz585k9e/Zmve6JE4cweWw/PjB2BGAbfYVrf2uqxuyhqxlGbIDTVDHB9+YIDg7fynWR07khepq9/Sbfv517UB2hLMrqBUEQBKG7IkNXuyEeXWO52Q+AIdp6177WNVRs2RFKzhEC+MkcxE+xQRRSb2/bRV/EUfpn/MrzAcsa7gaGA24hlG42miAIgiB0Z8QR6oZ4dZ0VppUv1EerJY9Ge19bRmxozTT4Sc4RUqkhj4lNTvXYPf572cvzPeN/ftDeps4gi4g7JAiCIPQwRAh1Qzy6Rh25bDLzARiiVdj7WhMac/oIZV6THBpLpoIS1pnFrm3BsDNlXhwhQRAEoScjQqgbkujtkwiPDdXW2ftaVzXW/IgNcCdLTz1lp7RrlpnlrvcRfPbrUDR94rQgCIIg9ARECHVDPHEhtNLOE3IcoeaqxuYs28SZj8xi8fo6QKmab04IFTiOUL/CgH1tleVJQsgba7Bfq+InKkJIEARB6GGIEOqGeD0JIWTlCQ3SNtj7mguNHXf/53y0qIKL/vs10HJnaYDiXMfd6ZPnpyCYmj+/3Ojnep8fchK41bygbCbUC4IgCEJ3QqrGuiEJV6bCtEZu9NFq7H3ZhMbW1zQBakPFzHpX0zQePmMXNtSFGNk3n8Kgj6qGiGvNZ9p4LucF+31eeAO3v7uQd+evY0z/Ant7RBwhQRAEoYchQqgb4o1XeW0wrS7Pfai292WjNYI+D+AIoZY6Pk8e54S+0jlCi/3bgHLd3MhG7n1/EQY6C9bW2ttFCAmCIAg9DQmNdUMSydIbsRyhMtURyqJqLOizfqx6FqGxZAqDvpRtQa+HA0P/4NnoJAA8GIzRVvC8/1pO8Hxor4tKaEwQBEHoYYgQ6oZ44jlCG+zQmOMIZSOEcvwea5x8gmZCY8mkc4R8Xo2fzEH8X/Q81prWUNa/+/7FRH0R//D9y14njpAgCILQ0xAh1A2xHaF4aKyUWvR4bCqWRY5Qjs8thJprqJhMYU6qI+TzOMeviwuhHfQlKeskWVoQBEHoaYgQ6oZ44sJlE1YiskczKcYqic8uNOYhMV7DIvvQWEluqhDyK0JofVwIpUMcIUEQBKGnIUKoG5JwhGJ47O7SicqxTI6Quj2Y7Ai1Ikfo4G37p2wb1ifXfp3cZVpFOksLgiAIPQ0RQt0QtalhIk+oLJ4nlMl0qQ9H7ddWaMxZ2JrQ2C7DHMfnuqO3ZcYf9qc0z+k+vS6tI2QJIJlELwiCIPQ0pHy+G+JVhNBGswhYRVm8hD5TaKyuyRFC1uFtc4Q0TePLv0zm4x8rOHr8IDy6ht/jHL+W0pRjcgnRQJCoIUJIEARB6FmII9QNUR2hingJfblWCTQjhEKOEIoaZlJorHU/5rL8AMfuONi+D7+3+RyhfBqB9MnSTZEY1UkNGgVBEAShuyBCKANTp05l3LhxTJw4cbNfW63SSozZ+IXnUw7U52TMEaptcsSGtUZZ14rQWEv3ky40VqBZs8fSJUvv948PGH/9u9Q0iRgSBEEQuh8ihDIwZcoU5s+fz+zZszf7tVVHKCGEttOX8m//bRREKtIeU9uU7AgpOUKtqBpLh+oIpUuWdhwhtxCKGSbrakIAzF9dk3KcIAiCIHQ1IoS6IWqO0CqzzLWvOLzWebP0E/h8KpimOzQWM9pcNZYOVQhVUsByo69rf57WFL+u262qaXRcIPUcgiAIgtBdkKdTN8TtCLmFUGHEmUTPY0fAO3+CH991JUtHk0Jjmt4+IZTnt3LqLTGj8Yvw33ifXe39BXFHKGqYmIoAq2wIO/ckzRYFQRCEbogIoW5IutBYguJ0obHKZS5HKJYUGmvNiI10HLnDAM7aczgX7DcKgE0UcnXwKj6MjQec0Bi4E6YrlSTp+aur2efW99nj5ul89pMi5gRBEAShCxEh1A1RGxNuNTApNBZLI4Q8XsJKfk401r6qsWT65Ae49uht2W5Qkb0tx++hjhwA8jVVCDn3EZz/HP/y3UYuTUxfsJ4VmxpZU93E/+atbtf9CIIgCEJHIUKoG6JWhr0yZS+ej+5rvy9JOEJGzDlA9xFTnBirn48ihNqZLJ1AzV3K8XmoNS0hlCjt70M1OfdsCy+eC8C2M//IwZ45nO15m2olX2hlZSOCIAiC0B0QIdQNUR0hr67xvxF/4eLwFABKYvGwUqTBOUD3uo6JJfcRamf5vH0vSmPFHL+HeoIA/M77GpP1OZzqmY5etxa+fc4l1Eq0WqqUMNmKTcq9C4IgCEIXIkKoGxJTwkuapvHY2bty1H57AFCaCI2F650DNN3lIrW3oWImPEmOUFRpTH6m5x1G6UrIa5Mznd5Eo0pJnF5V1ZixH5IgCIIgbE5ECHVDkoeX6rqGVjoMgD6xDRCqcwuhWMh1jFWh1XHl8wm8irPk9+q8azqVY7vqC9lfn+ssrlhgvzTRqFGq2iIxk3U1TR1yT4IgCILQHkQIdUPSuSXRnL6sMvugY8Dqr9xCKBoipsz5ihpJfYTaWT6fQHWEfB6N+fpohjf9h2VGPwJahEJNCXmt/cZ+aaTJUZI8IUEQBKE7IEKoG+JJI1x0TeMrY7T1ZsWsJEco7JpKr5bPG6bWgY6QKoR0fLrVV+iu6C+da/sLrRcrv1SOTCeEUvOETNOUCfaCIAjCZkWEUDfkV7sOZftBRVx+0Nb2No+eJIQimR2hiBIaM0knQ9qGmizt8+h44u9fMvbl75Ff8Ux0Ej9PvNZaULHQXusndc5YvdL3KMGUp79iwvXvsqk+nLJPEARBEDoDb8tLhM1NfsDL/y7a27VN1zXmGHFhtHIWTDjF2RkNZawaM9HoIEPIlSPk8+iu9w8aR2OY8Lw3LnBqVtr7cgilnCvdpPo3v7XGh7w6dxVn7zWiY25aEARBEJpBHKEegkfT+MEcRgg/NFa6cnCIhdJUjcVDY2gd1kcoOUfIpzhEffIDgNV1OpnELDKVqJE5BGZKQZkgCIKwmWiTEHr88cd544037Pd//OMfKS4uZs8992TZsmUddnOCg0fXiOBlkSceHvvpA2dnNJzkCDkNFTvWEXKHxtRQWZ88PwCPfp06Zd5yhEz20b+hSLdEke0IVa+E/54MSz6214sOEgRBEDYXbRJCN910Ezk5Vlfhzz//nKlTp3LrrbdSVlbGZZdd1qE3KFgkxMx8zxjrxeqvnJ3JjpBrxEbHCSFP2mRpi7K4IzR7ferF8mjiIH0OT/pv4R7/Pc49Arx8Pix8Ex4/0l5viiUkCIIgbCbalCO0YsUKttpqKwBeeeUVjjvuOM477zz22msvJk2a1JH3J8TxxNXMUn1w6s6kHCF1+nxHhsZ8HqWPkEdzOUKlcUcohocqM49izUnmztVCbKMtB2BfvmastoxIzPr9Yd33HXJvgiAIgtAW2uQI5efns3HjRgDeffddDjroIACCwSCNjdIfpjNIuDGVZmoOjlU+7+TcqOXzHRkaUx0hr0d3Caw++X779cake8whRP/4PDKAc71vEEncr9oGII4YQoIgCMLmok2O0EEHHcRvfvMbdtxxRxYtWsThhx8OwPfff8/w4cM78v6EOHpchGyiIHVnNOSEmoCIYWCaJhodXD6vCCGPrmEq2TyJ0BhAJQXAGvt9X62KYdpa+/1R+uf8WLOKmqatKIylVpSZkiUkCIIgbCba5AhNnTqVPfbYg4qKCl588UX69OkDwJw5czj55JM79AYFi0RobJNZlLozFnblCJmmM6/McoQ6qGpMCYVpmtu5KVMcoU2mW6yVanXs7bFCYGHNj0+LUf/Na+xw7btpryOOkCAIgrC5aJMjVFxczL333puy/brrrmv3DQnp0bUWHKGksRwxI4aXRI5Qx6A6QrqmYSiKpSQ3sxBS+TFnPNs2zGa0tirjGtFBgiAIwuaiTY7Q22+/zSeffGK/nzp1KhMmTOCUU06hsrKymSOFtpIo0KozAuAJuHdGQynzySKxWPyVZouo9qLmCGm4nZuCoM9+vTFNL6EEPxfsAsBW2uqMa8QREgRBEDYXbRJCf/jDH6ipsfrFfPvtt/z+97/n8MMPZ8mSJVx++eUdeoOCRUKEGAC5fdw7Y6GUBoWJnCETOixJSC2XT3aECoKOufhibF+WG33TnmNt8U4AbKWvtAbIxmk0HUcpEut588bSDcoVBEEQuj9tEkJLlixh3LhxALz44osceeSR3HTTTUydOpW33nqrQ29QsEjkCMUME2JJs7jSOELRuCNkoHdY1Ziuu3OE1EsWKo7Qz+ZADgjfxt6hO1POUV80CoC+Wg3DtHX29gYclysSMwhFY7z7/VqqG1PnlHU3/vLKt+x20zQ21qUmfguCIAjdmzYJIb/fT0ODNT182rRpHHzwwQCUlpbaTlF3YcWKFUyaNIlx48axww478Pzzz3f1LbWJhAiJGSY0bHDvjIUZ0zSPPlTbm6JRa+ZXR1aNqWjNOEIAUbysNPvZ79eYpdwUORl/bhGrTMvRmqgvsPd7FHcoHDW4/d1FnPfkHH77pDrFvnvy1BfL2VAX5qkvlnf1rQiCIAitpE3J0nvvvTeXX345e+21F7NmzeLZZ58FYNGiRQwenKbhXxfi9Xq58847mTBhAmvXrmXnnXfm8MMPJy8vr6tvrVUkHCEjnbKpWMANXMmUQCl7hKwk9pgdGuu4qjEVPalqLD+Y/lfp9Nz7KdEbeG3DAAD+FvDykzGQQZ6N7Ko7E+oD8Qn1+TSgNVXxn28sQf3Fz5s6/N47Cyn7FwRB6Hm0yRG699578Xq9vPDCC9x///0MGjQIgLfeeotDDz20Q2+wvQwYMIAJEyYA0L9/f8rKyti0qec8XBMkcoQiMQN2/x0An8a2da0ZoDmfKxEaMzuwakxFA5cj5PPo5Pg8Kesq/INZkz/Ofp/j97LYtH5fJulzne1amKd9N/Bd8Ddc+c2h6EY4+VTdHknyFgRB6Hm0SQgNHTqU119/nXnz5nHOOefY2++44w7uvvvuVp1rxowZHHXUUQwcOBBN03jllVdS1kydOpXhw4cTDAbZbbfdmDVrVltumzlz5hCLxRgyZEibju9KEg0LQ1GDqj3/xEmhq7kpemrKOi9WSCyiCqFOUEKapqU8+JPDYwABn4f8gLPd59FsIVSmucOoe3rm269LjZ5XfSg6SBAEoefRptAYQCwW45VXXuGHH34AYNttt+Xoo4/G40l1BZqjvr6e8ePH8+tf/5pf/vKXKfufffZZLr/8ch544AF222037rzzTg455BAWLlxIv35WDsqECRPsnBiVd999l4EDBwKwadMmzjjjDB566KHWftRuQY7fQ7+CAOtrQyyviTHTHMtwbU3Kuv5aJSvNvq6Gih1VPq+ia25HCKAwx8f6WnfCcMCrk68kUnt1ncXGwJYvYMRaXtPdEEtIEAShx9EmIbR48WIOP/xwVq1axZgx1jT0m2++mSFDhvDGG28watSorM912GGHcdhhh2Xcf/vtt3Puuedy9tlnA/DAAw/wxhtv8Mgjj3DllVcCMHfu3GavEQqF+MUvfsGVV17Jnnvu2eLaUMh5mHen5O9hfXJZXxti2UYrUT1s+lLWDGIDK+lL1BZCnUNysjRkcIS8OvkBRxx7FUeoOXxmz6vAEhkkCILQ82hTaOziiy9m1KhRrFixgq+++oqvvvqK5cuXM2LECC6++OIOu7lwOMycOXOYPHmyc8O6zuTJk/n888+zOodpmpx11lkccMABnH766S2uv/nmmykqKrL/dKcw2pDSXACWb2rA79UJkSqEBmpWRVmir1BHh8YOGldOnt/DkTsMILl1jtpUMUHAmxoa20QhPxkDmr1ODj1QCIkSEgRB6HG0SQh99NFH3HrrrZSWltrb+vTpwy233MJHH33UYTe3YcMGYrEY5eXlru3l5eWsXbs2w1FuPv30U5599lleeeUVJkyYwIQJE/j2228zrr/qqquorq62/6xYsaJdn6EjGVZqVbr9452FhKMG4TRCaFBcCJmRRgBCpq9D06X/dfrOfH3NwRTn+jGzcYR8OhOHO78nPo/1K3d+5DLqtDx+1EdQZwZTjsvRemCytHhCgiAIPY42hcYCgQC1tbUp2+vq6vD7/WmO6Dr23ntvDCP7TsWBQIBAINDywi5gWJ9c1/twmh/fKH01xIBwPQANBDvUEdI0Db9XKeVXKMwQGjtoXDn/OH4Hxg0spKbRyuX60RzM+X0eoaJR5791Z6ccpzpCC9bWMKa8wG4DUB+Kkqe4TGz8Cf57Mux1Mex4Wns/oiAIgrAF0SZH6Mgjj+S8885j5syZmKaJaZp88cUXnH/++Rx99NEddnNlZWV4PB7WrVvn2r5u3Tr69+/fYdfpKew6otT13h/MIYRbeB6sf8lh+kyGfWf1E2og0Cnl85CaLJ0pNKZpGifsMoRtBxbhUybYR3xFhDU/ehonRRVCh975MR8uqgDg1bmr2Pav7/DQjJ+dxa/8DjYshFentPcjtRrVFZPQmCAIQs+jTULo7rvvZtSoUeyxxx4Eg0GCwSB77rknW221FXfeeWeH3Zzf72fnnXdm+vTp9jbDMJg+fTp77LFHh12npzCwOIe3LtnHfl+U42elf4T93jA18rQQ9/vvomTDHADqzWCnVI1B6oO/IJDeEVLxepz3/vg+L6kVYjm4Q2Ovfm1Nq7/02bkA3PjmD87ONXOzveVWY5oms5duoqYp/aiPqGKLybgxQRCEnkebQmPFxcW8+uqrLF682C6fHzt2LFtttVWrz1VXV8fixYvt90uWLGHu3LmUlpYydOhQLr/8cs4880x22WUXdt11V+68807q6+vtKrLOYurUqUydOpVYrHuVcQ8uybFf+z06qz0jGRW2OjS/ZuzBLzyfudY3EuiUPkIAfzliLFe+9C3n7G2JsUxVYypeZV6ZPy6KfOmEkOZOlk6IprSuS7SpVffdGl6Ys5I/vPANY8oLeOeyfVP2y7BVQRCEnk3WQqilqfIffPCB/fr222/P+ga+/PJL9t9//5TrnHnmmTz22GOcdNJJVFRUcM0117B27VomTJjA22+/nZJA3dFMmTKFKVOmUFNTQ1FRUadeqzWo4aeGcIyqggFQZ71fYgyApDZO9QQ7ZcQGwK92Hcp+Y/rSvzCYcm8JkoWQT3GEEq8TTSBVbvA9Sl+tijuiJ6Qc5yKqOEfe1KTr9vJy3IlauC41Jw7inb7jSLK0IAhCzyNrIfT1119nta61D91JkyalVB8lc+GFF3LhhRe26rxbAvXhKOvztoaN1vsKilPWNJqdm/g9oMhxqApz0gihpLEbao5QwuXxaOl//pd4X6bRDHCadxovRB8Etk9dtGGR8zpYnP2NZ0lyHlQyqiMkOUKCIAg9j6yFkOr4CN2DxnCMpSV78tDPh7PYHES1mTpItoGOd0kykU1oLNkR6lcQsB2tdFzpewaAvTY8C0xOXVC51HkdbxnQkbQU+QpHFUdIlJAgCEKPo03J0kL3oDjXT8Dn48boaTwb259KsyBlTSObrxXAmPICinPdrtDgEnfJvzfJEbr1+B2yO7meQbNXK32eIvXZnasVNCdu3vl+Lbve5CTyR2IihARBEHoaIoQyMHXqVMaNG8fEiRO7+lZSeOqc3di6PJ8HT9+JHL8TetpIqhDanI5QSZ6fmX86kL226mNv23frMtcar65UjXk0hvVxu1ghM73giSQnPyWoXum8NqLunKEOoLlk6N8+Ocf1Xs0XEnon62qaqKzvec0+BUHIjAihDEyZMoX58+cze/bsrr6VFPYeXca7l+3HzsNKCSo5OGkdIW3zNocMeD2U5TvXzPW7hY2aI+TRU3/9/h49Of15wxmm0Vctd7+PNKSumf1vuHN72LA4dV8LtKYoLCqOUK+mpinCbjdNZ8e/vdfVtyIIQgfS5unzQvdAzcGpIj9lf6OZk7Kts7nysG3IC3j57b4jU/apfYTS5dU3kr4zeTBSlf5i1UkjUCINkFPs3jb/FUswLf0YyrJs8bBpCdRvaFXeT6QVHcyFnsfSDR0fehUEoesRIdTDUR2hWJrwUcNmdoTAqiS76dg0FV64+wilqy9Uq9xWm6UM1DYBkJNRCK10vw+ncYSaqq3/tiaZ+u4JAPQv/hfz0gjMdEiOUO9G1cSmaXZaWwpBEDYvEhrr4QR9GXJn4jRuxhyhbPClc4QmnArAw9HDXMndU8KX8IfIeQDkRasAKKCBizwvsa22BOa/BvXW6A27h1CkHmb8A/53ifPkaqxy9iXTVAOVyzLe74hI9uG0qOQI9WoMGaciCL0SEUI9nKDP+RGqbkuCzVk1lg0e5R7t0R9H3sFJoau5JXqyKzS2iQLmGlYoqzyyEjYt4ffe5/i97wXeCPwZnjs9flI/FA60Xofq4P0bYM5jsO47AKINVQDU19VS0xTh9H/P5Lkv4yG1e3aGu3aATcrsMhUz+87i4ghtOcRECQlCr0GEUAa6c9WYSo7iCOm6xtx9HmSl6VRqNbL5c4SyJqGJvAFmmmOJ4sVQfiUrzQI2qQngU3fjLO+7qecJFoMvXn1Ws8rZHg2DYaCFra7Qr85ezL8++pmPf9zAH1/4xlpTv97672KnDF79577HzN7lkaqx3o0qfVpqtCkIQs9BhFAGunPVmIoaGvPqGuGRB3NO+Ap7W1MX5AhlS7phsEFl2GoNue4E8FgoZb11UBH44/2Kqpww17RvlkK4Fg9xgRJp4IOF69OfQ80fMhwXSDNTx39kIirJ0r0ad45Q192HIAgdiwihHo4aGvPoGoU5XuqUSrFGrfs6QulSTX8yB7pWpEsATyGnGHzxz1nlVJE988n3TqI0kK81cdHGG7jU+0LKKZqalPyhmCPGdAmNCTbOz1ccIUHoPYgQ6uEEvG5HqDDoc+UFhTOUo3cH0hXdLGMAJ4f/zIGhf/DvM3cB4KrIObzuOxTG/QKAFUZf7ose7RwULHJCY4ojVEADtVUb7fcH6l9xqPYFl3pfAkxCYcdhqqyucc6nCCGN7F2etiZLr69p4plZy2kMZy+6hM2Pqn1a019KEITujZTP93DyA86P0KPrFAS9bKKQR6KHYqJR602dP9ZdUENj/QuDrK1p4vpjtmN97WhG9c3jwLHlvHjBnhx3P3zqy+XIX+zK3d/AtNjO7KHPd04ULHZUldJgsUBrYN36tXa/7TzNET4P+/5Jw49BWzLGQkpoLBaxX3pM53VLtNUROv6Bz1m+qYEFa2u59uhtM5zbwKNp6GkS4oXNg+ESQqKEBKG3IEKoh9O3wHF/QpEYeX4vugbXR88AoLAb/4TVR/rrF+/NvBVVTBrTz1VZlmgYGYrGwJ/H7dETARiHUvIeLLLGa4BbCNHIxg3rSddCcbLna3j+OPt9LKwKIUcw+c0MeUlpaGuy9PJNVu+j9+avSyuEmiIx9v77+wwtzeWl3+3VpmsI7UdtrtmKHHpBELo53fgxKWRDnuII1Yai6LpGfsBLTZMlDLpz0zf13sryAxw4tjxlTSIZPBQ1XA+iKlNJos4pdpKdDSe5uUBrpGrThuxuJpQ+NBYwMgshXXO7BNFOipd8tbySDXVhNtTJjKvugjhCgtB7kByhDPSU8vl0qOGy7hhJOXCbfgCcsMvgFtfajlDEcAmNapSQX7AIcktTjj3f+z92WP5EVvekh5ykajU0FsARQsnjNlTnqpQaiiMZKtKyJJNmlWK07oH605c+QoLQexBHKANTpkxhypQp1NTUUFRU1NW30yzJzkRAKanvjo7Qw2fuQkM45nKzMhHwOaGxcNRRBDWmKoSKnYaKSQwIL83qnnxhVQgpVWPRJmezYeL1aFZ/ojcu4wi9lFdiuwMmXwXPh0ag6RAIFmZ1zWxRH7qGYXZKnlDMMPl08QbGDymmKMfX4efvDRiGVI0JQm9EHKFeQJ98d68gdRBr95NBljjLRgSBUxVnmFAfdsJeVWaSIzRwp3bdky+ihMaijhDyxBwhZDtSK2bC109xif48ALmKa5QyBPaDm+Ddq7O6h8yOkPPQ7azw26OfLuGMR2Zx0oOfd8r5ewPqdy86SBB6DyKEegF98twl8i4h1B2VUCtQP0ttkyOEXKExbxDy+rTrOgWh9ax86gIqv3vP5QjlaM7rWOJBGO9NVIolnkq1WudE6hMy0ggf/R0+uxuqlY7XrSSmPIDV1xW1IdZWN6U7pNW8Mte6vwVra1tYueXicuZECQlCr0GEUC+gT36yEFKbEPZsJZRJCNUpo0Ne+XoldaEo7HQmAN/m7t7q6/jNJgYvfpqSF453CaGg4vbYjkDYar5YpNXjJUoxiniINDivQ3XO63Caga9JaBl+VuoDONG92jBMJt44jd1vnk7ThqUQzb66LR3punwLbtyhsS68EUEQOhQRQr2AISW5rvcBX+9xhDRNs8VQbZPa00fD8FgT52+cm8Pv/vMVHHwDnPUmz+efmvF83xgj+GPkXJ6NTsq4xlRERQ5pHKGwI3xKqKNEUwVPhtdqVVqc71dXU1HrXCub0FjiHjbUWceN1xYTvHc8PHpYxs+TDd0xl6y7obpxhighQeg1SLJ0L+APh4zhu9XVnLDzEMDtCHXHqrHWEvDqhKKGyxECeOuQD7nxpVlUUIx/fZ2VpDx8L2ojP2U813KzH8/F9sfjMTiJD9OuqVy5iEQNWo6mOkLxZG3F6SnVaighg/Ojvm6scl1j4dpajrj7k6x+Pm5HyHq9tsYKiR3r+cTasWpOyydqhl7wa9LpqOEwiYwJQu9BHKFeQJ/8AK9ftA9n7jkcSHKEesEjLsdvCbvKBncfneUNflZTBlh9hhJ8HxnIt8bwtOfa5OsPQL2ZeQbbu++/51xbcYQawzFWVzW6nJ5SrZZiJUfIDGVwhJqqXNf4+McKwB1iyfSTUqvlovHu1WviuUH1BDN+jtbQGwRzZ6P2y5TyeUHoPYgQykBP7iOk5tWooqin0q/Aetiv2NTo2r6qysnHCUWdOV1VITgqfCM/GQNSznXywXtx36k7UUtmITROd7pWqzlCp/17Jnve8j4bNm2yt5VS63KEYiHVEVKEUGOl6xpNkeznirmEUNyVSiRJq4LupTlJFWutQHKEWiaqNHSSZGlB6D30/KdkJzFlyhTmz5/P7Nmzu/pWWo0aGsvxZTG9vZtTXpgQQg2u7aurnIqphFiIxgyqGiOARoMyfBa/NXHMVzqcXL+HumYcoR30JfbrRNVYgDDjqj5iP30eP69aa+8v1WooVnKEDNURCmV2hBrSDFjNlKejul2JPJWEI1SnOELXPt/20veshdDab2H1122+Tk/GHRoTISQIvQXJEeqFqI5Qtv16ujMDiqyH/fIkIbSq0nGIEiM4vlpeZYsi16N9x9Pgx3dgyK7krDFcVWdrzRL6a27HJkFO3BG60vtfzva+A8CG2v72/lJqKVFCY0ZThnyhpByhxpYcoQ2LrdL7fX5POOpUBdo5QtXWZzeUf8uogqy1ZKWDYhF4YG/r9VWrIJDf/Ppehhoak1xpQeg99PynpJBCUHGBcv093xHqn0kIVblDZeGYwfsLnDEXmjoU4bBbrD9Ajr/K5aS8F9uZSvJZYAzlin5zGFn1qb0vGM8RGqWttreVRd2OUKlSPm+EMwihJEeoMZ0jpL75z3FQuRSWfER45zftzcmOUACnkk69j9aSlSOktgZoqtrihJB0lhaE3omExnohqiPUK4RQPDRW3Rhxba8LuavIdr9pOg98ZFWMbTuwMGPycY7PHRqL4OX26Im8aexObd5Q19qAFiWXpoxui5Us7eyrral28n+UMnuWz4T794aFbwPpHSHXo7VyafxDrnOFxn6uqCdmmHbZvQ/nO1CTtluLroOXKDtqP0Ismn6Rod7zlpdT5B510oU3IghChyJCqBeiJkjn+nu+6ZdwhFqissERSuMGFLodIYWgz0O9Ehrz4DzgY/FcIpU99O/dJfIKfahhqOa4UJ/+sJxdb5zGta99T12tMr9s44+w7lv470lgmrYjVEINF3leYgAbXUnRKur285+aw2XPzrXFliqEMt1jNuiaxjXeJ3k58Fd4//r0i9SmjWb2yd7J/GfmMu6e/mObj+8MFq+v4/R/z2TWkk0Z18TEERKEXokIoV6ImizdKxyhZoRQQTC90LPyijILoTDOYFGfIoTMQOrA1En6vIyO0AT9J4o1JwSWRxM1TVEe+2wpn3y/NP1NL3zLTpa+3Xc/v/e9wD3+ewjHk1C+W1XtWp4skF6bt5pwvIzepylCqB05QgBneONtAz69y962oS7kCICYIoRibneuNfz55e+4/b1F/FTRvvvtSC54ag4f/7iBE5uZtRaTWWOC0CsRIdQL6W2hsYFFOfZn6lcQYOLwEnvfJQeOTiuGinL9LDP7p2wHpy9RAr8iJsxAkf16iVEOwImeDynQ3PlICXI192iLXJxKtvra6uTlFs+fSW7dUgD298wDYBd9kS14rnn1O9fycCzVfYnERZNfEXHtCo0pOUKmx0rO/mFNDbvcMI3THp5p7VDFTxuFkCom6poyhOC6gNVV6X++Kq6Zb80oIakoE4SehQihXohbCPX80FiO38N/z9udvxwxlsfO3hWv7ny+g8aVJ81WsyqgmiIxromcxRuxXTHP/J9rf9Dr/rXP9zoPLi3oOELTjJ35OLYdAa3lB/Yq0xr6mqc5QqjQk2H+VyzMqIZ5KZsTQiiWVJKULmQWjQshNTTWpx2OkKuhYtwVe3a21Zfo8583xi+qOkLu5pbZWiQRpfSqp7UuMrIYuvr2d2uYcP17zFhUsbluSxCEdiJCqBcS6GVVYwA7DS3hN/uMZNzAQtbVOGJjcEmuS/gBFAZ9HDi2H+sp4baiP6ON2Ne13+tJEkI+56Gm5zhCqBE/d0SPz+r+ZhnbAE65vYZBgZZGCOVYwzvywhsJKF2rq81cOzSW7DaE0gihSCI0puYI6Q0p67JF7WFkJHoueZKUSqbQ2KsXwn27Q6RlV8UlhLpRwnU2s9bcobH0Quj8p76iujHCGY/M6rB7EwShcxEhlIGe3FladTxye0EfoWSWbHRycjy6ltI9uzjXxzb9C/ngikn876K9WzxfXaDcOV+w2H4dMb2s9w+2368xSzFM54EZNh2R+X18pEceTRyrf8yPgTPY3fgq9WL9xlr3GNvAVkpJvoHOdfrDmI8cmlK1lc4RMuOOjCqE8vVwyrpm+fkjeP9GiEXcbkdcCCULRpf4MZTXXz8JFQtgwRspl/ipoo6VlY5ASwg4ADNDDld3xVU11rNuXRCEZhAhlIEe3VladYR6QWfpZJL/MZ4cGkuEA0eU5TXbUPKM8P/xYmwfppefbW/Tc50coQhe/vYrx00qop5/RE8CIOrLZxOOe7TAtMruc7UQZ3nfwatlqK+OC6G+VDFSEUIlWh2neaejLf+cMZEFrkOShdAlnhf5NnAO22pLXflNOWbckTEM6kNRrn7lOz7/aWPGz88TR8OMW+GDG/FGHbGSqJzzJg8gay40lrwfq93Bgbd9xN5//yDtZ4l2IzWRVT9JRcTJ9HlB6D2IEOqFuDtL9z4h1LfAGp2x91bWwNXk0Fi2yaozjPH8PnIBXkX8eF1CyEM/pWItVwtxf+xoLghfwsxd7yFXmUO2yLCcoxJqGa//nPGas+r7AVCuVVKkVJupHB56y/U+HHMLoct8LxLUIlzjewK/0lAxx2yEl8+Hu8bz0HtzefKLZZz80BcZ78Vm5r/IiTidtQ3N+p1Rc7EMw0xKlk4jhAx3ArXqBCXCSmpoLBrrWWKiyxyhz+6Fb57fjBcUhC0LEUK9EFUY5PSCZOlknvj1rpy5xzDu+tUEIFUItZY85Tvy5xXbr4/ZvoxtBxalrH/L2I1lhbtQqDkP+vUUs8EsxKOlPiHV4a+3fW2JjL5aFX296XNqDox+5HqfLkcIwEvMVfqfQxPM+y9UL2fQ0hcIEOZI/fOU8R4pROrpF15pvzXjzo5XyRFqisaScoTiTpTaZLGZSrKEAFJFXSSWwTXrphhZ5Ah1OBUL4d0/w0u/2TzXE4QtEBFCvRA1VJTXS5KlVcYOKOS6Y7ajT77lDAXaGf7LV8rvAzl59usd+lnnf6ngFAAejB5h76tP6mp9zt4j+c4YkXLuBjPAcrOf/X5Z/HU/MguhZDI1WvRguHKEcpTS/ZLoBv7ifYp7/ffA82da8cSKRbDJcavMeOI2wOCIM2g2EeLyKKGxhnAsfWismXCZmgydEECRHiyEVEeoufL5DqWhmdCmIAgdggihXoiaPJzcM6c3khoaa93x+UoeUY4qquKztV4tOp1jQtfb+UEA9eEoH8bGW2+G7U1pnp9vzVQhtMYsJYQzNHUDRRimhk+LcWLs9azuz4w0uUROAg8xd7K0sqYouoFTPdOtNz9/CDcNhKkT4e4d7fkQTY2Oo1UWXWe/1uLiRq2SagzH0idLR5X7SnKE1GTohJiLRJ1tkZ4WGmvn0NUnv1jGoXfOcFU9toipXFT6EwlCpyBCqBfiHrra+0JjySSXeQdbKf5UIRRUK9DigkD3eJlnbkVUmVFcH4pyReR87tROhRMeI9fvYZ4xCoCGvCH2ukYCRHDuJ4qXdVgNIb1kN6ZiavWFzAhcRpCQq+Tei+FKllZDdcXRDehqmE4dmBquA9PEbzpuToGhNH+MOzuqE9UQTg6NRVxrrQ/nfsCrOUDdPjSWRbZ0Nn2EmuPqV75jwdpa/vnOwuwPUq9jdqPvSxB6ESKEeiF+T+/qLN0S6rMiz+/hxl9s16rj1cqyoFqBllMMgEdP/d+kPhxjA0U85TkW8vuS6/cw3diRv0VOZdn+99jrGvHzZPQgAD6PjQPg4vCFrnP9N7o/M+N9iNIx2FxNX62ag/U5vOb/i73dT8TlCKkUxTKHVL78cTnEIq58pjzD6UqtxQWPKloawtGWQ2Mhd2drVxgsmpos3a2EUBaowq49OUKZcr5C0ZjdKNO5kGpDdZ9O3ILQmxAh1AtRHZL2JhL3BNQQzrfXHsJ2g1ITnJsj4Qj5PTq6rsHxj8KYI2DPi4A0ZeRAQzxHKJFHk+v3YqLz79gRaIN3ZiaWGHsseiizzLFMCt3GWZE/AjDb3IaVOL2L3jN25qTwNVyUJJCSudt/L2N0J6m5VKvNKIRKIpk7G//f05+luDf5plPBlhBCEeWB3RhJCo2lc4TC7io4VUglxoSoLlNPC40ZHTR9Ps2vE+GowcQbprH/bR8m7VG+I6Ptg24FQchM74+bbIEU5/o5e6/h6JpGca6/5QN6OOoDSk/3lEnDxOElzF5aybE7DrKTpe3cqu1+af2J40nusIzlCIFTYq46b3l+LxcHr6KgehFzzK0BWGoOcB2/US9lsGHl5dSYudY2pS9RNpRodRkHrfrIXMGVR1MaIeScRzcscaM6No2ZQmPqecLue1GFTjiNI5TifnRzOmr6fLou1ks21FPTFKWmKYphmM7vsXodcYQEoVMQIdRL+etR23b1LWw22pK4+uDpuzBt/joO32EAm+qsB3+iP1EyqiNUlh9gQ13IrhpLOEJqpV5+wIseLGRO1ZiM16/2lEJcB1STD0ClWdD6D9IG8rSmlHEYBaoQSuQIxZJzhNL0EYoqjlDILYTc7k/3rhrLqqFiB/URSjfNw0iqSNMTd+RKlhZHSBA6AxFCQo8neUhpNpTm+TlxopXUnB/w8sBpOzOkNCftWrWMfHBJjksIJUSSWiGVG/C4ErDTUe3tQ8K0SThClWZ+qz9HW0jnCBXghLUSjlBYqfBqTC6fT1SNqS5RiiOkhsYSydI9t2rM6CBHSE+jhFxCyDCx6x1MCY0JQmfT+xNIhF5Pex5KCQ7drn/a5okAPiVZekipJVrs0Fg8bKb2zAl4PWwzoHl3J+J1RE8N1jnX0oevd7imDXffOvJoxExyhDw4okU3Y2DEXEKmIRx1iZ5NNfWc8cgsFqzc4JykGSEUscvnu84Rai7BWQ1XvTp3VdoRGh0WGkt7b+mv4+rWLaGxrGkMx5j+wzqaIiIehZYRIZSBnjx0dUujI4RQc0SUzNjBJZZr5CRLW/8LTRxRwsiyPCaPtZKgrzt6O16/aG9GlOWRDtPruE+NBOyy/e8GHs83aRozpkPtWF1jpnez0pGvNREJtTCpPhpyl88nJUu/++0KZiyq4NY35jnHNBMa6+qGimc9Ootjpn6alXt4yTNzeeGrlSnb3UKo7feSzhFyCSHXG1UIyUM9W/744jec8/iX/Omlb7v6VoQegAihDPTkoatbGm0JjbWGqgbnYdQnz0o+r0sKjQW8HqZdvh8PnbEzYIXTthtUxABlVpmK6VOFi8aAIut9VX2Yf0ZPTFkfNt2htj9EzmOdWeLcYyvCank0Em5qoat1LJSaLK2ExhoareP9atVauI7apghPz1zOxrpQUrJ0uj5CWf7cXrvY+tNGmiIxPlxYwTcrq9lQ53yG5n5vZv68KWWbKlDaUz6fphtDUkWaOELt5X/zrIHGL329qovvROgJiBASejzbZQhpdRSb6p2E4ESzyoZ4aEzNH9J1LaUiyOdJ/7/Y96WH0ugt4s3YrgCUF1qJ2psawswwxrNL0/322pDp4938o+z3uzZN5fnYJKpx3KYqWiGEtCbCTekHvtpEQ2mSpZVmjqb1UFaHvhKq48qXvuVPL3/LtY+9zqBVb5Io/47ETBaureWWtxbYy7NyhOoq4KvHrT91mVsCNEdNo3OPCUF217QfmXDduyxen77qLl1Cc0eFxtIFx9SzuQRaTBE/IoQEoVOQZGmhx3PZQVsT9Hk4bPv+nXL+yoZUIRSNP6zS9RhS8Wfo42TmlvKviW9yxwdLAWxHqDIuujZQxHPR/TjR+xHXRc/AM+IU1s9t5BNjO9bHO1PXmIoQaoUjlE8ToaZWhsbCMTDVrtbWQzmgKUIoXMcb36wGNO7Z8GvYAIfql/K2sSs0VfHve27DG5sIcdEWzcbJq1fET906yO/b8jHA96urGVycS1Guj2pVCMXF1x3TFgFwy1sLePjMXVKET7qfamf2EVJbCWTMEZLO0oLQKYgjJPR48gJerjhkTMZk5/aiOkLJDSo9bRRCuX4PDTEPiUduP9sRch58V0fP5ujQ33jLfwgF+flcHz2D942d7P11OOG11vQgyqORSGMLQigWTk2WVkJjifEgAVe/ItOqSFPYWbcExw5fXc2tvof4u+8he1+mYbIu6tc7r2vXtrwemLOskiPu/oR9//EBADVNqY5QggyGXYc7QmooLV2OkBomzJwjJI6QIHQGIoQEoQW2LrcqwAoC3hQhlCn0lSCQYX+u32OH1wDy4zPhNtU7YiOEn2/MUQwty8eb5jwNOH2PVpllLXwKJ7naSpZuOTSmPpxfnbuaL350hIjXtB7Q/qTO1oW4BVYkbjoPWfseAId5nJy7qGFA5bLmk4DVcFjtmubvOc4HCyzxlHCCqtOExhJkErJaGk9IjeS1NjKmiqh0IitqZHKE1NCYJEsLQmcgQkgQWuDOkyZw8q5DeHnKXgR87tltLTlCmYRSjt9rja2w31vnraxP7Qg9rDQ3bQiu0XS6hq80lZCRlnrNTWY+D8SsPKM8GgmHWkiWTgqNAa5jvMTYX/+aa3xPutYUafXoSil+pJno+5iNH8BdO8BrF6Xse+vbNex20zRWrFjibMzSEUr+qtKFxhKk6/JsbU/dFlPFSiuVkCoq0zlC6hwzd46Q0rBSHCFB6BRECAlCCwwpzeXmX+7AVv3yCSY5QutrQxmOsmguNNYYThVCalVTguF9ctMKribFEVpt9rFfm/mpuVImGvWmVcFWplUTi5fPZ2ziGK8a20P/ns8CF3KS5wPXpHsvUR71/yPlsELq6UON/T652k1l//WPWy/m/idl3wX/+Yp1NSGmf/mdszFLRyhZ3FQ3NOMIaYk+UMnngJWVDRx8x0c8M2s5AGqRW2tDY2oLhnQiK5IpR0jK5wWh0xEhJAitINkR2mlocbPrmxNCo8vzXe8h/WTyQSU5aR+eamhsrVlqvzbz+qWsNdGoj+cUba8vZbsf7gCaEUKbfiY3WsldvqkM1DZxmfcFgjgiLdOw19/7nmd28Hf2e78WwV0TBYfoszjJ8wGNevoeSypFRpXzJlkIff8yLHw75Zhk0VjT5NxrshDKZOhpmsbfXp/PonVWJRwkd5Zu8dZdqI5PurBbNFP+kStZWoSQIHQGUjUmCK1AzRE6ZNty/nT42GbXZwqNFQZ9/HbfUURjJgdvW87qqsyhqr4FATYqCdsJ1NBYla4IoVhqeM0kveipzlR2/+oUXgfbKumvVdJfq7R3+0j/UN5NX+B6n0uIEmpd2x703wnAuuhg98GGkdJkp4xq540qhGrXwfNnWa+v2QS6I1CTZYYaGksWmonhpskukobTKyqB6tS0to+Q6vikc5PcjpB6USmfF4TORhwhQWgFqhA6c4/h5LUwUyyTI9S3IECO38MVh4xhh8HF5Pgzn6dPXiBtcu5Cc6jzJsdprhgpHglAk+njoejhANwQOY1vzRFMi+3oOkdL883CpofPY+NStmdyhJI51/smf/M9lnZffqzKefPyBXDHOGhwNzLsoylCqE6pIFPL6pNGe+hJNk9zOULp8nXACl8lN1yMZWp6mAUtCSHVMVITp90NFcUREoTOQBwhQWgFqsOTTbl+cpVZgn5Jk+5zkkJuKmUFgbQOxAJzKL8NX8o6s5TS4gBUWdsjvnyOCP2DSrOATRTwr+gRVMR7D10ZOY8vPRfY56hsphHjs+ZBvBjenV30Rezhme/at5fn+4zHJXOk54u02/MMRcDMe9r675zHYJ/L7c1liQ8FEK63pt2vn+8eABuqg6Dzs1C1Tcwwm68ayySE0FJ6BbVnxIZb6KQRQsrFXNeV8nlB6HRECAlCKxhcksMvJgykb0GAolxfi+t9nvQP2j75biGUyBFKuzbPn/HB+45hdabeI9dvC6Gwv5ifzEH2moQIAthIAWHTg1+z3IVKM8Nw2EG7cPWycwibBsNpuVpLPWe7qFeGuGK6w2rRJnj7Svjy3zDqQOXiSY6QIm7CUcPuLK1jMHzxkzDwCGdtkk71EaVcq0TThqU4N6oQuv71+fTJ93PMhEFkQyRm4CVKf60SwxiSsl8VR66KtK4snzdisHI29N8B/Lmb99qCsBmR0JggtAJN07jzVzvy5yNSw0Xp8GfIEUpO6A024wgFfZ4Wq5RK8/ycE/49M3P2ZdFWv8m4zkRnnZJYvdx0J1b/JXI2n5o7YJ74hO2ebDRbbtZYSQZB1VoaNtov82l0h+CiTZYIAvhpurM9lCyEnNfhqGE7Qsd7PmKXH26BB/dR1rp/Dk/6b+aTwCUMrfsmpUQ++WdwyTNzs/1URGImT/hu4ZPAJQyrnZOyP3P5fBcmS38+FR45BJ45ZfNeVxA2MyKEBKET8XszCxyV5hwhaLmBX0mej+nGztyU938sb/Q3u3aTIlo+NbZzJV2/HduVU0NXEs5zSvA3mi2HAGNZ/FXylTG6xTV88wzmc2cCUKLVtrA4TrgWFk+DJ38JVStc31UoFrMdofHazymH6knl87vrPwCw86bXU1y49gz3jRoGe8bDi7ttei1lf+by+S7sIzQr3gX85w8273UFYTMjQkgQOpFMobFkmssRgparlEpzLTFTG4qyqsoac7HnqD5p1w7QnITkn8yB1BG034fj0fKaRuehuyGL8R06zd/fW7GJVJZOaPE8ANr8V9AwKE2ExQoGNH9AqA6eOs5yid76oyvMFIpYjtDW2grG6z+lHJpw5lLCYHhTEqLbI4RUoZPuZ+kKjRkmb3+3hmOmfkptg1JNuLlDYx0w26w+FOX2dxfyw5qalhcLQhchQkgQOpFMVWPJ5LTgCLX0DC7Jiwuhpqhdir/biPRC6DtjOGB1mwaNBlMRQpqV91Td6DgR2YTG+ipJzWeG/48G08mBeim2NxdFLiJSPKLF8yQopdZxhPL7gd5MPpaaI1S33iU6GiMxIuEm3g38H9vpSzOewhI5zpcc0bwp4qjZ8OTqr2FTquNkn08JfaU7TTSpquz8p75i3ooq5i1TcqY2tyPUAULon+8u5O73F3PYXR93wA0JQucgQkgQOpF0VWNl+amhq4BXT9s0MUE2OUIAdYoQGtonJ+3aqyK/4RXPwRwdvhGvrtGgOEIenyVgquLdmDUNGpX9Kg9HD+PbuKi6Mnquvf0jYzzHhq+z3681S4nixSwZ2exnUCnXKh1HKLcP+NJ/FgBCSgjNl+PKt9lQF2KCtjjNQdaahDsTM0xylYaREXwpDlC6ai8AqlfBvybB3Tum3487B8hM455FMlSVxaJtL59/+7s17P/PD/luVXXLi9PSdgcswTcr23ptQdh8iBDKwNSpUxk3bhwTJ07s6lsRejCqI3Tp5NGM7JvHI2el/k5pmkae0kvot/uOZOvyfJ4+dzfA/UgaP9jK2TlwGyfRuSQeGmuMxFi20RqfMbDILR5+NXEI+4/pyzpKuZ7zWGn2pTDHR70idHL81nkSCcZ+j87ofk6J/RKjHICYqXFD9HSOCt/IDk3/4vnYJC7U/8z+odsAd9frRK+iQL+tMn9RgLHLOdB/ewD6aVWUatkJoZoap9Ej/jzXOIuK2hC7xfN+VBKiJyE6Yqa7Qs00jVRHKGZwi/dfvOv/A6d53nN2rFfPb1JKDZP0r13CJeKa2Jr6Gdzl884CnzLWpLXJ0uc/9RVLNtRzwX9Sk7OzogNCcenCgPe+/yPPfbmi3ecWhI5ChFAGpkyZwvz585k9e3bLiwUhA2rfoWN3HMT7v5/EDoOL064tL3TEw75b9+Xdy/Zjz1HWVHm1+uy/5+3Oq1P24pc7OZ2ZE0IIYFXcERpYnMN/frMbe23Vh/d/vx+3HLcDgXjydqJrclGOzxXGSiRtb4p3svZ7dB779a72/pVmX/YL3c7OoQfiLpRGTbwX0esN27LEtPJ51HBbVXx/YfnwjN8TwNoNm+x8oH5apS2EYjl9wJvelQKY9b3i+CQ5QhW1ITsBWiUP6ztKuD4xw6RYq7f3+6P1KY5QH2MDv/J+yNb6Kn7teQsPMdj4k0ugBAnzqv9qHvP/A+Y9Y29vMUcoQ9WYVxU/bQyN1TW1MaTWAaGx5E86f3UN/3x3EX984Zt2n1sQOgoRQoLQiahl8gXB5vsODSl1erUkV5H9eq8RbNO/gD8cMoZcv5fxQ4pd587xewj6nP+dS/P89C8KstdWZfznN7szsq8lRnxxhypRGl+Y43OFxhLXrYgPf80LeBlUnAN7XkxM83Jz9BSWmf2p9xSy7+iyjJ9FdYRC8aq08uLmZ4s1hqNQYFWrlVNpOzQRf0mzjlCg1nEXQqaXFZsa7PcVdSFG6atTjinQLCGUcIQ8RphipUrNbzSk5GXlGI5QytOauMd3D9yzE3z3orOdJoboVtdrc9FbACxeX8cFT86y16SLsKmhMbVs30v7R2y0Oce7A4RQMpvSjIoRhK5GhJAgdCLqbKuCYPP9S4eUOEIoeXRHUa6Pty/dlyn7O+ElryKEfB7NVXn258PHpp1z5kvqX1SU46OeVEeoojYhhOLnPOh63jriC+abw+17HdM/cxJ1E45DFcISgP0KA3DeR6zb9xZmG1vb+x+IHsVKs4wvh/7adoTKtUrG6tbU9/9+X8/KWudp3mS6BeVwjzNu4/1vl/Lu/HUM1taTTwMbaxrd88ri5GFV1sUME7NiIV/7z+Wfvgft/f5Ygys0FjNMcgxHYOUQ4nBPXNx886y9PVdrsl+bgWIAfv/8PII4AiCdLlFDYy5HSJ3p1sZQVUv5ZRnpgL5FyZdWP2e289pWVzVy4G0f8vhnS9t9P4KQDhFCgtCJhCLOX/yZBrAmGFLquB4t9RUC8HhUIaRT2eAk1v5ix/Qdj5PvwQqNOY5QQoCtjwuh/IQg0zT8OU7/obL8AKP6ZnZ4TOWvlnWm1dk64PXAwAkYO59Fvel81orxF7B36G7WeAfbjtBp3ulMiJe7v7cun1UNmvt8e11iv+8TcRyfHMJsr/3MJ4FLucN3H001FXi1VGcjX3GEzM+mkqOFXW0FArEGlyCJxAxyTEcIJYRUMn0V0WXEx35UN4RdQijZaZn6wWKe+HyZ/d4thDqgs3RrdNDST+GTO6w5H20VUM2ghgCzdapufXsBP1XU89fXsh/rIgitQUZsCEInstOwYgCKsxjHMbBYFUIt/6+pOkJeRRSV5ftTOlcn8Hnd20tyk5KlfcmOkHMfavfrvgUBRvVLnVPm9+p22O3y8PkM0SqYa27F8D6O2zWgKAfPkDKI6xdvIBeoojEcg+JhrvM9ET2Iz4xtOd/8n72thlw46HooGwOv/o5cJWSVo4WY4n0VgIM8X/FKTfrxII4jZGD481L+RRgwGgCDI/XPmW2MIRqLuRwhj5b+KT5SX2O/NuJnzQt4iWqOEPIZIdcx/3hnoeu9KoR0o+3J0vZ9tEbQPGYN6aV4WKeExpL7JWX6PVVpinT8fQiCigghQehE+hUEmfWnA8lvISwG1pT5BNk4QhqKI6Tr/GriEJ6ZvYJbfrlDxmOSHaHiXD9PRA/m1563YPvjyY1a192QRgipvY7K8v0MLU2dP9U3P2Ana79k7AvAK1P2Ypv+7hEc/YrzbSEUCFgCsCEcgxH7udbdGD0V0Fyhtlozft1AqhDLIeRUmwFTay9OWQNOsnQ0ZmL4U0N8QbOR46Kvc5n/UQDM2/6PPc2WK0hHaUo+UrgBYhFOjrzMbM352frNUJojHerDjviJREKOb785c4Q2/dQxVWNJ72NJQigbmmsr0VswDJO73/+RHYeWsN/Wfbv6drY4JDQmCJ1Mv8JgVg7PuIHOAznT1HoV9V/6Pq/OX4/alg+vmMTkceUZj0mefTamvIDVlLF96N/4fvkAuXHhYztCivgJelUhFEgb6svxe+yy/vP3G8X03+/HhCHFqbPUPI4wyAlYbllDOAYeL//I/wMAd0V/SSgugBqVPKYa8vjLK98S9qQKsR30JYzWV2X8/AnyNSVHKNKYsj9oNHByzHGhtEg9x/Bhi+dVhZAZrocvH+W02n9zl/8+e7uvBSFUq1R5uXOE2iqE2qKEtE5yhJxzfr2ikl8/NpuFa7McpdKLefv7tdw57UfOfGRWy4uFDkccIUHoJhTl+PjsygPweXS0LP4ZrD7gvLpG0OdheFnzlVnepJEfI8ryeOPivcnze9F0ndy4YKmNl9e7HSFH+PQtsITJ9cdsyzWvOrkbQZ/O1FN34oc1NYwfXIyeKfThcRyeREJ2YyRKzDB5uGpnnonejye/DOqsvKcmU3WEcnjqi+VsR4RfNftpHRq1HHJMR/Cc5nmPYzyf8kLoSgiljn8IGo0U0bxgSYdbCDXAmrkpa1pyhNRyd18HJEu3