diff --git a/Project Report 6_5.pdf b/Project Report 6_5.pdf
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diff --git a/README.md b/README.md
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-<div>
-  <p align="center">
-    <img src="./client/src/assets/svgs/Logo.png" alt="logo" height="90rem" width="90rem">
-  </p>
-  <h1 align="center"> Dermify.AI </h1>
-</div>
-
-<h3 align="center">
-  🏆 Winner of the "Best AI Hack" @Diversion Hackathon 2024 by IEM-ACM
-</h3>
-
-## <img src = "https://media3.giphy.com/media/v1.Y2lkPTc5MGI3NjExODJ3YWRuajZ1d2pydm0zaW44bW1qMmF2eHlleW1peGt5ejhrMjJkYSZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9cw/kU4mcmHfmV7YhuIj6J/giphy.gif" width="45"> Project Details :
-
-<p align="center">
-  <img src="./client/src/assets/readmeImages/banner.jpg" alt="banner">
-</p>
-
-> Welcome to the `Dermify.AI` 👨🏻‍⚕️💉 web app! Dermify is the first AI powered Skin Diagnosis application which harnesses the power of Image Processing and Google's Bard (LLM) to offer cost-effective and accessible skin condition assessments worldwide.
-
-Author : [Sayan Maity](https://github.com/Sayan-Maity) | [Riya Mahanty](https://github.com/riyamahantyyy) | [Joybrata Das](https://github.com/joyous4) | [Samrat Das](https://github.com/das2Samrat)
-
-## 🛠️ Built with :
-
-![Bard](https://neuron.infura-ipfs.io/ipfs/QmTwgwYJV5bx3wnvZAVRUP9a9tVGT58pFeycvhMWhrgkGF) <img src="https://img.shields.io/badge/reactjs%20-%23333.svg?&style=for-the-badge&logo=react&logoColor=%2361DAFB"/> <img src="https://img.shields.io/badge/chakraui%20-%31BB.svg?&style=for-the-badge&logo=chakraui&logoColor=white"/> <img src="https://img.shields.io/badge/magic%20-8A2BE2.svg?&style=for-the-badge&logo=magic&logoColor=white"/> <img src="https://img.shields.io/badge/razorpay%20-2b83ea.svg?&style=for-the-badge&logo=razorpay&logoColor=white"/> <img src="https://img.shields.io/badge/nodejs%20-%23339933.svg?&style=for-the-badge&logo=nodedotjs&logoColor=white"/> <img src="https://img.shields.io/badge/expressjs%20-%23000000.svg?&style=for-the-badge&logo=express&logoColor=white"/> <img src="https://img.shields.io/badge/mongodb%20-%2347A248.svg?&style=for-the-badge&logo=mongodb&logoColor=white"/> <img src="https://img.shields.io/badge/vercel%20-%23000000.svg?&style=for-the-badge&logo=vercel&logoColor=white"/> <img src="https://img.shields.io/badge/render%20-%2346E3B7.svg?&style=for-the-badge&logo=render&logoColor=white"/> <img src="https://img.shields.io/badge/huggingface%20-yellow.svg?&style=for-the-badge&logo=huggingface&logoColor=white"/>
-
-- **Frontend**: ReactJS, Chakra-UI
-- **Backend**: NodeJS, ExpressJS, MongoDB
-- **Model**: Google Bard (LLM), Huggingface Model
-- **API**: Magic-sdk api, Razorpay api, Formspree api
-- **Version Control**: Git
-- **Hosting**: Render, Vercel
-
-## ✨ Features :
-
-- Comprehensive overview of users’ health and wellness, facilitating easy tracking and management of health data
-- Advanced image analysis through (AI model and google's Bard LLM) for instant identification and diagnosis of skin conditions, providing immediate insights into dermatological health
-- Personalized skincare recommendations and routines based on individual skin types and concerns, promoting healthier and more radiant skin
-- Access to a vast medical information database, enabling quick and accurate searches for comprehensive disease-related details
-- Data-driven insights for monitoring and optimizing overall health, offering valuable information for informed lifestyle choices
-- Used Magic-sdk for fast and most secure passwordless authentication
-- Receive personalized alerts based on health analytics, providing timely information for informed lifestyle choices
-- Supportive online community for sharing experiences, seeking advice, and connecting with others facing similar health challenges
-- Explore options to upgrade plans (premium users) for enhanced features and benefits
-- Connect with healthcare professionals for online consultations (Video Chat) and advice
-
-## 📸 Screenshots :
-
-<table>
-    <tr>
-        <td width="50%">
-            <img src="./client/src/assets/readmeImages/1.jpg"></img>
-            <br />
-            <p align="center">Landing Page Section</p>
-        </td>
-        <td width="50%">
-            <img src="./client/src/assets/readmeImages/2.jpeg"></img>
-            <br />
-            <p align="center">Features Section</p>
-        </td>
-    </tr>
-    <tr>
-        <td width="50%">
-            <img src="./client/src/assets/readmeImages/3.jpeg"></img>
-            <br />
-            <p align="center">Why Choose Us Section</p>
-        </td>
-        <td width="50%">
-            <img src="./client/src/assets/readmeImages/4.jpeg"></img>
-            <br />
-            <p align="center">Contact Us Section</p>
-        </td>
-    </tr>
-    <tr>
-        <td colspan="2">
-            <img src="./client/src/assets/readmeImages/5.jpg"></img>
-            <br />
-            <h3 align="center">User Dashboard Page</h3>
-        </td>
-    </tr>
-    <tr>
-        <td width="50%">
-            <img src="./client/src/assets/readmeImages/6.jpg"></img>
-            <br />
-            <p align="center">Derma Prediction Page</p>
-            </td>
-        <td width="50%">
-            <img src="./client/src/assets/readmeImages/7.jpg"></img>
-            <br />
-            <p align="center">Explore Derma Page</p>
-            </td>
-    </tr>
-    <tr>
-        <td width="50%">
-            <img src="./client/src/assets/readmeImages/8.jpg"></img>
-            <br />
-            <p align="center">Health Analytics Page</p>
-        </td>
-        <td width="50%">
-            <img src="./client/src/assets/readmeImages/9.jpg"></img>
-            <br />
-            <p align="center">Community Forum Page</p>
-        </td>
-    </tr>
-    <tr>
-         <td width="50%">
-            <img src="./client/src/assets/readmeImages/10.jpg"></img>
-            <br />
-            <p align="center">Subscription Plan Page</p>
-        </td>
-        <td width="50%">
-            <img src="./client/src/assets/readmeImages/10.jpg"></img>
-            <br />
-            <p align="center">Patient Video Call Page</p>
-        </td>
-    </tr>
-    <tr>
-        <td colspan="2">
-            <img src="./client/src/assets/readmeImages/10.jpg"></img>
-            <br />
-            <h3 align="center">Doctor's Dashboard</h3>
-        </td>
-    </tr>
-    
-</table>
-
-## 📸 Developers :
-
-<p align="center">
-    <img src="./client/src/assets/readmeImages/team2.png" alt="logo" height="100%" width="100%">
-  </p>
-
-## 📝 Endnote :
-
-While doing this project I (Sayan), Riya, Samrat and Joybrata have learnt how to work in a team and time bounded situation. We enjoyed every moment doing this project and taking part in this awesome hackathon. We are also thankful to all the organizers for organizing this hackathon.
diff --git a/Team 6 Final Report.pptx b/Team 6 Final Report.pptx
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diff --git a/skin_model/Baseline.ipynb b/skin_model/Baseline.ipynb
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+++ b/skin_model/Baseline.ipynb
@@ -0,0 +1,498 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6fa5a720",
+   "metadata": {},
+   "source": [
+    "## Skin Cancer Classification baseline model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fdf93ec8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import shutil\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import cv2\n",
+    "import sklearn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79ab8ac8",
+   "metadata": {},
+   "source": [
+    "### Loading Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "34c3ceb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['BKL', 'NV', 'AKIEC', 'MEL', 'DF', 'BCC', 'VASC']\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.mkdir(\"HAM_Dataset\")\n",
+    "base = \"HAM_Dataset\"\n",
+    "\n",
+    "os.mkdir(os.path.join(base, \"MEL\"))\n",
+    "os.mkdir(os.path.join(base, \"NV\"))\n",
+    "os.mkdir(os.path.join(base, \"BCC\"))\n",
+    "os.mkdir(os.path.join(base, \"AKIEC\"))\n",
+    "os.mkdir(os.path.join(base, \"BKL\"))\n",
+    "os.mkdir(os.path.join(base, \"DF\"))\n",
+    "os.mkdir(os.path.join(base, \"VASC\"))\n",
+    "\n",
+    "print(os.listdir(base))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e754affa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for image in os.listdir('ISIC2018_Task3_Training_Input'):\n",
+    "    if \"jpg\" not in image:\n",
+    "        os.remove('ISIC2018_Task3_Training_Input/'+image)\n",
+    "\n",
+    "for image in os.listdir('ISIC2018_Task3_Training_Input'):\n",
+    "    if \"jpg\" not in image:\n",
+    "        print(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b71a7814",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {0:\"MEL\", 1:\"NV\", 2:\"BCC\", 3:\"AKIEC\", 4:\"BKL\", 5:\"DF\", 6:\"VASC\"}\n",
+    "\n",
+    "df_labels = pd.read_csv(\"../input/isictruth/ISIC2018GroundTruth.csv\")\n",
+    "for i in range(len(df_labels)):\n",
+    "    labels = df_labels.iloc[i,1:]\n",
+    "    df_labels.loc[i,\"label\"] = mapping[list(labels).index(1)]\n",
+    "\n",
+    "#df_labels[\"label\"]=df_labels[\"label\"].astype(int)\n",
+    "df_labels.set_index('image', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "083c0229",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NV       6705\n",
+      "MEL      1113\n",
+      "BKL      1099\n",
+      "BCC       514\n",
+      "AKIEC     327\n",
+      "VASC      142\n",
+      "DF        115\n",
+      "Name: label, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_labels['label'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe973dfb",
+   "metadata": {},
+   "source": [
+    "### Computing class weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "da687f41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{0: 4.375273044997815,\n",
+      " 1: 2.78349082823791,\n",
+      " 2: 1.301832835044846,\n",
+      " 3: 12.440993788819876,\n",
+      " 4: 1.2854575792581184,\n",
+      " 5: 0.21338020666879728,\n",
+      " 6: 10.075452716297788}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.utils import class_weight\n",
+    "\n",
+    "class_weights = class_weight.compute_class_weight('balanced',\n",
+    "                                                  classes=['AKIEC', 'BCC', 'BKL', 'DF', 'MEL', 'NV', 'VASC'],\n",
+    "                                                  y=df_labels[\"label\"])\n",
+    "class_wt_dict=dict(enumerate(class_weights))\n",
+    "class_wt_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e6dc59ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "100%|██████████| 10015/10015 [00:06<00:00, 1625.40it/s]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "images = os.listdir('ISIC2018_Task3_Training_Input')\n",
+    "\n",
+    "for image in tqdm(images):\n",
+    "    fname=image[:-4]\n",
+    "    label=df_labels.loc[fname, \"label\"]\n",
+    "    src = os.path.join('ISIC2018_Task3_Training_Input', image)\n",
+    "    dst = os.path.join('HAM_Dataset', label, image)\n",
+    "    shutil.copyfile(src, dst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7db46759",
+   "metadata": {},
+   "source": [
+    "### Data Augmentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fc89f32c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 8516 images belonging to 7 classes.\n",
+      " Found 1499 images belonging to 7 classes.\n",
+      " {'AKIEC': 0, 'BCC': 1, 'BKL': 2, 'DF': 3, 'MEL': 4, 'NV': 5, 'VASC': 6}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.applications.resnet_v2 import preprocess_input as base_preprocess\n",
+    "\n",
+    "image_gen = ImageDataGenerator(rotation_range=30,\n",
+    "                               width_shift_range=0.1,\n",
+    "                               height_shift_range=0.1,\n",
+    "                               shear_range=0.1,\n",
+    "                               zoom_range=0.2,\n",
+    "                               horizontal_flip=True,\n",
+    "                               fill_mode='nearest',\n",
+    "                               rescale=1/255,\n",
+    "                               validation_split=0.15)\n",
+    "\n",
+    "data_dir = 'HAM_Dataset'\n",
+    "batch_size = 128\n",
+    "target_size = (224,224)\n",
+    "train_image_gen = image_gen.flow_from_directory(data_dir, \n",
+    "                                          target_size=target_size,\n",
+    "                                          color_mode='rgb',\n",
+    "                                          batch_size=batch_size,\n",
+    "                                          class_mode='categorical',\n",
+    "                                          subset=\"training\")\n",
+    "\n",
+    "test_image_gen = image_gen.flow_from_directory(data_dir, \n",
+    "                                          target_size=target_size, \n",
+    "                                          color_mode='rgb',\n",
+    "                                          batch_size=batch_size,\n",
+    "                                          class_mode='categorical',\n",
+    "                                          shuffle=False,\n",
+    "                                          subset=\"validation\")\n",
+    "\n",
+    "print(test_image_gen.class_indices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9d19125",
+   "metadata": {},
+   "source": [
+    "### Baseline CNN Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "eb4948e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Model: \"sequential_3\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_12 (Conv2D)           (None, 222, 222, 64)      1792      \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_12 (MaxPooling (None, 111, 111, 64)      0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_13 (Conv2D)           (None, 109, 109, 64)      36928     \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_13 (MaxPooling (None, 54, 54, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_14 (Conv2D)           (None, 52, 52, 128)       73856     \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_14 (MaxPooling (None, 26, 26, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_15 (Conv2D)           (None, 24, 24, 256)       295168    \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_15 (MaxPooling (None, 12, 12, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "flatten_3 (Flatten)          (None, 36864)             0         \n",
+      "_________________________________________________________________\n",
+      "dense_6 (Dense)              (None, 128)               4718720   \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 128)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_7 (Dense)              (None, 7)                 903       \n",
+      "=================================================================\n",
+      "Total params: 5,127,367\n",
+      "Trainable params: 5,127,367\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "add Codeadd Markdown\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout\n",
+    "\n",
+    "model = Sequential()\n",
+    "\n",
+    "model.add( Conv2D(filters=64, kernel_size=(3,3), input_shape=(224,224,3), activation=\"relu\") )\n",
+    "model.add( MaxPool2D(pool_size=(2,2)) )\n",
+    "\n",
+    "model.add( Conv2D(filters=64, kernel_size=(3,3), activation=\"relu\") )\n",
+    "model.add( MaxPool2D(pool_size=(2,2)) )\n",
+    "\n",
+    "model.add( Conv2D(filters=128, kernel_size=(3,3), activation=\"relu\") )\n",
+    "model.add( MaxPool2D(pool_size=(2,2)) )\n",
+    "\n",
+    "model.add( Conv2D(filters=256, kernel_size=(3,3), activation=\"relu\") )\n",
+    "model.add( MaxPool2D(pool_size=(2,2)) )\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128, activation=\"relu\"))\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "model.add(Dense(7, activation=\"softmax\"))\n",
+    "\n",
+    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c62414f2",
+   "metadata": {},
+   "source": [
+    "### Training and Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b387e8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
+    "\n",
+    "lr_reduce = ReduceLROnPlateau(monitor='val_accuracy', factor=0.5, patience=1,mode='max', min_lr=0.00001,verbose=1)\n",
+    "early_stop = EarlyStopping(monitor=\"val_loss\", patience=2, verbose=1)\n",
+    "model_chkpt = ModelCheckpoint('best_model_dn121.hdf5',save_best_only=True, monitor='val_accuracy',verbose=1)\n",
+    "\n",
+    "callback_list = [model_chkpt,lr_reduce]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "39ff5ca2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch 42/45\n",
+      "67/67 [==============================] - 126s 2s/step - loss: 0.3013 - accuracy: 0.7550 - val_loss: 1.3435 - val_accuracy: 0.6758\n",
+      "\n",
+      "Epoch 00001: val_accuracy improved from 0.61107 to 0.67578, saving model to best_model_dn121.hdf5\n",
+      "Epoch 43/45\n",
+      "67/67 [==============================] - 124s 2s/step - loss: 0.2686 - accuracy: 0.7810 - val_loss: 1.5529 - val_accuracy: 0.6851\n",
+      "\n",
+      "Epoch 00002: val_accuracy improved from 0.67578 to 0.68512, saving model to best_model_dn121.hdf5\n",
+      "Epoch 44/45\n",
+      "67/67 [==============================] - 124s 2s/step - loss: 0.2371 - accuracy: 0.8033 - val_loss: 1.4009 - val_accuracy: 0.7111\n",
+      "\n",
+      "Epoch 00003: val_accuracy improved from 0.68512 to 0.71114, saving model to best_model_dn121.hdf5\n",
+      "Epoch 45/45\n",
+      "60/67 [=========================>....] - ETA: 11s - loss: 0.2297 - accuracy: 0.8042\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit(train_image_gen,\n",
