AI_ANN_MODEL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb43419c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "#from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import precision_recall_fscore_support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a75d9508",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "preprocessing Image Number==>  6656\n",
      "preprocessing Image Number==>  6657\n",
      "preprocessing Image Number==>  6658\n",
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      "preprocessing Image Number==>  6897\n",
      "preprocessing Image Number==>  6898\n",
      "preprocessing Image Number==>  6899\n",
      "preprocessing Image Number==>  6900\n",
      "preprocessing Image Number==>  6901\n",
      "preprocessing Image Number==>  6902\n",
      "preprocessing Image Number==>  6903\n",
      "preprocessing Image Number==>  6904\n",
      "preprocessing Image Number==>  6905\n",
      "preprocessing Image Number==>  6906\n",
      "preprocessing Image Number==>  6907\n",
      "preprocessing Image Number==>  6908\n"
     ]
    }
   ],
   "source": [
    "data_path = r\"D:\\\\1\\\\train\"\n",
    "img_size=256             \n",
    "counter=0            \n",
    "X=[]\n",
    "Y=[]\n",
    " \n",
    "categories=os.listdir(data_path)\n",
    " \n",
    "for category in categories:                                                            # this loop to know how many images in categories\n",
    "    folder_path=os.path.join(data_path,category)                                       # make folder empty has the same path for dataset\n",
    "    img_names=os.listdir(folder_path)                                                  # put each image in this folder\n",
    " \n",
    "    for img_name in img_names:\n",
    "        img_path=os.path.join(folder_path,img_name)\n",
    "        fullpath=os.path.join(data_path,category,img_name)\n",
    "        try:\n",
    "            img = cv2.imread(fullpath, cv2.IMREAD_COLOR)\n",
    "            img = cv2.resize(img, (img_size,img_size))\n",
    "            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
    "            X.append(img)\n",
    "            Y.append(category)\n",
    "            counter+=1\n",
    "            print(\"preprocessing Image Number==> \",counter)\n",
    "        except:\n",
    "            print(\"Error in ==> \",counter)\n",
    "imgs=np.array(X)\n",
    "lbls=np.array(Y)\n",
    "del X\n",
    "del Y\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4092624",
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "#Label Encoding\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(lbls)\n",
    "lbls_encoded = le.transform(lbls)\n",
    " \n",
    "#Train and Test Split\n",
    "train_x, test_x,train_y, test_y = train_test_split(imgs,lbls_encoded,test_size=0.1)\n",
    " \n",
    "#Normalization\n",
    "train_x, test_x = train_x / 255.0,  test_x / 255.0\n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "151b0ceb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (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",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n",
      "195/195 [==============================] - 796s 4s/step\n",
      "22/22 [==============================] - 89s 4s/step\n"
     ]
    }
   ],
   "source": [
    " \n",
    "from keras.applications import VGG16\n",
    "#Feature Extraction\n",
    "VGG_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_size,img_size, 3))\n",
    " \n",
    "for layer in VGG_model.layers:\n",
    "    layer.trainable = False\n",
    " \n",
    "VGG_model.summary()  \n",
    "feature_extractor=VGG_model.predict(train_x)\n",
    "features = feature_extractor.reshape(feature_extractor.shape[0], -1)\n",
    "ann_features_train = features\n",
    "feature_extractor_test=VGG_model.predict(test_x)\n",
    "ann_features_test = feature_extractor_test.reshape(feature_extractor_test.shape[0], -1)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07ecf05b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "195/195 [==============================] - 9s 43ms/step - loss: 0.4487 - accuracy: 0.8607 - val_loss: 0.4782 - val_accuracy: 0.8726\n",