OdWnI4RQ8ugSxQU65aGZAHy3qppZf57c/mt1Q1ZXNdK3IP0/GlSWK5WOwuZHHCFB6EYMLM6xRUZLqI+Ylv6izbSuONfHtgOLbAGVm1Stlq+8DyQ5QgBn7DGcKfuPsrcHvR6CPg87Di3JLIIAPE7OVCIvqT4UY+nGekJRgwZfKbuPckIETTjrKygG4ItNmUXf14a7eaOhu7t5j9VXsKu+kOvW/o5vflxGMoE2iCCAEZqTk6RF6kFL/bkEWnSEnKT3Lqsa05Icodn/hhfOgVgk8zFpSL6ymiydIJGY350IRWM88skSFq+va3lxBuYs28Set7zPr/71RYtrOyEvXWgFIoQEoYeilh9nk3QK6YWQSnJuUqYcoT7KmBCv7pwzJQSWCcURSoQNG8MxllRYic+j+uUxRskrUnOEVplW/6KVTbmYvlQx1GAG2GAWuTfq6c3vHLORYbVf2e83mal5R61BV5Oow/Wgp34fLQqhkBIa0xTx09nJ0rGk0JsqhN64HL57Ab5/uU33kCAUa9llSnQ170oemvEz178+n8m3f9Tmczwzy+pvNWdZZQsr049dETYfIoQEoYfSln9FqjlCfo+eMvU+rxkhpOYtqc0h1TEialPHZhmxr/0yIb4aIlG743Vxjp+ty1Uh5Lhkq0xrmOwPa2v5KVyScuqNZiE3Rk/hJ2MA3xvDqDALmb3Pw7wZ25VYmr/y+mlVACw2BnJY6BYiZpZirgW0SANorRdC7tBY+xsqZv1rElVbAmjpj2ysatM9JAhFmhdzb3+3lvHXvct9HzrdwrsiWXr20pbFS0u05n9PcYS6FhFCgtBDaUs1kE/JESrK9aXkIuX4k0NjzoM8z+9lYFGQwqDXVTGmlvGr4bNm2eYIOOFxuPhr22lqCMfsiqlcv4etyx13Rs0RWmVaIbP6cIwGM7UtQR05LDUHcGD4No4I38yu4fspHL4jv4tcyvXe9FVkADdET2Mdpa52Au0i0pA2zyZA892V60IdLISy/T2Jpu+N5KKdqkRtMJqO3z83F4Bb317Y7LrmyLZRY3O0uQml6z5as1aUUFciQkgQeijbDszc2TkTXsURKs5JFRHNhcZ0XeP9KyYx68+TXS6QGm4LZOsIaRps+wsoHWkPm20Mx2gIxez7UDttq4IgERoDiJEqvOqShEzf/KD9ud41dk1Zn6A23uSx2mw+4Txb/JU/wpxHU7YHCFsNC5MIEuJfvtvYt9IJP3VEjlDWqEKolblAmUh+vmcSQndN+xHDMKkPt/8zZluW3xwdoUtaE+4SHdS1iBAShB7KwOIcpl2+L7P+fGDWx6ihMXVQa4LmhBBYOUDJeUCqy5R1jpBCwhGqD0WtEnqspG1d1/jDIWMAKNWcyi51NtpKRRQlULtWAwwozrGdqtqYl7fH3ECFWcgLsX1d62qxhFdiSGwmKszWC9AUoqkl+2d73uFgzxzOb3gAK7Bitr5qLBqGSPPOjmmarK1Os0Y9LpM7FK5Pvz1LQtH0n+GOaYt4+/v0zS/VflnZEO0IIdQROTutcYTafzWhHYgQEoQezFb9CuhXkH0oZ6jS4dmfpldRcr+j/EDLHTZURyiYbWjMdc1E+XyMhnhoLJGrNGX/rfjVxCFuZ0ThpsipzDFGc1H4QntbHUlCqDBof476cJRvSw9iYugBXozt41pXFxdQVS0kTG8wi7P8ZM3w5h9SbICd9EX263M8b7EocAY5mjqaQ/kOoiF4+CD436XOtlgUpk6E+3ZvVjT96eXv2P3m6fxvXtIwWlWcRTPkMaVpNbCpPkxVQ3bDVEPNdIleVZkqDgFaqYM6JKyVxrDrVMQR6lpECAnCFsSuw0vt1wvXpTayS3aEsulwrYbbsk6WTnONSMy0K4bUXKWgz8P90aP52ejP9ZHTXceuoQ/Hha/jf8ae9rY60y0M+xcF7YG3pglV8ZlsK8x+rnUJAaU6Qh/GxvOdMZwzwv/nXNMspd3M/Q/Mf8W1Se1BdLXvKfxakphRc4QWT4eVs9yht40/QuVSqFwCDRszXvq/s6xhtne8t8i9QxU/aRwrAJrcQigcNdjpb+8x4fr3iKapCEt2VlrKEUrg87Q9FynZEWpL/k2H5Ai1aq0ooa5EhJAgbEHousZfjxoHwCUHjk7Znyx8snOEOiY0BvDMbKvkWK1ey/F7WEVfDgjfziOxwwCr7P/qI8elPV+iz1CCE3YZTMCr20ndid4wyYLGFkJKjtDHxnYcGb6JGcZ41h31H2ZsczULzSGt/oxpmXGb/ZAuoo6RevrQkE1CCBkxqF7hbE8ImIoFzrZQhm7NG3/iLt+9jNGWp+5TO2yHMvTPSXKE1FJ3dehvgpQcoWaqxlQBUxhseTZfJmJJvYraEinrkByhVpykA6J5QjsQISQIWxhn7zWCmX86kFN2HZqyLzknKJsHkq+djlDy2A9wN3ZMLvEHuHD/rThn7xEMUgbV3hA5lbnGKB6KHgHACTsPZtrl+7HtwCI0TbPnvc1cYk2Zj+Hho5gzly0xJFV1hBJ5QwDa6MksGvRL9yT5BMHibD6qm00/24m92+tLWl6fiNc8dwa89Udne8KlWf+Dsy1NCAuAZ07hGM9nPOW/GbC6Ov9UERc9qiOU6fgkR0h1MhrCzVe1GYZJTVPmNRvrnOvnBtrewiDZEYq2Ic7VEQ5Nq84gsbEuRUZsCMIWSHlh+rwi1Z0JePWUhovpaFP5vEK6cSK5ivhRhZCmwUsX7Mn4wcWAM54D4OHYETwcO8J+/5t9RrJVP0fU5Pm9dlgswT+iJ7Kf5xvmGk53bDVHyB7wivXd6JrGAG1Tyv02lY4huNoaGRExPfiSw1px9gndQdCj8Z73UojUE22yRMgO2s9p17tIOEILXndtrqvZQH5+X1jnjDpJCJY+VNNHSTRPuEZ9tWo21oc58p5PAFh6yxHucFhTBiGUJJDUTtG1aUSO+ny/4oV5TPthXfrzAhWKEIrGTF6bt5rvV1Unnc9scfxMctVYW/J9OsKhaVX5fPsvJ7QDEUKCINioAqS8MJjVzDNfWxoqtoAqcIKKOCvK8bHjUKeJYnlhkEXrLDExaUxfPlxYYe9LnquWyBMC2G/rvny0qILvzJHsH7rNVTLvEkKKI5Tr9+LRNRabA1Pu93/LA5wQP30mEbS+YFtWNJXjMwFvjiU8vn+Zq7zvMEH/Ke0xKqYRZcX6apJ9vE0bKsgfOBbWz3c2hmo53jODv3v/hUcz4f2NcMCfXcepYS3DMNHVqrFMjlAzQqimhY7QL33V/DDcDYoQaorEuPi/XwNuIRw1zBbzh5IdIOt96wR6R+cItSTg1OsZhtn8eBqhw5HQmCAINmric3lhdjPPfG0ZsZHEQePKXe/V6jX1QZhclXb9MdsxrE8uNx67Hf8+cyJ/OWKsvS855KbmO6nz3JaYA9iEUxLvCo0ppfgeXUPX4P7o0dwd/QUPjHvSWacIJoCo6b72b8K/Z9pu/7b2GUCe1RQy+ObF/Nb7BrvpllOz1HB/D+Akfy9ZU8Htd96Ssj//05vhw1tgk+MqmU3VHK1/ZokggBm3wmuZm0mGY4a7ZD7L0FhEER01Te3rPVQfcgSkmlTdqOQVZdMjKLFmvLaYv3ofx2iobuGIVDrGEXJOEkkzY8291nkdkzDZZkeEkCAIaemXIXyWjPov9LaExgDuP3Un9hnt9ARSHSGXEEpynEaU5fHRH/bn1N2G4dE11z0nO0L5wfRCKBnVHUoWOJqmUU8Ot0dPJNLHEV0/mG6fZp/QXTwVdfo7TTN2xhOwzmuaYOb1JR01SdcDCMeN+5EbP+JO/30p+0vXfQYf3uzaZjTVME63hskaZvx7+OrxtNeEuPBQhVAbQmPp8n9a80hXxU9ThqTqbHoEJYTQq4FrONv7DoEZN7TiLiw6otOzeoaW8pTUtR3REFJoHSKEBEFIS3mW/YnaWz6fOMdgpZN0jk8d9pq946TOQ1OHwYLbESrLTy+EHjhtZ6pRhJDpFia6Et4ozvNzevhK7o0ew4uxfYnozjnX0Iebo6fQFOzLbN/E+L05927mpTaCDOPj9ugJAHyTv7eztrWNdACzYiFlWg0xU+PNZrpp96OSl/3X4P309uwdIZfToThCaUJjrREUakVZJi2QrkQ/mWQhoW9anGFlZtqig36uqHPfn3KO1jhCHRGWE1qHCCFBEFzsOsIqKz951+zKxP2e9ofGAErznMRsV46QL/3rlu4lOZekIAtHaO/RZdQo4scMuLtIq6kbpbl+PjZ24J/RkzDQqfK4xU09Obxz0Htck3cN4BZpRm6qI9QQLOdDYzyTQ7fy3LBrIcfKhZoe2yntvTaHvvwzAH4yBzLfGJ52zUO+25gVnMKO+mLyPrkJ3v2LszPNjDRre8yaoQYQDbscmpqmqFW2/9P79iT71jzT61uoOoNmHKHqlbB4GphmyhrDk12I13VMK8XIS1+t5IDbPuKSZ+fa29TKs0gLAk5dK47Q5qfXC6Gqqip22WUXJkyYwHbbbcdDDz3U1bckCN2aJ369K19cdSCjlenvzeFtZx+hBMU5zsiPTDlCqphJh5pk6m0mR6gsP3W8CFgVcBWUcGvkJP4WOQ187i7VqiNUklRRd0fRH9lk5nNV5Bx7W5PhsXM+1Dlshif1+nWBckBjsTkYbyAXfjeTD/d8gi+MsSlrZxcdwiPRQ+333wUmQOkozB2thpP6Bmto6XxzWNrkboCDPHPSbm+Rphr48hG4eRC5yz+wN9c0RuCFX8OTx8LMBwArh6iIOs70vEMJGVymOOmqzpLJKBLu2wOeOg6WfZpaNeZxO5szFlVwyTNfU52m71GC1poyD3xkJbu/8c0ae5vqAkVbcIRU96gzulqbpskPa2oyhhy3dHq9ECooKGDGjBnMnTuXmTNnctNNN7FxY+auq4KwpRP0eehflP3YDtfQ1TRjO7JFLd1XGzuO6pdPv4IAW/XL56IDUptAqqil/N6kyhtVpPXL4Ah54sfcFzuG13KPTcntUAt/ipNmtX3WOIydQg/y35iTGxSKGrZD4cqfakpN4G0IOJ2ugz4PFJRT138i0XQVT6E6Vz7Rs32mUPfbWby+zt0k8gdjGD+bA9J+1jYTqoHXL4NYmJEfOqNN3vx2Dfz4rvVm1oOAJVzu8k3lOt/j/MP3YLOnbchi4GpaRyhc74Tyln3eoiN0xiOzeHXuav7x7gIyoTpC2YTjfGl6YYWVnKeWHaH01+4o3vpuLYfd9TEn/euLDj93b6DXCyGPx0NurvUXRigUwjTNDkmEEwTBor2dpROoIkp1gQqDPr646kCmXb6fHbbLhOrYJD+c1H+hpxs4e+Yew1ziqV9BgEjSSAiPsj+5x9Ka6iaSB2OFIobtUKiz3SKDdgfc+T+qS5TItQp4PXajR5V1IS8xpTLt5Z81tvvrO3y41D0jbL45jJ/NgXwc2441Zilh0/3z2bnp/pQKtxZREqk9du8hk23qZzlrdMt9i8ZMJnnmATDZ83WLp/YTQSezaLC7Rr99Fbx/o/V6tXJe0yBmGPhRWgN40ov61VWZh9OqYmTsNW8za0lq7yiVdEJIFT8tCSHVxWpv1dia6kaufPEbfljj/JwSHdvnrahKe0xTJMa3K6u32GdjlwuhGTNmcNRRRzFw4EA0TeOVV15JWTN16lSGDx9OMBhkt912Y9asWaknaoaqqirGjx/P4MGD+cMf/kBZWWqioiAIbaO9naUTBBTxk9xHJdu+KmqYzpN0jPowSh44+9v9RnLt0du6er30KwgQSXIXVEco2f1KN0crFI3ZYRE1f6l+25PhyDtYfNKHyslThWDQp6c4Ql8bW3FL0y/J05wHeV3cHapNGjj7gzEMA53TI39ij9C9bB/6t2v/RoqoJP2QWdMThDxlHps3fu6Q42Zp8UGwZ3ne4Qn/3521uiUSW9PVebi2hnmBc7nO+5i9baK2gF00x7mJGIY1T+2L+6yWAJFGWKE8D6pXEI2ZFNBgbzL09E1Bm/uVUvVAJGby++fnpi768T0rryoWTdsdXf19a67arSEcZV2N87M02pkjdMl/5/LM7BUcfe8n9raW/vc594kvOereT3h+zsp2Xbun0uVCqL6+nvHjxzN16tS0+5999lkuv/xy/vrXv/LVV18xfvx4DjnkENavX2+vSeT/JP9ZvdoaYlhcXMy8efNYsmQJTz/9NOvWZe5uKghC61BdmLZMn0+w7+gydA12GFzU5nMMLsnJuE99MCVXlBUGfSkN7/oWBFL+Ja8+15PPkWBAUZAz9xgGJEJj1kFej2Y7Tk9/uQZ2+TWNBcNpNC0naHWZMzg24awFvB5iyl/Tn8S25djw9aw0+1Jhpn5Parn/erOYDbjXhEh1wirN9LlgDcMPhHxFCBXGQ2xJc8xKqeFa3xNpz6E6HTVmDqNz6hisVaRd+1vP6+RoYU73TgMgQJjnA9fzQuB6rvA+y1htmXU+9fqNlbD2W+d9zSpipkmh5ggh08iUe5Rdg0PIkLfzn+Phs3vguxfweVPPFVYcyOYcoT1veZ/XldyitjhCD3z0E79+bDbhqMF3q6vj13TOo7fQGPXjHzcA8MTnS1t97d5Al3eWPuywwzjssMMy7r/99ts599xzOfvsswF44IEHeOONN3jkkUe48sorAZg7d25W1yovL2f8+PF8/PHHHH/88WnXhEIhQiHHXq6paT7BTxC2dNSKF187coSKc/18c+0h7cozGlCUw5Pn7Jp2RtpJE4fwxOfL2GNknxS3KF234r4FAQ4Y04/pC9YzJp44rj6kkvsUJdA1zXa3wlEnNObVddsZuHPaj/xmn5FEDZODwreyf8EqRvc/CLC6Q+fYQkh3CaEV/pEkoj5PxQ7i5BGN3PizMx6kRumBpM5RU/k0ti17eb7ngehRQGqvpARNhcPJq1FGfxQMtJo21rr/Iflm4KqUY0PVa3j1iansFnVESy25vKL/gbxANbs03Z8i0jxKSKwMdw7Vhd5XudD7KvNjp0GTEqZqrIQGJeezeiUxw6SQemdb1B0uDBBmsFaBTj8y0SopUrcen2dkymZ3jlD6M0ZiRsrYl7ZUjd3yluWavfntmrT7s21UnUU6VK+ky4VQc4TDYebMmcNVVzn/o+m6zuTJk/n888+zOse6devIzc2loKCA6upqZsyYwQUXXJBx/c0338x1113X7nsXhC2F/oVB9hldRsCru6bGt4Vspt23xD6j0zcr3HZgETP/dCCleX48Sf9CTufu9M0P8JsTRvLslys4dsdBgLsvjtej8c6l+/L96mqueH6e3ftG152wmZosnSy+Vlc1Eo2ZrDT78al/BNuoSeeJHKGkUKMx+lCYa70O4Wf53rfwweIv7f0/mEN5Oro/GynivujRab+HSyIXsk/sG1439kjZ96WxNbvoiwBozBsCMUVEFPS3/vv2/7mO6a9VppwnEKnhxJ//xInKtiLqyYtZIaDd9B/41NiWUzzv81TsQGrIZ5C2wV67jb6cFWbqzzEWM9zCp2GTO/G8eiXRmOFyhLRoiKqGMGc9Opujxw/kPt9dHOj5mrubbgYmplwDUqvGUnJnVFcqkO8KDyfGabhCY/HXyaM21LEiCdpTNVYXiqateMtmVI51bckR6nZs2LCBWCxGebm77Xx5eTlr167N6hzLli1jn332Yfz48eyzzz5cdNFFbL/99hnXX3XVVVRXV9t/VqxY0a7PIAi9HU3TePKc3Xj4zIlZ/4XbVZQXBvF5dHRdc+X7pHOydhvZh5I8P+fvN8oeUhtLCo2N6V/AL3caTIHiQHk0zc4Zeeyzpfa/+JOr2FZXNdphM4+uucRZwhHS0PDjhHaaBjjNEQuD3pTKNQOdP0XP5bboiTTiThI+eVer+/UGinjZ2IdI/N/BqhNzfPiv9uu6/GEQDTsnyClO+Y4SrNQHuMaRpCNfyWnaXl/C0/6b+KPvWf4aD6uN1p1ZZGO1ZeSTmsxs1q9zC6HGTdBU5byPNKA3VlKo5AgRC/H0rOXMXVHF9a/P58B40vaB1S+n3uRHt8J/T0FLCqelyINa5fljmq4coaaI9X26k6VNfv/cPPb9xwfUhZxzr6tJFUIthcZM08zoGpmm6XJoE2TrCLUmp6s30a0doY5g1113zTp0BhAIBAgEWt+ASxCEnoVX1+yQhV8Jc027fF/W1YQYO6Aw5Rg1d0R1eApzvPYQU13XUpyc5PVgVS0NKc2x96kJ4Ykcof5FQT40xvNybC9+cdzplCldrwcU5bQqjJhpdpzmenBqPBOdRJlWzRtL+vGPcKPzkAikfh8JKr39uLTxXP7pe5DhuhU6qzCL+MEYyr6eb1PWn+d5HT0+B+04zye8E5tIP63K3n+KZzorzNTQlb/yRwipQqgSGqtca/TGDRRpTmhMi4ZSHEBQxE3NGivxepdfw4x/QizEsOCB/Ex/e21KSXutEoIK17t+tnWhKDl+j6viMBIzePErKxH5jW9Wc9JES5SqSdIJZi3ZyF3TFvGnI8bST+nuvmhdLW99u5bpC9bRGI7x5iX7pFSrZZJQLeUIJdhSmzl2ayFUVlaGx+NJSW5et24d/fv3z3CUIAhCy3gUIaQ+ULbqV8BW/dInEGcqLy4I+ACrlFzXtLQz15IfWqurGhkQ79fk8+huRygeYizK8fH2FYeQ4z8crTBI38VO+GhAcbBVs92KctJXTzUlJVBfGT3PejFvHdcFGihM3FYwsxCq8ZXzpbkNk8J3MHP0U5SveJNLIlP4xhjJr423udz3gmt9QgQl+Jf/Dus8Zg415DFCX8dvva+nXGfWrM8YOUJJ+W7Y6PQQ8hdAuBZv0yZ3jlAsZH/2II4Do6FRURui9JUpeH6ebjWJjJNjNKKS8mNXHaFwnctJaQhHgYArWdq93+mXtD6NEPq/Fy3hWBeK8fCZu9jbD7lzhus+lmyoZ+vyAlefI8Mw04bGshZCUj7f/fD7/ey8885Mnz7d3mYYBtOnT2ePPVLj2x3J1KlTGTduHBMnpo8hC4LQs1HzgpK7UGci07+YC3Ocf1N6NC2tU+PRNS46YCv7/b0fLOaLnzfa+1wduhWBM7wszw7NlSmNIAcUBdNeJ1PlXKb8q79GzqLRV8JzfVOn0weUsFyyI7TadHo61QTK7fDQO1tdzd6hO/nM2I46crk79kuaTEeE1Xoz94J6IHoUT5pW8cyOepoZYRWL+H6xksBdtdwZB9LHSlj+8OsfKFYdoViIcFws9NecRGst2sDEG6fR+PNnKZdpqHMna6f82JMcITUxOhH6CkcdwROOOidIhM4gfWgswY/r3dV5KWlK8fOobRsyGTrZRqy30MhY1wuhuro65s6da4evlixZwty5c1m+fDkAl19+OQ899BCPP/44P/zwAxdccAH19fV2FVlnMWXKFObPn8/s2bM79TqCIHQNajjDn6ECLJlMkxLUKjVNS01yBisU9/uDx3DXrybY2x6c8bO9T3c5Qun/au6rDIvtkxdIe52BxemFUEGaSjqABeZQnthrGvfU75+yL6A5FU1hr+OSXRa+gD9Efmu/r/H3s+fD/VRlsDIprFWjhPSmlf+afUN3MM9wV1o1mT5eiO3HGv+wlPv4xhgBwCBtA+Fapfx+0xLrv96gVdUG5FYuYDf9B3uJFgvboyXUxO7cJqsFSzoTJC8pPynFCVQdoVCty/1JOD6RDOXzD3z0E5/Gnb10obEE4TR9qVSqGq38LVUItSU0pn62LTU01uVC6Msvv2THHXdkxx13BCzhs+OOO3LNNdagwpNOOol//vOfXHPNNUyYMIG5c+fy9ttvpyRQC4IgtAY1eTldZ+B0ZAqNFSphJ4+ePjTmiYutQWmEitejuYRZppCXGt7yeXQCntR1xRlCYIXNzGlbVd3Eik2NaBrsNLQ47Zq5FY7D8Y05kvmGI1hC3iJ7PtyyTQ0px6qJ1NWBASw3yzkxfI1rzZHhG1lPCRtzRrm2vxLbk39GrfqzgdoGik3FKUkIoWAR5PYB4BLvSy43SYuFGbryDXbX59MfxxGy3KF0qcWQqzWhY7CH/j0BwmmSpZMdIee7qY87Qqr4aVAGylY3Rjj14ZkArKvN7AiFo4al0r5+CtZ+l7I/kYSvCqZM4qm5ZGlVSElorIuYNGmSPfZC/fPYY4/Zay688EKWLVtGKBRi5syZ7Lbbbl13w4Ig9ApU4dHe0Jg6DNaja2k7Dfviobgdh5aw87AS1z6vrrseVjkZ2hCoCdV98v1pHaHdRvaxXyeSsYHm+gcy82dLIIwpL6BPfvqk6opap4JsjdmHSpxQWUXeVnbobdnGVCE0XHMclNUF4wF3c8cfjUEsNgcDEM3rT40inOrNHFaZ1jSAgdpGStQeQ9VW5IBgMeSmD7nlVf/IoYuu5hn/DQzRnEa8QS1CMXWk+2LyaOIS70v8138jv/c+j2Ga/GvGTzw7O369RqVlQLiewvB6/uW7jT3176gPxTAM09VNOtNA2U31LQihFTPh1Snwv0tS9lfFk/NDighrjMTSCrvmHKGQEqoTR0gQBGELQhUR6RoqpiPTv5jdobHmq8Y8usa/lSTYxDb1wdnczLZrjhzHpDF9OX7nwSmC6/5Td2K/rZ3+O2X51rDaXL+HbQdk7ti9cJ3lsuw4tCRl/EiCB750mss2xEvz9wvdzomhq6nOHUZuPDS2bGN9yrGeeHK0ofuJ+fJS9jcqoqgkL8Bic5D9vp4ga0xL3BVpDfQjtW8ROcW2I5TgnZj1HatVcb9PStoerFXgI1WkJIQQwHneN6hqiHDTmwv4vxe/tXrtKPPWCNdxcs2jHOyZw9P+m6hrChN99kwe8/0dLd6aINNA2fpQ5kGzoZjhOF61qe1iqhtSQ2OhSIwdWMxH/ks5RHfSOtS2FsmuZqMykX5LnU4vQigDkiwtCL2bMsX5SOfgpCNT5MAVGtPSh7bUUFxRjs8lvnwedwO+YDNl8b/eewSPnb0rQZ8nZQbbYdsPcIXPdE3j9Yv25tP/O4Ci3PQhM5WdhhYTyPBdfGuO5C+Rszkt7DS4XWb2Z5Y5Fq+ukxcPjaUzFa6M/IY1ZinrTnzd9T38YFhl5C/E9rW3leT5WWY6qQ/1ZpAGglQpXbMjScNjrdCY4wjdHjme5wrOSPs51pol9nWP8MwkRwunrDnL+3baYwHqwlF3E8dwHXmGI4zuefl9/AtfZZJnHgOxkuHrQ+kdoboM2yHuCCVCcKGalP1VDRHe+GYNB98xw97WFIlxl+dOhunreTBeiQfu0Fhyl2tVCDWEY2mbKpqmyYMf/cQ732fXv6+nIUIoA5IsLQi9GzXxONscoeSmiAmSQ2PpqrlU0aJpGn3yAq5j1PyObEN16VCFUEM4RtDnoSQvdcZYOnYaltkRAmusxydGakNan0ezk6XT8UzsAPYI3Ytn0HjX93BG+ErOD1/KU7GD7G0luT5WKl2l6+Lu02rTGZY93diJ5cExzgWCxS5HqHjothTmpx8me1H4Ih6JHQrA6Z730q7pq7mFRyF1zv00JQmhUB3rcO7tAP1r+3W5VskR+hdE65XeR3FM06Q+FOU0z3tc630MD2ncGFsI1TJKW8W5ntcJYAm3qsYIU57+yrW8MRKjiFRHTg2NRWPu6zQmuVX14VRxNntpJTe/tYDfPjkn9R57ASKEBEHYIumrlKJnmhuWzCm7DWXsgEIunTzatT2b0FgyZQWOOPHquqvyqD2oQiaTE5GO4lwfI8vyXMffGrGSlP8SSa3SzVXymLwezXaEmiPH53GJyQqKedvYFUN5FJXm+e2cIIB6rHyhVaYjdL41RjDNUMKLeX1p8hXbb2tyBhPTU8XfC7F9mW1uw3uxnYmYHle36+YYrTldr+uaIm6HJlyPZjrf8688H9qvH/TfwVT/3Ryw6oGUc4ZjBg3hKDf4HuUs77sc67GmxR+hf8Fb/v9jmLZWSco2udn/CH/2Pc3h+kwm63MoqVQbVZo85LuNs5ZcQbrasUSniFM908j55zBYMcve15gUDksXrlPDnW0NnzWEo5z+75n86eXUBptdjQghQRC2SFQhlG1orCDo461L9uHSyVu7tpfkukdsZNPoUA3NeT2aqxNxR9EaITRhSLEl4hQhdF/sF+zSdL/LsUmw01An4dur6+QpfYoSjSKTyfF5UjpsJ1Oc63c5QvWmda6PjPH2ts+Mbbm7Zl82Dj8SJpzGTRv35biH5zrH5A7G8LiF0APRo7giXvJfRQHPx/ZLufaGYmdQ7XKjLzONbQAYrzu9i9ZurIKYE04zw3XkGI5QGKcvs1/31SznaK+q/wFQQg1F1NGXKqrrm1yNHw/SLbdlqv9uxuoruN77GA0bHQE2HqsSbh/Ptzzsv40/r55i7yuinoM8cxhXP8sl7px8IOs7v9H3CFqkHh51Bp0nC5u6kHsILGB3TQeoaKbSrTn++c4iPv5xA0/PXE5tU+o1uhIRQoIgbJGoQijb0Fgm+hU6D35dp9nwUgJVCHl0Le28s/aSHOY4cocBGdeOLLNCScn3njwlPsGOSpm9z6PZydIAk8emb2/i9egZw4tg9WAqzvGxUnGE6uKO0FOxg9gndAdHhG7ia3M0VRTwu9CFrDvgNh7+LsaP5iBWmmV8ZWwFOcVoXnf1W4VZhFohdkf0OJYZ/fjOGM5hoZs5LHQz3w88zt7/qbEdb8esHNFrfE/yK8/7ANz40kz3TYfryDNTw1EqjQTZV5/Hh4HLmRc8j9nB3+F/63JXX6N99W/ssBdAqVZD5TpHVCV6Ou2sLXK2xdeXaO7miwmisRi8eiGHr7rLvcOI2glvyaGxujSO0MpKp9P2+tokF800YdUc9yBakpeYPJOouMMaF9KdECEkCMIWSd/81ofGMtFPEVVNESOrGWAuR0jXOH6nwew+spSrDtumXfeionYxBrjluB345wnj7U7DB27jND5MlNr70/QmAvjljoNc4bA+St6R16OzdXwsSZ88vz3gNR0ePfN3E/Dq5Po9rnwgP457sMIs53tzuP1+4bpaXv56FYYJYXzsH7qd48LXEvR52GvMYNe5N5juztgVlLBf+A6ODN/ED+YwfjCHUWvkKvuLeDa2P5WmJRBv8T3M1toKog2WeIma1ufQTINSsyrjZwLIoYkn/H+nSHNaC+Qu+8DV6TpHC7OP7oSN/ETpR+p5B3md/KQ+WCG6EiWHSSX28wz4+kn22fg8QUKu8CLrraaTyaGxhlCUb1ZWUd2gfO9Kb6j1yd2wf/gfPHQAPHUcmahpjLoq5xas7V5CqFvPGutKpk6dytSpU4nFtsxyQkHo7ZS1ITSWCTUstKEulKUQcguJHL+HZ87r3NFB+QEvx+9sCa4f1tTSEI4yfYHVW2doqSUCMrlZp+8xjD8eug2nPvwFJ+4yxNXryKtrHLfzYHYeVsKgkhx8Hp1BxTmsqmpMOU9zjlCu30uO30MYJ9SoiqIEw/rksmxjA1UNEb5c6rgqkfgjLejzcPyuI+Bd55iNaZ0t973UmM7PpMIspoEgl0Sm8IT/7wDspv/Ad/Eu1+soYVC8Kqw83qjxb5HTKNFqqTLz+YvvPxk/J4C/cR3baUtc2w7zOG7TGH1l2uO8hiNEyrRqVptlGR0hFjlfQAGN5Cqz1tiwEMrHpQiheSur+fvbC9h/TF8ePXtXINkRShJCX/7b+u+KJKdMoaLO7SItVITQYXd9jFfXeOzsiRl7WHU24ghlQKrGBKF3k+xodBQb68JZ5Qi5krVbyJvpaAaX5HLQuHKXYGtJCPk8Ov2Lgkz//SR+u98oV6+jRGhxeFme/XpUv/RVW8kl/ypjygvsDtVHhm7govCFfGuOTFk3pCTXzjVaU50qtgJeHa/XLag2mekH6ap8vc4RBRtMSzjNMMZzW+R4ACbqCymIuzpVZr49by0x7f4zY1v+GT3JVf6vUqUXu95P9rirvvbRUztIN0dZPAcpkyPk++El+3WhVu+uKKtZDZ/eRfmKt7jU+wIlcXfJCluZ9NkwG0K1mKbJikrFEUoOjUXSJJzPfw0WT7Pfqs04wXGEYobJD2tq+HZVddaDYTsDEUKCIGyR9Ctw8nqCWVR5ZUtdKIrfq3PtUeOaXZecI9QVqKGzwSWWEMrkZiULJFXspcuxGts/vfBoTvTtMLjIDr99Z47kf8aeadcFfbotZNdUpz6IEyJNx/l8FWbmhpIJ5q1TkoKV9V+aVqn+RH2hLSZqzDy+NrZyHV8bz2dqIL2z8WNgO9f7nZIGy/bTqlq8RxVbCGVwhDz16+zXQ7QKdE2pKHvnT/DeNew37w9c6n2JF/zXAZajeZ7ndf5Z/yeYdh3fr65xhbVSQmNR5/vf/abpfP/jYnjudCtUFg3Z51TZVG8JI7VlRDaVlp2FCCFBELZIcvwenj53N548Z1fbhehIztprBLuPzDxpvY8SGmtvsnYyh2/fH4BtMoiRBDVK9U4i1NWcI6Siisd0OVYXTBrFqL6pXaSbE33bDy7KOF4k+RwJIZl4qLrvzTqHV+nNU0Fxi+dNVKglr//a2IqI6WGAtomxupX0W0sOXxvuNgq1piUm5xhbEy0ZxaeBfbk8fD5Npo9Twn+ixufufp1gk5nePWuJMpoXQirqeJF0jNLX8LTvBmprqviT77/WxtkPMe2Hda5162pDULPGCbspQqiipp47X/7EWVxpJXsnKs0Sc/YSY0HU8SDtDU+3BxFCgiBssew5qox9RvdteWEWpBMQzQkc1RHKNMy1NaiRhVuO24GrjxzH47/etdljjtphIIOKczhn7xH2tmwdIXdoLFXcFOf6mf77Sa4Ea2jeEdpzVFnK+nR4dM2V4wXYs86se3Pf63fGcJodthanUXFyNirJ1U0E+M60vqMzPe8AUEMeXyUJoUSFW2lREd6L5/D+9n/nJWNftgk9zmfGdtT50v+ufWmMcb2fZ6SGAwGiSY/svi2ExlT+5nusxTV7euZzZO1zrm2jv7yOICEOGWcl1lc3RuDxI+HpE+Cb5yHihM0KqSc3ojSP/Gk6RJpsR8gSQqY93ywxHsSjax0anm4tkiwtCILQAUwYXMyspZtc25r7V25JruMI1TRm3+8nE+pjvjDoc4mbjPeQ5+eT/9vfNYsq0z0ni50cn5osnflzqqNDwO0IDSrO4b/n7o6mQdQwKc3zpx3xkIyuaa5kc+uz+OyRFck5Wom5Yy2xiULujv6CwyYMJ3dhCXVKYvAsYww76ovJ06xtIwYP5Lsl7u84hnXdoM8DmsYlk0fz3apqZi6xfi/CvvThuf/F9uBgj9O1+StjtN27yNQ8aGaMBjPASn0gW5tOgnVLobG2cFjsQ9cv0xFNr7PIE+CstV9xli+He0N/hJp4SG/ef6HOcZqu9T3BMZHPnIPfvhIql1JRdwoAYwsauCcwhfciewKT7dBYV7pBII6QIAhCh3DXyRM4eFw5/z13d3tbc46QKgiqGlPDO61FrVxrDVpSkmqm0Fjyw6olRyhB8mwr9XMHvDpD++QypDSXEWVWGK25ZOoEXl1zJZuDW1gmHKFnh/yF56L78WDsqBbPmeD26Imsn3BRysy02YbT1uA7YziDJp3DTSfswnqzOOUcgfh3Uxj08fuDHbfHk3GOm1tQqSE3o9Ryh34yB7BMH+JaV0YNv/a8xWGejivqGaRtSNl2mGcWJQ1L2MMznyvq/unsqF3rCo0d4/ks5VhmPmA7QgeEP6Bcq+I03gTTtENjXZkfBCKEMiJDVwVBaA0DinL41xm7sMcoJw8k2yaJlQ1t77T70Bm7MKQ0h8fO7pi/qzJVvDWbI9SMI5SMKoTamiSu65qrDxQkCyHrMyzodzh/jP6WML5WXcs0U8OVHxgTeCR6KJeFL+DI8E0EBk/g+J0H86W+Xcrx6nejhux+KtkbgLnGSKZ79gHgqeApLDX7u45faDo9kPRRBwAwyxjLz7h7I22rL+Ea35NZf67meD/v8Iz7ttacUv4dDaWybf33WZ37jDU3Uc4m8vOcnDGzdo0dGsum3URnIqGxDEyZMoUpU6ZQU1NDUVHL1QaCIAjJZBo1kUx1O4TQQePKOWhc+nLttpCpuWRzOULNNaT0e3TCSnhMFU1tFUL9C4Mu4QPuMSfBuJhTw3c5Pk+z095VBhQHMZKEUAwP10edifYJB+7fub9hm9rFvKZUuAUVMakO5I3klnNO+bN8vKyJvrrBM+GJhAYfDFWbqDOD9ngMdcSItu0v2OfjbVhtlvFL/zeueyrU3K0DfjCG2sncmbg2cgbX+p5I2V5buh3Uv2l9VlPjpPDVvBC4HsBdbdYG9g9/wLP++Rj6r+xt0TXfEcqxxHs2ndg7E3GEBEEQOokLD9iKyWPLuefkHdPuH1NuVXUdvG3HCZn2kilvOznJWRVCzfGfc3djdL98nv7NbkD7HaGDxpVzwaRRKdcvThMaU4WQKkjSMaAoyM2/3J5Hz57IqL75aUaXOvg8mv3wNvP6cUD4du6MHm/vVx/sqiPk0TVCvhLC+FjVFOA9YxeK8iyxrCZe15ODMXAnyC+HARNYYZYTw8OP5iB7TbXpdMF+Oro/h4du4rLI75r9jLdEfsUTsYO5I3IcL8X2jieRW2woGGu//tTYji/Nbfht+LJmz9ccM2Lbu94P19dRVjnXfu/77wkMff9CnvH/jSJP184eE0dIEAShkygM+nj4zMyJuv89b3c+WbyBgzvQ0Wkv6SrY/B49JZdIDf/Emklwnji8lPcudwacqoKqNY0k9xldxiUHjmaX4VZLguRwSmleamhMLcUvDPrS9hxK8PlVB7reN5e0reY9JTtTyai5W9GYkeJ+FMYF2lWR3zBVu5tHoodaO85+B8wo+B3Bs9ToZycyf2xsz5HxTtR/j55MNVYJ/rZN/2YXfRGPx7thWwdY9/u1MRoDnbtix0GM/2/vzqOjqvI8gH9f7dkqlX0PiYQtQMIWQog0YqKAiKK2IIMtiMiowUGbprWhAZU5HWYYPKexkdZ2YejpMYPauAGOCBKVAYRIlACGRTQ0AkGWLBBCUnXnj6Je3qsteyqhvp9zck5S7+bVfTc5p37nd3/3XnxgWCzfuzqkaU+kIus4AHBb/9RSb1gnYIzhCCRrU8G5+VSxqk3kDx8hUgNMbPwMwPg2v1d7MRAiIvKR8CAD7sqM93U3VKzuAiE3UxfK4mnngmhvtNrmM0JBBi0uX7OiT3Qwjlbal4b/9ZFsVRvXjFDT1Jij+FbZxhzQuo875ydKDg9EheLMrab3dQ2ElL+rHLsGq3ApLA8x2ft9ClGYcm05AEAjARq9AYD63nVWDTBvJ74/fQ7/tuEEIlCDV62T5CAIsGeTjgvF4bqhSUCVfbrsZ6jPW9PrtHDsOak1BKCwYTqipUv42GbfdkEZCDUmZAH/KIFOsv/CJ9bh8kq3hoRs6E+pj9hogA5Ca1AFQp5YNM0v/+9MDISIiEg2JMni8pq7VWHKDJG3jJAzdUbIfXXGtgW3oPTkRZypuornPjzkto3zSqMgg3IfIdcaodAAPTzJdPPMzvFgcnggfrpUh0anZx3dOwLvfu3+XDBn9Y02GJyK0d0FaJ7GpdEmgNhBMJrqcFJcwfSG37ttpzqfzdq0IvGcU4ZH+Vc16DR40Wl1nXJTSSkxCxf/UY7I60dxbLGOlAOhPTc9CVTUogE6JEg/I0i6ap/qa3QfBP1XYx4e1G2TfzZJ7d8+oj0YCBERkSzEpEfZ8+NxvrYeY1fuAND8ztct2QTRQbns3dNis9hQEyaExmHrobPuG8B1akw5DSYXSztNjTlLiQjE67Oy5B2PlZyLpXN6R+D4uVqX6bV7hyXgaqMVQ5PCcMfqLwB43iCzwWpz2YbAXb88FZ87Ak5lYTgArPxlBl75/Hscu549synLf0VToXo1AlW/p2lm/6hr0OOCCEa4VAtN4ghUi42IlOyB0H6Rhnej5+GbUzVYv1UDwDHNJqCFDVZoobG53xbikEhR/RwpLrpt11VYLE1ERCrBRp2qyNdTILRscjruHZaAsX1bvjt3SkTTEurmNpLM6x+Nh3NT8OLUTJdrzsv8lbU6jgxWgGpqTI9Zo1NUvxNo0KF3VLDbwm9lLPPomFTM/cVNuLW/fXdlZdAgSRJmZPdCerzZ+RYuGqw2GHTqIMexf5JSc7VTyueSJOD+EUku+0g90/AozliGAeP/oHhVfd8S4/Wdx4OiPe7ls8k6Cud0cZB6j0Oj1PQe54QF/1F1K9ZbnWt7JHljSU8O2Xqpfo60nfPavrMxI+TBmjVrsGbNGlit1uYbExHdYIyKD1vn7IjDw7nN717tTBl0nHRTc6Ok0UhYNnmgh/uoP7gzk0LxQFYSos0medpOVSNk0uGp/L64Z2gC7l6zE4D3TJbymRdPsh+gu+iOAbAE6nFnRtvqukakhOPYWfUu0ElhgfjbnGwsfb8Mx8/ZD3RtLgOnnJZ0dDPAaTz+xzoOP4Xcj78OHolXNn2Bz2tc+/yheRqm540EeufBcMzlMgBgSeNsfJ4Ujb8EhkMvNX0eXoYJqPO+2utcQh6iTm1DpbDIB8o2QqvaJwkAwq2umzh2JWaEPCgoKMChQ4ewd2/H7dhJRNRTmBRTT54CofaqaeG+Pu44Z4QMWg1W3JeBX9/WV35NNTUWoIdGI6F/XIjb687cPXGQUYeF4/tjQFzz2R+lzxeOw8pfZmB6VpJLkBNk1CI3LRIjU5sO6HUuIv/VKHsGZe4v3J9BBrjfzuCLoz+jpOIi3tLdg522wS7XtXoTMHwWYEnyupePoyzKCOXfS8Lla94TBV8PWY4fhi/CPfXPy6/t0o9CHdT7a4U1Vnret6ELMCNEREQulIdgOh0X1m5ajdSqAmt3nGuE3B3aqcwaOWpx9IrCpAAveyG15yBcl0LriEAkR9jrc5wDDseUlrJexzlYWnp9CnJwgufNfZWbOD44KhlfnbiAI2dr8cPPV1wKvB2UU3yedhQHmlYS6qVG9xGiB9WSGcd6z8Kpnfvwv6YJGB97GS/X/AtQo84kmWx1wNUqIMDS8pt3IGaEiIjIq/YEBe7cmWFf3u18cGprtOR8KucaIUB9lpn3qbE2d80rZSCk1UhyQKfMAjkXS+u1GgxNDvN6QrsyuxVk1KHv9c06q682eNwTSdkXb8dcOIJWWytDhroGKy5fs2eR/jPiaWDWR7AaQlzaHQsZCTTUubzeVRgIERGRVx09NfavUwbhyVvTUDR3VPONPfCWwXAIcKoRcrnewhqhlpoyxF6H8/gtvT22UWZ7ggxaud5HmRFqyY7bzlsaqM43M+jkwK+6rtFzRkgR/HibGnM8zx+M83FZGPFsw5xm+wcAV65ZcbnePn3myHw5Aq6VcauwP2wCMq++ircHrAbMcR7v09k4NUZERF51dHYkxOlU9rZoSbBgUi6pdxP0BOg9fwS2JfZ7ceoQ/P7OdEQ6HQirZPRy/IaDvgWH2AbotWiwNtXsKAPDQKNOngqsvtrgcRrS2IKM0Jb5Y+SaqK81gzC4/nWPmaHEsABEhRgRbwnApm9Po+LCFfQKt08JBl0ff0c/15yIwxrYz27jWWNERNSteTtuojtTZoTc7ZMTYPD8Eej48A9qxR5JGo3kNQgCnDJCikBIGdd5O8TWYVivMNXPymLpYKNW3qixuq5BrvFZPmUQnr+raRVeSzJCyeFNew8J4X16bOH4ftj4RC7irx82/N97KlC45TsAioyQmylNX58+z0CIiIi86qxVY51Nr9UgPc6M6BAj+sQEu1wPNHjOCL36q+G4f3giNhbkdmiflAGHKhBq5Rls//7LDEwfmYSPnrwZgDroC3LOCF0/AuXmtEjV4bMGrSJQ9BCMKIOU5v4N4kLtG1MGuBnXYKepMSVfZ4Q4NUZERF710IQQAOCDebmwCfcftt5WjSWFB2Ll/a4bObaXMjOlmhqTlMXSzQcG0SEmFN6bIf+srBEKMurkv1l1XaOcEdJKkiojpcoIeXhPZV+aC4jjrmeC3I2rI+h0V9vVknqvzsSMEBERedVTM0KA/YPcOQjKHxADg1aDu4Z0/YG3wYqMjNFpBZlDSzJCzpRTY0EGnVwcXn21QS6W1molj9NhRi9BoYPy/8Bd4BRttk8LuluNF2R01Ah1v6kxZoQ84M7SRER2PTgOcusvDw1HfaPN7SaEne0XfZqOIzl0ulr+XiN5Xj7fEuqpMa18D+Xyea0keVwy7ykjpKT8PwgL0uNstfpQVaObM94c+sear9/D9Z/J11NjzAh5wJ2liYjsrDdYJCRJkk+CIMAeJDwzoT8AYLbiiBJ1Rqj1H83KIuRgRY1Q1RVFRkgjwaj1EAi1IBhRTpFOHOR5ubvz1NiTt6bh5j6R9v64OZbD11NjzAgREZFXPXlqrDt6/JbemDQ4DglhTafeq5bPtyEjZNQ51wjZ/2bVV5uW2Os0XqbGWhAIKbM5/zz2Jqz7vx8AACEmHeaOaTr+Q7lcf/+S2xAW1LRxprtAyNcZIQZCRETkFeOgjuc4csOhtRsqOlP+jYIMOkhubqFxDoS0Hr7XaXCt0fVclchgI85fvgbAvkLsy2fGQSNJiDWbVKvelGfIKYMgwFNGiFNjRETUDWUk2s+2Gj8wxsc9ufEpS3RasmrMmXL60qTXwKTXumRavGWENBoJN0UGwWzSIcbsfi+kl/5pKDKTLFj3cBYAIDEsEPGWAFUQBAB3Do5DZLABU0ckutwjb4Dr/xIzQkRE1C29MSsLmw+cxt1DEnzdlRue6tDVNmSElNNRjmM7zCY9fq5tKmjWaiSXzI/S+/Nyca3Rhmmv7nb7Hn1jQvB+C/ZVCgsyYM+ifLeZrTljUhEXasKvN3wjvxYR1PYz5zoCM0JERORWZLARD+WkIPT6uVXUeZRBg7YNxdK39I2GUafByNRw+TXH7tLK9/C2d1CISY+IYGOH1IR5mt4z6rS4d5g6U+Q8TdjVmBEiIiLysfYWS4cG6lG69HZVvY1j5Zj8HpLnqTGlrq4J8/WqMWaEiIiIfExq5z5CgH1pvrJex6zI5EmSvQ7IqDhWw1PWpneU63EkncXX9UEAM0JERNRD9YsJQfnZGkSHeD/otCdQHbHRhqkxd8yKXawdu1XrdU3vI8F9IPSHewchdIseD45K7pB+eBPVzCG1XYGBEBER9UivzRyBl3ccx5wxqc037uZq65uWlWelhHtp2XLKjJCjGLslO0hHh5iwamrHn7PmzoiUsC55H28YCBERUY+UFB6IwnsH+7obHUI5HTVxUGyH3FNZI+TICLVlaX5n+NucbBTtPYllkwf6uisMhIiIiHztln7ReGn6UIzuHeGyL09bKVeNubun4yBUX8hNi0RuWqTP3l+JgZAHPHSViIi6ilYjYXJmfIfe011GCACW3JmOH89fxpAkS4e+X0/FQMiDgoICFBQUoLq6GqGhob7uDhERUasoa4SUK8Qeubnn11R1pO4xWUhEREQdSrlqrC3nl/kLBkJEREQ3IFVGyN0prASAgRAREdENSVkj5HwKPDVhIERERHQDUq4amzU6xXcd6eZYLE1ERHQDigo24rb0GABwOeiUmjAQIiIiugFJkoS/PDTC193o9jg1RkRERH6LgRARERH5LQZCRERE5LcYCBEREZHfYiBEREREfouBEBEREfktBkJERETktxgIERERkd9iIERERER+i4EQERER+S0GQh6sWbMG6enpyMrK8nVXiIiIqJNIQgjh6050Z9XV1QgNDUVVVRXMZrOvu0NEREQt0NLPb2aEiIiIyG8xECIiIiK/pfN1B7o7x8xhdXW1j3tCRERELeX43G6uAoiBUDNqamoAAElJST7uCREREbVWTU0NQkNDPV5nsXQzbDYbfvrpJ4SEhECSpA67b3V1NZKSknDy5EkWYXcQjmnn4Lh2PI5px+OYdryePqZCCNTU1CA+Ph4ajedKIGaEmqHRaJCYmNhp9zebzT3yH6w745h2Do5rx+OYdjyOacfryWPqLRPkwGJpIiIi8lsMhIiIiMhvMRDyEaPRiGXLlsFoNPq6KzcMjmnn4Lh2PI5px+OYdjx/GVMWSxMREZHfYkaIiIiI/BYDISIiIvJbDISIiIjIbzEQIiIiIr/FQMhH1qxZg5SUFJhMJmRnZ+Orr77ydZe6rc8//xyTJ09GfHw8JEnCe++9p7ouhMDSpUsRFxeHgIAA5Ofn4+jRo6o2Fy5cwIwZM2A2m2GxWPDII4+gtra2C5+i+ygsLERWVhZCQkIQHR2NKVOmoLy8XNXm6tWrKCgoQEREBIKDg3Hffffh7NmzqjYVFRWYNGkSAgMDER0djYULF6KxsbErH6VbWbt2LTIyMuTN53JycrBlyxb5Ose0/VasWAFJkvDUU0/Jr3FcW+e5556DJEmqr/79+8vX/XI8BXW5oqIiYTAYxBtvvCEOHjwoHn30UWGxWMTZs2d93bVuafPmzWLx4sXi73//uwAgNm7cqLq+YsUKERoaKt577z3xzTffiLvuukukpqaKuro6uc2ECRNEZmam2L17t/jiiy9EWlqamD59ehc/Sfcwfvx48eabb4qysjJRWloq7rjjDpGcnCxqa2vlNo899phISkoS27ZtE/v27ROjRo0So0ePlq83NjaKQYMGifz8fLF//36xefNmERkZKX73u9/54pG6hQ8++EBs2rRJHDlyRJSXl4tFixYJvV4vysrKhBAc0/b66quvREpKisjIyBDz58+XX+e4ts6yZcvEwIEDxenTp+Wvc+fOydf9cTwZCPnAyJEjRUFBgfyz1WoV8fHxorCw0Ie96hmcAyGbzSZiY2PFypUr5dcuXbokjEajeOutt4QQQhw6dEgAEHv37pXbbNmyRUiSJE6dOtVlfe+uKisrBQBRXFwshLCPn16vF2+//bbc5vDhwwKA2LVrlxDCHpxqNBpx5swZuc3atWuF2WwW9fX1XfsA3VhYWJh47bXXOKbtVFNTI/r06SO2bt0qxo4dKwdCHNfWW7ZsmcjMzHR7zV/Hk1NjXezatWsoKSlBfn6+/JpGo0F+fj527drlw571TCdOnMCZM2dU4xkaGors7Gx5PHft2gWLxYIRI0bIbfLz86HRaLBnz54u73N3U1VVBQAIDw8HAJSUlKChoUE1pv3790dycrJqTAcPHoyYmBi5zfjx41FdXY2DBw92Ye+7J6vViqKiIly+fBk5OTkc03YqKCjApEmTVOMH8H+1rY4ePYr4+HjcdNNNmDFjBioqKgD473jy0NUu9vPPP8Nqtar+iQAgJiYG3333nY961XOdOXMGANyOp+PamTNnEB0drbqu0+kQHh4ut/FXNpsNTz31FHJzczFo0CAA9vEyGAywWCyqts5j6m7MHdf81YEDB5CTk4OrV68iODgYGzduRHp6OkpLSzmmbVRUVISvv/4ae/fudbnG/9XWy87Oxrp169CvXz+cPn0azz//PMaMGYOysjK/HU8GQkR+rKCgAGVlZfjyyy993ZUbQr9+/VBaWoqqqiq88847mDlzJoqLi33drR7r5MmTmD9/PrZu3QqTyeTr7twQJk6cKH+fkZGB7Oxs9OrVCxs2bEBAQIAPe+Y7nBrrYpGRkdBqtS5V+GfPnkVsbKyPetVzOcbM23jGxsaisrJSdb2xsREXLlzw6zGfN28ePvroI3z22WdITEyUX4+NjcW1a9dw6dIlVXvnMXU35o5r/spgMCAtLQ3Dhw9HYWEhMjMz8cc//pFj2kYlJSWorKzEsGHDoNPpoNPpUFxcjNWrV0On0yEmJobj2k4WiwV9+/bFsWPH/Pb/lIFQFzMYDBg+fDi2bdsmv2az2bBt2zbk5OT4sGc9U2pqKmJjY1XjWV1djT179sjjmZOTg0uXLqGkpERus337dthsNmRnZ3d5n31NCIF58+Zh48aN2L59O1JTU1XXhw8fDr1erxrT8vJyVFRUqMb0wIEDqgBz69atMJvNSE9P75oH6QFsNhvq6+s5pm2Ul5eHAwcOoLS0VP4aMWIEZsyYIX/PcW2f2tpaHD9+HHFxcf77f+rram1/VFRUJIxGo1i3bp04dOiQmDt3rrBYLKoqfGpSU1Mj9u/fL/bv3y8AiBdffFHs379f/Pjjj0II+/J5i8Ui3n//ffHtt9+Ku+++2+3y+aFDh4o9e/aIL7/8UvTp08dvl88//vjjIjQ0VOzYsUO1hPbKlStym8cee0wkJyeL7du3i3379omcnByRk5MjX3csob399ttFaWmp+Pjjj0VUVFSPXkLbXs8++6woLi4WJ06cEN9++6149tlnhSRJ4pNPPhFCcEw7inLVmBAc19ZasGCB2LFjhzhx4oTYuXOnyM/PF5GRkaKyslII4Z/jyUDIR1566SWRnJwsDAaDGDlypNi9e7evu9RtffbZZwKAy9fMmTOFEPYl9EuWLBExMTHCaDSKvLw8UV5errrH+fPnxfTp00VwcLAwm83i4YcfFjU1NT54Gt9zN5YAxJtvvim3qaurE0888YQICwsTgYGB4p577hGnT59W3eeHH34QEydOFAEBASIyMlIsWLBANDQ0dPHTdB+zZ88WvXr1EgaDQURFRYm8vDw5CBKCY9pRnAMhjmvrTJs2TcTFxQmDwSASEhLEtGnTxLFjx+Tr/jiekhBC+CYXRURERORbrBEiIiIiv8VAiIiIiPwWAyEiIiLyWwyEiIiIyG8xECIiIiK/xUCIiIiI/BYDISIiIvJbDISIiFphx44dkCTJ5TwmIuqZGAgRERGR32IgRERERH6LgRAR9Sg2mw2FhYVITU1FQEAAMjMz8c477wBomrbatGkTMjIyYDKZMGrUKJSVlanu8e6772LgwIEwGo1ISUnBqlWrVNfr6+vxzDPPICkpCUajEWlpaXj99ddVbUpKSjBixAgEBgZi9OjRKC8v79wHJ6JOwUCIiHqUwsJCrF+/Hn/+859x8OBBPP3003jwwQdRXFwst1m4cCFWrVqFvXv3IioqCpMnT0ZDQwMAewAzdepUPPDAAzhw4ACee+45LFmyBOvWrZN//6GHHsJbb72F1atX4/Dhw3jllVcQHBys6sfixYuxatUq7Nu3DzqdDrNnz+6S5yeijsVDV4mox6ivr0d4eDg+/fRT5OTkyK/PmTMHV65cwdy5czFu3DgUFRVh2rRpAIALFy4gMTER69atw9SpUzFjxgycO3cOn3zyifz7v/3tb7Fp0yYcPHgQR44cQb9+/bB161bk5+e79GHHjh0YN24cPv30U+Tl5QEANm/ejEmTJqGurg4mk6mTR4GIOhIzQkTUYxw7dgxXrlzBbbfdhuDgYPlr/fr1OH78uNxOGSSFh4ejX79+OHz4MADg8OHDyM3NVd03NzcXR48ehdVqRWlpKbRaLcaOHeu1LxkZGfL3cXFxAIDKysp2PyMRdS2drztARNRStbW1AIBNmzYhISFBdc1oNKqCobYKCAhoUTu9Xi9/L0kSAHv9EhH1LMwIEVGPkZ6eDqPRiIqKCqSlpam+kpKS5Ha7d++Wv7948SKOHDmCAQMGAAAGDBiAnTt3qu67c+dO9O3bF1qtFoMHD4bNZlPVHBHRjYsZISLqMUJCQvCb3/wGTz/9NGw2G26++WZUVVVh586dMJvN6NWrFwDghRdeQEREBGJiYrB48WJERkZiypQpAIAFCxYgKysLy5cvx7Rp07Br1y786U9/wssvvwwASElJwcyZMzF79mysXr0amZmZ+PHHH1FZWYmpU6f66tGJqJMwECKiHmX58uWIiopCYWEhvv/+e1gsFgwbNgyLFi2Sp6ZWrFiB+fPn4+jRoxgyZAg+/PBDGAwGAMCwYcOwYcMGLF26FMuXL0dcXBxeeOEFzJo1S36PtWvXYtGiRXjiiSdw/vx5JCcnY9GiRb54XCLqZFw1RkQ3DMeKrosXL8Jisfi6O0TUA7BGiIiIiPwWAyEiIiLyW5waIyIiIr/FjBARERH5LQZCRERE5LcYCBEREZHfYiBEREREfouBEBEREfktBkJERETktxgIERERkd9iIERERER+i4EQERER+a3/BxV3ZK3U3blEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Epoch_Anz = 250\n",