+    "                    epochs=15, \n",
+    "                    validation_data = test_image_gen,\n",
+    "                    class_weight=class_wt_dict,\n",
+    "                    callbacks=callback_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78865b82",
+   "metadata": {},
+   "source": [
+    "### Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "336bcbf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "array([[ 12,  16,   7,   1,   5,   8,   0],\n",
+      "       [  2,  46,   6,   4,   9,  10,   0],\n",
+      "       [  6,  23,  67,   2,  30,  34,   2],\n",
+      "       [  3,   4,   2,   6,   1,   1,   0],\n",
+      "       [  3,   4,  29,   0,  78,  51,   1],\n",
+      "       [  8,  27,  44,   6,  78, 836,   6],\n",
+      "       [  0,   3,   1,   0,   0,   2,  15]])\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "cm = confusion_matrix(test_labels, predictions)\n",
+    "cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5c15da6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.35      0.24      0.29        49\n",
+      "           1       0.37      0.60      0.46        77\n",
+      "           2       0.43      0.41      0.42       164\n",
+      "           3       0.32      0.35      0.33        17\n",
+      "           4       0.39      0.47      0.43       166\n",
+      "           5       0.89      0.83      0.86      1005\n",
+      "           6       0.62      0.71      0.67        21\n",
+      "\n",
+      "    accuracy                           0.71      1499\n",
+      "   macro avg       0.48      0.52      0.49      1499\n",
+      "weighted avg       0.73      0.71      0.71      1499\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(test_labels, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ceef50c8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/skin_model/DenseNet121.ipynb b/skin_model/DenseNet121.ipynb
new file mode 100644
index 0000000..a513379
--- /dev/null
+++ b/skin_model/DenseNet121.ipynb
@@ -0,0 +1,923 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Skin Cancer Classification Transfer Learning DenseNet121"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2021-09-20T09:53:25.793096Z",
+     "iopub.status.busy": "2021-09-20T09:53:25.792824Z",
+     "iopub.status.idle": "2021-09-20T09:53:25.801106Z",
+     "shell.execute_reply": "2021-09-20T09:53:25.800410Z",
+     "shell.execute_reply.started": "2021-09-20T09:53:25.793067Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import shutil\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "import cv2\n",
+    "import sklearn\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "plt.style.use(\"classic\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T05:53:07.683746Z",
+     "iopub.status.busy": "2021-09-20T05:53:07.682980Z",
+     "iopub.status.idle": "2021-09-20T05:53:07.709720Z",
+     "shell.execute_reply": "2021-09-20T05:53:07.708962Z",
+     "shell.execute_reply.started": "2021-09-20T05:53:07.683709Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['MEL', 'VASC', 'DF', 'NV', 'BKL', 'AKIEC', 'BCC']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(os.listdir('../input/ham-dataset/HAM_Dataset'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T07:40:53.011177Z",
+     "iopub.status.busy": "2021-09-20T07:40:53.010316Z",
+     "iopub.status.idle": "2021-09-20T07:40:53.019292Z",
+     "shell.execute_reply": "2021-09-20T07:40:53.018318Z",
+     "shell.execute_reply.started": "2021-09-20T07:40:53.011131Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.applications.densenet import preprocess_input as base_preprocess\n",
+    "\n",
+    "\n",
+    "image_gen = ImageDataGenerator(preprocessing_function=base_preprocess,\n",
+    "                               rotation_range=20,\n",
+    "                               width_shift_range=0.1,\n",
+    "                               height_shift_range=0.1,\n",
+    "                               shear_range=0.1,\n",
+    "                               zoom_range=0.1,\n",
+    "                               horizontal_flip=True,\n",
+    "                               fill_mode='nearest',\n",
+    "                               #rescale=1/255,\n",
+    "                               validation_split=0.20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T07:40:53.907860Z",
+     "iopub.status.busy": "2021-09-20T07:40:53.907598Z",
+     "iopub.status.idle": "2021-09-20T07:40:54.802207Z",
+     "shell.execute_reply": "2021-09-20T07:40:54.801390Z",
+     "shell.execute_reply.started": "2021-09-20T07:40:53.907829Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 12085 images belonging to 7 classes.\n",
+      "Found 3019 images belonging to 7 classes.\n",
+      "{'AKIEC': 0, 'BCC': 1, 'BKL': 2, 'DF': 3, 'MEL': 4, 'NV': 5, 'VASC': 6}\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dir = '../input/ham-dataset/HAM_Dataset'\n",
+    "batch_size = 64\n",
+    "train_image_gen = image_gen.flow_from_directory(data_dir, \n",
+    "                                          target_size=(224,224), \n",
+    "                                          color_mode='rgb',\n",
+    "                                          batch_size=batch_size,\n",
+    "                                          class_mode='categorical',\n",
+    "                                          subset=\"training\")\n",
+    "\n",
+    "test_image_gen = image_gen.flow_from_directory(data_dir, \n",
+    "                                          target_size=(224,224), \n",
+    "                                          color_mode='rgb',\n",
+    "                                          batch_size=batch_size,\n",
+    "                                          class_mode='categorical',\n",
+    "                                          shuffle=False,\n",
+    "                                          subset=\"validation\")\n",
+    "\n",
+    "print(test_image_gen.class_indices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T07:40:55.225838Z",
+     "iopub.status.busy": "2021-09-20T07:40:55.225619Z",
+     "iopub.status.idle": "2021-09-20T07:40:56.697774Z",
+     "shell.execute_reply": "2021-09-20T07:40:56.697080Z",
+     "shell.execute_reply.started": "2021-09-20T07:40:55.225813Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f2638597390>"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 216x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3,3))\n",
+    "plt.imshow(train_image_gen[0][0][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transfer Learning using DenseNet121 Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Model: \"model_12\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_13 (InputLayer)           [(None, 224, 224, 3) 0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "zero_padding2d_8 (ZeroPadding2D (None, 230, 230, 3)  0           input_13[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv1/conv (Conv2D)             (None, 112, 112, 64) 9408        zero_padding2d_8[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv1/bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1/conv[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv1/relu (Activation)         (None, 112, 112, 64) 0           conv1/bn[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "zero_padding2d_9 (ZeroPadding2D (None, 114, 114, 64) 0           conv1/relu[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "pool1 (MaxPooling2D)            (None, 56, 56, 64)   0           zero_padding2d_9[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_0_bn (BatchNormali (None, 56, 56, 64)   256         pool1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_0_relu (Activation (None, 56, 56, 64)   0           conv2_block1_0_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 128)  8192        conv2_block1_0_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_1_bn (BatchNormali (None, 56, 56, 128)  512         conv2_block1_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_1_relu (Activation (None, 56, 56, 128)  0           conv2_block1_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 32)   36864       conv2_block1_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_concat (Concatenat (None, 56, 56, 96)   0           pool1[0][0]                      \n",
+      "                                                                 conv2_block1_2_conv[0][0]        \n",
+      ".\n",
+      ".\n",
+      ".\n",
+      ".\n",
+      "\n",
+      "__________________________________________________________________________________________________\n",
+      "bn (BatchNormalization)         (None, 7, 7, 1024)   4096        conv5_block16_concat[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "relu (Activation)               (None, 7, 7, 1024)   0           bn[0][0]                         \n",
+      "__________________________________________________________________________________________________\n",
+      "flatten_12 (Flatten)            (None, 50176)        0           relu[0][0]                       \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_24 (Dense)                (None, 128)          6422656     flatten_12[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_12 (Dropout)            (None, 128)          0           dense_24[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_25 (Dense)                (None, 7)            903         dropout_12[0][0]                 \n",
+      "==================================================================================================\n",
+      "Total params: 13,461,063\n",
+      "Trainable params: 13,377,415\n",
+      "Non-trainable params: 83,648\n",
+      "__________________________________________________________________________________________________\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.applications import *\n",
+    "from tensorflow.keras.layers import Flatten, Dense, Input, Dropout\n",
+    "from tensorflow.keras.models import Model\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "\n",
+    "base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
+    "\n",
+    "for layer in base_model.layers:\n",
+    "    layer.trainable = True\n",
+    "\n",
+    "x = base_model.output\n",
+    "x = Flatten()(x)\n",
+    "# x = Dense(4096, activation='relu')(x)\n",
+    "# x = Dropout(0.5)(x)\n",
+    "x = Dense(128, activation='relu')(x)\n",
+    "x = Dropout(0.5)(x)\n",
+    "x = Dense(7, activation='softmax')(x)\n",
+    "tl_model = Model(inputs=base_model.input, outputs=x)\n",
+    "\n",
+    "\n",
+    "tl_model.summary()\n",
+    "optimizer = Adam(0.0001)\n",
+    "tl_model.compile(loss=\"categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T07:41:32.803559Z",
+     "iopub.status.busy": "2021-09-20T07:41:32.802865Z",
+     "iopub.status.idle": "2021-09-20T07:41:32.809583Z",
+     "shell.execute_reply": "2021-09-20T07:41:32.808787Z",
+     "shell.execute_reply.started": "2021-09-20T07:41:32.803522Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
+    "\n",
+    "lr_reduce = ReduceLROnPlateau(monitor='val_accuracy', factor=0.5, patience=2,mode='max', min_lr=0.00001,verbose=1)\n",
+    "early_stop = EarlyStopping(monitor=\"val_loss\", patience=2, verbose=1)\n",
+    "model_chkpt = ModelCheckpoint('best_model_aug.hdf5',save_best_only=True, monitor='val_accuracy',verbose=1)\n",
+    "\n",
+    "callback_list = [model_chkpt,lr_reduce]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T07:41:33.998735Z",
+     "iopub.status.busy": "2021-09-20T07:41:33.998425Z",
+     "iopub.status.idle": "2021-09-20T09:37:55.639900Z",
+     "shell.execute_reply": "2021-09-20T09:37:55.639133Z",
+     "shell.execute_reply.started": "2021-09-20T07:41:33.998702Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "189/189 [==============================] - 352s 2s/step - loss: 1.7499 - accuracy: 0.4074 - val_loss: 0.9937 - val_accuracy: 0.6615\n",
+      "\n",
+      "Epoch 00001: val_accuracy improved from -inf to 0.66148, saving model to best_model_aug.hdf5\n",
+      "Epoch 2/20\n",
+      "189/189 [==============================] - 340s 2s/step - loss: 0.7136 - accuracy: 0.7340 - val_loss: 0.8230 - val_accuracy: 0.7284\n",
+      "\n",
+      "Epoch 00002: val_accuracy improved from 0.66148 to 0.72839, saving model to best_model_aug.hdf5\n",
+      "Epoch 3/20\n",
+      "189/189 [==============================] - 339s 2s/step - loss: 0.5030 - accuracy: 0.8223 - val_loss: 0.8826 - val_accuracy: 0.7479\n",
+      "\n",
+      "Epoch 00003: val_accuracy improved from 0.72839 to 0.74793, saving model to best_model_aug.hdf5\n",
+      "Epoch 4/20\n",
+      "189/189 [==============================] - 339s 2s/step - loss: 0.3668 - accuracy: 0.8721 - val_loss: 1.0351 - val_accuracy: 0.7440\n",
+      "\n",
+      "Epoch 00004: val_accuracy did not improve from 0.74793\n",
+      "Epoch 5/20\n",
+      "189/189 [==============================] - 340s 2s/step - loss: 0.3209 - accuracy: 0.8906 - val_loss: 0.7385 - val_accuracy: 0.8019\n",
+      "\n",
+      "Epoch 00005: val_accuracy improved from 0.74793 to 0.80192, saving model to best_model_aug.hdf5\n",
+      "Epoch 6/20\n",
+      "189/189 [==============================] - 340s 2s/step - loss: 0.2600 - accuracy: 0.9136 - val_loss: 0.8868 - val_accuracy: 0.7943\n",
+      "\n",
+      "Epoch 00006: val_accuracy did not improve from 0.80192\n",
+      "Epoch 7/20\n",
+      "189/189 [==============================] - 338s 2s/step - loss: 0.2152 - accuracy: 0.9310 - val_loss: 0.8217 - val_accuracy: 0.7764\n",
+      "\n",
+      "Epoch 00007: val_accuracy did not improve from 0.80192\n",
+      "\n",
+      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
+      "Epoch 8/20\n",
+      "189/189 [==============================] - 338s 2s/step - loss: 0.1439 - accuracy: 0.9501 - val_loss: 0.9309 - val_accuracy: 0.8066\n",
+      "\n",
+      "Epoch 00008: val_accuracy improved from 0.80192 to 0.80656, saving model to best_model_aug.hdf5\n",
+      "Epoch 9/20\n",
+      "189/189 [==============================] - 338s 2s/step - loss: 0.1155 - accuracy: 0.9583 - val_loss: 1.0752 - val_accuracy: 0.7870\n",
+      "\n",
+      "Epoch 00009: val_accuracy did not improve from 0.80656\n",
+      "Epoch 10/20\n",
+      "189/189 [==============================] - 343s 2s/step - loss: 0.0943 - accuracy: 0.9686 - val_loss: 1.0865 - val_accuracy: 0.7900\n",
+      "\n",
+      "Epoch 00010: val_accuracy did not improve from 0.80656\n",
+      "\n",
+      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
+      "Epoch 11/20\n",
+      "189/189 [==============================] - 349s 2s/step - loss: 0.0774 - accuracy: 0.9745 - val_loss: 1.0638 - val_accuracy: 0.8089\n",
+      "\n",
+      "Epoch 00011: val_accuracy improved from 0.80656 to 0.80888, saving model to best_model_aug.hdf5\n",
+      "Epoch 12/20\n",
+      "189/189 [==============================] - 371s 2s/step - loss: 0.0580 - accuracy: 0.9834 - val_loss: 1.1004 - val_accuracy: 0.8115\n",
+      "\n",
+      "Epoch 00012: val_accuracy improved from 0.80888 to 0.81153, saving model to best_model_aug.hdf5\n",
+      "Epoch 13/20\n",
+      "189/189 [==============================] - 356s 2s/step - loss: 0.0491 - accuracy: 0.9837 - val_loss: 1.3549 - val_accuracy: 0.7917\n",
+      "\n",
+      "Epoch 00013: val_accuracy did not improve from 0.81153\n",
+      "Epoch 14/20\n",
+      "189/189 [==============================] - 355s 2s/step - loss: 0.0460 - accuracy: 0.9837 - val_loss: 1.2851 - val_accuracy: 0.7976\n",
+      "\n",
+      "Epoch 00014: val_accuracy did not improve from 0.81153\n",
+      "\n",
+      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n",
+      "Epoch 15/20\n",
+      "189/189 [==============================] - 357s 2s/step - loss: 0.0422 - accuracy: 0.9839 - val_loss: 1.2144 - val_accuracy: 0.8119\n",
+      "\n",
+      "Epoch 00015: val_accuracy improved from 0.81153 to 0.81186, saving model to best_model_aug.hdf5\n",
+      "Epoch 16/20\n",
+      "189/189 [==============================] - 353s 2s/step - loss: 0.0345 - accuracy: 0.9874 - val_loss: 1.3847 - val_accuracy: 0.7950\n",
+      "\n",
+      "Epoch 00016: val_accuracy did not improve from 0.81186\n",
+      "Epoch 17/20\n",
+      "189/189 [==============================] - 353s 2s/step - loss: 0.0345 - accuracy: 0.9870 - val_loss: 1.2980 - val_accuracy: 0.7996\n",
+      "\n",
+      "Epoch 00017: val_accuracy did not improve from 0.81186\n",
+      "\n",
+      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
+      "Epoch 18/20\n",
+      "189/189 [==============================] - 361s 2s/step - loss: 0.0302 - accuracy: 0.9890 - val_loss: 1.3043 - val_accuracy: 0.8062\n",
+      "\n",
+      "Epoch 00018: val_accuracy did not improve from 0.81186\n",
+      "Epoch 19/20\n",
+      "189/189 [==============================] - 351s 2s/step - loss: 0.0285 - accuracy: 0.9912 - val_loss: 1.3658 - val_accuracy: 0.8062\n",
+      "\n",
+      "Epoch 00019: val_accuracy did not improve from 0.81186\n",
+      "Epoch 20/20\n",
+      "189/189 [==============================] - 354s 2s/step - loss: 0.0262 - accuracy: 0.9906 - val_loss: 1.3751 - val_accuracy: 0.8162\n",
+      "\n",
+      "Epoch 00020: val_accuracy improved from 0.81186 to 0.81616, saving model to best_model_aug.hdf5\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = tl_model.fit(train_image_gen,\n",
+    "                       epochs=20, \n",
+    "                       validation_data = test_image_gen,\n",
+    "                       callbacks=callback_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T09:40:42.140577Z",