      "Epoch 2/5\n",
      "195/195 [==============================] - 8s 40ms/step - loss: 0.1224 - accuracy: 0.9554 - val_loss: 0.2407 - val_accuracy: 0.9161\n",
      "Epoch 3/5\n",
      "195/195 [==============================] - 8s 40ms/step - loss: 0.0605 - accuracy: 0.9773 - val_loss: 0.1695 - val_accuracy: 0.9392\n",
      "Epoch 4/5\n",
      "195/195 [==============================] - 9s 47ms/step - loss: 0.0232 - accuracy: 0.9910 - val_loss: 0.1447 - val_accuracy: 0.9522\n",
      "Epoch 5/5\n",
      "195/195 [==============================] - 8s 42ms/step - loss: 0.0130 - accuracy: 0.9963 - val_loss: 0.1736 - val_accuracy: 0.9522\n",
      "22/22 [==============================] - 0s 6ms/step\n",
      "Accuracy : 0.9522431259044862\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "#ANN\n",
    "#val_loss_(dec)_val_acc_(inc)\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(256, activation=\"relu\"),\n",
    "    keras.layers.Dense(128, activation=\"relu\"),\n",
    "    keras.layers.Dense(7, activation=\"softmax\")]) # change number based on output classes\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\",optimizer=\"adam\",metrics=[\"accuracy\"])\n",
    "ann=model.fit(ann_features_train, train_y, epochs=5,validation_data=(ann_features_test, test_y))\n",
    "y_preds = model.predict(ann_features_test).argmax(axis=1)\n",
    "Accuracy = accuracy_score(test_y,y_preds)\n",
    "print(\"Accuracy :\", Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e28eabb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y_Normal = le.inverse_transform(test_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c852983c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 502ms/step\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "1/1 [==============================] - 0s 359ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 376ms/step\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "1/1 [==============================] - 0s 365ms/step\n",
      "1/1 [==============================] - 0s 38ms/step\n"
     ]
    }
   ],
   "source": [
    "from tkinter import *\n",
    "import tkinter as tk\n",
    "from tkinter import filedialog\n",
    "from tkinter.filedialog import askopenfile\n",
    "\n",
    "from PIL import Image, ImageTk\n",
    "     \n",
    "root = tk.Tk()\n",
    "root.geometry(\"1250x950\")  # Size of the window \n",
    "root.resizable(width=True, height=True)\n",
    "root.title('A I project')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "root['background']='#263d23' \n",
    "my_font1=('Helvetica', 25, 'bold')\n",
    "my_font2=('Verdana', 15, 'bold')\n",
    "label = tk.Label(root,text='Upload Files & Detect',width=30,font=my_font1)\n",
    "label.grid(row=1,column=1)\n",
    "label.place(anchor = CENTER, relx = .5, rely = .025)\n",
    " \n",
    " \n",
    "root2 = tk.Button(root, text='Press me To Upload Images', \n",
    "   width=25,command = lambda:upload_file())\n",
    "root2.grid(row=2,column=1,pady=5)\n",
    "root2.place(anchor = CENTER, relx = .5, rely = .070)\n",
    "def upload_file():\n",
    "    f_types = [('Jpg Files', '*.jpg'),\n",
    "    ('PNG Files','*.png'),('Jpeg Files', '*.jpeg')]   # types of files to select \n",
    "    filename = tk.filedialog.askopenfilename(multiple=True,filetypes=f_types)\n",
    "    col=1 # start from column 1\n",
    "    row=3 # start from row 3 \n",
    "    for pathgui in filename:\n",
    "        img=Image.open(pathgui)# read the image file\n",
    "        list_of_images = []\n",
    "        preprocessed = cv2.imread(pathgui, cv2.IMREAD_COLOR)\n",
    "        preprocessed = cv2.resize(preprocessed, (img_size,img_size))\n",
    "        preprocessed = cv2.cvtColor(preprocessed, cv2.COLOR_RGB2BGR)\n",
    "        list_of_images.append(preprocessed)\n",
    "        arr = np.array(list_of_images)\n",
    "        feature_extractor_input=VGG_model.predict(arr)\n",
    "        features_input = feature_extractor_input.reshape(feature_extractor_input.shape[0], -1)\n",
    "\n",