    "if (Training_Percentage > 0):\n",
    "    history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    history = model.fit(train_iterator, epochs = Epoch_Anz)\n",
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(val_loss_ges)\n",
    "\n",
    "\n",
    "plt.semilogy(loss_ges)\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9888 - loss: 5.3001e-04 - val_accuracy: 0.9667 - val_loss: 8.4156e-04\n",
      "Epoch 2/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9938 - loss: 4.7175e-04 - val_accuracy: 0.9667 - val_loss: 4.3191e-04\n",
      "Epoch 3/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9892 - loss: 4.6274e-04 - val_accuracy: 1.0000 - val_loss: 3.4501e-04\n",
      "Epoch 4/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9936 - loss: 6.6163e-04 - val_accuracy: 0.9667 - val_loss: 7.2213e-04\n",
      "Epoch 5/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9916 - loss: 4.4398e-04 - val_accuracy: 1.0000 - val_loss: 4.2688e-04\n",
      "Epoch 6/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9985 - loss: 5.2642e-04 - val_accuracy: 0.9667 - val_loss: 7.7268e-04\n",
      "Epoch 7/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9941 - loss: 4.9209e-04 - val_accuracy: 1.0000 - val_loss: 3.0639e-04\n",
      "Epoch 8/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9965 - loss: 5.3199e-04 - val_accuracy: 1.0000 - val_loss: 3.9143e-04\n",
      "Epoch 9/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9955 - loss: 5.1323e-04 - val_accuracy: 1.0000 - val_loss: 4.1882e-04\n",
      "Epoch 10/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9910 - loss: 4.5862e-04 - val_accuracy: 1.0000 - val_loss: 2.5729e-04\n",
      "Epoch 11/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9987 - loss: 4.7074e-04 - val_accuracy: 1.0000 - val_loss: 3.5260e-04\n",
      "Epoch 12/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9985 - loss: 5.0662e-04 - val_accuracy: 1.0000 - val_loss: 3.6849e-04\n",
      "Epoch 13/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9992 - loss: 4.3567e-04 - val_accuracy: 1.0000 - val_loss: 4.5490e-04\n",
      "Epoch 14/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9979 - loss: 3.9800e-04 - val_accuracy: 1.0000 - val_loss: 3.6111e-04\n",
      "Epoch 15/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9985 - loss: 7.1840e-04 - val_accuracy: 1.0000 - val_loss: 4.1304e-04\n",
      "Epoch 16/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9909 - loss: 4.5350e-04 - val_accuracy: 1.0000 - val_loss: 4.7364e-04\n",
      "Epoch 17/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9997 - loss: 5.8661e-04 - val_accuracy: 0.9667 - val_loss: 8.0396e-04\n",
      "Epoch 18/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9970 - loss: 5.2226e-04 - val_accuracy: 0.9667 - val_loss: 4.5739e-04\n",
      "Epoch 19/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9921 - loss: 4.3960e-04 - val_accuracy: 1.0000 - val_loss: 2.3554e-04\n",
      "Epoch 20/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9909 - loss: 4.9290e-04 - val_accuracy: 1.0000 - val_loss: 6.8630e-04\n",
      "Epoch 21/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9944 - loss: 4.3800e-04 - val_accuracy: 1.0000 - val_loss: 3.7417e-04\n",
      "Epoch 22/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9969 - loss: 6.2471e-04 - val_accuracy: 1.0000 - val_loss: 3.3411e-04\n",
      "Epoch 23/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9967 - loss: 4.7569e-04 - val_accuracy: 1.0000 - val_loss: 3.8042e-04\n",
      "Epoch 24/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 5.0384e-04 - val_accuracy: 1.0000 - val_loss: 3.9307e-04\n",
      "Epoch 25/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9999 - loss: 6.4802e-04 - val_accuracy: 1.0000 - val_loss: 4.0617e-04\n",
      "Epoch 26/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9967 - loss: 5.6663e-04 - val_accuracy: 1.0000 - val_loss: 4.7135e-04\n",
      "Epoch 27/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9885 - loss: 4.5048e-04 - val_accuracy: 1.0000 - val_loss: 5.1304e-04\n",
      "Epoch 28/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9965 - loss: 5.1796e-04 - val_accuracy: 1.0000 - val_loss: 8.4076e-04\n",
      "Epoch 29/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9974 - loss: 5.6951e-04 - val_accuracy: 1.0000 - val_loss: 4.2108e-04\n",
      "Epoch 30/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 4.7258e-04 - val_accuracy: 0.9667 - val_loss: 9.5149e-04\n",
      "Epoch 31/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9941 - loss: 5.4794e-04 - val_accuracy: 1.0000 - val_loss: 5.3679e-04\n",
      "Epoch 32/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9972 - loss: 4.7029e-04 - val_accuracy: 1.0000 - val_loss: 5.8440e-04\n",
      "Epoch 33/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9957 - loss: 5.5759e-04 - val_accuracy: 1.0000 - val_loss: 3.9064e-04\n",
      "Epoch 34/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9966 - loss: 4.8435e-04 - val_accuracy: 1.0000 - val_loss: 3.4531e-04\n",
      "Epoch 35/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9911 - loss: 4.8959e-04 - val_accuracy: 1.0000 - val_loss: 1.9388e-04\n",
      "Epoch 36/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9994 - loss: 4.4402e-04 - val_accuracy: 1.0000 - val_loss: 4.7027e-04\n",
      "Epoch 37/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9958 - loss: 4.6565e-04 - val_accuracy: 0.9667 - val_loss: 4.7180e-04\n",
      "Epoch 38/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9925 - loss: 4.8369e-04 - val_accuracy: 1.0000 - val_loss: 2.9029e-04\n",
      "Epoch 39/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9966 - loss: 4.0519e-04 - val_accuracy: 1.0000 - val_loss: 4.5324e-04\n",
      "Epoch 40/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9920 - loss: 7.6968e-04 - val_accuracy: 1.0000 - val_loss: 4.2640e-04\n",
      "Epoch 41/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 4.6383e-04 - val_accuracy: 1.0000 - val_loss: 4.5811e-04\n",
      "Epoch 42/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9979 - loss: 5.2953e-04 - val_accuracy: 1.0000 - val_loss: 0.0010\n",
      "Epoch 43/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9863 - loss: 5.7057e-04 - val_accuracy: 1.0000 - val_loss: 4.4273e-04\n",
      "Epoch 44/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9967 - loss: 4.7335e-04 - val_accuracy: 1.0000 - val_loss: 3.7355e-04\n",
      "Epoch 45/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9980 - loss: 3.8562e-04 - val_accuracy: 1.0000 - val_loss: 3.3563e-04\n",
      "Epoch 46/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 5.2795e-04 - val_accuracy: 1.0000 - val_loss: 2.7760e-04\n",
      "Epoch 47/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9957 - loss: 5.4668e-04 - val_accuracy: 1.0000 - val_loss: 4.5102e-04\n",
      "Epoch 48/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9945 - loss: 4.1645e-04 - val_accuracy: 0.9667 - val_loss: 2.2956e-04\n",
      "Epoch 49/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9968 - loss: 4.4022e-04 - val_accuracy: 1.0000 - val_loss: 7.3049e-04\n",
      "Epoch 50/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9948 - loss: 4.0854e-04 - val_accuracy: 1.0000 - val_loss: 4.6425e-04\n",
      "Epoch 51/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9905 - loss: 8.0651e-04 - val_accuracy: 1.0000 - val_loss: 5.3028e-04\n",
      "Epoch 52/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9996 - loss: 4.0680e-04 - val_accuracy: 1.0000 - val_loss: 5.4224e-04\n",
      "Epoch 53/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9998 - loss: 3.8413e-04 - val_accuracy: 1.0000 - val_loss: 8.7837e-04\n",
      "Epoch 54/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9971 - loss: 5.5514e-04 - val_accuracy: 1.0000 - val_loss: 5.6331e-04\n",
      "Epoch 55/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9912 - loss: 4.8504e-04 - val_accuracy: 1.0000 - val_loss: 4.1171e-04\n",
      "Epoch 56/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9952 - loss: 3.8557e-04 - val_accuracy: 1.0000 - val_loss: 2.8179e-04\n",
      "Epoch 57/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9926 - loss: 3.2519e-04 - val_accuracy: 1.0000 - val_loss: 5.6016e-04\n",
      "Epoch 58/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9985 - loss: 4.8050e-04 - val_accuracy: 1.0000 - val_loss: 4.1349e-04\n",
      "Epoch 59/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.7402e-04 - val_accuracy: 1.0000 - val_loss: 3.4676e-04\n",
      "Epoch 60/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9901 - loss: 3.5924e-04 - val_accuracy: 1.0000 - val_loss: 3.4211e-04\n",
      "Epoch 61/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9976 - loss: 4.7713e-04 - val_accuracy: 1.0000 - val_loss: 5.3635e-04\n",
      "Epoch 62/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9981 - loss: 4.7998e-04 - val_accuracy: 1.0000 - val_loss: 6.0880e-04\n",
      "Epoch 63/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9996 - loss: 5.8131e-04 - val_accuracy: 1.0000 - val_loss: 3.2213e-04\n",
      "Epoch 64/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9997 - loss: 4.4127e-04 - val_accuracy: 1.0000 - val_loss: 5.7891e-04\n",
      "Epoch 65/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9915 - loss: 5.3510e-04 - val_accuracy: 1.0000 - val_loss: 2.4172e-04\n",
      "Epoch 66/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9954 - loss: 3.6708e-04 - val_accuracy: 1.0000 - val_loss: 8.9571e-04\n",
      "Epoch 67/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9922 - loss: 4.7506e-04 - val_accuracy: 1.0000 - val_loss: 2.4037e-04\n",
      "Epoch 68/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9945 - loss: 4.5382e-04 - val_accuracy: 1.0000 - val_loss: 5.6303e-04\n",
      "Epoch 69/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9894 - loss: 4.4624e-04 - val_accuracy: 1.0000 - val_loss: 4.2062e-04\n",
      "Epoch 70/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9968 - loss: 5.1541e-04 - val_accuracy: 1.0000 - val_loss: 3.3203e-04\n",
      "Epoch 71/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9914 - loss: 3.6619e-04 - val_accuracy: 1.0000 - val_loss: 3.6823e-04\n",
      "Epoch 72/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9942 - loss: 4.3041e-04 - val_accuracy: 1.0000 - val_loss: 4.0205e-04\n",
      "Epoch 73/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9984 - loss: 5.0639e-04 - val_accuracy: 1.0000 - val_loss: 3.7543e-04\n",
      "Epoch 74/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 4.5395e-04 - val_accuracy: 0.9667 - val_loss: 2.3976e-04\n",
      "Epoch 75/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.8738e-04 - val_accuracy: 1.0000 - val_loss: 4.1999e-04\n",
      "Epoch 76/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9919 - loss: 3.4863e-04 - val_accuracy: 1.0000 - val_loss: 2.0073e-04\n",
      "Epoch 77/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9957 - loss: 4.4417e-04 - val_accuracy: 0.9667 - val_loss: 3.0446e-04\n",
      "Epoch 78/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9980 - loss: 4.2951e-04 - val_accuracy: 1.0000 - val_loss: 2.0296e-04\n",
      "Epoch 79/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 3.7174e-04 - val_accuracy: 0.9667 - val_loss: 7.1184e-04\n",
      "Epoch 80/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9945 - loss: 5.8454e-04 - val_accuracy: 1.0000 - val_loss: 0.0012\n",
      "Epoch 81/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9990 - loss: 5.4627e-04 - val_accuracy: 1.0000 - val_loss: 3.5180e-04\n",
      "Epoch 82/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9970 - loss: 4.8918e-04 - val_accuracy: 1.0000 - val_loss: 3.0059e-04\n",
      "Epoch 83/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 4.0986e-04 - val_accuracy: 0.9667 - val_loss: 0.0010\n",
      "Epoch 84/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9857 - loss: 5.1376e-04 - val_accuracy: 1.0000 - val_loss: 5.7892e-04\n",
      "Epoch 85/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 4.6713e-04 - val_accuracy: 1.0000 - val_loss: 3.3457e-04\n",
      "Epoch 86/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 4.4258e-04 - val_accuracy: 1.0000 - val_loss: 5.3303e-04\n",
      "Epoch 87/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9997 - loss: 3.8019e-04 - val_accuracy: 1.0000 - val_loss: 2.3607e-04\n",
      "Epoch 88/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9955 - loss: 3.1580e-04 - val_accuracy: 1.0000 - val_loss: 3.2014e-04\n",
      "Epoch 89/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 4.6500e-04 - val_accuracy: 1.0000 - val_loss: 3.5517e-04\n",
      "Epoch 90/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9912 - loss: 4.9195e-04 - val_accuracy: 1.0000 - val_loss: 3.3932e-04\n",
      "Epoch 91/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9919 - loss: 4.5253e-04 - val_accuracy: 0.9667 - val_loss: 4.8041e-04\n",
      "Epoch 92/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9995 - loss: 4.8823e-04 - val_accuracy: 1.0000 - val_loss: 4.0366e-04\n",
      "Epoch 93/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9926 - loss: 4.3719e-04 - val_accuracy: 1.0000 - val_loss: 2.1971e-04\n",
      "Epoch 94/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9963 - loss: 4.0691e-04 - val_accuracy: 1.0000 - val_loss: 2.5919e-04\n",
      "Epoch 95/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9964 - loss: 3.4546e-04 - val_accuracy: 1.0000 - val_loss: 4.0685e-04\n",
      "Epoch 96/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 3.9907e-04 - val_accuracy: 0.9667 - val_loss: 4.0001e-04\n",
      "Epoch 97/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9995 - loss: 3.7120e-04 - val_accuracy: 1.0000 - val_loss: 3.8148e-04\n",
      "Epoch 98/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9961 - loss: 3.6915e-04 - val_accuracy: 1.0000 - val_loss: 5.2087e-04\n",
      "Epoch 99/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9964 - loss: 3.8482e-04 - val_accuracy: 1.0000 - val_loss: 6.5517e-04\n",
      "Epoch 100/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 4.4630e-04 - val_accuracy: 1.0000 - val_loss: 3.4988e-04\n",
      "Epoch 101/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9925 - loss: 4.8088e-04 - val_accuracy: 1.0000 - val_loss: 5.6661e-04\n",
      "Epoch 102/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 4.3162e-04 - val_accuracy: 1.0000 - val_loss: 2.0636e-04\n",
      "Epoch 103/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9990 - loss: 4.0211e-04 - val_accuracy: 1.0000 - val_loss: 1.9276e-04\n",
      "Epoch 104/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9987 - loss: 4.1800e-04 - val_accuracy: 1.0000 - val_loss: 2.6515e-04\n",
      "Epoch 105/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9958 - loss: 3.9086e-04 - val_accuracy: 1.0000 - val_loss: 2.6919e-04\n",
      "Epoch 106/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 3.1783e-04 - val_accuracy: 1.0000 - val_loss: 7.9739e-04\n",
      "Epoch 107/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9925 - loss: 4.2887e-04 - val_accuracy: 1.0000 - val_loss: 5.3924e-04\n",
      "Epoch 108/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9948 - loss: 3.7506e-04 - val_accuracy: 1.0000 - val_loss: 7.4216e-04\n",
      "Epoch 109/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9848 - loss: 5.1619e-04 - val_accuracy: 1.0000 - val_loss: 2.4963e-04\n",
      "Epoch 110/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 4.1238e-04 - val_accuracy: 1.0000 - val_loss: 3.1234e-04\n",
      "Epoch 111/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 3.9555e-04 - val_accuracy: 1.0000 - val_loss: 5.6081e-04\n",
      "Epoch 112/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9974 - loss: 3.6115e-04 - val_accuracy: 1.0000 - val_loss: 4.2633e-04\n",
      "Epoch 113/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9922 - loss: 4.1660e-04 - val_accuracy: 1.0000 - val_loss: 2.6992e-04\n",
      "Epoch 114/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9998 - loss: 3.2377e-04 - val_accuracy: 1.0000 - val_loss: 5.6504e-04\n",
      "Epoch 115/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9984 - loss: 3.8077e-04 - val_accuracy: 1.0000 - val_loss: 4.0234e-04\n",
      "Epoch 116/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9982 - loss: 3.8239e-04 - val_accuracy: 0.9667 - val_loss: 2.7463e-04\n",
      "Epoch 117/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9962 - loss: 4.2842e-04 - val_accuracy: 1.0000 - val_loss: 2.6324e-04\n",
      "Epoch 118/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9993 - loss: 2.9507e-04 - val_accuracy: 1.0000 - val_loss: 2.9633e-04\n",
      "Epoch 119/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9901 - loss: 3.7194e-04 - val_accuracy: 1.0000 - val_loss: 3.5139e-04\n",
      "Epoch 120/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9990 - loss: 3.3005e-04 - val_accuracy: 1.0000 - val_loss: 2.0845e-04\n",
      "Epoch 121/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9901 - loss: 4.2765e-04 - val_accuracy: 1.0000 - val_loss: 2.8245e-04\n",
      "Epoch 122/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9966 - loss: 4.3040e-04 - val_accuracy: 1.0000 - val_loss: 3.3034e-04\n",
      "Epoch 123/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9970 - loss: 3.9128e-04 - val_accuracy: 1.0000 - val_loss: 3.4919e-04\n",
      "Epoch 124/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9964 - loss: 3.9842e-04 - val_accuracy: 1.0000 - val_loss: 2.0277e-04\n",
      "Epoch 125/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9983 - loss: 3.5795e-04 - val_accuracy: 1.0000 - val_loss: 4.3301e-04\n",
      "Epoch 126/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9972 - loss: 3.5030e-04 - val_accuracy: 1.0000 - val_loss: 3.2763e-04\n",
      "Epoch 127/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9990 - loss: 3.6777e-04 - val_accuracy: 0.9667 - val_loss: 3.0298e-04\n",
      "Epoch 128/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9930 - loss: 4.4676e-04 - val_accuracy: 0.9667 - val_loss: 5.6239e-04\n",
      "Epoch 129/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9989 - loss: 3.8317e-04 - val_accuracy: 1.0000 - val_loss: 3.0362e-04\n",
      "Epoch 130/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9939 - loss: 3.2288e-04 - val_accuracy: 1.0000 - val_loss: 3.5553e-04\n",
      "Epoch 131/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9958 - loss: 5.3754e-04 - val_accuracy: 1.0000 - val_loss: 3.3341e-04\n",
      "Epoch 132/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9972 - loss: 4.0699e-04 - val_accuracy: 1.0000 - val_loss: 1.6038e-04\n",
      "Epoch 133/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 3.4515e-04 - val_accuracy: 1.0000 - val_loss: 2.4596e-04\n",
      "Epoch 134/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9926 - loss: 3.1466e-04 - val_accuracy: 1.0000 - val_loss: 3.1568e-04\n",
      "Epoch 135/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9926 - loss: 4.0261e-04 - val_accuracy: 1.0000 - val_loss: 2.5736e-04\n",
      "Epoch 136/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9928 - loss: 4.9978e-04 - val_accuracy: 1.0000 - val_loss: 4.7376e-04\n",
      "Epoch 137/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9982 - loss: 3.7969e-04 - val_accuracy: 1.0000 - val_loss: 4.0300e-04\n",
      "Epoch 138/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9919 - loss: 3.4910e-04 - val_accuracy: 1.0000 - val_loss: 3.3730e-04\n",
      "Epoch 139/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9959 - loss: 3.6871e-04 - val_accuracy: 1.0000 - val_loss: 5.8869e-04\n",
      "Epoch 140/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9948 - loss: 3.8611e-04 - val_accuracy: 1.0000 - val_loss: 3.5341e-04\n",
      "Epoch 141/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9930 - loss: 3.6676e-04 - val_accuracy: 1.0000 - val_loss: 2.5842e-04\n",
      "Epoch 142/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9998 - loss: 3.2359e-04 - val_accuracy: 1.0000 - val_loss: 2.8921e-04\n",
      "Epoch 143/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9996 - loss: 3.2398e-04 - val_accuracy: 1.0000 - val_loss: 2.5169e-04\n",
