6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j92Sj0HhrngF"
   },
   "source": [
    "# Loading Dataset from Buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "fujNL6rr1GsU",
    "outputId": "a9dfc361-0d8d-49ed-c3da-0fcde0b71589"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying gs://proobj/test.zip...\n",
      "Copying gs://proobj/train.zip...\n",
      "| [2/2 files][  1.7 GiB/  1.7 GiB] 100% Done  60.3 MiB/s ETA 00:00:00           \n",
      "Operation completed over 2 objects/1.7 GiB.                                      \n",
      "Archive:  test.zip\n",
      "   creating: test/\n",
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     ]
    }
   ],
   "source": [
    "# Loading dataset from Google Buckets (proobj)\n",
    "!gsutil -m cp -r gs://proobj/t*.zip .\n",
    "!unzip test.zip\n",
    "!unzip train.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J-YzdINxrtmG"
   },
   "source": [
    "# Loading Libraries and Tranformations \n",
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9iTP_3bfTNGW"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "TRAin_DATA_FOLDER= \"train\"\n",
    "\n",
    "\n",
    "images = []\n",
    "classes=[]\n",
    "missing=0\n",
    "for class_folder_name in os.listdir(TRAin_DATA_FOLDER):\n",
    "    class_folder_path = os.path.join(TRAin_DATA_FOLDER, class_folder_name)\n",
    "    class_label = class_folder_name\n",
    "   \n",
    "    for image_path in glob(os.path.join(class_folder_path, \"*.png\")):\n",
    "        image_bgr = cv2.imread(image_path, cv2.IMREAD_COLOR)\n",
    "        if image_bgr is None: # if the file contain any missig value ignore it\n",
    "          missing += 1\n",
    "          continue\n",
    "        images.append(image_bgr)\n",
    "       \n",
    "        classes.append(class_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xaXyorEzTRUp",
    "outputId": "bcaaa527-f2b6-4d66-f214-de4bc1ba9218"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(214, 214, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def resize_images(img):\n",
    "\n",
    "  img = np.array(img).astype(np.uint8)\n",
    "  #print(img.dtype)\n",
    "  #res = cv2.resize(img,(256,256), interpolation = cv2.INTER_CUBIC)\n",
    "  return img\n",
    "\n",
    "#save resized images into images.\n",
    "images = [resize_images(img) for img in images]\n",
    "images[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_l9ANIEvTjKN"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "X7BVblyiTmOR",
    "outputId": "85494bea-0bfb-4d56-d6eb-986702aa1282"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 256, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def resize_images(img):\n",
    "\n",
    "  img = np.array(img).astype(np.uint8)\n",
    "  #print(img.dtype)\n",
    "  res = cv2.resize(img,(256,256), interpolation = cv2.INTER_CUBIC)\n",
    "  return res\n",
    "\n",
    "#save resized images into images.\n",
    "images = [resize_images(img) for img in images]\n",
    "images[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "BISnW7RgTtVv",
    "outputId": "928b63e0-a354-47a6-a0e0-0a1069779c37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images shape:  (4750, 256, 256, 3)\n",
      "classes shape:  (4750,)\n",
      "number of Scentless Mayweed images is  -> 516\n",
      "number of Common wheat images is  -> 221\n",
      "number of Common Chickweed images is  -> 611\n",
      "number of Maize images is  -> 221\n",
      "number of Fat Hen images is  -> 475\n",
      "number of Sugar beet images is  -> 385\n",
      "number of Charlock images is  -> 390\n",
      "number of Black-grass images is  -> 263\n",
      "number of Cleavers images is  -> 287\n",
      "number of Small-flowered Cranesbill images is  -> 496\n",
      "number of Shepherds Purse images is  -> 231\n",
      "number of Loose Silky-bent images is  -> 654\n"
     ]
    }
   ],
   "source": [
    "images = np.array(images)\n",
    "classes = np.array(classes)\n",
    "print(\"images shape: \", images.shape)\n",
    "print(\"classes shape: \", classes.shape)\n",
    "#for key,value in images_per_class.items():\n",
    "   # print(\"number of {0} images is  -> {1}\".format(key, len(value)))\n",
    "  \n",
    "for label in set(classes):\n",
    "\n",
    "  print(\"number of {} images is  -> {}\".format(\n",
    "        label, \n",
    "        len(images[classes == label])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "MvwAKKVvTt5o",
    "outputId": "c073460c-91eb-4c71-9d0d-790ac23e47e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "enc = LabelBinarizer()\n",
    "y = enc.fit_transform(classes)\n",
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "YKBjS6rfT3xf",
    "outputId": "a5d8efad-0c40-4231-b06d-d571a9ea4277"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3325\n",
      "1425\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(images, y, test_size=0.3, random_state=50)\n",
    "print(len(X_train))\n",
    "X_train[0].shape\n",
    "print(len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "3BjTRlH5T9Hy",
    "outputId": "7b3cfa98-bd1f-46f7-ced6-4458f65f726c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 256, 3) 712\n",
      "(256, 256, 3) 713\n"
     ]
    }
   ],
   "source": [
    "random_seed = 2\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_test, X_val, y_test, y_val = train_test_split(X_test,y_test, test_size = 0.5, random_state=random_seed)\n",
    "print(X_test[0].shape, len(X_test))\n",
    "print(X_val[0].shape, len(X_val))\n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)\n",
    "X_val = np.array(X_val)\n",
    "y_val = np.array(y_val)\n",
    "X_test = np.array(X_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e11-S6Czzxnl"
   },
   "source": [
    "# VGG 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GHVkOQ85T-cE"
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential,Model\n",
    "from keras.layers import Dense, Dropout, Flatten, Activation\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "def vgg16_model(num_classes=None):\n",
    "\n",
    "    model = VGG16(weights='imagenet', include_top=False,input_shape=(256,256,3))\n",
    "    model.layers.pop()\n",
    "    model.layers.pop()\n",
    "    model.layers.pop()\n",
    "\n",
    "    model.outputs = [model.layers[-1].output]\n",
    "\n",
    "    model.layers[-2].outbound_nodes= []\n",
    "    x=Conv2D(256, kernel_size=(2,2),strides=2)(model.output)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)    \n",
    "    x=Conv2D(128, kernel_size=(2,2),strides=1)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x=Conv2D(128, kernel_size=(2,2),strides=1)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x=Flatten()(x)\n",
    "    x=Dense(num_classes, activation='softmax')(x)\n",
    "\n",
    "    model=Model(model.input,x)\n",
    "\n",
    "    for layer in model.layers[:15]:\n",
    "\n",
    "        layer.trainable = False\n",
    "\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HhW2kr44XE02"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Gw4egxd3UQtn"
   },
   "outputs": [],
   "source": [
    "def precision(y_true, y_pred):\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "    precision = true_positives / (predicted_positives + K.epsilon())\n",
    "    return precision\n",
    "\n",
    "\n",
    "def recall(y_true, y_pred):\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "    recall = true_positives / (possible_positives + K.epsilon())\n",
    "    return recall\n",
    "\n",
    "\n",
    "def fscore(y_true, y_pred):\n",
    "    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:\n",
    "        return 0\n",
    "\n",
    "    p = precision(y_true, y_pred)\n",
    "    r = recall(y_true, y_pred)\n",
    "    f_score = 2 * (p * r) / (p + r + K.epsilon())\n",
    "    return f_score\n",
    "  \n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, classes,\n",
    "                          normalize=False,\n",
    "                          title=None,\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if not title:\n",
    "        if normalize:\n",
    "            title = 'Normalized confusion matrix'\n",
    "        else:\n",
    "            title = 'Confusion matrix, without normalization'\n",
    "\n",
    "    # Compute confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    # Only use the labels that appear in the data\n",
    "    classes = classes[unique_labels(y_true, y_pred)]\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=classes, yticklabels=classes,\n",
    "           title=title,\n",
    "           ylabel='True label',\n",
    "           xlabel='Predicted label')\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    fig.tight_layout()\n",
    "    return ax\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Pf5kpB7-UYQx",
    "outputId": "2cf98df2-d642-48bc-b5cf-7aafce03fc14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 256, 256, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 4, 4, 256)         524544    \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 3, 3, 128)         131200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 3, 3, 128)         512       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 3, 3, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 2, 2, 128)         65664     \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 2, 2, 128)         512       \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 12)                6156      \n",
      "=================================================================\n",
      "Total params: 15,444,300\n",
      "Trainable params: 7,808,012\n",
      "Non-trainable params: 7,636,288\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras import backend as K\n",
    "num_classes=12\n",
    "model = vgg16_model(num_classes)\n",
    "model.compile(optimizer=SGD(lr=0.01, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()\n",
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "cjiblda2UYnM",
    "outputId": "b44912f8-44a5-4a84-d84c-95eff9ea1356"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3325 samples, validate on 713 samples\n",
      "Epoch 1/20\n",
      "3325/3325 [==============================] - 49s 15ms/step - loss: 1.3425 - acc: 0.5510 - val_loss: 1.3853 - val_acc: 0.5820\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.58205, saving model to ./weights.best_01-0.58.hdf5\n",
      "\n",
      "Epoch 00001: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 2/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.4658 - acc: 0.8415 - val_loss: 0.6232 - val_acc: 0.7700\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.58205 to 0.76999, saving model to ./weights.best_02-0.77.hdf5\n",
      "\n",
      "Epoch 00002: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 3/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.2813 - acc: 0.9035 - val_loss: 0.5201 - val_acc: 0.8233\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.76999 to 0.82328, saving model to ./weights.best_03-0.82.hdf5\n",
      "\n",
      "Epoch 00003: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 4/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.1770 - acc: 0.9414 - val_loss: 0.7394 - val_acc: 0.7518\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.82328\n",
      "\n",
      "Epoch 00004: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 5/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.1169 - acc: 0.9693 - val_loss: 0.6742 - val_acc: 0.7714\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.82328\n",
      "\n",
      "Epoch 00005: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 6/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0671 - acc: 0.9838 - val_loss: 0.8327 - val_acc: 0.7153\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.82328\n",
      "\n",
      "Epoch 00006: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 7/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0415 - acc: 0.9913 - val_loss: 0.4899 - val_acc: 0.8331\n",
      "\n",
      "Epoch 00007: val_acc improved from 0.82328 to 0.83310, saving model to ./weights.best_07-0.83.hdf5\n",
      "\n",
      "Epoch 00007: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 8/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0198 - acc: 0.9982 - val_loss: 0.3206 - val_acc: 0.8934\n",
      "\n",
      "Epoch 00008: val_acc improved from 0.83310 to 0.89341, saving model to ./weights.best_08-0.89.hdf5\n",
      "\n",
      "Epoch 00008: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 9/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0129 - acc: 0.9985 - val_loss: 0.3287 - val_acc: 0.8920\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.89341\n",