    "        prediction_input = model.predict(features_input).argmax() #edited\n",
    "        prediction_input_Normal = le.inverse_transform([prediction_input]) #edited\n",
    "        img=img.resize((150,150)) # new width & height\n",
    "        img=ImageTk.PhotoImage(img)\n",
    "        root1 =tk.Label(root)\n",
    "        root1.grid(row=row,column=col,pady=100,padx=10)\n",
    "        root1.image = img\n",
    "        text_answer=prediction_input_Normal[0] #edited\n",
    "        # text_answer=text_answer.tolist()\n",
    "        xz = tk.Label(root,text=text_answer,width=10,font=my_font2)  \n",
    "        xz.grid(row=row+1,column=col,pady=0,padx=10)\n",
    "        root1['image']=img # garbage collection\n",
    "        if(col==7): # start new line after third column\n",
    "            row=row+2# start wtih next row\n",
    "            col=1    # start with first column\n",
    "        else:       # within the same row \n",
    "            col=col+1 # increase to next column                                                                                 \n",
    "root.mainloop()  # Keep the window open# your code goes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5569bccd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[102   0   0   0   0   0   0]\n",
      " [  0  96   0   0   2   0   1]\n",
      " [  0   0  95   0   0   0   0]\n",
      " [  0   0   0 117   2   0   4]\n",
      " [  0   0   0   0  79   0  22]\n",
      " [  0   0   0   0   0  91   0]\n",
      " [  0   0   0   1   1   0  78]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       102\n",
      "           1       1.00      0.97      0.98        99\n",
      "           2       1.00      1.00      1.00        95\n",
      "           3       0.99      0.95      0.97       123\n",
      "           4       0.94      0.78      0.85       101\n",
      "           5       1.00      1.00      1.00        91\n",
      "           6       0.74      0.97      0.84        80\n",
      "\n",
      "    accuracy                           0.95       691\n",
      "   macro avg       0.95      0.95      0.95       691\n",
      "weighted avg       0.96      0.95      0.95       691\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(test_y, y_preds)\n",
    "print(cm)\n",
    "print(classification_report(test_y, y_preds))\n",
    "# drawing confusion matrix\n",
    "sns.heatmap(cm, center = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "025154f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Value  :  0.969885433715221\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculating ROC:  \n",
    "#roc_curve(y_test, y_pred, pos_label=None, sample_weight=None,drop_intermediate=True)\n",
    "\n",
    "fprValue, tprValue, thresholdsValue = roc_curve(test_y,y_preds,pos_label=6)\n",
    "#print('fpr Value  : ', fprValue)\n",
    "#print('tpr Value  : ', tprValue)\n",
    "#print('thresholds Value  : ', thresholdsValue)\n",
    "\n",
    "#Calculating Area Under the Curve AUC :  \n",
    "\n",
    "fprValue2, tprValue2, thresholdsValue2 = roc_curve(test_y,y_preds,pos_label=6 )\n",
    "AUCValue = auc(fprValue2, tprValue2)\n",
    "print('AUC Value  : ', AUCValue)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(5, 5), dpi=100)\n",
    "plt.plot(fprValue,tprValue, marker='.', label='ANN (auc = %0.2f)' % AUCValue)\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d82e21ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss Curve : [0.4486837685108185, 0.1224052757024765, 0.060516927391290665, 0.023212002590298653, 0.01303118746727705]\n"
     ]
    },
    {
     "data": {