      "Epoch 144/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9983 - loss: 3.6816e-04 - val_accuracy: 1.0000 - val_loss: 1.4002e-04\n",
      "Epoch 145/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9970 - loss: 2.9244e-04 - val_accuracy: 1.0000 - val_loss: 2.8878e-04\n",
      "Epoch 146/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9902 - loss: 4.2354e-04 - val_accuracy: 1.0000 - val_loss: 3.0613e-04\n",
      "Epoch 147/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9948 - loss: 3.6664e-04 - val_accuracy: 1.0000 - val_loss: 2.1903e-04\n",
      "Epoch 148/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9982 - loss: 3.3349e-04 - val_accuracy: 1.0000 - val_loss: 3.7935e-04\n",
      "Epoch 149/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 3.0169e-04 - val_accuracy: 1.0000 - val_loss: 4.6367e-04\n",
      "Epoch 150/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9999 - loss: 4.0124e-04 - val_accuracy: 1.0000 - val_loss: 1.6449e-04\n",
      "Epoch 151/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9946 - loss: 5.3956e-04 - val_accuracy: 1.0000 - val_loss: 2.3563e-04\n",
      "Epoch 152/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 3.5166e-04 - val_accuracy: 1.0000 - val_loss: 2.2041e-04\n",
      "Epoch 153/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9978 - loss: 4.0310e-04 - val_accuracy: 1.0000 - val_loss: 4.1650e-04\n",
      "Epoch 154/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9975 - loss: 3.5163e-04 - val_accuracy: 1.0000 - val_loss: 3.0205e-04\n",
      "Epoch 155/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9835 - loss: 7.0391e-04 - val_accuracy: 1.0000 - val_loss: 3.8659e-04\n",
      "Epoch 156/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9911 - loss: 4.9906e-04 - val_accuracy: 1.0000 - val_loss: 4.1143e-04\n",
      "Epoch 157/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.4274e-04 - val_accuracy: 1.0000 - val_loss: 3.3867e-04\n",
      "Epoch 158/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9956 - loss: 3.6105e-04 - val_accuracy: 1.0000 - val_loss: 3.5394e-04\n",
      "Epoch 159/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9958 - loss: 3.0241e-04 - val_accuracy: 1.0000 - val_loss: 2.6187e-04\n",
      "Epoch 160/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9909 - loss: 3.2297e-04 - val_accuracy: 1.0000 - val_loss: 2.4054e-04\n",
      "Epoch 161/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9972 - loss: 3.5825e-04 - val_accuracy: 1.0000 - val_loss: 2.2159e-04\n",
      "Epoch 162/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9897 - loss: 4.1840e-04 - val_accuracy: 1.0000 - val_loss: 2.2289e-04\n",
      "Epoch 163/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.3374e-04 - val_accuracy: 0.9667 - val_loss: 3.2266e-04\n",
      "Epoch 164/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9952 - loss: 2.9716e-04 - val_accuracy: 1.0000 - val_loss: 3.3807e-04\n",
      "Epoch 165/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9986 - loss: 3.9229e-04 - val_accuracy: 1.0000 - val_loss: 2.8456e-04\n",
      "Epoch 166/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9987 - loss: 3.1219e-04 - val_accuracy: 1.0000 - val_loss: 1.4729e-04\n",
      "Epoch 167/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9981 - loss: 4.0774e-04 - val_accuracy: 1.0000 - val_loss: 2.6268e-04\n",
      "Epoch 168/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 3.7333e-04 - val_accuracy: 1.0000 - val_loss: 1.8756e-04\n",
      "Epoch 169/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 3.0073e-04 - val_accuracy: 1.0000 - val_loss: 2.5120e-04\n",
      "Epoch 170/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9947 - loss: 3.2875e-04 - val_accuracy: 1.0000 - val_loss: 2.8168e-04\n",
      "Epoch 171/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9988 - loss: 2.8205e-04 - val_accuracy: 1.0000 - val_loss: 1.9070e-04\n",
      "Epoch 172/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9904 - loss: 3.3760e-04 - val_accuracy: 1.0000 - val_loss: 2.4308e-04\n",
      "Epoch 173/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9995 - loss: 2.9265e-04 - val_accuracy: 1.0000 - val_loss: 2.3131e-04\n",
      "Epoch 174/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9969 - loss: 2.9217e-04 - val_accuracy: 1.0000 - val_loss: 3.3392e-04\n",
      "Epoch 175/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9967 - loss: 3.5981e-04 - val_accuracy: 1.0000 - val_loss: 4.6082e-04\n",
      "Epoch 176/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9932 - loss: 4.1494e-04 - val_accuracy: 1.0000 - val_loss: 1.7796e-04\n",
      "Epoch 177/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9977 - loss: 4.4528e-04 - val_accuracy: 1.0000 - val_loss: 2.5185e-04\n",
      "Epoch 178/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9977 - loss: 3.0247e-04 - val_accuracy: 1.0000 - val_loss: 3.0268e-04\n",
      "Epoch 179/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9990 - loss: 3.9270e-04 - val_accuracy: 1.0000 - val_loss: 1.4009e-04\n",
      "Epoch 180/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9969 - loss: 3.1968e-04 - val_accuracy: 1.0000 - val_loss: 5.3912e-04\n",
      "Epoch 181/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 3.5087e-04 - val_accuracy: 1.0000 - val_loss: 4.3766e-04\n",
      "Epoch 182/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9999 - loss: 3.6853e-04 - val_accuracy: 1.0000 - val_loss: 3.5559e-04\n",
      "Epoch 183/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.5911e-04 - val_accuracy: 1.0000 - val_loss: 2.1144e-04\n",
      "Epoch 184/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9971 - loss: 3.8470e-04 - val_accuracy: 1.0000 - val_loss: 4.0837e-04\n",
      "Epoch 185/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9990 - loss: 3.5662e-04 - val_accuracy: 1.0000 - val_loss: 2.0688e-04\n",
      "Epoch 186/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9995 - loss: 3.0774e-04 - val_accuracy: 1.0000 - val_loss: 2.3461e-04\n",
      "Epoch 187/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9989 - loss: 3.4597e-04 - val_accuracy: 1.0000 - val_loss: 1.6652e-04\n",
      "Epoch 188/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9977 - loss: 2.9944e-04 - val_accuracy: 1.0000 - val_loss: 2.8267e-04\n",
      "Epoch 189/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9944 - loss: 3.9902e-04 - val_accuracy: 1.0000 - val_loss: 1.9279e-04\n",
      "Epoch 190/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9900 - loss: 3.9882e-04 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
      "Epoch 191/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9842 - loss: 6.3242e-04 - val_accuracy: 1.0000 - val_loss: 4.0829e-04\n",
      "Epoch 192/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9984 - loss: 3.1396e-04 - val_accuracy: 0.9667 - val_loss: 2.2608e-04\n",
      "Epoch 193/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9919 - loss: 4.2789e-04 - val_accuracy: 1.0000 - val_loss: 1.7796e-04\n",
      "Epoch 194/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9948 - loss: 3.1366e-04 - val_accuracy: 1.0000 - val_loss: 2.4928e-04\n",
      "Epoch 195/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9960 - loss: 2.9514e-04 - val_accuracy: 1.0000 - val_loss: 1.3942e-04\n",
      "Epoch 196/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.6909e-04 - val_accuracy: 1.0000 - val_loss: 2.9736e-04\n",
      "Epoch 197/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.9921e-04 - val_accuracy: 1.0000 - val_loss: 3.9865e-04\n",
      "Epoch 198/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9988 - loss: 3.5509e-04 - val_accuracy: 0.9667 - val_loss: 3.0482e-04\n",
      "Epoch 199/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9922 - loss: 3.2463e-04 - val_accuracy: 1.0000 - val_loss: 3.4024e-04\n",
      "Epoch 200/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9993 - loss: 3.1797e-04 - val_accuracy: 0.9667 - val_loss: 1.5390e-04\n",
      "Epoch 201/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9965 - loss: 2.8027e-04 - val_accuracy: 1.0000 - val_loss: 2.8982e-04\n",
      "Epoch 202/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9926 - loss: 3.1981e-04 - val_accuracy: 1.0000 - val_loss: 1.7260e-04\n",
      "Epoch 203/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9967 - loss: 3.7359e-04 - val_accuracy: 1.0000 - val_loss: 3.2352e-04\n",
      "Epoch 204/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.1724e-04 - val_accuracy: 0.9667 - val_loss: 3.7840e-04\n",
      "Epoch 205/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9958 - loss: 3.4209e-04 - val_accuracy: 1.0000 - val_loss: 1.8181e-04\n",
      "Epoch 206/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.6374e-04 - val_accuracy: 1.0000 - val_loss: 2.1582e-04\n",
      "Epoch 207/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9999 - loss: 3.1274e-04 - val_accuracy: 1.0000 - val_loss: 2.8191e-04\n",
      "Epoch 208/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.9282e-04 - val_accuracy: 1.0000 - val_loss: 4.0572e-04\n",
      "Epoch 209/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9969 - loss: 3.3565e-04 - val_accuracy: 1.0000 - val_loss: 1.3622e-04\n",
      "Epoch 210/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.1570e-04 - val_accuracy: 1.0000 - val_loss: 1.5536e-04\n",
      "Epoch 211/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9977 - loss: 3.9058e-04 - val_accuracy: 1.0000 - val_loss: 1.4014e-04\n",
      "Epoch 212/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9976 - loss: 2.7375e-04 - val_accuracy: 1.0000 - val_loss: 1.6396e-04\n",
      "Epoch 213/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9989 - loss: 2.7607e-04 - val_accuracy: 0.9667 - val_loss: 1.8790e-04\n",
      "Epoch 214/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 3.7281e-04 - val_accuracy: 1.0000 - val_loss: 3.0727e-04\n",
      "Epoch 215/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9874 - loss: 2.9485e-04 - val_accuracy: 1.0000 - val_loss: 1.7204e-04\n",
      "Epoch 216/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9900 - loss: 2.6611e-04 - val_accuracy: 1.0000 - val_loss: 3.1696e-04\n",
      "Epoch 217/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.6986e-04 - val_accuracy: 0.9667 - val_loss: 4.0176e-04\n",
      "Epoch 218/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9956 - loss: 3.2635e-04 - val_accuracy: 1.0000 - val_loss: 3.9160e-04\n",
      "Epoch 219/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9981 - loss: 3.2166e-04 - val_accuracy: 0.9667 - val_loss: 3.0419e-04\n",
      "Epoch 220/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9991 - loss: 3.0002e-04 - val_accuracy: 1.0000 - val_loss: 1.5730e-04\n",
      "Epoch 221/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9975 - loss: 2.8374e-04 - val_accuracy: 0.9667 - val_loss: 2.9587e-04\n",
      "Epoch 222/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9986 - loss: 3.0835e-04 - val_accuracy: 1.0000 - val_loss: 2.1512e-04\n",
      "Epoch 223/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9996 - loss: 2.5055e-04 - val_accuracy: 1.0000 - val_loss: 2.3675e-04\n",
      "Epoch 224/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.8011e-04 - val_accuracy: 1.0000 - val_loss: 2.1924e-04\n",
      "Epoch 225/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9991 - loss: 2.6345e-04 - val_accuracy: 0.9667 - val_loss: 2.0713e-04\n",
      "Epoch 226/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.9171e-04 - val_accuracy: 1.0000 - val_loss: 3.0780e-04\n",
      "Epoch 227/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9961 - loss: 3.8679e-04 - val_accuracy: 0.9667 - val_loss: 2.5145e-04\n",
      "Epoch 228/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9982 - loss: 2.7981e-04 - val_accuracy: 0.9667 - val_loss: 2.7359e-04\n",
      "Epoch 229/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.1001e-04 - val_accuracy: 1.0000 - val_loss: 3.7373e-04\n",
      "Epoch 230/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9978 - loss: 3.9449e-04 - val_accuracy: 1.0000 - val_loss: 3.4790e-04\n",
      "Epoch 231/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.7381e-04 - val_accuracy: 1.0000 - val_loss: 1.9386e-04\n",
      "Epoch 232/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 2.7074e-04 - val_accuracy: 1.0000 - val_loss: 3.2540e-04\n",
      "Epoch 233/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9975 - loss: 2.8585e-04 - val_accuracy: 1.0000 - val_loss: 3.1338e-04\n",
      "Epoch 234/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9964 - loss: 3.1295e-04 - val_accuracy: 1.0000 - val_loss: 2.5819e-04\n",
      "Epoch 235/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9991 - loss: 3.0870e-04 - val_accuracy: 1.0000 - val_loss: 5.7609e-04\n",
      "Epoch 236/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9960 - loss: 3.5460e-04 - val_accuracy: 1.0000 - val_loss: 1.2336e-04\n",
      "Epoch 237/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9948 - loss: 3.0303e-04 - val_accuracy: 1.0000 - val_loss: 1.5753e-04\n",
      "Epoch 238/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9933 - loss: 5.9605e-04 - val_accuracy: 0.9667 - val_loss: 2.9407e-04\n",
      "Epoch 239/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9978 - loss: 3.0841e-04 - val_accuracy: 1.0000 - val_loss: 2.6825e-04\n",
      "Epoch 240/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9973 - loss: 2.4834e-04 - val_accuracy: 1.0000 - val_loss: 2.0388e-04\n",
      "Epoch 241/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 2.9452e-04 - val_accuracy: 1.0000 - val_loss: 4.0270e-04\n",
      "Epoch 242/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9986 - loss: 2.8130e-04 - val_accuracy: 1.0000 - val_loss: 2.4131e-04\n",
      "Epoch 243/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9905 - loss: 3.4876e-04 - val_accuracy: 1.0000 - val_loss: 2.0468e-04\n",
      "Epoch 244/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9988 - loss: 2.7273e-04 - val_accuracy: 1.0000 - val_loss: 7.1776e-04\n",
      "Epoch 245/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9926 - loss: 3.7439e-04 - val_accuracy: 0.9667 - val_loss: 2.1999e-04\n",
      "Epoch 246/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.5996e-04 - val_accuracy: 1.0000 - val_loss: 1.4833e-04\n",
      "Epoch 247/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9979 - loss: 3.4154e-04 - val_accuracy: 1.0000 - val_loss: 9.1021e-04\n",
      "Epoch 248/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9946 - loss: 5.0875e-04 - val_accuracy: 0.9667 - val_loss: 2.9368e-04\n",
      "Epoch 249/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9980 - loss: 3.1884e-04 - val_accuracy: 1.0000 - val_loss: 2.8629e-04\n",
      "Epoch 250/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9972 - loss: 2.6022e-04 - val_accuracy: 1.0000 - val_loss: 2.1748e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Epoch_Anz = 250\n",
    "if (Training_Percentage > 0):\n",
    "    history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    history = model.fit(train_iterator, epochs = Epoch_Anz)\n",
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(val_loss_ges)\n",
    "\n",
    "\n",
    "plt.semilogy(loss_ges)\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9983 - loss: 3.1204e-04 - val_accuracy: 1.0000 - val_loss: 4.3345e-04\n",
      "Epoch 2/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9957 - loss: 2.9688e-04 - val_accuracy: 1.0000 - val_loss: 3.4447e-04\n",
      "Epoch 3/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 5.1813e-04 - val_accuracy: 1.0000 - val_loss: 2.9073e-04\n",
      "Epoch 4/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9921 - loss: 3.2576e-04 - val_accuracy: 1.0000 - val_loss: 4.6408e-04\n",
      "Epoch 5/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9999 - loss: 2.8676e-04 - val_accuracy: 1.0000 - val_loss: 2.3651e-04\n",
      "Epoch 6/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 2.9333e-04 - val_accuracy: 1.0000 - val_loss: 2.6309e-04\n",
      "Epoch 7/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9949 - loss: 2.3597e-04 - val_accuracy: 1.0000 - val_loss: 1.7772e-04\n",
      "Epoch 8/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9993 - loss: 3.3989e-04 - val_accuracy: 1.0000 - val_loss: 1.5177e-04\n",
      "Epoch 9/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9966 - loss: 2.6216e-04 - val_accuracy: 1.0000 - val_loss: 3.4113e-04\n",
      "Epoch 10/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9973 - loss: 3.8779e-04 - val_accuracy: 1.0000 - val_loss: 1.4835e-04\n",
      "Epoch 11/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9928 - loss: 2.9519e-04 - val_accuracy: 1.0000 - val_loss: 2.5593e-04\n",
      "Epoch 12/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.9623e-04 - val_accuracy: 1.0000 - val_loss: 3.1179e-04\n",
      "Epoch 13/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.0415e-04 - val_accuracy: 1.0000 - val_loss: 2.7297e-04\n",
      "Epoch 14/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.3347e-04 - val_accuracy: 1.0000 - val_loss: 1.5639e-04\n",
      "Epoch 15/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9999 - loss: 2.3425e-04 - val_accuracy: 1.0000 - val_loss: 1.5193e-04\n",
      "Epoch 16/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9969 - loss: 3.0893e-04 - val_accuracy: 1.0000 - val_loss: 2.0765e-04\n",
      "Epoch 17/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 2.5274e-04 - val_accuracy: 1.0000 - val_loss: 1.8345e-04\n",
      "Epoch 18/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9988 - loss: 3.3843e-04 - val_accuracy: 1.0000 - val_loss: 3.1759e-04\n",
      "Epoch 19/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.1153e-04 - val_accuracy: 1.0000 - val_loss: 2.1993e-04\n",
      "Epoch 20/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9906 - loss: 2.9024e-04 - val_accuracy: 1.0000 - val_loss: 2.9743e-04\n",
      "Epoch 21/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9935 - loss: 3.1831e-04 - val_accuracy: 0.9667 - val_loss: 2.5613e-04\n",
      "Epoch 22/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9991 - loss: 2.8990e-04 - val_accuracy: 1.0000 - val_loss: 3.9447e-04\n",
      "Epoch 23/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9860 - loss: 3.0061e-04 - val_accuracy: 0.9667 - val_loss: 4.0184e-04\n",
      "Epoch 24/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.5697e-04 - val_accuracy: 0.9667 - val_loss: 3.5264e-04\n",
      "Epoch 25/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9970 - loss: 3.3080e-04 - val_accuracy: 1.0000 - val_loss: 2.0498e-04\n",
      "Epoch 26/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 2.9788e-04 - val_accuracy: 1.0000 - val_loss: 1.5204e-04\n",
      "Epoch 27/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 3.7654e-04 - val_accuracy: 1.0000 - val_loss: 1.6703e-04\n",
      "Epoch 28/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9997 - loss: 2.5288e-04 - val_accuracy: 1.0000 - val_loss: 1.5386e-04\n",
      "Epoch 29/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9999 - loss: 2.6352e-04 - val_accuracy: 1.0000 - val_loss: 1.7150e-04\n",
      "Epoch 30/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.4633e-04 - val_accuracy: 1.0000 - val_loss: 1.6347e-04\n",
      "Epoch 31/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.6331e-04 - val_accuracy: 1.0000 - val_loss: 2.4051e-04\n",
      "Epoch 32/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9954 - loss: 3.0952e-04 - val_accuracy: 1.0000 - val_loss: 2.8833e-04\n",
      "Epoch 33/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9954 - loss: 3.8698e-04 - val_accuracy: 1.0000 - val_loss: 2.5044e-04\n",
      "Epoch 34/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9980 - loss: 2.9244e-04 - val_accuracy: 1.0000 - val_loss: 1.1506e-04\n",
      "Epoch 35/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 2.9989e-04 - val_accuracy: 1.0000 - val_loss: 1.8795e-04\n",
      "Epoch 36/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9981 - loss: 2.9416e-04 - val_accuracy: 1.0000 - val_loss: 2.9513e-04\n",
      "Epoch 37/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9992 - loss: 2.7520e-04 - val_accuracy: 1.0000 - val_loss: 3.5282e-04\n",
      "Epoch 38/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9976 - loss: 2.6101e-04 - val_accuracy: 1.0000 - val_loss: 1.1004e-04\n",
      "Epoch 39/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9997 - loss: 2.5710e-04 - val_accuracy: 1.0000 - val_loss: 3.2129e-04\n",
      "Epoch 40/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.4518e-04 - val_accuracy: 1.0000 - val_loss: 2.8706e-04\n",
      "Epoch 41/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9967 - loss: 2.7256e-04 - val_accuracy: 1.0000 - val_loss: 2.0427e-04\n",
      "Epoch 42/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9973 - loss: 2.5971e-04 - val_accuracy: 1.0000 - val_loss: 1.3313e-04\n",
      "Epoch 43/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9986 - loss: 2.9826e-04 - val_accuracy: 1.0000 - val_loss: 2.7863e-04\n",
      "Epoch 44/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9948 - loss: 2.8042e-04 - val_accuracy: 1.0000 - val_loss: 4.6426e-04\n",
      "Epoch 45/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9963 - loss: 2.8575e-04 - val_accuracy: 1.0000 - val_loss: 1.9986e-04\n",
      "Epoch 46/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9999 - loss: 2.5838e-04 - val_accuracy: 1.0000 - val_loss: 2.3911e-04\n",
      "Epoch 47/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9974 - loss: 2.8565e-04 - val_accuracy: 0.9667 - val_loss: 3.9190e-04\n",
      "Epoch 48/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9950 - loss: 2.5075e-04 - val_accuracy: 1.0000 - val_loss: 3.0738e-04\n",
      "Epoch 49/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9993 - loss: 2.6786e-04 - val_accuracy: 0.9667 - val_loss: 2.9882e-04\n",
      "Epoch 50/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9912 - loss: 3.1088e-04 - val_accuracy: 1.0000 - val_loss: 2.3238e-04\n",
      "Epoch 51/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.5361e-04 - val_accuracy: 1.0000 - val_loss: 1.8043e-04\n",
      "Epoch 52/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9980 - loss: 2.3566e-04 - val_accuracy: 1.0000 - val_loss: 2.0464e-04\n",
      "Epoch 53/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.3452e-04 - val_accuracy: 1.0000 - val_loss: 2.9433e-04\n",
      "Epoch 54/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9993 - loss: 2.8319e-04 - val_accuracy: 1.0000 - val_loss: 2.3462e-04\n",
      "Epoch 55/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9991 - loss: 2.6816e-04 - val_accuracy: 1.0000 - val_loss: 2.8868e-04\n",
      "Epoch 56/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.6920e-04 - val_accuracy: 1.0000 - val_loss: 1.5296e-04\n",
      "Epoch 57/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.6801e-04 - val_accuracy: 1.0000 - val_loss: 2.6059e-04\n",