+     "iopub.status.busy": "2021-09-20T09:40:42.139902Z",
+     "iopub.status.idle": "2021-09-20T09:40:42.381795Z",
+     "shell.execute_reply": "2021-09-20T09:40:42.381131Z",
+     "shell.execute_reply.started": "2021-09-20T09:40:42.140540Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metrics = pd.DataFrame(tl_model.history.history)\n",
+    "metrics[[\"loss\",\"val_loss\"]].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T09:40:53.282394Z",
+     "iopub.status.busy": "2021-09-20T09:40:53.282130Z",
+     "iopub.status.idle": "2021-09-20T09:40:53.514401Z",
+     "shell.execute_reply": "2021-09-20T09:40:53.513688Z",
+     "shell.execute_reply.started": "2021-09-20T09:40:53.282368Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metrics[[\"accuracy\",\"val_accuracy\"]].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T09:44:16.187572Z",
+     "iopub.status.busy": "2021-09-20T09:44:16.186664Z",
+     "iopub.status.idle": "2021-09-20T09:45:25.831164Z",
+     "shell.execute_reply": "2021-09-20T09:45:25.830423Z",
+     "shell.execute_reply.started": "2021-09-20T09:44:16.187530Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "48/48 [==============================] - 68s 1s/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = tl_model.predict(test_image_gen, verbose=1)\n",
+    "predictions = predictions.argmax(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T09:45:42.454735Z",
+     "iopub.status.busy": "2021-09-20T09:45:42.454469Z",
+     "iopub.status.idle": "2021-09-20T09:45:42.459930Z",
+     "shell.execute_reply": "2021-09-20T09:45:42.457985Z",
+     "shell.execute_reply.started": "2021-09-20T09:45:42.454706Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "test_labels = test_image_gen.classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-09-20T09:46:20.935434Z",
+     "iopub.status.busy": "2021-09-20T09:46:20.935181Z",
+     "iopub.status.idle": "2021-09-20T09:46:21.000189Z",
+     "shell.execute_reply": "2021-09-20T09:46:20.999547Z",
+     "shell.execute_reply.started": "2021-09-20T09:46:20.935406Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "cm = confusion_matrix(test_labels, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized confusion matrix\n",
+      "[[0.67346939 0.09183673 0.1505102  0.00510204 0.06632653 0.0127551\n",
+      "  0.        ]\n",
+      " [0.02919708 0.86861314 0.04136253 0.         0.01459854 0.03892944\n",
+      "  0.00729927]\n",
+      " [0.02277904 0.00683371 0.79498861 0.00455581 0.07061503 0.10022779\n",
+      "  0.        ]\n",
+      " [0.05949657 0.02517162 0.03432494 0.77116705 0.01830664 0.09153318\n",
+      "  0.        ]\n",
+      " [0.00898876 0.01573034 0.10786517 0.         0.63820225 0.21348315\n",
+      "  0.01573034]\n",
+      " [0.         0.00226757 0.01587302 0.         0.02040816 0.96145125\n",
+      "  0.        ]\n",
+      " [0.         0.         0.0154185  0.         0.         0.01101322\n",
+      "  0.97356828]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import itertools\n",
+    "\n",
+    "def plot_confusion_matrix(cm, classes,\n",
+    "                          normalize=True,\n",
+    "                          title='Confusion matrix',\n",
+    "                          cmap=plt.cm.Blues):\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",
+    "    plt.figure(figsize=(7,7))\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+    "    plt.title(title)\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(len(classes))\n",
+    "    plt.xticks(tick_marks, classes, rotation=45)\n",
+    "    plt.yticks(tick_marks, classes)\n",
+    "\n",
+    "    fmt = '.2f' if normalize else 'd'\n",
+    "    thresh = cm.max() / 2.\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        plt.text(j, i, format(cm[i, j], fmt),\n",
+    "                 horizontalalignment=\"center\",\n",
+    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    \n",
+    "cm_plot_labels =['AKIEC','BCC','BKL','DF','MEL','NV','VASC']\n",
+    "\n",
+    "plot_confusion_matrix(cm, cm_plot_labels, title='Confusion Matrix', normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AKIEC</th>\n",
+       "      <th>BCC</th>\n",
+       "      <th>BKL</th>\n",
+       "      <th>DF</th>\n",
+       "      <th>MEL</th>\n",
+       "      <th>NV</th>\n",
+       "      <th>VASC</th>\n",
+       "      <th>micro-average</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.940378</td>\n",
+       "      <td>0.962902</td>\n",
+       "      <td>0.919510</td>\n",
+       "      <td>0.965552</td>\n",
+       "      <td>0.920172</td>\n",
+       "      <td>0.926466</td>\n",
+       "      <td>0.992713</td>\n",
+       "      <td>0.946813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1</th>\n",
+       "      <td>0.745763</td>\n",
+       "      <td>0.864407</td>\n",
+       "      <td>0.741764</td>\n",
+       "      <td>0.866324</td>\n",
+       "      <td>0.702101</td>\n",
+       "      <td>0.792523</td>\n",
+       "      <td>0.975717</td>\n",
+       "      <td>0.813846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
+       "      <td>0.164557</td>\n",
+       "      <td>0.139759</td>\n",
+       "      <td>0.304781</td>\n",
+       "      <td>0.011730</td>\n",
+       "      <td>0.219780</td>\n",
+       "      <td>0.325914</td>\n",
+       "      <td>0.022124</td>\n",
+       "      <td>0.186154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
+       "      <td>0.326531</td>\n",
+       "      <td>0.131387</td>\n",
+       "      <td>0.205011</td>\n",
+       "      <td>0.228833</td>\n",
+       "      <td>0.361798</td>\n",
+       "      <td>0.038549</td>\n",
+       "      <td>0.026432</td>\n",
+       "      <td>0.186154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.019794</td>\n",
+       "      <td>0.022239</td>\n",
+       "      <td>0.059302</td>\n",
+       "      <td>0.001549</td>\n",
+       "      <td>0.031080</td>\n",
+       "      <td>0.079519</td>\n",
+       "      <td>0.003899</td>\n",
+       "      <td>0.031026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
+       "      <td>0.952645</td>\n",
+       "      <td>0.979263</td>\n",
+       "      <td>0.964243</td>\n",
+       "      <td>0.962659</td>\n",
+       "      <td>0.939360</td>\n",
+       "      <td>0.992887</td>\n",
+       "      <td>0.995325</td>\n",
+       "      <td>0.968974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
+       "      <td>0.835443</td>\n",
+       "      <td>0.860241</td>\n",
+       "      <td>0.695219</td>\n",
+       "      <td>0.988270</td>\n",
+       "      <td>0.780220</td>\n",
+       "      <td>0.674086</td>\n",
+       "      <td>0.977876</td>\n",
+       "      <td>0.813846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>precision</th>\n",
+       "      <td>0.835443</td>\n",
+       "      <td>0.860241</td>\n",
+       "      <td>0.695219</td>\n",
+       "      <td>0.988270</td>\n",
+       "      <td>0.780220</td>\n",
+       "      <td>0.674086</td>\n",
+       "      <td>0.977876</td>\n",
+       "      <td>0.813846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recall</th>\n",
+       "      <td>0.673469</td>\n",
+       "      <td>0.868613</td>\n",
+       "      <td>0.794989</td>\n",
+       "      <td>0.771167</td>\n",
+       "      <td>0.638202</td>\n",
+       "      <td>0.961451</td>\n",
+       "      <td>0.973568</td>\n",
+       "      <td>0.813846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sensitivity</th>\n",
+       "      <td>0.673469</td>\n",
+       "      <td>0.868613</td>\n",
+       "      <td>0.794989</td>\n",
+       "      <td>0.771167</td>\n",
+       "      <td>0.638202</td>\n",
+       "      <td>0.961451</td>\n",
+       "      <td>0.973568</td>\n",
+       "      <td>0.813846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>specificity</th>\n",
+       "      <td>0.980206</td>\n",
+       "      <td>0.977761</td>\n",
+       "      <td>0.940698</td>\n",
+       "      <td>0.998451</td>\n",
+       "      <td>0.968920</td>\n",
+       "      <td>0.920481</td>\n",
+       "      <td>0.996101</td>\n",
+       "      <td>0.968974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
+       "      <td>0.980206</td>\n",
+       "      <td>0.977761</td>\n",
+       "      <td>0.940698</td>\n",
+       "      <td>0.998451</td>\n",
+       "      <td>0.968920</td>\n",
+       "      <td>0.920481</td>\n",
+       "      <td>0.996101</td>\n",
+       "      <td>0.968974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
+       "      <td>0.673469</td>\n",
+       "      <td>0.868613</td>\n",
+       "      <td>0.794989</td>\n",
+       "      <td>0.771167</td>\n",
+       "      <td>0.638202</td>\n",
+       "      <td>0.961451</td>\n",
+       "      <td>0.973568</td>\n",
+       "      <td>0.813846</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              AKIEC       BCC       BKL        DF       MEL  \\\n",
+       "accuracy                   0.940378  0.962902  0.919510  0.965552  0.920172   \n",
+       "f1                         0.745763  0.864407  0.741764  0.866324  0.702101   \n",
+       "false_discovery_rate       0.164557  0.139759  0.304781  0.011730  0.219780   \n",
+       "false_negative_rate        0.326531  0.131387  0.205011  0.228833  0.361798   \n",
+       "false_positive_rate        0.019794  0.022239  0.059302  0.001549  0.031080   \n",
+       "negative_predictive_value  0.952645  0.979263  0.964243  0.962659  0.939360   \n",
+       "positive_predictive_value  0.835443  0.860241  0.695219  0.988270  0.780220   \n",
+       "precision                  0.835443  0.860241  0.695219  0.988270  0.780220   \n",
+       "recall                     0.673469  0.868613  0.794989  0.771167  0.638202   \n",
+       "sensitivity                0.673469  0.868613  0.794989  0.771167  0.638202   \n",
+       "specificity                0.980206  0.977761  0.940698  0.998451  0.968920   \n",
+       "true_negative_rate         0.980206  0.977761  0.940698  0.998451  0.968920   \n",
+       "true_positive_rate         0.673469  0.868613  0.794989  0.771167  0.638202   \n",
+       "\n",
+       "                                 NV      VASC  micro-average  \n",
+       "accuracy                   0.926466  0.992713       0.946813  \n",
+       "f1                         0.792523  0.975717       0.813846  \n",
+       "false_discovery_rate       0.325914  0.022124       0.186154  \n",
+       "false_negative_rate        0.038549  0.026432       0.186154  \n",
+       "false_positive_rate        0.079519  0.003899       0.031026  \n",
+       "negative_predictive_value  0.992887  0.995325       0.968974  \n",
+       "positive_predictive_value  0.674086  0.977876       0.813846  \n",
+       "precision                  0.674086  0.977876       0.813846  \n",
+       "recall                     0.961451  0.973568       0.813846  \n",
+       "sensitivity                0.961451  0.973568       0.813846  \n",
+       "specificity                0.920481  0.996101       0.968974  \n",
+       "true_negative_rate         0.920481  0.996101       0.968974  \n",
+       "true_positive_rate         0.961451  0.973568       0.813846  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import disarray\n",
+    "\n",
+    "# Instantiate the confusion matrix DataFrame with index and columns\n",
+    "#cm = confusion_matrix(a,b)\n",
+    "df = pd.DataFrame(cm, index= ['AKIEC','BCC','BKL','DF','MEL','NV','VASC'], columns=['AKIEC','BCC','BKL','DF','MEL','NV','VASC'])\n",
+    "df.da.export_metrics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/skin_model/ResNet50.ipynb b/skin_model/ResNet50.ipynb
new file mode 100644
index 0000000..7a2a9a2
--- /dev/null
+++ b/skin_model/ResNet50.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:20:28.286681Z","iopub.status.busy":"2021-06-04T03:20:28.286326Z","iopub.status.idle":"2021-06-04T03:20:30.515434Z","shell.execute_reply":"2021-06-04T03:20:30.514622Z","shell.execute_reply.started":"2021-06-04T03:20:28.286608Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["['skin-cancer-mnist-ham10000']\n"]}],"source":["%matplotlib inline\n","# python libraties\n","import os, cv2,itertools\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","from tqdm import tqdm\n","from glob import glob\n","from PIL import Image\n","\n","# pytorch libraries\n","import torch\n","from torch import optim,nn\n","from torch.autograd import Variable\n","from torch.utils.data import DataLoader,Dataset\n","from torchvision import models,transforms\n","\n","# sklearn libraries\n","from sklearn.metrics import confusion_matrix\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import classification_report\n","\n","# ensure results are reproducible\n","np.random.seed(10)\n","torch.manual_seed(10)\n","torch.cuda.manual_seed(10)\n","\n","# return directory\n","print(os.listdir('/kaggle/input/'))"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2021-06-03T23:27:38.678740Z","iopub.status.busy":"2021-06-03T23:27:38.678367Z","iopub.status.idle":"2021-06-03T23:27:38.693007Z","shell.execute_reply":"2021-06-03T23:27:38.692221Z","shell.execute_reply.started":"2021-06-03T23:27:38.678707Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["['hmnist_8_8_RGB.csv', 'hmnist_28_28_RGB.csv', 'HAM10000_images_part_1', 'ham10000_images_part_1', 'hmnist_8_8_L.csv', 'HAM10000_images_part_2', 'ham10000_images_part_2', 'hmnist_28_28_L.csv', 'HAM10000_metadata.csv']\n"]}],"source":["print(os.listdir('../input/skin-cancer-mnist-ham10000'))"]},{"cell_type":"markdown","metadata":{},"source":["# Load data"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:20:38.979551Z","iopub.status.busy":"2021-06-04T03:20:38.979182Z","iopub.status.idle":"2021-06-04T03:20:40.289683Z","shell.execute_reply":"2021-06-04T03:20:40.288806Z","shell.execute_reply.started":"2021-06-04T03:20:38.979521Z"},"trusted":true},"outputs":[],"source":["data_dir = '../input/skin-cancer-mnist-ham10000'\n","all_image_path = glob(os.path.join(data_dir, '*', '*.jpg'))\n","imageid_path_dict = {os.path.splitext(os.path.basename(x))[0]: x for x in all_image_path}\n","lesion_type_dict = {\n","    'nv': 'Melanocytic nevi',\n","    'mel': 'dermatofibroma',\n","    'bkl': 'Benign keratosis-like lesions ',\n","    'bcc': 'Basal cell carcinoma',\n","    'akiec': 'Actinic keratoses',\n","    'vasc': 'Vascular lesions',\n","    'df': 'Dermatofibroma'\n","}"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2021-06-03T23:27:47.051082Z","iopub.status.busy":"2021-06-03T23:27:47.050757Z","iopub.status.idle":"2021-06-03T23:27:47.059686Z","shell.execute_reply":"2021-06-03T23:27:47.058814Z","shell.execute_reply.started":"2021-06-03T23:27:47.051051Z"},"trusted":true},"outputs":[],"source":["def compute_img_mean_std(image_paths):\n","    \"\"\"\n","        computing the mean and std of three channel on the whole dataset,\n","        first we should normalize the image from 0-255 to 0-1\n","    \"\"\"\n","\n","    img_h, img_w = 224, 224\n","    imgs = []\n","    means, stdevs = [], []\n","\n","    for i in tqdm(range(len(image_paths))):\n","        img = cv2.imread(image_paths[i])\n","        img = cv2.resize(img, (img_h, img_w))\n","        imgs.append(img)\n","\n","    imgs = np.stack(imgs, axis=3)\n","    print(imgs.shape)\n","\n","    imgs = imgs.astype(np.float32) / 255.