      "image/png": 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EuHr05rOD2ahtbBachoiIyL6w3AiQ0C8AOl9XVNQ1YfPhS6LjEBER2RWWGwGUCglPXFlQc82+DOgNXEmWiIjIUlhuBJkyLBRaF2dkldZix5lC0XGIiIjsBsuNIK4qJzw+smWNKU7qR0REZDksNwLNjNPBWSnhcNZlHM2+LDoOERGRXWC5ESjAU4MHokIAAKv2ZghOQ0REZB9YbgSbM6blwuLvT+Yjp6xWcBoiIiLbx3IjWL8gT4zu6QeDDHy8L1N0HCIiIptnFeVmxYoV0Ol00Gg0iI2NRWpqaptet2HDBkiShEmTJnVuwE529ejNxkPZqKxvEpyGiIjItgkvNxs3bsSCBQuwZMkSpKWlISoqComJiSgqKjL7uszMTLz44osYM2ZMFyXtPGN7d0Mvf3fUNOqxITVbdBwiIiKbJrzcvP3225g7dy5mz56N/v3744MPPoCrqyvWrFlz09fo9XpMnz4dr776KiIjI7swbeeQJMl49ObjfZlo0hsEJyIiIrJdQstNY2Mjjhw5goSEBOM2hUKBhIQEpKSk3PR1S5cuhb+/P5588slb/oyGhgZUVlaaPKzRg9Eh8HNXIb+iHt+dzBcdh4iIyGYJLTclJSXQ6/UICAgw2R4QEICCgoJWX7N3716sXr0aK1eubNPPWLZsGbRarfERFhZ227k7g8ZZiZlxOgDAqj0ZkGUuyUBERNQRwk9LtUdVVRVmzJiBlStXws/Pr02vWbRoESoqKoyPnJycTk7ZcdNju0PtpMDJ3AoczCgTHYeIiMgmOYn84X5+flAqlSgsNF1bqbCwEIGBgTfsf/HiRWRmZuL+++83bjMYWq5PcXJywrlz59CjRw+T16jVaqjV6k5Ib3m+7mpMHhaKfx3Mxqo9GRgZ6Ss6EhERkc0ReuRGpVJh2LBhSE5ONm4zGAxITk5GXFzcDfv37dsXJ0+exLFjx4yPBx54AHfddReOHTtmtaec2uPJK6uFJ/9SiPTiasFpiIiIbI/QIzcAsGDBAiQlJWH48OGIiYnBu+++i5qaGsyePRsAMHPmTISEhGDZsmXQaDQYOHCgyeu9vLwA4IbttqpHN3eM6+uP5F+KsHpvBv760CDRkYiIiGyK8HIzdepUFBcXY/HixSgoKEB0dDS2bdtmvMg4OzsbCoVNXRp02+aMiUTyL0X4Iu0S/vvuPvBxU4mOREREZDMk2cFuy6msrIRWq0VFRQU8PT1Fx2mVLMu4f/lenMqtxH+P741543qJjkRERCRUe35/O9YhERshSRLmjmmZnPCTlCzUN+kFJyIiIrIdLDdW6t5BQQjSalBS3YCtx/NExyEiIrIZLDdWylmpwKx4HQBgNSf1IyIiajOWGyv2aEx3uKmUOFdYhd2/loiOQ0REZBNYbqyY1sUZj4xombtn1Z50wWmIiIhsA8uNlXtiVAQUErDn1xL8UmCdi34SERFZE5YbKxfm44p7BgYBaFlQk4iIiMxjubEBT45pWZLh62O5KKqsF5yGiIjIurHc2ICh3b0xLNwbTXoZ61KyRMchIiKyaiw3NmLulaM3nx7MQm1js+A0RERE1ovlxkaM7x+I7j6uKK9twhdpuaLjEBERWS2WGxuhVEh4YpQOALBmbwYMBk7qR0RE1BqWGxvy++Fh8NQ4IaOkBjvPFoqOQ0REZJVYbmyIm9oJj8WGAwBW7eVt4URERK1hubExs+J1cFJISM0ow4lL5aLjEBERWR2WGxsTqNXggahgAMBKTupHRER0A5YbG3R1Ur/vTuYjt7xOcBoiIiLrwnJjgwYEaxHfwxd6g4y1+3j0hoiI6FosNzZq7phIAMCG1BxU1TcJTkNERGQ9WG5s1Nje3dCjmxuqGpqx8VCO6DhERERWg+XGRikUEuZcOXrz8b5MNOsNghMRERFZB5YbG/bQkBD4uqmQW16H708ViI5DRERkFVhubJjGWYkZcVcm9duTDlnmkgxEREQsNzbu8ZHhUDkpcPxSBQ5nXRYdh4iISDiWGxvn567G5KEhAICVu9MFpyEiIhKP5cYOPDm6ZVK/HWcLkVFSIzgNERGRWCw3dqCnvwfu6tMNsgx8zEn9iIjIwbHc2Imrk/ptPnwJ5bWNgtMQERGJw3JjJ+J6+KJ/kCfqmvT47GC26DhERETCsNzYCUmSMOfKgppr92eioVkvOBEREZEYLDd25L7BwQjwVKO4qgH/OZ4vOg4REZEQLDd2ROWkwKz4lqM3nNSPiIgcFcuNnXkspjtcVUr8UlCFfRdKRcchIiLqciw3dkbr6oxHhocBAFbu4aR+RETkeFhu7NAToyIgScDP54txvrBKdBwiIqIuxXJjh7r7uiKxfyAAYPUeTupHRESOheXGTs29o+XC4i1Hc1Fc1SA4DRERUddhubFTw8J9MKS7Fxr1BqxPyRQdh4iIqMuw3NixOaNblmRYfyAL9U2c1I+IiBwDy40dSxwQgFBvF1yubcIXaZdExyEiIuoSLDd2zEmpwBOjWq69Wb0nAwYDJ/UjIiL7x3Jj5x4ZEQYPjRPSS2rw07ki0XGIiIg6HcuNnXNXO+GxmO4AOKkfERE5BpYbBzBrlA5OCgkH0stwKrdCdBwiIqJOxXLjAIK0LrhvcBAAHr0hIiL7x3LjIOaMabkt/NsT+cgrrxOchoiIqPOw3DiIgSFajIz0QbNBxif7M0XHISIi6jQsNw5k7pWjN/9KzUZ1Q7PgNERERJ2D5caB3NXHH5Hd3FBV34xNh3JExyEiIuoULDcORKGQ8OTolkn91uzLQLPeIDgRERGR5bHcOJjJQ0Ph7eqMS5frsP10oeg4REREFsdy42A0zkrMGBkOAFi1l7eFExGR/WG5cUAz4nRQOSlwNLscR7LKRMchIiKyKJYbB9TNQ42HokMAACt3ZwhOQ0REZFksNw7qyTEtFxZvP1OArNIawWmIiIgsh+XGQfUO8MDY3t0gy8DH+zJFxyEiIrIYlhsHdnVSv02Hc1BR2yQ4DRERkWWw3DiwUT190TfQA7WNevwrNVt0HCIiIotguXFgkiQZF9Rcuz8Djc2c1I+IiGwfy42DeyAqGP4eahRWNuCbE3mi4xAREd02lhsHp3JSICleBwBYtScDsiyLDURERHSbWG4I02O7w8VZiTP5lUi5WCo6DhER0W1huSF4uarw++GhAICVe7gkAxER2TaWGwIAPDEqApIE/HSuGBeKqkTHISIi6jCWGwIA6PzcML5fAABg9V4uyUBERLaL5YaM5t7Rclv4F2m5KKluEJyGiIioY6yi3KxYsQI6nQ4ajQaxsbFITU296b5ffvklhg8fDi8vL7i5uSE6Ohrr16/vwrT2a3i4N6LCvNDYbMCnB7JExyEiIuoQ4eVm48aNWLBgAZYsWYK0tDRERUUhMTERRUVFre7v4+ODl156CSkpKThx4gRmz56N2bNnY/v27V2c3P5IkoQ5o1sW1FyfkoX6Jr3gRERERO0nyYInNomNjcWIESOwfPlyAIDBYEBYWBjmzZuHhQsXtul7DB06FBMnTsRrr712y30rKyuh1WpRUVEBT0/P28puj5r1Boz9313ILa/DsocHYVpMd9GRiIiI2vX7W+iRm8bGRhw5cgQJCQnGbQqFAgkJCUhJSbnl62VZRnJyMs6dO4c77rijM6M6DCelArNH6QC0XFhsMHBSPyIisi1Cy01JSQn0ej0CAgJMtgcEBKCgoOCmr6uoqIC7uztUKhUmTpyIf/zjHxg/fnyr+zY0NKCystLkQeZNHREGD7UTLhRV4+fzxaLjEBERtYvwa246wsPDA8eOHcOhQ4fw17/+FQsWLMCuXbta3XfZsmXQarXGR1hYWNeGtUEeGmc8GtPy98RJ/YiIyNYILTd+fn5QKpUoLCw02V5YWIjAwMCbvk6hUKBnz56Ijo7Gf//3f2PKlClYtmxZq/suWrQIFRUVxkdOTo5Fx2CvZo2KgFIhYf/FUpzOqxAdh4iIqM2ElhuVSoVhw4YhOTnZuM1gMCA5ORlxcXFt/j4GgwENDa3Py6JWq+Hp6WnyoFsL8XLBvYOCAACr93BSPyIish3CT0stWLAAK1euxCeffIKzZ8/imWeeQU1NDWbPng0AmDlzJhYtWmTcf9myZdixYwfS09Nx9uxZvPXWW1i/fj0ef/xxUUOwW3PHtNwWvvV4Hgoq6gWnISIiahsn0QGmTp2K4uJiLF68GAUFBYiOjsa2bduMFxlnZ2dDofitg9XU1ODZZ5/FpUuX4OLigr59++LTTz/F1KlTRQ3Bbg0O9UJMhA9SM8qwdn8mFt7TV3QkIiKiWxI+z01X4zw37fPD6QI8tf4IPDVOSFk0Dm5q4X2YiIgckM3Mc0PWL6FfACL83FBZ34zNh3kxNhERWT+WGzJLoZDwxJUlGdbsy4Sek/oREZGVY7mhW5oyNBRers7ILqvFjjM3n1yRiIjIGrDc0C25qJR4PDYcALCSt4UTEZGVY7mhNpkZHw6VUoEjWZeRln1ZdBwiIqKbYrmhNvH30OCB6GAAnNSPiIisG8sNtdmcK5P6fX8qHzlltYLTEBERtY7lhtqsb6AnxvTyg0EG1uzj0RsiIrJOLDfULnPGRAIANh3KQUVdk+A0REREN2K5oXa5o5cf+gR4oKZRjw2p2aLjEBER3YDlhtpFkiQ8eeXam7X7M9GkNwhOREREZIrlhtrtwehg+LmrkV9Rj+9O5ouOQ0REZILlhtpN7aREUtzVSf3S4WBrrxIRkZVjuaEOeXxkODTOCpzKrcSB9DLRcYiIiIxYbqhDvN1UmDIsFACwem+64DRERES/YbmhDntiVAQkCdh5tggXi6tFxyEiIgLAckO3IbKbO8b1DQAArN7LSf2IiMg6sNzQbZl75bbwL45cQllNo+A0RERELDd0m2IifDAoRIuGZgM+PZAlOg4RERHLDd0eSZKMC2quS8lEfZNecCIiInJ0LDd02+4dFIRgrQYl1Y3YeixPdBwiInJwLDd025yVCswapQMArNrLSf2IiEgslhuyiEdjusNd7YTzhdX4+Xyx6DhEROTAWG7IIjw1zpg6IgwAbwsnIiKxWG7IYmbF66CQgD2/luBsfqXoOERE5KBYbshiwnxccc+gIADAqj08ekNERGKw3JBFzR0TCQDYejwXhZX1gtMQEZEjYrkhi4oO88LwcG806WWsS8kUHYeIiBwQyw1Z3JwrR28+PZCN2sZmwWmIiMjRsNyQxY3vH4BwX1dU1DXh8yOXRMchIiIHw3JDFqdUSHhiVMuSDGv2ZkBv4KR+RETUdVhuqFP8fngotC7OyCytxc6zhaLjEBGRA2G5oU7hqnLC9NjuAIBVe9IFpyEiIkfCckOdJileB2elhEOZl3Esp1x0HCIichAsN9RpAjw1uD8qGACP3hARUdfpULnJycnBpUu/3QWTmpqK+fPn46OPPrJYMLIPc0a33Bb+/akCXLpcKzgNERE5gg6Vm8ceeww//fQTAKCgoADjx49HamoqXnrpJSxdutSiAcm29Q/2xOieftAbZKzdlyk6DhEROYAOlZtTp04hJiYGALBp0yYMHDgQ+/fvx2effYa1a9daMh/ZgSfHtNwWvuFQDirrmwSnISIie9ehctPU1AS1Wg0A2LlzJx544AEAQN++fZGfn2+5dGQX7uzdDb383VHd0IyNqTmi4xARkZ3rULkZMGAAPvjgA+zZswc7duzAhAkTAAB5eXnw9fW1aECyfZIkYc6Vozcf78tAs94gOBEREdmzDpWbN954Ax9++CHuvPNOTJs2DVFRUQCArVu3Gk9XEV3rwegQ+LmrkFdRj+9OFYiOQ0REdkySZblDc+Pr9XpUVlbC29vbuC0zMxOurq7w9/e3WEBLq6yshFarRUVFBTw9PUXHcSj/t/NXvLPzPAaHavH1c6MgSZLoSEREZCPa8/u7Q0du6urq0NDQYCw2WVlZePfdd3Hu3DmrLjYk1uMju0PtpMCJSxU4lHlZdBwiIrJTHSo3Dz74INatWwcAKC8vR2xsLN566y1MmjQJ77//vkUDkv3wdVfj4aGhAICVnNSPiIg6SYfKTVpaGsaMGQMA+PzzzxEQEICsrCysW7cO7733nkUDkn15cnTLhcU7zxYio6RGcBoiIrJHHSo3tbW18PDwAAD88MMPePjhh6FQKDBy5EhkZWVZNCDZl57+7hjX1x+yDKzZmyE6DhER2aEOlZuePXviq6++Qk5ODrZv3467774bAFBUVMSLdOmWrk7qt/lIDi7XNApOQ0RE9qZD5Wbx4sV48cUXodPpEBMTg7i4OAAtR3GGDBli0YBkf+IifTEg2BP1TQZ8dpBH+oiIyLI6VG6mTJmC7OxsHD58GNu3bzduHzduHN555x2LhSP7JEkS5o5pWVDzk5QsNDTrBSciIiJ70qFyAwCBgYEYMmQI8vLyjCuEx8TEoG/fvhYLR/Zr4uAgBHpqUFzVgK3H8kTHISIiO9KhcmMwGLB06VJotVqEh4cjPDwcXl5eeO2112AwcGp9ujVnpQKzRukAAKv3ZqCDc0kSERHdoEPl5qWXXsLy5cvx+uuv4+jRozh69Cj+9re/4R//+AdefvllS2ckOzUtpjvcVEr8UlCFvRdKRMchIiI70aHlF4KDg/HBBx8YVwO/6uuvv8azzz6L3NxciwW0NC6/YF1e/c9pfLwvE3f07oZ1T3BdMiIial2nL79QVlbW6rU1ffv2RVlZWUe+JTmoJ0ZFQCEBu88X41xBleg4RERkBzpUbqKiorB8+fIbti9fvhyDBw++7VDkOMJ8XDFhYCAAYBWXZCAiIgtw6siL/v73v2PixInYuXOncY6blJQU5OTk4LvvvrNoQLJ/c8ZE4ruTBfj6WB7+OKEP/D00oiMREZEN69CRm7Fjx+L8+fN46KGHUF5ejvLycjz88MM4ffo01q9fb+mMZOeGdvfG0O5eaNQbsD6Fk/oREdHt6dAFxTdz/PhxDB06FHq99U7KxguKrdP3J/PxzGdp8HZ1xv6F4+CiUoqOREREVqTTLygmsrS7BwQizMcFl2ub8EXaJdFxiIjIhrHckFVQKiQ8MaplQc01ezNgMHBSPyIi6hiWG7IajwwPg6fGCeklNUj+pUh0HCIislHtulvq4YcfNvt8eXn57WQhB+emdsJjseH44OeLWLUnHeP7B4iORERENqhd5Uar1d7y+ZkzZ95WIHJsSfHhWLUnHQczynDyUgUGhZr/d46IiOh67So3H3/8cWflIAIABGldcH9UMLYczcXKPel4b9oQ0ZGIiMjG8JobsjpPjm65sPjbk/nIK68TnIaIiGwNyw1ZnYEhWsRF+kJvkLF2f6boOEREZGOsotysWLECOp0OGo0GsbGxSE1Nvem+K1euxJgxY+Dt7Q1vb28kJCSY3Z9s09w7Wo7e/PtgNqrqmwSnISIiWyK83GzcuBELFizAkiVLkJaWhqioKCQmJqKoqPVbgXft2oVp06bhp59+QkpKCsLCwnD33XcjNze3i5NTZ7qztz96dHNDVUMzNh3mpH5ERNR2Fl1+oSNiY2MxYsQI4yrjBoMBYWFhmDdvHhYuXHjL1+v1enh7e2P58uVtulOLyy/Yjn8dzMaft5xEiJcLfv7jnXBSCu/iREQkiM0sv9DY2IgjR44gISHBuE2hUCAhIQEpKSlt+h61tbVoamqCj49PZ8UkQR4eGgJfNxVyy+uw7XSB6DhERGQjhJabkpIS6PV6BASYTtYWEBCAgoK2/TL705/+hODgYJOCdK2GhgZUVlaaPMg2aJyVeHxkOABg5Z4MCD7ISERENsKmj/O//vrr2LBhA7Zs2QKNRtPqPsuWLYNWqzU+wsLCujgl3Y4ZceFQOSlwPKccR7Iui45DREQ2QGi58fPzg1KpRGFhocn2wsJCBAYGmn3tm2++iddffx0//PADBg8efNP9Fi1ahIqKCuMjJyfHItmpa/i5q/HwkBAAwMo96YLTEBGRLRBablQqFYYNG4bk5GTjNoPBgOTkZMTFxd30dX//+9/x2muvYdu2bRg+fLjZn6FWq+Hp6WnyINsyZ0zLbeE/nClEVmmN4DRERGTthJ+WWrBgAVauXIlPPvkEZ8+exTPPPIOamhrMnj0bADBz5kwsWrTIuP8bb7yBl19+GWvWrIFOp0NBQQEKCgpQXV0tagjUyXr6e+CuPt0gy8CavRmi4xARkZUTXm6mTp2KN998E4sXL0Z0dDSOHTuGbdu2GS8yzs7ORn5+vnH/999/H42NjZgyZQqCgoKMjzfffFPUEKgLzBkTCQDYdPgSymsbBachIiJrJnyem67GeW5skyzLuPe9vTibX4n/mdAHz97ZU3QkIiLqQjYzzw1RW0mShLlXrr35ZH8mGpsNghMREZG1Yrkhm3Hf4GAEeKpRWNmA/xzPEx2HiIisFMsN2QyVkwJJ8ToAwKq9nNSPiIhax3JDNmV6TDhcnJU4m1+J/RdLRcchIiIrxHJDNkXr6oxHhocC4KR+RETUOpYbsjlPjI6AJAG7zhXj18Iq0XGIiMjKsNyQzQn3dUNi/5blOVZzUj8iIroOyw3ZpKtLMnx5NBfFVQ2C0xARkTVhuSGbNCzcG9FhXmhsNmD9gSzRcYiIyIqw3JBNapnUr2VJhk8PZKG+SS84ERERWQuWG7JZiQMCEOLlgrKaRnyZlis6DhERWQmWG7JZTkoFnhjdcu3Nqr3pMBg4qR8REbHckI2bOiIMHmonpBfXYNf5ItFxiIjICrDckE1zVzthWmx3AMDK3bwtnIiIWG7IDsyK18FJISElvRSncitExyEiIsFYbsjmBXu5YOLgIACc1I+IiFhuyE7MGd1yW/h/juchv6JOcBoiIhKJ5YbswqBQLWIjfNBskLF2f6boOEREJBDLDdmNq5P6/etgNmoamgWnISIiUVhuyG78rq8/Iv3cUFXfjE2Hc0THISIiQVhuyG4oFJJxUr81+zKg56R+REQOieWG7MrkoaHwdnVGTlkdfjhdIDoOEREJwHJDdsVFpcSMkeEAgJV70gWnISIiEVhuyO48HhcOlVKBtOxyHMm6LDoOERF1MZYbsjv+HhpMGhIMAFi9l0dviIgcDcsN2aU5V24L33aqADlltYLTEBFRV2K5IbvUO8ADd/TuBoPMJRmIiBwNyw3ZrbljWm4L33Q4BxV1TYLTEBFRV2G5Ibs1uqcf+gZ6oLZRj3+nZouOQ0REXYTlhuyWJEl48sqkfmv3ZaKx2SA4ERERdQWWG7JrD0QHo5uHGgWV9fjuZL7oOERE1AVYbsiuqZ2UmBWvA9AyqZ8sc0kGIiJ7x3JDdu+xmO7QOCtwOq8SKemlouMQEVEnY7khu+ftpsLvh4UBAFbv4W3hRET2juWGHMIToyMgSUDyL0W4UFQtOg4REXUilhtyCBF+bkjoFwCAk/oREdk7lhtyGHOvLMnwZdollFY3CE5DRESdheWGHMYInTeiQrVoaDbg0wOc1I+IyF6x3JDDkCQJT145erP+QCbqm/SCExERUWdguSGHcu/AQIR4uaCkuhFfHc0VHYeIiDoByw05FCelArNH6QAAq/ZmcFI/IiI7xHJDDmfqiDC4q51woagau84Xi45DREQWxnJDDsdD44xHR7RM6rdqT7rgNEREZGksN+SQZo+OgFIhYd+FUpzJqxQdh4iILIjlhhxSiJcL7h0UBABYtZdHb4iI7AnLDTmsOaMjAAD/OZ6Hwsp6wWmIiMhSWG7IYUWFeSFG54MmvYxP9meKjkNERBbCckMObc6YlqM3nx3MRm1js+A0RERkCSw35NDG9QuAztcVFXVN2Hz4kug4RERkASw35NCUCglPXrn2Zs2+DOgNnNSPiMjWsdyQw5s8LBRers7IKq3FjjOFouMQEdFtYrkhh+eqcsL02O4AOKkfEZE9YLkhApAUp4NKqcDhrMvY+2uJ6DhERHQbWG6IAPh7avBAdDAA4PHVBzHtowPYdqoAzXqD4GRERNReTqIDEFmLP9/bDzUNzdh+ugAp6aVISS9FsFaD6SPDMS2mO3zcVKIjEhFRG0iyLDvU7SGVlZXQarWoqKiAp6en6DhkhXLL6/DZgSxsOJSDsppGAIDKSYH7BwdjVrwOg0K1ghMSETme9vz+Zrkhuon6Jj2+OZGPT/Zn4mRuhXH7kO5eSIrT4d5BQVA58cwuEVFXYLkxg+WG2kuWZRzNKce6/Zn49mQ+mvQtHxk/dzUeiwnDY7HhCNRqBKckIrJvLDdmsNzQ7SiqqseG1Bx8djALhZUNAAAnhYTEgYFIitNhhM4bkiQJTklEZH9YbsxguSFLaNIbsP10Adbtz0JqZplxe78gTyTFhePB6BC4qJQCExIR2ReWGzNYbsjSTudVYH1KFr46lov6ppZbx7Uuzpg6IgyPx4aju6+r4IRERLaP5cYMlhvqLOW1jdh0OAfrUrJw6XIdAECSgHF9/TEzTofRPf2gUPCUFRFRR7DcmMFyQ51Nb5Dx0y9F+CQlE3uume040s8NM+PCMXlYKDw0zgITEhHZHpYbM1huqCtdLK7G+pQsfH7kEqobmgEAbiolJg8Lxcy4cPT09xCckIjINrDcmMFyQyJUNzTjy7RL+GR/Ji4W1xi3j+rpi6Q4Hcb1C4CSp6yIiG6K5cYMlhsSSZZl7L9YirX7M5F8thCGK5++EC8XzIgLx9ThYfDmMg9ERDdguTGD5YasRU5ZLT47mI0Nh7JRXtsEAFA7KfBAVDCS4nUYGMJlHoiIrmrP72/hc8evWLECOp0OGo0GsbGxSE1Nvem+p0+fxuTJk6HT6SBJEt59992uC0pkYWE+rlh4T18cWDQOf58yGAOCPdHQbMDmI5dw3z/2YvL7+7H1eB4am7kyORFRewgtNxs3bsSCBQuwZMkSpKWlISoqComJiSgqKmp1/9raWkRGRuL1119HYGBgF6cl6hwaZyUeGR6Gb+aNxhfPxOGBqGA4KSQcybqMP/z7KEa98SPe2XEeRZX1oqMSEdkEoaelYmNjMWLECCxfvhwAYDAYEBYWhnnz5mHhwoVmX6vT6TB//nzMnz+/XT+Tp6XIFhRV1uNfqdn47GA2iqt+W+bhnkFBmBUfjqHducwDETmW9vz+duqiTDdobGzEkSNHsGjRIuM2hUKBhIQEpKSkWOznNDQ0oKGhwfh1ZWWlxb43UWfx99RgfkJvPHtnT2w7XYB1+zNxOOsy/nM8D/85nocBwZ5IitPhgehgaJy5zAMR0bWEnZYqKSmBXq9HQECAyfaAgAAUFBRY7OcsW7YMWq3W+AgLC7PY9ybqbKorFxh//kw8vpk3Go8MD4XaSYHTeZX4ny9OYOSyZCz7/iwuXa4VHZWIyGoIv6C4sy1atAgVFRXGR05OjuhIRB0yMESLv0+JwoFF47Dwnr4I8XJBeW0TPvw5HXf8/SfMXXcYe38tgYPdAElEdANhp6X8/PygVCpRWFhosr2wsNCiFwur1Wqo1WqLfT8i0bzdVHh6bA/MHROJ5LOFWJeShb0XSrDjTCF2nClEj25uSIrX4eGhoXBXC/uIExEJI+zIjUqlwrBhw5CcnGzcZjAYkJycjLi4OFGxiGyGUiHh7gGB+HROLHYuuAMz48LhplLiYnENFn99GiP/loxXtp7GxeJq0VGJiLqU0P+tW7BgAZKSkjB8+HDExMTg3XffRU1NDWbPng0AmDlzJkJCQrBs2TIALRchnzlzxvjn3NxcHDt2DO7u7ujZs6ewcRCJ1tPfA0sfHIg/JvbBF0cuYV1KFtJLarB2fybW7s/EmF5+SIrT4a6+/lzmgYjsnvAZipcvX47//d//RUFBAaKjo/Hee+8hNjYWAHDnnXdCp9Nh7dq1AIDMzExERETc8D3Gjh2LXbt2tenn8VZwcgQGg4x9F0vwyf5MJP9ShKuf8jAfF8wYGY5HhofBy5XLPBCR7eDyC2aw3JCjySmrxfoDWdh4KAcVdS3LPGicFZgUHYKZcTr0D+bngIisH8uNGSw35KjqGvX4+lguPknJwtn83+Z7itH5YGZ8OBIHBMJZafc3UBKRjWK5MYPlhhydLMs4nHUZa/dnYtupAuivLE0e4KnG9NhwPBoTBn8PjeCURESmWG7MYLkh+k1BRT3+dTAL/0rNRkl1IwDAWSlh4qAgzIzXYUiYF5d5ICKrwHJjBssN0Y0amvXYdqoAa/dn4mh2uXH7oBAtkuJ1uG9wEJd5ICKhWG7MYLkhMu/EpXJ8sj8L/zmRh8ZmAwDAx02FR0eEYfrIcIR4uQhOSESOiOXGDJYborYprW7AxsM5+DQlC3kV9QAAhQSM7x+ApHgd4iJ9ecqKiLoMy40ZLDdE7dOsN2Dn2SJ8sj8TKemlxu29A9wxM06Hh4aEwI3LPBBRJ2O5MYPlhqjjzhdWYV1KJr5My0Vtox4A4KF2wpThoZgZp0OEn5vghERkr1huzGC5Ibp9FXVN+OLIJaw/kIWMkhrj9rG9u2FWvA5je3eDgss8EJEFsdyYwXJDZDkGg4zdvxZjXUoWfjr32zIP4b6umDEyHL8fFgatq7PYkERkF1huzGC5IeocmSU1+PRAFjYdzkFlfTMAwMVZiUlDQpAUH46+gfy8EVHHsdyYwXJD1LlqG5vx1dE8fLI/E+cKq4zbYyN8kBSvw939A+DEZR6IqJ1YbsxguSHqGrIs42BGGdalZGL76ULjMg9BWg2mx3bHozHd4eeuFpySiGwFy40ZLDdEXS+/og6fHcjGv1OzUVrTssyDSqnAfYNblnmIDvMSG5CIrB7LjRksN0TiNDTr8e2JfHySkoXjOeXG7VFhXkiKC8fEwUFQO3GZByK6EcuNGSw3RNbhWE451u3PxDcn8tGob1nmwddNhWkx3TF9ZHcEabnMAxH9huXGDJYbIutSUt2ADanZ+PRANgoqW5Z5UCokJA4IwMw4HWIjfLjMAxGx3JjDckNknZr1Buw4U4i1+zNxMKPMuL1voAdmxukwaUgwXFVc5oHIUbHcmMFyQ2T9fimoxLqULGxJy0VdU8syD54aJzwyPAwz4sIR7stlHogcDcuNGSw3RLajorYJm4/kYP2BLGSV1gIAJAm4q48/ZsaF445eXOaByFGw3JjBckNkewwGGT+fL8YnKZnYda7YuD3Czw0zRoZjyvBQeGq4zAORPWO5MYPlhsi2ZZTUYH1KFjYfzkFVQ8syD64qJR4eGoKZcTr0DvAQnJCIOgPLjRksN0T2oaahGVuO5mJdSibOF1Ybt8dE+ODOPt0Q38MPA4M9udQDkZ1guTGD5YbIvsiyjJT0Uqzbn4UfzhTAcM1/0dzVToiJ8EF8D1+MjPRF/yBPXqNDZKNYbsxguSGyX7nlddh+qgAp6aU4mF5qXJ38Kq2LM0ZG+iAu0hdxPfzQO8Cdc+gQ2QiWGzNYbogcg94g42x+JfZfLEHKxVKkZpShplFvso+fuwqxkb6Ii/RFfA9fRPi5sewQWSmWGzNYbogcU5PegJO5FUi5WIoD6aU4lFmG+iaDyT4BnuorRccPcT18EebjKigtEV2P5cYMlhsiAloW8Tye01J29l8swdHscuMaV1eFeLkgvocv4q48uN4VkTgsN2aw3BBRa+qb9EjLuoz9F0uRkl6K4znlaDaY/ucxws8NIyOvlJ1IX3TzUAtKS+R4WG7MYLkhoraoaWjGocwypKSX4sDFUpzMrcB1XQe9/N0R16Plep3YCF94u6nEhCVyACw3ZrDcEFFHVNQ14VBGmfHIztn8SpPnJQnoF+hpPKoTE+nDWZOJLIjlxgyWGyKyhMs1jTiYUdpSdi6W4teiapPnFRIwKESLkT1aLlAeofPmquZEt4HlxgyWGyLqDEVV9TiQXma8GyujpMbkeSeFhKgwr5YLlCN9MTTcGxpnpaC0RLaH5cYMlhsi6gr5FXVIuXJUZ//FUuSW15k8r3JSYGh3L8RFttx2Hh3mBZUTl4oguhmWGzNYbohIhJyyWuNt5ynppSisbDB53sVZieE6b4y8MqHgoBAt18UiugbLjRksN0QkmizLyCipMV6cfOBiKUprGk32cVc7YYTO2zihYL8gTyi5LhY5MJYbM1huiMjayLKM84XVSLlyVOdAehkq6ppM9tG6OCM2wufKredcF4scD8uNGSw3RGTtrq6LdSC95Xqd1IwyVDeYLgLq66b6bULBHr6I5LpYZOdYbsxguSEiW9OsN+BU3m+LgB7OvIy6JtNFQP091L8tFRHphzAfF5YdsissN2aw3BCRrWtsNuD4pXLj3VhHsi+jsfnGdbGuTigY18MXwV5cF4tsG8uNGSw3RGRv6pv0SMu+bCw7x1pZF0vn63rlFJYfRkb6wN9DIygtUcew3JjBckNE9q6moRmHs66UnfRSnLxUfsO6WD393Y0TCo6M5LpYZP1YbsxguSEiR1NZ37Iu1tUJBc8WVOL6//L3C/I0nsKKifCB1oXrYpF1Ybkxg+WGiBxdeW3jlaUiWm49P19447pYA0O0xrIzQucDNzXXxSKxWG7MYLkhIjJVXNWAA+m/TSiY3sq6WINDtcYJBYdxXSwSgOXGDJYbIiLzCirqkZJeYrxmJ6fsunWxlAoM6e5lvBsrursX1E4sO9S5WG7MYLkhImqfnLJa41Gd/RdLUVBZb/K8xlmB4eE+xgkFB3NdLOoELDdmsNwQEXWcLMvILP1tEdAD6aUoqTZdF8tNpUTMlaUi4iL90D+Y62LR7WO5MYPlhojIcmRZxoWi6pZFQC+W4kBGKcprTdfF8tQ4ITay5RRWfE9f9Pb3gIJlh9qJ5cYMlhsios5jMMg4W1DZUnTSS3EwvQxV162L5eOmwshIH8T18ENcpC96dOO6WHRrLDdmsNwQEXWdZr0Bp/MqW47spJficGYZahtvXBfr2qUiuvu4suzQDVhuzGC5ISISp7HZgBNX18VKL8XhrBvXxXJXO8HL1bnl4aKC1tUZWhdneLmYbvNycb7yTxW8XJ15e7qdY7kxg+WGiMh61DfpcTS7HCnppUi5WIJjOeVo0nfs15LaSfFb+TEWnyuFyPXKtmsKkpdryz4eaiceKbIBLDdmsNwQEVmvukY98ivqUF7XhIq6JlTUNqG8thHldU0or72yre63bRW1TSiva4L++sWz2kGpkOCpcTIWIC9jKVLB89ojRq7O0F4tRVeKkjNvee8y7fn9zfm0iYjIariolIjs5t6u18iyjOqG5iulp8n4z/K6RpTXNqHyuq+v3a+uSQ+9Qcbl2iZcvu4ur7ZwVzv9VoiMpUd1TUFq+frafbxcVNA4K3i0qBOx3BARkU2TJAkeGmd4aJwR6t2+19Y36VvKz9UCVNt4zdEh00JUcc0+lfUtd4BVNzSjuqEZueV1t/hJplROipZrhlxMjwj9VoicoXVV3XCdkYfaibfRtwHLDREROSyNsxIaZyX8PTXtep3eIBtL0dXTZL+Vn5ZSVHHNabNrn282yGhsNqCoqgFFVQ3t+rkKCcZTZdeWH20r2649kqR1cYbKyXFOobHcEBERtZNSIcHbTQVvN1W7XifLMmob9b8Vntomk+uJyuuubLv+tFpdE2ob9TDIMBYolNa262e7qZQ3Xlh93VEj7XV3oHm5OsPFWWlzp9BYboiIiLqIJElwUzvBTe2EEC+Xdr22oVlvcjSotaNCV7++9lRbZX0TZBmoadSjprGu/afQlIqWo0VmriPSXrkA+2pB8nZtOY0mCssNERGRDVA7KeHvoYS/R/tPoVXVX3t06MbTaC3XFDVeU5Bavm7Sy2jUG1BS3YCS6rafQhsQ7Ilv/zCmvUO0GJYbIiIiO6ZUSC1HVVzbfwqtrklveh3R1aNG1xSg356/emdaI7wEHrUBWG6IiIioFZIkwVXlBFeVE4LbeQrtduYdsgTHuXSaiIiIuoRS8O3qLDdERERkV1huiIiIyK6w3BAREZFdsYpys2LFCuh0Omg0GsTGxiI1NdXs/ps3b0bfvn2h0WgwaNAgfPfdd12UlIiIiKyd8HKzceNGLFiwAEuWLEFaWhqioqKQmJiIoqKiVvffv38/pk2bhieffBJHjx7FpEmTMGnSJJw6daqLkxMREZE1kmRZFnq/VmxsLEaMGIHly5cDAAwGA8LCwjBv3jwsXLjwhv2nTp2KmpoafPPNN8ZtI0eORHR0ND744INb/rz2LJlORERE1qE9v7+FHrlpbGzEkSNHkJCQYNymUCiQkJCAlJSUVl+TkpJisj8AJCYm3nT/hoYGVFZWmjyIiIjIfgktNyUlJdDr9QgICDDZHhAQgIKCglZfU1BQ0K79ly1bBq1Wa3yEhYVZJjwRERFZJeHX3HS2RYsWoaKiwvjIyckRHYmIiIg6kdDlF/z8/KBUKlFYWGiyvbCwEIGBga2+JjAwsF37q9VqqNVqywQmIiIiqyf0yI1KpcKwYcOQnJxs3GYwGJCcnIy4uLhWXxMXF2eyPwDs2LHjpvsTERGRYxG+cOaCBQuQlJSE4cOHIyYmBu+++y5qamowe/ZsAMDMmTMREhKCZcuWAQBeeOEFjB07Fm+99RYmTpyIDRs24PDhw/joo49EDoOIiIishPByM3XqVBQXF2Px4sUoKChAdHQ0tm3bZrxoODs7GwrFbweY4uPj8a9//Qt/+ctf8Oc//xm9evXCV199hYEDB4oaAhEREVkR4fPcdLWKigp4eXkhJyeH89wQERHZiMrKSoSFhaG8vBxardbsvsKP3HS1qqoqAOAt4URERDaoqqrqluXG4Y7cGAwG5OXlwcPDA5IkWfR7X22V9npUyN7HB9j/GDk+22fvY+T4bF9njVGWZVRVVSE4ONjkcpXWONyRG4VCgdDQ0E79GZ6ennb7Ly1g/+MD7H+MHJ/ts/cxcny2rzPGeKsjNlfZ/SR+RERE5FhYboiIiMiusNxYkFqtxpIlS+x2RmR7Hx9g/2Pk+GyfvY+R47N91jBGh7ugmIiIiOwbj9wQERGRXWG5ISIiIrvCckNERER2heWGiIiI7ArLTTutWLECOp0OGo0GsbGxSE1NNbv/5s2b0bdvX2g0GgwaNAjfffddFyXtmPaMb+3atZAkyeSh0Wi6MG377N69G/fffz+Cg4MhSRK++uqrW75m165dGDp0KNRqNXr27Im1a