      "\n",
      "Epoch 00009: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 10/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0118 - acc: 0.9982 - val_loss: 0.2766 - val_acc: 0.9144\n",
      "\n",
      "Epoch 00010: val_acc improved from 0.89341 to 0.91445, saving model to ./weights.best_10-0.91.hdf5\n",
      "\n",
      "Epoch 00010: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 11/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0053 - acc: 1.0000 - val_loss: 0.2869 - val_acc: 0.9116\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.91445\n",
      "\n",
      "Epoch 00011: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 12/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0032 - acc: 1.0000 - val_loss: 0.2565 - val_acc: 0.9215\n",
      "\n",
      "Epoch 00012: val_acc improved from 0.91445 to 0.92146, saving model to ./weights.best_12-0.92.hdf5\n",
      "\n",
      "Epoch 00012: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 13/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.2567 - val_acc: 0.9257\n",
      "\n",
      "Epoch 00013: val_acc improved from 0.92146 to 0.92567, saving model to ./weights.best_13-0.93.hdf5\n",
      "\n",
      "Epoch 00013: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 14/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.2435 - val_acc: 0.9313\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.92567 to 0.93128, saving model to ./weights.best_14-0.93.hdf5\n",
      "\n",
      "Epoch 00014: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 15/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.2494 - val_acc: 0.9257\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.93128\n",
      "\n",
      "Epoch 00015: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 16/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.2521 - val_acc: 0.9299\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.93128\n",
      "\n",
      "Epoch 00016: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 17/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0016 - acc: 1.0000 - val_loss: 0.2480 - val_acc: 0.9355\n",
      "\n",
      "Epoch 00017: val_acc improved from 0.93128 to 0.93548, saving model to ./weights.best_17-0.94.hdf5\n",
      "\n",
      "Epoch 00017: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 18/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.2508 - val_acc: 0.9327\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.93548\n",
      "\n",
      "Epoch 00018: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 19/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.2514 - val_acc: 0.9299\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.93548\n",
      "\n",
      "Epoch 00019: saving model to ./weights.last_auto4.hdf5\n",
      "Epoch 20/20\n",
      "3325/3325 [==============================] - 48s 14ms/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.2534 - val_acc: 0.9299\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.93548\n",
      "\n",
      "Epoch 00020: saving model to ./weights.last_auto4.hdf5\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,CSVLogger\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "filepath=\"./weights.best_{epoch:02d}-{val_acc:.2f}.hdf5\"\n",
    "checkpoints = ModelCheckpoint(filepath, monitor='val_acc', \n",
    "                              verbose=1, save_best_only=True, mode='max')\n",
    "filepath=\"./weights.last_auto4.hdf5\"\n",
    "checkpoints_full = ModelCheckpoint(filepath, monitor='val_acc', \n",
    "                                 verbose=1, save_best_only=False, mode='max')\n",
    "\n",
    "callbacks_list = [checkpoints, checkpoints_full]\n",
    "\n",
    "#MODEL\n",
    "# hist = model.fit_generator(generator.flow(X_train,y_train, batch_size=75), \n",
    "#                            epochs=35, validation_data=(X_test, y_test), \n",
    "#                            steps_per_epoch=x_train.shape[0], callbacks=callbacks_list)\n",
    "history = model.fit(X_train,y_train,\n",
    "          batch_size=64,\n",
    "          epochs=20,\n",
    "          verbose=1, shuffle=True, validation_data=(X_val,y_val),callbacks= callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 638
    },
    "colab_type": "code",
    "id": "7WSasMhVf6hv",
    "outputId": "bb116586-e244-4dd7-d731-f6627c886f72"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-fcd7206c8ce7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclassification_report\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mclassification_report\u001b[0;34m(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict)\u001b[0m\n\u001b[1;32m   1850\u001b[0m     \"\"\"\n\u001b[1;32m   1851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1852\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1853\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m     \u001b[0mlabels_given\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n\u001b[0;32m---> 81\u001b[0;31m                          \"and {1} targets\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of multilabel-indicator and continuous-multioutput targets"
     ]
    },
    {
     "data": {
      "image/png": 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qFY0x0REvvY/OAd6o7KCIjBWRWSIya926dfsdz8zMpKioKPIPsgYNyNjrpoewVdj2p6oU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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "epochs = 20\n",
    "fig, ax = plt.subplots()  \n",
    "ax.plot(np.arange(epochs), history.history['loss'], 'r')\n",
    "ax.plot(np.arange(epochs), history.history['val_loss'], 'b')\n",
    "ax.legend(['train', 'validation'], loc='upper left')\n",
    "ax.set_xlabel('Iterations')  \n",
    "ax.set_ylabel('Cost')  \n",
    "ax.set_title('Cost vs. Training Epoch') \n",
    "from sklearn.metrics import classification_report\n",
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "mhxOj5ThrdRZ",
    "outputId": "dc8de48e-9def-4ee7-a40d-dec7217d3043"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.26552980972893453\n",
      "Test accuracy: 0.9185393258426966\n"
     ]
    }
   ],
   "source": [
    "# Printing test accuracy\n",
    "score = model.evaluate(X_test, y_test, verbose=0, batch_size = None)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ERHSV8AkUruy"
   },
   "source": [
    "Prediction using the saved Model and saved weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GryLzBfug8FM"
   },
   "outputs": [],
   "source": [
    "from keras.models import *\n",
    "model = load_model('model.h5')\n",
    "m = model.load_weights(\"weights.best_10-0.92.hdf5\")   # Call the last model weights best -- The \n",
    "#next argument is the accuracy of the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "xGup0dseg6Kc",
    "outputId": "1f27a22b-6ca2-49ed-b306-b69e916f6306"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loose Silky-bent\n"
     ]
    },
    {
     "data": {
      "image/png": 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gobBeXvDSOR4X0LEXHSNyc36h5hsffvErnPM0bdS84sW9FRYJkYGgXsyc5oQx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\nwxcnrq9KoTE99PTduy85v2gGQEMVZqC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HeEd1YMRS9qzBOrWo2M6Itrlp1y2K\n3LkS8Qe/jz+KQ0GkkdaVfVt6QpE++DlnvZ1j4Hy+MU96ey/rquirpFTjUjMhDMRxoLTK9XYhDkFX\nUn2KPs0j4xSYH4/MhxEfhz5AqzCO2KdHXuJH9mMhpcz1cyNtK9u245tXeSqVPek2Yt0KoKEhfjho\nQlNUk5TpSkpjfmfNraIvfFNFjbIFnMO0pg95XfWbbhDj8K/9fFwfOJp+RRigVV3F5rpTqvo3jEGB\nnaK7dWusJi/bA60VrNWfn7MdGGs8oNBW75Q0rNPumVu7Ihiul1ewcHn9xBBnrA2cP77oS1sq18sr\nX3z1jjjoajRvCayuMauozqB1WnQD9pR18IYqH30Y3n5H7k1WLj1pS9tHBG7Xs64eU+pVmw53axPi\nfND2IJf+gjumae66BemfV2XXrRVy3nVA2wVu3qlk3Vun9mWB2+0GVas9d29FnCVlhc3SWQVa9SVa\nyv2AEXIrhHECZ5kPR/K+I03FVK1WsHqJpWVlT5pQMs0j+54opbAuC0/vHwB6zqVWOjFo5IFzjjiN\nXF5eyetOTYXW1Cg3HVSJSlGLvIBOts1PrFIAoJYeNw657Nggbw9Xa3A4PipItQNJ231/31eWVRQF\nL60SYiQ6ukZgxzsYDwPzaeTwcCQGvXH9NKnlN60gwpc/e0eujT1n3v/sC37zF3/B97/9npIs66a6\neNdf9HF+7ElGO1IzwzDghwnrA2A5zAdu1zM+OJZ11S2KgUqlVpW3eus5xglvLWtJSGmMo2YEDiGy\n501VdG4g7QtNBk6nB/ZtpaZESonb9YLQ8EFvghCGDmMdlCAlQnGFMIyUovvw3exvOn8/RIyB42nS\njAunLY3xB/YtMduDrlGdJy2ZVhJ//vKneOeZnyaO706cvnpWsEhVW3YpGTeqKIq+8RHv2FPCrsLr\nh8/EUbcy3jmi91wur8zDQG4Fa7Q6sjZwvbxQ9sR23WhN2O8Sc6vrttP7Z8JBBUnafkGulRgGgh9Y\n17XLhCvOeZZ9o5bC+eUTBjg+PlIRQlBfjR8CUvQwkCYa9C6maxgU1tJax7lLZV9vlJx1XWu0LXX3\nWLtS+fa3v0Fq/5mKvm7SGlhPRaMI55Pnsu7YWrndrlj3wHq5Mc0D8zQwzxNp1fg37z12jDRp7OvC\ner6y3Ra8t6TbleBgmh4Jwwi1cb5cGMYJyk9M0QhK/bViGGIP5RC6KEdvGNdNKWrquVOJKrp8o1OT\nYr8B6TCRrIquEHHBMk6TipNEQzmMMYzTzCWvmgUQFQ4aPIhUpsOBUr7BB0f7vV7YWIWC2BhxYeiL\nXcE7pRzX0ricX99uJWvU6OW6lVpoOOtwxqo8WCreea7XF1otTPNJ99nOdeGOSsBFKrfLWecj66Zb\nGozKiiUxDlP3k+lK1LnAnnaG6QBiaE2lvWI0OSrGSBxGtn0hVZUZRzPqz2bdqdJwoqhuB/gIW9GH\ns0glHCLHd48qMmsNRIVgtQmSs7Y+RQlWzUiXmMP54yd8cDjvOJ5OeAvTNGk4qvMa6Godt8urqhZL\n05DfsumLGzSzwhjNIDV9w2AUkolzXofMnUYkfVhYem7Fvm66mfH+LY9SByq/E4nVqupZ71zHuemB\n6kNQEnYH1NQqYAJ7rkDFi8f+3kxPke1amUFWJP98UEBrfUFkJYgw9OFliSP7trGvKzHY/jyeaEHp\n1CUnhjFwPBz4rqgl3ljDvm9YFyg5U3LBic7ehmHuxr8f/i7+KA4FzXFUhFne9h5kYjREIzjePb9j\nWxbW5apDSXRl1apOzgUFTaTlpt58Y0giHI8HxsMjGGE+jHzx8z9gmGZFl5VCbZXSCr/823/Cvlx5\n/fBbTg8PFGl8XL8nb2diN1T5ISo1qevfh2FgGgcdGOUVqYm0XhQAagcFffSyz1jTnYuJkm9YHwl+\nIJiAaULuX/MQtPfP26I4M+9Vvt0luuv60oUz8U3VWUvWjYj1bMvKstx4eHqvKHrvOAwPOqirCsQF\nqweu8zSBLe3Ew4FCRbD4ODEMkT1nbIBIJAxBScRmY/IW03RO8cs//iOmOapoJyiWbk9FQbfLQqgN\nSsMFjw+eUlZaFtre2JuWvMvrK1/87GdgwAXNtHz5+IHry6uW7Fb1+63oFkERY5V1vTCOI9PxoAeC\nyNvQdJ5nrPMKSulaBucdPgau15sKovDkUpkEak2UrIdvaQp1FaMYN2s0ddr0mUPaE7XCMB2pOPak\nGZx7b0XYE8E13fikwjd/9qecjk/Mx4npNOCdhVJJ5xfStmGNZfC68fJAtA4/RMYpovq28qZYLE0F\nTGlZWK8Xrp8+9+9FuvZDZ0V5SzQMVgzWBtJ+I04/sdg40PWToGqtKjqJN2ilMI0j+7oArcMyGiIG\naaX/OS0PjVXkeNpWTGs8Pb1DmjAfZ3LdOF8+Y64vTNMB77yubPLO7fUFby3BOmX3CXjrGEIkdPlx\n61NllUYPOik30mXCM2mHltc+2b6LbeiLIKG0TEPVeLVWTFRIx76vWBoheNbWg1+buh41wapSiiLP\nc9kwOHUXutA3Lxox3lpTk09r5FY1irw1ai0cHx576JMeUt6qInAYdAZTcsFFh7bpgmyp26KjDqmc\nHnKtNcbTA8v5ynicGcahe/ZdH7qpCK0UzZIoOTN01HgYNeVLwaJqha8i7PtGSgnnPS5aLucXLp8+\nqGbEGtUCtEZ0amGehkHbHAtxGggxknclEGmGg85ehiGyrwu1MyRrbQS04qRrUSyGfd+VldEDiW3n\nODqvWQ6tXwIiovZ+qy3ClhLOB02kkoKp6ti1Vu3Ng9ftDP5IqYIYh1hPHGdK23HGq9qxR8QH43HB\nc3xwxHHEWp39lFI4v3zuvImqztaty+iz0EqjSdaBdtP5iyo5TTd9ZbZ9/ellSWovpvTmdCcu14pk\nwZjGh+++AVEpZ+5ORWcC4zCo9txavDWKMO9OvevLC3/2p/+CL7/6kuV2YZgjZVtxFhbvNT2p/0I2\np4eJdZYSBlIt7OvONI88PM7subKLhrQ8PDz1r1qUMVALe64sy9YFTo1admotDONEM5qhUEztMFBL\nfIO3GEy34rYmhDD1UlfHy9Zpb7p1aIymyuvLfEe6i6HrMe43YmTddg5B4S/TYVZTVYi00sh5w4SR\n2hrbvuOjxzRD3bWtaQYKgojrTrvE7fqqg799oW4rrQlxfGK5XLsJh25KQzUgzjIfDl04o2ay23rT\ndsdCKlrdNWOpufDp48fOUdQDvaWk7WL/+ZR9xY8RsCyXV5DGeBh5fP8eavsdy6H/d7d15bYsGGcZ\nwgA9H7OWQt0WpCTu8Xz7ulMdyKB7f18y1t/5EfLWNjRpWjl0gZXpYT7PX35J2hJz1n+/LldSV082\nafzib/+K88dvqUZJ2pfLK+++/FIvGDfqy43a66UToBDBW5X2lwy3666yeISaMq/ffw8N8pY0gNc7\nXl9eOT0eGIaRfduZxkHl5lm3Puv2E/M+GGtUh4By/1x3e4mtlJZgF32JrChtxyp6axqPahiqmYYO\nX7Z9ZRhGTu/f4Z2n1oQk7f+oGW9gjIrKOh4O/eVUKaw/HHn59IlhOuCdg+j52a/+gG1PfPjw2vX2\nC6kUfvHLv8Xt/ArGUKpw/nxmiE575eCx3rGVDTNEcI7r7coQR70RjIei6U+hqRCp1kqpCcRgbD80\njNp+Q4eQWCOqxe/bjThHihRsp/UYA9PhxOvtzNAah9OR+XBk7KyKvCWOpwdqqvgxIEZ0wr4njOtq\nUKNeAxsCT+8ez30sRwAAIABJREFUWJaVj/U79m3DFNUsHJ6eCGHACqppcO5t01Obls6t6Qp0nmeG\nOPDy8kIuun2ZD0dSz+CcphO15jersjEejCaBS1WfyOAH5VnU1iXulV/+8u/roVvVRm1EuQ/LdSEO\nA00qxuq6UFBjVF43yp5AKnhd43hrsfSsDXEUXSdw+/wZ66KyNHTijfXuTQzlvRqrxArTceJ50JZl\n23eur59ZL68qfg+OyhOmVYw0rW57OpkNDmsc67oSQqCJwRilit9e9XmxTjM6XPBa9RjP8b36Txpn\ntm1HRAOFa7NUMapT2BLWOpZ1QWom/jVwbD8O7wNd1SgQ4qC0G2mYVvDGqGlEpAtA/nXCzj101nci\n0zAqc0BFQ+V3obPYfjsIVSpYS7WWavVBKBjGjsLOOXfDimC9hn6Mw8AQh45Pn1k2peLcS9YQIuty\nI+0JjMN08VVtlZQ1gKaURPC6f9bVoO3+eJ3Y6xC1qyqtgVYIzjEEj2m5OxydRo7ReQt960BfrY3D\nwDQddE7jvWrxe+is7Wg6Y8zbnv0+gRqGsQM8VESDGPY9UxvEYVKGY7OM4+EtYk2VhHdBjuZARh9w\nxvQ1o/5eb7cbORftvffEnjNCn+g3pSe1jicHg+mHirVKDkKUrNRapVEZD4ee4SBabfXvFUFbzu52\nND03xKDPTd533ecD1ltCDITgmOcZ7/zbf+8eAddjH7TVsB5E7dUxjG9tW6mF2gr7vrFtC4+PJx6f\nnlRZ6XUoHmJ4a2u8j29+FumOT2uNoux8ZDocqU21DrWzRFoTDp2CZaxhPByYHk64GAjRYtDfZa6a\ne5pyJeWqw08bETzpr2Gd/lFUCup+RJVkxpBSwfsBUwo2K7Qk5UScpm4PTUzTkdZtqNao0aWJbgas\nNRjb+n63Yp3OH4zRodf8eEIQrvuN4/FR/3yIvF6vOoDrIAsdEFakCvM8stxWnp/f06zV8tZpnzzE\nwDxPSF6oOXG7XLTctDvuMGhvWdWNaPrBZsV01aCw3lRye89uDN0A07o1u1EZp9APOFXa1dYl187q\njl10gLreruQ9sa0LX/3s636IQLDKRCjG0j1G4PSms8bSaHgf+OLxa9ZlUV8ICs492CelO98q620j\nNsj7hsyq329FLe4heA7Tgcv5DNIIceDl0yvnz5/1UDFGB2JDZzeu21vLU9IOaPCO0EN8++9WGkpn\nEkcYI89ffoWzBgcYp0BT6xxircJvRDrqTZWD6+sr23JjPX+iUfBBEfMxBm6vrzjUlGZao+aKi4OG\nzPavuZWqVvLer4fBMsRBtQ/S+/heCby8fkJqJU6jYvF9JJ3P6gZ9UPOeNAtiuw/Dv+Huam2kZcXb\nQDHCeJjZ9wXTYLldCVFl3DUXrPc8fvklt5fPLLcVwVKN71+TQ5GMgnMzUi0fvvvzH/w6/jgOBdBo\nN7UPUlthDF5JPwZySer4E4sRo7v8HvulwRie0j3mWt/pw2DQPi2lzBgjraqwRmWraHlmHd4pnzH3\n6bazDpHa+X9K/3E9znzdMpUeEpI3mjVISeTtqsagVtmzAlf3tDEevOYaim4L9CAoiGiMWc1Fo8o6\n79EaQ6u6brNWZbUGi3E6YyhVPQjW9AT0ou1H3tcuydVbLoZBDyFjOmNC1W7OWppzHA5HruvCOE/s\nm6Z0256/YLrYJY6DUqya/gzvUWlvblW973V33quel5ePOrTrjtN91QwG8UZL4Kr8Quc9r58+czh2\ncxsqT1dbhrZHqmlXYEATrRSGcSYOY2dSaMqXdab7UvoKsZR+sejX1ErBomtqFzSvQoxCX2Ic9bM4\n2yPtXK865A3+QnepGufxPT1KW6Wiw9j+DDd6eWHAhkgQUd7Evt9FiJQ9U4KucE0Xa90DaQ1Qk4bN\ntFK4nc+aojWo6Mx7T9IOsw9FPX4YCUXAOLak9ncbo3JH0AE8OA4PXwH34LZ/88eP4lAwGEou+DBS\npZI62MJge/nmu9bd45320WIUMyWibYI1QTP6bO/7RN5izePoFW7qjJKGWu2qQMO2rcQw6vyiqkov\n9/xE7zxJdHiJNGrdWD5/Yl0Th4dHnBOKVJwzzHNEqCrAcoa9VKbTI8U2pCmAdggz2+3KEAZaSYr9\naoKJA6XseIv2lmUDa3BecXM2jN0SPeJcVKR8s7y+vlBbJjidglvjKC0TiOzbynq98vj4SJGi/P+q\nWRkIrKtawW/XC957RcRX1S6sRVHoKSVqrVxfL7pO7S+bdQNgCXFU6nLNOgu6m3RM4Px64/XzC3nP\nmr7ljIqsXGC5XvA+MI5Db5tAulZAN0ja4qisWi3TQsM5j3e+szsLwzizbRvDPLHtSdsF77Gt8vT4\nwOeP36s+oGlmiI/hdwatTbcHtaqHhNz08DXqLA3WsRe1pQ+T6j/iNLHtu5rpkDcVqEEl7KVVglOk\nnPVB08IMHB4f3qArrWRaUvZnQ/SwN5b1dsNbT4yRbVkJzut56GDfbhiJukqX3NOkuoQZo61wrT2t\n6z6sV98Gom12mE/8pA4Fua8g9d1Dj1pHkdxLQ+UBWusoTWm8YqHUqpRdY0h9//9/UvfuMLZte3rX\nbzznYz2qau99zj3n3ut2G+wmaEsEBJaIkIhASM4sEQFCcgI5zpw6RUJCcoDACY8MAjIkRESECGxM\ny23Tfbv73vPYj6q11nyMN8F/rDrXBtzbTds6vaSrc3ft2ntX1ZpzzDG+//f9PrHzamqTNKLtwBIa\neCtIsprF2FNbg5yIVZ64Dfoo8X4elKfRvcOgloQicZgt1hS0loTc45t3LMsNtNCMW1xROXLLG3Kh\ni5JvrcMeho4dEz0glp2YdoyWKUyp6dXWLOdM98oFkHy9IufIum7kInyGkpLQn5SoDUaL6SnsG9fL\nC8NhQht5Akt7lhxhKshWurXXI9y+bRwPJz7u76UvopQftIdOU/aT4OC3Dp71Vm7amqIkL5vmdr2x\nvNzw09T9BQqDaA0KRSzy75ciqVb6eHLwXvQE24VVZWhaQTMYJTsBmTY5KbipjZxrP1plWmxohMqV\no3z9dw1Ha4Hs1CoLQL3DTPuOqjUByQ7jhHGOcLnhxxHVa+BTDK9awt1G3v+w7EByD6R1SpR3nj1u\nQva2HoroFa96mHXUKrRto0Xey1nawuzge+t5RQ5KInCOk5QJ1SJCbqlVFoXODm29wqBWAdrIbsa9\nMkk+5/UjWRTgcDyjtMFrzUE9EUvEjrI1PZ3P7OtCCRtU4QSkkqUwBIFPiOW590agKVXRrKb1uvLb\nhxsPx8Y8jrjBiQ04BhG5ciO10NuKM81YrBa+v7VampSUwtA4P50w1jJNB8G0t0Jrmekw0rShANPk\n0HFnvQhGSzeYhxOH8YjDst4WShWnYzWmU5+lBk2pRolL9+NL+5LqqndrilwzKQeg4EwP45TEtgeM\nHfqcWjom1+uVkgJf/vw3MK78ELTqCcvckXQlJ0rtmPxa2Vah//hhIO1XSm7MxzPVeIy1HE4nxsHz\n6cP3KNXY1xvTNDGNA4+Pb9hD4OMvP7DeAoeHxnw+MJzFvp1jZFsD4zgxHw6ghA/ZSsF7mRrQCobG\n4B0aKYyVbXDlfDziO42qloobp1dzUauV0qBRxVOgGsbAvkqCMedCzeKWRckCV3ORSVcV4XZ4ekss\nFd0S3vnX4ww07L1whx+0q5KjoO0GeQ9TP2apVskpst8ujF7eF9E/RIOpteKUjMXFqWvQRlKt4zxJ\ngrZlnHUiVLb2amdPcaGESI5R+kwOx36k6CYoP8jYtzXG6SQt7H1h+ZzXH7soKKX+C+DfAb5rrf3l\n/rE3wH8L/Cbwe8Bfa619UmL7+k+BfxtYgX+/tfa//XH/hnDvPLkr1k01GQXlTFPiZb/XX+UiuX3V\nQa+tr7ymb6dqP6tpLfVeYdtJe+TjN98x/fRIeTjhu+nGWEvZdznrd8FInHfl18g6MgajCHHHWMsw\nzdLF4MYOyhTDU1ZCWk7bQsqBSsMaR80VVTVh3Qi5vjb9tFQwSvXASpMwWJM8gEIAITkHVHV91i4g\nTuhOBiVqvXYebcXnYZpl6Op9TTvUwnq54ga5oVOJnSYtIznvHFv3hmitezWbk11FjJ2AJKKYMhrt\nBFVfugEIZC3ebjth2SX40yBHsW7HPWF9FLMZYiMep04f3vf+1JbvpeQghKOacc5iZWzQTT6irUzT\nhL2nUIHDLJqE7ACFmH0nMEHFD57by7P8uguFkqq1xBBEm+gIfaUBY2QxaI3WOuWrVUqtGC2Tntd2\n7Jx7EE2zb5tcy7ngB08OIjxO49iPZkV+Lll2Lqo1KO31/TT2B7ao0L4K1jr5eJZsTW1y3M0hUlJB\n1cY8HaQPxUo9n5ItpUxTjLzHg/Lk9Ke7U/gvgf8M+Du/9rG/AfxPrbW/pZT6G/3X/wnwbwF/qf/v\nrwD/ef/vP/WljXmNF+vudrPGQk+53YMzdjzghypbMtUwNWKaeMKVtq8QjRwzRje228L10zPLdaHF\ngNYPgjnfo4wSDUzzgRx2AZJIiAKrLKWm3uQrb1COgnG3XlRt7SZAvl5U7QnIQsqZmFZCXCkl440j\nhZ3IlceHtxgnixW1UZ080fflwh42vB/JJWK1x1ojXD9st+RXqq4003riL2O0xfgJ40eaHXqRTelj\nT03LSYpK1hthXbFeyk28H6hVxoLPn96LcOk01lps7yq8uw/9OOGnk4wmlQUjinwqcoyjNubDiVIy\nNSc+vv/I9XJhPAhtO4TAcl2kWGaUGXxrmdv1WQxYSH2bsYamNJrKOB0wRhZOefWYeJPxjbGGWoWk\ntceANUaMYQg/IHdhupRKeLkIn6JCU0aeonFHKD7y1MYprFeEFEhpe11MBYJrcNMsW/8q3grjBmqJ\naGRcbjvsRI5CUEui5ZWaItYo4Y0qRy5VeiNze81r5CzHj9Qi3S1GTVGi6tb2nZNMe7QxpFRYni/y\n0OjftzMa70VXKrlgmiO3BEY6NuKe72bWz3r9sYtCa+1/UUr95j/x4b8K/Bv9//9XwP+MLAp/Ffg7\nTagW/6tS6vHeQP3HfiXGSNwzV+bjiel4IMQAKEJYxdFXez24Vjy+e8f3v/yljLIa5JjQxrLdLlCF\ne6cUTENjGmYUA9NpwA2Ca8t7wI6DJDK16iAOg9b2dduWY+L68hGlNMfzOxl7NVGcK4qSRC9Aa5qS\nZp8QN0LcKbUxGM/T+Qvco0HFLIJWaX2+rokqUoDT+ZGpHMTGbB0pBnJJDOMk8/LSUzUN8Vgo1R1/\nFT/PFGQcd7fjatVoOfYnLJy/+IrldpV8SauEKEYnKd4xpL0yHAUjbxvoThuepxF/nDpN2qBCkTNv\n1zwOh1nkLCv23lYL+21lPh3Jpb4SiKWaPbDsG/NhEL4DIPMTKYWhVkYLzjqGzuvU2vTxa+ltUVZq\n8O5MQxQKQ676B3xbSq9HCRk3KnQTlV9rRYoBqx0QgcKbL96I6t8aQxtoWZx/uQi5qlRoQaYpynqM\n0VADqrZXvF0MUToYtCGEBWcNg7kzJiXLYJyVxGZr5FowwyBekZQlhdl1DYV4aUxvdwKZzFmrqA2e\nv/ledsFGdqjDMIqLtMnsZppGSoHz4xsqcoRp7ZnWd4Of8/qTago/+bUb/RukbBbgZ8Af/Nrn/WH/\n2P9jUVBK/XXgrwMc56EXdAgxJ+57HzMJeDWEgD8dX48Lpqut1Ir3Tqrq08627qSQ8EYCOEo1rNUM\nw8C2XpjngzQE5STn8yhb4tYvrpKlJEYpw7YIGOPy/MK7n/xMeiW1JqfaU3xKHGt9BNVo0oZcErkK\nM8DbmcF55mEklCuqVln9k+C36WyFPWw4Z/HD1A1XlpQCe9iF1NyMAE3i9grzjDnivO9GF0dBy+Sg\n76pCimIsUkKMGoa5t0TPrMsNa0So255XtNKc2iPzcZZSVDeJo9JJcau2yFGhGbYSWG83lNWvpCRl\nJQ9ArbRSySHhlUBvlM6AYZ4m9m3p/g85R8coI0HBadZ/gi4kF3mt3NeO/v3Idp4qI2NjZDysjSxM\n1klSMMVICgHjPcY4WWCQLIFRCm0aznkOxyMlxz4SN2iqFPCkIC1jgJxpM62IZjAM068F0jL7vrNe\nb5ScGQ4z3hnGSaA13EEngO42L+O8TD5qBK1fi21pMp40CCFL9ZtfGzHBWWPIe8IMDnuf6DT5ed4T\nw9JP4qQxS+awGGNp/PNfFF5frbWmBLn7z/rn/jbwtwG+eHNu8qZp6BXa+7piBwnimN7QrO6se60I\n64rKmU8f38vYzhtx/40OVeWNNc72bXzD6Mbzh2+xxuCGkQaM84F1uWC1Jd3hH3e77r7RauOnf+G3\n8OMojMR7KWuWGTX6fs4XTsK6X9njSurdFPMwYakMWpNrz0o0mE9n6UmIUYpDZzEeNSXFH8Y55uOJ\nbYtcPn3i8vyMtYZx8tjJU1PCKce6bEyPsjtQtUDKONXVeSv2WNuqPGmMozWhWDvv0cbw5U9/xj/6\n9u9y+XjldJ5htJzePkhhjNJYN5Aq2EHq0y7hSokrYb0wHQ6Mw4P0VQxjX0wsu99Zl4WcM+fTgXVd\npSk5J9AQth03TIQgX4dqldlqvPWSIFQdIjIMDG7ker1hjCWFlVQa3gt2fd8rMQVxKzaFwnUfRBKL\ncSkdcQ+pys4IY6XqrxZai0yHA85Lj8YeAmhkkYiJsPVuCycuVnnqVzAV8gpNplUC1o0s33/Pd7/6\nnt/+1/8K0+HA8fHUH0YbMcgiJeJkwyn36iNQRkuTFuIhSSnIw6rKMUmPAqzVWr1i7WiKWCKDG9lT\nxFiPMV5cl0oW6ztYJsVIK5Xry+2z780/6aLw7f1YoJT6Gviuf/yPgD/3a5/38/6xf/qre45Ka2jv\nQIl1s5RCU1KQabtRqdUq/YUVqIKnqsjqq4yiBBk3ql7Wap3jnpNP+0q1BjsMHM5PKKM4OUPaA60K\nV29ZNpwbKTExTSMVqYg3uo/dujnoHqhpiLEm5cC23yhKBDhjNOM4oloVArM2QvGxA+uyIP0LlqyL\nbAe1dEHq3u5Te5Bo33bGaZanYxdQjRHjk9fCVRT0uLAERbCTsd+9xdtYqTzzgydnAYLIiLFxfveG\nmsTl553FGQVNMwyz+ATWXWbsCnFuTiMxCOpO+iDvRTsyBqv9yaWd4ng6Mx2OrMvCnqIcEZXidr2h\ntYKSOR5PuJbFVagUTcnTuZbKXlZUNy2pJiShfVsp/RE0zRM1S+38/WuJ+46qAi1VCFtB+jzl51C7\nOauielU7P4B5xpHpMLJzoxRLeo7oendHiv4kBTQ7GkH6NyWhPdUUo5fuj1IzVVXWfeGVF9qxaCKE\nin6QswS/dIfBKtW7Q41c/xrkevaeHBM5BHIJUMEpL/0XtWLciPde3I5FGAvnceR2uwqMZ9tww/TZ\nN/efdFH4H4B/D/hb/b///a99/D9WSv03iMD48jl6Qq0VZTQPT09UJRe9KZWP338juwAFMQhnIafc\nQa1Z2qT9ICGpcZA5te6gDWs783+UVqewUWri8PDEm69+Jk/kjn7LKVJS4vym8bt//3e4vCycH87M\nb97gBqEtiRdeBCLTG395rbm/EPJOs551v2KVkxSmUljVqCX20WclLldxzFkjZ3Nt0LZXxHWIhulH\nlWEc+clPf84wP4jj0BuULjIu7Z4AekdCSgVvh95SVQn7zjCNr1kH6x21Zqy5t11Dq43ju0e0VZwf\nHjmdZvECeCfpU2sYR9edk4nRGsY3b6m1sMfUcWkSOPrii5/w8umTjDhdISwLL8/PsotKiRSi3FBZ\nJguqIQW2dae1LLxNBhEcS6TQ5EihNSkn4raireXl228ww8ieM0oZjuc3r01RaVtI24obZlBy3m5N\nSMzKWk4PJ261kvaAs1YWWqVkkei06mEa2bcXDg8n9nWnobltsltZl0XO/UpGf+N0IKQdazXjPFDz\nE/vywuMXT1Qtad0acv/5e3nS9zC90ZbaDV0FjRmG1/pESkVl2d04q0gvV3HYhsDhKIJ6Wi+cjifM\nJLSv4+HI5fkDqMo4jnz87ltyTuy3lRQyqX4+ZeVzRpL/NSIqvlNK/SHwN/ti8N8ppf5D4PeBv9Y/\n/X9ExpG/i4wk/4PP+SJ0zxiEuMv62BRe69fORoUSHSFlQFgFEmEVW7AxtvP2qoiAJZNzIqaNYXgr\nqK5h4Hh4yzDPzIdjv1n6ea8LmNoqrNPcLt/z8PQkY552F8PkDFs7Ms4Yy5oDuWViCcQSWMJNtnC1\nyjHFDugmsJU+vZP0JXA8P7GFgHWePYV78kbOf10LMMaiJycXh3P4aaCUgLMGN09s20auEOOO9zO5\nZBEJgamLlNRGU7KTMNaRm4hYJSVQMB8PXJ4/Ybp1V3casvAEpItBLtb2CgKdxokQO51ZC8MhhCgQ\n25T7k9sQQiCsW6+t/0E8U/cgU7dyW6XRxuO9p1TBpOt7zV8plBj6uLmR1oWyruw501Ds6/4aNsr7\nIuO4Ti+epokYk4irKXI6nlieX3iV4nvuoqTE5fKMmyaGg6PUwuw92lpx2ho5otGE+GWsFWK2RCYx\n2oo5TJXeCK5lcqXlYfJ6LxkDfcyei+xuShHD1L1EWHZESToordQI1rBircVbTUYT4g2nh1fUvFJi\nnU8pMk4j63rDGEMMOyVLXb19neT88a/PmT78u/8fv/Vv/r98bgP+o8/+1+9/jg5GUYrUI8XZj5K2\nyzISDCFIDHgcWa/PpD1IfNgNDOPI9fKhOyNFjJKJ5oCiMc5HptMJO0l3ojFOsguldsOQ9DcO08BX\nv/HnJXJ6faaVh872lzOhdqIJtCIz4tQioUa2spCqmJ9S2Hn79DVff/EzSPL00spgfC9Gvclo6eXl\no5x3U5TuAi0OO2udEJeU7CKUttQmC1hYd8bRUnORUZwVpqDSkZdPH5imScCjSuAhwzj2Y4SUsI7H\nWRqwQ3ce1kYqO4/v3mG91J/d4bC1ZkpOTIcjddsYhgNpl4anwXuOpyMvm5TVrOvGvv+SsgW89zw8\nPPHd99/JsUQL4UgSjrK4hXWRHkVvGJzDxETNWWAkKHTnRaAVMUVSKZI3yYVmZPTa+kQhRllYaQ03\nH+XnqcQLcluvaGU6dGXiV3/0R9LfoFuPaleeX176NMiyXq4Cj5mObJvciGlf8c4Kjq/2UXKMYkfe\npfS11CDHjnDjy5/+jNF7VGqCxLOOVMVM1WrmviCVnCmh947mxvbyUdLcZaNVGWWrLC3fo5WHnlUa\nrRtvH56IMbBfP+GGA7UalpQ5Hk6i26SdvMkUpTYFeoD2+YHoH4WjEWQLLD0EAoVoiMqvtMFbJxZO\nmowpq3AG6JHjthfh9ylDo4mt1CicP/Dw9iusc9IlqCXKW7IYQyQNiZiWrEUpxXiYOT48cvn0HpSw\n+u7hFXmCdz8DDW06yqv2SvomSPdxmIWOhEJVhXNOoqsNUFLxljpuXSHUalpjnmZqFTuudYM4NpVE\ngeVBJZVixqgeCxYQie2mp5wiw3Cm5swwDZJMbMKgaEocfeM4kmN6tcbewTD+4SwTm76llVcTg0//\nXJTcTJXaadqJqivHQSrOROeRCVHeAznEfuyqYjfv/n/vBzEYxUymCP/ROErIHfJaqEo8KjEmiTAr\nTaoVaiPl2t8/2dkY19/Lbua6U6JcR6k3DPyak1B2Ql58IVm+xpQLKiZqEfNQSRKiEniLYXAjtYg2\nEG6517zJiFMrjdKV6TgxDEMvq9Zd7BG6kvUDORe886SUCTmRe4VdCxvb5SOneabm0LWOXqyLWNut\nFeu60ppQIkYrUtqJDYbZiR2/Cmyn9UleqxLkyqnS9Off6j+ORaGJNbnWyjB4xmlm23b8OPZvrMpR\noiRS2Fkvzz+cj3Mkhu6Eo0KV7eLD259zfnqHH0dqSSKsVVBKnGOtJVLJmB6zjXHHO88wj7z9+is+\nfP97KCWdipL7F+BH7f2Pxhn2cGENF9I9uYIUuGg0JSRZ8e1I2SMlRhFQjUIbwW+VXNjWFVXkqbcs\nQWAqg0Uh6C6Ufs3dD6OTdmPVcF5i5KULfU9P77i+fGLbFuww9C21whnPuq+i6DvLcrt1oVR3doCg\n4WNY0TjOT1+KMJYysRSOpyMhV3LOaF3IMaCsp0Yp2lFaotnUwnwQ80xYrsTbFWol5oqy4o/Q1vSe\nB/DOYlQRMtA047Vl32/EfRf+g1LElAlJdg+xZNCOsNw7P6vkNhDGpdKGGgXxvvenpD+c5PKSu1ka\npPtok6rYl42mGy8vz/zky6+5fPrE+1+9ZxwV1IIzlnGUFifvPGoUxHralg6AAWcEj+e9ZZzfcpgG\nvBv6YtaI5R4Fzxx7MU5VmmGaSDlTcyVcXphGwboZI72iNInGWy9GthL3jgsEVSRAtiO8j1oD2nhS\nWBmnmRpyz/LIqBYyOe6ffTv+KCArEk22XcSRII9ScDqfOD2cEReAXMCC2M60cu/vS53GK1Sb1grD\neGB+eEI7K0yCzhC4p8ZyLtRGP7/Td3QC0NDa4LzneH4iRbHhypNu4B7rblRSDaQqbMA7Mk0p0REM\nmnvVeq1NuIkhyPa3ya/D1ktXYiLGxL4FbteV621D2QGMJUtRE7WJmGec6wsU4uFovNKIrLNopTus\nVPwCYqvN/RiWWW+3V/X97dt3ryEt0/sl5eL2r+GccRwFV5ZEBBXkmoxvby9XSpI6+BxkQtF6DmRZ\nXpgOA/PpIMp6EyR6idJWdC/7FROOHFms82itcN5L4jAXSTh2YI2UmYh4J0q7TDLkGFQoKVByFKHW\nmP4kDZQkCH76WDL3EJScdBWHwxnvPDFG8adcF2pur63ecIcEF1LYBbLbRdh7JkIgJ/U1eLdvq5il\n7L3DRP6OfbnJQ0V83TL+RrPte59wqFfNQvXxtNIK30fouRZyu98N0ipe8w41sq2X7unptn2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eGGAWm1Gs4vL2gt5+A7iqw2qFWxrZucO/vPQ5OR2DDOneTsUdqQkmRKprhJ\n0nfP9kxR4uhzLYzHB4bjEeMHYmlUDGE64ucjhw/v8ccj4/HI8d0TT7/4Rh5o81M5a4xMUVqvPiRD\nU5yYqSRKLQzz1MVNrTc+JUeytcaPv/0Nz58/9R1Y+jVvYS1d7JR6XD0Izq/ezVVKv4miWiuiIcmR\ndV0lhxKZHCilZJpiZQFtSqoNUU1KxXZPotq3VShZRWLpb7eLHGe1BuswPqCMYRjFewPSz7lH1aec\nyKWwx0jc40+4eiUZFNpobPAih6ZxXTb2VPn08RNfPn7ier7JmDjJv98UXF6fv/rZ/AtVCq21H+5/\nVkr9e8B/3v/zN8Dv/qlv/Z3+tT/zMsZQaiKVxnA68c18lPlw2dCmYWyg5igOtesNPx0YDw8kRMop\ngR8C4XQqUHRj2zPXbac2TdKKvSgwDTcbwgeFmx1buYDunfyaMXrAqRHXPKoqoJI7b2GvjVTK2yr6\n+PSA+/AOjWI+PYn1uLVOYAKrFcvtlePpAYV0jqkZYwPBe0qrjKcHmjUoo0WF2ZCodi/y5pIEMW+9\nY3p8IK6RTz984eHdI9ZpOU5VRYqF2kNxtJcx6ZYS5Xzhplf84AhTQFtRX15fLhK1Fw6SGKU1VbXO\noShoOwgxKGVcGHtWgidFcVMqIxQjEwLz4YFxPtFo3C5n6RE0UNYxhBF/Ej1JqZIUPj8eObxcuF5u\n7FuiGfsWytOKHL+M95js4dKY5gnnNRiZBCgnI1iM5nA8YVWjJPG5pCwVC7pJIzmlt/m9t4Z9vVHp\nnMO3ZKo7bEWgNCVHatXkXKWiQ70BWeh2fgDnArU1Ym9S5m0nrRvN7JTWKMtKLY35eGCPOykn2iYj\nyGmciNsurI1aqAoMXcMCXYMjQJ+cRb4dQiDtCeMtpWam48RtcqRtZRwGUinsBa4/XLi8ZqbRMB9H\nwiGJw9d9fU/hL1QpKKW+/1P/+U8D/0P/838G/HWlVFBK/T7wDwP/3Z/3erVIjPzdKmqsrPRoTV/8\n2VaRltaa2baVdVnww0AY5z5r1tQGl/OZbY3EWIkZEpaqHUVp7Dhg5oAaNFlt0LFecnZ3OBNIy07d\nCxqh+6jWiYDtLvapGCNS7HEaBNHVDS2qATQxuhjFNI2U0jX5TWLHttuFnPZ+whOIDEqjjBVVo5N0\nImngyTgxl0LsxOe0ZdbbRhOM8VuZeY+a11p4i9u2kkrlcr3w8YcfeP5yRtuZUg3X65XrshDmI9oF\nmd27gPMjp4f3HVhiBDemFLUK+CSmgupoNaU0Jgw0Y2Ri1KcXKSdqK6QYSalPg6qM9GrJhCAZF3fx\nkXcOhYwEa5HvPZ6OhHEU7YVqOO9ASZmuVOds9l2/lNpHpLwlgIm5TniRCkjbxu3yKv4JDVo1tvUV\npQtKVxG7qE72LiKtt076WnvcKEV+DxBoTgiDBAOVTpZu0r+IMUnTsIJCjgRhkAal1pp936FplmXt\nBj2pCGu3pEufTKqgOzgoRglJKllyJk2fMHhv8cERgsMosXhLi0lzW3a2WFn3wuvlxuVyZdniVz/f\nXzOS/I+BvwZ8UEr9GvjXgb+mlPoryFvwR8C/ANBa+x+VUv8p8D8BGfgX/7zJg/w9cRBaP2JspSHU\nmoyYlfz4hFcD8faMH2YBq+5XYs8HrKWQS2VfFm57I5VGc4H0p7zk2ij0SWFPlqI3NIYSM7UodNU4\nZRjshDeKshWg4m2gpl0SnEoS3uGHd4RhkEDQ2rA5UnIkroUhDL3p5PHOiY6+dMUkBa0EdxZj5fDt\nI9pJt1hphXWWfY0YpUSEXGQHak0RpiPGeLbzlXVdSOkLPnzLMARu1wthmHj/3Xd8+fJJmld+IpfC\n9Xol7pF9u/Hy+sp12TnMJ/7oD/6Qpw/f8P1B3JyCEJcP5PUiGQVFZ5Ebr7e+Y4p3IDWEmDyMND+Q\ngXVbsUr/lM9J69ZkIR+hIEZRme7LiipwPBwp6cJyXRjnk4BbSgEjzMNhHBnnmXJ7ZfAORUF3TalR\nimAlnQlaB7DuqGZQVuOshBGbVjHOcS07RhW29cJ3v/u7YscOAocpVcJdtBN/hfOhcxrAYLCDllAV\nJWxO650E4ThHrY3QF7MlF8bDiWrk2GO7F+Px3RPaGl5fXmixEqvkPWqlGaaB1mTxKkmOm9Y7Sqtd\nfKaxfdOh+4LiXpDAQEWwGqUzbnS4YinXXZSrRuOnI1jHba1UZUn1L7HR2Fr7Z/5vvvzv/xnf/zeB\nv/nVPwHI2KlXA9q4t4eiEim5cCmJmhItNzljtkxDEWygFjnX7bmw5kZWYh8uKJQP5FZxxoCpKN9w\ngyFjscqTyyZ0ZSU7T8tNouONRVeRISuEomyduCzHae78fTkDojSl7LI49fJynEaRrBoDTdh+TYsN\nN8eEbkqIvNZ3s4voLEznEIpSTyzDMpLrWQhakYpQmXIs7OxM00wYR9Z1kd5LRUahrSFZAAplAvPx\nyPOnz5w/P2P0gNa+i3Kg5oofHMZa4nLpjUUhPsd9g6qwxou4qFZsmNDeUxq0IuG6Wkm5SxdkKSMS\n8BACfhg4n2USs++C5C9Z7N/TOIivxRju8Wz0gBeRrmsJBVKqBz3pbiGv7HGXZHIUxgVaqgzDxPF0\n4OX5C8564rbJptMKp4cnhnliHAfW5YbRluCl9JbmdHkLdY1JcjBr99TUKu9t7j2iWLv8OVcZc1fp\nZaBVF9NJM/Ht3qDYLwLjxWiqltQw6WH5t1j60qnQ0uO4d7nkugcwc39WkMpHjp8KbwwxihHQhwAa\npsNJJihfvyb8fBSN67aRqwZnOi25W0INbHGnJE1KXsrS2PDeoW6vOO9oynC5ZWrzUk4bCMeTxMsZ\nzXo74w+Rp28fWOIzYEl7pXSGwDw84sxAuYmmwTmHrsJGuGWJNT+entBOqD1KKVST5OngPPt+k650\nlzfH9UatGdWhsPTm2J4zdj5ixgN2mOQD1xOivTdv0mGla5/1S5Bu2TfImaYaylmWdeXHH37keDoS\n44a5eabTE+uysm2ZdY29vG4oZd+EYcNBko50Kmy3nU+//djLZYcq7W1xa7WAMYI5l7q89wQs+74z\nTgeK1L2gEL1Ag1QK621h33eO8ywCp5Z5ff2C5C8otDO9ARcZZ0OKhRg3StICAmkNKMzHA68+YFrA\nKBhc4HA4sS8bLSViuZOS+tELhTOSq/l6uWC8g00R40ppmadffMvTt7+g5szl/EraxcacS6LBW0Sh\n1o3D45HDNPE///d/m8PxPaWPdXVXtBrrxHiVM+v5lcvLC+P8QIw7dgykfSN4yfWgaw6GMHLJLyLo\naqCsLPCuVfYU0Vo2GWMczln2fX1LOcs54a1DG81eMroWvIKWE8FLjoY2HqqWY3MSg6CxXkyFwQlk\n5yuvn8eioA2oAaUVKXUffBG5ZiuN5Xyl1UrOlT1CYyBuDWtHXrcEBmx4YB4nbufnHrutJGOgJMxQ\nePjuyBo/y267R/ZtwTTNPD0R9IgulqA1apLR4L6scuQIA7/41SN7ihjvO5hVnGjDMFGL6P9L2vj0\nw2+Y5hMlCUBDLLyWfd+w4wE3zrj5BNoKVUfGCrTWBGCqFBqZqrRWsD1/8LJuAvTUFheChMu0RiyV\nsiZ0jFxuCylmUm7QU6GU0j2JOL9lO6AUVSn2WPjy+QsPjyeR+OZEGAaWtVcISTIV9m0ROW1tLJcb\nIUzdmKTF0tzj43Mt7C9nUd4Fmfps+/aG0b8Tip2zpFtEG3h4944vnz5zu17E/NUcLcHx3QlaZTwe\nWNZnwnBEY4hRQmLue+edv2i14Q4u1dagnGDr9xgppXJ698TTt98guRGNVhVNOVKCpmS6FfdELZlh\nFObl4APfvP8F5+fPkn7dHYsohTGh93vAKMs0n0gl9bzQhnca32nKFUmqCk8Dcd1YbytrKRSliHvE\nWIMPDmMUzjn2EtlqxNhOxzbSWNxLhKrEa1ML55dPBC2pVKplrNV4D9u28O7bX2LGQRq2zqGU4unD\nu69/HP9yn+6/2NVqQ4xhkv3YahUBU4N0z2DI0poQ37woFjEWN0xYH4gx8vL8zPV2k5AXpQiDQ5uM\nHxXoiHSQm/jptcP6QVDZXkjIdA/7HjcZORVpHOWeA/jGZIBusxVJSFOqs/AsKUW2dWPbItsa2ZaV\nuEfJcBhniRcz+u1oQFegxRjZ17WXq+L/KKVrGnpzr7SG6uBXrKM0yAVSktyEnAulCkFKGU3KmVwb\nxg1dkWfkDGus5FvESEqZbd9IJXO9XqlNUSrEnOR4gBKhUkPSnro+4V5maxTTNL3dl9Ya4zhKCdx7\nFHFPbNtOa3A8nMSHgRaptZJ8xLgvUDOqFuIiQBrnHdo6IRqpbgvv4BRthcGZOhWqdSm4CAnv8mtp\nEFvv5ffozcK+tUoF1DTGBXKtLPtG04qUIrlk3DgSxhGrTR+HF6iNbV3fSnuMYT4dMd5ivVjYbYem\naITMpI2mqcp4mgnzIEE1SlFL4XJ+Ze/al5RES+F96A3URm6F0prEDeRMTonr82fitgmNyqi3e1JK\nIgwOVGGYBk6Pj/ghyJg1/SU2Gv9+XLUhFCR9z8qrxFaIObMn0YHvqeA6vDTnREoFnWQGm7sj0hnN\n4+OI9QbqDdUy7395ALtRzNrP6yLP3feN8fAdVMX2spBuO9PDt+RWsFpL3l9/gHIRgYs2Pa+vikU2\nl8g0z1zyRji+I3f8W5XpVZewBkIwaOMw2krFUxMg1GqjPSlLmnRMCWoRgU4z4jTMhdtt6aRoxTDN\n3VIsWo20rug+sw9hEG1BFJSXH2ZG64klMQwjOfeRoq74wZO2K1uM7GnHOSfR6lEaXkpLHkMpPe/C\nWB4/fIvRgsdLJWMQNeXr87MEyjTIMVKSPEBKKcbDiVIbIQyUmPny8UcGLc7E5eUFYxzv339Aa816\nvXB9PbMA2+uF09M7jg9P1BrZ405tXWJcEjGn3oCD6/kFmnAd903EUvuysp6fuxFtFPUlShDvIJL3\npsRs1GCYJ+bj1KcTjn3dcWHk8PiBWn8kp4TVQfJOl4X59CSvUStbitSWqc1gvORumI5y08F3k5XD\nOM1wnKi/+S3bIjQorRQvH39kvQaGccLo7uPQrft4pJG+LjdKStQYqeuV0Vu+effA4TAStwUajLOS\nno1VlJYwTrPFSKuF2+Xy1c/jz2JRoMuGWq2UzjIsNcscOEaUsazrgjEjrSmMtrhpoDQFqqK1COC8\nU7z/8Mi2L1QSjZWmYZo9t32FKje4tcoQ5j5y1LRYccpJyd2bS8pYqR7ulxISlABXRbjilWNdbhyO\nDygFS98ZKDKSc8OEm+b+K8oO40fpSVQE/Jpz7sKhn+hB2hjJoqhdTBUGSpUmWylNjH+1H7+15bZe\nORwOUt62e/EnASl7yuL0E7O95Gf2JpbxI3tMaFU7X7L2SYTCO9+FNJnQsXf3SLiSMqoqlJGJhdGa\nIQzk3BfUroJsrVG0jAdzkvI8eItRdxWgaBhMd03OhwPOGD7+9reM84mXFFE6SxhLShhtyUruF0oa\nwNoMpH3Dasuedpko9HCZdbkRej5CzQU0/R4JuSt2WXRthXEYqWUXUHJF3Fdyy0gpkmJiGAzbtjEd\nHt4akKIjqMJGUJL/yX3xabWPUIVw5Toh7OnbD1xfznz54QWl5B7EdSPvEdtZE/eg3dp5GetyFeGd\n0jgZmNNq5fJ6xijpiSgkqFdpTc2Z8/NnGSu31nkTX3f9PBaFJnp4bSxxEwPRel24Xq/STDKG8fQg\nuvNublJa4UxDGXk6rK4cDiPWZk5+oFoDoTA9WGK+Sdhn3IkpYczM6fCe9JJIS8RUjzFCVXbOkmvG\n+Il8vVGiOBWt8cR9J1YBnChtqKVhrOF6vZF6ulWtMAwT83iUZKiecKVq4/LywvfHX4HWxHyf6Wt0\nLYTguV1eJduwx65bF0hNGlJKSW7A3kNmG4rcF1E/jIKVU4ZhmEkxSTmZpImmnWddV6zzWGOo1ghf\nUUHapIR/WV5FWanlaLCnzHA4MZ6kIZqj8BjJhc/tI8d371CTw3vHdBTTjiQ3vWK1Yd8lBEUZQ4oC\n1K1F+hXWWvZlk14FEPflLUE6jAOjt6TrC2W9YQeLNyesE21AGEdKyW9Hu7Rn8lbYa8J6DarQSiRu\nK4WKdo64b9zOZw5Pj13HUIn7SsoKG0aCsXhjUHoQ1mXnJMrio9DW48MkjVdjKECJu0y+kEpV95Qn\n54+UUokliVWdirNeqN9eiM9+Dpzen9huN85frhJfb8ObdTqlLCzIksVq3brKk0aJGasKYThwfXnG\najg8PEluiBbp+O1yJjSFSoVhlOatHFG+7vp59BSadP3jthG3jeVy5fIs0Wu1z1JkVCPlqjYKbWA+\nDEyTZ5o8Phi0qThnQGX0AHYwrNulA06kwaS1RytL3DMqK2yzEhKKnE9rB3IorXFhYLleiOuC1UpG\nYU1+3refqQLKsu6JUsC4gTAesGGCLsq5T4OcFbu09Eu6F75J/HrcNzGE2S5nNZZcCuu6SDMSxR73\ntx23lNwzBkTAo5QW2W+t0mxTP93Xe0lfcmJf17dm3TjNGDeIiEtZYi6crwvPL2digW1PrOvOdtuI\nW2K7bSyvN+IqdOSGRluHcZbpMBPjjlLSCxinmXGaese+W91bkaCYKP2Ke2y6THR0n7hknPedTFTk\nSFML3lvxLAyeD99+281YPVU5998/JWqFnEsPVdWyqVTIKeOcgGFba+zrglYClDFGjl+ldDdtrbSm\n3vDw1o9UVF+M6bRxTcoJlIQIa91NX32q+tPOLNF7Comod87gXU/fCo5G19rUSlNafCRBdnttZcTp\nQ3gTVMVt5fgkOSBaCe5eGSMTIPnn2LdN9CFNmu2tyLH5a6+fR6Wg4Pn5k3S7YyTFTDOC+q5FusvO\nWvKehFGoE/PhwOlhpGTx6udSoEVyXSkq4UYPVuTLrULaIw15M7VymGKoW8RghajTFHVfOZ2OpJqx\n2mB04/XTjwzTRAhBjDhKGlbz6SiqyjASc+Xpw3fM40FYkXd4SJFUaO8DNQl4dblc2LcVNxxQVWzc\nVUti0/Xl5acHOSVSTpKKZURdaY3tngrkA9kbatYaITSXQoo3wnxE6UZO8gDkbZeUbD8I869mbsuN\nME9YF8hVgZUyfxoCxlrWded2XSkpkXdB3nkcrVaWZUVZh/XfdpDrZ1LcyTH2Bawjx2g8Pj3w8uWL\nxBmgWK430iZWb7RmmmfW9QZIwK6xjvF4ABSqGebjkdIScZfpzOvzZ66vUtWUXKGJlKfWjtlLme1y\nEQu8qVxen3EmsG+Jzz98ZH6Quf3p4aF7TxLWD8zjA3Hd2GMUKEqrrOtN+ARuZDgq8tat58iDzydd\nswAAIABJREFUO1kjSdAlCXvRePabWLOVFShrLZXldmPwDt0j5dGCtRvmiadffEPFcrvesFrTnKPQ\nsOPAtq5M4yTeDGRBDVayJB6PM7fnH2itUKk8PLzj/PwJqy0GaEXs8bFtop+o/08P3997/SwqBQXS\nZFNweHzi9OEDxyfpnEqKTz8iWE1rCWcbzkKOC7V0MKkx+A4ScaNDWWjUPufNQqpRCq0d8/hISVKS\naSt5EaUIlen6+kpJldeXM8+fPpH2jbTLg9g6wUcpRcpJCMj7hjJiW25ITgXAw8ND/+CWt/RjyZV1\n3C43ltcz8bZAKXg/sG7bW0e7daFMyZncDWK11r4TA/3fkYBSjaY3EKm0mlEUrOlcRSUVjuokYWPk\nTO2cl1J1k4ULwFjpoPshEIYBhSLFyjgdcT7gJkGAKa1ZXy+UmKUh5wJibBSmpLXCrLyPJZUWs5s2\nRqTP3NXIjaWDdftwh9YKYRrJFMbDAYAhjLRcULXJQ5gSGunel5xFZ9xxfrUU9m2nZgl8McbjwoA2\njrRGWhFU/DhPDNMkNmxr5POnfzI/3eXL67IKI8EobAi9Msvknj1iTM+YRMl7WJtUYveKsu/kuSsn\nWxXfZ20FbbVMBvp7bDpMRshaMEyT9ED6e7NvkkquaOz72qlLourclqtg36cRazTL5cq2SqqVsfZN\npv0118+iUiglczg8yFkqJqwPfPP9t1xeXkTH3sTu6qyAPmq6YYyURa2HqdpBYr313FADZCXI+FIg\nxivGegZ/xOqB7cuKw9N0Y91kN2y5oGulvZ5JJRP3XXIGuq12vV3x04hxYuCJm6gvlbW4cZAxl0ys\noDVevnxB8AhGAj9aRVvP4XjAhYk/+aM/4no+M5wemd59IISAMUpKxiqBJCXLmVJpzXw8dfqP6OKt\nkWYWOdMozFMQ9V7ZWZdLdzk6arnzEDJumEBDjg3jAufPnxmMRVFlYqMK3/3ye2kgKs31fOH8+YoP\nYzcPSWOXXDBNsV6vuOCJce+Zj6YfKZRQonNm+fIFYxSHx1MXdm2Sf9DvrUIRvKMWTVIJ5w0fvv8l\n0+nI+eMztVTW2w2lmiDrg8VYJU7Z7kysSiGhvpLh+PrxEy4MlCr07FpB1YZtmi+/+YHhcODDr36F\nd57JW7ZtZ9vlaOaM3P9GxXmP7m5WpYEqMnUXJmmA6yIKVWUoKjPMR1KMb0fPuwdDaZFu76kwTRO3\n5UppjaoqTUNeM/QIOdMj6/wYiDFhtaUWustWziY1J27rC6MTUMxhlGbqdTlTayQMEx//6BOZFx6/\n+wWXV1l4v/b6WSwKSomhRyvDdlmoXDEGvBNDi+pgEmoRHDiuN4erlJAK/DhivEJNldiWN017znsf\nCcmuXUukRc2ed3QTC/Hy/LkvAJr5dMKPo4z+msLYjW3bJKkJWbGF7lTeYtitE02FUYaWSofGtLdu\ncOuUXjHFSIl9eHhiXzc+/uZPeFcUp/fvGCbpXrcqc2mZTIhOoGQprWuJ/bUb1mhylj/XknHWScJw\nijgtQTDWW1Q17LVgvWgcfJUIeG8dFLl/gw8YD+MQmA6TyGgfZkoslKr7GDLivMMEjS4/ZTDcz9qt\n9TEsirzu7MvC+eMnHt6dcNaybeubZJeGvOcohhBIEZRyaIOYn4xCWWEd5pSlg99dg3dxW6Oj3hGy\n0eg9++WFFK+UmgiHgX0TsIluGqMdrWmu51eMdwzzLOnNTYjhRgvhSQYIssNrpUl1ZwgDJUr/IqeI\nDYG4iF4hpSiejZ4BIga+W2c93pWQ9+dSplhNi9LVOcd5OzOMB6lEu81+8HJs0BhKbWx565/5u5VO\nYY0m+EDaVpSCaRgpgDKKYQ5iiloWSm1vmSpfc/0sFgWtFAZNLQ2nHbnsmFLwgxd2QL+hOUpTRtRl\ncuw4PH0jFGZ7QwVP1TulbJRiyLmwpYRzs5SXSyEvhboaCZKtirQnluuNeX7gw3ff9wNVw3bP7TAG\nTu0EpjP4N7EyayNATms9RmlSljHkvYQ2phuElOkAbxHM5CT8BjdOPH73O/z61x/5O3/wx8w/fuH7\n3/8HsF5i2Gpt7DH2j4BwGKyRWWQrBdUyhST5klpoUa1W7OhpSnG9vHB8fE/JldTHm7/5O79BG4P1\nI9MspXmKGyE43v/yA4/vHnGmkuJC0BkfQL2beb1lwKCyNEf9MOOMxRpNU4rT45NAWTunIK4r5x8/\nsZxfub0+44Lc75wT1jqMvSP0c1/gJjFMqYYLnsvLMynK6LihMFYakWI1jmy73JdWKi0lIS85x3a7\nkdOVX/7e78tRwIqv5HAYiesueDdtCOOBbY+Uqnh9fsU60RQoozCuB7f0I4H1joJoR/YszdpUModh\nRFsrQivjUFa/pY7v28a67ViXsd5gjWwcg/O8fPpEqaJ09NZBgGAUwVv8KH2rVgxWVZrRlNSw2qG1\nOEVjjgyH96RbRavEfntB5Znl9sLTd7+DUZoyeT78zowynlyFIr6sK8I8+vOvn8WiALAvF5RyTNOI\nG4/Mh0mixLWhxI3S7pjtbolVjQ/f/lLCPFKjTTu4wr5fySWzb4qUQZsR60bqVmA1qKhopTL4SbrN\nTfH4/jvmhyPDYZIdqBQZzVnDcJxJSXIEc0yYMKIKUBomWMI9z1BpjLesy8IwSOnqrGffRMPufacq\n5Z5RUA1b3JnefUPMjeYcHz8+k2sW0ZSW9OP77n57vWCVwiqNqhVTNsbRYcX9LSxsLXFkJUUejidu\nr8JWvLysaB3YkngxhlB5vrxyOM7MHz4wPRz48N17qBlrNFoF0iY+jGGw7FthWzcJHMkZNSnGcaSk\nhLGW23Jj33aClyNca5WHb77h3S++Y10uYERvYpQwKmJasc51yKviejlLL0YrFIa8bpQsZ3PJW63E\nKMwEPw7EFBmmmUbh9eUjzj5ibcOozMPjY5/4VGrZuwoxYp3itu2M83u0dWAMVRtQ8r/Lugr9yhus\nd9wdqjEK0u/15YW0SABMyoXldmGaZrJ34idpFhBH5fXllS8/PGND4P0vvyWMFpMLNVWohbjeCONA\nKxnTKr/61bcc339DGAa8D6QoI9Va4fXTjeW64uwg5Gcl792gFXsRwniMMm6+XZ6x45HSnCRb60YY\nArkUhsNfIs3579flvaQ1j4dRaLdJpMElisuQJuft5XphcOIWW9Ybxjfc4MhuYy8rrY+O9rihlMht\nrelU371RYpGOtRI8mnUOow3OGUk6VuKAdK433YIozS7PV4yyXa1XMd0YY4yldS97pRHGgdqJPPcj\ng6QxiQJPSLuZbd3lKKEtbjAddFKxzqOVYl3EamyDI0wTsRRaaeTexNLKkEuVrEmEp1hK4fgwYH0g\nx63TnMSpGKOkNrciKPFxCAyj58P3v8B4jfRzG9sqZWpTGpTpEX0WozNYhwsBrTWX25V5PKCMkSCc\nMMiRoI/xShJupAmStVlyFkIVjXEcoMfN3ePpUIJXLyVjehxea1BqEaaFNoQeejuEQI4rxhiePrzD\nB4Mx8r5L8G5Gms+W3Hq4CppxmgBBtwNyZLBWdDJVRGG1tLfA3H3dSSmxLBeCdwQrvZaG6ni6EWsN\nrUpoUSmF2+XK86cv4iFZM2G+YuyDwGyaROUZLQExRhum0wEfJoZxJIQgjUhvmQ/vZazanPybt5Ut\nR/YsyLbcKll3J69xEmVQKvtyRYUHqUxb67zT3sj/yutnsyhMDyf5MGc5Q4prTcZbyjjh1msYD4+k\n6wvKauK+ouxG0IFYL1QlpqkUCwqRw87zI+tlZf+YMdG/YbWUElhGVRrvDba79w7zI3Hb8aPj4cM7\nXPCcP39mDAGa4vPnZ6wP2HHqWYES8GJd4HpdRKh0b4D1oXVwDt9FTAU5i6/Lyu0sQa+5JwIZJ0yB\nYRp4evqeXCrn65Xr7UYqFRDRkVXi13h+/cx3356oLaOVwQcvJpru9Zfud0PpgnVK7k1J+GHkw3cf\nOD6dOJxmoBCcZ01CfUoxio8iVWoT5eF0OGD80CPc5IONsdIpr5WcC97LGbrVhrp7ExpM08z6+kWS\nwsOAKpVWlcBLlFSAukl8m1JCgW6lYZQl1ShCrmDFc0HFGunt1FYw1uOMJsWd2jmH9MZjo4lNu3TY\nrxa5+W1b8cMILdOMeF7oVVBF+gLbuvLbv/Mb9k2a1d5bPvziHcY1wugoLXE+f4SqUDhaMVJRfDlT\n8SStAM3f/fUnluvKeJgIweGM4XicORwf+fD9N1hrGaeR5fVCialPoApP3zyRS8FPE6cPD/zx//KH\n6K1RqyenlaYKzQpgJ9WKcwMuHCWvokosYunP0r7dgUJfd/0sRpJydgt9BZc04m5666MtIzbQ0qBq\nVFW0ImMoNwRwIksuVfDuNVeMDljrUdVAMujmO8y1YayAKHKP/ZbdXFFzYblKZJl1jpgSh+MjIUwY\nLWWxUY31dhN5rrOS2Ow980E0CqobiO4OxTt3T2zMd8aeEb+9ERSbnJudgESbIkYBpBz6a94j3iS+\nvIemWHkgS5Od426sqrWRYhQdwFvHWaK0GhnnZXcaDxPWmXuEhRiWlCLlQlOa1gQKm7q5zFgJrNFK\ndjjnXR9lCnHp7kqstb4tKuuySh8mZfaYuVyuQo/KuZvg5OdtVVKXAGj3IFrXtRUe5+W8b52RBdw0\nQjAEL2KvVjPWGIySBGqrNafTo4wKe2/gznrIVcJe2psLt7wpKwWxL4zFbblIWlXKpAp7anz59ML5\n+ZVlWcSw1uT9ak2xrTvLdUEbhxsC43HGTyNuGDm/Xvjy+ZnL+crr+QLaoJ2DnnlZahXkm5Km5T08\nRmtNGAPvfvGBw9OR+TThrUxsalWse6Y0x23dqdqDDTTtaEiWhdKWdd3uE/2vvn4mlYIil9Z31o6k\nUqJPt4CyAiaNHZlubSDuVwZrqC6TWiamSk6KHCu5SGPHm4nl80pZoFVHmAdyjgKz5D5XL4Doxrfb\ninVCvLmdL7g98utlwyhDGCdU3MWXvi9CB+60Iq0UcV3wRlNyZZomckykGlFezq9NtFjC0tt26Qo3\nhM6rNblWwjjjnGO53YilsP/Jx55AXHrmo5S7sRV++PFHTvNA7ch0N4xSbueK6vF4rYk2wA3Q9oj3\nGuc9D+8f8cFCS+xbn/5o+9YoU8C2S9NUa0sYZsGLl4wzhmGeyblQjSbGrWcdZko1AmRxgdeX81uu\nYo6ZMEy0EASrrrQ4H5F+gdEifR7HSUa9TqONk1FgkiPE4+OjoNlUIsUF5y2t9cZtj9CrUVSLSim2\n6wWrNakVci2kPaKtw02BvO/oKj4CkYJXyY/wXlK+tSKEIFTm68ZtSSzrTkoGc5N4OesnSmms1yvG\nDux7FFPZ4YBu9/zTRk0JqnA21i0xuwntA6ovAqq7RI2zjMcDfpBwY+ec+CpyohrL+++/4fL8wnY7\nszVH3Aq1OV4vK4+Pj4TTBwoFZR1x2d9Ss402PaH96/f/n8Wi0BCFnraOdVs5HA6YTi1uiI03dZlm\nA5puuNFiBqgqC9k4ZUqu1KYBkS4PfuZy+xFTJ2hI41L1pOnWib6tkbOh7yfcVTRvwZ5JHo5xPort\n2V27/dnIiLSJGSb1BqW1pp9pBY6hi6VUTTOWcZ5Ir2cR3ZSCNo7r7ZXTwyOlNW7LjcPhAEokrmEM\n1GWRQJOcSS0SY+zVjmGPO22ySFiKdJl9B4pWEUz0vobFONi3mwBitQBVtLGdjynVk1LicWi1Ye4J\n1VlIQK01vBNYR0mFXPIb6XmaJhkT9twC65yQf+5vGLLwtCZ4+FrlZ9Lqpw1Aa8O+bW9CLW17NdJa\nN3nJ4tgQhWgYvSRu1YLWsnv6LpduSqhIpaeFad1Y14hpAdcfjtbkd4xxFwR82ns+gpimlDYcTieM\nDdyWTwzD8JbZuW+Z8/OFWguX1yuPHwJumkRo18RcZrrMPG1KHmwg5SKW9RR7FVnx48R90qiMFqMb\nldPDI8vtynbdKVGCfK0zPH54x8sn8ZE0bD+yKdZ9p3VQSxUZW39dyaccx/+/QVYUVCXSUj8MpJyJ\nJfaSupAVGGdRraKNxgwK7TThQdHsyPn6QtwyIu7SWD9AUpxfnmGXYBSF7KhxWxhGhxaLJLT25voz\nWug74zhScyGnjNeOSmXdroDCjZ6pTaTtRts3comoMEi6taridHOS9RCCY9tXXHAoK6PJnBKXl2eW\ny5WiLY8PR5blig8BozT7ujLOB+Ie+fLluQePtu73bWBFTWkRZ+G6rDgtnAnrPDFLRYEylLgTxkkm\nNK1SSWLlHp2g5rvPo5ZKiZHSISatiXqwlo4e28W8lGIipyIAlt7zsXYgp4ZWtuclCmvCOnE2tq6v\n1Vr1D7wihFEWRS2uwRBcT7EWloUxsghO48wed6bDAUVmHifS9ipHv5zF4GVH1pskdDenMCHw8OED\n+7py/fwJpy2Xy5maFLfri5igrCVvqwiCrCKMA+/ff5D7alz/eS2X1wvKGo7rkefPL9CPsakorr/9\nglKKx29kmhHmGa2VjI6t69ZuTRlHUg9xCWGm5czl/MzpYRJNCkKGLilLWHBeyTGS941pnHiun7g8\nXxjnmahFBOaHwPnLM9eXs3g1SmZfNpz30mspWe5Ha8SSMdZxW69f/Tj+PBYFaSVIWnKW1GfJQJRu\ndu3wjVYzzmn8ZPGjIbczJTURKMVMa+D9o/AEb5m6N1pRGNcBGzUzeItR4iO4LVe8FTUY2vedXbHe\nrjh7N0lJf0BXiX43xghmrBt2mhFbcMnSOW9NshyMVgJRtaZDVTJxr6Q9kvadfdt4XRaevvsd5nmS\nFOweGlOydOtjjCKWqpKNabVGoylIVkItIt5prZCTuCfDeMBbw3K7AD0ZCfH9H44nxsOh8wbpBi/Z\nyu+QFIX0cGLcMD1rwDqJNRPZdcVaYQvKNANSD8011ryZxXSHiFgnUesppTdqsfRe6NOYJr6WtMv9\n4ycj17asPWMiQosEJwYnFwL7dusjzv7zI81NGwbhEhjL5dNnSm7UIpH2cY80dZbMSAUx7sxPj2hj\n2fb9zcJ9OJ468ERDyYRx4HA6sNx2UOJOXbeFaZy7qE2qKKX1W8lem9yPrRasF4ygaFdk0pNS7Pkd\njVJlQiIaDkm/SvsKCqbj3AEqkihtnSe2XdSgxnT7/E9Hg1pEVltKksat+UlN+7XXz2JRuHvQa66o\n1kv4Xk5RylvslfXgQmF8UBRWSq0sy42WvWTlKZjHE3Et5NdE2y2qZvJ2Q7eEVeL3N01DURgf0Hag\n5Ma23BjGEdOS7Kq5KwczHB7eUVLidnmhGUeNiRIz6/mMGwJ+nkSQ5ANViShHKVAtM3lP2W40BWnf\nefn8hZQSh4cDfp7YtxsZ2PdIGEbpBZQItTJaQ1MZoxTL7YYJA9teKK2gvKakjXWJBG8BhdOGfV/Z\ntiZGLOt5vZ45PpzQ2jBNB/w4vOHraY1UJWau1oRqShYMI1Vb2iTp+sPxidv1RqnSUY/bRnemYazB\nOo31UnH8H9S9u49lXZrm9VvXvfc5JyIy87vUrXtm0MAfgDUGJhY4442HGIQ0DhhIGIz4C8ZCGrc9\nRkICJJDAwEFIGBhgMELCaIwRDN1V3fVdMjMizmXvva4YzzpRX0s0/TUqjapC+ipVmRkZJ07stda7\n3vd5fk9OieVwoHUBbGsp1NLfHK+9ZNwyMU2BmneWaSGtF2kvhqfEOsf1+irRjvO0tPGLP/gDbtcX\nbS5BmRIK2hHOrLeOGdeHb//sV8MZ2qkF9nUnpUTtDdI2ILAGYxnfuxL+9pYJMbAlXalSLqSccMER\n58jl9UavmhIdn96zHBbeffGBaZ5wPoz+QBuhM8oujdOsZ9cHrpcL1ltu+42n1tm2RIidaTpgA7pO\nTJHgH2kt48KR+XGhUfn2T35NT5Xz84V125UuPgRdPgSNn4uAKt1ovbQipJ4xlv77OJIUrrvhrObG\n3Tq2bceYzr5fOZ4WloNlPnqM22QD3hN5VZnvvErJfMvkSyVdZJ4JFJZocV3BsbSMaTIFNeOgbJof\nm0o0nrZlsoG0X3DW8O6Lr1ls55o2ZmfY8qYnqDbqvqtUj5bD4UBtskXfxz97TZy/+3NKqpLHDjaf\nKgiLd4aH2RHnWYyBXN9ErNZa2V7v/YF3E+ueeHm5Eb0jbZ85HY7M0bPMEzUlhZm0+naPX44Pgn00\nbYYhTEyTsh3vlEPnDGnbsMYRpkC5Xtn2wufvv9ep+eFrPn7+DMbhrFfDDsNhWdjzDlYh8NaAcY5U\nN26XM3FZsEZ2714b+ZY4f36m18LDF0+EybPMlm29qNpBiyl4Ay1xPE6aQI1Tfb1cVGWlQtlfJAk3\nUNEVJddMbJH19VVJWyo9Ba3dbxgYupSRGmbB9UrbN0rv9P6A9Z6cDa/PV3rvnB4/sK4r7XqTOQ9d\neXz0LA9HpjmyHBYOhwPbrnwGGFRop/ySVBI1p9FMdbRuKdlRUqNW8Di2vHOaxwbaxUVoGGprnB4O\nhGD57le/wnpwXuMiNSElqa+5KvWqFTU4Rxl2hwZRCzb8vkFWkHqQfo/PUhk7zxO1Jh78hLHiKGAb\npSourJUmRPvonFvnyRvULDdkKTuGhjMOB7KmdmHba026D5eV4APdQS8rIYilMB8fMRQOwbOfn6nr\njgsTpje8s9QGtewYE3l4eGKaoxZyb6OHYbC9cmmJtG04twBCfsd5Yb9eicEzH4/UstEIBB8xRo3K\nUhLeyjuxrTem5cB8CBgSJReWsIgFYIVAs0YnjVx7VglEtSi+Pm9v7D9QeW6s01SjaiRYW2GrjZIr\nNUkotafMw2BCmpGLGbxnvVzZB89Q1xfFvd0uV14/fwID7774Sm2QnGlZcXMxRD6fz6SyspwW4k/e\nK6+yqOm5zDKa3ZkH6tbpWTg/fyJOquo6DZcrzhus1ZSqlcJ2uahJOLwnwu9X5mUhl4p3wtJLsFRk\nR7d3nUild0etBTuew7RnNQebCErHpyO1VObTAWORa5cuBy6wp/vERp6dVquuUcPb45zXlbNpStRr\nH4u5DWm1PCmmg8Uq3DfBPM88fHji8nyl7/torKeBymOwPjvQ6K2yHB9Eobq3z9/w+T9yLf5Vf8EY\n84cohv4n6Az6o977PzbGfAD+C+BvoUCYv9d7/2zkI/3HwL8J3IC/33v/p3/FV9Ed3kiVxmAWYOB0\nmsh1xYSMmxW+mqoyDtKWpZoLE9P8yHbNnD9u1G2EoFoHpStOLnqMaYT5gf16YVn0xi3TTC3nkV2Y\nmcyCdxPvv/gZ1lpevv9WkI7bhokz1nmO80Jtr8zHdxyfTsTJE6IgGNhOnCV99f4I9hfET595+XST\ncWtSYtDDcRn3+sI8BTCDexgDPkws7gg0StpZ5oA1lfm44Nqi8ZJ2Aeq2k9ZVJ1Da9QM1lmny9JYp\naWX2Hovusd5BoQnCWjLltpL3HYemAWnd2K8XUZmNY72e9fnOE0ZT0gUDTovBWBGeai68fv8d1/ML\nPka+z4mHx3fs6zr6CJUwB+JpFhh3vWDMBylLB316X5WRoBaTria9DgKRC+S0kzJse+HlfOXx3SMP\njxFaZYkL5G2kPivdat91NbIj0q92XcVMK1gr3UYt2kijrxibSaWI1lw66+WVMkJUWqs8vHsAwPmJ\nOEUOx4N6PkYNvQZMMYyJilSq3jDyMRWQY52jTTOfvvueJXhO7554+uo90apR23Jlu914/+FL6lWK\nyrI0lsMjpke21GgvV3KuNKtg4rTdhIGfBPj9+Okz0zxhbWOKk2jeo4H6W9kUUNLTf9h7/6fGmAfg\nfzXG/PfA3wf+h977PzLG/EPgHwL/EfBvoLi4fwX4OyiM9u/8VV/kroCzTqeEdw7Ti0oiL0ZCNyud\nEYXeDKVVrFvo1pJTZ79lbq83HDPBR0pJQ9TToDfCNBO8J12vstXGRS7H+RFj4OHhibS+CDyyD1gG\nljxQ5evlFbscxd7rmWmZ8H4k+PT2ZujBVMLIr/AxcHh45PyyaUzUxmzaKI5enzMreKQ3LGL/2yFf\n9jGQbpJWl20jjvn9w9N79n3juq6CpFpFsPUuey/dk5G0utc6dAGdO9DyHuf25sTcd3IupF1xb2bw\nDMt6wz69x9BZb9c3SrEbVYKi3UcgbKvqMUTJoVurGtMZsEEjVBcd3vjBQdAphgKvFazrxshQWl6R\nqLoo36019j2zbYmcVlyI6uIjm7wZkvJuGmGSuCun+5RjuFfrTnCO4ANmigqacY7goVPwi9NpbgxU\nIzt80Ug1TJN6DN4RQpSHxRhpOSZHsUnsSe/lX3GiMb1F23EniCkS7na5jgAaD1nEqd41ri05E+eJ\nWhq3yypaVa2SPPeOcQ6w7CnjY3yr2LCGaTlIyFcq9I3g/Vs181vZFHrvf46Spem9n40xf4zi5f8u\nipMD+E+B/xFtCn8X+Cddbej/2Rjzzhjzs/Hv/CU7ghmATztOowy9q7/gCuFo8VMj1UZrUOusRlI3\nslXbI5dvNl6/e1V4iIPWMvTCenvGTY7D4R09Z7bbivdRgZ+9Mx8e6T0R4sS+3jAV0nZma8/s65nl\n9FPdwSfBRyqd3guH+cjj45Oi6YIXaJNGLYnDIWIYxqvDTO1Iwr0mQvBDhltwA176s5//Ad/+2a/I\nRQaebb3iYyAuC9F7wunEPVpveXjUCLc1TscTS5i4vL7y8uk7uUi7GJG1KerNGKHI6J2cK74Z2aGz\nxl5lT+R1w6wiYqeSsX4EqtaGXRP5+QU3zYoeG5VZWjPremOaZh6fHsnbyr5dmB9OkoFHGXAOB7EV\naV1Xnjax7Td6n0TxzlnJzwbiSGDOaQcjpWIpGuMa2ylFjWXRrQOvn8+EaHn3KE9DdRp5tt64rlec\niaTtiqmJlm48vnuvpp41xOAoNQ1JeIXWRvZEofm7DTzgqhGlaj5ggmMKB6x1TIflLbSoll32HEZP\np5jRBEzDvKdJxG29jClP5/HDO2wMpJT55v/6JZ1GmCe+/sXPOB4Xzs/P1Fo4PJxG2I5rg2HAAAAg\nAElEQVR6S95H8TJ7pwdlS9aUqb1hjXvrJVg55JTM3jLhOP/2NoW/uHbN3wL+VeB/AX7yg4X+a3S9\nAG0Yf/qDT/vl+L2/sCkYY/4B8A9AHv7etIMaJyOHFF03psljPNSWlPhrDNt6ATwxHInTibobFn+i\nLeC9Yu1rTpjgMP2B1laGVxdqBePwI3W45gQU1rTircF26RVqSoTpiZp3WjcwL/RehmQZ4vKAdV5Z\ngq1yPJ24bRecm0Ycm7iE3nucj2+iKOMC0zzRyorpSrG+vLyMDrWjVTXtSto5Pj7I0msMed/hPg5t\nlbRtbOuNMMZYcZ7J+0pwjpL3QfV1Q60nXFmrgpz0UuitqFtdKr0ospw2QloZXXSn8n37/IybFpqf\naMZQ2Gm1kvaNFIRha3nHe4XVuBjFQzRGjTGCpOmAR2U8+DdZu7oGjI66OAaqcNoQNkE36jF0ZKl2\nVhi222XDWvUW3HivhK0rmJ5oaQcaT+/ecTgesK4PtkEnuiAC0sAsma4qyKB8iyk4bSD+LkrSa5uG\nRweMzFrGYL0leM/1euVwEL2ZjrQawyzVm0bHcRLw9uHxidvrheeP32OcgcuZw3Emr1eC9xjgRh9C\nrglr1OiVlqSLE+L0Proo/qTpBu+cGp+o8qztr8Fi46+xKRhjTsB/BfwHvfdX8wOSS++9m79OJ0Of\n80fAHwE8zLG3dcNPUeqvWui+M50c2A1jpRC8XS/krNyGLZ15evqF7tgNXIwcvn6U1NOIw1NThscH\nbEmwv2JqwfZBa7KBnDaacWogMrGnmx4KU8GKwDOFA6ZVPnz1c3796z9FliYI00Gl3pgDX69njOm6\nny8LmMa7L77k82XFrIlSEh3BUw4+kq7PTMEzx8B+fR2QD5WiDmVA1Fz48uufkLYbp+OR15cXnA9M\nbuHl4zMxRrEnDcyHEz74gf5W8zUsB1yI5JKY5yNlWxW5drlCa8Tg2YeeYa9J985coRlKb0QfOB6P\nbC+v1L2w9SuXbadPC7U1rINru3B5eWaaAu+/fs98OkqVOHBkuTQOhxN51Wx921bmsGCdQClryRyW\naYzPhuPyoMi59fxZi9uK22DpPL1/ZF4W0i7fxb4War4O2fNNYBIj4MwUA95Zvvzp18LB2aY0cwNt\nmI+C89SkYNZutNH0JvXsnm8YH1kmbRqNjnOW6J08Nq2/Gcack54kxkgMkdv5rMZzGyAgEX/wRte8\n+bC8Scof371jvb3QWuVP/9kfA42f/fwPYYQjxXuyurU8f//M99/8GYenD3x4/xWX11eWaVal4lSd\nuoGtf/fuPa2Mq9YPtAx/1ceP2hSMMQFtCP9Z7/2/Hr/9zf1aMKLpvx2//yvgD3/w6X8wfu8v/bDW\nktYLzp1w0Y9uahWMwzZyUUhozoladCePYdIosTosDroEPWbc1VvOOpER0LNtumr0LoYC3UgTMbh5\nSjTSqW+6koyNM/I4YHn5/EkVgBVqzIYfxKPTx70VxaLlHWuhNPEG3BAwXa834mHicn4lrxveHKhO\n4zPrLJZOSlnjP6eqQR4CkabW2wp4qm844wHN8x/fv6d7RymiObsw4XwkLieJj+KMMYblcKLXynX/\npMDT64WasuLFvHwPrWWZkYbkd72cJWgyqrKOx5lryrgxNsPIM2CdouFKydowrH9LwbpcLvIZ/IDI\n7ZoEXndIrbEj7clachIkZzx8SrDuYjE4pyCeWgVasX7BGH2NKQT29UrwjfgujkkMI3A1kvMVHxcM\nkGsjpxXvpDEoOY8SXdmWcZpx3tJoTHOglc5eM86ILv0b+bZk3apsC3lPrNeb+iNdDcq8S/NSs/Qr\nzjpaqdSKqmJvORdF6R1OJ+iVfbvRaqfkStgGLt861stVE4taubw803IlLhPdoI3Dd5yx3AZrcoqR\nfb28AX9/zMePmT4YlDL9x733/+QHf/TfAv828I/Gr//ND37/3zfG/Oeowfjy/9lPAJy3RNcwdWO7\n3Ki94o8TuV2l1uudlLcRnZWI4YGIZbEHfJ1HarISnNsoYHowasCFQDOO62boRgEspnel7bSCCxEX\nD+SSqCUTwiwFoZFazMXIertQV11BWkrMjx/o3tGdpyE2QeuVGAKH5UBOK60VbInUoodgmmZOR8Oe\nd7oBGyYu604qleCtQKLHA6yr4trvWK+bcOpSCBpeny8DDlowKTEfjzJ3OaHGau0c3r2XTTjM4wog\nJ+AdJx8f30nme71gmiWtO8wBesVNYwqUiubrHdzkZcted5mTTgdy69z2XVbdmqnOUavm5dY1pXyP\n4JVWym9szcjn0FHmhOmV1DMxOlxQ+rY1kVZ2NRhHuMzp+J5t24jWYkPnum64ONG6SvzH5UC+XokT\nOJ8VJzjp8Y7zDLYTloXasqCsRgeDuIwBeqOUhPMTx9OjRsJqa+OCp9oOWd9r8x7rPbaJ41hb43a9\nDSk65CJdAkb9kNdPz0KsHWfcCJxN+42CZQ4Txla++PKDRHxW488Y1QNQ5oeoZPu68vGbXxIOT2JF\nrKtk4ocnQoycX17UyPaOGALXlxeitxyWWbySH/nxYyqFfw34t4D/3Rjzv43f+4/RZvBfGmP+XeD/\nBv7e+LP/Do0j/xkaSf47f9UXMHSm6KktE6aJWndhsUzFOFURdbgFS92Ypw94E3EE9QCcJzgn3XyI\no+vb6VbmkFoFzew9Mc0zVFmQrY9YF3n68DXPL59pTWAXkNOvZgFcc85KMUa5fdYFxXn1+4y/MsUI\nvbLeLhL7GaOZu5lValZlDK77rhxEYymtU9aNKUr9lopss+t65Xh4IIRInHXKl5Klf/BiBdS6Y7yU\nh/u+0rK0BT5MEkc5N2TZXtmQecc5S5gj67WA9xAmetWdvEgvO3wIhTYSkoxVElQZ8nNnHXbMvquv\nanB5uTFbQ0IZZPndthEw2zu5aRLinFMU37hr3wnNCroWd7CXfTjfJH03zrDdLij1ShXMNE3UYdvG\nuDcBVGuiEXGPaMeMqm5kZCRtCjXrSjfHhZS24dZ00MRgrEUcB2Mshja8Hwnj2lvIjRsSZ2PUOzAY\nSk6agAzq9/VyZbteuV7PfBW/GrkjGlvaLrZkCAqR1SRqoTU5O411GG/fXsNyPNCtqo86GBaHo8jb\n+64wIh88aV9VXU4TLW9jGvJb1Cn03v8nwPwlf/yv/7/8/Q78ez/6FaAS7LAcMM6y253FRapdSS1R\nK2xZqjhH4DB/yXF6D9UR+qyxkO1UKm4KtAFJ5Q7RbNphv//8DV9/+IroPMbIPef8jHWRT999i40B\nF4/UvFJr1WvI0kpM8zSuLzvHL3/O4fE9qRRIhhAF8cglj9AP8C4On3xn3Vf2def6elZepvO0UjQS\nnQIlF5qprOlCZ9Kp9/hAq40QDJY60qgSy+lAriIKYTSa7VWbWCuNUjvTYdKfj4eg1gFFCW70YhI4\nh+kR//Sebl5peaelDesca1rV9AIaFRcXtnUdpbgfEnQ1sKIzlAZxntjyznZbIWWy37ldrlhn1Qir\nlSZFzmBS+IG9a6TbyukwsHnNgLEEE2hIYgJGjTRrh/vU3BXWythonR48lU4ZWoF927heHKfHn2Cs\n4fR0Yl0VPOy6oeadmla8dWzbRaM+zAieybpaWkNJu/B2vYAJUBLf/9mvePjwc5bHp0FpVp+oGXkW\nyqqU7usu4O/r57OeCQOmVSg7pmlTmOPEFKO8EHVstK3JeGUDKe0Sn7Umdod1/OJf/ts8f3od60ZJ\nVpfzq66Js1iTcYmsl4umaQOKM/2WK4V/AR+N3EeK81QwvrDXjdIUrZbShjOT7n82UrPhEI/YrFDU\n2irrvitJB20ylMq+bezrRsmVw7ufklvmcrlyOgSVkK1Tykb3lpobOEeulegDz5++ZVmOEry4IIOJ\n9XQs67qDU6ZloZC8Zsd+CFd61q7vp5mybqzrjbLtYwLR6KZzOp5wBqYPD3TbSWWXXdkocwCgI/ZD\nyzIY7dvGbR3Btz7oVySHbt2S8s7B/Mb+DdobZDyy2DB6Ks3RbWXPhenhkbLeqGfRrfER6yxbXvFT\npFCFKjMdF8a/PcJP7TA03a5XAUP2NE5Vy369KmR1OWCaMHWVRso7+7rRqvDo0zST2sZkOpML1F7I\nJXMPeGmtjk3FMs8LWy4ogVssS+cteb+SW2ffb/Qxwdi//4R3Au+2uuGDw1unBPEifmbrBUzFB0sI\nwxjXGxhHrQrpTTmxPL6n1IJ3cJgs54+/pKzPBB+UCu4Dl30XHaxIS9BywbXGYRGNOacdykaYjjjz\nGyMbhoGQd6z7isGSUuLDuy/Z8zaoUfp5tt6J88SXX3/F+fX1DfdvaSzHI8Y7phC5PH9me/1ISZmS\nlK/51U9/9qNX4+/GpjB2uea64Kt1p/RMrYZSbjg7ZqzW4vzC5BZ66QoEaZrPLgfdrY1BVOG3slRj\nrdPTOwIV2zM9yQZ99+LveSfGoGTm7czt+swyPTDNM7dr0gPS77Pnihuos946BoczEjm10sCPxqNz\n7LtwWLUUck4o2HYhhECMCmZljGLjMNTohBceviNBkzEW0zXy2m434izRld46bUg/5CRKBOb0r1ij\nBdB0369NcWvOednRbcfWwL5t+BE/VnshThq7tTeZ7L17rddmjBmhqurd0NX9Pj2dlPJ0lKy75jJG\nqCvGuyHtLWNTUFaE9bCuV5ZFRGo7Fue9kSmzTyXtwvVXxs+iZkBhQbUUYpioxo7YdcfLx+9Jy4x3\nHzBzJMyLWAfOU8t6f/SATm2ZUjLj/47MUkccnX1gxNbNGNd0RagJeqWWTPSOXlaEEBQA1nWLmww5\nZ5b5iKGKqdkLo2x6Czyub74fQ/QT+7Yr7m0AWN6a4cbQKYria0VTh5YpeWcaYbv7+TPedqyHOYgQ\nbfm9M0R1WDq172z1hY6uAClt0B3WRZwNRH8k2AVv1EBzSJPfjWGaJ7Ai6aRVltuSB/QVsR7j0xOP\nx4WXb3+F7fD6+VsOy0KYZ40VL89s26v8/m3lct4wTqlAab3gw/K24fgpjqSiTrGGaqHT8DFiUEfa\nThOlKE36+F6bgfVGaDErug7NDHqSCEzGCEcHAsH2rpl0Tis0w+PpSBkKTe8D6bbpYRoLqN+19tZi\nRkPMOK/czBHDXnum1KxpjTP4+Mj0+UjZt6FXkGHLGcc0LWzXG51ON7o/W2fppUg/0ArOFlwIfPjJ\nex7fP0pD0C3OelJu7OuNz99+S06V1Dq2a7x4OgnOcr2+cCByu1yHLFcPfMMKptq001VThv3JUFOH\nIvOPs9JvdCPLeRubRs6Guu08njLHeaDtDNgQ1HS2hrYPlHyvGNsxXWNRHya29UbZbsx2HCCmAUUq\nSyuvTiuCqtxPcsZB9fT4RM4J5zy5BAnD9is+WDU7K7TuSHsbX0/cj96alIg1vwFxSu0Eq0lXuE86\n9sR+ubKnDUtiff3Iw8MT1MLBQ/zwpEdfOGyM/fFahd8NRqOBYhKZnWaauPi1UsvGbX3B2YBzE9ZE\nDEJpK4vMKs3IW8qg7PgQBGQdZeed5PPGRgSMD1RjSC1TUFe+tkLvhfnwiJ9mupw2b4x+Ny20wS7s\njOuCDyIRd1Ul1rgx5xa/wFs3lISbTo6gcWkcwp44lGoGCD4SfSS4iLP+zdhkTRgNN43owvgce7fH\nDqnyPcmKkYMo6bFyDKbDgg1+UI0Z41f71nwy1uLmmdw0+wdlb7aqDAP9LCS1bq2xXs/UsksERMN7\ng/d9TIAKzkJwME+eh8cj77/8UhtgqaR1g66rx7ZdAOHvnPXUnKgpqSpImXW7aeF1MNYPT4QCZEzr\nSvXq94pJtO/p8KCULgw5V0qDUtuICNCm0HsdFYAqHmPsOKU79Ir3nlo1Tp6XmV53LXwMLkRK1whV\n3EjZu0GszzsrsqZdjUQ9LRgqT0/vf0N3GpMYSfrH2dxV+YUQSSm9bWDC/Otga7UPV6tGtDEGequs\n1zPpdsaazjxNLMvM8XRkWmbiHJkPv2eI92465/Qdzel+RevUfMXbI37xGDyeCdcWvFswVovK+qjR\nD0AdJ4dVeZu3TQSbJhOSDQJdrJdnvOnYEDk+fYkxHTN6AsJ6aSSukWRX8k+tlC746Nenh6F9sODV\nHbZB4BDn1AzCS6d/Pp/J242edy3moWeoVaXier0C4FzEe49wjQ43TXQ6+1VNKowlRM9l/UytkjF3\nDPt2Y54ivZuRTg2t66qVthU/SWbdjfod+/X6prKjN2xrqnhKIT48MZVEvrwKNefs6FWqfDfGQFW0\nfW+ZlCUSa23n6Rd/QDwuxOiIzuLoHMaY1sdAKo2f/s2/wXe/+iV8fpVq1XSOcca6znJ4wJiBZ2+N\ntaz4ODHPB7yTZFyIfCPHp3UEq9i8PkAxjU6YF4J1fDFN7OuVW/pEKXC+3Yj7Eecsddc9PSNoKnQO\nhxPXyzPuXpk1VXOH04N+TrdVwNm8Y4Jnnh4xjLKraclPCOFnu8FYneQGjWONt/I3DIKq0v42TDzI\nkbsslKKpRR0b8zzPGGeZlgN9iOI0lk54hOuzvemQs413TyfePz0QQ2DyDj9FcivEZaHXQu+/d9cH\ntJhdJKerYstbIY6AUIulbDuOmR4CzOpi11oEZkF0Z2M9qSZiCMpVbA1jPXcYSM5FpajVbrqVCK3I\nTamLpH5FXfDeJGhyNlD6hp8cpTe89ZRS8c1hO8yHJ9K+vUmEMapgeskyI2WNi7gbiO6mFqPsA+uV\neXDXrvsQKK0Q4iQZ9jjRfRDff18veD9L4uwUg57TNjiWXUo4C85axZLXpgcxZ1UF3tNH6AzjO+7W\nKMpM94O31wZgh33ZjMYWGGqtHI8nrJs5nk5jYyzSgYwKhiEnPhxkeX748I5eK7f1LLnwPKlsp6o3\nai0NlKdpVBO8Rbo5R2nKiZCORPJnSfztMK+ZIWhy+Gl665/sSbbo7XbD9aqpBfeRq+V6feV4fORy\n/jSqyYZxkTXt9LrR9hFki8U4Xcl67aoKYZC+h4V9oOWneYGuUbBeLDCqglwy19dnTk+BOItLGnyg\ntsI0z+RdlKZ6rwK9ZN+tysNSSsFhlWZuGvNhInrLsswYGsbJ/+Gck7HLut+ueOlfxEej0cxE2hJp\nF03HO+2QtXXKZaevlf165XB8T3+sGOeoo9uv+8dopNXGx8uZ9aYA0nAUybaMsNZqBChd14vulF09\niVqqQBjTLA5BjBoXNpWmx8MXw9nXaKZhTaAUBkBVidWtFuK8UHvFhYA3hpYL0cmAVbvYf73fk4YD\nfj68lbtb2jgeH5kOJ/q+4XOGpoe0NnXD9Z84iK023n1YWA4H1vXMtu+iISMXYN939m2lLYfBUNC0\ngN55fPee88szpeh73bebGmTLAq3iR+hLr5227/QO0+HA9XYFKy3+44f3zIcJH60s41NUGDB6iDEo\n1Qmj0+yLJ6YlkLZ3XC9ntttlXMMkPcapoebjrJAT0yQ371KWYoRCswYpKKvel26cmrZG+LnaK9Px\nhD89sV9XXl52tu2f8zf/xtfMQyjpbFTDs0PHcjk/6w5vLM7N1G5Zb4mSGumqydD8+ISbApVCtRVv\nA2kVncpgRN4a+PqSV6a46LpiFE50mA7kop9RzxsvH78B5wnLIzVrQz8+PSqAyOt72m/KpBQXxFCG\nQjeXwnycCJPn8d0D3himMCnnNMtuHuIs2pR1tL9GFv3vxKag+18a5fPEPTDUuQnTAvu6wtapa2Hv\nZ1op2BAJy0GnSgczRCclF7b1Rm0NH+S6a72xrysxeHqrMg+lDYxhWk5v8296o/ZOmA+klHV6vjEH\nLbl1yrayjDGoZ6DUR9n/ZkX2ajrt2z5OY6vkae51iBgCutcPE1drxDjhQ+RyOWMx4475g3fJO/pe\nxBS8XaVqy5m0b9SWSfmGK+qn1NJpTsKjPNiPLSViiCIxOfd2mu/7Bq3hnaPkiguO+eGRECOtZm4f\nv4cO122lYvBWrtC4RFwQ6MUMknEqmcNy4q5edNby/sMX/Omf/HNJwR08PL3TRGLf9G4M9Z91ARf9\n+HsKfxlvA4zKJQRpRtoQ+3A/E3ImRC+jkjVczmd8nLhdbkOa3MhF5ib1bKSALXnDWjkijel048BM\n5G0nb420F1rTTmLvxrYxiWqt/yZAuCRK06ledyHSSs1S547GcS75N028kYcRXBxaB8ndr5er/p49\nDQMglLy/TZk7VT2rqgzNaQkE73S16G0kdLk3JakLXklc/veMvNRp7Psry/Il58uvsQbeHb4gXxLt\nVujn4eGPgT3vkiS3TsUyLyed+LUom2+kFlnv2fPKwzIrYTk4elNZtqcNUJJTqY3oReRpLSvIpSrc\nBBj3ZlF0tuurOvve8fjhKynjSuH8+jzUZAFTLLknHh6e2G43odBLelMCltreEqLp0gDg5Yd3IdB6\nY3IT1hi8gW3rVFMZCEqMt1xfXmjNUmm8vnzCXix+iUxzUG7BIFUbx0DZ17fmVkkbzlo+ffeN1pS3\nOGOwk8e7R3h60MPlDQTDNB3w009EoP74SmsOS2NeLPF0wFDEdGwVFyLxcBzXnjJETo5v//xXYjD6\nQC1w/vSRvK1YH3RtckEUqnkm56w0bxgIMTkRaWr2GYc4GbQRFWfZ9sR8OA3VqBZBsI5uZVDKt51U\n4fVVpq/jYR5XC0OcTuS8yavRO3tx3J5v9G7BHsEnSllJKRHWhJ8CGAjuLoIWVzHZu8MXJnckbVex\nEkMc4USOvF/V8PYB4xd6ytpwmgEb6Mia3jrkJiDM3TJthnLSWcv54zfUtPPub/9LHA6zpj1mkJoG\n3LekVVehWkTWHk3mH/PxO7EpqJvu+fj5T1gOjzjTCWZmfblgtgh1NP+cI4RJ2oTWOSwn0XBrodbM\nvq988eVPZRt1htPhQacghpZ3lmUm553SlQwU/CLTSSvqLneL6Z2yJyUIlY04H3HOUGoakWsztldM\nVb9ACdgSsZS0jeRiy/njZ8qe1KV3amqVUglx1j24AWM60JucgPe+Rq0F49S3gEFkHnZhZwxTnPju\n+18T44Hl8Uil0fKoSlLGtEoICjaRLrbTjVGYjFVcmx1pSCVXBYeMO3yIcQhqZKXe1w3vLccP78ml\ns9+qGnIjb2NZDuPryVjUjSGlJC1DNxoRNgFu9vEa16tCgLFOJ1gr9IrcolYZlmCoe8LHOHiHApPu\n+z6csKqwam48nE601kltTBhKJXhPt45lPpAuG2GKXC8b1Bv5sfH4dCIsM+t6oZvA62Xjertxy5YQ\nT2BgiYFeq0J1jeC5R05M0VLTVRg1Y+g1YX2k1IQ1IlPrWiIjWRnvh7Oekoq8D6noKtkGeIc2mpwV\nb/2YqnRNIJxG7d5Hck7kXMZ1SarWeT5Ra1OPpjWZC7H00ojHo66z9vdM0dgBWuMwKYXYGY/DYXR8\nI3ajJMJp3wlxwjlDLgnjvEaTd2vo24nbx4ag0l34cU0VclrZ1xvHWdMGPy+CrjSZX4xR/JjxusvV\nWjEuMB8PhGnCmM5+fcEaL4ZiyoP6Y6DCYXlk21bKvoNtmmG39lbRANSqkWUbU4COI7dt4NM7277C\nGKsaBpWoKjB0p1PrjdqiGI9NkA3no4JvQU7P3odV/P6AqZ8wL+qbqPk0aNndjGaWOvJm6BLiNElb\nYQzTsmC6hDqtayR5pxdb6+g5KyvTOpkakVeilqzoPyu/R+sjZbMVrFOwr0WhOtZ4JOFUOK5hIobA\nvt0wyCcQ3cxtFVi3d3EJexcG3ZmAQZtZbTIkrduuAyUGti0Rl053keYMbj6wXq+sW2ZPnY5EX602\n9tFL8TFSa6HUyvV6xruTMiyGUCyXDEWL3VJkxDN6z3Vg6T1qg/fYmiUETdHumHZr7yE1BdP6QOuO\nPMyBjc8p4cPE8fEDFl0j3YDEWGeZp5n1esaaJhm+cfJvoGnYj/34ndgU6NBLJ/iJyT0QbCBsnQ9P\nPyWvicvrhVwaVC2wlBM+yF9AvY95dGqFGClFXdxSddqBEUI9Rqb5iOkF29C9ca+ktQ3ZQ+O0nFhO\nJ6CR95WSL7gwY2JkhjdVY02F1vYhI9X4MgxV4svH76DD5fWZ5VHBMmagu2pvpG3nsJyoNHquYAUP\nMdZjo2XbRROep5mSN5w1pCrsVi6JOEW++PkfEg8n/OSZvcCkxjh8t1pgraDhoFSReoAbzcLp8R3r\n9cotXdXfG2IjC5pcODOEldpEQpioNXM4TARrOJ2Oyt9wiHCMwyBIqa5khTo2hVa1QLrRyHZbrxLT\ndJGnvAvM84HgHfv1VbTlYXHvNeNNJ61X/LCxA9SS3vow3ge61RJq26apCxbnLSVLGHQ4njBWLtMw\nv8dP77HLEzZYogF3fM9l/SU2J9pWua5nasv4gZRzIaj/MKqG/OmTXIdNSVlxWnAOvJskMuvqsTgf\nCNFxKWduu64u1ka6tcT5SJwWvPeKna9Jb3ezROdx3Q15t74uVsnbtVR8nGAQyU0D7+zwalwxbYde\nmA8LLk7c0pVhC/3Ry/F3Y1PA4N2ERZkMtoCtFm88dnZs66ootKFmuxtH7oGgvTfd/UtmX29v2Xmy\n6HYOhyOmN+UMMrgL00RGSq9aOtt5ZYqG/k7xZvPxKENPkdy4WzNKY3kgcBrB3WW+nU5vdozy6gjG\nVXcohLFBGS22MizMwJgH3sdjMi31JhjIul5R76MyTROlSh0JhjDPA19WSWnFukhrhTAdlezkg6TY\ngx9hxtXE+8jl9Zk+xnw5Z5X99U7RBmcHW6F3Si6EKsaERTbxED1eDp8RYjK/fSttiMEYOg8X4lt2\nZ2+Nsm+qQmqRPHxIffEejHoItagx5p0nbzeW5Uje9t8Ep4jKC12vdc2KFexvb2kf73WVsK0XJj/h\np8jThy9lYQ5R+hSUu/D+q5/x+dvvSeuroD1G1zelUennSx/wnmZ4eT1zWA6stw0fROWqQybtRtgx\nHS6XZzqBaT7CyOaMLopZYe6NbP1njcMGJ0XriL8bQ1eJu4r0JXTpXWjaFGra9R8MKEUAACAASURB\nVF23TK8ZWqFZQX9by2zblXn6PRMvAQR/ZHITLntctfSt6H5uDB++/AnbtvH8cqY1QymdnDaOhyMu\nCIRBr8Qp8t2v/08eHr5geRApuNXKvl6x3pKLkG7eOXCGp6++YE+N28cXlmVmu1y4nS8cnt5xeHpH\nbydeP/16KFoVd3a7rsQ4c71c2ct1BIkOYwvKDKxdqrPaC6FpwamFIBXacjiI74+BLgOM93J7bnkV\nIfgHiditDYAt5u3XeV604G1nOSyklLWurE6VMjaWsm7styvreuXdh/fQLTmpjI1hBjylFkxwY2Pr\npPE1xENQSKp3liXGYV0uYDSB8HEiLsdRvkuDb0OQAMgYSleYzuX8SkmJ9fVM3jdh6owjuIA1lnVd\ncS6ShzK0ZGlGWqu8vH4i+InW9e91YyUSwgzMXB/9hULtyGS17+Si2Lyfvf8D1tuN0/HE4xciVB2O\nDyjtW4rXhyfDFI9sl/+DnHastey1qKs/8Gc+OBqGLVW8mdm3jrULl/ON6O2IB1Aj1PtIWgutBNx0\nACfthHW6zraRgA7w9OFL1vVGWq/a+EY26L4JcXdPRbcmUHsTtNUY1i2xXV95OESs7Zi2vxnqetuH\nP0ehwL39eJ3C74TMudOxeAwBU80Ia7Gg2zHTNCmz4I3px0B+SxJrOsQwQWucjk9i4aWNXnXC9rso\nyUjc0kdoRzeGMEVO7544vntkOR1lv22ddVtFXXJu9DBGU6x3JUaPuPmOoVvt6iZ4TAgSAXmP9Vpo\nbajU2kg0jjGO72F0161OrN77kDMbbrcVZUDU8QAxoJ/6d6IP2mQ6ilVrGdsqaVPk3TRFaey9Iy4z\nh+OB+4l0/1p9fG3nPdPhiJ8XTPD4eaKO6seNUJoyWIca3w2hzJBKT/OBaV40bh2jWecD9xvI+XzG\nGEdJQqpv2z54FBrhllJ+s8CbQnPWbRvNuiFqauopqeLo3Nbr6Cdocd1TwDGGy+XMtovWVUoixMDD\n44N0D1SNHlvHOY/38S1d24fIh5/+lDhFhb4E5XCUolGoc4E4zYQwYW2UsM56rA3k3Nm3Qu8O8KQk\n6Cw44nQYqtUwNs86vlcxGvYtcXp4woVZjsoqVoIZAcsS1sprYUdmqIxrhm4CKSvs93p+odXhEyk7\nrUklaUd/48d+/E5UCtZYFvcAGfq2oyxG//YD37ZMSmM+2wr7rp24WkftBm+dAl+8JWdh3fftzLyc\nxtgpKDgGGUrW8xU3mmgdC07Nxa9+8XO++fabsSgYzIVA2ldcCKScODw8qWP/6TMdWZhTqoruGou9\npSKwxxDg3GfimJHag3lbbOBk9DNgkCDm+PAk/0XPlF51bRiLDaxAHAMkKuVsx4xI+DgdsWZMVFCT\nDBeHHyRRexkekUjOO95PwythWY5H9VFSUjOy3CuUPlyXdUA/hCybpyOtda6v8vfXpqAdb4eitBvR\njzrs643L8ydy7vh4IJdGKZnQO8FYScy7VIIy/kRqHxZt4Ha5yAnbiiTNcaJ0hafsKWNDeMuCrOOi\n6bws0DVtQpjlTHk4ConuA95HvFEZX2yFYHn64gs68PLxI+2meLzEhnceGxyn04nz87M2JGNwccEY\nUaTWbcd6M94r2aHDNOvIc5a8Ccvmp0CqO3E60GtlX2+kPY2ks4le+zADdkLUqLIYXZe79az7jveO\nQ5jo2ZCbIddEsAv7rmcu1/p2jfJhxo0goB+1Hn+rq/v/54c1FipY4wdvUbgxiVYsKRX2fR/YqYlp\n0gNbsrr2pXVSqaSc1TnuCjbFVHVzex08wKoIcMCFSBjlsPUOFwI2ON5/+ZVYdyjQg3GC1arXcL1e\n2fadfd9l5S1lzInlblRUPCpBzQgbrZLnGiMhUdq3sTEoF6B1xYAxfLtp3yh5p1TNnZ0Pb40iY92I\nmq9DxNMGdcioITrovr0Na3ktbxqFOM3EOKu3Ygx3kbO1hrTeePn0HfvtKnhuEY7t7iBsbx11vRRd\nm/rbFe0O9tFr0sTkTqLuvY1MySRu413QNdSopjccoxL6QUXkrMN73a/n5SBaFrqG5UFTLkm5Ffu2\nvdGSzRD9AG+BtjqiDXVAaay1hKig3FKKwl2dx4dAnGcOpwdKylA7D6dHMTVK1c+3tbf3QmHA+v5b\nh+vlxj4wdTLs3XMrDGEkbIWgKq/0Qpwn9ry/TRDu/QLTzZBPj/f4Xt12VTgWcTvswPYZ4+km0LCk\nUjF+JkzH0QRvv5nO/YiP34lKgW7w3WO6ZatjTus9BRlkbluGAVUN00Scj9jrjfW8s10Tzlu2smNt\nZTlMBC/en3OWKUTSvuG6OtTrepUm30fW9apd1A/UmnrYKtWs7ojrfqZ3Q94z1gZez89M04E4LXR/\nx3GpDN/XjZYbLy8vnI5Hek8aZxqrDjKKs1MprB+W8x5FkotW/fAQ2NfL0AqMjWIw/btTeC3Atq7K\nIkDz+jafNE+PkxgSb/t9Z08r0UfSesPHSJwXRdFNUR19a6UgBWqSutHAcDw6vHMKTfFyeXorv8W9\nSRaCREfX21lUaSdnpxmiGoy0H3TPvl3AaKM5PixAx1Gp+wUbDpSW9Xow0B3zrKj51jqlZUrdcGGG\nblhviet5pxgI80xKAr+mtEvy2wrTNJH3DYtjPV/58PXXgMVYQWa98SzLwr7tzEES9fl4xPnA+fk8\nQLo6/Y8HZYNMc5QVOiVoSn4KcxS1mUZtnTkucujWxrxMb1g77wO39TocppZcigKEEPujpMK+bcR5\nEQS4ZnJNGGfGE9ppRaPzKYhW3XHSw8QAVGyY6d1Qx5W201j39KOX4+/IptCHAk7NIzC0t187Lk7j\n/qreQC2NOM+s552SMq0YWkt4D3HSm2mcrhopZWpR0Ad0YgxvHEfF7zVwQ7gTptHw0kPTh6HmdrsR\nY0RoeGHB/CD3DNHZoODIoTnNi05rtLPbEV2PcSIgjV6BqjupAe+MwJwUCHMXrdwX11svwEpDoSmF\nZL6y/3qRn1wQXPQuWy55LGDe8iictQKXNGkSoL39eW9taEOaTnp7d0mqmRdCUGjp2BCMVc6EQYRt\n76cBmpVPww6bufQW+rq3baX1xEN4pNZEqfpZ1yJBk7kLtozoWc4HGClL3o5qqDbyyHrEq6IzI3hl\nmRYY77/3gbxlnahv92pF0xMgxDD6JdJx+Bjp24qPUdVjqeRa1FTtDqeUYqV9DURWbeJNWmuwYWKa\nIz7ooKGJoXk4HXFR6dB2d28J22bY6O9xASXfZNAzUGrWgdGkRLVmko16jGCnecYa+ThSdhhzr+o0\nijfWYGwn7Svd/L5VCg16UuPOOpV0xgbBLzsErze/1IT3gTgLbhHdNBSDlofHJ7b9qnkvBmOCQljQ\nydZKHgariukFYyK0xu38ytP7GWudmns+YryaiiVnOnZkHd4jwgwpb7RWicGreTjGn/eS3vnB2sNB\ngUZRyGnKtKbRUx8svg73dSlXW1UZeRf8hBCG/l927VwyeRCj1tvK8nDUwscKAzYCS+9p1b11WskY\nU7CmYLphvT5zr4y0oXnW1ys1CUwTgmFaPL1npjBjjTbV+0gsOEuMs6jU9w2rd6a40Lpgq33QqS6f\nn4Ur7+rdxOOB9z//OdMyYa1GpufPH8lbwjbh0aZ5wjkrO3lX9gHeYYwj5cTtfOHymsi5U41ligvG\na8EaYzCjSRpCYFtveB+VUHU6kvNGqwfxIkqlpTSk7Bop4z2nd4+Y2vn6Zz/l4zff8ny+kFJmmgKm\nK8HKh4jzln1bmeZFTW/TiNERYuTp3XtyHqPYXnEx4qfIsiykkulJNK9cK6YVXTn3ldfXZ/WUmihN\nIWqM3nOCVqn7UKWWNoJsxuZrHYfTI3u6yXDVO9aJB2L9SOn6kR+/G5tCl6HF+4mOtOEhLlJ/jftq\n742wRHXjU4JcmLzFHR/U2DFDeTZ6EncijtR6nXAIpLISTRinbKXXyjIvethdx08LcVapt95uMg11\nAOnKR80nhsPoe0Ant/YXcg3u4I5WDet2Yz4slFhpCC6iiYVOHG0KdfAU+pjEqKnYutx1b2COsUnW\nKsT59fWFr6NnWQ50c1cJ6n9azRgClqbmF515OdBHo7G1xp46mIntdmW77rQixWCYFuaDFpkzVfd6\nLLUmVTh2lgbAqmwNfqLkopNpaEaMU0T67XaV+zCqyphHQ1OZlOBiYPrpifVyoWxaoGGOgteYPkjY\n7a3/k28buWxUEyAw5NmdO2QlBM3zY4ykXeRmazy9NpbTzPVyJsyzgnKdZ17ugb1WngSykrzLxulh\nwvQPXC+v1JKoyeKcJe0ZLZ3+BqbFKDk9xgljDOu6jSuDZ09Ses7O0q43TRO8J1grcI25B7g0Go11\nu+GnCTeuQELIOeZJh2D0QuzRMq0PmEzJ+Kgpg7XC3u3rSjOVUpLezx/58buxKRgIcaFjeUuLBgV9\n3ptYhjG2UjPKYuitEOOBOM9su1xqPkykfWM+HLXRWEOvojJZL0FKCIH7DNzUCnZQhbpizXItMtLk\nPNKv5eRTg0+d+5KKJhcofTmlRAhRrs09EXwgl8R63XDOMh8n7jIVDJQi0ZDKWrUOBNhAUt1xXeo0\nTB9Xp3vZPppRrTVqruSQFTM2eAwtJ6hZ+gPraD3j4zQ64kZNvT40EN0MZ50lbRvTHHDeMi0zbZhy\n3DD/WCe7sTIeoWa5L3Xtkz3dB+nvc0ns66bXOXzU1jpC8GoUVr2vrklOPR8O1DCiz6zs4dM8cT1f\nRPJ2gWU5UKrleKqs24v0JnRiiDin76EbKTLXbSUMGTHN4P4f6t7l1bZ1Xe/6fdfWWr+NMeZct73P\nJUfFBCJCRLDiHyBYEUtqIRa1oKBgKVa0kprGohCxIihBxIKIFQVBLKgkIaAnUUg4ybnsy9prz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YHOTyIEbVqv2d3tgdjuRlZls21k9vUL/nGFhjKE0j65wlxsDz118Q3s7k+cww7LFdqOHGQV5u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\n3zDuF4X7cT/uxzeM/x8wEBHS7QeYCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.preprocessing import image\n",
    "from sklearn.preprocessing import *\n",
    "import matplotlib.pyplot as plt\n",
    "import os, random\n",
    "dir = \"/content/test/\"                # Randomly selecting a image for prediction from test \n",
    "filename = random.choice(os.listdir(\"/content/test//\"))\n",
    "filepath = os.path.join(dir, filename)\n",
    "# filepath = \"/content/test/4e18ab737.png\"   # File path of the image you want to predict\n",
    "test_image = image.load_img(filepath, target_size = (256, 256))  # Target size must be the\n",
    "# input size of the image which is passed into the training.\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "\n",
    "#predict the result\n",
    "result = model.predict(test_image)\n",
    "import matplotlib.image as mpimg\n",
    "testimage=mpimg.imread(filepath)\n",
    "plt.imshow(testimage)\n",
    "result_fin = enc.inverse_transform(result)\n",
    "print(*result_fin,sep='')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 231
    },
    "colab_type": "code",
    "id": "nJpFl4O8g64v",
    "outputId": "4e8efd73-d11d-4421-d642-1bb31ff72078"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-b1e7e53cdf63>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Plot non-normalized confusion matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m plot_confusion_matrix(y_test, y_pred, classes=class_names,\n\u001b[0m\u001b[1;32m      5\u001b[0m                       title='Confusion matrix, without normalization')\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'class_names' is not defined"
     ]
    }
   ],
   "source": [
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plot_confusion_matrix(y_test, y_pred, classes=class_names,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yaSi1qHOg7NB"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JpQb2QSyWw0U"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3_tOr0uvW1jh"
   },
   "source": [
    "#ResNet- 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HecCzsR6W7fe"
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential,Model\n",
    "from keras.layers import Dense, Dropout, Flatten, Activation, GlobalAvgPool2D\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "def resnet_model(num_classes=None):\n",
    "\n",
    "    model =  ResNet50(weights='imagenet', include_top=False,input_shape=(256,256,3))\n",
    "#     model.layers.pop()\n",
    "#     model.layers.pop()\n",
    "#     model.layers.pop()\n",
    "\n",
    "    model.outputs = [model.layers[-1].output]\n",
    "\n",
    "    model.layers[-2].outbound_nodes= []\n",
    "    x = BatchNormalization()(model.output)\n",
    "    x=Conv2D(256, kernel_size=(2,2),strides=2)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)    \n",
    "    x=Conv2D(128, kernel_size=(3,3),padding='same')(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x = Conv2D(64, kernel_size=(2,2),strides=1)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x=Conv2D(12, kernel_size=(2,2),strides=1)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "#     x=Flatten()(x)\n",
    "#     x=Dense(num_classes, activation='softmax')(x)  \n",
    "    x = GlobalAvgPool2D()(x)\n",
    "    x = Activation('softmax')(x)\n",
    "\n",
    "    model=Model(model.input,x)\n",
    "\n",
    "    for layer in model.layers[-3:]:\n",
    "\n",
    "        layer.trainable = False\n",
    "\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "M4eby0wMW7t7",
    "outputId": "3689240a-cf6d-4964-b375-56d365859607"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94658560/94653016 [==============================] - 4s 0us/step\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            (None, 256, 256, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 262, 262, 3)  0           input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 128, 128, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 128, 128, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 128, 128, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 130, 130, 64) 0           activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 64, 64, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 64, 64, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 64, 64, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 64, 64, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 64, 64, 64)   36928       activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 64, 64, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 64, 64, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 64, 64, 256)  16640       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 64, 64, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 64, 64, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 64, 64, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 64, 64, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 64, 64, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 64, 64, 64)   16448       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 64, 64, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 64, 64, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 64, 64, 64)   36928       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 64, 64, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 64, 64, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 64, 64, 256)  16640       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 64, 64, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 64, 64, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 64, 64, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 64, 64, 64)   16448       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 64, 64, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 64, 64, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 64, 64, 64)   36928       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 64, 64, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 64, 64, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 64, 64, 256)  16640       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 