\n","\n","    for i in range(3):\n","        pixels = imgs[:, :, i, :].ravel()  # resize to one row\n","        means.append(np.mean(pixels))\n","        stdevs.append(np.std(pixels))\n","\n","    means.reverse()  # BGR --> RGB\n","    stdevs.reverse()\n","\n","    print(\"normMean = {}\".format(means))\n","    print(\"normStd = {}\".format(stdevs))\n","    return means,stdevs"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:20:47.524056Z","iopub.status.busy":"2021-06-04T03:20:47.523717Z","iopub.status.idle":"2021-06-04T03:20:47.530079Z","shell.execute_reply":"2021-06-04T03:20:47.529346Z","shell.execute_reply.started":"2021-06-04T03:20:47.524026Z"},"trusted":true},"outputs":[],"source":["# Values stored to save future run time\n","norm_mean = [0.7630392, 0.5456477, 0.57004845]\n","norm_std = [0.1409286, 0.15261266, 0.16997074]"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:20:56.784205Z","iopub.status.busy":"2021-06-04T03:20:56.783894Z","iopub.status.idle":"2021-06-04T03:20:56.848099Z","shell.execute_reply":"2021-06-04T03:20:56.847171Z","shell.execute_reply.started":"2021-06-04T03:20:56.784175Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>lesion_id</th>\n","      <th>image_id</th>\n","      <th>dx</th>\n","      <th>dx_type</th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>localization</th>\n","      <th>path</th>\n","      <th>cell_type</th>\n","      <th>cell_type_idx</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>HAM_0000118</td>\n","      <td>ISIC_0027419</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>HAM_0000118</td>\n","      <td>ISIC_0025030</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>HAM_0002730</td>\n","      <td>ISIC_0026769</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>HAM_0002730</td>\n","      <td>ISIC_0025661</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>HAM_0001466</td>\n","      <td>ISIC_0031633</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>75.0</td>\n","      <td>male</td>\n","      <td>ear</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     lesion_id      image_id   dx dx_type   age   sex localization  \\\n","0  HAM_0000118  ISIC_0027419  bkl   histo  80.0  male        scalp   \n","1  HAM_0000118  ISIC_0025030  bkl   histo  80.0  male        scalp   \n","2  HAM_0002730  ISIC_0026769  bkl   histo  80.0  male        scalp   \n","3  HAM_0002730  ISIC_0025661  bkl   histo  80.0  male        scalp   \n","4  HAM_0001466  ISIC_0031633  bkl   histo  75.0  male          ear   \n","\n","                                                path  \\\n","0  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","1  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","2  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","3  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","4  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","\n","                        cell_type  cell_type_idx  \n","0  Benign keratosis-like lesions               2  \n","1  Benign keratosis-like lesions               2  \n","2  Benign keratosis-like lesions               2  \n","3  Benign keratosis-like lesions               2  \n","4  Benign keratosis-like lesions               2  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df_original = pd.read_csv(os.path.join(data_dir, 'HAM10000_metadata.csv'))\n","df_original['path'] = df_original['image_id'].map(imageid_path_dict.get)\n","df_original['cell_type'] = df_original['dx'].map(lesion_type_dict.get)\n","df_original['cell_type_idx'] = pd.Categorical(df_original['cell_type']).codes\n","df_original.head()"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:00.497779Z","iopub.status.busy":"2021-06-04T03:21:00.497489Z","iopub.status.idle":"2021-06-04T03:21:00.543392Z","shell.execute_reply":"2021-06-04T03:21:00.542655Z","shell.execute_reply.started":"2021-06-04T03:21:00.497754Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>lesion_id</th>\n","      <th>image_id</th>\n","      <th>dx</th>\n","      <th>dx_type</th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>localization</th>\n","      <th>path</th>\n","      <th>cell_type</th>\n","      <th>cell_type_idx</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>HAM_0000001</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>HAM_0000003</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>HAM_0000004</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>HAM_0000007</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>HAM_0000008</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     lesion_id  image_id  dx  dx_type  age  sex  localization  path  \\\n","0  HAM_0000001         1   1        1    1    1             1     1   \n","1  HAM_0000003         1   1        1    1    1             1     1   \n","2  HAM_0000004         1   1        1    1    1             1     1   \n","3  HAM_0000007         1   1        1    1    1             1     1   \n","4  HAM_0000008         1   1        1    1    1             1     1   \n","\n","   cell_type  cell_type_idx  \n","0          1              1  \n","1          1              1  \n","2          1              1  \n","3          1              1  \n","4          1              1  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Determine how many images are associated with each lesion_id\n","df_undup = df_original.groupby('lesion_id').count()\n","# Filter out lesion_id's that have only one image associated with it\n","df_undup = df_undup[df_undup['image_id'] == 1]\n","df_undup.reset_index(inplace=True)\n","df_undup.head()"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:06.043553Z","iopub.status.busy":"2021-06-04T03:21:06.043177Z","iopub.status.idle":"2021-06-04T03:21:12.334592Z","shell.execute_reply":"2021-06-04T03:21:12.333801Z","shell.execute_reply.started":"2021-06-04T03:21:06.043522Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>lesion_id</th>\n","      <th>image_id</th>\n","      <th>dx</th>\n","      <th>dx_type</th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>localization</th>\n","      <th>path</th>\n","      <th>cell_type</th>\n","      <th>cell_type_idx</th>\n","      <th>duplicates</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>HAM_0000118</td>\n","      <td>ISIC_0027419</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","      <td>duplicated</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>HAM_0000118</td>\n","      <td>ISIC_0025030</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","      <td>duplicated</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>HAM_0002730</td>\n","      <td>ISIC_0026769</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","      <td>duplicated</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>HAM_0002730</td>\n","      <td>ISIC_0025661</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","      <td>duplicated</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>HAM_0001466</td>\n","      <td>ISIC_0031633</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>75.0</td>\n","      <td>male</td>\n","      <td>ear</td>\n","      <td>../input/skin-cancer-mnist-ham10000/ham10000_i...</td>\n","      <td>Benign keratosis-like lesions</td>\n","      <td>2</td>\n","      <td>duplicated</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     lesion_id      image_id   dx dx_type   age   sex localization  \\\n","0  HAM_0000118  ISIC_0027419  bkl   histo  80.0  male        scalp   \n","1  HAM_0000118  ISIC_0025030  bkl   histo  80.0  male        scalp   \n","2  HAM_0002730  ISIC_0026769  bkl   histo  80.0  male        scalp   \n","3  HAM_0002730  ISIC_0025661  bkl   histo  80.0  male        scalp   \n","4  HAM_0001466  ISIC_0031633  bkl   histo  75.0  male          ear   \n","\n","                                                path  \\\n","0  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","1  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","2  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","3  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","4  ../input/skin-cancer-mnist-ham10000/ham10000_i...   \n","\n","                        cell_type  cell_type_idx  duplicates  \n","0  Benign keratosis-like lesions               2  duplicated  \n","1  Benign keratosis-like lesions               2  duplicated  \n","2  Benign keratosis-like lesions               2  duplicated  \n","3  Benign keratosis-like lesions               2  duplicated  \n","4  Benign keratosis-like lesions               2  duplicated  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# Identify lesion_id's that have duplicate images and those that have only one image.\n","def get_duplicates(x):\n","    unique_list = list(df_undup['lesion_id'])\n","    if x in unique_list:\n","        return 'unduplicated'\n","    else:\n","        return 'duplicated'\n","\n","# Create a new colum that is a copy of the lesion_id column\n","df_original['duplicates'] = df_original['lesion_id']\n","# Apply the function to this new column\n","df_original['duplicates'] = df_original['duplicates'].apply(get_duplicates)\n","df_original.head()"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:16.748710Z","iopub.status.busy":"2021-06-04T03:21:16.748343Z","iopub.status.idle":"2021-06-04T03:21:16.760263Z","shell.execute_reply":"2021-06-04T03:21:16.759147Z","shell.execute_reply.started":"2021-06-04T03:21:16.748678Z"},"trusted":true},"outputs":[{"data":{"text/plain":["unduplicated    5514\n","duplicated      4501\n","Name: duplicates, dtype: int64"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["df_original['duplicates'].value_counts()"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:20.120034Z","iopub.status.busy":"2021-06-04T03:21:20.119726Z","iopub.status.idle":"2021-06-04T03:21:20.131063Z","shell.execute_reply":"2021-06-04T03:21:20.130108Z","shell.execute_reply.started":"2021-06-04T03:21:20.120005Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(5514, 11)"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# Filter out images that don't have duplicates\n","df_undup = df_original[df_original['duplicates'] == 'unduplicated']\n","df_undup.shape"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:27.174233Z","iopub.status.busy":"2021-06-04T03:21:27.173913Z","iopub.status.idle":"2021-06-04T03:21:27.189333Z","shell.execute_reply":"2021-06-04T03:21:27.188394Z","shell.execute_reply.started":"2021-06-04T03:21:27.174193Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(1103, 11)"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["# Create a val set using df as none of these images have augmented duplicates in the train set\n","y = df_undup['cell_type_idx']\n","_, df_val = train_test_split(df_undup, test_size=0.2, random_state=101, stratify=y)\n","df_val.shape"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:33.199241Z","iopub.status.busy":"2021-06-04T03:21:33.198853Z","iopub.status.idle":"2021-06-04T03:21:33.208576Z","shell.execute_reply":"2021-06-04T03:21:33.207648Z","shell.execute_reply.started":"2021-06-04T03:21:33.199208Z"},"trusted":true},"outputs":[{"data":{"text/plain":["4    883\n","2     88\n","6     46\n","1     35\n","0     30\n","5     13\n","3      8\n","Name: cell_type_idx, dtype: int64"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df_val['cell_type_idx'].value_counts()"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:38.420094Z","iopub.status.busy":"2021-06-04T03:21:38.419781Z","iopub.status.idle":"2021-06-04T03:21:39.662953Z","shell.execute_reply":"2021-06-04T03:21:39.662049Z","shell.execute_reply.started":"2021-06-04T03:21:38.420066Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["8912\n","1103\n"]}],"source":["# This set will be df_original excluding all rows that are in the val set\n","# This function identifies if an image is part of the train or val set.\n","def get_val_rows(x):\n","    # create a list of all the lesion_id's in the val set\n","    val_list = list(df_val['image_id'])\n","    if str(x) in val_list:\n","        return 'val'\n","    else:\n","        return 'train'\n","\n","# Identify train and val rows\n","# Create a new colum that is a copy of the image_id column\n","df_original['train_or_val'] = df_original['image_id']\n","# Apply the function to this new column\n","df_original['train_or_val'] = df_original['train_or_val'].apply(get_val_rows)\n","# Filter out training rows\n","df_train = df_original[df_original['train_or_val'] == 'train']\n","print(len(df_train))\n","print(len(df_val))"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:42.240000Z","iopub.status.busy":"2021-06-04T03:21:42.239669Z","iopub.status.idle":"2021-06-04T03:21:42.248516Z","shell.execute_reply":"2021-06-04T03:21:42.247503Z","shell.execute_reply.started":"2021-06-04T03:21:42.239970Z"},"trusted":true},"outputs":[{"data":{"text/plain":["4    5822\n","6    1067\n","2    1011\n","1     479\n","0     297\n","5     129\n","3     107\n","Name: cell_type_idx, dtype: int64"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df_train['cell_type_idx'].value_counts()"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:46.121136Z","iopub.status.busy":"2021-06-04T03:21:46.120825Z","iopub.status.idle":"2021-06-04T03:21:46.128655Z","shell.execute_reply":"2021-06-04T03:21:46.127721Z","shell.execute_reply.started":"2021-06-04T03:21:46.121107Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Melanocytic nevi                  883\n","Benign keratosis-like lesions      88\n","dermatofibroma                     46\n","Basal cell carcinoma               35\n","Actinic keratoses                  30\n","Vascular lesions                   13\n","Dermatofibroma                      8\n","Name: cell_type, dtype: int64"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df_val['cell_type'].value_counts()"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:21:58.083581Z","iopub.status.busy":"2021-06-04T03:21:58.083136Z","iopub.status.idle":"2021-06-04T03:21:58.216283Z","shell.execute_reply":"2021-06-04T03:21:58.215372Z","shell.execute_reply.started":"2021-06-04T03:21:58.083535Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Melanocytic nevi                  5822\n","Dermatofibroma                    5350\n","dermatofibroma                    5335\n","Vascular lesions                  5160\n","Benign keratosis-like lesions     5055\n","Basal cell carcinoma              4790\n","Actinic keratoses                 4455\n","Name: cell_type, dtype: int64"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# Copy fewer class to balance the number of 7 classes\n","data_aug_rate = [15,10,5,50,0,40,5]\n","for i in range(7):\n","    if data_aug_rate[i]:\n","        df_train=df_train.append([df_train.loc[df_train['cell_type_idx'] == i,:]]*(data_aug_rate[i]-1), ignore_index=True)\n","df_train['cell_type'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["# Split dataset"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:22:15.122368Z","iopub.status.busy":"2021-06-04T03:22:15.122029Z","iopub.status.idle":"2021-06-04T03:22:15.134392Z","shell.execute_reply":"2021-06-04T03:22:15.133554Z","shell.execute_reply.started":"2021-06-04T03:22:15.122337Z"},"trusted":true},"outputs":[],"source":["# Split the test set again in a validation set and a true test set:\n","df_val, df_test = train_test_split(df_val, test_size=0.5)\n","df_train = df_train.reset_index()\n","df_val = df_val.reset_index()\n","df_test = df_test.reset_index()"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:22:36.369623Z","iopub.status.busy":"2021-06-04T03:22:36.369171Z","iopub.status.idle":"2021-06-04T03:22:36.387232Z","shell.execute_reply":"2021-06-04T03:22:36.386480Z","shell.execute_reply.started":"2021-06-04T03:22:36.369584Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["552\n"]},{"data":{"text/plain":["Melanocytic nevi                  444\n","Benign keratosis-like lesions      47\n","dermatofibroma                     20\n","Basal cell carcinoma               15\n","Actinic keratoses                  12\n","Vascular lesions                    8\n","Dermatofibroma                      6\n","Name: cell_type, dtype: int64"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["print(len(df_test))\n","df_test['cell_type'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["# Set model"]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:23:16.168696Z","iopub.status.busy":"2021-06-04T03:23:16.168379Z","iopub.status.idle":"2021-06-04T03:23:16.174226Z","shell.execute_reply":"2021-06-04T03:23:16.173338Z","shell.execute_reply.started":"2021-06-04T03:23:16.168669Z"},"trusted":true},"outputs":[],"source":["# feature_extract is a boolean that defines finetuning or feature extracting. \n","# If feature_extract = False, the model is finetuned and all model parameters are updated. \n","# If feature_extract = True, only the last layer parameters are updated, the others remain fixed.\n","def set_parameter_requires_grad(model, feature_extracting):\n","    if feature_extracting:\n","        for param in model.parameters():\n","            param.requires_grad = False"]},{"cell_type":"code","execution_count":18,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:23:20.188492Z","iopub.status.busy":"2021-06-04T03:23:20.188141Z","iopub.status.idle":"2021-06-04T03:23:20.202550Z","shell.execute_reply":"2021-06-04T03:23:20.201622Z","shell.execute_reply.started":"2021-06-04T03:23:20.188461Z"},"trusted":true},"outputs":[],"source":["def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):\n","    # Initialize these variables which will be set\n","    model_ft = None\n","    input_size = 0\n","\n","    if model_name == \"resnet\":\n","        \"\"\" Resnet18, resnet34, resnet50, resnet101\n","        \"\"\"\n","        model_ft = models.resnet50(pretrained=True)\n","        set_parameter_requires_grad(model_ft, feature_extract)\n","        num_ftrs = model_ft.fc.in_features\n","        model_ft.fc = nn.Linear(num_ftrs, num_classes)\n","        input_size = 224\n","\n","    else:\n","        print(\"Invalid model name, exiting...\")\n","        exit()\n","\n","    return model_ft, input_size"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:23:26.064174Z","iopub.status.busy":"2021-06-04T03:23:26.063865Z","iopub.status.idle":"2021-06-04T03:23:31.891025Z","shell.execute_reply":"2021-06-04T03:23:31.890051Z","shell.execute_reply.started":"2021-06-04T03:23:26.064144Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4e067ea165124d63b949b093fd5073d5","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0.00/97.8M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["model_name = \"resnet\"\n","num_classes = 7\n","feature_extract = False\n","# Initialize the model\n","model_ft, input_size = initialize_model(model_name, num_classes, feature_extract, use_pretrained=True)\n","# Define the device:\n","device = torch.device('cuda:0')\n","# device = torch.device('cpu') # If using cpu\n","# Put the model on the device:\n","model = model_ft.to(device)"]},{"cell_type":"markdown","metadata":{},"source":["# Data augmentation"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:24:21.197101Z","iopub.status.busy":"2021-06-04T03:24:21.196784Z","iopub.status.idle":"2021-06-04T03:24:21.203256Z","shell.execute_reply":"2021-06-04T03:24:21.202467Z","shell.execute_reply.started":"2021-06-04T03:24:21.197072Z"},"trusted":true},"outputs":[],"source":["# define the transformation of the train images.\n","train_transform = transforms.Compose([transforms.Resize((input_size,input_size)),transforms.RandomHorizontalFlip(),\n","                                      transforms.RandomVerticalFlip(),\n","                                      transforms.RandomRotation(20),\n","                                      transforms.ColorJitter(brightness=0.1, contrast=0.1, hue=0.1),\n","                                      transforms.ToTensor(), \n","                                      transforms.Normalize(norm_mean, norm_std)])\n","\n","# define the transformation of the val images.\n","val_transform = transforms.Compose([transforms.Resize((input_size,input_size)), \n","                                    transforms.ToTensor(),\n","                                    transforms.Normalize(norm_mean, norm_std)])\n","\n","# define the transformation of the test images.