      "Epoch 58/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9985 - loss: 2.1133e-04 - val_accuracy: 1.0000 - val_loss: 2.2124e-04\n",
      "Epoch 59/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9944 - loss: 2.2862e-04 - val_accuracy: 1.0000 - val_loss: 2.6481e-04\n",
      "Epoch 60/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9930 - loss: 2.3265e-04 - val_accuracy: 1.0000 - val_loss: 3.5521e-04\n",
      "Epoch 61/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9922 - loss: 3.1109e-04 - val_accuracy: 1.0000 - val_loss: 1.6438e-04\n",
      "Epoch 62/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9988 - loss: 2.6413e-04 - val_accuracy: 1.0000 - val_loss: 1.6185e-04\n",
      "Epoch 63/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9944 - loss: 2.4585e-04 - val_accuracy: 1.0000 - val_loss: 2.7690e-04\n",
      "Epoch 64/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9990 - loss: 2.8808e-04 - val_accuracy: 1.0000 - val_loss: 1.9574e-04\n",
      "Epoch 65/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9948 - loss: 2.5711e-04 - val_accuracy: 1.0000 - val_loss: 1.4993e-04\n",
      "Epoch 66/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.2663e-04 - val_accuracy: 1.0000 - val_loss: 2.1303e-04\n",
      "Epoch 67/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9998 - loss: 2.5472e-04 - val_accuracy: 1.0000 - val_loss: 2.6011e-04\n",
      "Epoch 68/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9993 - loss: 2.7194e-04 - val_accuracy: 1.0000 - val_loss: 1.6166e-04\n",
      "Epoch 69/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9946 - loss: 2.5690e-04 - val_accuracy: 1.0000 - val_loss: 1.1709e-04\n",
      "Epoch 70/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9993 - loss: 2.6851e-04 - val_accuracy: 1.0000 - val_loss: 2.6458e-04\n",
      "Epoch 71/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.4901e-04 - val_accuracy: 0.9667 - val_loss: 2.3269e-04\n",
      "Epoch 72/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 2.2016e-04 - val_accuracy: 1.0000 - val_loss: 2.4541e-04\n",
      "Epoch 73/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9987 - loss: 2.7840e-04 - val_accuracy: 1.0000 - val_loss: 1.7554e-04\n",
      "Epoch 74/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9989 - loss: 3.0404e-04 - val_accuracy: 1.0000 - val_loss: 2.5853e-04\n",
      "Epoch 75/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9971 - loss: 2.6408e-04 - val_accuracy: 1.0000 - val_loss: 3.1603e-04\n",
      "Epoch 76/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.6016e-04 - val_accuracy: 1.0000 - val_loss: 1.9318e-04\n",
      "Epoch 77/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9954 - loss: 2.5920e-04 - val_accuracy: 1.0000 - val_loss: 2.5649e-04\n",
      "Epoch 78/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9934 - loss: 2.2782e-04 - val_accuracy: 1.0000 - val_loss: 1.9534e-04\n",
      "Epoch 79/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9998 - loss: 2.5755e-04 - val_accuracy: 1.0000 - val_loss: 2.1697e-04\n",
      "Epoch 80/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 3.1009e-04 - val_accuracy: 1.0000 - val_loss: 3.6758e-04\n",
      "Epoch 81/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9952 - loss: 2.6667e-04 - val_accuracy: 1.0000 - val_loss: 2.6123e-04\n",
      "Epoch 82/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 2.6420e-04 - val_accuracy: 1.0000 - val_loss: 2.2823e-04\n",
      "Epoch 83/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.4505e-04 - val_accuracy: 1.0000 - val_loss: 2.0174e-04\n",
      "Epoch 84/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.7360e-04 - val_accuracy: 1.0000 - val_loss: 2.2426e-04\n",
      "Epoch 85/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9962 - loss: 2.5628e-04 - val_accuracy: 1.0000 - val_loss: 2.6541e-04\n",
      "Epoch 86/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9994 - loss: 2.6896e-04 - val_accuracy: 1.0000 - val_loss: 2.9659e-04\n",
      "Epoch 87/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9999 - loss: 3.3673e-04 - val_accuracy: 1.0000 - val_loss: 1.6475e-04\n",
      "Epoch 88/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9928 - loss: 2.8877e-04 - val_accuracy: 1.0000 - val_loss: 1.6030e-04\n",
      "Epoch 89/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9952 - loss: 2.5897e-04 - val_accuracy: 1.0000 - val_loss: 2.3538e-04\n",
      "Epoch 90/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9985 - loss: 2.4639e-04 - val_accuracy: 1.0000 - val_loss: 1.4618e-04\n",
      "Epoch 91/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9942 - loss: 2.7862e-04 - val_accuracy: 1.0000 - val_loss: 1.9809e-04\n",
      "Epoch 92/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9972 - loss: 2.3862e-04 - val_accuracy: 1.0000 - val_loss: 1.7852e-04\n",
      "Epoch 93/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9959 - loss: 3.2072e-04 - val_accuracy: 1.0000 - val_loss: 2.5409e-04\n",
      "Epoch 94/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 2.7059e-04 - val_accuracy: 1.0000 - val_loss: 2.4832e-04\n",
      "Epoch 95/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9983 - loss: 2.2269e-04 - val_accuracy: 0.9667 - val_loss: 2.1128e-04\n",
      "Epoch 96/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9907 - loss: 2.6474e-04 - val_accuracy: 1.0000 - val_loss: 3.4793e-04\n",
      "Epoch 97/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9989 - loss: 2.5325e-04 - val_accuracy: 1.0000 - val_loss: 1.9368e-04\n",
      "Epoch 98/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.5724e-04 - val_accuracy: 1.0000 - val_loss: 1.7941e-04\n",
      "Epoch 99/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.5307e-04 - val_accuracy: 1.0000 - val_loss: 2.3955e-04\n",
      "Epoch 100/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9991 - loss: 2.5648e-04 - val_accuracy: 1.0000 - val_loss: 1.5026e-04\n",
      "Epoch 101/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9997 - loss: 2.8901e-04 - val_accuracy: 1.0000 - val_loss: 1.9299e-04\n",
      "Epoch 102/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9951 - loss: 2.4373e-04 - val_accuracy: 1.0000 - val_loss: 2.1175e-04\n",
      "Epoch 103/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9993 - loss: 2.5665e-04 - val_accuracy: 1.0000 - val_loss: 2.0388e-04\n",
      "Epoch 104/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9943 - loss: 2.7562e-04 - val_accuracy: 1.0000 - val_loss: 1.8387e-04\n",
      "Epoch 105/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9971 - loss: 2.5486e-04 - val_accuracy: 1.0000 - val_loss: 2.3090e-04\n",
      "Epoch 106/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9935 - loss: 2.7699e-04 - val_accuracy: 1.0000 - val_loss: 1.4087e-04\n",
      "Epoch 107/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9941 - loss: 2.8292e-04 - val_accuracy: 1.0000 - val_loss: 4.6829e-04\n",
      "Epoch 108/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.1672e-04 - val_accuracy: 1.0000 - val_loss: 1.4830e-04\n",
      "Epoch 109/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.5368e-04 - val_accuracy: 1.0000 - val_loss: 3.0504e-04\n",
      "Epoch 110/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9977 - loss: 2.0638e-04 - val_accuracy: 0.9667 - val_loss: 2.3598e-04\n",
      "Epoch 111/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9993 - loss: 1.9093e-04 - val_accuracy: 1.0000 - val_loss: 1.9139e-04\n",
      "Epoch 112/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.3394e-04 - val_accuracy: 1.0000 - val_loss: 1.1746e-04\n",
      "Epoch 113/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.7718e-04 - val_accuracy: 1.0000 - val_loss: 2.1839e-04\n",
      "Epoch 114/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9935 - loss: 2.2204e-04 - val_accuracy: 1.0000 - val_loss: 1.1074e-04\n",
      "Epoch 115/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9961 - loss: 3.1179e-04 - val_accuracy: 0.9667 - val_loss: 2.6489e-04\n",
      "Epoch 116/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9818 - loss: 2.8097e-04 - val_accuracy: 1.0000 - val_loss: 1.5169e-04\n",
      "Epoch 117/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 2.2038e-04 - val_accuracy: 1.0000 - val_loss: 2.4732e-04\n",
      "Epoch 118/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9985 - loss: 2.3072e-04 - val_accuracy: 1.0000 - val_loss: 2.5744e-04\n",
      "Epoch 119/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9995 - loss: 2.8116e-04 - val_accuracy: 1.0000 - val_loss: 2.4858e-04\n",
      "Epoch 120/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9913 - loss: 2.4701e-04 - val_accuracy: 1.0000 - val_loss: 2.6456e-04\n",
      "Epoch 121/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9978 - loss: 2.4785e-04 - val_accuracy: 1.0000 - val_loss: 2.9041e-04\n",
      "Epoch 122/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 3.2504e-04 - val_accuracy: 1.0000 - val_loss: 1.8743e-04\n",
      "Epoch 123/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9867 - loss: 3.0110e-04 - val_accuracy: 1.0000 - val_loss: 1.9045e-04\n",
      "Epoch 124/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 2.1510e-04 - val_accuracy: 0.9667 - val_loss: 2.9585e-04\n",
      "Epoch 125/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9981 - loss: 2.1495e-04 - val_accuracy: 0.9667 - val_loss: 1.9122e-04\n",
      "Epoch 126/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9973 - loss: 2.8630e-04 - val_accuracy: 0.9667 - val_loss: 1.6731e-04\n",
      "Epoch 127/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9992 - loss: 2.3295e-04 - val_accuracy: 0.9667 - val_loss: 3.4371e-04\n",
      "Epoch 128/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9956 - loss: 2.1617e-04 - val_accuracy: 1.0000 - val_loss: 2.8935e-04\n",
      "Epoch 129/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9992 - loss: 2.4823e-04 - val_accuracy: 1.0000 - val_loss: 1.6746e-04\n",
      "Epoch 130/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9952 - loss: 2.0394e-04 - val_accuracy: 1.0000 - val_loss: 2.0034e-04\n",
      "Epoch 131/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9996 - loss: 2.2531e-04 - val_accuracy: 1.0000 - val_loss: 2.3844e-04\n",
      "Epoch 132/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.3231e-04 - val_accuracy: 1.0000 - val_loss: 1.7754e-04\n",
      "Epoch 133/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9937 - loss: 3.1361e-04 - val_accuracy: 1.0000 - val_loss: 3.3478e-04\n",
      "Epoch 134/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9994 - loss: 2.8684e-04 - val_accuracy: 1.0000 - val_loss: 1.0270e-04\n",
      "Epoch 135/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.1778e-04 - val_accuracy: 1.0000 - val_loss: 1.4136e-04\n",
      "Epoch 136/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.4105e-04 - val_accuracy: 1.0000 - val_loss: 1.3222e-04\n",
      "Epoch 137/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.2791e-04 - val_accuracy: 1.0000 - val_loss: 1.5071e-04\n",
      "Epoch 138/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9956 - loss: 2.3724e-04 - val_accuracy: 1.0000 - val_loss: 1.5851e-04\n",
      "Epoch 139/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9986 - loss: 2.3648e-04 - val_accuracy: 0.9667 - val_loss: 4.9222e-04\n",
      "Epoch 140/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9993 - loss: 3.0056e-04 - val_accuracy: 1.0000 - val_loss: 1.3644e-04\n",
      "Epoch 141/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9986 - loss: 2.2016e-04 - val_accuracy: 1.0000 - val_loss: 1.6864e-04\n",
      "Epoch 142/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9978 - loss: 2.2469e-04 - val_accuracy: 1.0000 - val_loss: 1.8213e-04\n",
      "Epoch 143/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9984 - loss: 2.2250e-04 - val_accuracy: 1.0000 - val_loss: 1.4344e-04\n",
      "Epoch 144/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9996 - loss: 2.0388e-04 - val_accuracy: 1.0000 - val_loss: 1.6960e-04\n",
      "Epoch 145/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9978 - loss: 2.4271e-04 - val_accuracy: 1.0000 - val_loss: 1.8861e-04\n",
      "Epoch 146/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9992 - loss: 1.7879e-04 - val_accuracy: 1.0000 - val_loss: 2.2606e-04\n",
      "Epoch 147/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9973 - loss: 1.7657e-04 - val_accuracy: 1.0000 - val_loss: 1.7383e-04\n",
      "Epoch 148/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.2436e-04 - val_accuracy: 1.0000 - val_loss: 1.4265e-04\n",
      "Epoch 149/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9971 - loss: 2.7761e-04 - val_accuracy: 1.0000 - val_loss: 1.9749e-04\n",
      "Epoch 150/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9984 - loss: 1.9895e-04 - val_accuracy: 1.0000 - val_loss: 1.4519e-04\n",
      "Epoch 151/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9975 - loss: 2.3811e-04 - val_accuracy: 1.0000 - val_loss: 2.2605e-04\n",
      "Epoch 152/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9991 - loss: 2.4584e-04 - val_accuracy: 1.0000 - val_loss: 2.8504e-04\n",
      "Epoch 153/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9940 - loss: 2.2342e-04 - val_accuracy: 1.0000 - val_loss: 4.8514e-04\n",
      "Epoch 154/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9994 - loss: 3.6337e-04 - val_accuracy: 1.0000 - val_loss: 1.8572e-04\n",
      "Epoch 155/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9968 - loss: 2.0897e-04 - val_accuracy: 1.0000 - val_loss: 2.5476e-04\n",
      "Epoch 156/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9909 - loss: 2.5427e-04 - val_accuracy: 1.0000 - val_loss: 1.6888e-04\n",
      "Epoch 157/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9963 - loss: 2.2443e-04 - val_accuracy: 1.0000 - val_loss: 2.0195e-04\n",
      "Epoch 158/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9979 - loss: 2.0892e-04 - val_accuracy: 1.0000 - val_loss: 2.4655e-04\n",
      "Epoch 159/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 3.4123e-04 - val_accuracy: 1.0000 - val_loss: 1.6701e-04\n",
      "Epoch 160/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9986 - loss: 2.1376e-04 - val_accuracy: 1.0000 - val_loss: 1.1840e-04\n",
      "Epoch 161/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9986 - loss: 2.1998e-04 - val_accuracy: 1.0000 - val_loss: 2.2897e-04\n",
      "Epoch 162/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9994 - loss: 1.9943e-04 - val_accuracy: 1.0000 - val_loss: 1.4217e-04\n",
      "Epoch 163/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9970 - loss: 2.2018e-04 - val_accuracy: 1.0000 - val_loss: 1.8583e-04\n",
      "Epoch 164/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9980 - loss: 2.4430e-04 - val_accuracy: 1.0000 - val_loss: 3.3440e-04\n",
      "Epoch 165/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.2139e-04 - val_accuracy: 1.0000 - val_loss: 2.3529e-04\n",
      "Epoch 166/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9996 - loss: 2.3696e-04 - val_accuracy: 1.0000 - val_loss: 2.5880e-04\n",
      "Epoch 167/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 2.7711e-04 - val_accuracy: 1.0000 - val_loss: 1.8740e-04\n",
      "Epoch 168/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9983 - loss: 2.1277e-04 - val_accuracy: 1.0000 - val_loss: 1.3489e-04\n",
      "Epoch 169/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9975 - loss: 2.4558e-04 - val_accuracy: 1.0000 - val_loss: 1.9547e-04\n",
      "Epoch 170/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9996 - loss: 2.6551e-04 - val_accuracy: 0.9667 - val_loss: 1.8328e-04\n",
      "Epoch 171/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9967 - loss: 2.4116e-04 - val_accuracy: 1.0000 - val_loss: 1.2958e-04\n",
      "Epoch 172/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9970 - loss: 2.3180e-04 - val_accuracy: 1.0000 - val_loss: 2.1314e-04\n",
      "Epoch 173/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9923 - loss: 3.5116e-04 - val_accuracy: 0.9667 - val_loss: 1.6376e-04\n",
      "Epoch 174/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9963 - loss: 2.2903e-04 - val_accuracy: 1.0000 - val_loss: 8.0171e-05\n",
      "Epoch 175/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9937 - loss: 2.6252e-04 - val_accuracy: 1.0000 - val_loss: 2.2560e-04\n",
      "Epoch 176/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9935 - loss: 2.4322e-04 - val_accuracy: 1.0000 - val_loss: 2.6920e-04\n",
      "Epoch 177/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9998 - loss: 2.2100e-04 - val_accuracy: 0.9667 - val_loss: 2.3611e-04\n",
      "Epoch 178/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9986 - loss: 2.0670e-04 - val_accuracy: 1.0000 - val_loss: 1.7025e-04\n",
      "Epoch 179/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9996 - loss: 2.1863e-04 - val_accuracy: 1.0000 - val_loss: 1.7632e-04\n",
      "Epoch 180/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9991 - loss: 2.0248e-04 - val_accuracy: 1.0000 - val_loss: 2.8672e-04\n",
      "Epoch 181/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.6118e-04 - val_accuracy: 1.0000 - val_loss: 1.4131e-04\n",
      "Epoch 182/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9993 - loss: 2.3577e-04 - val_accuracy: 1.0000 - val_loss: 1.8940e-04\n",
      "Epoch 183/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9969 - loss: 2.1122e-04 - val_accuracy: 1.0000 - val_loss: 3.7442e-04\n",
      "Epoch 184/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.4439e-04 - val_accuracy: 1.0000 - val_loss: 1.6431e-04\n",
      "Epoch 185/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9978 - loss: 2.5006e-04 - val_accuracy: 1.0000 - val_loss: 1.7715e-04\n",
      "Epoch 186/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9996 - loss: 3.5177e-04 - val_accuracy: 1.0000 - val_loss: 1.6164e-04\n",
      "Epoch 187/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.8267e-04 - val_accuracy: 1.0000 - val_loss: 2.4133e-04\n",
      "Epoch 188/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 3.1614e-04 - val_accuracy: 1.0000 - val_loss: 1.2500e-04\n",
      "Epoch 189/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9928 - loss: 2.3019e-04 - val_accuracy: 1.0000 - val_loss: 1.5149e-04\n",
      "Epoch 190/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9957 - loss: 1.7718e-04 - val_accuracy: 1.0000 - val_loss: 2.6427e-04\n",
      "Epoch 191/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9977 - loss: 2.4003e-04 - val_accuracy: 1.0000 - val_loss: 2.2926e-04\n",
      "Epoch 192/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9975 - loss: 2.4797e-04 - val_accuracy: 1.0000 - val_loss: 3.1690e-04\n",
      "Epoch 193/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 2.2608e-04 - val_accuracy: 1.0000 - val_loss: 2.2471e-04\n",
      "Epoch 194/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9984 - loss: 1.9849e-04 - val_accuracy: 1.0000 - val_loss: 2.5777e-04\n",
      "Epoch 195/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 1.0000 - loss: 2.1565e-04 - val_accuracy: 1.0000 - val_loss: 1.9211e-04\n",
      "Epoch 196/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9991 - loss: 2.3837e-04 - val_accuracy: 1.0000 - val_loss: 2.0872e-04\n",
      "Epoch 197/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9954 - loss: 2.5529e-04 - val_accuracy: 1.0000 - val_loss: 1.5301e-04\n",
      "Epoch 198/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9991 - loss: 1.9585e-04 - val_accuracy: 1.0000 - val_loss: 1.6432e-04\n",
      "Epoch 199/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9917 - loss: 3.2316e-04 - val_accuracy: 1.0000 - val_loss: 1.7729e-04\n",
      "Epoch 200/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9987 - loss: 1.9653e-04 - val_accuracy: 1.0000 - val_loss: 1.3419e-04\n",
      "Epoch 201/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 1.9747e-04 - val_accuracy: 1.0000 - val_loss: 1.2519e-04\n",
      "Epoch 202/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 3.3320e-04 - val_accuracy: 1.0000 - val_loss: 1.2018e-04\n",
      "Epoch 203/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9991 - loss: 1.9066e-04 - val_accuracy: 1.0000 - val_loss: 1.3176e-04\n",
      "Epoch 204/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9976 - loss: 2.3026e-04 - val_accuracy: 1.0000 - val_loss: 1.9920e-04\n",
      "Epoch 205/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9989 - loss: 2.5659e-04 - val_accuracy: 1.0000 - val_loss: 1.4750e-04\n",
      "Epoch 206/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9948 - loss: 1.9158e-04 - val_accuracy: 1.0000 - val_loss: 1.4815e-04\n",
      "Epoch 207/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9988 - loss: 2.0264e-04 - val_accuracy: 1.0000 - val_loss: 1.3882e-04\n",
      "Epoch 208/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9997 - loss: 2.3359e-04 - val_accuracy: 1.0000 - val_loss: 2.0184e-04\n",
      "Epoch 209/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9979 - loss: 2.4431e-04 - val_accuracy: 1.0000 - val_loss: 1.4267e-04\n",
      "Epoch 210/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9974 - loss: 1.8386e-04 - val_accuracy: 1.0000 - val_loss: 1.0462e-04\n",
      "Epoch 211/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9983 - loss: 1.7761e-04 - val_accuracy: 1.0000 - val_loss: 3.8381e-04\n",
      "Epoch 212/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 2.9155e-04 - val_accuracy: 1.0000 - val_loss: 1.2493e-04\n",
      "Epoch 213/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9971 - loss: 2.3399e-04 - val_accuracy: 1.0000 - val_loss: 1.7349e-04\n",
      "Epoch 214/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9999 - loss: 2.6526e-04 - val_accuracy: 1.0000 - val_loss: 2.0534e-04\n",
      "Epoch 215/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 2.0117e-04 - val_accuracy: 1.0000 - val_loss: 6.6293e-05\n",
      "Epoch 216/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9989 - loss: 2.0211e-04 - val_accuracy: 1.0000 - val_loss: 1.2170e-04\n",
      "Epoch 217/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 2.1603e-04 - val_accuracy: 1.0000 - val_loss: 1.1044e-04\n",
      "Epoch 218/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 2.3369e-04 - val_accuracy: 1.0000 - val_loss: 1.4152e-04\n",
      "Epoch 219/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9904 - loss: 2.0187e-04 - val_accuracy: 1.0000 - val_loss: 1.1179e-04\n",
      "Epoch 220/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9999 - loss: 2.1140e-04 - val_accuracy: 1.0000 - val_loss: 1.7996e-04\n",
      "Epoch 221/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 1.7471e-04 - val_accuracy: 1.0000 - val_loss: 2.0988e-04\n",
      "Epoch 222/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9982 - loss: 2.0830e-04 - val_accuracy: 1.0000 - val_loss: 1.0709e-04\n",