64, 64, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 64, 64, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 64, 64, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 32, 32, 128)  32896       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 32, 32, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 32, 32, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 32, 32, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 32, 32, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 32, 32, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 32, 32, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 32, 32, 512)  131584      activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 32, 32, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 32, 32, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 32, 32, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 32, 32, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 32, 32, 128)  65664       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 32, 32, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 32, 32, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 32, 32, 128)  147584      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 32, 32, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 32, 32, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 32, 32, 512)  66048       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 32, 32, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 32, 32, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 32, 32, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 32, 32, 128)  65664       activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 32, 32, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 32, 32, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 32, 32, 128)  147584      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 32, 32, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 32, 32, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 32, 32, 512)  66048       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 32, 32, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 32, 32, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 32, 32, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 32, 32, 128)  65664       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 32, 32, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 32, 32, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 32, 32, 128)  147584      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 32, 32, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 32, 32, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 32, 32, 512)  66048       activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 32, 32, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 32, 32, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 32, 32, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 16, 16, 256)  131328      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 16, 16, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 16, 16, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 16, 16, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 16, 16, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 16, 16, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 16, 16, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 16, 16, 1024) 525312      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 16, 16, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 16, 16, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 16, 16, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 16, 16, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 16, 16, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 16, 16, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 16, 16, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 16, 16, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 16, 16, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 16, 16, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 16, 16, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 16, 16, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 16, 16, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 16, 16, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 16, 16, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 16, 16, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 16, 16, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 16, 16, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 16, 16, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 16, 16, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 16, 16, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 16, 16, 256)  262400      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 16, 16, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 16, 16, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 16, 16, 256)  590080      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 16, 16, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 16, 16, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 16, 16, 1024) 263168      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 16, 16, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 16, 16, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 16, 16, 256)  262400      activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 16, 16, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 16, 16, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 16, 16, 256)  590080      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 16, 16, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 16, 16, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 16, 16, 1024) 263168      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 16, 16, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 16, 16, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 16, 16, 256)  262400      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 16, 16, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 16, 16, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 16, 16, 256)  590080      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 16, 16, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 16, 16, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 16, 16, 1024) 263168      activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 16, 16, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 16, 16, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 8, 8, 512)    524800      activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 8, 8, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 8, 8, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 8, 8, 512)    2359808     activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 8, 8, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 8, 8, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 8, 8, 2048)   1050624     activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 8, 8, 2048)   2099200     activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 8, 8, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 8, 8, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 8, 8, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 8, 8, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 8, 8, 512)    1049088     activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 8, 8, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 8, 8, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 8, 8, 512)    2359808     activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 8, 8, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 8, 8, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 8, 8, 2048)   1050624     activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 8, 8, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 8, 8, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 8, 8, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 8, 8, 512)    1049088     activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 8, 8, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 8, 8, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 8, 8, 512)    2359808     activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 8, 8, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 8, 8, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 8, 8, 2048)   1050624     activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 8, 8, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 8, 8, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 8, 8, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 8, 8, 2048)   8192        activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 4, 4, 256)    2097408     batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 4, 4, 256)    1024        conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 4, 4, 256)    0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 4, 4, 128)    295040      activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 4, 4, 128)    512         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 4, 4, 128)    0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 3, 3, 64)     32832       activation_60[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 3, 3, 64)     256         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 3, 3, 64)     0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 2, 2, 12)     3084        activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 2, 2, 12)     48          conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 2, 2, 12)     0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 12)           0           activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 12)           0           global_average_pooling2d_1[0][0] \n",
      "==================================================================================================\n",
      "Total params: 26,026,108\n",
      "Trainable params: 25,967,972\n",
      "Non-trainable params: 58,136\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras import backend as K\n",
    "num_classes=12\n",
    "model = resnet_model(num_classes)\n",
    "model.compile(optimizer=\"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()\n",
    "model.save('model1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 615
    },
    "colab_type": "code",
    "id": "5bn7Fq7LW7zl",
    "outputId": "b12fcb5c-472b-4db1-aa85-f37ea63a250e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3325 samples, validate on 713 samples\n",
      "Epoch 1/10\n"
     ]
    },
    {
     "ename": "ResourceExhaustedError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-8b9455cfcb9a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m           \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m           verbose=1, shuffle=True, validation_data=(X_val,y_val),callbacks=callbacks_list)\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1456\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1457\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1458\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1459\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1460\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mResourceExhaustedError\u001b[0m: 2 root error(s) found.\n  (0) Resource exhausted: OOM when allocating tensor with shape[64,16,16,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node training_2/Adam/gradients/zeros_61-0-1-TransposeNCHWToNHWC-LayoutOptimizer}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[metrics_3/acc/Mean/_6333]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n  (1) Resource exhausted: OOM when allocating tensor with shape[64,16,16,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node training_2/Adam/gradients/zeros_61-0-1-TransposeNCHWToNHWC-LayoutOptimizer}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n0 successful operations.\n0 derived errors ignored."
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,CSVLogger\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "filepath=\"./weights.best1_{epoch:02d}-{val_acc:.2f}.hdf5\"\n",
    "checkpoints = ModelCheckpoint(filepath, monitor='val_acc', \n",
    "                              verbose=1, save_best_only=True, mode='max')\n",
    "filepath=\"./weights.last1_auto4.hdf5\"\n",
    "checkpoints_full = ModelCheckpoint(filepath, monitor='val_acc', \n",
    "                                 verbose=1, save_best_only=False, mode='max')\n",
    "\n",
    "callbacks_list = [checkpoints, checkpoints_full]\n",
    "\n",
    "#MODEL\n",