\n","test_transform = transforms.Compose([transforms.Resize((input_size,input_size)), \n","                                     transforms.ToTensor(),\n","                                    transforms.Normalize(norm_mean, norm_std)])"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:24:26.798282Z","iopub.status.busy":"2021-06-04T03:24:26.797939Z","iopub.status.idle":"2021-06-04T03:24:26.807490Z","shell.execute_reply":"2021-06-04T03:24:26.806498Z","shell.execute_reply.started":"2021-06-04T03:24:26.798232Z"},"trusted":true},"outputs":[],"source":["# Define a pytorch dataloader for dataset\n","class HAM10000(Dataset):\n","    def __init__(self, df, transform=None):\n","        self.df = df\n","        self.transform = transform\n","\n","    def __len__(self):\n","        return len(self.df)\n","\n","    def __getitem__(self, index):\n","        # Load data and get label\n","        X = Image.open(self.df['path'][index])\n","        y = torch.tensor(int(self.df['cell_type_idx'][index]))\n","\n","        if self.transform:\n","            X = self.transform(X)\n","\n","        return X, y"]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:24:44.124369Z","iopub.status.busy":"2021-06-04T03:24:44.124031Z","iopub.status.idle":"2021-06-04T03:24:44.131464Z","shell.execute_reply":"2021-06-04T03:24:44.130505Z","shell.execute_reply.started":"2021-06-04T03:24:44.124337Z"},"trusted":true},"outputs":[],"source":["# Define the training set using the table train_df and using the defined transitions (train_transform)\n","training_set = HAM10000(df_train, transform=train_transform)\n","train_loader = DataLoader(training_set, batch_size=32, shuffle=True, num_workers=4)\n","\n","# Same for the validation set:\n","validation_set = HAM10000(df_val, transform=train_transform)\n","val_loader = DataLoader(validation_set, batch_size=32, shuffle=False, num_workers=4)\n","\n","# Same for the test set:\n","test_set = HAM10000(df_test, transform=train_transform)\n","test_loader = DataLoader(test_set, batch_size=32, shuffle=False, num_workers=4)"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:24:56.903736Z","iopub.status.busy":"2021-06-04T03:24:56.903424Z","iopub.status.idle":"2021-06-04T03:24:56.910334Z","shell.execute_reply":"2021-06-04T03:24:56.909351Z","shell.execute_reply.started":"2021-06-04T03:24:56.903708Z"},"trusted":true},"outputs":[],"source":["# set optimizer and loss function\n","optimizer = optim.Adam(model.parameters(), lr=1e-3)\n","criterion = nn.CrossEntropyLoss().to(device)"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:25:00.808115Z","iopub.status.busy":"2021-06-04T03:25:00.807802Z","iopub.status.idle":"2021-06-04T03:25:00.814720Z","shell.execute_reply":"2021-06-04T03:25:00.813552Z","shell.execute_reply.started":"2021-06-04T03:25:00.808085Z"},"trusted":true},"outputs":[],"source":["# this function is used during training process, to calculate the loss and accuracy\n","class AverageMeter(object):\n","    def __init__(self):\n","        self.reset()\n","\n","    def reset(self):\n","        self.val = 0\n","        self.avg = 0\n","        self.sum = 0\n","        self.count = 0\n","\n","    def update(self, val, n=1):\n","        self.val = val\n","        self.sum += val * n\n","        self.count += n\n","        self.avg = self.sum / self.count"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:25:07.849330Z","iopub.status.busy":"2021-06-04T03:25:07.848994Z","iopub.status.idle":"2021-06-04T03:25:07.858612Z","shell.execute_reply":"2021-06-04T03:25:07.857601Z","shell.execute_reply.started":"2021-06-04T03:25:07.849280Z"},"trusted":true},"outputs":[],"source":["total_loss_train, total_acc_train = [],[]\n","def train(train_loader, model, criterion, optimizer, epoch):\n","    model.train()\n","    train_loss = AverageMeter()\n","    train_acc = AverageMeter()\n","    curr_iter = (epoch - 1) * len(train_loader)\n","    for i, data in enumerate(train_loader):\n","        images, labels = data\n","        N = images.size(0)\n","        images = Variable(images).to(device)\n","        labels = Variable(labels).to(device)\n","\n","        optimizer.zero_grad()\n","        outputs = model(images)\n","\n","        loss = criterion(outputs, labels)\n","        loss.backward()\n","        optimizer.step()\n","        prediction = outputs.max(1, keepdim=True)[1]\n","        train_acc.update(prediction.eq(labels.view_as(prediction)).sum().item()/N)\n","        train_loss.update(loss.item())\n","        curr_iter += 1\n","        if (i + 1) % 100 == 0:\n","            print('[epoch %d], [iter %d / %d], [train loss %.5f], [train acc %.5f]' % (\n","                epoch, i + 1, len(train_loader), train_loss.avg, train_acc.avg))\n","            total_loss_train.append(train_loss.avg)\n","            total_acc_train.append(train_acc.avg)\n","    return train_loss.avg, train_acc.avg"]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:25:29.588357Z","iopub.status.busy":"2021-06-04T03:25:29.588015Z","iopub.status.idle":"2021-06-04T03:25:29.597137Z","shell.execute_reply":"2021-06-04T03:25:29.596261Z","shell.execute_reply.started":"2021-06-04T03:25:29.588324Z"},"trusted":true},"outputs":[],"source":["def validate(val_loader, model, criterion, optimizer, epoch):\n","    model.eval()\n","    val_loss = AverageMeter()\n","    val_acc = AverageMeter()\n","    with torch.no_grad():\n","        for i, data in tqdm(enumerate(val_loader)):\n","            images, labels = data\n","            N = images.size(0)\n","            images = Variable(images).to(device)\n","            labels = Variable(labels).to(device)\n","\n","            outputs = model(images)\n","            prediction = outputs.max(1, keepdim=True)[1]\n","\n","            val_acc.update(prediction.eq(labels.view_as(prediction)).sum().item()/N)\n","\n","            val_loss.update(criterion(outputs, labels).item())\n","\n","    print('------------------------------------------------------------')\n","    print('[epoch %d], [val loss %.5f], [val acc %.5f]' % (epoch, val_loss.avg, val_acc.avg))\n","    print('------------------------------------------------------------')\n","    return val_loss.avg, val_acc.avg"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T03:25:35.649129Z","iopub.status.busy":"2021-06-04T03:25:35.648811Z","iopub.status.idle":"2021-06-04T04:52:17.376454Z","shell.execute_reply":"2021-06-04T04:52:17.375208Z","shell.execute_reply.started":"2021-06-04T03:25:35.649100Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["  0%|          | 0/10 [00:00<?, ?it/s]"]},{"name":"stdout","output_type":"stream","text":["[epoch 1], [iter 100 / 1124], [train loss 1.67071], [train acc 0.36156]\n","[epoch 1], [iter 200 / 1124], [train loss 1.51895], [train acc 0.41734]\n","[epoch 1], [iter 300 / 1124], [train loss 1.42696], [train acc 0.45510]\n","[epoch 1], [iter 400 / 1124], [train loss 1.36015], [train acc 0.48320]\n","[epoch 1], [iter 500 / 1124], [train loss 1.30096], [train acc 0.50900]\n","[epoch 1], [iter 600 / 1124], [train loss 1.24955], [train acc 0.52938]\n","[epoch 1], [iter 700 / 1124], [train loss 1.20955], [train acc 0.54625]\n","[epoch 1], [iter 800 / 1124], [train loss 1.17708], [train acc 0.55922]\n","[epoch 1], [iter 900 / 1124], [train loss 1.14762], [train acc 0.57017]\n","[epoch 1], [iter 1000 / 1124], [train loss 1.12040], [train acc 0.58037]\n","[epoch 1], [iter 1100 / 1124], [train loss 1.09462], [train acc 0.59045]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:02,  2.36s/it]\u001b[A\n","3it [00:02,  1.48it/s]\u001b[A\n","5it [00:04,  1.37it/s]\u001b[A\n","7it [00:04,  2.20it/s]\u001b[A\n","8it [00:04,  2.66it/s]\u001b[A\n","9it [00:05,  1.65it/s]\u001b[A\n","11it [00:05,  2.55it/s]\u001b[A\n","13it [00:07,  1.93it/s]\u001b[A\n","15it [00:07,  2.74it/s]\u001b[A\n","18it [00:08,  2.16it/s]\u001b[A\n"," 10%|█         | 1/10 [08:44<1:18:39, 524.37s/it]"]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 1], [val loss 0.43959], [val acc 0.85243]\n","------------------------------------------------------------\n","*****************************************************\n","best record: [epoch 1], [val loss 0.43959], [val acc 0.85243]\n","*****************************************************\n","[epoch 2], [iter 100 / 1124], [train loss 0.78675], [train acc 0.71281]\n","[epoch 2], [iter 200 / 1124], [train loss 0.77605], [train acc 0.71578]\n","[epoch 2], [iter 300 / 1124], [train loss 0.78844], [train acc 0.70833]\n","[epoch 2], [iter 400 / 1124], [train loss 0.77909], [train acc 0.71195]\n","[epoch 2], [iter 500 / 1124], [train loss 0.77660], [train acc 0.71144]\n","[epoch 2], [iter 600 / 1124], [train loss 0.76373], [train acc 0.71620]\n","[epoch 2], [iter 700 / 1124], [train loss 0.75496], [train acc 0.71826]\n","[epoch 2], [iter 800 / 1124], [train loss 0.74978], [train acc 0.72062]\n","[epoch 2], [iter 900 / 1124], [train loss 0.74213], [train acc 0.72333]\n","[epoch 2], [iter 1000 / 1124], [train loss 0.73485], [train acc 0.72528]\n","[epoch 2], [iter 1100 / 1124], [train loss 0.72428], [train acc 0.72918]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:01,  1.71s/it]\u001b[A\n","3it [00:01,  1.97it/s]\u001b[A\n","5it [00:03,  1.64it/s]\u001b[A\n","7it [00:03,  2.57it/s]\u001b[A\n","9it [00:05,  1.67it/s]\u001b[A\n","10it [00:05,  1.96it/s]\u001b[A\n","11it [00:05,  2.27it/s]\u001b[A\n","13it [00:07,  1.79it/s]\u001b[A\n","15it [00:07,  2.62it/s]\u001b[A\n","18it [00:08,  2.25it/s]\u001b[A\n"," 20%|██        | 2/10 [17:24<1:09:34, 521.86s/it]"]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 2], [val loss 0.63905], [val acc 0.76637]\n","------------------------------------------------------------\n","[epoch 3], [iter 100 / 1124], [train loss 0.66187], [train acc 0.74781]\n","[epoch 3], [iter 200 / 1124], [train loss 0.65413], [train acc 0.75547]\n","[epoch 3], [iter 300 / 1124], [train loss 0.63975], [train acc 0.75677]\n","[epoch 3], [iter 400 / 1124], [train loss 0.62647], [train acc 0.76125]\n","[epoch 3], [iter 500 / 1124], [train loss 0.62480], [train acc 0.76138]\n","[epoch 3], [iter 600 / 1124], [train loss 0.61806], [train acc 0.76568]\n","[epoch 3], [iter 700 / 1124], [train loss 0.60691], [train acc 0.77098]\n","[epoch 3], [iter 800 / 1124], [train loss 0.60426], [train acc 0.77219]\n","[epoch 3], [iter 900 / 1124], [train loss 0.59682], [train acc 0.77517]\n","[epoch 3], [iter 1000 / 1124], [train loss 0.59158], [train acc 0.77772]\n","[epoch 3], [iter 1100 / 1124], [train loss 0.58846], [train acc 0.77898]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:01,  1.76s/it]\u001b[A\n","3it [00:01,  1.93it/s]\u001b[A\n","5it [00:03,  1.47it/s]\u001b[A\n","6it [00:03,  1.90it/s]\u001b[A\n","7it [00:03,  2.44it/s]\u001b[A\n","9it [00:05,  1.83it/s]\u001b[A\n","11it [00:05,  2.68it/s]\u001b[A\n","13it [00:06,  2.06it/s]\u001b[A\n","15it [00:06,  2.90it/s]\u001b[A\n","18it [00:07,  2.36it/s]\u001b[A\n"," 30%|███       | 3/10 [26:03<1:00:43, 520.52s/it]"]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 3], [val loss 0.39348], [val acc 0.85590]\n","------------------------------------------------------------\n","*****************************************************\n","best record: [epoch 3], [val loss 0.39348], [val acc 0.85590]\n","*****************************************************\n","[epoch 4], [iter 100 / 1124], [train loss 0.48886], [train acc 0.81563]\n","[epoch 4], [iter 200 / 1124], [train loss 0.51154], [train acc 0.80906]\n","[epoch 4], [iter 300 / 1124], [train loss 0.51402], [train acc 0.80583]\n","[epoch 4], [iter 400 / 1124], [train loss 0.52348], [train acc 0.80023]\n","[epoch 4], [iter 500 / 1124], [train loss 0.52134], [train acc 0.80144]\n","[epoch 4], [iter 600 / 1124], [train loss 0.52076], [train acc 0.80094]\n","[epoch 4], [iter 700 / 1124], [train loss 0.51915], [train acc 0.80232]\n","[epoch 4], [iter 800 / 1124], [train loss 0.51415], [train acc 0.80453]\n","[epoch 4], [iter 900 / 1124], [train loss 0.51147], [train acc 0.80507]\n","[epoch 4], [iter 1000 / 1124], [train loss 0.50939], [train acc 0.80541]\n","[epoch 4], [iter 1100 / 1124], [train loss 0.50668], [train acc 0.80676]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:01,  1.89s/it]\u001b[A\n","2it [00:02,  1.18it/s]\u001b[A\n","3it [00:02,  1.97it/s]\u001b[A\n","5it [00:03,  1.70it/s]\u001b[A\n","6it [00:03,  2.12it/s]\u001b[A\n","8it [00:03,  3.37it/s]\u001b[A\n","9it [00:05,  1.90it/s]\u001b[A\n","10it [00:05,  2.24it/s]\u001b[A\n","12it [00:05,  3.43it/s]\u001b[A\n","13it [00:06,  1.94it/s]\u001b[A\n","14it [00:06,  2.36it/s]\u001b[A\n","16it [00:06,  3.64it/s]\u001b[A\n","18it [00:07,  2.41it/s]\u001b[A\n"," 40%|████      | 4/10 [34:41<51:58, 519.75s/it]  "]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 4], [val loss 0.60235], [val acc 0.79762]\n","------------------------------------------------------------\n","[epoch 5], [iter 100 / 1124], [train loss 0.44517], [train acc 0.82719]\n","[epoch 5], [iter 200 / 1124], [train loss 0.44394], [train acc 0.83453]\n","[epoch 5], [iter 300 / 1124], [train loss 0.45473], [train acc 0.82937]\n","[epoch 5], [iter 400 / 1124], [train loss 0.44372], [train acc 0.83305]\n","[epoch 5], [iter 500 / 1124], [train loss 0.44812], [train acc 0.83213]\n","[epoch 5], [iter 600 / 1124], [train loss 0.44275], [train acc 0.83380]\n","[epoch 5], [iter 700 / 1124], [train loss 0.44186], [train acc 0.83330]\n","[epoch 5], [iter 800 / 1124], [train loss 0.44120], [train acc 0.83289]\n","[epoch 5], [iter 900 / 1124], [train loss 0.43693], [train acc 0.83375]\n","[epoch 5], [iter 1000 / 1124], [train loss 0.43713], [train acc 0.83397]\n","[epoch 5], [iter 1100 / 1124], [train loss 0.43748], [train acc 0.83398]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:01,  1.78s/it]\u001b[A\n","3it [00:01,  1.91it/s]\u001b[A\n","5it [00:03,  1.58it/s]\u001b[A\n","7it [00:03,  2.51it/s]\u001b[A\n","9it [00:04,  1.95it/s]\u001b[A\n","11it [00:05,  2.74it/s]\u001b[A\n","13it [00:06,  2.15it/s]\u001b[A\n","15it [00:06,  2.97it/s]\u001b[A\n","18it [00:07,  2.50it/s]\u001b[A\n"," 50%|█████     | 5/10 [43:21<43:19, 519.82s/it]"]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 5], [val loss 0.39599], [val acc 0.85417]\n","------------------------------------------------------------\n","[epoch 6], [iter 100 / 1124], [train loss 0.38775], [train acc 0.84937]\n","[epoch 6], [iter 200 / 1124], [train loss 0.40960], [train acc 0.84359]\n","[epoch 6], [iter 300 / 1124], [train loss 0.40877], [train acc 0.84344]\n","[epoch 6], [iter 400 / 1124], [train loss 0.40887], [train acc 0.84352]\n","[epoch 6], [iter 500 / 1124], [train loss 0.40630], [train acc 0.84294]\n","[epoch 6], [iter 600 / 1124], [train loss 0.40302], [train acc 0.84401]\n","[epoch 6], [iter 700 / 1124], [train loss 0.40298], [train acc 0.84379]\n","[epoch 6], [iter 800 / 1124], [train loss 0.39737], [train acc 0.84566]\n","[epoch 6], [iter 900 / 1124], [train loss 0.39568], [train acc 0.84681]\n","[epoch 6], [iter 1000 / 1124], [train loss 0.39284], [train acc 0.84778]\n","[epoch 6], [iter 1100 / 1124], [train loss 0.39017], [train acc 0.84841]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:01,  1.62s/it]\u001b[A\n","2it [00:01,  1.18it/s]\u001b[A\n","3it [00:02,  1.86it/s]\u001b[A\n","4it [00:02,  2.53it/s]\u001b[A\n","5it [00:03,  1.30it/s]\u001b[A\n","6it [00:03,  1.69it/s]\u001b[A\n","8it [00:04,  2.90it/s]\u001b[A\n","9it [00:05,  1.52it/s]\u001b[A\n","10it [00:05,  1.91it/s]\u001b[A\n","12it [00:06,  2.98it/s]\u001b[A\n","13it [00:07,  1.71it/s]\u001b[A\n","15it [00:07,  2.62it/s]\u001b[A\n","18it [00:08,  2.19it/s]\u001b[A\n"," 60%|██████    | 6/10 [52:04<34:42, 520.72s/it]"]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 6], [val loss 0.44647], [val acc 0.85069]\n","------------------------------------------------------------\n","[epoch 7], [iter 100 / 1124], [train loss 0.33824], [train acc 0.87187]\n","[epoch 7], [iter 200 / 1124], [train loss 0.33447], [train acc 0.87000]\n","[epoch 7], [iter 300 / 1124], [train loss 0.34940], [train acc 0.86552]\n","[epoch 7], [iter 400 / 1124], [train loss 0.35003], [train acc 0.86555]\n","[epoch 7], [iter 500 / 1124], [train loss 0.35678], [train acc 0.86313]\n","[epoch 7], [iter 600 / 1124], [train loss 0.34993], [train acc 0.86594]\n","[epoch 7], [iter 700 / 1124], [train loss 0.34690], [train acc 0.86741]\n","[epoch 7], [iter 800 / 1124], [train loss 0.34685], [train acc 0.86730]\n","[epoch 7], [iter 900 / 1124], [train loss 0.34627], [train acc 0.86753]\n","[epoch 7], [iter 1000 / 1124], [train loss 0.34707], [train acc 0.86794]\n","[epoch 7], [iter 1100 / 1124], [train loss 0.34825], [train acc 0.86759]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:01,  1.83s/it]\u001b[A\n","3it [00:02,  1.86it/s]\u001b[A\n","5it [00:03,  1.55it/s]\u001b[A\n","7it [00:03,  2.42it/s]\u001b[A\n","9it [00:05,  1.84it/s]\u001b[A\n","10it [00:05,  2.16it/s]\u001b[A\n","11it [00:05,  2.52it/s]\u001b[A\n","12it [00:05,  3.07it/s]\u001b[A\n","13it [00:07,  1.59it/s]\u001b[A\n","15it [00:07,  2.55it/s]\u001b[A\n","18it [00:07,  2.28it/s]\u001b[A\n"," 70%|███████   | 7/10 [1:00:45<26:02, 520.86s/it]"]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 7], [val loss 0.40779], [val acc 0.85764]\n","------------------------------------------------------------\n","*****************************************************\n","best record: [epoch 7], [val loss 0.40779], [val acc 0.85764]\n","*****************************************************\n","[epoch 8], [iter 100 / 1124], [train loss 0.30284], [train acc 0.88219]\n","[epoch 8], [iter 200 / 1124], [train loss 0.30542], [train acc 0.88266]\n","[epoch 