9d2es6Oau/4du3adcP7J0kSCgoKuiZwOy1btgwjRoyAh4cH/P39MWnSJJw7d+6Wr7Olz2BHxmhLn8P3338fgwcPNk7uFhcXh++//97sa2zp/Wvv+GzpvWvN66+/DkmSMH/+fLP7iXgPWW7aYePGjViwYAGWLFmCtLQ0REVFITExEUVFRa3uv3//fkybNg1PPvkkjh49ikmTJmHSpEk4depUFydvm/aOD2iZgTI/P9/4yMrK6sLE7VNTU4OoqCisWLGiTftnZGRg4sSJuOuuu3Ds2DHMnz8fc+bMwfbt2zs5ace0d3xXnTt3zuQ99Pf376SEt+fnn3/Gc889hwMHDmDHjh1oamrC3XffjZqampu+xtY+gx0ZI2A7n8PQ0FC8/vrrOHLkCA4fPozf/e53ePDBB3H69OlW97e196+94wNs57273qFDh/Dhhx9i8ODBZvcT9h7K1GYxMTHyc889Z/xar9fLwcHB8rJly1rd/5FHHpEnTpxosi02Nlb+r//6r07N2VHtHd/HH38sa7XaLkpnWQDkLVu2mN3nf/7nf+QBAwaYbJs6daqcmJjYicksoy3j++mnn2QA8uXLl7skk6UVFRXJAOSff/75pvvY2mfwem0Zoy1/DmVZlr29veVVq1a1+pytv3+ybH58tvreVVVVyb169ZJ37Nghjx07Vn7hhRduuq+o95BHbtqosbERR44cQUJCgnGbQqFAQkICUlJSWn1NSkqKyf4AkJiYeNP9RerI+ACguroa4eHhCAsLu+X/odgaW3r/bkd0dDSCgoIwfvx47Nu3T3ScNquoqAAA+Pj43HQfW38P2zJGwDY/h3q9Hhs2bEBNTQ3i4uJa3ceW37+2jA+wzffuueeew8SJE294b1oj6j1kuWmjkpIS6PV6BAQEmGwPCAi46TUKBQUF7dpfpI6Mr0+fPlizZg2+/vprfPrppzAYDIiPj8elS5e6InKnu9n7V1lZibq6OkGpLCcoKAgffPABvvjiC3zxxRcICwvDnXfeibS0NNHRbslgMGD+/PkYNWoUBg4ceNP9bOkzeL22jtHWPocnT56Eu7s71Go1nn76aWzZsgX9+/dvdV9bfP/aMz5be+8AYMOGDUhLS8OyZcvatL+o99DhVgUny4mLizP5P5L4+Hj069cPH374IV577TWByagt+vTpgz59+hi/jo+Px8WLF/HOO+9g/fr1ApPd2nPPPYdTp05h7969oqN0mraO0dY+h3369MGxY8dQUVGBzz//HElJSfj5559vWgBsTXvGZ2vvXU5ODl544QXs2LHD6i98ZrlpIz8/PyiVShQWFppsLywsRGBgYKuvCQwMbNf+InVkfNdzdnbGkCFDcOHChc6I2OVu9v55enrCxcVFUKrOFRMTY/WF4fnnn8c333yD3bt3IzQ01Oy+tvQZvFZ7xng9a/8cqlQq9OzZEwAwbNgwHDp0CP/3f/+HDz/88IZ9bfH9a8/4rmft792RI0dQVFSEoUOHGrfp9Xrs3r0by5cvR0NDA5RKpclrRL2HPC3VRiqVCsOGDUNycrJxm8FgQHJy8k3Pp8bFxZnsDwA7duwwe/5VlI6M73p6vR4nT55EUFBQZ8XsUrb0/lnKsWPHrPb9k2UZzz//PLZs2YIff/wRERERt3yNrb2HHRnj9Wztc2gwGNDQ0NDqc7b2/rXG3PiuZ+3v3bhx43Dy5EkcO3bM+Bg+fDimT5+OY8eO3VBsAIHvYadermxnNmzYIKvVannt2rXymTNn5Keeekr28vKSCwoKZFmW5RkzZsgLFy407r9v3z7ZyclJfvPNN+WzZ8/KS5YskZ2dneWTJ0+KGoJZ7R3fq6++Km/fvl2+ePGifOTIEfnRRx+VNRqNfPr0aVFDMKuqqko+evSofPToURmA/Pbbb8tHjx6Vs7KyZFmW5YULF8ozZsww7p+eni67urrKf/zjH+WzZ8/KK1askJVKpbxt2zZRQzCrveN755135K+++kr+9ddf5ZMnT8ovvPCCrFAo5J07d4oaglnPPPOMrNVq5V27dsn5+fnGR21trXEfW/8MdmSMtvQ5XLhwofzzzz/LGRkZ8okTJ+SFCxfKkiTJP/zwgyzLtv/+tXd8tvTe3cz1d0tZy3vIctNO//jHP+Tu3bvLKpVKjomJkQ8cOGB8buzYsXJSUpLJ/ps2bZJ79+4tq1QqecCAAfK3337bxYnbpz3jmz9/vnHfgIAA+d5775XT0tIEpG6bq7c+X/+4OqakpCR57NixN7wmOjpaVqlUcmRkpPzxxx93ee62au/43njjDblHjx6yRqORfXx85DvvvFP+8ccfxYRvg9bGBsDkPbH1z2BHxmhLn8MnnnhCDg8Pl1UqldytWzd53Lhxxl/8smz77197x2dL793NXF9urOU9lGRZljv32BARERFR1+E1N0RERGRXWG6IiIjIrrDcEBERkV1huSEiIiK7wnJDREREdoXlhoiIiOwKyw0RERHZFZYbInJ4kiThq6++Eh2DiCyE5YaIhJo1axYkSbrhMWHCBNHRiMhGcVVwIhJuwoQJ+Pjjj022qdVqQWmIyNbxyA0RCadWqxEYGGjy8Pb2BtByyuj999/HPffcAxcXF0RGRuLzzz83ef3Jkyfxu9/9Di4uLvD19cVTTz2F6upqk33WrFmDAQMGQK1WIygoCM8//7zJ8yUlJXjooYfg6uqKXr16YevWrZ07aCLqNCw3RGT1Xn75ZUyePBnHjx/H9OnT8eijj+Ls2bMAgJqaGiQmJsLb2xuHDh3C5s2bsXPnTpPy8v777+O5557DU089hZMnT2Lr1q3o2bOnyc949dVX8cgjj+DEiRO49957MX36dJSVlXXpOInIQjp9aU4iIjOSkpJkpVIpu7m5mTz++te/yrLcslL2008/bfKa2NhY+ZlnnpFlWZY/+ugj2dvbW66urjY+/+2338oKhUIuKCiQZVmWg4OD5ZdeeummGQDIf/nLX4xfV1dXywDk77//3mLjJKKuw2tuiEi4u+66C++//77JNh8fH+Of4+LiTJ6Li4vDsWPHAABnz55FVFQU3NzcjM+PGjUKBoMB586dgyRJyMvLw7hx48xmGDx4sPHPbm5u8PT0RFFRUUeHREQCsdwQkXBubm43nCayFBcXlzbt5+zsbPK1JEkwGAydEYmIOhmvuSEiq3fgwIEbvu7Xrx8AoF+/fjh+/DhqamqMz+/btw8KhQJ9+vSBh4cHdDodkpOTuzQzEYnDIzdEJFxDQwMKCgpMtjk5OcHPzw8AsHnzZgwfPhyjR4/GZ599htTUVKxevRoAMH36dCxZsgRJSUl45ZVXUFxcjHnz5mHGjBkICAgAALzyyit4+umn4e/vj3vuuQdVVVXYt28f5s2b17UDJaIuwXJDRMJt27YNQUFBJtv69OmDX375BUDLnUwbNmzAs88+i6CgIPz73/9G//79AQCurq7Yvn07XnjhBYwYMQKurq6YPHky3n77beP3SkpKQn19Pd555x28+OKL8PPzw5QpU7pugETUpSRZlmXRIYiIbkaSJGzZsgWTJk0SHYWIbASvuSEiIiK7wnJDREREdoXX3BCRVeOZcyJqLx65ISIiIrvCckNERER2heWGiIiI7ArLDREREdkVlhsiIiKyKyw3REREZFdYboiIiMiusNwQERGRXWG5ISIiIrvy/wEcvHtrNAxFwAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Loss Curve :\",ann.history['loss'])\n",
    "plt.plot(ann.history['loss'])\n",
    "plt.title('loss curve')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train'], loc='upper right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1837e193",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf",
   "language": "python",
   "name": "python3"
  },
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