    "# hist = model.fit_generator(generator.flow(x_train,y_train, batch_size=75), \n",
    "#                            epochs=35, validation_data=(x_test, y_test), \n",
    "#                            steps_per_epoch=x_train.shape[0], callbacks=callbacks_list)\n",
    "history = model.fit(X_train,y_train,\n",
    "          batch_size=64,\n",
    "          epochs=10,\n",
    "          verbose=1, shuffle=True, validation_data=(X_val,y_val),callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pKlgxzlagLKj"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "epochs = 10\n",
    "fig, ax = plt.subplots()  \n",
    "ax.plot(np.arange(epochs), history.history['loss'], 'r')\n",
    "ax.plot(np.arange(epochs), history.history['val_loss'], 'b')\n",
    "ax.legend(['train', 'validation'], loc='upper left')\n",
    "ax.set_xlabel('Iterations')  \n",
    "ax.set_ylabel('Cost')  \n",
    "ax.set_title('Cost vs. Training Epoch') \n",
    "from sklearn.metrics import classification_report\n",
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FHAJ6D2HhLwM"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "REoQ3AJbW8Hg"
   },
   "outputs": [],
   "source": [
    "score = model.evaluate(X_test, y_test, verbose=0, batch_size = None)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1yWxI9KYW82P"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mytNSFDkW8xQ"
   },
   "outputs": [],
   "source": [
    "from keras.models import *\n",
    "model = load_model('model1.h5')\n",
    "m = model.load_weights(\"weights.best1_05-0.73.hdf5\")   # Call the last model weights best -- The \n",
    "#next argument is the accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SZBvpq-9sDaQ"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "from sklearn.preprocessing import *\n",
    "import matplotlib.pyplot as plt\n",
    "import os, random\n",
    "dir = \"/content/test/\"                # Randomly selecting a image for prediction from test \n",
    "filename = random.choice(os.listdir(\"/content/test//\"))\n",
    "filepath = os.path.join(dir, filename)\n",
    "# filepath = \"/content/test/4e18ab737.png\"   # File path of the image you want to predict\n",
    "test_image = image.load_img(filepath, target_size = (256, 256))  # Target size must be the\n",
    "# input size of the image which is passed into the training.\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "\n",
    "#predict the result\n",
    "result = model.predict(test_image)\n",
    "import matplotlib.image as mpimg\n",
    "testimage=mpimg.imread(filepath)\n",
    "print(filepath)\n",
    "plt.imshow(testimage)\n",
    "result_fin = enc.inverse_transform(result)\n",
    "print(*result_fin,sep='')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xv_gi-7Urz7t"
   },
   "source": [
    "# Inception V-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bSoCTbJnW8sW"
   },
   "outputs": [],
   "source": [
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.preprocessing import image\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, GlobalAveragePooling2D, BatchNormalization, Flatten\n",
    "from keras import backend as K\n",
    "\n",
    "# create the base pre-trained model\n",
    "model = InceptionV3(weights='imagenet', include_top=False,input_shape=(256,256,3))\n",
    "model.layers[-2].outbound_nodes= []\n",
    "# add a global spatial average pooling layer\n",
    "x = model.output\n",
    "\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.4)(x)\n",
    "x = Flatten()(x)\n",
    "predictions = Dense(12, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=model.input, outputs=predictions)\n",
    "#model.summary()\n",
    "\n",
    "# first: train only the top layers (which were randomly initialized)\n",
    "# # i.e. freeze all convolutional InceptionV3 layers\n",
    "# for layer in base_model.layers:\n",
    "#     layer.trainable = False\n",
    "\n",
    "# # compile the model (should be done *after* setting layers to non-trainable)\n",
    "# model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy',fscore])\n",
    "\n",
    "# # train the model on the new data for a few epochs\n",
    "# model.fit(X_train,y_train, batch_size=64,epochs=3, verbose=1, shuffle=True, validation_data=(X_val,y_val))\n",
    "\n",
    "# at this point, the top layers are well trained and we can start fine-tuning\n",
    "# convolutional layers from inception V3. We will freeze the bottom N layers\n",
    "# and train the remaining top layers.\n",
    "\n",
    "# let's visualize layer names and layer indices to see how many layers\n",
    "# we should freeze:\n",
    "# for i, layer in enumerate(base_model.layers):\n",
    "#    print(i, layer.name)\n",
    "#model.summary()\n",
    "# we chose to train the top 2 inception blocks, i.e. we will freeze\n",
    "# the first 249 layers and unfreeze the rest:\n",
    "for layer in model.layers[-2:]:\n",
    "   layer.trainable = False\n",
    "# for layer in model.layers[250:]:\n",
    "#    layer.trainable = True\n",
    "\n",
    "# we need to recompile the model for these modifications to take effect\n",
    "# we use SGD with a low learning rate\n",
    "from keras.optimizers import SGD\n",
    "model.compile(optimizer=SGD(lr=0.01, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.save(\"model2.h5\")\n",
    "# we train our model again (this time fine-tuning the top 2 inception blocks\n",
    "# alongside the top Dense layers\n",
    "from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,CSVLogger\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "filepath=\"./weights.best2_{epoch:02d}-{val_acc:.2f}.hdf5\"\n",
    "checkpoints = ModelCheckpoint(filepath, monitor='val_acc', \n",
    "                              verbose=1, save_best_only=True, mode='max')\n",
    "filepath=\"./weights.last2_auto4.hdf5\"\n",
    "checkpoints_full = ModelCheckpoint(filepath, monitor='val_acc', \n",
    "                                 verbose=1, save_best_only=False, mode='max')\n",
    "\n",
    "callbacks_list = [checkpoints, checkpoints_full]\n",
    "\n",
    "\n",
    "\n",
    "history = model.fit(X_train,y_train,batch_size=64, epochs=30,verbose=1, shuffle=True, validation_data=(X_val,y_val),callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1hpdTNqj5UoH"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "epochs = 30\n",
    "fig, ax = plt.subplots()  \n",
    "ax.plot(np.arange(epochs), history.history['loss'], 'r')\n",
    "ax.plot(np.arange(epochs), history.history['val_loss'], 'b')\n",
    "ax.legend(['train', 'validation'], loc='upper left')\n",
    "ax.set_xlabel('Iterations')  \n",
    "ax.set_ylabel('Cost')  \n",
    "ax.set_title('Cost vs. Training Epoch') \n",
    "from sklearn.metrics import classification_report\n",
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8pOXWPtSzac1"
   },
   "outputs": [],
   "source": [
    "score = model.evaluate(X_test, y_test, verbose=0, batch_size = None)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V3qlVez9Cbt0"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "from sklearn.preprocessing import *\n",
    "import matplotlib.pyplot as plt\n",
    "import os, random\n",
    "dir = \"/content/test/\"                # Randomly selecting a image for prediction from test \n",
    "filename = random.choice(os.listdir(\"/content/test//\"))\n",
    "filepath = os.path.join(dir, filename)\n",
    "# filepath = \"/content/test/4e18ab737.png\"   # File path of the image you want to predict\n",
    "test_image = image.load_img(filepath, target_size = (256, 256))  # Target size must be the\n",
    "# input size of the image which is passed into the training.\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "\n",
    "#predict the result\n",
    "result = model.predict(test_image)\n",
    "import matplotlib.image as mpimg\n",
    "testimage=mpimg.imread(filepath)\n",
    "plt.imshow(testimage)\n",
    "result_fin = enc.inverse_transform(result)\n",
    "print(*result_fin,sep='')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iXc4jG12W8nf"
   },
   "outputs": [],
   "source": [
    "# To save the models and weight files into google buckets into a folder Model_dev\n",
    "! gsutil -m cp -r w* m* gs://proobj/Model_dev/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vT0MRgJdW8ir"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2WC-bW5lW8d2"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ehf4bMfIW8ZH"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8qwVsW6xW8TM"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GfnQo8CsW8OU"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-4qdg66BW8C8"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KqUovZ8ZEYI8"
   },
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XgZZ0O0cEbMH"
   },
   "outputs": [],
   "source": [
    "\n",
    "## Downloading dataset from Buckets\n",
    "!gsutil -m cp -r gs://proobj/t*.zip .\n",
    "!unzip test.zip\n",
    "!unzip train.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UUTLkUyMEpdp"
   },
   "outputs": [],
   "source": [
    "# Downloading models with weights from Buckets  0-VGG 1-RESENT50 2-Inception-v3\n",
    "# Model_dev consists the model files for VGG16, RESNET50, INCEPTION-V3\n",
    "# Note the last epoch number file is the best accuracy file\n",
    "! gsutil -m cp -r  gs://proobj/Model_dev/ ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-fFlvexSIHe0"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "TRAin_DATA_FOLDER= \"train\"\n",
    "\n",
    "\n",
    "images = []\n",
    "classes=[]\n",
    "missing=0\n",
    "for class_folder_name in os.listdir(TRAin_DATA_FOLDER):\n",
    "    class_folder_path = os.path.join(TRAin_DATA_FOLDER, class_folder_name)\n",
    "    class_label = class_folder_name\n",
    "   \n",
    "    for image_path in glob(os.path.join(class_folder_path, \"*.png\")):\n",
    "        image_bgr = cv2.imread(image_path, cv2.IMREAD_COLOR)\n",
    "        if image_bgr is None: # if the file contain any missig value ignore it\n",
    "          missing += 1\n",
    "          continue\n",
    "        images.append(image_bgr)\n",
    "       \n",
    "        classes.append(class_label)\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "enc = LabelBinarizer()\n",
    "y = enc.fit_transform(classes)\n",
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N_U98MVHEpbR"
   },
   "outputs": [],
   "source": [
    "### PREDICTION FROM VGG 16 ####\n",
    "\n",
    "from keras.models import *\n",
    "model = load_model('./Model_dev/model.h5')\n",
    "m = model.load_weights(\"./Model_dev/weights.best_10-0.92.hdf5\")   # Call the last model weights best -- The \n",
    "#next argument is the accuracy of the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j7ejQGfPEbOv"
   },
   "outputs": [],
   "source": [
    "### PREDICTION FROM RESNET 50 ###\n",
    "\n",
    "from keras.models import *\n",
    "model = load_model('./Model_dev/model1.h5')\n",
    "m = model.load_weights(\"./Model_dev/weights.best1_05-0.73.hdf5\")   # Call the last model weights best -- The \n",
    "#next argument is the accuracy of the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3kOXOFwmEbWc"
   },
   "outputs": [],
   "source": [
    "### PREDICTION FROM INCEPTION ###\n",
    "from keras.models import *\n",
    "model = load_model('./Model_dev/model2.h5')\n",
    "m = model.load_weights(\"./Model_dev/weights.best2_30-0.95.hdf5\")   # Call the last model weights best -- The \n",
    "#next argument is the accuracy of the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i8YXCrelEbbG"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "from sklearn.preprocessing import *\n",
    "import matplotlib.pyplot as plt\n",
    "import os, random\n",
    "import numpy as np\n",
    "dir = \"/content/test/\"                # Randomly selecting a image for prediction from test \n",
    "filename = random.choice(os.listdir(\"/content/test//\"))\n",
    "filepath = os.path.join(dir, filename)\n",
    "# filepath = \"/content/test/4e18ab737.png\"   # File path of the image you want to predict\n",
    "test_image = image.load_img(filepath, target_size = (256, 256))  # Target size must be the\n",
    "# input size of the image which is passed into the training.\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "\n",
    "#predict the result\n",
    "result = model1.predict(test_image)\n",
    "import matplotlib.image as mpimg\n",
    "testimage=mpimg.imread(filepath)\n",
    "plt.imshow(testimage)\n",
    "result_fin = enc.inverse_transform(result)\n",
    "print(*result_fin,sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m637-IZ8H3ls"
   },
   "outputs": [],
   "source": []
  }
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