8], [iter 300 / 1124], [train loss 0.30747], [train acc 0.88187]\n","[epoch 8], [iter 400 / 1124], [train loss 0.30964], [train acc 0.88062]\n","[epoch 8], [iter 500 / 1124], [train loss 0.30813], [train acc 0.88200]\n","[epoch 8], [iter 600 / 1124], [train loss 0.30144], [train acc 0.88474]\n","[epoch 8], [iter 700 / 1124], [train loss 0.30611], [train acc 0.88366]\n","[epoch 8], [iter 800 / 1124], [train loss 0.30823], [train acc 0.88199]\n","[epoch 8], [iter 900 / 1124], [train loss 0.30943], [train acc 0.88101]\n","[epoch 8], [iter 1000 / 1124], [train loss 0.31108], [train acc 0.88084]\n","[epoch 8], [iter 1100 / 1124], [train loss 0.31001], [train acc 0.88205]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:01,  1.69s/it]\u001b[A\n","3it [00:01,  1.98it/s]\u001b[A\n","5it [00:03,  1.69it/s]\u001b[A\n","7it [00:03,  2.63it/s]\u001b[A\n","9it [00:04,  1.96it/s]\u001b[A\n","11it [00:05,  2.73it/s]\u001b[A\n","13it [00:06,  2.12it/s]\u001b[A\n","15it [00:06,  2.92it/s]\u001b[A\n","18it [00:07,  2.50it/s]\u001b[A\n"," 80%|████████  | 8/10 [1:09:23<17:19, 520.00s/it]"]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 8], [val loss 0.42649], [val acc 0.88368]\n","------------------------------------------------------------\n","*****************************************************\n","best record: [epoch 8], [val loss 0.42649], [val acc 0.88368]\n","*****************************************************\n","[epoch 9], [iter 100 / 1124], [train loss 0.26681], [train acc 0.89750]\n","[epoch 9], [iter 200 / 1124], [train loss 0.28456], [train acc 0.89234]\n","[epoch 9], [iter 300 / 1124], [train loss 0.28605], [train acc 0.89240]\n","[epoch 9], [iter 400 / 1124], [train loss 0.28377], [train acc 0.89281]\n","[epoch 9], [iter 500 / 1124], [train loss 0.28172], [train acc 0.89312]\n","[epoch 9], [iter 600 / 1124], [train loss 0.28351], [train acc 0.89141]\n","[epoch 9], [iter 700 / 1124], [train loss 0.28676], [train acc 0.89080]\n","[epoch 9], [iter 800 / 1124], [train loss 0.28608], [train acc 0.89156]\n","[epoch 9], [iter 900 / 1124], [train loss 0.28343], [train acc 0.89187]\n","[epoch 9], [iter 1000 / 1124], [train loss 0.28373], [train acc 0.89156]\n","[epoch 9], [iter 1100 / 1124], [train loss 0.28293], [train acc 0.89165]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:01,  1.57s/it]\u001b[A\n","2it [00:01,  1.37it/s]\u001b[A\n","4it [00:01,  3.08it/s]\u001b[A\n","5it [00:03,  1.55it/s]\u001b[A\n","7it [00:03,  2.62it/s]\u001b[A\n","9it [00:04,  1.97it/s]\u001b[A\n","11it [00:05,  2.79it/s]\u001b[A\n","13it [00:06,  2.17it/s]\u001b[A\n","15it [00:06,  2.82it/s]\u001b[A\n","18it [00:07,  2.44it/s]\u001b[A\n"," 90%|█████████ | 9/10 [1:18:03<08:39, 519.80s/it]"]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 9], [val loss 0.47329], [val acc 0.85069]\n","------------------------------------------------------------\n","[epoch 10], [iter 100 / 1124], [train loss 0.23296], [train acc 0.90781]\n","[epoch 10], [iter 200 / 1124], [train loss 0.23931], [train acc 0.90781]\n","[epoch 10], [iter 300 / 1124], [train loss 0.24999], [train acc 0.90396]\n","[epoch 10], [iter 400 / 1124], [train loss 0.25598], [train acc 0.90125]\n","[epoch 10], [iter 500 / 1124], [train loss 0.25670], [train acc 0.90156]\n","[epoch 10], [iter 600 / 1124], [train loss 0.25729], [train acc 0.90208]\n","[epoch 10], [iter 700 / 1124], [train loss 0.26279], [train acc 0.90031]\n","[epoch 10], [iter 800 / 1124], [train loss 0.26221], [train acc 0.90059]\n","[epoch 10], [iter 900 / 1124], [train loss 0.25793], [train acc 0.90215]\n","[epoch 10], [iter 1000 / 1124], [train loss 0.25548], [train acc 0.90269]\n","[epoch 10], [iter 1100 / 1124], [train loss 0.25473], [train acc 0.90261]\n"]},{"name":"stderr","output_type":"stream","text":["\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:01,  1.64s/it]\u001b[A\n","3it [00:01,  2.03it/s]\u001b[A\n","5it [00:03,  1.68it/s]\u001b[A\n","6it [00:03,  2.12it/s]\u001b[A\n","7it [00:03,  2.65it/s]\u001b[A\n","8it [00:03,  3.28it/s]\u001b[A\n","9it [00:05,  1.37it/s]\u001b[A\n","10it [00:05,  1.83it/s]\u001b[A\n","12it [00:05,  2.99it/s]\u001b[A\n","13it [00:07,  1.57it/s]\u001b[A\n","14it [00:07,  1.96it/s]\u001b[A\n","16it [00:07,  3.10it/s]\u001b[A\n","18it [00:08,  2.24it/s]\u001b[A\n","100%|██████████| 10/10 [1:26:41<00:00, 520.17s/it]"]},{"name":"stdout","output_type":"stream","text":["------------------------------------------------------------\n","[epoch 10], [val loss 0.46001], [val acc 0.88021]\n","------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["epoch_num = 10\n","best_val_acc = 0\n","total_loss_val, total_acc_val = [],[]\n","for epoch in tqdm(range(1, epoch_num+1)):\n","    loss_train, acc_train = train(train_loader, model, criterion, optimizer, epoch)\n","    loss_val, acc_val = validate(val_loader, model, criterion, optimizer, epoch)\n","    total_loss_val.append(loss_val)\n","    total_acc_val.append(acc_val)\n","    if acc_val > best_val_acc:\n","        best_val_acc = acc_val\n","        print('*****************************************************')\n","        print('best record: [epoch %d], [val loss %.5f], [val acc %.5f]' % (epoch, loss_val, acc_val))\n","        print('*****************************************************')"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T04:53:22.538395Z","iopub.status.busy":"2021-06-04T04:53:22.538054Z","iopub.status.idle":"2021-06-04T04:53:22.692883Z","shell.execute_reply":"2021-06-04T04:53:22.691907Z","shell.execute_reply.started":"2021-06-04T04:53:22.538360Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fig = plt.figure(num = 1)\n","fig2 = fig.add_subplot(1,1,1)\n","fig2.plot(total_acc_val, label = 'validation accuracy')\n","fig2.plot(total_loss_val, label = 'validation loss')\n","\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T04:53:27.036106Z","iopub.status.busy":"2021-06-04T04:53:27.035782Z","iopub.status.idle":"2021-06-04T04:53:27.174596Z","shell.execute_reply":"2021-06-04T04:53:27.173577Z","shell.execute_reply.started":"2021-06-04T04:53:27.036075Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fig = plt.figure(num=1)\n","fig1 = fig.add_subplot(1,1,1)\n","fig1.plot(total_acc_train, label = 'training accuracy')\n","fig1.plot(total_loss_train, label = 'training loss')\n","\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","execution_count":32,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T04:53:32.216765Z","iopub.status.busy":"2021-06-04T04:53:32.216333Z","iopub.status.idle":"2021-06-04T04:53:32.231308Z","shell.execute_reply":"2021-06-04T04:53:32.229160Z","shell.execute_reply.started":"2021-06-04T04:53:32.216724Z"},"trusted":true},"outputs":[],"source":["def plot_confusion_matrix(cm, classes,\n","                          normalize=False,\n","                          title='Confusion matrix',\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","    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n","    plt.title(title)\n","    plt.colorbar()\n","    tick_marks = np.arange(len(classes))\n","    plt.xticks(tick_marks, classes, rotation=45)\n","    plt.yticks(tick_marks, classes)\n","\n","    if normalize:\n","        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n","\n","    thresh = cm.max() / 2.\n","    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n","        plt.text(j, i, cm[i, j],\n","                 horizontalalignment=\"center\",\n","                 color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n","    plt.tight_layout()\n","    plt.ylabel('True label')\n","    plt.xlabel('Predicted label')"]},{"cell_type":"code","execution_count":33,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T04:54:11.835095Z","iopub.status.busy":"2021-06-04T04:54:11.834764Z","iopub.status.idle":"2021-06-04T04:54:19.913520Z","shell.execute_reply":"2021-06-04T04:54:19.912474Z","shell.execute_reply.started":"2021-06-04T04:54:11.835067Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# Validation data evaluation\n","\n","model.eval()\n","y_label = []\n","y_predict = []\n","with torch.no_grad():\n","    for i, data in enumerate(val_loader):\n","        images, labels = data\n","        N = images.size(0)\n","        images = Variable(images).to(device)\n","        outputs = model(images)\n","        prediction = outputs.max(1, keepdim=True)[1]\n","        y_label.extend(labels.cpu().numpy())\n","        y_predict.extend(np.squeeze(prediction.cpu().numpy().T))\n","\n","# compute the confusion matrix\n","confusion_mtx = confusion_matrix(y_label, y_predict)\n","# plot the confusion matrix\n","plot_labels = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc','mel']\n","plot_confusion_matrix(confusion_mtx, plot_labels)"]},{"cell_type":"code","execution_count":38,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T04:56:59.349243Z","iopub.status.busy":"2021-06-04T04:56:59.348933Z","iopub.status.idle":"2021-06-04T04:57:07.968791Z","shell.execute_reply":"2021-06-04T04:57:07.967926Z","shell.execute_reply.started":"2021-06-04T04:56:59.349213Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# Test data evaluation\n","\n","model.eval()\n","test_y_label = []\n","test_y_predict = []\n","with torch.no_grad():\n","    for i, data in enumerate(test_loader):\n","        images, labels = data\n","        N = images.size(0)\n","        images = Variable(images).to(device)\n","        outputs = model(images)\n","        prediction = outputs.max(1, keepdim=True)[1]\n","        test_y_label.extend(labels.cpu().numpy())\n","        test_y_predict.extend(np.squeeze(prediction.cpu().numpy().T))\n","\n","# compute the confusion matrix\n","confusion_mtx_test = confusion_matrix(test_y_label, test_y_predict)\n","# plot the confusion matrix\n","plot_labels = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc','mel']\n","plot_confusion_matrix(confusion_mtx, plot_labels)"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T04:55:52.375673Z","iopub.status.busy":"2021-06-04T04:55:52.375342Z","iopub.status.idle":"2021-06-04T04:55:52.388309Z","shell.execute_reply":"2021-06-04T04:55:52.387120Z","shell.execute_reply.started":"2021-06-04T04:55:52.375642Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","       akiec       0.80      0.22      0.35        18\n","         bcc       0.62      0.90      0.73        20\n","         bkl       0.50      0.61      0.55        41\n","          df       0.50      0.50      0.50         2\n","          nv       0.96      0.94      0.95       439\n","        vasc       1.00      0.40      0.57         5\n","         mel       0.38      0.46      0.41        26\n","\n","    accuracy                           0.86       551\n","   macro avg       0.68      0.58      0.58       551\n","weighted avg       0.88      0.86      0.86       551\n","\n"]}],"source":["# Generate a validation classification report\n","report = classification_report(y_label, y_predict, target_names=plot_labels)\n","print(report)"]},{"cell_type":"code","execution_count":37,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T04:56:23.697461Z","iopub.status.busy":"2021-06-04T04:56:23.697110Z","iopub.status.idle":"2021-06-04T04:56:23.709389Z","shell.execute_reply":"2021-06-04T04:56:23.708426Z","shell.execute_reply.started":"2021-06-04T04:56:23.697431Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","       akiec       0.78      0.58      0.67        12\n","         bcc       0.52      0.80      0.63        15\n","         bkl       0.69      0.66      0.67        47\n","          df       0.80      0.67      0.73         6\n","          nv       0.97      0.95      0.96       444\n","        vasc       1.00      0.62      0.77         8\n","         mel       0.41      0.60      0.49        20\n","\n","    accuracy                           0.89       552\n","   macro avg       0.74      0.70      0.70       552\n","weighted avg       0.91      0.89      0.90       552\n","\n"]}],"source":["# Generate a test classification report\n","report = classification_report(test_y_label, test_y_predict, target_names=plot_labels)\n","print(report)"]},{"cell_type":"code","execution_count":39,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T04:57:23.153279Z","iopub.status.busy":"2021-06-04T04:57:23.152821Z","iopub.status.idle":"2021-06-04T04:57:23.381504Z","shell.execute_reply":"2021-06-04T04:57:23.380401Z","shell.execute_reply.started":"2021-06-04T04:57:23.153231Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Text(0, 0.5, 'Fraction classified incorrectly')"]},"execution_count":39,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["label_frac_error = 1 - np.diag(confusion_mtx) / np.sum(confusion_mtx, axis=1)\n","plt.bar(np.arange(7),label_frac_error)\n","plt.xlabel('True Label')\n","plt.ylabel('Fraction classified incorrectly')"]},{"cell_type":"code","execution_count":40,"metadata":{"execution":{"iopub.execute_input":"2021-06-04T04:57:44.247587Z","iopub.status.busy":"2021-06-04T04:57:44.247234Z","iopub.status.idle":"2021-06-04T04:57:44.382344Z","shell.execute_reply":"2021-06-04T04:57:44.381410Z","shell.execute_reply.started":"2021-06-04T04:57:44.247557Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Text(0, 0.5, 'Fraction classified incorrectly')"]},"execution_count":40,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAX70lEQVR4nO3dfZRddX3v8feHBMqDCQ83sdI8mIhBb2oRYQy3hWWpAg3FFXoRFSRdYq3p9RpB6bUNt4qWrq4iveVe1LQaEQUsIg/iSkuulOJDixc0ExofEhqbxtBMhCYiGIgKBD73j7MHT4aZc/ZMsveZmf15rXXWnP38GRZrvtn799u/n2wTERHNdUCvA0RERG+lEERENFwKQUREw6UQREQ0XApBRETDTe11gNGaMWOG582b1+sYERETyrp1635oe+Zw2yZcIZg3bx79/f29jhERMaFIenCkbZU+GpK0WNImSZslrRhhnzdJ2ihpg6Qbq8wTERHPV9kdgaQpwErgdGAAWCtpte2NbfssAC4FTrb9qKQXVpUnIiKGV+UdwSJgs+0ttp8CbgLOHrLPO4CVth8FsL2jwjwRETGMKgvBLGBb2/JAsa7dscCxkr4u6T5Ji4c7kaRlkvol9e/cubOiuBERzdTr7qNTgQXAqcD5wCclHTF0J9urbPfZ7ps5c9hG74iIGKMqC8F2YE7b8uxiXbsBYLXtp21/H/gercIQERE1qbIQrAUWSJov6SDgPGD1kH2+SOtuAEkzaD0q2lJhpoiIGKKyQmB7D7AcuBN4ALjZ9gZJl0taUux2J/CIpI3AV4D32X6kqkwREfF8mmjzEfT19TkvlEVEjI6kdbb7hts24d4s3hfzVtzR6wh72XrFWb2OEBHR815DERHRYykEERENl0IQEdFwKQQREQ2XQhAR0XApBBERDZdCEBHRcCkEERENl0IQEdFwKQQREQ2XQhAR0XApBBERDZdCEBHRcCkEERENl0IQEdFwKQQREQ2XQhAR0XApBBERDZdCEBHRcCkEERENl0IQEdFwKQQREQ2XQhAR0XApBBERDZdCEBHRcJUWAkmLJW2StFnSimG2Xyhpp6T1xef3qswTERHP17UQSHq3pCNHe2JJU4CVwJnAQuB8SQuH2fXzto8vPteM9joREbFvytwR/CKwVtLNxb/wVfLci4DNtrfYfgq4CTh7rEEjIqIaU7vtYPv9kj4AnAG8DfiYpJuBT9n+tw6HzgK2tS0PACcNs98bJL0G+B7wXtvbhu4gaRmwDGDu3LndIkfEODRvxR29jrCXrVec1esI40apNgLbBh4uPnuAI4FbJV25j9f/W2Ce7eOAu4DrRrj+Ktt9tvtmzpy5j5eMiIh2ZdoILpa0DrgS+DrwK7bfCZwIvKHDoduBOW3Ls4t1z7H9iO0ni8VrinNGRESNuj4aAo4CzrH9YPtK289Ken2H49YCCyTNp1UAzgPe0r6DpKNtP1QsLgEeKJ08IiL2ixELgaSjiq9XD1kGwPaPbI/4h9v2HknLgTuBKcC1tjdIuhzot70auEjSElqPm34EXLgvv0xERIxepzuCdYCB4XoJGXhJt5PbXgOsGbLusrbvlwKXlkoaERGVGLEQ2J5fZ5CIiOiNMo3Fd5dZFxERE1OnNoKDgcOAGcWbxYOPiKbTekcgIiImgU5tBL8PvAf4JVrtBYOFYBfwsWpjRUREXTq1EVwNXC3p3bY/WmOmiIioUZk3i5+VdMTggqQjJf336iJFRESdyhSCd9h+bHDB9qPAOypLFBERtSpTCKa0jzhaDC99UHWRIiKiTmWGmPgS8HlJnyiWf79YFxERk0CZQvBHtP74v7NYvovWAHERETEJlJmP4FlJnwG+bHtT9ZEiIqJOZd4sXgKsp3gcJOl4SasrzhURETUp01j8QVrTTj4GYHs9kHGIIiImiTKF4GnbPx6yzlWEiYiI+pVpLN4g6S20upEuAC4C/l+1sSIioi5l7gjeDfwy8CRwI/BjWmMQRUTEJNDxjqB4eewO278B/HE9kSIiok4d7whsP0NrrKHDa8oTERE1K9NG8ATwHUl3AbsHV9q+qLJUERFRmzKF4AvFJyIiJqEybQQXFm0EERExCaWNICKi4dJGEBHRcGkjiIhouDKjj14n6SDg2GLVJttPVxsrIiLq0rUQSDoVuA7YCgiYI+mttv+x0mQREVGLMo+G/hI4Y3AuAknHAp8DTqwyWERE1KPMWEMHtk9IY/t7wIFlTi5psaRNkjZLWtFhvzdIsqS+MueNiIj9p8wdQb+ka4DPFssXAP3dDireQVgJnA4MAGslrba9cch+04CLgW+MJnhEROwfZe4I3glspDX89EXF93d2PKJlEbDZ9hbbTwE3AWcPs9+fAh8GflYqcURE7FdlCsFU4Grb59g+B/gIMKXEcbOAbW3LA8W650g6AZhj+45OJ5K0TFK/pP6dO3eWuHRERJRVphDcDRzStnwI8A/7emFJBwBXAX/QbV/bq2z32e6bOXPmvl46IiLalCkEB9t+YnCh+H5oieO2A3PalmcX6wZNA14BfFXSVuC/AKvTYBwRUa8yhWB38QgHAEknAj8tcdxaYIGk+cULaecBqwc32v6x7Rm259meB9wHLLHdtSE6IiL2nzK9ht4D3CLpB7ReKHsR8OZuB9neI2k5cCetNoVrbW+QdDnQb3t15zNEREQdygwxsVbSy4GXFatKDzFhew2wZsi6y0bY99Qy54yIiP2rzB0BwKuBecX+J0jC9vWVpYqIiNqUGWvoBuAYYD3wTLHaQApBRMQkUOaOoA9YaNtVh4mIiPqV6TX0XVoNxBERMQmVuSOYAWyU9E3gycGVtpdUlioiImpTphB8qOoQERHRO2W6j36tjiAREdEbIxYCSffYPkXS47R6CT23CbDt6ZWni4iIyo1YCGyfUvycVl+ciIioW5leQxERMYmlEERENFwKQUREw5UdaygionHmreg4eWLttl5xViXn7dRraGhvob2k11BExOTQqdfQNABJfwo8BNxAq+voBcDRtaSLiIjKlWkjWGL7r2w/bnuX7b8Gzq46WERE1KPsVJUXSJoi6QBJFwC7qw4WERH1KNNY/Bbg6uJj4OvFuohJYzw1ClbVIBgxkjJjDW0lj4IiIiatro+GJB0r6W5J3y2Wj5P0/uqjRUREHcq0EXwSuBR4GsD2t4HzqgwVERH1KVMIDrX9zSHr9lQRJiIi6lemEPxQ0jEUL5dJOpfWewURETEJlOk19C5gFfBySduB7wNLK00VE9p46oED6YUT0U2ZXkNbgNMkHQYcYPvx6mNFRERdOo01tNT2ZyVdMmQ9ALavqjhbRETUoFMbwaHFz2kjfLqStFjSJkmbJa0YZvt/k/QdSesl3SNp4SjzR0TEPur0aOiY4udG27eM9sSSpgArgdOBAWCtpNW2N7btdqPtjxf7LwGuAhaP9loRETF2ne4Ifkut50CXjvHci4DNtrfYfgq4iSFvKNve1bZ4GB2GvY6IiGp0uiP4EvAo8AJJ7X+wBbjEfASzgG1tywPASUN3kvQu4BLgIOC1w51I0jJgGcDcuXO7XDYiIkZjxDsC2++zfQRwh+3pbZ9p+3NSGtsrbR8D/BEw7NAVtlfZ7rPdN3PmzP116YiIoMQLZbbHOuDcdmBO2/LsYt1IbgJ+e4zXioiIMRqxEEi6p/j5uKRdxc/Bz66RjmuzFlggab6kg2iNT7R6yDUWtC2eBfzr6H+FiIjYF52mqjyl+Fmqq+gwx++RtBy4E5gCXGt7g6TLgX7bq4Hlkk6jNaDdo8Bbx3KtiIgYu65vFhfjDA3YflLSqcBxwPW2H+t2rO01wJoh6y5r+37xKPNGRMR+VmbQuduAZyS9lNaYQ3OAGytNFRERtSlTCJ61vQf4r8BHbb8POLraWBERUZcyheBpSefTen7/d8W6A6uLFBERdSpTCN4G/CrwZ7a/L2k+cEO1sSIioi5lhqHeCFwEIOlIYJrtD1cdLCIi6lFm8vqvSpou6SjgfuCTkjIEdUTEJFHm0dDhxeBw59DqNnoScFq1sSIioi5lCsFUSUcDb+LnjcURETFJlCkEl9N6O3iz7bWSXkKGgoiImDTKNBbfAtzStrwFeEOVoSIioj5lhpg4GHg78MvAwYPrbf9uhbkiIqImZR4N3QC8CPhN4Gu0hpN+vMpQERFRnzKF4KW2PwDstn0dreGinzfTWERETEylhpgofj4m6RXA4cALq4sUERF16tpGAKwq3ij+AK2JZV4AXNb5kIiImCjK9Bq6pvj6NeAl1caJiIi6jVgIJF3S6UDbGWYiImIS6HRHMKYpKiMiYmLpNGfxn9QZJCIieqPM6KPXSTqibflISddWmioiImpTpvvoce0T1dt+FHhVZYkiIqJWZQrBAUX3UQCKeQnKdDuNiIgJoMwf9L8E7pU0OPDcG4E/qy5SRETUqcx7BNdL6gdeW6w6p5i+MiIiJoFSj3iKP/z54x8RMQmVaSOIiIhJLIUgIqLhKi0EkhZL2iRps6QVw2y/RNJGSd+WdLekF1eZJyIinq/MC2XnSPpXST+WtEvS45J2lThuCrASOBNYCJwvaeGQ3f4Z6LN9HHArcOXof4WIiNgXZe4IrgSW2D7c9nTb02xPL3HcIloT3m+x/RRwE3B2+w62v2L7J8XifbRmP4uIiBqVKQT/YfuBMZx7FrCtbXmgWDeStwP/d7gNkpZJ6pfUv3PnzjFEiYiIkZTpPtov6fPAF4EnB1fa/sL+CiFpKdAH/Ppw222vAlYB9PX1eX9dNyIiyhWC6cBPgDPa1hnoVgi2A3PalmcX6/Yi6TTgj4Fft/3k0O0REVGtMm8Wv22M514LLJA0n1YBOA94S/sOkl4FfAJYbHvHGK8TERH7oEyvodmSbpe0o/jcJqlro67tPcBy4E7gAeBm2xskXS5pSbHbX9CaA/kWSeslrd6H3yUiIsagzKOhTwM30hpsDmBpse70bgfaXgOsGbLusrbvp5VOGhERlSjTa2im7U/b3lN8PgPMrDhXRETUpEwheETSUklTis9S4JGqg0VERD3KFILfBd4EPAw8BJwLjLUBOSIixpkyvYYeBJZ02y8iIiamEQuBpD+0faWkj9J6b2Avti+qNFlERNSi0x3B4LAS/XUEiYiI3hixENj+2+LrT2zf0r5N0huHOSQiIiagMo3Fl5ZcFxERE1CnNoIzgd8CZkn6SNum6cCeqoNFREQ9OrUR/IBW+8ASYF3b+seB91YZKiIi6tOpjeBbwLck3Q7stv0MPDfz2C/UlC8iIipWpo3g74FD2pYPAf6hmjgREVG3MoXgYNtPDC4U3w+tLlJERNSpTCHYLemEwQVJJwI/rS5SRETUqcww1O+hNV/ADwABLwLeXGWoiIioT5mxhtZKejnwsmLVJttPVxsrIiLqUuaOAFpFYCFwMHCCJGxfX12siIioS9dCIOmDwKm0CsEa4EzgHiCFoAbzVtzR6wh72XrFWb2OEBH7WZnG4nOB1wEPFxPZvxI4vNJUERFRmzKF4Ke2nwX2SJoO7ADmVBsrIiLqUqaNoF/SEcAnaQ018QRwb5WhIiKiPh0LgSQBf277MeDjkr4ETLf97TrCRURE9ToWAtuWtAb4lWJ5ax2hIiKiPmXaCO6X9OrKk0RERE+UaSM4CVgqaSuwm9bbxbZ9XJXBIiKiHp0mpplr+9+B36wxT0RE1KzTo6EvAth+ELjK9oPtnzInl7RY0iZJmyWtGGb7ayTdL2mPpHPH9BtERMQ+6VQI1Pb9JaM9cTGBzUpabyIvBM6XtHDIbv8OXAjcONrzR0TE/tGpjcAjfC9rEbDZ9hYASTcBZwMbnztp0QtJ0rNjOH9EROwHnQrBKyXtonVncEjxHX7eWDy9y7lnAdvalgdoNTyPmqRlwDKAuXPnjuUUERExgk5zFk+pM0gntlcBqwD6+vrGcncSEREjKPMewVhtZ+8xiWYX6yIiYhypshCsBRZImi/pIOA8YHWF14uIiDGorBDY3gMsB+4EHgButr1B0uWSlgBIerWkAeCNwCckbagqT0REDK/sDGVjYnsNrcls2tdd1vZ9La1HRhER0SNVPhqKiIgJIIUgIqLhUggiIhouhSAiouFSCCIiGi6FICKi4VIIIiIaLoUgIqLhUggiIhouhSAiouFSCCIiGi6FICKi4VIIIiIaLoUgIqLhUggiIhouhSAiouFSCCIiGi6FICKi4SqdqjIiqjFvxR29jrCXrVec1esIsQ9yRxAR0XApBBERDZdCEBHRcCkEERENl0IQEdFwKQQREQ2XQhAR0XApBBERDVdpIZC0WNImSZslrRhm+y9I+nyx/RuS5lWZJyIinq+yQiBpCrASOBNYCJwvaeGQ3d4OPGr7pcD/Bj5cVZ6IiBhelXcEi4DNtrfYfgq4CTh7yD5nA9cV328FXidJFWaKiIghZLuaE0vnAott/16x/DvASbaXt+3z3WKfgWL534p9fjjkXMuAZcXiy4BNlYQubwbww657jS/JXL2JlheSuS7jIfOLbc8cbsOEGHTO9ipgVa9zDJLUb7uv1zlGI5mrN9HyQjLXZbxnrvLR0HZgTtvy7GLdsPtImgocDjxSYaaIiBiiykKwFlggab6kg4DzgNVD9lkNvLX4fi7wZVf1rCoiIoZV2aMh23skLQfuBKYA19reIOlyoN/2auBTwA2SNgM/olUsJoJx85hqFJK5ehMtLyRzXcZ15soaiyMiYmLIm8UREQ2XQhAR0XApBKPUbdiM8UbStZJ2FO9sjHuS5kj6iqSNkjZIurjXmbqRdLCkb0r6VpH5T3qdqSxJUyT9s6S/63WWMiRtlfQdSesl9fc6TzeSjpB0q6R/kfSApF/tdabhpI1gFIphM74HnA4M0OoZdb7tjT0N1oGk1wBPANfbfkWv83Qj6WjgaNv3S5oGrAN+e5z/NxZwmO0nJB0I3ANcbPu+HkfrStIlQB8w3fbre52nG0lbgb6hL52OV5KuA/7J9jVF78lDbT/W41jPkzuC0SkzbMa4YvsfafXImhBsP2T7/uL748ADwKzepurMLU8UiwcWn3H/LyxJs4GzgGt6nWUyknQ48BpavSOx/dR4LAKQQjBas4BtbcsDjPM/UhNZMRrtq4Bv9DhKV8UjlvXADuAu2+M+M/B/gD8Enu1xjtEw8PeS1hVDz4xn84GdwKeLx2/XSDqs16GGk0IQ45KkFwC3Ae+xvavXebqx/Yzt42m9Qb9I0rh+DCfp9cAO2+t6nWWUTrF9Aq1Rjd9VPPocr6YCJwB/bftVwG5gXLYrphCMTplhM2IfFc/ZbwP+xvYXep1nNIpb/68Ai3scpZuTgSXFM/ebgNdK+mxvI3Vne3vxcwdwO63HtePVADDQdnd4K63CMO6kEIxOmWEzYh8UDa+fAh6wfVWv85QhaaakI4rvh9DqTPAvPQ3Vhe1Lbc+2PY/W/8dftr20x7E6knRY0YGA4hHLGcC47Q1n+2Fgm6SXFateB4zLTg8TYvTR8WKkYTN6HKsjSZ8DTgVmSBoAPmj7U71N1dHJwO8A3ymeuQP8T9trehepq6OB64peZQcAN9ueEN0xJ5hfBG4vpiyZCtxo+0u9jdTVu4G/Kf7huAV4W4/zDCvdRyMiGi6PhiIiGi6FICKi4VIIIiIaLoUgIqLhUggiIhouhSAaQdJ/KkasXC/pYUnb25YP2k/X+KqkUhOUSzp1tCN+jub8EaOR9wiiEWw/AhwPIOlDwBO2/9fgdklTbe/pTbqI3sodQTSWpM9I+rikbwBXSvqQpP/Rtv27xcB3SFpazDmwXtInipfHylxjnqR/knR/8fm1ts3TJd1RzG/xcUkHFMecIeneYv9binGXIiqTQhBNNxv4NduXjLSDpP8MvBk4uRhY7hnggpLn3wGcXgyU9mbgI23bFtF683QhcAxwjqQZwPuB04pj+oERs0XsD3k0FE13i+1nuuzzOuBEYG0xvMEhtP7Al3Eg8DFJx9MqIMe2bfum7S3w3FAgpwA/o1UYvl5c6yDg3pLXihiTFIJout1t3/ew913ywcVPAdfZvnQM538v8B/AK4tz/6xt29DxXVxc6y7b54/hWhFjkkdDET+3lWKYYEkn0JpYBOBu4FxJLyy2HSXpxSXPeTjwkO1naQ2m1962sKgYyfYAWo+N7gHuA06W9NLiWodJOnboSSP2pxSCiJ+7DThK0gZgOa35qSnmS34/rZmxvg3cRWvE0eHcIWmg+NwC/BXwVknfAl7O3ncga4GP0ZqO8/vA7bZ3AhcCnyuudW9xXERlMvpoRETD5Y4gIqLhUggiIhouhSAiouFSCCIiGi6FICKi4VIIIiIaLoUgIqLh/j+QtgfBfP14WQAAAABJRU5ErkJggg==","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["label_frac_error = 1 - np.diag(confusion_mtx_test) / np.sum(confusion_mtx_test, axis=1)\n","plt.bar(np.arange(7),label_frac_error)\n","plt.xlabel('True Label')\n","plt.ylabel('Fraction classified incorrectly')"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.10"}},"nbformat":4,"nbformat_minor":4}
diff --git a/skin_model/VGG16.ipynb b/skin_model/VGG16.ipynb
new file mode 100644
index 0000000..04c1870
--- /dev/null
+++ b/skin_model/VGG16.ipynb
@@ -0,0 +1,1057 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Skin Cancer Classification Transfer Learning VGG16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "ALvhhkxr7rEx"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import shutil\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import cv2\n",
+    "import sklearn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "817dtMFO_z4I"
+   },
+   "source": [
+    "### Creating Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "nW5BVkRW8rXT"
+   },
+   "outputs": [],
+   "source": [
+    "os.mkdir(\"HAM_Dataset\")\n",
+    "base = \"HAM_Dataset\"\n",
+    "\n",
+    "os.mkdir(os.path.join(base, \"MEL\"))\n",
+    "os.mkdir(os.path.join(base, \"NV\"))\n",
+    "os.mkdir(os.path.join(base, \"BCC\"))\n",
+    "os.mkdir(os.path.join(base, \"AKIEC\"))\n",
+    "os.mkdir(os.path.join(base, \"BKL\"))\n",
+    "os.mkdir(os.path.join(base, \"DF\"))\n",
+    "os.mkdir(os.path.join(base, \"VASC\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "vP-rLwF6_6gi"
+   },
+   "outputs": [],
+   "source": [
+    "for image in os.listdir('HAM_images'):\n",
+    "  if \"jpg\" not in image:\n",
+    "    os.remove('HAM_images/'+image)\n",
+    "\n",
+    "for image in os.listdir('HAM_images'):\n",
+    "  if \"jpg\" not in image:\n",
+    "    print(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 286
+    },
+    "id": "D4OAvtliR_AJ",
+    "outputId": "f4bd1cb1-e994-46e3-ee43-f46c0e30b9e3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(450, 600, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "img = os.listdir('HAM_images')[0]\n",
+    "skin=cv2.imread('HAM_images/'+img)\n",
+    "plt.imshow(cv2.cvtColor(skin, cv2.COLOR_BGR2RGB))\n",
+    "plt.show()\n",
+    "\n",
+    "print(skin.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TpClbZy0ARZ7",
+    "outputId": "0ecd3b6a-7fc4-4a0e-ce6a-916a2be59057"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['AKIEC', 'NV', 'BKL', 'DF', 'VASC', 'MEL', 'BCC']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(os.listdir(base))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 235
+    },
+    "id": "A6MtATLAA7Dp",
+    "outputId": "e851da45-0c9e-44e2-cc9d-656e83951afe"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MEL</th>\n",
+       "      <th>NV</th>\n",
+       "      <th>BCC</th>\n",
+       "      <th>AKIEC</th>\n",
+       "      <th>BKL</th>\n",
+       "      <th>DF</th>\n",
+       "      <th>VASC</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>image</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ISIC_0024306</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ISIC_0024307</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ISIC_0024308</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ISIC_0024309</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ISIC_0024310</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>MEL</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              MEL   NV  BCC  AKIEC  BKL   DF  VASC label\n",
+       "image                                                   \n",
+       "ISIC_0024306  0.0  1.0  0.0    0.0  0.0  0.0   0.0    NV\n",
+       "ISIC_0024307  0.0  1.0  0.0    0.0  0.0  0.0   0.0    NV\n",
+       "ISIC_0024308  0.0  1.0  0.0    0.0  0.0  0.0   0.0    NV\n",
+       "ISIC_0024309  0.0  1.0  0.0    0.0  0.0  0.0   0.0    NV\n",
+       "ISIC_0024310  1.0  0.0  0.0    0.0  0.0  0.0   0.0   MEL"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mapping = {0:\"MEL\", 1:\"NV\", 2:\"BCC\", 3:\"AKIEC\", 4:\"BKL\", 5:\"DF\", 6:\"VASC\"}\n",
+    "\n",
+    "df_labels = pd.read_csv(\"ISIC2018GroundTruth.csv\")\n",
+    "for i in range(len(df_labels)):\n",
+    "    labels = df_labels.iloc[i,1:]\n",
+    "    df_labels.loc[i,\"label\"] = mapping[list(labels).index(1)]\n",
+    "\n",
+    "#df_labels[\"label\"]=df_labels[\"label\"].astype(int)\n",
+    "df_labels.set_index('image', inplace=True)\n",
+    "\n",
+    "df_labels.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 336
+    },
+    "id": "AfM_Vqb8FrnZ",
+    "outputId": "cfa67eff-c7f4-44da-bfc1-4e72258863e9"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "  FutureWarning\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(df_labels[\"label\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "U2dtXnJvA83m"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.utils import class_weight\n",
+    "\n",
+    "class_weights = class_weight.compute_class_weight('balanced',\n",
+    "                                                  ['AKIEC', 'BCC', 'BKL', 'DF', 'MEL', 'NV', 'VASC'],\n",
+    "                                                  y=df_labels[\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Iu-zhW27LG5c",
+    "outputId": "1cbb6617-bc28-4608-ccd5-6d53a0f9d698"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 4.375273044997815,\n",
+       " 1: 2.78349082823791,\n",
+       " 2: 1.301832835044846,\n",
+       " 3: 12.440993788819876,\n",
+       " 4: 1.2854575792581184,\n",
+       " 5: 0.21338020666879728,\n",
+       " 6: 10.075452716297788}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_wt_dict=dict(enumerate(class_weights))\n",
+    "class_wt_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LvQ-UKPPBECF",
+    "outputId": "9f8e7e19-a3c3-4454-ddad-d66a95a82dd5"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10015/10015 [01:07<00:00, 148.08it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "images = os.listdir('HAM_images')\n",
+    "\n",
+    "for image in tqdm(images):\n",
+    "    \n",
+    "    fname=image[:-4]\n",
+    "    label=df_labels.loc[fname, \"label\"]\n",
+    "    src = os.path.join('HAM_images', image)\n",
+    "    dst = os.path.join('HAM_Dataset', label, image)\n",
+    "    shutil.copyfile(src, dst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cjJ7GntVBOMC"
+   },
+   "source": [
+    "### Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "Y7gIMayCBFVX"
+   },
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.applications.vgg19 import preprocess_input as vgg_preprocess\n",
+    "\n",
+    "\n",
+    "image_gen = ImageDataGenerator(preprocessing_function=vgg_preprocess, validation_split=0.15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "nwZ_VCb_BQcH",
+    "outputId": "91eea299-f091-418e-ac46-66144c6be41b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 8516 images belonging to 7 classes.\n",
+      "Found 1499 images belonging to 7 classes.\n",
+      "{'AKIEC': 0, 'BCC': 1, 'BKL': 2, 'DF': 3, 'MEL': 4, 'NV': 5, 'VASC': 6}\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dir = 'HAM_Dataset'\n",
+    "batch_size = 128\n",
+    "target_size = (224,224)\n",
+    "train_image_gen = image_gen.flow_from_directory(data_dir, \n",
+    "                                          target_size=target_size,\n",
+    "                                          color_mode='rgb',\n",
+    "                                          batch_size=batch_size,\n",
+    "                                          class_mode='categorical',\n",
+    "                                          subset=\"training\")\n",
+    "\n",
+    "test_image_gen = image_gen.flow_from_directory(data_dir, \n",
+    "                                          target_size=target_size, \n",
+    "                                          color_mode='rgb',\n",
+    "                                          batch_size=batch_size,\n",
+    "                                          class_mode='categorical',\n",
+    "                                          shuffle=False,\n",
+    "                                          subset=\"validation\")\n",
+    "\n",
+    "print(test_image_gen.class_indices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UHVT1i2jBWI7"
+   },
+   "source": [
+    "##     "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "arTOLtguTPBz"
+   },
+   "source": [
+    "## Transfer Learning using VGG16 Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "gol_645xVaA-",
+    "outputId": "f95f4ed7-0c6d-47c9-8a19-292760338c6b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+      "58892288/58889256 [==============================] - 1s 0us/step\n",
+      "58900480/58889256 [==============================] - 1s 0us/step\n",
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
+      "_________________________________________________________________\n",
+      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
+      "_________________________________________________________________\n",
+      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
+      "_________________________________________________________________\n",
+      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
+      "_________________________________________________________________\n",
+      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
+      "_________________________________________________________________\n",
+      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
+      "_________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
+      "_________________________________________________________________\n",
+      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
+      "_________________________________________________________________\n",
+      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
+      "_________________________________________________________________\n",
+      "flatten (Flatten)            (None, 25088)             0         \n",
+      "_________________________________________________________________\n",
+      "dense (Dense)                (None, 512)               12845568  \n",
+      "_________________________________________________________________\n",
+      "dropout (Dropout)            (None, 512)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 7)                 3591      \n",
+      "=================================================================\n",
+      "Total params: 27,563,847\n",
+      "Trainable params: 12,849,159\n",
+      "Non-trainable params: 14,714,688\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.applications.vgg16 import VGG16\n",
+    "from tensorflow.keras.layers import Flatten, Dense, Input, Dropout\n",
+    "from tensorflow.keras.models import Model\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "\n",
+    "vgg16_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
+    "\n",
+    "for layer in vgg16_model.layers[:-1]:\n",
+    "    layer.trainable = False\n",
+    "\n",
+    "x = vgg16_model.output\n",
+    "x = Flatten()(x)\n",
+    "x = Dense(512, activation='relu')(x)\n",
+    "x = Dropout(0.5)(x)\n",
+    "x = Dense(7, activation='softmax')(x)\n",
+    "tl_model = Model(inputs=vgg16_model.input, outputs=x)\n",
+    "\n",
+    "\n",
+    "tl_model.summary()\n",
+    "tl_model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uFlL2JP3BmEL"
+   },
+   "source": [
+    "## Model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "gPKEUxH7PeTK"
+   },
+   "outputs": [],
+   "source": [
+    "from keras.callbacks import ReduceLROnPlateau\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
+    "\n",
+    "lr_reduce = ReduceLROnPlateau(monitor='val_accuracy', factor=0.6, patience=2,mode='max', min_lr=0.00001,verbose=1)\n",
+    "early_stop = EarlyStopping(monitor=\"val_loss\", patience=2, verbose=1)\n",
+    "model_chkpt = ModelCheckpoint('best_model.hdf5',save_best_only=True, monitor='val_accuracy',verbose=1)\n",
+    "\n",
+    "callback_list = [model_chkpt,lr_reduce]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "H2GxTdc2BjnL",
+    "outputId": "1311d3bf-d0e7-4a6d-8073-3204abd48335"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "67/67 [==============================] - 125s 2s/step - loss: 0.3214 - accuracy: 0.7919 - val_loss: 0.7819 - val_accuracy: 0.7658\n",
+      "\n",
+      "Epoch 00001: val_accuracy improved from 0.75117 to 0.76584, saving model to best_model.hdf5\n",
+      "Epoch 2/20\n",
+      "67/67 [==============================] - 122s 2s/step - loss: 0.3196 - accuracy: 0.8073 - val_loss: 0.8108 - val_accuracy: 0.7592\n",
+      "\n",
+      "Epoch 00002: val_accuracy did not improve from 0.76584\n",
+      "Epoch 3/20\n",
+      "67/67 [==============================] - 118s 2s/step - loss: 0.3069 - accuracy: 0.7932 - val_loss: 0.8110 - val_accuracy: 0.7692\n",
+      "\n",
+      "Epoch 00003: val_accuracy improved from 0.76584 to 0.76918, saving model to best_model.hdf5\n",
+      "Epoch 4/20\n",
+      "67/67 [==============================] - 124s 2s/step - loss: 0.3213 - accuracy: 0.7977 - val_loss: 0.7915 - val_accuracy: 0.7665\n",
+      "\n",
+      "Epoch 00004: val_accuracy did not improve from 0.76918\n",
+      "Epoch 5/20\n",
+      "67/67 [==============================] - 119s 2s/step - loss: 0.2616 - accuracy: 0.8116 - val_loss: 0.8262 - val_accuracy: 0.7672\n",
+      "\n",
+      "Epoch 00005: val_accuracy did not improve from 0.76918\n",
+      "\n",
+      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.00012960000021848827.\n",
+      "Epoch 6/20\n",
+      "67/67 [==============================] - 119s 2s/step - loss: 0.2675 - accuracy: 0.8222 - val_loss: 0.8032 - val_accuracy: 0.7718\n",
+      "\n",
+      "Epoch 00006: val_accuracy improved from 0.76918 to 0.77185, saving model to best_model.hdf5\n",
+      "Epoch 7/20\n",
+      "67/67 [==============================] - 119s 2s/step - loss: 0.2778 - accuracy: 0.8202 - val_loss: 0.8109 - val_accuracy: 0.7752\n",