      "Epoch 223/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9992 - loss: 2.0516e-04 - val_accuracy: 1.0000 - val_loss: 2.1863e-04\n",
      "Epoch 224/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9988 - loss: 2.4062e-04 - val_accuracy: 1.0000 - val_loss: 1.1470e-04\n",
      "Epoch 225/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9976 - loss: 2.2973e-04 - val_accuracy: 1.0000 - val_loss: 1.3333e-04\n",
      "Epoch 226/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9995 - loss: 2.3731e-04 - val_accuracy: 1.0000 - val_loss: 1.3290e-04\n",
      "Epoch 227/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.0664e-04 - val_accuracy: 1.0000 - val_loss: 2.0093e-04\n",
      "Epoch 228/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9994 - loss: 1.5949e-04 - val_accuracy: 1.0000 - val_loss: 1.3366e-04\n",
      "Epoch 229/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9963 - loss: 1.9634e-04 - val_accuracy: 1.0000 - val_loss: 1.5671e-04\n",
      "Epoch 230/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.9960 - loss: 2.3942e-04 - val_accuracy: 1.0000 - val_loss: 1.6175e-04\n",
      "Epoch 231/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9944 - loss: 2.0982e-04 - val_accuracy: 1.0000 - val_loss: 1.1864e-04\n",
      "Epoch 232/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 1.8088e-04 - val_accuracy: 1.0000 - val_loss: 1.8003e-04\n",
      "Epoch 233/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9997 - loss: 2.1133e-04 - val_accuracy: 1.0000 - val_loss: 2.2100e-04\n",
      "Epoch 234/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9942 - loss: 2.2843e-04 - val_accuracy: 1.0000 - val_loss: 2.1499e-04\n",
      "Epoch 235/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.2822e-04 - val_accuracy: 1.0000 - val_loss: 1.2831e-04\n",
      "Epoch 236/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9973 - loss: 1.9511e-04 - val_accuracy: 1.0000 - val_loss: 2.0420e-04\n",
      "Epoch 237/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9984 - loss: 2.0756e-04 - val_accuracy: 1.0000 - val_loss: 9.9838e-05\n",
      "Epoch 238/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9982 - loss: 1.6907e-04 - val_accuracy: 1.0000 - val_loss: 1.8691e-04\n",
      "Epoch 239/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 1.9173e-04 - val_accuracy: 1.0000 - val_loss: 1.1501e-04\n",
      "Epoch 240/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9999 - loss: 2.4801e-04 - val_accuracy: 1.0000 - val_loss: 1.5563e-04\n",
      "Epoch 241/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9998 - loss: 2.1090e-04 - val_accuracy: 1.0000 - val_loss: 1.6025e-04\n",
      "Epoch 242/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9985 - loss: 2.2598e-04 - val_accuracy: 1.0000 - val_loss: 2.3886e-04\n",
      "Epoch 243/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 1.0000 - loss: 2.2492e-04 - val_accuracy: 1.0000 - val_loss: 1.5946e-04\n",
      "Epoch 244/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9970 - loss: 2.3760e-04 - val_accuracy: 1.0000 - val_loss: 1.5557e-04\n",
      "Epoch 245/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9978 - loss: 2.1652e-04 - val_accuracy: 1.0000 - val_loss: 2.2650e-04\n",
      "Epoch 246/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9975 - loss: 1.7958e-04 - val_accuracy: 1.0000 - val_loss: 2.4489e-04\n",
      "Epoch 247/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 1.9316e-04 - val_accuracy: 1.0000 - val_loss: 1.2448e-04\n",
      "Epoch 248/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9989 - loss: 2.0606e-04 - val_accuracy: 0.9667 - val_loss: 2.2906e-04\n",
      "Epoch 249/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9974 - loss: 2.0622e-04 - val_accuracy: 1.0000 - val_loss: 1.4709e-04\n",
      "Epoch 250/250\n",
      "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 1.0000 - loss: 1.7968e-04 - val_accuracy: 1.0000 - val_loss: 1.9542e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Epoch_Anz = 250\n",
    "if (Training_Percentage > 0):\n",
    "    history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    history = model.fit(train_iterator, epochs = Epoch_Anz)\n",
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(val_loss_ges)\n",
    "\n",
    "\n",
    "plt.semilogy(loss_ges)\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the model by hand\n",
    "\n",
    "* The following code uses the trained model to check the deviation for each picture.\n",
    "* The evaluation takes the periodic character of the results into account (dev1 ... dev2).\n",
    "* Images, that have a bigger deviation as the parameter \"deviation_max_list\" are printed in a list to check the picture and labeling itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_resize_all\\1.8_75dd3145e3bdcb42663f4e448cf9d138.jpg 0.18 0.47326115 -0.29326114\n",
      "data_resize_all\\2.3_4cd7838798b0a1a656fa5c1c5267339c.jpg 0.22999999999999998 0.48396614 -0.25396615\n",
      "data_resize_all\\3.2_4904930dbce0bc29d32254e676022863.jpg 0.32 0.5651895 -0.24518949\n",
      "data_resize_all\\7.3_a4accb1ac1e16cc3d5fa1a28ccbe35e8.jpg 0.73 1.0570004 -0.32700038\n"
     ]
    }
   ],
   "source": [
    "Input_dir='data_resize_all'\n",
    "#Input_dir='test_result'\n",
    "files = glob.glob(Input_dir + '/*.*')\n",
    "res = []\n",
    "stat_Anz = []\n",
    "stat_Abweichung = []\n",
    "i = 0\n",
    "deviation_max_list = 0.15\n",
    "\n",
    "for i in range(100):\n",
    "    stat_Anz.append(0)\n",
    "    stat_Abweichung.append(0)\n",
    "\n",
    "for aktfile in sorted(files):\n",
    "    base = os.path.basename(aktfile)\n",
    "    target = (float(base[0:3])) / 10\n",
    "    \n",
    "    target_sin = math.sin(target * math.pi * 2)\n",
    "    target_cos = math.cos(target * math.pi * 2)\n",
    "\n",
    "    test_image = Image.open(aktfile)\n",
    "    test_image = np.array(test_image, dtype=\"float32\")\n",
    "    img = np.reshape(test_image,[1,32,32,3])\n",
    "    classes = model.predict(img, verbose=0)\n",
    "    \n",
    "    out_sin = classes[0][0]  \n",
    "    out_cos = classes[0][1]\n",
    "    out_target = (np.arctan2(out_sin, out_cos)/(2*math.pi)) % 1\n",
    "\n",
    "    dev_sin = target_sin - out_sin\n",
    "    dev_cos = target_cos - out_cos\n",
    "    dev_target = target - out_target\n",
    "    \n",
    "    if abs(dev_target + 1) < abs(dev_target):\n",
    "        out_target = out_target - 1\n",
    "        dev_target = target - out_target\n",
    "    else:\n",
    "        if abs(dev_target - 1) < abs(dev_target):\n",
    "            out_target = out_target + 1\n",
    "            dev_target = target - out_target\n",
    "            \n",
    "    target_int = int ((float(base[0:3])) * 10)\n",
    "    stat_Abweichung[target_int] = stat_Abweichung[target_int] + dev_target  \n",
    "    stat_Anz[target_int] = stat_Anz[target_int] + 1\n",
    "               \n",
    "    res.append(np.array([target, out_target, dev_target, out_sin, out_cos, i]))\n",
    "    if abs(dev_target) > deviation_max_list:\n",
    "        print(aktfile + \" \" + str(target) + \" \" + str(out_target) +  \" \" + str(dev_target))\n",
    "\n",
    "    \n",
    "for i in range(100):\n",
    "    stat_Abweichung[i] = stat_Abweichung[i] / stat_Anz[i]\n",
    "\n",
    "res = np.asarray(res)\n",
    "res_step_1 = res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res[:,3])\n",
    "plt.plot(res[:,4])\n",
    "plt.title('Result')\n",
    "plt.ylabel('value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['sin', 'cos'], loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res[:,0])\n",
    "plt.plot(res[:,1])\n",
    "plt.title('Result')\n",
    "plt.ylabel('Counter Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Orginal', 'Prediction'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stat_Abweichung)\n",
    "plt.title('Result')\n",
    "plt.ylabel('Counter Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Orginal', 'Prediction'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zxn0goygKpk6dismTJyObzdZ6OaHlp0++jd+ueA8A8OlFM/CVY+e4+vl4PE6ZGIIgCMI14z6QYUSjUXqQVkBfRsGWgTwAYOdIgTrzEgRBEIEwrs2+hH/khVKl4Uy+hishCIIgxhMUyBC+kBPa+Y5QIEMQBEEEBAUyhC/khUCGMjIEQRBEUFAgQ/hCThfI5Gq4EoIgCGI8QYEM4QuUkSEIgiBqAQUyhC+QR4YgCIKoBRTIEL4gVi0NkbREEARBBAQFMoQv5PIkLREEQRDBQ4EM4QskLREEQRC1gAIZwhfEQCZXUJHJFWy+myAIgiD8gQIZwhdEjwxAJdgEQRBEMFAgQ/iC6JEByCdDEARBBAMFMoQviH1kAApkCIIgiGCgQIbwhZwhkCHDL0EQBBEEFMgQvmDMyFAvGYIgCCIIKJAhfIEyMgRBEEQtoECG8IXyqiUKZAiCIIjqQ4EM4QssI5NKRAGQtEQQBEEEAwUyhC+w8uuWhjgAkpYIgiCIYKBAhvAFZvZtbYwBIGmJIAiCCAYKZAhfyJU8MlpGhqQlgiAIovpQIEP4As/INBQzMkOUkSEIgiACgAIZwheY2ZdlZEhaIgiCIIKAAhnCF/J5vUeGpCWCIAgiCCiQIXzBmJEhaYkgCIIIAgpkCF/I80CGZWQokCEIgiCqDwUyhC+wqqVW7pEhaYkgCIKoPhTIEBVTKKhgo5ZYRobMvgRBEEQQUCBDVIw4MLKVqpYIgiCIAKlpIPPUU0/hIx/5CKZNmwZFUfDAAw/wr2WzWXz729/GAQccgFQqhWnTpuGss87Ce++9V7sFE6bkhUCGMjIEQRBEkNQ0kBkaGsJBBx2EO++8s+xrw8PDWLlyJa666iqsXLkSf/zjH7F69Wp89KMfrcFKCTtywuTr1kbq7EsQBEEER6yWv3zJkiVYsmSJ6dfa2trw6KOP6j53xx134PDDD8emTZswc+bMIJZISJA3k5ayeaiqCkVRarUsgiAIYhwQKo9MX18fFEVBe3t7rZcSWl7ZvBuvbt7t62uKHpnmkrSkqsBotmD1IwRBEAThCzXNyLhhdHQU3/72t/G5z30Ora2tlt+XTqeRTqf5x/39/UEsLxSkc3l8/ufPIaIoePnqExGP+hPHsoxMLKKgMR7lnx/O5NCYiFr9GEEQBEFUTCgyMtlsFp/+9Kehqip+8pOf2H7v0qVL0dbWxv/NmDEjoFXWP8PpPIYzeQymcxjN+mfGZRmZaERBNKKgIV48rcjwSxAEQVSbug9kWBCzceNGPProo7bZGAC44oor0NfXx/9t3rw5oJXWP9m8JvXk8qrNd7qDzVmKRYp+mKYEVS4RBEEQwVDX0hILYtauXYsnnngCnZ2djj+TTCaRTCYDWF34yAiBjBjUVAqrWoqWAhkmL1F3X4IgCKLa1DSQGRwcxLp16/jH69evxyuvvIKOjg5MnToVn/zkJ7Fy5Uo89NBDyOfz2LZtGwCgo6MDiUSiVssOLZmcFrxkfAxkuEem5LlJJYuBDM1bIgiCIKpNTQOZFStW4Pjjj+cfX3bZZQCAs88+G9deey0efPBBAMDBBx+s+7knnngCxx13XFDLHDNkBTnJT2mJvS7PyJC0RBAEQQRETQOZ4447Dqpq/UC1+xrhHjEj46e0JFYtAUBTSVoaImmJIAiCqDJ1b/Yl/EOUk/yUlowemaYESUsEQRBEMFAgM44QMzK+Vi2VMjKsL01TkqQlgiAIIhgokBlHZKtWtaT3yDRR1RJBEAQREBTIjCOqXrXEzb4skKGMDEEQBFFdKJAZR1SrIZ4xI8PKrymQIQiCIKoNBTLjiGo1xMuXzL7Gzr5k9iUIgiCqDQUy4wh9+bWPGRljHxkqvyYIgiACggKZcYQYvFSnj0ypaonKrwmCIIiAoEBmHJHJaYGFn4FM1li1ROXXBEEQREBQIDOOqNaIAu6RiRrKr7MUyBAEQRDVhQKZcUTVOvsaPDJMWhpOk0eGIAiCqC4UyIwjqj9rqXg6UR8ZgiAIIigokBlHZKrcRybG+8iUyq9JWiIIgiCqDAUy44hslTv7RqOG8muSlgiCIIgqQ4HMOKLas5ZiBo9MOlfgQQ5BEARBVAMKZMYR1ZKWWNVS1CAtASQvEQRBENWFAplxRCZXnYZ4xoxMMhaBUvxfmoBNEARBVBUKZMYR+llLPmZkePl18XRSFEXrJZOmjAxBEARRPSiQGUdkq1R+nTVkZACgMUHdfY0MpXO484l1eHvnYK2XMi55871+/OQfb+vaEBC1Y3v/KH60fC12DqRrvRQi5FAgM46o9vTrqBDIpJKleUtZkpYYD7++Df/zyGr84LG1tV7KuOSmh1fh5odX4Z9rd9Z6KQSAXz27Abc+ugb3PLex1kshQg4FMuOIbJWkJaNHBtBKsCkjo7FrOAMA6BvJ1ngl45P+0nHvHcrUeCUEAOwazpb+S+8HURkUyIwj0tXq7FsKimJR7XRiJdhD5JHhsONP0kZtyJUyh1RJVx+Mlt4H2uwQlUKBzDgiqD4ygNjdl6QlBrtxp3N0464F2VLVHgXX9UE6W7wHUWUjUSkUyIwjqiUt8c6+JC3ZwgIZP7sqE/Kw83+EHpx1AWVkCL+gQGYcUa2hkWYZGW0CNt2kGKOlHSjbiRLBki2wDACdk/XAaI4CGcIfKJAZR4hZmKpULUWp/NoOysjUFi4t0TlZF4yStET4BAUy4wgxI1PN6dcAkGIZGfLIcEZzlJGpJdzsSw/OuoCkJcIvKJAZR4iZAD+zAjlDZ19Ak5ZG6CbFoYxMbWGBPD046wN2PdA9gqgUCmTGEbqMTMFPacm6sy9ViGjwqiUq/60JTFql8uv6QJOW6P0gKoMCmXGErmop56e0VN7Zl2dkSFriMEmJMjK1gZ2nQ2k6J+uBNDf70vtBVAYFMuOIavWRMcvI8Kol2m1xWJVGNq+iUPAvkCScUVWVZ2TonKwPWEYmm1d9vR8R4w8KZMYJhYKqr1ryUVriZl9dZ99S1RJJS5xRQdKgrEywiOc+SUv1gXg9UHBJVAIFMuMEY+Dip7Rkm5EhaYkzKlQrUeVSsIieMPJt1Z5cvsA3QAAZfonKoEBmnGCc7+NrQ7x8eWdfkpbKEXeg6TwdlyARA3cqv649o4b70RC9J0QFUCAzTjCOJKi+R6Y0a4kCGY4ukKGMTKCIUt5wNg9VJY9SLRk1yHt0nyAqgQKZcUJ5Rqa6VUuNfPo17bQY4i40TROwA0WUllSVjn+tMQYylLklKoECmXGCMQNTlYxM1Kz8mm5QQNFsLQaTxsCSqC5GTxgF2LVlNEvSEuEfFMiME4w70FxB9S29njXp7JsqSUtUWlnEePxZDw0iGIxmd8oA1BaSlgg/oUBmnMCCiUQsInzOn0DGvLNvlP8/PTTKb9yUkQkWYzBNmcLaYgzk6R5BVAIFMuMEdiNPCQGGX5kSM49MIhbhgQ117tSa4THIoxEsJC3VF0ZpiSrJiEqgQGacwDIAqWSMf86vCdhmGRmASrBFjDduysgEi1FaIimjthgzlEP0fhAVUNNA5qmnnsJHPvIRTJs2DYqi4IEHHtB9XVVVXH311Zg6dSoaGxuxePFirF27tjaLDTms/LQxHi37XKWYdfYFqARbxHjjpoxMsGRz5JGpJ4yBPb0fRCXUNJAZGhrCQQcdhDvvvNP067fccgt++MMf4q677sLzzz+PVCqFk046CaOjowGvNPywDEAiFkGiFHD4NQHbKSNDaXwTjww1xAsUox9smDwyNaXc7Ev3CMI7MedvqR5LlizBkiVLTL+mqipuv/12/Nd//RdOO+00AMCvfvUrdHV14YEHHsBnP/vZIJcaetiNPB6NIB5VkMn7N6aAZWSihkCmkY8poIeGcQdKDfGCpaxqiYLrmmL0jJG0RFRC3Xpk1q9fj23btmHx4sX8c21tbXjf+96HZ5991vLn0uk0+vv7df8IfUaGSUB+SUtWGZmUR2npR8vX4q4n33b1M71DGXzjt6/ihfW9rn4uKIw3bhoaGSz1KC0NZ3L41u9fxROrd9R6KYFTbvat/ftBhJe6DWS2bdsGAOjq6tJ9vquri3/NjKVLl6KtrY3/mzFjRlXXGRZ4+XU0grjP0lIuX161BAANrCmei5tU30gWtz66Bjc/vKos/WzHo29uwx9Wvouf//Md6Z8JkrTRI0MZmUAxSkv1UH79z7Xd+O2Kd/HjJ9bVeimBU97ZlzJkhHfqNpDxyhVXXIG+vj7+b/PmzbVeUl2g98gUAw6/pCUtI6M/nfjvcZF96BvOAii2kXcTyAyWJhrX6w2xrGqJMjKBYgza68G3NTCa0/13PMEC+5ZSFWU9ZMiI8FK3gcyUKVMAANu3b9d9fvv27fxrZiSTSbS2tur+EdqDMx5VfJeWsswjE9VnZFjmh31dhv7RLP9/48PfDhb0uPmZICmrWqqDjMB4wljuXg8PTmZwrYe1BA2bO9bRnAAwPo8B4R91G8jMmTMHU6ZMwfLly/nn+vv78fzzz+OII46o4crCidbZN4p4KeDIVdkjwwImoz/BDnF36iYjk+aBTH3eEMsCGcrIBErOEEzXgyeDPbzH40OcXQ8TmiiQISqnplVLg4ODWLdO04fXr1+PV155BR0dHZg5cyYuueQSfPe738U+++yDOXPm4KqrrsK0adNw+umn127RIYXtSONRRcuU+NAQT1VVHsgYPTJxD9LSgJiRcTGPiO3w6jaQMc5aqtPM0VjFeA7WQyXdUKa+5dBqwq7TjlQxkKHya6ISahrIrFixAscffzz/+LLLLgMAnH322Vi2bBm+9a1vYWhoCF/+8pexe/duHH300Xj44YfR0NBQqyWHFjOzrx8jCvLCTjdu8Miwj427YTv0GZmxKy2RRyZYyqSlOvDIsIf3SDYPVVWhKIrDT4wd2HVKGRnCD2oayBx33HG2E5gVRcH111+P66+/PsBVjU1Es6+XTIkVYpBS5pGJKbrfLYMuI+Ni18y+t16nSlMfmdrCztNELIJMrlAXD062hqKxvaAbtDrWYddrJ3lkCB+oW48M4S8ZoSFezEdpSczIGD0yXsq8vXpkWKBQ7xkZNrSTMjLBwnxabY1xAPUhLYk+naFxJq0wqVXLyORsN7UEYQcFMuOEao0o0GVkLAIZNwHTQLpSaan2DygzWKaotfQgpaqlYGHZRx7I1IG0JAYv9WA+DhLNI1N8PwoqzR8jvEOBzDghy8uvNWnJjwnMYkYmqpibfb1KS25kIrbDyxVU36qx/IQFZexBShmZYGEtAHggUweBg7iGelhPkKQNVUvA+DsGhH9QIDNOYMFEMuavtMSyOhEFiPggLfV7lpa07zVWCNUDbMfd2sAyMvW3xrGMUVqqh86+I7pApvYZoiBhgX0qGUMiVrxPjLdjQPgHBTLjhKzQEM9Pacmqq2/xd7E+MtWvWhKlmnqUl0YN0hJlZIKlTFqqg4fm0DjOyLDroSEeQZOHUSYEIUKBzDghoyu/9k9ayuXNe8gAPvSR8WD2dftzQcHW1NpYLBSs1+qqsQqTllobisd/NFvQyaK1QOydMu4CmdL1kIxF+XDZ8XYMCP+gQGacwBvi+S4tmXf1BbyNKPDcRyYnZmTqL9tR5pGpQ/lrLMOlJcGTUWt5aZikJTTEo7zsfLxVbhH+QYHMOEFv9i1JS740xCtNvo6WBzLeRhR47Oxb79JS1uCRoUAmUFjA3ZKMgXnSax08iFLKeJNV2PVA0hLhBxTIjBOYtJQUp1/72BDPzCPj5fdU2kcGqE/ZJp2jjEwtEYemNsWLD87hdO3OE1VVdRmIoXH0EFdVlV8PDfEoGtn7MY6OAeEvFMiME5jhVmyIl/FDWspbS0ssuJGVlnJ5fcdVL31k3P5cUGgeGcrI1AKWFYxFI2hK1t6Tkc4VIF4W42nWkHjuN8SjSPH3Y/wcA8JfPAcy69atwyOPPIKRkREAoK6MdU7aZNaSP9KSjdk35k5aGjQ0KZNtGifu8ID6lpYoI1Mb+IiCqCBlZGv34DTKKOMpGyFenw2xCPfIjKdjQPiL60Cmp6cHixcvxty5c3HKKadg69atAIAvfelL+MY3vuH7Agl/yApm32rMWoqZeGSYtCRb5i3KSoC8R8aY3ajPjExxTaxqph7lr7EM94jFlLqQMozG1vH0EGfXQiyiFDNkdfB+EOHGdSBz6aWXIhaLYdOmTWhqauKf/8xnPoOHH37Y18UR/pExm37tQ/mpXUaGSUuyEla/YPQF5M1/xu+rt4yMqqo8KGtrKmZksnkVhRqX/44nWAYsFtEyMkM19MiUZ2TGj6yiGX2L7wOTlsjsS3jF9fTrv//973jkkUewxx576D6/zz77YOPGjb4tjPAXtiNNxBShUZ0fGRltd2XErbTUP2LIyEhmVoyZGzfVTkGQyRfAlFdWtcQ+3xAZPxOPawnLHMajEe3BWUNpyZh9GE/ZCLEZHgAqvyYqxnVGZmhoSJeJYfT29iKZTPqyKMJ/WDCRiEZ9lZa0jIxZZ1+30pI+IyMbkBgDnnqTlsT1MI8MQGMKgkQM5OtBWjL+7vGUjWDXQzJWfB+YtDSejgHhL64DmQ984AP41a9+xT9WFAWFQgG33HILjj/+eF8XR/hHRvAI+Ckt2VUtuZ1+zTwy7KWkMzLZ+paWmGlZUYCmRJT3MUnn62udYxkzaamW5ddGKWk8ZSPEHjIAyOxLVIxraemWW27BCSecgBUrViCTyeBb3/oW3njjDfT29uJf//pXNdZI+ADv7Ct6ZHyRluxGFER0v9sJlpHpSCXRPZiWrlqq90CGdzGNRaEoxVlX6VyBMjIBIkpL9VB+Pb4zMuYeGQpkCK+4zsgsWLAAa9aswdFHH43TTjsNQ0ND+PjHP46XX34Ze+21VzXWSPiAaPaN+Sot2XhkPFYtTWopSpSyAUm5tFRfN0SjJyAZYyZoCmSCQpSWeJVMHZRfN43DbIQ4ngAQj8H4yUoR/uI6IwMAbW1tuPLKK/1ey5jjjff6cMmvXykrK54/tQW/OGsRb0wXBEzeScQiwvRrH2ctmZRfu5aW0log89ZWYFQyk1Nm9q2zTIdxB5qMR4HRnOeMTCZXwHm/WoGFM9px6YlzfVunFT/5x9t4cs0O3H3O4VwGCBuaBFofLfGZlNTZnMBw70gggcy1D76BnQNp3PH5hVCU8utVlvtf2ITfv/Qufn7WInSkEs4/YKBMWqoDz1JYKBRUfPWelzCrswlXnrqfr6+9o38UX73nJXzh/bPw8UP2cP6BOsJ1IPPUU0/Zfv2YY47xvJixxiNvbMfaHYNln9/WP4rV2wew/7S2QNaRL6jclCuWX/vRlC1vM6LArYTFpKVJze4yMkYJqu4yMoYdaCJaWUbm9ff68NSanXhjS18ggcw9z23Elt0jeGnjLhy9z8Sq/75qkBFmjTWWpi3XsvyaPbQnNiexuXek6tkIVVXxy2c3QFWBqwf2Q1drg+fX+u2KzXh50278c+1OnHbwdNc/zwMZZvZNUPm1LO/uGsHf39yOaETBf56yb0UBqZF/vd2NlZt2IxmLjv1A5rjjjiv7nHgw82Rg5PSPFB/Mn1k0A2ceMQsAcNGvX8Y7O4fQM5gJbB2ihFScfu1jQ7y8XR+Z0u+RlJb6TaQlVVUdL9YyaanOuuayGzeTlJKlnaisB8gIO3eCCthYf5+eoXQgv68aiNJSKlk/nX0nloL2amcjcgWVtwCodAPDMole72HGDGVTksqvZWET2/MFFSPZPA8CfXntTPF9DWOzTtfaxq5du3T/duzYgYcffhiHHXYY/v73v1djjaGFSUpzJqWwYHobFkxvw/T2RgBA92BwDwWx820i6q+0pGVkTDr7xrxVLbFApqDK/Wz9m331N+5KMzLs3BnNFao+GqRQUPnoiJ0D4Q1kRGmpHqQMLSNTlGbSuQK/lqqBuGmpdAPDft7rPYxtNFhAXw9SX1gQ721Gy4Jfrx3GOXCuw7m2tnI55MQTT0QikcBll12Gl156yZeFjQWYVNLSoB3mzpKmXLOMTFThGRk/pCWZqqV8odjFNmLyPSJcWmrR+hGN5vI8ILKi7gMZPumXZWSKN26vHpme0gMkX1CRzatIxPxLLxsZyuT4Tr5nKLhz1m+09gMRvouth/JrlpFhn2sRGib6CRsaC8hvLCxfK+9zRiZOVUuyjOgCmWxFEqHVa4dxDpxvbtOuri6sXr3ar5cbE7CIWbw5sRtXkBmZrFCxpChaHxl/MjJsqrCJtCR8TkZeYsdrYirBe63IBCUsUGA7u3oray4z+1ackdEeINXuYizu+rpDnJHhs5YiCpcyalm1xB7a7U0J3jepmhkJ8fqrNCPDHnSeMzJCOwJAk5ZGsnka2+GAeD/s9zkjkx5PGZnXXntN97Gqqti6dStuuukmHHzwwX6ta0wwkDbJyPBAJrjdrdZDRin9l0k+fmZkymPihFCVlc2rSDqcbVoGK45kLILRrFyvFXZxtzfGMZzJ192IgrTB3Mg9Mh7XuVN4gIxm87qxB34jBjJhzcjkBX9IvE6GFA4L5ddNiRgG07mqrsdPaYnNTuv2eD4Yq5aahEq4kWye95UhyhH9gL5LS6XnRBgzMq7PmIMPPhiKopRp8+9///vxv//7v74tbCzATrRWIZBhmngtMjJs9hEfUeCHtCTR2bf4ffIZmZaGGBriUYxmC3IZmdLF3daUwHt9o/UnLZXWx0qXExVWjfUI5061s0/i2Iggz1k/MZrd66FKhhmNmxJRNCaiGEznqmp29VNaypQCcK8ZunROn6FkAT5QDPAokLFG3PwYR7pUiuaRqa/7pwyuz5j169frPo5EIpg0aRIaGvzT6sYK9SItpXOatATA3xEFNh6ZaESBogCq6iyj5PIFviNtaYiVbm5ZqZ4wYkam+HF97SiMO1AtI+ODtFTloG0sSEviuReLKNqQwnTtpCVW+t2UiCGViGIngpOWZDYVtq/FMjKDaamqQiNaO4LidRCJFOdfjWTzZPh1IAiz77jIyMyaNasa6xhzqKpqavZlgUywZl+tPbv432p39mW/K5MrOO4CB4WHSktDnN/kZGQitoNob2KBTH3dDNnfwIbk+ZmRqXbQ1i9mZIYynh5ctSYnnHvF6deaJ6NWiJ19WV+boKSlSjtKs59P5woYyuTR7DKDYvSMAUAqWQxkqATbHr205HdGRntfw4bUGfjDH/5Q+gUvuugiz4sZS6SFh7eYkeksSUs9Q952M15gD8xkNaQlm86+QPGhnckVHHeBbHfREI8gEYvwm5wbaaluAxlDQzwW0Hi5YWTzBewa1m5gQZp9M7kCBtK5qnpyqgF7iEcjCqIRhVfJZPMqMrmCY1VcNWBG48ZENJAxBX5JS2JzTaCYpfMcyAiSEg2OlCOIjEyu9B6bZdnrFakz8Pvf/77UiymKQoFMCbaTjShASjCzsUAmm1fRP5JDW1P1HwpZoaup+F8/pCW7zr4ApJvv9QtGX0ArUXYjLbU1Fo9tvTbEY1km9uD0EsjsMhgsg5SWgGImMayBDMsaimMWRjLO5f3VYDitZWSCmDXkl7RkvI57htKYPTHl6jV4YC+8Dyy4JGnJniDMvkBx0xKmcSRSgYzRF0M40z9SPMmakzFd1iUZi6K1IYb+0Rx2DqYDCWT4wMhYubRUaVbIziMj/q5Mzj5oEo2+ANBQWqtcRkYvLWVyBam+NUFRnpHxXrW00+CtClJaAoq+iDkuH1y1hs8Zi2qBZCyiIFdQMZzNoQ3BB2Ys85BKxALKyPgjLRl/dueAe4mcD1EVAkheEk/Ski1iBtZ4bVb82sK9NmyBTPBbkXHCgCHDIKL5ZIIxT5aXXxf/q6qouJuoXWdfAEIXYTlpiR0vT9JSo3as60nnNd64WUDpxSNj9FZVPyOjv1kGdc76ibFqD6jt1OlCqb08wKSl6mcjxJ5RuQqkJaMc7WVshTGwB8bnFHAv6PrIjFSnjwwQvsolT3Vu7777Lh588EFs2rQJmYz+xnrbbbf5srCwY8wwiExsTuKd7qHAeslkLTIyQPEGF6sg8LabtQTIS0vsgclK1TWzr4S0lGPSkhbIjGbzdbOjSBvMjZV4ZIzVbkFLSzsDNKn7hVFaAorVQv2juZp09xV31az8GqjuQzzjUx8ZY0am20NGxng9AEAjdfeVIgizL1BfG0EZXAcyy5cvx0c/+lHsueeeWLVqFRYsWIANGzZAVVUccsgh1VhjKNF6yJRnZETDbxBoGZlicCAaczP5gu6G4haZqqXiGlxKS7yNv3xGJpWMccmgnprilU2/riAjUxbIVPmGw96XRDSCTL4Q0oyMvmoPQCC+FCtY6bWiFA2vqSA8Mjl/Apms4Tr20kbC6BkDavt+hIl0Nc2+OTEjE65AxrW0dMUVV+Cb3/wm/v3vf6OhoQF/+MMfsHnzZhx77LH41Kc+VY01hhKz0msG7yUTUF8OduPiVUuCMbeSNDNg39kX0AIcZ2mpdLySJWkpJi8tiTu8Bhcm4aAo6yNTgdnXKC15naAtC3tfZnY2AQhnUzzN7C5kZPiYguADXiYhNcajxR4qAZRfi9JSJVVLmbx+jZ6kpVy5tJRKkrQkgxhssM7xvr22wSMTJlwHMm+99RbOOussAEAsFsPIyAiam5tx/fXX4+abb/Z9gWHFTlpiGZmg0vTGjEwkovAAo9JeMryzr1X5dUwzFttRnpFhZl/5qqWGeET4ufq5IfI+MmUZGT/MvsFIS8zg60VKqDXGqj2gtlUyw0JXX/G/YRhRYMysejkfTMuvSVqSoqpVSzppKVzvg+tAJpVKcV/M1KlT8fbbb/OvdXd3+7eykFNXZl+T1LpfE7Blq5acdoH9lZh9hR1e0kUmJyiMQ/L8yMgwOaLamSdjIBOUHOonZtJSLbv7MmmpsSyQqd5aMj5JS2UeGZfng6qqttLSCElLthj7yBhHBfn12mHLyLj2yLz//e/H008/jX333RennHIKvvGNb+Df//43/vjHP+L9739/NdYYSvptzb7BzltiJ6XYLyMeLQ5lrHQCtpNHRjbzY5TieB8ZiZ2BuMNzk8kJCqs+MpV4ZPaY0ITV2weqHrCxEk+ekQmj2TdnIi2xB2cNpaVUSVJqCpG0xK5jNnrErTyezatgS0mKZl+qWpJCvN7zpeo3dv5Ugqqquo1V2Dwy0kegt7cXHR0duO222zA4OAgAuO666zA4OIjf/OY32GeffahiScBszhKDZ2QCmiZsllpP+DSmIOdUfu1RWmqU9LoYd3gNLgKgoPCzsy/LyEyf0FgMZKr4dxYKKh8dMbuTBTLhy8gwf5be7Fs7KYNlXowZmarOWvJNWir+7KTmJHYMpNE/mnPVHVk8X8WMTIoCGSmM98OB0ZwvgYzxXjRmA5lp06bh9NNPx5e+9CWceOKJAIoy01133VW1xYUZO7NvZ43NvoB/0hLrIxONmt/IZKUloxQn63Ux7vDcVDsFRXn5tbeMjKqqXNrZY0IjgOpmnoYyObDM9Z6TioHMwGgOo9l8RZVuQWMmrXI5pwbSEssCsTXwbEQ2PNLSxOYkeocyyBWK5+TUtkapn2fXs6JomylADCxJWrLDuHEZGM2iq7Xygc3G+2zYpCVpj8zPf/5z7Ny5EyeffDJmz56Na6+9Fhs2bKji0sKNfR+ZorQ0lAlm2quxIV7x/1mjOn+qliqXlli5ur782ulBbdzh1aW0lNNLS147+/aNZHlAOL2dBTLVO3/YexKPKpjckuTnT29AmUS/YC35YybSUi0yAOLka0CTmKrZ08Y3aYnNbYtHeNGCG8NvWvCLiR3FSVqSI224r/X7ZPg13i/HrNn3zDPPxPLly7Fu3TqcffbZ+OUvf4m9994bJ554In7zm9+UNcbzg3w+j6uuugpz5sxBY2Mj9tprL9xwww2+GpyqBSuNM+sj05yM8YdZEKl644gCwD9pKe9k9mXSkkOEX97ZV+5hb9zhuSnbDoJ8QeUPDm72jXvLyDB/SmtDjB+nagZs4nuiKAo6U6VMYsjkJd4Q0iwDUIPzhGUdAq1a8jkjk4hGtDYSLgy/ZkZfAHUxkTwMsOPH7rd+VS6Nm4wMY86cObjuuuuwfv16PPzww5g8eTLOPfdcTJ061feBkTfffDN+8pOf4I477sBbb72Fm2++Gbfccgt+9KMf+fp7qoFdRkZRlEB9Msbya0DouOtT1ZLziAKnqiW9FCcbkBh3eG6qnYJAXAdviBf15pFhAcTE5qR0oFcJRnmUN3IMmeHXTlqqRfk1+51l0lJVh0b6NKJA2BR5kcjNxhMAWvl1LarIwgS7n7Csvl/dfY2SVdg8MhXNWlq8eDHuvfde/OpXvwIA3Hnnnb4sivHMM8/gtNNOw6mnnorZs2fjk5/8JD70oQ/hhRde8PX3eEFVVQyMZi0vPDuzLyBULlXgkylIykLGEQWA/QRsVVWlX5tVLVmOKCh93m5QXS5f4LtRY2dfR2nJ2GzOxWgDN8geDyNiIMOycF4zMiyAKAYy/gRsdu+1MRhnwbexl00tcPN+mElLtSy/Zlkg9vAOVx8ZbVM0kXcolw9sNZlVH8hUGlh6vT7NXqeeM/7svjappXgt+peR0Z8TYz4jw9i4cSOuvfZazJkzB5/5zGdwyCGH4N577/VzbTjyyCOxfPlyrFmzBgDw6quv4umnn8aSJUssfyadTqO/v1/3rxp883ev4YBr/477nt9U9jUW5ADmGRlAMPx6fCj89Mm3sfCGR7Fm+4Dj9/Lya11GxlryueDelVh825NSD0neEM+is68mLVnfHAaFhwkL/JKSZl/jDq8aGZnfv/QuDrr+73j+nR7XPzsqlL6zadzsffCakelsTvjmBTrvlytw0u1PmWZ2+g3dljsDbhtgxWvv7sZB1/8dv3xmg9T3m0tLtZMymMGYySlM5soV1Ko9QPybfq1NEvfSoZxdl0lDlRMP5jy8Hxt7hnDodx/FDx5b6/pnRTK5Ak66/Sl8cdmLFb1OtcjmC1zKn9TMAhmfMjJZY0amPjLasrgKZNLpNO677z4sXrwYe+21F+6++26cddZZWLduHR599FF89rOf9XVx3/nOd/DZz34W8+fPRzwex8KFC3HJJZfgjDPOsPyZpUuXoq2tjf+bMWOGr2titDcVb+5mN/V0rsB9EVaBjJfdjMgTq3egbySLlzbucvxes4Zgiaj16IDHV+3AO91D2NQ77Pjajg3xJEYUsF1FMhbhWSPZMmrjDk+TpPx7IDy9dicGRnN4YX2v659lO8wG4cad9CgL9YjSkg9eoEJBxeOrd2DtjkFs7Cl/r40ZmUm8kWNtpaUX1vdiYDSHx1ftkPr+rEn36dqWX5s3xAOqJ3X5Nf2aZ2RiHjMyFtJSU9K74fnlTbuxaziLJ9fInQ9WbOwZwtodg/jH6p11mZEQr3X/MzLjxCNzwQUXYOrUqTj33HPR2dmJv/71r9iwYQOuu+46zJ49uyqL++1vf4t7770X9913H1auXIlf/vKX+N73vodf/vKXlj9zxRVXoK+vj//bvHlzVdam7U7LL2K2k1UUrSKh/OdLaXqP0hI7gWVuxOmctbSUMdzU8gWtMZLMazt5ZLTfY31h9Jt0QZYNSIw7vGqMKGBr8NKzRZO+tBt3QihJd5MSZyMtOpsTrhoGWiGWV5vtqo3yaL1kZNjDXnYdpiMK6kBaaiq9h/FohFeEVasE26/p12J2y4v528rsy45FJl/gUqDb16w0KBUl03qszBPvhZ3N1ZWWwuaRke4j8/TTT+Oaa67BF77wBXR2dlZzTZzLL7+cZ2UA4IADDsDGjRuxdOlSnH322aY/k0wmkUwmq762iTbSEH8AJGNcTrD6ea8ZGfbwl+mDYXYjt5KWRMOhzGvzzr4Ws5aYtGS3CzSWXgPyAYmVtORnapQFC16yPGkTT4DY0TSTL6AhIteTxczsW0nmSSzd7DY5D43y6MQ6ycgMlR5YsuuwC2RqKS2xLERxPTH0jWR5abbf+CYt8U2Rgokt7B7oJiNj7pFpFLJSw9k8Wi36Utm9ZqWBjHg+dQ+mMaWt8v4sfiIGgW2Nxc1Fv0/SkvF+OWYDmddee62a6zBleHgYEYP3IhqNouAwSTkItLSq807W7ue9mn15RkbiRmxm9rWSlsTUtlRGxskjI9FHxqzCS2ts587sW40+Mux3eMnyaIFWeek7ULxhyDaX06SlhC9eIFFft8vItBoCmdpnZIrr6hlKQ1VVXT8SMzRptb6kJVFSakpE0TeSrXtpSZ+RcZ+h43PRYvpzPhmLIBpRim33M3nTthVOr1npeyn+HbU+x80QN0XsXlktaWnMBjK14CMf+QhuvPFGzJw5E/vvvz9efvll3HbbbTj33HNrvTTB6Oa8k7X7eS9D+Ipm4uIJLHPj08y+5Q3xjNKSeDOQCZIc+8hI9KsxG7DZIKSa8wXV8vWNO7xqmH25tOQhODLbgcajCp9VU7w5yd20u02qlpwCPTvEm6B5QK5/X+zk1CBh52g2r6J/JIe2JvvjZ5uRqUX5dbY8kKl2CbZf0lJGOJbMp9E7lEGhoFpmn0XSFtKSoihoikcxkM65lvvYNVbpwEkxI1PrrKMZ4vBZdk36Z/YNd9VSXQcyP/rRj3DVVVfhggsuwI4dOzBt2jR85StfwdVXX13rpekCEeOuUNvJWt9gK3kojGTzPICQufGZZWSspSXtxi5zY2DrcJKW7LqJmmdkxKyF9WA04w6vGg3xeEbGk0emfAeqKAoS0QjSuYKrG4be7Kt5j+wCPTv0GRmzgNzc7Ns7lPb8O/1ADLB3DqYrCmSKPiHnrI6fcLNvXJSWqluC7VtDPMFv11HKyOQLKnaPZPnHdlhJS0AxmBtI51wfA3aNDWfzFb2X9Z6REbPP1c/IjOGqpaBpaWnB7bffjo0bN2JkZARvv/02vvvd7yKRcL5gqg27aNmuUMRNRmbXcMa1uU08eYdcmH3FG3ncQloSAyMZvd6paklmRIHZ8RIf/HaZEOMOL1kFaYkdPy/zm7gZ2bAD1cYUyK1zJJPn73WnIC0VX8PbTcc5I1MKyEt6/ITSOV9Qi+dtrRC9Wz0SD5ycibTEMiDFrFiwu09j+TVQfanL7+nXRYNyxLZ60wyrqiXAu2+JXWOqWtl1323wyNQb4rFr9T2QKb42iwHDlpFxFcjkcjlcf/31ePfdd6u1ntAg6pTGBmF2XX0ZE5oSiJTkhV6XDwVxJy2TGjfroyFWzojoMjIupKVKpl+beYoiEYWv0S67Yikt+Wn25R4ZD9KSRQOwRClQk71hsBtrMhZBczKmez2vN2/R7LvTpvqOncfxaAQTSg+uWqbexXNUJqOZMc3IaNdm0D6ZYRNpqanK0pLfDfHYde3WN2UV2APae+JWWhID+UqOn/g31Ke0xI5dFaSl0jFkKkLYPDKuAplYLIb/+Z//QS5HbaQBsYpDfxH3S5h9oxGFZ3XcDF0TXx+Qu3D5jVxi+rXOIyPx2jmHzr4y06/7LQI/maZ41g3x6s3sW25uBORvGGLFkqIoiEYUnmHwKqPJmn3F87geDL9igC2zDrM+StGIwt+DoEuwtT4y5dJStaqoxOClkkGxWaEhHgDB8Ct3D+OBfcwmI+NRWgIqC0rFc6keulcb0Y6dXlryoxMxu4e0NhZfd0xnZADggx/8IJ588slqrCV0TLTwuchIS8Wf92b4HdAFMhIZmZz+5gOI068rk5a0jIz5qeROWtIHfjJBiXGHx7wjXmQg69/hQx+ZmLm0JJ+RYUZfTVat1A9klJaMN0Sz87geeskMuZaWWEZGH2zXogQ7l9d8UalEubRUtfJrYSNRyUNKG1FQPJasBFvmfQDspSWvE7DF87+SQCY0Zl8hI5MrqL5s2thrsLLusHlkXJt9lyxZgu985zv497//jUMPPRSpVEr39Y9+9KO+La7eYQ2hjIGITPk14P2hIO6kZS5c++nXNtKSlGxl75FxIy21GgI/Xkptc1EZd3h+Vy2pqlpZHxkLc2OCZ2Tk1skeFKwRFlBMMQ+kc55vZOJ5NJotYCiTR3Opt0mhoPLREWIgo2VkanejF89LM0nMiJnZFygGD7uGs4FKS6JRudFEWqq08sYK36Qlfi8prneiyxJsq4Z4gPcxBfpAxtvxG0rnXGf6gkY8dqlEFBGl6FcbGM3qziUvsPsUk5Yq6TVUC1wHMhdccAEA4Lbbbiv7mqIoyOfDFclVwsQW814w/SMuMzIuHwriTloq2LCZfm3cnY14LL926uxrX7VkkZFhGQebv3Eko9/hsQvar6GRmXyBd7/1JC2xqioLs69bj4wuIyMR6NlhNAr2DKZ5IDOUyYEpEK0m0pLsDrwaiOelzDoyfESBxXyfKk6dNsKur6jgAQO8ZyNkEa+/yqQlQ0bGpg2FGXYZGdYFXaYRp9lrAt7L6Y33YDcl5UEh+gEVRUFzMob+0Rz6R3OY3Frha5fuITwj46M0HwSupaVCoWD5bzwFMYCWkTF2RZUx+4o/71aP1Wdk5D0yZiMKyqUld+XXOYfOvnLSklVGxtm4q5lpmbTkb0ZGvEn61UcGAJIx1oHYnbQkZmQqzT4ZAxlxF8q+Fo8qugF/E+tAWtKbfSuXlrzM9/EKb4ZXehjxtcRZAFl9j4zZoFhZjGbfTpfyeDpnnZHxLC3pzL7ejh+7B08uSWW5goq+EX+MtH6RNrSa8NPwa5SWwpaRqaj8enR01K91hBLeotuQkRlIF08sp+6ULKNTSUbG6canqqpQtVHeEM84ldpvj0zcY9USINel1yjdiKMN/DDBiV6bSsqvraQl9xkZMZBhfqDKpaXi78gIX9PeE/GB21njMQWixwSQG/FhVrUHCA/OAD0yQ3w8gf58YKXYQUhLlTykjMeSBbYyEh8gesb8LL/W/p4hj8ePZfamtjfyh3m9yUtGWY5tlPt9KMHWzL7jJCOTz+dxww03YPr06WhubsY777wDALjqqqvw//7f//N9gfXMJIvdqWxGZqKHoWvi6wPFB2HeJlWcL6hcGklG9d1lASBrk5GRucE7T7+WmbVkLsXJZByMIwDYHKOCWlm/DOPrA5U1xEtamH3lPTLVM/s2lo6ZPiNj/p7UumrJeE7KjPiwkpaYlFGt4MEMrauv/rhWW1rK+SQtWWZkfOkjw3rpuCy/Fs4Jr9ISC+InNScsizhqjXFT1OprRkYvLY35jMyNN96IZcuW4ZZbbtE1pluwYAF+8Ytf+Lq4eqfTYvCjrNmXe2xcPhSMg8LsLnzxhIzHTDIytmZf+xtKQQiSrD0yStk6RPIFlWeVysqvJSZgG3d4Ysraj14y4mtk86pt0Gi7vqpkZCrrmcPO09kTi4b9HtOMjDGQqe1N3vigGsrkHR9eVtISCx6qVSlkhtbVV38+VLuCSrz+8gX357H2OvpS9klCYCuTAbXvI+NN6vOjaomb6VNJfl+vv4xMaVMUZ9KSf03x2Gszed/Pqs8gcB3I/OpXv8LPfvYznHHGGYgKO/yDDjoIq1at8nVx9Y5mdNNO+OIcJDmzL696qkBaAux3IaJ0lNCZfSWmXzs9IISbYdTj9OtB4W+xlpZkPDLFczERjfDulH74ZIyv4fY1NbOvf31kGJUOyGQB8ZyJTbrfIX6tJal/Tya6fHD5DZNmWpIxHgw6PXCspKValF+zzUEqaQxkvDWDk8Uo7XqtXMqUrjctI1MMbEezBakgwqpBJOB9TINo7Pf6XvLrqyXBg7NaGtrNsJKWfMnIsIZ4jeOgIR4AbNmyBXvvvXfZ5wuFArLZ+jJHVRt2EYu7wnSuwLMcjtJSixbIuHkoGE9cO59MumTAVhS9/GM1/VrfEM/+piDu6iwzMhF7jwx7YCZjEZ0ZGZA0+/JdSvFnFUXh2Rk/dF5jkOA6kLEoN024CGRy+QJ2DRePU6cgLSUrMPuK5dVzShkZM7Ov8Rxmvz+dK/CfDxKtmVxUlw2wI2dZteRNyqgElv1pNEhL1Z61ZNxIeJWXjM0FU8mYqTRphdnsMQY7JpWUX3sNBFnBRjEjU6fSkqXZt/Lzl90rWSCTqyBrVwtcBzL77bcf/vnPf5Z9/ve//z0WLlzoy6LCgtmukD2YFUXT4K1gXTEz+YIrw5bxxLW7EYs3HtG0aTX9Wld+7XCDF4MgS49MzL5qyU6Gk8k4mEk3MpkcWcoyMi53KmmD9MVwU7XUW7rJRpTiaAtGo0TDQCuKwxKL/z+7kwUy5mZfkaZEjD90a2H4ZTvuVDLGHzhO6zAzuwPVDx7M4OMJaigtAd4rl7hHRggK3Ujkdn1kUlxakr8Xqqrqi7TEsuoTW5KeG5VWG+O9zl9pSe+RAcLV3dd1H5mrr74aZ599NrZs2YJCoYA//vGPWL16NX71q1/hoYceqsYa6xZFUTAxlcB7faPoGcpgRkcTP6makzHHHgQN8ShakjEMpHPoHkzrTiI73EhL7GRMGnajltJSVnvt0az9ZGV9RsaiasmhjwzLLhlLrwEImRUJs29MDGSiALI+dbysUFpyGFEgc7NgAUZHKqF7LyoJ2MTy6ukTGku/x9nsCxTlpU29w+geTHN/TVCIHhNZ47F1Q7zgy6+ZtNRkaGDGJmFXLyNjCGQK3q6NrEkrh85UEpt7R6QyGGkbs68Xw3M2r0JMHHg3+2p9mti5v9Pl6JhqUy4tFZ8XRs9kJa8tVtpmcoWKG+0FheuMzGmnnYa//OUveOyxx5BKpXD11Vfjrbfewl/+8heceOKJ1VhjXWMswdZ6osgFJaK8JAu70NgO005a4jdxg2xjKS0Zbup2O0QxOLGK2ZykJbsKL5mqJeP0a93P+WL2rVBasuib4aazr5k/BhCqljz8nWLGZZJJSbVVbx8ANU29s916UyLKM5pOJdg5k1lLgHcpoxKYtGRVfu22GZwM+YL+YQ94r+gzy27JBpT5gtYKwq5qyU1Wynjuey6/HmJVgcmaV+ZZUa2MjKqq/Ji3NMS4xzBMYwpcZ2QA4AMf+AAeffRRv9cSSrSbKQtk5Iy+4s+v7x6SvmiKZuLiiTu5pQFbdo/YVheZpYIBa2nJuBsazuR4t1cjYldfUbbS/R4naSlt3tUXkJSWTMyDLNvhdXeme/2yjIy7naxTQzyZjAw7t0R/jPiaXrxA4nnKqjT6RrLI5ApIxCKW3ZaB2pZgix4ZtgnY6VCCbSUtpao8FsAMx/LrUv8jq+vJC2bXXsXSkkmTRKfNmPhgtB1R4OL9MF6fXq75bL6A3SUP2sTmJD/3609a0rea8MvsK2a1GmJRJGMRjGYLoTL8us7I7Lnnnujp6Sn7/O7du7Hnnnv6sqgwYZw9I9tDxvjzbvowMKNeV2vxZ+1Ssdrka/2NUaZqCbC/MTh19QX00pKZoVkqI2OxM8gXVL6z1Htk/Ovua5S13JYlGm8+DDdmX9b+vSwj44O01NIQQ3tjnEtWzI9j9754Ha3hByx7kkrEpDMyVtJSbcqv9b17GCywUVX/K0bEQIYFC8ZMrNvX0nlkJANbcRNg1xDPjdRnDOK9SHPsnI9GFLQ3xl2PXQgKY6uJVp/MvuL9NRmP8Pd2TAcyGzZsMB1FkE6nsWXLFl8WFSbYbnbngDEjIyctdbrsjDkgmInZBWd38WYtMzLOVUtOr+3U1RfQpKXi73IXyDhV5Yif10tLbAZRFaqWXKZbed+MMrOvG4+MhbRUQcAmlldHIgoPCtjvsjNh13JMgegxmWTRWduIlbTUVANpiV1PxvJrMbDxuwRblJHY78nk3EtLuXyB79x1HhnJjAw7TxPRiKl/UHw/ZKs4jee+l/eS3bs7UglEIgq/zkay+UAr2pxg9x6/+8iwY6goxftSMi6fLa4XpKWlBx98kP//I488gra2Nv5xPp/H8uXLMXv2bF8XFwZ4WlViJ2v+8+7S9P2CmZhJPjIN8Yw3cTMTbianZXvam+LYPZy1fW2nrr6APhOUzRfK1tFvE/g1xOylJV0gU2b2rVYfGfmLW1VVbT5KBdOvtTlLemkpWUHVkvE87WxOYsdAuqz6zi4j4zaQUVUVOwbS6GptcL1ehla+HLWcPi9SKKj8PLWqWqqWtNQ7lEGbkO0CtGyDsfw6GlHQEI/wfiydPq6DZVEiihZAe+kjI94r4iYZmU29w3h9Sx//fFtjHDM6mvjHds3wAC1Dxrw0xuDfDOO578Vj1MNLr4vXV1Miyt+L7oEMZnZ6cmCUsa1vtOyamdSSlL4eyqUlfzr7sqxWMlasbNUyMnL3z3U7BjCaLWBGR5N0wYrfSL9Dp59+OoBipc7ZZ5+t+1o8Hsfs2bNx6623+rq4MGDcFfa7DmRKGRmJVuuAWOUTl3L5sxuWsUW+FsgIzaSE15nYnCwFMjIZGetARszWmBkMKzH7jgrZJnGHJ1PtJIsxA+MmOBJTs5VMv2YPajbSgr8mC/QqMPuyvhHGjr1270uny3OWccfj63Dro2vw0zMPxUn7T3G9ZkD0mESFsl/rTIBYnWM1/boa0tLTa7vxhf/3PC5dPBcXL96Hf96q/Lq4nhhGsxnfS7BFaY03qPQgLYnnqllG5t9b+vDhHz2t+5m7zzkMx8+fDMB+PAGgr+QaTuflAhnDue9FWmL3bnYvV5RiVubdXSPoHkpjZmeT3Y9L8fqWPnzkjqdhTDRFFODvlx6DvSe3OL6Gndm3El+V8XVZoCmbkfn+Y2vxf69txdUf3g/nHj3H0xoqRVpaYhOuZ86ciR07duimXqfTaaxevRof/vCHq7nWusS4K3QrLbEdyzs7B6W+X3zAaDtK5/Lr8oxMuQmXlV7HIgqvVrG7MbCUvW1GJqrPyBhhazeWowKiR8Y+I2Pc4VXa8Vb/O4xVS/KvqZe+rDIyzq/HjIjtTcbOx94zT0ZTujHLYncez2TnbPeQq0aOq7cPACg+5L0yzKWlGL/2dg1nysqLGWIjOKuhkdWozli+arvuvwyr8mtAk338LsFmG4hENKKZ/D1IS2IvGnHzcsjMCThiz05MbWvg/1pK2eKn12nvtVUFHyMejfAAX7bZIjv32T3ISxDINwqCdMvHFLgM1q1YvW0Aqlq87tkxSkQjKKjAW1sHpF7DWLrOrt1cQa3oXmdsYeHWI8OOkTFjHCSuc2br16+vxjpCi3FX6FZa2ndqKwBgQ88wRrN5y90KQ3x9lp62KzlkVUnGrrlm0pKYtuelkBVmZBRFQTyqIJtXTccUWFX1FD/Hpjvbe2SMP+untGS8Mbp5TXaDiEaUskDSTdWSVVDB/s4RDzcxo6SnVZ4URw+wB4lZ+fU+k1sQUYrSyc7BNCa3yKXG2d+6epvcjdsMJs00JaJFT4NSHBDaO5wxXYcYPBulpQaJWV5eYX/jmu0Dul5MWvl1+XGtVgk2OwaxqMKvVS/SUkYw+oq7/4Z4FPd/+f267/3Ni5vw7T/8W/de202+ZrQ0xJEeTEv3RmHv3YSmBLoH0548LVy6TWkP4kk+txhgQdxxcyfhZ2ctAgCc98sX8dhbO6Q8LrrS9dK9PJUolkqravEe4bXnizHAdOuRYZufSQYPX5B4Ev+WL1+O5cuX88yMyP/+7//6srCwYNwVus3ITG5Jcj/Kuh2DWDC9zfb7xddPyUhLlhkZa2kpJXRvtQuSWHraas4SIxaJIJvPm9487QMZJ7OveUWQr31kyjr7uglk2I27fAfqZtaSVXDsFOjJvGZrWUYmg6FMnps6zc7jxkQUsztTeKd7CKu3DUgHMuxvXbWt33MqfFjI4EUjCjpSCXQPZtA9YBXICPPADAG3nwGvEfYAH80WsKl3mI+BEKUxI7yvje8ZGe0ekKhAWsqalF5bMW9KcYO2Sghk7JrhMVobYugeTEsbWNl715kqBjJOTTzN0OYsCRmZlLtqUud1lv/tbjwu+tL14mtEIgqakzEMjObQP5rD5FavazNISy4zMtxjVMNAxnXV0nXXXYcPfehDWL58Obq7u7Fr1y7dv/FGRyrBo+Le4YxtIzEzFEXBvK6iPiqzUxVfX0pasjT7mkhLQtpb5rVlqpbE32U2AdtON3faMVu2//dRWjKWd466eMjYDchz45GxarJYmbRUbvYFUHqIFG+ssZIB1Yx5U+TPWQb7W/tHc9jWP+p6zYDgMSk99J0Mv1mLLAKgBYK5gmopTXlh50BaVxK+els//3+r8mtA8834XUUljimpRFrSAiLnIGFuVzMUpXg+sWDAbjwBw20lDnvNCSnt2nArL5llZNyMXZDB7G9387fqSteFc6fVB8Ovcao298iYVCcb0ffgCZG0dNddd2HZsmU488wzq7Ge0BGNKOhoSqBnqLgrdCstAcD8KS14fn0v9xDYIWZkZHZwzmZf7YbGbqCNiajkazt7ZABtB2cqLeWssxZaGbWV2Vdbr+7nYv7ttNlrtDQUdz5uSrrtgjTZqqVsvqDruilSiTRizByKZl/xHLbKmsyb0oK/vb5Nt+N2QvxbV20bwNS2RtfrNnpMJrYksHq79QPH7uErvi+juQKao673daYYg7tV2wZw8oKpAMTy6+CkpZxwDJi05CUjk3aRkWlKxDCzowkbe4axetsAjtw7aRvYM9xW4rDrsa0xzjeUdk08zRDnLDFYgNzt0KNIlrRJ5tlNQzt2H4pHFd391o8SbGPmmHtkJO4rVnPggsb1lZvJZHDkkUdWYy2hRRwyJlYVyWKWhrWi38Tsa1t+nTO/kbNAJl9QUShlVpj/QFZakvHIFL9uXfJpm5GRlZYMGRnt53ww+5Zuvsxo684jY11uKjs0clC4QTVbSEuVjSjQS0s9QkbGTh6d7yUjI7z/Xn0yoo8LECUA8wdO1mLyNaAP7v2Ul1YJGRhA+1tVVdVJY0aqJS1lTKQlb+XX5tldK1imeZUgswFOgYy7BzMLEBrjUS2j5bIKzawq0Dh6plJGTdowuJlebeUv8qMpnmXVksQ5ovXgSTrOFqwmrgOZ8847D/fdd1811hJaOoUGYXaNxKzQ0vT9Dt+pb1QmM72XG/TKMjJCNVFpd8bT3omo0L7dpmpJorMvoPWSMbswzHYqDFEiMquOcaxa8sUjU1xze2NC9zvlftba3JiQlJbY+90Yj5Y9QPyRllhGhgXjGfSPOGcVWfDNzKwyiDs8r4GMscU/W/dOx4xM+a1OURQezPgZyLC/7ZCZ7bqPM/kCP1Zmxkz2IPa//LpcWsp6qVpykZEBtGCXBXZ2fjiG27b74mt6CQQLBZUHwUxOAsr7g1WKmV+O/a39LqSlpOHY+TGmQAuy3GdktBlVtcvGAB6kpdHRUfzsZz/DY489hgMPPBDxuP6Bfdttt/m2uLAgtmz3Ii2xQGZ7fxq7hjKYkLI+KcSyWZnKIuvya31/l2RMb0SUuSnkeUM8J4+MjbRko5uLN7x0rlB2A7Ta4WkziPyTlrSMjBdpybvZ164xnTHQc2OeNb5uR+mcyxdUbN41DMA+qzizo4k3DdvYM4Q9JzU7/k7xb3UjSYkMG6Qlp66yTr6OhngU6VzB18olJhGfvnA6Vm7ajQ09QxjJ5HXSmlkfmUaJDKsXzKQlL9OvxTJuGViwu9qYkbEJhNxkKXSvGRd8fVn549c/muUNEzvFjIzP88S0TZc3s69V6bof0pJxM8krKiUyMlyWq6HRF/AQyLz22ms4+OCDAQCvv/667mt+DjoLE+xN3LJ7hL/5bgKZ5mQMe0xoxLu7RrBq2wCO2Mu6r2e/LpBxln+ylhkZYXRA6Xv05dcyGRk5acluAvaIzS5NzGSks2aBjFX5tZ99ZIq/g3WsdNcQz9zDA7jPyNg1DCz+rvLjY0WhoJVXs9dNxCJoa4yjbySL9d1Dlr+TEY0omNvVgtfe7cOqbQNSgYz4t67bMWDa6dkJsfwa0Eo+rT0y5uMJGA3xCPpG/MvI5Asqf3AfvfdEdKQS6B3KYO2OAW6oTsQiplIX88j43aDPrCGel6GRzPwpm5FhG7Q12wdRKKi21zrDTZYC0GdlZTLURth509oQMwzCLL5Xu4ezns7T8nWaSUtuzL7mx86P7r7GzHHSRTWk1oMnZBmZJ554ohrrCDVsV8geAIpS9Jm4Yf6UVry7awSrt/XbBjJiBYuUtGQxaykaUbg5jgVfzEiZSsS0gXoSHhkns6+dtGTlcwGKO0jWJ2Q0l0cb9BkCK6Owv2bfkrTEMjKuzL7W0hKvWsoXUCiolvqynV/FKdCzYiiT4x1GxazLxOaEIZCxl0fnCYHMKQdMdfy9YkYim1exvnsIc0s+ChlUVS2vWvIhI2NcWyVs7BkqBZURzOpMYV5XC559pwertg3gkJnR0trN3yeZDKsXMtwnpCDO+8h4kZbsg0IjszubkIxFMJLNY1PvsLDzt/55t1U42j1A23y5CQS7uaykzyiwQar5goreoUxFYzUA88wz/1vTEuXXFtldt4Gf+dospCWZjMxg7UuvAQ8eGaIctitkD4DmZMy18YmbJx0ql/SdfZ1vfFmLhnhAueQjGhFTEtKSbEaGmX2N0pKqqrbdPhVFsfWBOElLfnhk2AOOOfK9NMSzq1oC7FO4dhkZFugB7v5W9prxqKIzvHYazmOnrKIbbxegBdVs9+ZWXhI9Jk2l7IWTBOBkUPW7KR7LxuwzuQXRiKIrU+fXl0XAyTv7+uyRyYkZGeaR8TKiwEX5NVA0WO/TVczUrdo24NIj40VaKt0PXUhLvIeMYfxHpNSjCHA/isN0nbnyDVurl4xMzCoj47/ZV8YjE1pp6fjjj7eVkB5//PGKFhRG2K5wc6+zt8CKedwY5xTIiOXXxRMvV1CRyRVMgxWrPjJAMfLO5Ar8Zi+WX8v1kSk1xHMqvzZpvsfWxjIDRhMboyEexXAmb/qgsdrh+dlHhr0Gk5bc+G5kqpYAe1nIrgqOBXrF4+M+kGlpiOuuZRaQa+ex/e1hvsED4QTzyBy4RzseX7WjGAAdNE163eK5yIIBMSNj5hOymnzN0GRIf4IHdv2y63m+SSBj1YGVZ1ir1Nk3oZOWPPSR4WZfucwfAMzrasXrW/qxetuAZNWSy4yMcA/wIi2ZGX0ZnalEWU8gr5gFcWIQ4uRxsypd98fsq/fvJKIuPDJ1YvZ1nZE5+OCDcdBBB/F/++23HzKZDFauXIkDDjigGmuse1g0qnVDdd8wmd3w1mwb4OXQRlRVNZ21BFgbBK3MvoBWbcQDmbQ7aYk9JBw9MjHztuj6Jk9WO2brB00QIwo0sy/LyPjTR0bc1dr5ZJzM415KzY1zlhgsKLDr6ivCHtYbe4cdDap5YQr1AaXu1W4rl4ZKD6hEVPOYsGsvky+Yptedsgjs5u1XpRD7m9j1LG5Q2DEy6yEDaGML/C+/NpOWvJdfJyQzMoC+cok/MG08Nm6yFICF2deDR6YzVZ5RMA4ErgSzTRe7/vKCf8gKq8IBf/rI6F97XGRkvv/975t+/tprr8XgoNzgw7GGcViWl0Bm9sQUEtEIhjJ5bNk9wodJioxmC/xh0NIQK/aFiEaQyRcwnMmj3WRIq5XZFyhviifuGFMuZi05VS1pfWT0ARq7uBXFuhKiIeFBWvJJLsjlteM9gXtkXGRkBP3eCCv9TecKtv6MgbRDIOOhfNgqODLejJzO40ktSUxsLo4IWLt9EAfNaLf8XjFYO2hGMZBxKy2NCO0BGA3xKFqSMQykc+geTPPMGSNn00eG/Tzgo7S0XZ+RYR6g7sE03t01Uly/1fTnkEhLsmZfQN8BmslMchkZyT4ygjTNZ8958ciYPIhZp1+rrtFuMDM6szEb+UJxg9pk46s0q3oC3PlsnF7bODRSJiNjNnCzFvjmkfnCF74w7uYsMcofAO6lpXg0gr0ma3qyGWwnLZqJGx3SqZrZt3wXZZR89OXXzmnarGzVkoW0JBp9rdKqPCgxyVpY7fAqmUGkf33td1bSEM8q2yRTueTUnM5L9olXviX1r1kekDufx7KjCsS/8YDp7QCAd3eNSE85BoSuuAZpxs7wK8oqZngJBK0YyeSxoafoL2LHJZWM8Wnhr2zaDcDO7MsyCvUpLdlld61gGZkNPUO8lb2ffWTYRks0+w578ciYSEvi/LFKMcuoKIrCOxA7/b1WRRHc7DviR0bGXdWS2IOnlpOvAR8DmWeffRYNDZU5u8NKQzyqa4ntJSMDiHq6uXmSpc5FM7FTOtUuI2OUlobSLmctCVN17UiUpCXjPBs7oy/DzsPgKC1VaPYVfyfb+fglLQFy3X37HaSlZNw60LPCr4wMUPRAAM7ZFbZzjijFTE5Xa/F3uZGXjF19GXaGXydpyU8Zcu2OAahqsSePOAmYBTUrNxVn0ZlNvhY/73/5dbm05GVEQcYhKDRjUksSE5riKKjAm+8V72sys5aGMnmpRouid0SmiacRO2nJz+6+PKNiFYg4ZKCsNkV+lF+nDfdhXrUk0eOK9+AJW/n1xz/+cd3Hqqpi69atWLFiBa666irfFhY2JjYnynpzuMXJ8Gtm/HTysqRtdlFW0lKTMKIgky9Y9lFwW7WUMUhLMlUMlVQtZfOq60m4ZutLxiKeHnhOGRmZwZFOnaK9mFWtXtNLIKNV29lXLrHzkN3I501pxfb+nVi9bQCHzpogtW5WjWJMwWsZmfIHjrO0JHfTloEbfbtadBnG+VNa8Oib27Ghp2iitqpa0hq6+R3ImEhLXjwyrPzahbSkKMXKrefe6eUyqZnUyhDPycHRHNqa7LOCWsdbb9ISyyhMsjD7Av7MW7LvAzPiKKU5m32dDcNOa2vkGZnSvd/hmmBBYEtDrCxACxrXGZm2tjbdv46ODhx33HH461//imuuuaYaawwFYh29F2kJkAlkygMlJy+LnEemXFoSHxZW8pLbzr6W0pJEIGNmPLMKFMSPK9lpi+vT+o2Yj0sw/3n7QE2mu6+VMZfhpWeO1WsaKw/cSEurtjplZPTnobF9vQxWVT/amAIP0pKPGRlu9J3aovs8q+5iWElLvPy6StKS2BDPy/Rr3hDPZXM4499vd70nYhF+XfS7GKbotbNvYBkZwxgAhqyU5tRHJldQPfu8jPdhLSNjf00wyW1Sjf0xgIeMzN13312NdYQe8SHgNSOzb+mCX989hHQuXxblmgUysh4Z84yMoWpJeFAkYhHEIgpyBRUjmXyZiRKQz8hYSktCxsMKu7lJZr0ZjB+PZvOWVSJOiIGSeAOR7aJr1xAPkPXIOFUtyVcYGF/TWF5tzMg4lV8DRTOrohRnruwcSPNKDyMZnpEprtc4UFAGa4+MNvDSiGxDPD8CGRaUsSCNMc/wcaOFqbOJG9sLFWUSjWgl6JVNv7brSWWH8XjYSUtAMYBOC3Pr7BC9IzK+PpHhTI5/r7EhHqD1lqnU7FsotccAyoM42Sotq3tJKhHjjU0HRrOWpf0yr500VC05mX25v6gOAhnPHpmXXnoJ99xzD+655x68/PLLfq4plEz0ISPT1ZpEW2Mc+YKKdTvKK8DMjJ9OYwpkGuJxaSmtT907zX7hGRkHj0xF0pJNxsFq4GQkovBdhRvvSNnrC+lc8XfIPvTEtLcZWkbGpmrJYZq6Fz+QlYG4KRE1lIc6n8eNiShmlcysdn4X9jey81A0CctmuIznJ2OSMLTViN30a0A0+1YuLa3mPWT0GYjZnU26688YiPHPCwG3n/KSX9OvrbqEO2EM5Jw2Aa0uDL9iWbNME08RJislYxHT94QZgHsGM5YtMWQQM65eRwxY3SsjEc0w7LW7r1G2SkoOjawXoy/gIZDZsWMHPvjBD+Kwww7DRRddhIsuugiHHnooTjjhBOzcubMaawwForQks5M1g+nJgPlDoSJpyayPjNBTQmz/zi5qpwZT0rOWrKQli3SrCDezupCWij9XeTWKuNuLRyN8hyz70LPStfka2XA2O7OvwyRqLxkFqyyPoig8II9FFMedM2OehExkzMjsPbkZ0YiCvpEstvfL7XjFho0iWkbGWlqyqrRJ+pSR6R5Mo3swA0UB5nbp507FohHsM1n7nNWuORmLgFkc/JSXstyUH9E2FZ6kJfdVSwDKxlA4Z2RcdLwVrrEmh42XETGjYOYtYZ19cwVVSuayXKNwbhnHqcj+rXYDaN2OdbB8bcOspTGdkfn617+OgYEBvPHGG+jt7UVvby9ef/119Pf346KLLvJ9gVu2bMEXvvAFdHZ2orGxEQcccABWrFjh+++plEk+SEuAvhOoETNvQyXSEtud5fJqyfuhf02nHY5sZ9+4g7Rk75Gxq1qy9tg0+vCAMgZKbkt1nTxACQePTDZf4DtzZ7Ovl6ql8tdkN6WWhpi0cdA45dgMzSNTPBYN8SjmTEwBkPfJjFhIS3ZVS9LSUoVmX/a3z+xoMu0HImYlrPqFKIrCjcB+zlvK8anVCj8OnqQldi+JuZO8xBJ0oLxyx0iLZG+UfEHlGbeGuHtpyWrOkrhOtimtZAo2C7ZiEaUsMygdyBi673p5DcvXNtznWGdfp4wM9xeFMSPz8MMP48c//jH23Xdf/rn99tsPd955J/72t7/5urhdu3bhqKOOQjwex9/+9je8+eabuPXWWzFhglyVQ5D4YfYF7A2//SYPIKfeE3ZNrFhwk8kXeOl18TXlpCX306/NG+LZVTHYNSyzK9/2o9GZJg1pD1/x9zqh/X3eqpYGhRuTZfm1B7Nvv42BmHm93JzDMnPCjBkZQL4HDUPzcJmblM36fchMvwYqz8iIFUtmiD4RNuXajGqUYPsmLXkov2aIgZyTtCSfpRAyHYK0JBsE9vA5S9YPYm4kH/BeuWS3oWHXmVPGx1hZpH+NygKZtEUfGeeMjHUzwaBxnTooFAqIx8tvcvF4HAUPUb4dN998M2bMmKEzGM+ZM8fX3+EXeo9MtTIy1mbfIceMTHmwIUpL7CGRjGkSipO0lM+7q1oyXhh26VKG1hDPuo+M2Q7Pj6Z4xoyR2+DIKeOUcPDIsPe7IR6xeRC7D9jsDMRiRkYW9pBas33A0qRqrFoCgPldLfg/bHURyGh9jkTYJmIwncNoNq873vJDIysLHFZbGH0Zom/GqrMvAE+VN06IPiHeZdvLrCUPnX0ZrAQd8E9a0ks2XjIyztLIxOYk3ukeqsjwayeByza0kwmGvEhL+YLK78tlVUsO14R2/GqfkXH9xP3gBz+Iiy++GPfffz+mTSsOfNuyZQsuvfRSnHDCCb4u7sEHH8RJJ52ET33qU3jyyScxffp0XHDBBTj//PN9/T1+0KmTlrxnZJievK1/FH3DWV0fBTOTppP8w24+ZpVBcUFaEkuvGU0Ory3dRybqv7Skqqrtxe1HUzxtxIDezS8tLVlUKjCcyq/7Hbr6Fl/buqrLCrtuwZ08IyN/a5jdmUIyFsFotoBNvcNcMhJhpbtmGRnZyiVxOrtIa0OMj+roHkxjjwmajJGTlJbs0uhrtg/gra328teKjcVmd0ajL2O+hLQEaEHO8rd28JEGAPC+OZ2Y0uat4WiOZ1I0acnTiAKPZl/AbUamlKUYcchSCOuJRBRHj8zKTbv4QFRAe8/spBFm+K2kBNtuw+Xe7GsdDHnJyIibKOOsJaeMTE+YMzJ33HEHPvrRj2L27NmYMWMGAGDz5s1YsGAB7rnnHl8X98477+AnP/kJLrvsMvznf/4nXnzxRVx00UVIJBI4++yzTX8mnU4jndZOuv5++T4VlSCWnZqVKsvS0hDH9PZGbNk9gre29eP9e3byr5mVzTpKS3YeGcGEKzbDk31tprM7dvY1VEcxnMyw4teMDxp9JYCJtOTDvCVjoOR29y47osAqkHEqvRbXJrumQkHljcnMTOmTW4oPy/ZG+V1WNKJgn65mvL6lH2u2D5gGMuz9EwMZ1l9k3Y5BFAoq71Zthdk5ChS9JZ3NCWztG0XPYEYXyGRkpSWLQHA4k8Npd/xLuorI2EOGMbnU4XbXcBbNNu8nM27++B9v6z6/39RW/PXiD0itwYhZHxlv0pL9sbSDvdeJqHV2keG22y178LL7VTavljXxXN89hI//+BnT15ls4ZEBtP4ylUzAtss8ywYhaZtNETtndo+4X6NucK9h1lI2r9pel/Vk9nUdyMyYMQMrV67EY489hlWrVgEA9t13XyxevNj3xRUKBSxatAj//d//DQBYuHAhXn/9ddx1112WgczSpUtx3XXX+b4WJ1ob4vjWyfOgqtCNK/DC/Ckt2LJ7BKu3DegDmbS12ddKWrIrv2aZlEy+wEtbxYoKp1RtXrpqyX76tVV5MmCdkREDG7OL25+qJYPZ14WxNl9Qtb4nFueDU9WS05wlQMsWyfaRGcrkuKnb7HVPPXAqXt28G59/30yp12NMKE0Ht7ohZ3hmUHuvprY38K/tHsnyKhErrKQloLh52No3WuY1cJSWHALBnsEMRrJ5RBTgiL06Tb+HcdAe7djTJIgDisHWNR/ZHys29vLp32ZccPxeaPhXlBvpCwXg2Xd68ObWfgylc556ImUEaYn51XJ5D1VLhhJ6N+w1KYWvHb+343sMeC9JNjbxbGvU1vl2qZ1FS0MMB+6hHf/2pgROPXCa5e9gm1Kn7JDtOm02bLzU3MHYbNeTalZnMXB/e8eQ+7WVXpdltQC9oTiTL6AhUv47xR489WD29fTEVRQFJ554Ik488US/16Nj6tSp2G+//XSf23ffffGHP/zB8meuuOIKXHbZZfzj/v5+njmqNhcct7cvrzN/aguWr9pRlnLXMjJmZt/yG7GqqrYlk6K0ZNZsrMkhSMpJemRiUfNdoFPDOMBaImIfRyOK6d/mh9nX2KdG6+7rHBzJGHVlMzJ25fxuMzLsNa3Kqyc2J3HbZw6Wei0362CBlvgQjEcjaG+KY/dwFt2DaYlAxrz8GrDe2TpLS/bBKfudE5oSuPe899uuz4nTF07H6Qun237PcfMm47h5k3WfW/Tdx9A9mMaa7QNYONN9oYN4DNhxkJlsbMTJOG2Hoij45knzpL7Xa0my2MRzOJPTZcZZ9mDRrAm4+4uHS6+7UiMtYN3vqvj6ctO+7bK7LNtlZ7Z3el1xMylKh+msefNPsQdPpRt3P5A+Ix9//HHst99+plJNX18f9t9/f/zzn//0dXFHHXUUVq9erfvcmjVrMGvWLMufSSaTaG1t1f0LG1o5q/5Ym5XNaj6W8gtBlHLMdlE6acmkR4dWBWDfEM+xs6+VtCQxooDt4I2Bmjb11ttOWwajx8XNa7LMQLHlur1HxjkjIxHISHpkeHDUGPc0l8VxHRbHJmPh1eLzbCTKW0csPDKA9S7e6eHrVPXFrisvHVP9wq4AQAbT6dcVNMSz68TtB7IN8cyqHq2yyEwaciuDaFVFfkyXtpeW7BpD2t0rmf9oQ8+Q67J9s9eNRxXezyidN389px48QSN9Rt5+++04//zzTQODtrY2fOUrX8Ftt93m6+IuvfRSPPfcc/jv//5vrFu3Dvfddx9+9rOf4cILL/T199Qb83kVyCA/uVVVNX2w2VUWiTcru4Z4orQkpmedpKUcn7Uk10emvCGevYdE/Jpxx+zkr+E9Xyox+xrKp91IS2bZMyOyVUt2r+G2j4xMcOSFBofsEnvoGANqrQeMs75v5ZEBrHfOTk3cnAIwu+ApKNyaoo1kfZKWnGQ6v5DOUuTsNl/693Nnyaxr1TPGei3yXYYt12mTeWZ/a76gWvqwVFUV+siUH/tJLUl0phJQ1eIEdldrM7kHK4oiVC6ZX89a6XXtZSXARSDz6quv4uSTT7b8+oc+9CG89NJLviyKcdhhh+FPf/oT7r//fixYsAA33HADbr/9dpxxxhm+/p56Y87EFOJRBYPpHK9cSOcK/IYkG8iIO31ZaanJRFqyivJ5RkZyRIExkLFLuTIspSWHbI4/fWTYzcO92VcbLWAdMDh6ZCSmqXuVlnwPZJykJYvdPHuwmM1JMmLnkbEyiDJZxeoc1cy+5u/BkE3wFBRu++0YEZsCWm0qZMg4yHR+0Sott+g70gLW90OWkemU8OiI+CEt2VVnphJRsH2gnb+MJWus7ndeg12rIItniy3Okx7eDK/2Rl/AhUdm+/btpv1j+AvFYlUZUfDhD38YH/7wh31/3XomHo1gr0nNWLVtAKu3DWBGRxOXKhRF23UAQGPcukSa3ayiEcU0axIX+siYlV+zxmNWc5xysp19HaUlu4yMedWSmbar/zk/+sgYG0XJB0cyAYOzR0am/NpdIMNLupPeK+vM12FvrjbrIwNozchkpCWr8mvAumSXm90dMjKZXMG0QsMueAoKseGgqqquU/k6aYn1dPLQyThj8R76jWwWxOweoBU/6O9ZrHzaaqip9VrkugzbrpMF8Sb3KkUpzkrqH81hYDSLrtbyEnuzyiIj86a04Jm3e1wHu8ZmeIxkPAqM5mwyMvXTQwZwkZGZPn06Xn/9dcuvv/baa5g6daoviyLKu6WyB2NzMqa72bIuoWY+lrRNM7zi57UAQ7thi3Oc/Klacpp+bW/2NX9AOv2sLx4Zq6olCblKqzCzDhicPDL9rsqvZaWlamdkLKQlnpHRv18TbeYkiRQKYq8j99KSdUZGW49ZQFkP0tI+k4sTxnuHMtjpoVV+zkxa8jAEMThpiW2g8vweY4ZZVtYqi8wa2rFyallYRtWpYZ0dTv2ynHw4bDMWUazv5fMl5p3Zr03/nrLA3yojU09dfQEXgcwpp5yCq666CqOjo2VfGxkZwTXXXDPuMifVhBl+WarQyi/BfSzZfJlZzG5gJACd8Y+1RG90IS1pHhmHqiWr6dcyfWQs5BynbI4v0lJOn7p2Exy5y8jYe2RkGuLJVFLJvqYXHM2+Frv5Tps5SSKjuTxPr7sx++acPDLCeszWbjUWIUgaE1HM7iyWdXuRlzJ+SUsBmX3Fc3PQRtIxCxCsmnhqc5XcSkvFtQym7c24dphJYPrfYS9fiQGbVTZOZt6Z6Wtb3IOTDhnt7jqTlqTPyP/6r/9Cb28v5s6di1tuuQV//vOf8ec//xk333wz5s2bh97eXlx55ZXVXOu4gkfYpY6iViZNduGqavlD227OEqAfUWA2kE+TluzLrz1Pv5bqI8M8MsaGePZBUNJPs2+ZR8YfaclJh5aqWnLZ+K9qZl+H4Yvs/SrzyNjMSRIRH0xmLf5bLR4GTtJSsW1/aaq5ybnCMpXGQZVBw2Y4eQlkxExKTJh75vbBXEn5tRuKlX7F32E3g8jYeRsQPTLaeZDLF7BrmHlkvJl9xb5QbnFqjOk0vVpmwze3qxmKUryO3Ay45PdgQ5DlnJGpL2lJ+m7W1dWFZ555Bv/xH/+BK664gl8EiqLgpJNOwp133omurq6qLXS8wcxb73QPIZ3LWz4YxZv6cCany6iweSpWN3Fx+vWwWr7zTPGMTGWdfeM+jCgoTrrVunU6/awf0lLaQlqS8d3IjBfgIwosgpBqdPaV6U3jBSePTKUZGV5uH4+Ydhrlu9q00SPjfI42xKOlOU3l74Nd75ogmTelBQ+/sc1T5VJOCEDEe0GuoLoy7gblkQGK1016MG1rsjWTlswqLXuHM1DVojQj05BPpCkRRTSiIF9QMTDqrSGh06bLOSNj32qiuM7ihPGNPcNYvW0AE/eWC9isgizmB7S6N9XTeALAZUO8WbNm4a9//St27dqFdevWQVVV7LPPPnU5jTrsTG1rQGtD0QT29o4hS+NntNTYbDRbHDPQKXyNzbeJW1wA4jBHtRT06M2+/nhkHM2+Eg3xit+fFwKZAKQl44gCFz1b5DIypZuFg9lXpvw6V1CRyxd480HrdTkHWF5wquiy8shMEgIZOyOr1rDR/HhalezK+Doa4hEMpu2lpVp6ZABg36nePBCAubQEoKyNvx1OzTX9prUhhu7BtK3h17jRAMxnz3WXJld3pBKOhQlGmBm3bySLgdGsp3lXTvcqJ3OzTL8toJjF39gzjFXbBnDU3hNdrs0gLUlnZOojkPF0Rk6YMAGHHXYYDj/8cApiqoSiKELHxn7bB6OVLpxxyMiI0hLzyJgNjazUI2MlLZndiIwkdR6GgvD/TmZf+wyBDMYeC176yNgFDAkHs6+bjExxvf5IXl5wGr5o5a9g7c1ZIG7FkENjOqtdrYwcYtcUz8wEXwuYB2Lt9kFbA6wZZtJS8fPyryOag4PJyDiXPZt7ZMqzyF6Nvsa1eG2KJ2v2dcrIJB0CGatGqnJrM2ZkrL13RamuGHTVw3gCwGMgQwSD2BvAroKFyUvG7r5Ou1GdtGRSEdIklDKa6ekVz1qS0H4VRdH8LsKDRvPXWDXEs/dsyGCcWuvO7OvsRUnamH1zwiBPGXlKfl3VMvvae5LY32h8CKaSMX7+2slLTtVDos9APFedRhTo1m4jLdU6IzOzowkN8QjSuQI29LibqSNKS+JxcGP4FYNtL9Ov3SJT9mwnLYm+Pp49cGn0LVuLx6Z4Tpuu1kb7oG1EYsMHeOsAbbU2u4Z4vaWePBFFm7FWayiQqWPERlh2koBWgm3MyNhr2qK0NGLSL4P9f0E1lz+0WUvupaWi56X4sVPK1GzGkVNXYC1D4GMfGVcN8Zy9KHYN8QbTzrOaAOtAz4r+apt9XY4oALQHjJ3h16l6iP092byqO1dlJjbbSYb1UH4NFK+xuR4Mv6qq8mxKsfW8YrmxsEP83mo3xAMkMzImBnKzSkvm56g0I+O1KZ5TUYNWfm0lLTm3qQC058Wa7cVp8nJrs69aMpOW2HXqRaqrFhTI1DFihM36GJhmZCykJafyazNpqdFEWgLM5SXpjIzJfBfxgee00zDbMTubfX2QliqYfi3jRbFriMdumg3xiKMnwY0fqHrSkv2xMRsayWAPGLuMjFP1UCoR4/NhxAeCnEfGOuith/JrBqtccmP4FTcPzD8V8zCmgAXbEQWOPiw/8C4tlc+e21mhn8OqIk4Wp8yz09+advDYMGZ3ppCMRTCSzWNT77Dc2ixe2y4jU2/+GIACmbpmbimQ2do3ind3FU9MswdjU9y8myU358XMAw1xRAFLX4pmymhE4Q8es+6+0p19I+U7QF0g47DTMNvtOxmFKzX7Fgrazr56Zl9rj4xM1RPDTdBWLbOvU7ZKy8iUv18yTfGcJJ5IROFTeMUHQuXSUn2UXwNihlbeA2E2b83LBOwgjb6Ac5YCsG+IZ2b29ernqFxasjfrOr2+jAQPFO/D+3Q1A5APdi37yLBssWlGhgIZwgWtDXFMb28EALz67u7S58zMvg7SkpVHRpx+bdGK3a4pnuysJXbzK6jaz4wKazMrpxUx65XiZBR204XXDDFLYgxkrAytIm7MvnYZGZnMiZn0Zoaqqlyy8r/82qFqiferMJGWmp3HFMhkRsxm9MiYfRtt1l4v5dcANPO/q4xMuSSUMMmQOhFk6TXgNiNj1kdGkJZKZt9JHh+8lUpL2pRup6olh/Jrh0AGAOZ1sUaqcsGulZGY35tMrgku1dWJ0RegQKbuYbsw9hA3rVpKWlQtOeyimCSUzhX46xtv2GbljAzpqiXhAmY3T6dZSSJmGQenXYobP4sZ+oyRUVqyf818QQsYZBviGc3Ubky5sk3xhjJ5MOm8emZfi6olO49Ms/PgyBGJzIixjFUsGbYLtpMSgUytq5YA7V6wsXe4zNhvhSgtscypF2nJqbGg38hMwDZ23gbMZfbKzb6VTcBm55VVMOwkXcnMpGO4NfxqmW1D1RILZCgjQ/gBu3kx7KSlsqolJ7Mv654pDNoz9umw6yUj29lX/LoxkDHr0mokaSLpuOkj46W1OPtdsYjCPQGy06/FtuoyIwqA8qyMzPRshmyAxV4zVuo95CfG4YtG2M7O1CMj0d13SCIzYtzZimXKdg9guwq3epKWJrUk0ZlKQFWLZdgyiD451qOHSc2upKWaZWRk+sho741ZE89Kzb6y07itGDVI1EYcpSVD9aQdbielW2V7tIyMtdmXMjKENPPLAhkzs695sOG0i2ID5PpGtMnaxgecWctvRs6ltCSuSbbJk/g9Xsy+gHXDOTvsSjtHc/bBEdP1i63Wrf8+8WvGh4obaUl2Krf4mm4nKDvhNHxRxiNTSfk1UP5AELMRTg3xgPqXlgBgvsvGeGadjdl1n3VxXQTtkZEx2JpJS8bya1VVtS60LidfM5yGOjrhVHUkBuBm9xU390p2fmzoGZLKRlsFWeSRIXyF6eIMu/JrK2nJOiNTGh1QCkgaTYaSmWnOjDwbUeCQkYlGFJ7WZuZLmWZ4jAaT8mKnXYqxI7BbzG6S7EYklo6bITsGQDSgGnc+3JSblDH7ymWKqmX0BWA7fLEgHC+zc1EmkJFpTGfMyGQL2jF1GlFQXLf+PcgLhu96kJYA0QMht+M28wix/3czATvrcC/xGxmDrbnZV9/Es380x++DnS7HE2hr8S4tqarqOGuJ/a25gmq6GXFqNSEyqTmJjlQCBcmsndV92C4jwzxH9TJnCaBApu7Zc1JK98Cz6+xrZfa12kUZP2+227Xr7ivrkQG0YIfdVGSd+OL3mFYtWVzc8WiEB09eKpfMAiXRz2NnIpYNGMQeMJVkZHig52D2tWuqWCl2wxfFv83e7Ou9agko78AqZhziNueoVUZmRPi41n1kGG49EGbl55VIS8FVLcn3kdGPKNAyyKqq8uC4JRmTutd4XYsV2bzKfWlWzTtTiSjYXtAsWHJj9lUURSjTd87aWfaRsRloy6rAKCNDSBOPRrDXpGJJnaIAzSY7Q+YzsSq/tszIRIyBjJ1sVX4Ry/aRAcQKKYO0JKH78mGNwoNJJhAyy+TIYhYoJWMR3qvE7jXdBCFW1QH9bsy+HqSlamC1DnFXZ5eR6RvJWo5rkAtkzKWlWESxrYxj56Cx6mu4ZNiOKOYBWC1w64HQAhnt7/dm9q1VRsZZWkrqzL7F/2dNPLsHSuMJKsgeyHQZtlyjcE5ZbbrYPCfAXL5KWxhyrXBzjljdh63uS6qqChkZCmQIF7ATszkRM70hW5VIZ50yMjFzGUn3OR4k2WVknAOZmGECtruqpfKMTFpCN3bT98WIWaAkZlDsSrDZDU/K32KhRcuMOGC4NftWQ1qyW0e6NLw0opgHvW2NcX4OsfbnRob5rCUX0pJExVJx3eYBmFix5LenyCtzu1qgKEDPUAY7B+wnhgPm0pLYdkEWrZVDMMeBvZeD6ZzpbKmiZGMtLQHF969nqPLsQSUN8di1oCj2hnM7Kc1NRgYQsnbbJQIZC9nKKiPTP5Lj55TbSeLVpD6EX8IWFsi0Npo/gJzKr61uPsYAx8zQyMbWV9LZV/xdXFryYPb917pu/kDZNZwpfc1OMvDeFM+sIoJ9PJotyGVkJPwtVoGRpz4yDoGMXXdoP7AqeRe7+poFBJGIgs5UAjsG0ugeTJtOGGbnn335tTEjIyeHWAVg9Wb0BYprmdXRhA09w7jl4VWYWuozBQBH7tWJ9+/Zqft+s4wM28C4CmQCb4innaODozm0NemvJX2fJ21NrIlnJlfsjcWkJV8yMiUzrvEcHs7k8MeVW3Difl3oatWfu2kh42EXDNvJV25keEA/o88Jt1VL3aVsTEuDd6muGlAgEwIWTGsDYG2u4uXXhhsxGztgpc0apSVj6TVgXRGlqioPZGQyMtxgyKUl+YuzvXQTW7lpN1Zu2q3/WqP1DYplmLyY9Kw8OMUUbNY2OHIXhBRfX5ytVHwNN519Sxk5p0CGl3RXOyOjPzZ2FUuMzuYkD2TMkCm/Nu6cZXufWPWRGcnWT+m1yP7T2rChZxi/e+ld3eeX/Ws9Xr3mQ7oHZs4kI8OkJTfTr4Muv07GokjGikMy+0ez5YFMVgxkjL2vosjkChjJ5LnvqpKMDLuO84ViB3SjBP+HlVtw1QOv462t/bjxYwfovuZk9GXYlXi76SMDgM/k2jmQRs9gGp0Wf7uY1TJmxq0yxUyqqydZCaBAJhQcvfdE/Ocp87Fodofp13llkeFhuG5HMSKfMzFl+nNGacnsIWHVo0ZM98YkzL7GQXV8lyFxY/zsYTMxlM6VXeT7T2s13b0zZk9MYe2OQazbMYgP7DPJ8feIWJVMynQMdjNeYM7EFN7eOYR1OwZx1N4T+edlK5+KazQPIIz08n4a1UkJW0l5dnOWGCxItxpTMCLRmM5vaUmbP1Zft8nLT5qHqW0N/CGTzau4/4VNvEJHDBhNzb4epCWZDsl+09IQR3owbZuliEYUk6KFGHYNZzGcyftSKtyUiCIaUZAvqBgYzZWdg5tLc42295cH4bKZZ82obi0tWW1IjaSSMczsaMKm3mGs3jaAI/c2/9vNupczeKbYcC1rgWH9yEoABTKhIBJR8OVj9rL8ulnWpFBQsaZUfmdsqseQqVqyysiIpZtRCd28EmmpI5XA5SfNd/w+I/OntODRN7e7aunOsMoYyZQ6u8nIzJvSgsfe2lGWBnbT2deuM62I1uG0OrspK4krbTKl2IhTCbbVCA0Rz9KSRdWXjMG4FsyemMJ/fXg//nEuX8D9L2wCAAyn86b9iURpKeFFWspZNzSsFq0NMXQPpu19Iybr0XrJ5Hi36EoevMyM2zeSxcBotkw+Yues6TolZSG7Em/Z6dci86a0YFPvMFZtG8CRwgZJRJfVipkHMkbzPTP6em0uWC3I7DsG4D4W4QGyqXcYI9k8krEIZneaZ2SM3hazG3bKwn+T02VkKpGWqncKsh48bqYFM1ijqLKUq4Tvxk0go61RXyrpzuyrNeqzo3uo2hkZC2lJQpbgGRlLs698+bVRWvI6PZxJS/UWyBiJRSP82BrlZd59u0JpKegRBYCDb8RmI5QSih/8kJbEtZhVFbHfYb5O5yC++Pr+SUsAsK9E5RILsiJK+VBVqzlwXFryOO6hWlAgMwbg5deCtMQejHO7Wiw9LIqi6E5gs7Q9r4jKGqSlvNtAxiAtuXTie4FlotZsHzBtm2+HZUZGoqR7wIUXhVUYrNmmrTGXL3BPiJ9VS9pNqEoZGSuzLwsKbXaUPCNjUokj25jOaMqUmXwNWGeSmLRU74EMIFYuGsaUCCMKGF6kpYzJ61Qbu7Jnu/uHmEXWzL6VBjLWgQbL+piv0520ZPb6aZdmXwCYxzZINpVL4jE0GpG5R8YYyPhQBVYNKJAZA7CbWDpX4N4VloWwkpUYor/FVFriQZIxI6Od4G7MvkEGMrM7m5CIRTCcyWPzrmFXP2vVX8FvaWn2xBQS0QiGMnls2T0CQG/8dTc00npNuv4PVUoLWx0bmYwMe9B0m2RkRH+WTEYmky8gnStIV9pYNsSro4GRTmheNvPKRd2IArapcDOigLVyiAVTfg04ZWSs2zc08cGROW08QYWeDjvphwVLrCrQbJ1O2RRtDIL3YEiE3ffX2mzi7F43adK7C4DQl4cCGcJnmPwDaPISSykaZzUZEXerbsqvxYolmR4bcYuGeNVsNBaLRrDP5GIzQbfyktUNyGnKMyBkZCzK5UXi0Qj2MqyR3biTsYiUJ0HLKFivaSiT58e8WmnhpMWx0TIyztKSWUaGnXuKQ2O65kSMNyzsH82ayipmWElz9Vh+bQVrwVC24bAZUZD1MqIgGtyxsC9Jtm6oyd6v3qEs3xRU+uC16iUjznIaTJfPSpLdsMkEbW6kJZlNnJ3PiGXeyj0yxb91Up2ZfSmQGQOIHWfZ7nW1ZEZGfFDall8bpCU3zfAAbUcYZEYGcN8JlWGVzpXp2eK2g67Wdr4oB7qpeiquybmSigUITYlo1TIMlhmZvLzZl2WNRIZ4Dxn7xnSRiMI7Xw+M5oSHr4O0ZJHRYud8U5XPUT+wkoB9k5ZqkpGxy1JYP9yZR4Y9wBPRiFT1n8xajBmZvpEsvxfmC2pZRswu4NK/vnnGJ5cv8Nd3Y/aV2cTZ3YO1jIyxaokyMkSVUBRFSy2n8xjN5rGhZwiAO2nJtPzaomuwm2Z4QHk3UafR9n7hdjYNw76PjKy0JBeIGBtYuSm9Lq7ReU282qCKOynt2JiPKJAKZAYzZalwrauv87ki7mzdSksj2bxuRz3MPDLJ+peWGi2kJdPp14Yu2zKYBUTVRi5LYXbPKv4cK4ue2JyouDOz1VqM88GMXx/NVNZHZtSmRNoJp02cVtBQ/rriSBnxeuzxyTztNxTIjBEauS6cx9rtgyioxbLlSQ4nnLjDMh9RYJGy9pyRCa5qCRBMbxID1ESsy6/te7bkCypPZ8tmZIw3HLcZHac1AcDOAAa9WXlNnGZ+AVq781xBRd+Iflc64qIMWtw5y0pL7EauqvoGYPVafm2GVXWh3fRrVw3xamn2tZs/ZGP23VQKZPzIHlgHMvoMojGjUqm0JF5LbmV4p02c3T1YDG4yQhZdk+pIWiKqQCqppZbZQ3teV4vjTkS8wZkGMvx187rIPF8y+8pmZIzpbD4CwEW61AvsYt7QM+xqeKR1Qzz77IfeqOtOWnqnewjpXN71TCSr0QAiQfR/4LKbRUM8u6qlRCyCtpKnyCgvDbsw3YoPBGlpSZxqLgSDYSm/BgQJOG0uLZkFMu6mX5cCogD7yNj2VrGYEQRoUuCWXUXzvB/N26xkLmMgYyzPlu8jYy5dsWs6EYvYDj41w2kTZ9efRgxY2fXLZnslYhG01FmWkgKZMYJYXSRbsQToxxTYlV8Deg+GlpGRO4XYhZEzTr+usrQ0uSWJ9qY48gUV63YMSv+cZetuh0GU7EaUiEVsH9wiU1ob0NoQ42t0n5FxDmS6SxmZSVXs/+A0osBpN892eSx7xJBphscQH36yDfESUc1jJnqftPLr+rppm2E1psSsBN04wFWGWmRk7IY12j2EmRTI7lF+ZmSMlUnGTtTlgYj5fcTq9VnrAOPPy06+FmEbpPXdQ6b3hrRNf5p4VNGuiZLHjQ/gTFUu1fkNBTJjhCahdwJLJe47VSKQcZCWxBuFKC/xtL1LaYmnKW12VH6iKIonn4zVTspJxnHrb+FrnNrK1+imGZ5uTTaVVEFmZMqHRlqXyopYGX7dSDysUqyYkZGTlhRFMfX3uJG0ao2Vly1jO/3aRdUSN/vWQlqyLkk283cY3y8/5FSrtZRLS+bSkKzZN1dQdedgJUURk1uSmNAUR0GF6SbOLlukKAo/T1jAU+0+VJVAgcwYge0ai9ISy8i0Ov6ck7QUiSg82yPeJNlux2mOjfH3BF21BFh3z7XDKmPkZPZ1a/TV1qgFW25fg60xkytY9ozo9qFVu+M6rBriSe7mJ1k0xeNl0BLnitiB1WzysxVmlV+sainM5dfm0pJ+UyEDe51k3Zl9zfrIGAMZP6Qlj2ZfycxzSmgdIAZLXprhMRRFsZ2E7XQP5mMKSu8970NVZ0ZfgAKZMQO7eN/tHUH3YBqKAsztanb8OSdpCdD8N2IJtlePTLm0VP1T0M1Ye4bm4TH2kTGvzGG4zaaYrbGfZ3XcBTJAeQMrBrvhVrNsMmmRrUpLptc7LcYUMGkpJaHLiztn/hCXkD/Nskk8ExSG8ms+Ad3QJsFUWoroviYDrwCrQfm12xEFxvuYHw9eLnOlnTIyBmlJMvMciRTnOQF6n02l90m2iVttsolzeu1ETN+fqrvKQ2crgQKZMQILZF7evBsAMLOjSUrbd5KWAPPBkSwgka1ashpRIOsjqQQvvWScqpaMhlaGW38LQ5+RcSktCcGWVabIjynAjuuw8A9p/gr799pqcKSbxnR6sy8zqMpkZMoDVFZ+LRNA1Rqr4a5+SUu8O3MNGuINpnO83QODBwhmHpmqSktGj0zxXO1qTZp+Pe0i89xqIl9VmrmWychY3YONGZlqD52tBApkxgis/PrlTbsAFCuWZHDqIwNoJdjDQtpa6yMjdwqJlRKqqs3OCUJamls6FjsG0thlMZTQiFWfGydjLQ9Cku6kJbbGbf2jeLdUbSEbyMSiEZ4ZszIh+9Wq3Q6rbJXbjIwxXT/iIjNimpGRkEOSJjO03PSvqTVNQvsFEbM+MsbmlDKYTdGuNuL5P5i28J6YnFPG98uPUmErMy47V+dMTJW+bm72lcmomMlXVqNSZLHbxLHXtjq/2TXBgjHKyBBVh+1Cdg0XLySn0QQMdpOPRhRLD4O229MuMLd9ZERpKa1r8lT9U7A5GcOMjkYA8vKS44gCC2mp32NGpqUhjuntxTW+vqWPf04WO8krkyvw3ixVzcgYbnz890t6ZJwyMlJmX+FhwM5RmUDGLEBl4z7CYPbl8m/GXFqqtLMvDwoDNPsmY1Hee8gYINj1kalmRiZfUPl5AWgZGS2Q8Wb2Bcyb4vGf93gO2m3i7LJagNb3iXtkSn/rJMrIENUiZTjRZYy+AJAopd2bEuUTUPlrC71kGHnXZl9tFyg+LILIyADAvC5rrdiIqqp8jUaDabXMvoAWfLIHsJvKJ7tMETPpxSIK79VSDVjA671qqeSRMWRkhlj5tZRHRujsm5PPIhgrvzK5ApdewlB+bd3Z16eGeKwLbIBmX8C6BNt+RIH2fikKMKGp8nM+lYgiws24xbWMZPJ8fAYLZLz2kQHM++ZowYa34263iXNqSqplZPTSUjUrH71CgcwYodFws5XpIQNo0pDdrrPRpLuv14xMJl/gWYNoRJHaLfsB96DYjLVnZPMqmCRvLO906iPT79HsC5S/Z+4yMuZddQEtMOhIJVw31XKD1fBFtqNz8kNZZWS8dvZ1Iy0ZA0GxQi8MGRletehi+rW7EQXBN8QD7Fr3WwcIolTS0ZRwLL+XQVE0My4LNNh5moxFMKWtUfc1vk5JWRVwkJYq2PBZbeLsslqAdr1qGZmSPF3FXlReoUBmjCDebBOxCGZ3Nkn9HLvJ2+06m0ykpUqqluymrlYLN5VLYpDiVlryavYV18hw8xp20tLOAIy+gE35ddZ5RAGgVVQNZ/K6c81L+fXAaM508rPT2ln2iFXoxaPBBduVwDpwD1lIS+adfb2YfYM9FlbdfXmA4GD29bOVvtbdt3iMRQO904gBuYyMndnX+3G32sQ5vTa7XtO5PHL5AnqHmUeGMjJElRAv3rldzdK7EFFasoJLSyZ9ZLxULblJt/oFaw64etuAZa8VBrvAFaX8xi1r9pUtndavUS8HugtkyvugMHp46XV1d1JWwxe1jIz9OZlKRPlriPKSt/Jrj31kSg9IN8FTPWDVEI9lUiqefu0iu+UnVtVCdg/hhliU92TxM3g3BivdgoHeqjzbjVlX7IHEcFP1ZMX80r3vra2GQMbhPsyrlnLFIEZVgYiizUWrJ0IVyNx0001QFAWXXHJJrZdSd4gZFZZKlMGNtDRs5pFxWbWUzau+pEvdMrszhUQsguFMnlcFWZEWbj5G3xCfJ1SFjMyciSndQ9eVtGTIKIiwnaPTANFKsRq+yErVnTIyiqLwB89OQV7yUn6dyRd4pYsXaSlMpdeAUFlY7enXAUtL1hkZ64ew2MTTz75JxvLoHl1GxjzgSrvIqJi9hlX1pBtYRmbNdv0mzi6rBYgZmYJOnpbdvAZJaAKZF198ET/96U9x4IEH1nopdYkYiMhWLAHaTd7osTF7bXEgndb+XXZEgbYLHJU0f/pJLBrB3pOKDQKdOvza7/Y0r4+xtwUgNsRzn5GJRyPYq7TGZCzi6qFhJy2xG25QGRnjOrhRVOLvYQ8eMSPjpvy6WeiQ2luq0pDJThp74ISp9BrQ1mkc7mo3NNJbH5naBDJlJlqHzRC7Z/nZbqA8I6NdV5azkio1+/ogw1tt4mTNvplcoa6NvkBIApnBwUGcccYZ+PnPf44JEybUejl1iXjDlTX6AlqzMLuHhFmzLfcemfKqpWpPvjYiO3PJ7iap76Jbnv2oJCMjrtFtIGRn9tVS4NW9CVkNX0y7CGQm8V4y5RkZmeqhSERBc+n7WCDjNP0aKO9KzLKPYTD6Apr8q6p6edFOWvIyoiD4jIxFpiNnL/018kCmGtISM/tq15VZeXa+oPLj7y6QKS+/NpspJYvVJs7Jv2OWkalHoy8AhCJveuGFF+LUU0/F4sWL8d3vftf2e9PpNNJp7SbY3+9cbjsWEEsOXWVkmLSUtPHIsEBGeDi59cgkdGbf4MYTiLAA73cvvaszvnWmEvjWyfO5jGC3ixI/N5otoMlwXVcayBTL5t9zVXoNCNVUNtJSNccTANrwxZFsXpeRSTuksEXYju9Xz27Ev9Z1A9ACErtzVKSlIYaBdI6bE2XkT6NRecRF8FQPiJuC4Uyer9sPaakgPJCDbIgHiBkZvbTE3h+rewi7H/qbkdEHVeJ1xcqzC2rx602JmKHNhPM5yKSrt7b142v3rQQAvLxpd+nnKwuo509pwZtb+/GD5Wvx4KvvAQC29o3avja7XtMhyMjU/VX661//GitXrsSLL74o9f1Lly7FddddV+VV1R+TW5NQFGBqa4OrhkXse6e2NVh+D5so3Cuk+916ZGLCLrCSQWiVcPCMdgDApt5hbOod1n1t/2lt+PRhMwCIrbvL/7ZiybhS8vrog4Z8QeW+DC/SkrjGaaXmeLK0lIIw1hBRpDuArr6MhnikGMgIWYGMi9387FI/jre29uOtrdomJB5VMFHyJtrSEAf6RrG7dCxkSoaN0txQ6X0MS0aG+UJGsnmd4dcPaSlb0AKeoDMyE0o7BeMgUSfvyJS2BqzaNoDZnSnf1mLMmIjdsll5dv9oDgOjWXS1NugDGYkgfvqE4jW/eziLh17bqvsaG4HglYNntuOPL2/BG+/144339Jt7q+eFWLXUPVg83vU4MBKo80Bm8+bNuPjii/Hoo4+iocH6QStyxRVX4LLLLuMf9/f3Y8aMGdVaYt3Q1dqAe7/0PkxqSVo2tjPjM4fNwOSWJI7aZ6Ll9zDfxtodWhajkllLQU6+Fjl8Tgfu+sKh2Nan6cSPr96Jp9bsxJtbxZSr/U2yIRZFNp8rC2TENupeMzLv37MDPz9rEfabJm/YBszfI0YQc5YYxWOW1R2btE1gaOTsI2dhYnOCBxKM/aa1oU2ysZnx2MtIS8aqrzB19WWkksVARizBNsukxFxOv84IfYGCrlpi5/UaIYOaFfxpVgHC0o8fgH+/24fD53T4that/FrfR4ZdV62NcfSP5rifZ1TwFcn0b5rb1YK7zzkMG3uGdJ+fkErgpP2nVLT2zxw2oxhojeg3OntPbuEdxY2IHhkWvFXbZ+eVug5kXnrpJezYsQOHHHII/1w+n8dTTz2FO+64A+l0GlHDELNkMolksj6jxmpz5N7WwYgVDfEolhww1fZ75na1QFGKO/udA2lMakkKGZnwSEuKouDkBfobQnNDHE+t2WmhHVuY4OJRDKRzZcZapp0nohHPQZqiKDhxvy7XP2fVJ6dQULk0E1wgYzD7usjINCVi+NSiyjYexkBGSlri1WjM7Ms8GHV9i9RhPty1PCOjXYtygYyYuQna7MvO6429wxjOlEs2VgUDU9saMbXNXVbTidZGQ0bGcF0VA50R/nUvRQ3Hz5/s13J1JGNRfPyQPVz/DMA8MsFUPnqlrs2+J5xwAv7973/jlVde4f8WLVqEM844A6+88kpZEENUh8ZEFLM6ig32mFHWrUfGrGopaLOvGaIBmFUbOGWMrHq2VOqPqQT2d2zoHtLd6HePZHnQGUT/B+PwRdFfEcSkc6Bc1pOTlgxm3zTrXVP7c1QWVoItSktm06/Z/xdUmFbeGeE+m4hS1c7QZkxqSaIzlYCqAmu3DwLQ3iNFkcvy+YXYsC6XL2DXsF6yNZqBa5V59ouErmopmF5UXqnrQKalpQULFizQ/UulUujs7MSCBQtqvbxxxfzS7CaWueBVSy5nLYkjCipx4vvF3pObEVGK3pKdJR2e6+8WD95GC2NtLQOZSS1JdKQSKAg3fEArvW5rjAfibzD2YxHli6D8FcbjL9UQz2D2ddO7pl4wy8iYNQUUr1mZpnjazKraPC7mC80sAb2HzY2MXimiR6Z3SGsQ117y8RjnQtUq8+wXSaFqKUh52gvhPMJE4BjHwVcy/VqrCqr96dcQj3KDKZNlRh0qIqya4lXSQ6ZSFEXBvC4mL2kymTaeIJidlHH4oniMgto9l2VkvPSRYR6ZEElLZhOw7UYUAJKBTI1KrxmswSe7PmtVLCAGKt28QVyS3wONIwbSdZR59gI3+2bzQvl1fQYy4blKS/zjH/+o9RLGJcZ5Hd47+9bO7GvF/CkteGfnEFZvG8AxcyfJS0t1lJEBisHms+/06PrkaOMJgrkBGTMy6bw27kHWT1Up5RkZ53PU2EfGzaDKeqHRpLuv3fRr8et21DwjM0UfoLtp++8nYqDSbbJBMFY11WIUi5+wjUf3YJoHs511OJ4AoIwMIck8oc212OjJbUM8UVqql52KccfndAMy7t4ZlcxZ8gOz4XBBjSdgGEclaD1kgpMBjD145GYt6QMwXn4dJo+MQVpSVZWXTotyUjSigF22MoZflrUJ0o8iYswG+zFI0QstuoxMudRSFsiEXFpiGZn3dhf7zbQkY3UblIXzCBOBM6szhYZ4BKPZAjb2DHGPTFTaI6NJS27mjwQBv1Fu1+/4rKoNktxPoX8I9NdBRgbQVy4FNTCSYTTNclkiwN28J2nJ8J6Gtfwa0IzK+YIK1i3fePzddPfVMjK1mbHDqiZ7hopVk7WY1QZo51WuoGJLqdV/py4jUyrPHhkbZl92n9sxUAxk6tXoC1AgQ0gSjSjYZ7K2M8q5LL/WSUt1lnJlmYy12weRL6iOVVXO0lJtMjJzSx6ZnQNpXnIdtEmvTFqqgbHbi7TEjLKhLr82DHcVZSPjMUi4aIpXa4+MsWrSj7b9XmDdewFgfXex14tZRqa/LCNTH/c5t7AMHCtsq1ejL0CBDOECccef52ZfWY9MqS16QeX+A6s5KUEzs6MJjfEo0rkCNvQMOTfEsxjQqJl9a/PwSyVjmFm64TM/QdBlk0bZrRYZGdaJmiEnLelL6lkgE6ryazY4srR2sSOvsbow5mJMQa09MoB47+nXNkIBB1asey8ArC81rTPLyIyV8mujlEgZGWJMIPZccZuREScQsw64QU6/tiMSUTC3q9hBdPW2Aceqqno1+wLlfoKgMzJG02zaQ1OwSvGSkWHZt2xeRb6g8sqfMElLWvl1ce1ZsSNvxLu0ZGYYDpp5pfYPxYxM7TIdLFixy8iUmX1rlMmqFGMGjjIyxJhgnmAmzbscUSDuyNmFXk87Fd4nZ2u/4OGxHlEAmAUytTX7AuUTvgOXlgzHJjQeGd0w0HwopSU23HUoo5eWoiaN7NzMW6rV5GsR0cheK7MvoAUrbI6XaKLn5dlplpEJu7SkX3dQlY9eoECGkIY97Df0DPGsituqJaA+AxlRNnOqNjD6QBj1kJFh79FbpUCmJ8CBkUC57FYPHhmZpo1iGn1UGLwYpowMm3jNpSWTZngMNxOwM7ngg1EjYoDOMk61uH8YNynm0lJxffVW1OAWY+A6iaQlYiwgtgt/q+TBkM3IiN/HMhf1lHI13fE5mn2NHpnamn0BLSBbu30AA6NZXn0TnNlX7zVhGZlkgA/B5kQMYqW3zAM4ElH4jXs0V+CDF8NUfl0mLZk0w2O4qlqqg4wMq5pM5wpYU+pcXYv2DcYg2UpaUlV1zHlkSFoixgzsQck0YtmMjKIo/IHCsjn1dIGzv2tT7zB6SzNUvPaRqWVGZnZnExKxCIYzebyyeTeAYnARVGbBOHyRdWEN0iMTiShoTmjvgay3gwXWfcNZXrbclAiPtFRm9rXxtsTcVC3VuPwa0FdNviqc10FjvLbF+WVsA5MvqBjJ5kMvLRkDV5KWiDEDe+CzG33UxU6b3QhZOV89XeCdzUlMbE5CVbUgzbKPTB1LS7FoBPtMLhqX/7WuB0BxJxVUM7qyPjI1kiXYe6Ao8llDdj6yYYBA/VTWycCCriEJaSnhQlrSPDK1PRbs3rNuZykjU0Ozb/H/9Q3ixPLsgdEc3+jUqpFgpZRnZEhaIsYITIJhxF20nY8ZHmb1ph3PNwRp1mbfcmmpUFAxmKm9tARoN/x/resGEOxOymj2TefsmwtWC/YeuKm0Ye93zxDLyEWkg6B6oDwj4ywtuRsaWdtjYbw+azF0VtykGKUWsTx7YDQbemmJMjLEmIWVQTLc3OiNN9R6u8DnGYI0a49MeUZmMJPjN9haZmQA7Yb/+nt9AII16ZX1kalxRsbN72WB9a5SIBMmWQkQRxQwj4ydtKTovseOWo8oYJRdnzWRlrRNilmGgnf3Hc2FXloSq5YS0UjZ6I96ggIZwhVzu5p1RkqZihCGcUdXL7OWGLI3Su1hre1mmayUiEZqfuNiwSYLrDpTwe2kyvrIsIxM4AP+ijddN+cne99YV+QwyUoA0JTUS0s526olLxmZOgtkamz2NbuuRMNvLcvE/UA8bzqbE4HJ014I5xEmakZTQuseC8h39gXKb4T10hCPYZTNnKZfp4WMTD0YfRnGv2NiSw0yMgZpKeiKF0/SUkwfyISp9BoAmkrHPpMrIF9QebWR2TFIuAlk6qAhHlDs2SKaa2vjkRGkJZPrqlXo7ss2OvW2YZNFURSehavniiWAAhnCA+KDUrZqCdBH+IpS+1S1kX0mt0D8c5xHFGiBTP9I7Y2+jMktSUxoElPgtfDIsIxMbQyPXqQlFlizqjWW4QgLjULgNZzJIccm1PskLdWy/BooPljFe08tMh2tDfbXlZiRcWqsGQbYe17P4wkACmQID4g+Ga8emWQsUnepysZEFLM7U/xjS2nJZPq1lpGprdEXKN7wxTR8oGZfQ7aKNcSrVUbGi7TEPTIhewAlYxEeiI9k8loAMkakJUAvL9U6I2N2XWmBTDb00hKgScKUkSHGHN4zMtrpVq+7FN2N0qkhXk6UluonIwNoHX6BYMsmrYZG1soj46VqKazSkqIoSAkl2OzYx0zkXzeBTL2YfQHUPCMjblTMTPRid9+wm30BkLREjF3Eh727jIz2vfWqG7O/LRGNlM2nYZhJS/XkkQH071Gg0lJcP3xRG1EQ7K2GVVi4CbRZWT0PZEImLQH67r5MWoqbBCBxF9JSvZRfA/pscM3Nvg7SktPw2TCgBTL1LS2F70olas7szhSSsWK7cLPdnhUxXUamPi9utuOze/CKlTlLfvBPAEBPaThjPUhLQC0DGf3MoloMjQS098GNpGVsiBc2aQnQ95LxTVqq0XtoBquaVNX66yNT/Dorv9akpaCzkX6SoIwMMVaJRhQcPqcDADB9QqP0zyVCIC0tnDkBiVhE55Ux0t6Y4Gbat7b2462t/dgxUAxk9i511a0186e0oK0xjsktSbQ3Bhdcibvk0Wyee2WCzsiw92GGUGHnBAvCWJaiMWTSEqDv7pv1SVriGZk6kJaaEjEcuEc7ohEFe7i49/hFKhHD1LYGtDXGMbWtoezrLNDpHxkb0hK7DxpL3+sNysgQnvjxGYegezCDOROtH/hGxNR0LXZTMnS1NuCxS49FW5P1wz8Ri+DhS47B6tKEaUZTIoqFMydUe4lSNCVieOSSYxCJwFIiqwaRSHGmViZfwGiuULPd/ILpbVj+jWMxvV3+YWd84ITNIwOIGZmc1hDPRlrKualaqoOMDAD86tzD0TecRVdreSBRbSIRBX/5+tHI5VXTAKW1tGnoGUrzz9Vr9lmG73/mYFzeN4K9JtXHBs0KCmQIT7Q0xF3LKDppqQ52d1bM7HTexXe1NtTkRuqGKSY7xiBoiJcCmWxe8MgEHxS4vfkaH0ypUHtk8razlsI2/VqkrTGOtgCzjEbsZBaWkdk5IAYy4QuIGY2JaN0HMQBJS0SAhEFaIipHNEPXk7/CCWNVTtg6+wLimAIhkDGRltimQiojkyt+Txjew1rTaghkohGlLsrWxzp0hInA0FUthTjdStijBTIFrSFeCN7vsSAtsfLrYZ20ZD392o3Zlx7IzrAsdZp39aVjFgR0lInAiFFGZlwgNsVjRtFkCB6CZYHMmJGWrM2+UtJSHZl96x1j+wW6zwVD+K5UIrToGuKFuCSRsEdsiseHRoYiI6NfY9jLr5loZD792oW0FCJ5sNYYfYMUyAQDBTJEYJC0ND4QRziw3XwiWv83dGNwHUZpSSu/zvGyazOzrxdpKWEiURF6UokoIgpQKMWHYQjgxwJ0lInACMOIAqJytIaBYcvI6M/JcPaRKZeWzPrIxNyMKAhRMFprFEVBsyBJUuY5GOr/7kKMGXRDIymQGbOIZl8tI1P/txpjljCM5deitMRkI7Oyaa0hnsSIAua1oYyMFKK8RJnnYKCjTAQGSUvjA7H8OsxVS2Esv27kVUtOfWRcSEshCkbrAdHwS5nnYKAzkwgMMvuOD1jJ6Ug2z3f8YXgIlpl9QygtpYShkX5Nv6bya3e06jIy4TuHwgidmURgkEdmfMDe2/7SRHAgHFKicbgfM86GCbH82n76tby0xL7H2DCQMEefkaFjFgR0lInAIGlpfMDe2/4RLZAJR0ZGC2QUJZznKAu+nKdfy0lL+YKKfKkEhzIycugCGco8BwKdmURgUEZmfMAzMiM5AMWgwMynUW+IgUtTPApFqf81G2Fy2JCktOTUR0YMdKghnhyi2TcMmcixAJ2ZRGDoAxk69cYqLJDpK2VkEtFIKIICMbhuDKGsBOjLr+WkJfuMjNj5NwxZtXqApKXgoaNMBEZMlJYo5TpmYV4K5pEJi7ciHo0gGimeo6lkOM9Pt9KS04gCVrEk/gxhTwuZfQMnHHcYYkyQoD4y44KyjEyIglZWcRXG0mtAM/vmCiqGM8XSdz+kpbBk1eoB8sgEDwUyRGDEyOw7LjAGMmHJyADa2sNYeg3o182Of0XSUs66Fw1hDklLwUNHmQgMMvuOD4xVS+EMZMLpkYlHIzzzyY5/PFIehMQkpSWekQnRe1hrqI9M8NDZSQRGggKZcQFLp7PBeWF6CLIOxGHNyACavDSQLlaNmWVkEpLSUjpHzfDcQhmZ4KGjTASG3uxLp95YxRikhiojEwu3tASUr90sCJGVlrI285oIc8jsGzx1fXYuXboUhx12GFpaWjB58mScfvrpWL16da2XRXiEpKXxgXEXauyYW8+wtYe1/BooD2RiNtJSrqBCVa2zMqLZl5BDzMiE6dwPM3V9dj755JO48MIL8dxzz+HRRx9FNpvFhz70IQwNDdV6aYQHKJAZHxjf2zDt5tnaU6HOyOiDMLvp14D9mIIMSUuuIWkpeOp62/Hwww/rPl62bBkmT56Ml156Ccccc0yNVkV4hVU+xKMK79dBjD3KMzLhuZmHvWoJ0DwyDLMgJKELZAqWwWaGzL6uSSViUBRAVWnDFhR1HcgY6evrAwB0dHRYfk86nUY6neYf9/f3V31dhBzshkq9FcY2xnR6mB6CY0FaMmaT7KQlQG/4ve4vb+D+FzaBqU0Flc1Zoo2HLJGIguZkDAOjOQpkAiI0d5hCoYBLLrkERx11FBYsWGD5fUuXLkVbWxv/N2PGjABXSdgxo6MJE5sTOGTWhFovhagiYTb7HjJzAmIRBQfNaKv1UjwjIy2JwQ3LuuQLKu57fhNGswWkc8V/THY6ZCZds25435wOTGiKY87EVK2XMi4IzbbjwgsvxOuvv46nn37a9vuuuOIKXHbZZfzj/v5+CmbqhOZkDE9/+4NkHBzjGKWlMGVkzvvAnvjC+2eFeictIy0pioJENIJMvsANvRt7hpDOFZCMRfDYZcciUgp2YhEFXa0N1V/4GOJnZy5CJl8I9XkUJkIRyHzta1/DQw89hKeeegp77LGH7fcmk0kkk8mAVka4hS7ssU95RiZc73nYz9GyqiULWSgWVZDJa9LS6m0DAIC5XS2Y0dFU3UWOcSIRBQ2RcJ9HYaKut0qqquJrX/sa/vSnP+Hxxx/HnDlzar0kgiAcEIcvAuGSlsYCZdKSRQaUZWqYtPRWKZCZP6WliqsjCP+p64zMhRdeiPvuuw9//vOf0dLSgm3btgEA2tra0NjYWOPVEQRhRUMsgqHS0MIwSUtjAZk+MoBm4GXS0uptxcKIeRTIECGjru8wP/nJT9DX14fjjjsOU6dO5f9+85vf1HppBEHYIMozYZOWwo4YyCgKLFsdGCdgr+YZmdYqr5Ag/KWuMzJ2HScJgqhfxECGMjLBIkpL8WgEimIfyGTyBQxnctjYOwyAMjJE+KA7DEEQvpMUKpfIIxMsYkbGbPI1IyZIS2u3D0JVgc5UApNaqFiCCBd1nZEhCCKciE0PKSMTLGL5tdnka4Y4AXt1T1FWomwMEUboDkMQhO80UEamZugyMjY9m8QJ2Ku2USBDhBe6wxAE4TvkkakdOo+MpLS0enuxYolKr4kwQncYgiB8h6qWakeTpLSkZWRUXrE0jyqWiBBCgQxBEL7TqAtk6DYTJLLSEvPIbO0bQfdgBooCzO1qrvr6CMJv6A5DEITvUNVS7RDNvlbN8ABNWnrjvaKsNKujqawrMEGEAbrDEAThO+SRqR0pIRixO/YsW/PvLX0AyOhLhBe6wxAE4Tti+TV5ZIJFlPXsMjJsRMHbOwcBkD+GCC8UyBAE4Tti+TVlZIIlElH48Zcpv2YN1KliiQgrdIchCMJ3GsjsW1OYvCQjLTFIWiLCCt1hCILwHcrI1BZm+JWRloBisDm7M1X1dRFENaA7DEEQvkMZmdrCSrBlpCUA2Ker2XJKNkHUO3SHIQjCd2jWUm1pLElLMg3xAGBeFxl9ifBCdxiCIHxH30eGqpaCJsUyMhJ9ZAAy+hLhhgIZgiB8R5SWRC8GEQwy0lJC+Nr8qRTIEOGFAhmCIHyHBTLJWASKQoFM0LiWligjQ4QYCmQIgvCdhtIDlPwxtcGNtNSRSmBSczKQdRFENaC7DEEQviNmZIjg4eXXEtLSvK4WypoRoYbuMgRB+M68KS1YML0Vpx08vdZLGZcs3rcLMzua8MH5ky2/54i9OjGzowmfPmyPAFdGEP6jqCprUD026e/vR1tbG/r6+tDaSiWGBEEQBBEGZJ/flJEhCIIgCCK0UCBDEARBEERooUCGIAiCIIjQQoEMQRAEQRChhQIZgiAIgiBCCwUyBEEQBEGEFgpkCIIgCIIILRTIEARBEAQRWiiQIQiCIAgitFAgQxAEQRBEaKFAhiAIgiCI0EKBDEEQBEEQoYUCGYIgCIIgQgsFMgRBEARBhJZYrRdQbVRVBVAcB04QBEEQRDhgz232HLdizAcyAwMDAIAZM2bUeCUEQRAEQbhlYGAAbW1tll9XVKdQJ+QUCgW89957aGlpgaIovr1uf38/ZsyYgc2bN6O1tdW31yXMoeMdHHSsg4OOdXDQsQ4Ov461qqoYGBjAtGnTEIlYO2HGfEYmEolgjz32qNrrt7a20kURIHS8g4OOdXDQsQ4OOtbB4cextsvEMMjsSxAEQRBEaKFAhiAIgiCI0EKBjEeSySSuueYaJJPJWi9lXEDHOzjoWAcHHevgoGMdHEEf6zFv9iUIgiAIYuxCGRmCIAiCIEILBTIEQRAEQYQWCmQIgiAIgggtFMgQBEEQBBFaKJDxyJ133onZs2ejoaEB73vf+/DCCy/UekmhZ+nSpTjssMPQ0tKCyZMn4/TTT8fq1at13zM6OooLL7wQnZ2daG5uxic+8Qls3769RiseO9x0001QFAWXXHIJ/xwda//YsmULvvCFL6CzsxONjY044IADsGLFCv51VVVx9dVXY+rUqWhsbMTixYuxdu3aGq44nOTzeVx11VWYM2cOGhsbsddee+GGG27QzeqhY+2Np556Ch/5yEcwbdo0KIqCBx54QPd1mePa29uLM844A62trWhvb8eXvvQlDA4OVr44lXDNr3/9azWRSKj/+7//q77xxhvq+eefr7a3t6vbt2+v9dJCzUknnaTefffd6uuvv66+8sor6imnnKLOnDlTHRwc5N/z1a9+VZ0xY4a6fPlydcWKFer73/9+9cgjj6zhqsPPCy+8oM6ePVs98MAD1Ysvvph/no61P/T29qqzZs1SzznnHPX5559X33nnHfWRRx5R161bx7/npptuUtva2tQHHnhAffXVV9WPfvSj6pw5c9SRkZEarjx83HjjjWpnZ6f60EMPqevXr1d/97vfqc3NzeoPfvAD/j10rL3x17/+Vb3yyivVP/7xjyoA9U9/+pPu6zLH9eSTT1YPOugg9bnnnlP/+c9/qnvvvbf6uc99ruK1USDjgcMPP1y98MIL+cf5fF6dNm2aunTp0hquauyxY8cOFYD65JNPqqqqqrt371bj8bj6u9/9jn/PW2+9pQJQn3322VotM9QMDAyo++yzj/roo4+qxx57LA9k6Fj7x7e//W316KOPtvx6oVBQp0yZov7P//wP/9zu3bvVZDKp3n///UEsccxw6qmnqueee67ucx//+MfVM844Q1VVOtZ+YQxkZI7rm2++qQJQX3zxRf49f/vb31RFUdQtW7ZUtB6SllySyWTw0ksvYfHixfxzkUgEixcvxrPPPlvDlY09+vr6AAAdHR0AgJdeegnZbFZ37OfPn4+ZM2fSsffIhRdeiFNPPVV3TAE61n7y4IMPYtGiRfjUpz6FyZMnY+HChfj5z3/Ov75+/Xps27ZNd6zb2trwvve9j461S4488kgsX74ca9asAQC8+uqrePrpp7FkyRIAdKyrhcxxffbZZ9He3o5Fixbx71m8eDEikQief/75in7/mB8a6Tfd3d3I5/Po6urSfb6rqwurVq2q0arGHoVCAZdccgmOOuooLFiwAACwbds2JBIJtLe36763q6sL27Ztq8Eqw82vf/1rrFy5Ei+++GLZ1+hY+8c777yDn/zkJ7jsssvwn//5n3jxxRdx0UUXIZFI4Oyzz+bH0+yeQsfaHd/5znfQ39+P+fPnIxqNIp/P48Ybb8QZZ5wBAHSsq4TMcd22bRsmT56s+3osFkNHR0fFx54CGaIuufDCC/H666/j6aefrvVSxiSbN2/GxRdfjEcffRQNDQ21Xs6YplAoYNGiRfjv//5vAMDChQvx+uuv46677sLZZ59d49WNLX7729/i3nvvxX333Yf9998fr7zyCi655BJMmzaNjvUYhqQll0ycOBHRaLSsemP79u2YMmVKjVY1tvja176Ghx56CE888QT22GMP/vkpU6Ygk8lg9+7duu+nY++el156CTt27MAhhxyCWCyGWCyGJ598Ej/84Q8Ri8XQ1dVFx9onpk6div3220/3uX333RebNm0CAH486Z5SOZdffjm+853v4LOf/SwOOOAAnHnmmbj00kuxdOlSAHSsq4XMcZ0yZQp27Nih+3oul0Nvb2/Fx54CGZckEgkceuihWL58Of9coVDA8uXLccQRR9RwZeFHVVV87Wtfw5/+9Cc8/vjjmDNnju7rhx56KOLxuO7Yr169Gps2baJj75ITTjgB//73v/HKK6/wf4sWLcIZZ5zB/5+OtT8cddRRZW0E1qxZg1mzZgEA5syZgylTpuiOdX9/P55//nk61i4ZHh5GJKJ/rEWjURQKBQB0rKuFzHE94ogjsHv3brz00kv8ex5//HEUCgW8733vq2wBFVmFxym//vWv1WQyqS5btkx988031S9/+ctqe3u7um3btlovLdT8x3/8h9rW1qb+4x//ULdu3cr/DQ8P8+/56le/qs6cOVN9/PHH1RUrVqhHHHGEesQRR9Rw1WMHsWpJVelY+8ULL7ygxmIx9cYbb1TXrl2r3nvvvWpTU5N6zz338O+56aab1Pb2dvXPf/6z+tprr6mnnXYalQR74Oyzz1anT5/Oy6//+Mc/qhMnTlS/9a1v8e+hY+2NgYEB9eWXX1ZffvllFYB62223qS+//LK6ceNGVVXljuvJJ5+sLly4UH3++efVp59+Wt1nn32o/LqW/OhHP1JnzpypJhIJ9fDDD1efe+65Wi8p9AAw/Xf33Xfz7xkZGVEvuOACdcKECWpTU5P6sY99TN26dWvtFj2GMAYydKz94y9/+Yu6YMECNZlMqvPnz1d/9rOf6b5eKBTUq666Su3q6lKTyaR6wgknqKtXr67RasNLf3+/evHFF6szZ85UGxoa1D333FO98sor1XQ6zb+HjrU3nnjiCdP789lnn62qqtxx7enpUT/3uc+pzc3Namtrq/rFL35RHRgYqHhtiqoKLQ8JgiAIgiBCBHlkCIIgCIIILRTIEARBEAQRWiiQIQiCIAgitFAgQxAEQRBEaKFAhiAIgiCI0EKBDEEQBEEQoYUCGYIgCIIgQgsFMgRB1CWzZ8/G7bffXutlEARR51AgQxBEVdi5cycSiQSGhoaQzWaRSqX4oESgGKgoigJFUZBKpXDIIYfgd7/7Hf/6iy++iC9/+ctSv2vZsmVob2/3+08gCCIEUCBDEERVePbZZ3HQQQchlUph5cqV6OjowMyZM3Xfc/3112Pr1q14+eWXcdhhh+Ezn/kMnnnmGQDApEmT0NTUFPi6s9ls4L+TIAjvUCBDEERVeOaZZ3DUUUcBAJ5++mn+/yItLS2YMmUK5s6dizvvvBONjY34y1/+AqBcWtq9eze+8pWvoKurCw0NDViwYAEeeugh/OMf/8AXv/hF9PX18QzPtddeCwBQFAUPPPCA7ne2t7dj2bJlAIANGzZAURT85je/wbHHHouGhgbce++9AIBf/OIX2HfffdHQ0ID58+fjxz/+sb8HiCAIX4jVegEEQYwdNm3ahAMPPBAAMDw8jGg0imXLlmFkZASKoqC9vR2f//znTYOCWCyGeDyOTCZT9rVCoYAlS5ZgYGAA99xzD/baay+8+eabiEajOPLII3H77bfj6quvxurVqwEAzc3Nrtb9ne98B7feeisWLlzIg5mrr74ad9xxBxYuXIiXX34Z559/PlKpFM4++2wPR4YgiGpBgQxBEL4xbdo0vPLKK+jv78eiRYvw/PPPI5VK4eCDD8b//d//YebMmaZBRiaTwa233oq+vj588IMfLPv6Y489hhdeeAFvvfUW5s6dCwDYc889+dfb2tqgKAqmTJniad2XXHIJPv7xj/OPr7nmGtx66638c3PmzMGbb76Jn/70pxTIEESdQYEMQRC+EYvFMHv2bPz2t7/FYYcdhgMPPBD/+te/0NXVhWOOOabs+7/97W/jv/7rvzA6Oorm5mbcdNNNOPXUU8u+75VXXsEee+zBgxi/WbRoEf//oaEhvP322/jSl76E888/n38+l8uhra2tKr+fIAjvUCBDEIRv7L///ti4cSOy2SwKhQKam5uRy+WQy+XQ3NyMWbNm4Y033uDff/nll+Occ85Bc3Mzurq6oCiK6es2NjZ6Wo+iKFBVVfc5MzNvKpXi/z84OAgA+PnPf473ve99uu+LRqOe1kEQRPWgQIYgCN/461//imw2ixNOOAG33HILDj30UHz2s5/FOeecg5NPPhnxeFz3/RMnTsTee+/t+LoHHngg3n33XaxZs8Y0K5NIJJDP58s+P2nSJGzdupV/vHbtWgwPD9v+rq6uLkybNg3vvPMOzjjjDMe1EQRRWyiQIQjCN2bNmoVt27Zh+/btOO2006AoCt544w184hOfwNSpUz2/7rHHHotjjjkGn/jEJ3Dbbbdh7733xqpVq6AoCk4++WTMnj0bg4ODWL58OQ466CA0NTWhqakJH/zgB3HHHXfgiCOOQD6fx7e//e2yYMqM6667DhdddBHa2tpw8sknI51OY8WKFdi1axcuu+wyz38HQRD+Q+XXBEH4yj/+8Q8cdthhaGhowAsvvIA99tijoiCG8Yc//AGHHXYYPve5z2G//fbDt771LZ6FOfLII/HVr34Vn/nMZzBp0iTccsstAIBbb70VM2bMwAc+8AF8/vOfxze/+U2p3jTnnXcefvGLX+Duu+/GAQccgGOPPRbLli3DnDlzKv47CILwF0U1CsgEQRAEQRAhgTIyBEEQBEGEFgpkCIIgCIIILRTIEARBEAQRWiiQIQiCIAgitFAgQxAEQRBEaKFAhiAIgiCI0EKBDEEQBEEQoYUCGYIgCIIgQgsFMgRBEARBhBYKZAiCIAiCCC0UyBAEQRAEEVookCEIgiAIIrT8f0gDmV/B9Q0mAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stat_Anz)\n",
    "plt.title('Result')\n",
    "plt.ylabel('Counter Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Orginal', 'Prediction'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deviation from Expected Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.0006  0.0301 -0.327   0.0981]\n"
     ]
    }
   ],
   "source": [
    "plt.plot(res[:,2])\n",
    "plt.title('Deviation')\n",
    "plt.ylabel('Deviation from expected value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Deviation'], loc='upper left')\n",
    "#plt.ylim(-0.3, 0.3)\n",
    "plt.show()\n",
    "\n",
    "statistic = np.array([np.mean(res[:,2]), np.std(res[:,2]), np.min(res[:,2]), np.max(res[:,2])])\n",
    "print(statistic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model\n",
    "\n",
    "* Save the model to the file with the \"h5\" file format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: test\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: test\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved artifact at 'test'. The following endpoints are available:\n",
      "\n",
      "* Endpoint 'serve'\n",
      "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 32, 32, 3), dtype=tf.float32, name='keras_tensor')\n",
      "Output Type:\n",
      "  TensorSpec(shape=(None, 2), dtype=tf.float32, name=None)\n",
      "Captures:\n",
      "  2833359401040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359398736: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359399312: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359400080: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359400464: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359399696: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359399120: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359402000: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359401424: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359402960: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359403920: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833359404688: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833360191952: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833360192912: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833360193104: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  2833360193680: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
     ]
    }
   ],
   "source": [
    "model.export(\"test\", \"tf_saved_model\")  # replace tf.saved_model.save with this line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "183940"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size\n",
    "\n",
    "converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n",
    "tflite_model = converter.convert()\n",
    "open(FileName + \".tflite\", \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ana-cont_1400_s2_q.tflite\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "53576"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import tensorflow as tf\n",
    "\n",
    "FileName = FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \"_q.tflite\"\n",
    "\n",
    "def representative_dataset():\n",
    "    for n in range(x_data[0].size):\n",
    "      data = np.expand_dims(x_data[5], axis=0)\n",
    "      yield [data.astype(np.float32)]\n",
    "\n",
    "converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n",
    "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "converter.representative_dataset = representative_dataset\n",
    "converter._experimental_disable_per_channel_quantization_for_dense_layers = True\n",
    "tflite_quant_model = converter.convert()\n",
    "\n",
    "open(FileName, \"wb\").write(tflite_quant_model)\n",
    "print(FileName)\n",
    "Path(FileName).stat().st_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  },
  "vscode": {
   "interpreter": {
    "hash": "3431513051aa6ef6a02e4978d18932220794b1f41fa21fbae2ad068757488314"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}