+      "\n",
+      "Epoch 00007: val_accuracy improved from 0.77185 to 0.77518, saving model to best_model.hdf5\n",
+      "Epoch 8/20\n",
+      "67/67 [==============================] - 119s 2s/step - loss: 0.2972 - accuracy: 0.8248 - val_loss: 0.8086 - val_accuracy: 0.7765\n",
+      "\n",
+      "Epoch 00008: val_accuracy improved from 0.77518 to 0.77652, saving model to best_model.hdf5\n",
+      "Epoch 9/20\n",
+      "67/67 [==============================] - 125s 2s/step - loss: 0.2459 - accuracy: 0.8335 - val_loss: 0.8178 - val_accuracy: 0.7779\n",
+      "\n",
+      "Epoch 00009: val_accuracy improved from 0.77652 to 0.77785, saving model to best_model.hdf5\n",
+      "Epoch 10/20\n",
+      "67/67 [==============================] - 120s 2s/step - loss: 0.2419 - accuracy: 0.8345 - val_loss: 0.8243 - val_accuracy: 0.7839\n",
+      "\n",
+      "Epoch 00010: val_accuracy improved from 0.77785 to 0.78386, saving model to best_model.hdf5\n",
+      "Epoch 11/20\n",
+      "67/67 [==============================] - 120s 2s/step - loss: 0.2540 - accuracy: 0.8341 - val_loss: 0.8511 - val_accuracy: 0.7765\n",
+      "\n",
+      "Epoch 00011: val_accuracy did not improve from 0.78386\n",
+      "Epoch 12/20\n",
+      "67/67 [==============================] - 122s 2s/step - loss: 0.2461 - accuracy: 0.8337 - val_loss: 0.8071 - val_accuracy: 0.7812\n",
+      "\n",
+      "Epoch 00012: val_accuracy did not improve from 0.78386\n",
+      "\n",
+      "Epoch 00012: ReduceLROnPlateau reducing learning rate to 7.775999838486313e-05.\n",
+      "Epoch 13/20\n",
+      "67/67 [==============================] - 120s 2s/step - loss: 0.2256 - accuracy: 0.8397 - val_loss: 0.8322 - val_accuracy: 0.7799\n",
+      "\n",
+      "Epoch 00013: val_accuracy did not improve from 0.78386\n",
+      "Epoch 14/20\n",
+      "67/67 [==============================] - 121s 2s/step - loss: 0.2323 - accuracy: 0.8461 - val_loss: 0.8581 - val_accuracy: 0.7899\n",
+      "\n",
+      "Epoch 00014: val_accuracy improved from 0.78386 to 0.78986, saving model to best_model.hdf5\n",
+      "Epoch 15/20\n",
+      "67/67 [==============================] - 120s 2s/step - loss: 0.2184 - accuracy: 0.8518 - val_loss: 0.8566 - val_accuracy: 0.7805\n",
+      "\n",
+      "Epoch 00015: val_accuracy did not improve from 0.78986\n",
+      "Epoch 16/20\n",
+      "67/67 [==============================] - 122s 2s/step - loss: 0.2186 - accuracy: 0.8468 - val_loss: 0.8779 - val_accuracy: 0.7738\n",
+      "\n",
+      "Epoch 00016: val_accuracy did not improve from 0.78986\n",
+      "\n",
+      "Epoch 00016: ReduceLROnPlateau reducing learning rate to 4.6655999904032795e-05.\n",
+      "Epoch 17/20\n",
+      "67/67 [==============================] - 124s 2s/step - loss: 0.2070 - accuracy: 0.8579 - val_loss: 0.8654 - val_accuracy: 0.7852\n",
+      "\n",
+      "Epoch 00017: val_accuracy did not improve from 0.78986\n",
+      "Epoch 18/20\n",
+      "67/67 [==============================] - 122s 2s/step - loss: 0.2146 - accuracy: 0.8605 - val_loss: 0.8624 - val_accuracy: 0.7819\n",
+      "\n",
+      "Epoch 00018: val_accuracy did not improve from 0.78986\n",
+      "\n",
+      "Epoch 00018: ReduceLROnPlateau reducing learning rate to 2.799360081553459e-05.\n",
+      "Epoch 19/20\n",
+      "67/67 [==============================] - 121s 2s/step - loss: 0.2027 - accuracy: 0.8637 - val_loss: 0.8656 - val_accuracy: 0.7845\n",
+      "\n",
+      "Epoch 00019: val_accuracy did not improve from 0.78986\n",
+      "Epoch 20/20\n",
+      "67/67 [==============================] - 123s 2s/step - loss: 0.1923 - accuracy: 0.8584 - val_loss: 0.8682 - val_accuracy: 0.7832\n",
+      "\n",
+      "Epoch 00020: val_accuracy did not improve from 0.78986\n",
+      "\n",
+      "Epoch 00020: ReduceLROnPlateau reducing learning rate to 1.6796160707599483e-05.\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = tl_model.fit(train_image_gen,\n",
+    "                    epochs=20, \n",
+    "                    validation_data = test_image_gen,\n",
+    "                    class_weight=class_wt_dict,\n",
+    "                    callbacks=callback_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gkLns_WxED91"
+   },
+   "source": [
+    "### Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "id": "V9rwGHtn1s2w"
+   },
+   "outputs": [],
+   "source": [
+    "df=pd.DataFrame(tl_model.history.history)\n",
+    "df.to_csv('hist2.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 283
+    },
+    "id": "NCmNdKJ6EEaz",
+    "outputId": "320b7723-d6a5-4b10-f5ee-224658153ec7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ca21d9b50>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metrics = pd.DataFrame(tl_model.history.history)\n",
+    "metrics[[\"loss\",\"val_loss\"]].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 282
+    },
+    "id": "jgusI856EHuX",
+    "outputId": "07464be5-445e-4c3e-f5da-9e93cebbbe46"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ca224cbd0>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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MUUbtiUtlwoKdHDqXxvCOobzUvwmVPG9MI8F+3nwxIpw/YhL495J9jPp6K72aVmfygKaEVPUu8jhaa15evIcjCWl8P7aj3WaBchYOk+i11iUyTnNJatGiBTt37rR3GDZX1qr7RMnLzjXx2ZpYpq2Nxb+SB9+Mbk+3xkXXl3dtFMjyZ7ow669jfLrmED0/+IPHuzXg0a71Cr2jdd6Wk/y88zTP9WpE5/oBBZYT1nGI70JeXl4kJSVJgikDtNYkJSXh5SVXWOXFwbMXGfS/DXy8+hD3tKrFyme6WpXkr/B0c+Wf3eqz+rmu9GxanQ8jY+j94Xoi98fnW37vqVSm/LqPOxsF8sRdDWz1Mso1h2iMzc7OJi4u7qb7mouS4eXlRXBwMO7u7vYORZSgXJPmyz+P8MHKGCp7ufHmoBb0bV74zYDW2BCbyGtL9hF7Lo3uYdWYPKApdcyzQV3IyGbAJ3+RlWNi2dNdqFpCE3E4o8IaYx0i0QshStfRxHSeW7CT7SfO07dZDd4c1Bz/Sp422392rolvNhzjo8gYsnM1j3atxz+71WfCD7uIjI7nh0c70a52VZsdrzyQRC+EsIrJpPlu83H+szwaD1cXXo9oTkTrWiXWPnbuQgZvLYvm552n8angTurlbF7u34Rxd9YrkeM5s8ISvcM0xgohSlZcyiUmLtzNxsNJdGscyNv3taSGT8m2xVSr4sVHQ9swrEMoU5dGUy+wImO71C3RY5ZHkuiFEPy+9wwvLNyNyaR5+74WDGkfUqq93DrW8+fXp+4oteOVN5LohSjHMnNy+c+yA3yz8Ritgn34dFhbQv2L7ucuHIskeiHKqRNJl3hi7nb2nErlkdvrMqlfmIwK6aQk0QtRDi3fc4aJC3ejFHwxoh19mhW/26QouyTRC1GOZObk8tbSaL7ddJxWIb58NqyNVUMSCMcmiV6IcuJ4UjpPzt3BnlOpjLmjLv/qK1U15YUkeiHKgWV7zvAvc1XNjBHt6C1VNeWKJHohnJhlVU3rEF8+laqackkSvRBO6nhSOk/M3c7eUxcY16UuL/SRqpryyqq/ulKqr1LqoFIqVik1KZ/1oUqptUqpHUqp3Uqp/vmsT1NKPW+rwIUQBVu6+wwDPvmLk8mX+fLhcF6+u6kk+XKsyCt6pZQrMA3oBcQBW5VSS7TW+y2KvQIs0FpPV0o1BZYBdSzWfwAst1nUQoh8ZWTn8ubSaL7bfJw2oUZVTbCfVNWUd9ZU3XQAYrXWRwCUUvOBCMAy0WugivmxD3D6ygql1EDgKJBui4CFEPk7lmhU1ew7fYHxd9bjhT6NcZfp9wTWJfog4KTF8zigY54yU4CVSqmngIpATwClVCXgXxjfBgqstlFKjQfGA4SGhloZuhACjMlgFm6LY8qSfbi5ujDz4XB6Nq1u77BEGWKrj/thwDda62CgP/CdUsoF4wPgQ611WmEba61naK3DtdbhgYGBNgpJCOd3/lIWT8zdzgsLd9M8yIdlT3eRJC9uYM0V/SkgxOJ5sHmZpTFAXwCt9SallBcQgHHlf79S6h3AFzAppTK01p8VO3IhyrmNsYlMWLCLpPRM/tU3jPF31sPVxbHmVRalw5pEvxVoqJSqi5HghwLD85Q5AfQAvlFKNQG8gAStdZcrBZRSU4A0SfJCFE9mTi7vrTjIl38epV5gRWaOvJ3mQT72DkuUYUUmeq11jlLqSWAF4ArM0lrvU0q9DkRprZcAzwFfKqWexWiYHaXL2tRVQjiBmPiLPD1/J9FnLvBQp1Be7t+UCh6u9g5LlHEylaAQDkBrzexNx3lrWTSVPN145/6W9GgidfHiGplKUAgHdu5iBhMX7mbdwQTuahzIO/e3IrCy7SbqFs5PEr0QZVjk/ngmLtpNemYOb0Q046FOtUt1ij/hHCTRC1EGXcrKYerSaOb+fYJmtarw8dDWNKhW2d5hCQcliV6Ua9m5Js6czyAu5RJx5y+jtaaSpzuVvdyo5OVGZU/zby93vN1dcSmF7ot74lJ5ev4Ojial82jXejzXq7GMUyOKRRK9cGpZOSbOpF4mLuUycSmXOJVy5bHx/OyFDExW9kdQCip5XEn8blTydKOSlzuVPa899/Z0o4K7K94erlTwcM3nsRveHq54XVlu8eGRa9J8/sdhPlwVQ2BlT+aM7Ujn+gEleHZEeSGJXjiN9TEJbD2WfDWJx6Vc5uyFDCw7lrkoqOlTgSC/CnSq70+wnzfBfhWMH19vXFwgLTOHtIwcLpp/p2XmcDEj+4ZlaZk5pF7O5lTKJXOZHC5l5d503J5uLnh7uOKiFEnpWQxoWZM3B7bAx9vdhmdHlGeS6IVTmPv3CV5avOdqIg/2q0Dn+gFXk3iQXwVC/Lyp4eNVogN9aa3JyDZxOTuXS1k5XM7KNT/OzfM454bll7NyuaNhAPe2qiUNrsKmJNELh/dj1EleWryHuxoHMv2hdni52+8GIqWUUU3j4UrVih52i0MIS9LCIxzazztOMXHRbro0DLB7kheirJJELxzW0t1nmLBgJ53q+jNjRLgkeSEKIIleOKQV+87y9PwdtKvtx8yR4TLeixCFkEQvHM6aA/E8OXc7zYN8mDWqPRU9palJiMJIohcOZX1MAo99v52wGlX49pEOVPaSLohCFEUSvXAYGw8nMm52FPUDK/HdmA74VJAkL4Q1JNELh7DlaDJjvomitr8334/pgK+3dF0UwlqS6EWZt/1ECqO/3kJNXy/mjO2EfyUZoleImyGJXpRpu+POM3LWFgIrezJvXCcZh12IWyCJXpRZ+06nMuKrLfhUcGfuuE5Ur+Jl75CEcEiS6EWZdPDsRUZ8tYWKHq7MG9eJWr4V7B2SEA5LEr0oc2LPpfHgzM24uyrmjutESFVve4ckhEOTRC/KlKOJ6Qz/cjOgmDO2E3UCKto7JCEcniR6UWacTL7E8C83k2PSzBnbkQbVKtk7JCGcgtw7Luwu16RZtC2Od1ceJCvHxLxxnWhcQ+ZHFcJWJNELu9Faszr6HP/9/QCHzqXRKsSX/wxqQdNaVewdmhBORRK9sIttx1N4e3k0W4+lUDegItMfbEvf5jVkZiUhSoAkelGqDiek8c7vB1ixL56ASp5MHdicIe1DSnR6PyHKO0n04qqM7Fw8XF1wcbH9VXX8hQw+ijzEgqiTeLm5MKFXI8bcUVeGGBaiFMh/mQDgWGI6g/63gRyTJry2H+F1qtK+TlVaBvsUa+amCxnZzPjjCDP/OkKuSTOiU22e7N6AABmvRohSI4lekJaZw/jvotDA3S1qEnU8hbUHDwLg4epCy2Af2tetSvs6frQLrYqPd9HDA2fm5DJn8wk+XXOIlEvZ3NOqFs/3bkRtf+kXL0RpsyrRK6X6Ah8DrsBMrfXbedaHAt8CvuYyk7TWy5RSvYC3AQ8gC3hBa73GhvGLYtJa88KPu4g9l8bsRzpyR8MAAJLTs4g6lkzU8RS2HE3my/VHmL5OoxQ0rl6Z8Dp+tDdf9VsOT2AyaZbsOs17Kw8Sl3KZzvX9mdQvjJbBvvZ6iUKUe0prXXgBpVyBGKAXEAdsBYZprfdblJkB7NBaT1dKNQWWaa3rKKXaAPFa69NKqebACq11UGHHCw8P11FRUcV7VcJq09bG8u6Kg7zcvwnj7qxXYLnLWbnsPHmerceS2Xosme3HU0jPygUgyLcC4XX8aF7Lh593nmLf6Qs0rVmFSf3C6NIwQHrSCFEKlFLbtNbh+a2z5oq+AxCrtT5i3tl8IALYb1FGA1c6P/sApwG01jssyuwDKiilPLXWmTf3EkRJWHMgnvdWHiSidS3GdqlbaNkKHq7cVt+f2+r7A5CTa+LA2YtXE//Gw0n8svM0wX4V+GhIa+5tVatEGnWFEDfPmkQfBJy0eB4HdMxTZgqwUin1FFAR6JnPfv4BbM8vySulxgPjAUJDQ60ISRTX0cR0np6/kyY1qvD2fS1v+qrbzdWF5kE+NA/yYfTtddFac/ZCBv4VPfFwk66SQpQltvqPHAZ8o7UOBvoD3ymlru5bKdUM+C/waH4ba61naK3DtdbhgYGBNgpJFCQtM4fxs6Nwc1F8MaIdFTxuvVfNFUopavpUkCQvRBlkzX/lKSDE4nmweZmlMcACAK31JsALCABQSgUDi4GHtdaHixuwKB6TSfPcgp0cSUxn2vC2MgSwEOWANYl+K9BQKVVXKeUBDAWW5ClzAugBoJRqgpHoE5RSvsBSjF44G2wXtrhV09bGsmJfPC/2C6NzgwB7hyOEKAVFJnqtdQ7wJLACiAYWaK33KaVeV0rday72HDBOKbULmAeM0kZ3nieBBsBkpdRO80+1EnklokhrDsTzQWQMA1vXYswdhTe+CiGcR5HdK0ubdK8sGUcS0oj4bAO1A7xZ+FjnYt3tKoQoewrrXiktZ+XAxYxsxn+3DXc3F74YES5JXohyRoZAcHImk2bCgl0cTUzn+zEdCZJJtoUod+SK3sl9uiaWVfvjebl/k6s3OwkhyhdJ9E4scn88H0bGcF/bIEbfXsfe4Qgh7EQSvZM6nJDGsz/spEWQD28NaiHjzQhRjkmid0IXM7IZPzsKDzcXvhjRThpfhSjnpDHWyZhMmmd/2MXxpEt8P7bjdUMICyHKJ7midzKfrDlEZHQ8r9zdhE71pPFVCCGJ3qms3HeWjyIP8Y+2wYzsXMfe4QghyghJ9E4i/kIGzy3YRctgH94c1FwaX4UQV0midxJTl0aTmWvi02FtpPFVCHEdSfROYENsIr/uOs3j3erL5NtCiBtIondwWTkmJv+yl9r+3jzWtb69wxFClEHSvdLBzfzrCIcT0vl6dHupshFC5Euu6B1YXMolPll9iD7NqnNXYxnmXwiRP0n0Duz1X/ejUEy+p5m9QxFClGGS6B3U2gPnWLk/nqd6NJChh4UQhZJEb3YxI5uHZ21h7cFz9g6lSBnZuby2ZB/1Aysy9o569g5HCFHGSaI3+/yPw6yPSWDSot2kZebYO5xCTV93mBPJl3gjojkebvInFEIUTrIEcCb1MjP/PErrEF/iL2TyyepD9g6pQMeT0pn+x2HuaVWLzg0C7B2OEMIBSKIH3l8Zg9bw6bA2DAkPYdZfR4mJv2jvsG6gtWbyL/vwcHXhlbub2DscIYSDKPeJfv/pCyzaHseo2+sQUtWbiX0bU9HTjcm/7EVrbe/wrrNi31n+iEng2V6NqF7Fy97hCCEcRLlP9P9ZHk0VL3ee6NYAAP9KnrzQpzGbjySzZNdpO0d3zaWsHF7/dT9hNSoz8rba9g5HCOFAynWiXx+TwJ+HEnmqewN8vN2vLh/WIZQWQT68uTS6zDTMfrI6ltOpGUwd2Bw313L9ZxNC3KRymzFyTZq3lkUTUrUCI/JcIbu6KF6PaMa5i5l8HBljpwiviT13kZl/HuH+dsGE16lq73CEEA6m3Cb6n7bHceDsRSb2CcPT7cYxYtqE+jG0fQizNhzj4Fn7NcxqrXn15314e7gyqV+Y3eIQQjiucpnoL2fl8v7KGFqF+DKgZc0Cy03sG0YlOzfMLtl1mk1HknihbxgBlTztEoMQwrGVy0Q/a8NRzl7I4KV+YYXOxFS1ogcT+zbm76P2aZi9kJHN1KXRtAz2YXiH0FI/vhDCOZS7RJ+Ylsn0dYfp2aQ6Ha2YPHto+1BaBvswdWk0FzOySyHCaz5cFUNiWiZvRDTH1UWmBhRC3BqrEr1Sqq9S6qBSKlYpNSmf9aFKqbVKqR1Kqd1Kqf4W6140b3dQKdXHlsHfik9WH+Jydq7V9d2uLoo3IpqTmJbJx5Gld8fs/tMX+HbjMYZ3CKVViG+pHVcI4XyKTPRKKVdgGtAPaAoMU0o1zVPsFWCB1roNMBT4n3nbpubnzYC+wP/M+7OLIwlpzP37BEPbh9CgWiWrt2sV4svQ9qF8vbF0GmZNJs2rv+zF19uDF/o0LvHjCSGcmzVX9B2AWK31Ea11FjAfiMhTRgNVzI99gCsV2hHAfK11ptb6KBBr3p9d/Pf3A3i6ufBMz0Y3ve3EPo2p7OXGq6XQMLtwexzbjqcwqV8Yvt4eJXosIYTzsybRBwEnLZ7HmZdZmgI8pJSKA5YBT93EtiilxiulopRSUQkJCVaGfnOijiWzYl88j3atT0yt6pQAABqoSURBVGDlm++94lfRg4l9wthyNJlfdpZcw+z5S1m8vfwA7Wr7cX/b4BI7jhCi/LBVY+ww4ButdTDQH/hOKWX1vrXWM7TW4Vrr8MDAQBuFdN3+eWtZNNUqezK2S91b3s+Q9iG0CvbhzWUl1zD77oqDnL+UxRsRzXGRBlghhA1Yk4xPASEWz4PNyyyNARYAaK03AV5AgJXblrjle8+y/cR5nuvdCG+PW58P3bhj1miY/agEGmZ3nTzP3C0nGNm5Dk1rVSl6AyGEsII1iX4r0FApVVcp5YHRuLokT5kTQA8ApVQTjESfYC43VCnlqZSqCzQEttgqeGtk5Zj47+8HaFy9Mve3Cyl6gyK0CvFlWIdQvtl4jANnL9ggQsPRxHQm/bSHgEqePNvr5tsQhBCiIEUmeq11DvAksAKIxuhds08p9bpS6l5zseeAcUqpXcA8YJQ27MO40t8P/A48obXOLYkXUpA5fx/neNIlJvUPs1lf9Bd6N6aKlxuTf95X7IbZs6kZvPjTHnp+8AfHEtN5a1ALqni5F72hEEJYSZW1MdfDw8N1VFSUTfaVejmbbu+upWmtKnw/pmOhd8HerHlbTvDiT3v4cEgrBrW5+UbTlPQspv9xmG83HsOkNcM7hPJE9wZUqyzjzAshbp5SapvWOjy/dbdeYe0Apq87TMqlbF7s18SmSR5gSHgI87ee5M2lB+jRpLrVV+FpmTnM+usoX64/QlpWDoPaBPFsz0aEVPW2aXxCCHGF0yb6U+cvM2vDUQa1CaJ5kI/N9+/iongjohkR0zbw4aoYXrunWaHlM3NymbP5BNPWxpKUnkXvptV5vk9jGlWvbPPYhBDCktMm+vdXHATgud4l17DZMtiX4R1Cmb3pOA+Eh9Ck5o09ZXJyTfy04xQfRx7i1PnLdK7vzwt9GtMm1K/E4hJCCEtOmej3nkpl8c5TjL+zHsF+JVsl8kKfxizbc4bJv+xlwaO3Xa0i0lqzfO9Z3l95kMMJ6bQK9uGd+1tye4OAEo1HCCHycrpEr7XmP8uj8a3gzuPmeWBLkq+3B5P6hfGvRXtYvOMUg9oE8eehRN5dcZA9p1JpWK0Snz/Ujj7Nqtu8nUAIIazhdIl+XUwCG2KTmDygKT4VSqeb4uB2IczbcpK3lkWzIOokm48kE+RbgfcGt2JQmyAZYlgIYVdOlehzTZq3lx2gtr83D3WqXfQGNuJiHso4YtpfxJ5LY8o9TRnWMTTfKQqFEKK0OVWiX7jtJAfjLzJteFs83Ep3TpUWwT5ETuhK9SpeVPR0qtMqhHBwTpORLmXl8P7KGNqE+tK/RQ27xFAv0Pox7oUQorQ4TaJPvZxNo+qVebpnQ2n0FEIIC06T6Gv6VOD7sR3tHYYQQpQ55W5ycCGEKG8k0QshIDcbFo6BVZOhjA10KIrPaapuhBC3SGtYOgH2LjSeu1WAu160b0zCpiTRC1HebfwUts+GOyZA+jn4422oFAjtx9o7MmEjkuiFKM8OLDWqa5pGQPdXQZsgPQmWPg/eAdBsoL0jFDYgdfRClFdndsGisVCrDQz8HFxcwNUN7p8FIR3hp3FwdL29oxQ2IIleiPLowmmYOxQqVIVh88HDYpRXD28YNg+q1od5w40PBOHQJNELUd5kpcO8oZB5AYbPh8rVbyzjXRUeWgRePvD9/ZB8tPTjFDYjiV6I8sRkgsWPwtk98I+voEaLgsv6BMGIn8CUDd8NgrRzpRensClJ9EKUJ2teh+hfofeb0Lhv0eUDG8PwH+HiWZhzP2ReLPkYb5XJJPcAFEASvRDlxY7v4a8Pod1o6PRP67cLaQ8PzIaze2H+g5CTWXIx3qqcLPi6L3zQFNa8CedP2DuiMkXpMvYJGB4erqOiouwdhhDO5dhfMHsg1LkDHvwRXG9hUp6d8+Dnx6DZfUa1j0sZuk5cPgn+ng6ht8GJzcay+t2h3Uho1A/cPEru2BmpELMSDi6FjAsQ0AgCGhq/AxtDxUAohYEWlVLbtNbh+a2TfvRCgPG1f9dcqNYEgtrZOxrbSjoMPzwEVevC4G9uLckDtB5m3FC1arKRvPr9t1QSWJGifzOSfIdHof87xtX8jjmw4ztY8LARa6th0HYkBNhoetGLZ417EA78Bkf/NNoxKlWHyjVg+2bITr9W1svHnPwbX/8B4Fvb6M5aCuSKXojUU8aV6tH14OoJg6ZD83/YOyrbuJQMX/Uyfo9bDVXrFW9/WsPKV2DTZ8YNVnc+b5s4b1XKcfiiC/jVhTErwc3z2jpTLsSuhu3fwsHloHOh9h3GVX6Te8Hd6+aOlXTYaN848BvEbTWWVa0HYQOgyT0QFG58y9EaLpyCxBhIPAQJB689Tjt7bX8u7uBf3/whYP6p3rTwBvJCFHZFL4lelG/7FsOvzxiDevX6N+xdBCc2Qc8pcPszZeOK9VblZMH398HJv+HhJVD7Ntvs90rPnT0L4J5PjMRpDzlZ8HU/I4k+ut74xlKQi2dh51xjqIeUo+DlC62GQtuHoXqz/LfRGs7sNL4xHPgNEg4Yy2u2grB7oMkACAy7uffI5fOQFGvEnHDQSP6JMZB8xPggqtUWxq+1fn8WJNELkVfmRVj+L9g5x6ique9L4+oqOwN+edxI+O1GQf/3S+3rtU1pDUueMqovBn1hJDVbysky+uIfWQtD5kBYf9vu3xorXja+WQz+1vqhGkwmOPancZUf/SvkZhlX4u1GGm0Pbl5wYqM5uS+FC3GgXKD27caVe9jd4Bti+9eSk2V8AGWlQ1DbW9qFJHohLJ3cYtzef/4EdHkeuk68vt7aZII1b8BfH0D9Hka9tlcVu4V7SzZ8AqteNV5fj1dL5hiZafDtPXBuP4z42XbfGKxxcLnxQdN+LNz9/q3tIz0Jds+Hbd9C4kHwqGS8Dy6nGAm/fncjuTfqCxX9bRt/CSh2oldK9QU+BlyBmVrrt/Os/xC4y/zUG6imtfY1r3sHuBujK+cq4GldyEEl0YsSk5sDf74Hf7xj3Aw0aEbhyWnbt/Dbs0YD7fAFxjYlxZQL+38xvmlcqa+91eQS/ZvR+No0Au7/umR7x6Qnwqw+kJ4Ao3836phL2vmT8PkdxpX1mMibr2vPS2vjw3/Hd2DKgcb9oUEP8Khom3hLSbESvVLKFYgBegFxwFZgmNZ6fwHlnwLaaK0fUUp1Bt4F7jSv/gt4UWu9rqDjSaIXJSL5KPw0HuK2QMuhRu8ML5+it4uNhAWjwLOSkexrtrR9bLGRsHIynNt3/XJvf4uueo2vPfYNBRfX/Pd1ZhfM6mt8OI1aCu4VbB9vXinH4aveRl31mJVGfCUlNxu+7g/nouHRP4zqNgEUv3tlByBWa33EvLP5QASQb6IHhgGvmR9rwAvwABTgDsRbH7oQxaQ17JoPy14w6lr/8RW0uN/67Rv0hEd+h7kPGA1/g7+Bhr1sE9vZPbDyVaOe26+OcfUd1PZaA11iDCTEwIFlcGn2te3cvMC/gcUHgLnLnkfFawOVDZ1XOkkewK+2MS7O1/3hqz7GzVUh7UvmWGveMD6s758lSf4mWJPog4CTFs/jgHxn4VZK1QbqAmsAtNablFJrgTMYif4zrXV0sSIWwlqXU4yql32Ljca0QV/cWkNajeYwNtJI9nOHwN3vQfgjtx5X6ilYMxV2zYMKvtDnP9B+zLWugX51bvwwuZR8Lflf+QA4vdOo7tGma+U8KsEjK/IfqKwk1WgOo5cad85+3c/4xtRutG17LcWshA0fG/t1lu6vpcTW3QmGAgu11rkASqkGQBMg2Lx+lVKqi9b6T8uNlFLjgfEAoaEl+LVPlB9H/zS6AKbFQ4/X4PanC67usEaVWjB6OSx8xPjwSDkGPabcXP13xgXY8BFsmmYk585PQZcJUMGv6G29q0JoJ+PHUnYGJB82kn9SrNGAWKP5zbwy26nRAsavMxq6f3sWTm0zei0Vtw4djA/HxY9C9ebQ9z/F3185Y02iPwVYXgYFm5flZyjwhMXzQcBmrXUagFJqOXAbcF2i11rPAGaAUUdvVeRC5CcnC9a+aVz5+deHMatuubvaDTwrG1Uiy18w9p9y3PiWUFQiy82Gbd/AurfhUiK0GGzcbORXu/gxuXsZ/cAL6gte2ryrGm0Z696G9e8Y4+MM+a549fa5ObBojNEVcvA3pVcl5USsuRzZCjRUStVVSnlgJPMleQsppcIAP2CTxeITQFellJtSyh3oCkjVjbA9U67REPlVT+Oqud1I4yYaWyX5K1zd4O4PoNfrsP9nmH2v0U0vP1obfbX/1wmWPW80kI5bC/+YaZskX1a5uEL3l40JTZKPwBdd4fCt3QQEGB/cJzbBgI+M9ghx04q8otda5yilngRWYHSvnKW13qeUeh2I0lpfSfpDgfl5uk4uBLoDezAaZn/XWv9q01cgypfsyxaNlRaNlkmxkJNhboica9zYUlKUMqqCfEPhp0eND5cHF17fOBgXZQwVcGKT0WA67Ado1Mex77S9WY37GVU58x807tDtMfnm7zaOjTTuZ2j7MLQcXFKROj25YUqUPVrDpaTrxwhJND8+fxLjmgFAGVfGV8cKaWj0ga5UrfRiPfE3zB9mxDx0rjGo1ep/Gw3AFavBXS9Cm4cd8+5aW8lMM+7S3feTMcbMwP8Z1WBFuXDG6C9fqRqMXX39dIfiBnJnrCibTLlw/rjRgyQxz8/llGvl3CoYow7mHQHQv37ZqK9NPmJMt5d60kj4ru5GQ2vnp6xLaOWB1kYj9KrJxt9tyBwIbFRw+dwcmB0Bp7cb3woCG5dWpA5LhikW9pWVblSt5E3oSYch12ISC+8A4x+6acT1Sd0npGyNfZ5X1XpG98tfnoSKAdDtRahS095RlS1KQecnjQHBfhwFX3Y3Rgltck/+5f/4Lxz/CwZ+LkneBuSKXtjOpWRj3BPLUfkSY4wr3SuUi9FPPL87Pr2r2i10UYpST8GCEUb3yzueNXogWXZ9PbzWmKO29YMwcJr94nQwckUvSo4pFw6vuTbmtynHWO7ubSTv0E4QMPL66hbLMcNF+eMTZNyTsHyiMbXh6Z3GHcsV/Y3hhH8aZ1zF93/H3pE6DUn04takxhlzkO743rhi9/aHjo8ZN+wENobKtcp2dYuwLzdPuOdjY/z1Zc/DjG5GH/nI14zG25G/OtygYmWZJHphvdxsiFlhXL3HRhp3d9a7C3q/AY3vLtl5OYVzajfSuJP3h4dhZndjWcQ0454DYTOS6EXRko/A9u+MSTrS4qFyTbhjArQdYdS3C1EcQe2MkSh/Nd+b0PpBe0fkdCTRi/zlZBp3dW7/1phLVblAwz7GjSsNe5fvfuHC9ioGwNA59o7Cacl/q7hewkFjwo1d8+ByMviEwl2vQJsHjYG9hBAORxK9MJz4G1a/bvRddnE35gBtO9Kog5dGVSEcmiT68i7pMEROgeglUKmGMVhXq+FQKdDekQkhbEQSfXmVnmTcfRj1Fbh6QreXjDsXpUubEE5HEn15k30ZNk83blTJSjOqZ7q9WPozEgkhSo0k+vLCZILdPxhT2F2Ig0Z9oee/oVqYvSMTQpQwSfS2knkRfn3GGKY2bACEdCje1HW2dGSdMQn12d1QszUM+hzqdrF3VEKIUiKJ3hZMubBwjHG3qIsrbPoMKgYaEy+E3QP1utpnfJf4/cawsLGrjG6S9800JlWWXjRClCuS6G1h5StwaIUxxVyLwUZiPbAU9i6G7bPBoxI07GVc6TfsDV5VSjaei2eN6dd2fA8elaHXG9BhvG0maRZCOBxJ9MW19SvY/D/o+E9oP8ZY1vwfxk9OpnFX6YHf4MAyY9YhF3fjCj9sgDEbki0bQTPTYOMnsPFTY1yajo/BnS/I8L9ClHMyHn1xxK6GOYOhQU8YNq/wOnlTrjGP6IFfIfo3SDkKKKMuP2yAMcep5ZyjYCTrS8nGtHqXEs2/kyyWJUF64rVl6QnGRB7NBhnzc1atV6IvXwhRdshUgiXh3AH4qpcx+9GYFTc3ZZzWxgQdB5Ya48mc3W0sDwwz9pOeaCTuzNSC9+HpY4zf7Z3np2kEBOf7txZCOLHyMfGI1vDHO8agWyU9jVt6Isx9ANy8YPgPNz8vqFJQvZnx03UipByHg8vg0EpjvV8di+Rd1Zhi77qEXtWYl1QIIazgPFf0iYfgi67GnZ0PfAu1O9s+ODDq3b+9F87shFFL5epZCFEmFHZF7zz97AIaGhM0e1aGb++BzZ8bV/m2pDUseQpOboaB0yXJCyEcgvMkeoDqTWH8WqML4+//MuaezLpku/2vf8+4u/SuV6D5fbbbrxBClCDnSvQAXj4wZI6RjPcsNBpMk48Wf797f4K1U6HlELjz+eLvTwghSonzJXow7vzs+gI8+KMxifWMrnBo1a3vLy4Kfv4nhHSCez81GlOFEMJBOGeiv6JhLxi/zpiHcs5go1eOyXRz+zh/AuYNg0rVjanO7DGUgRBCFINzJ3qAqnXhkZVGlcvaN2H+cLh83rptMy/C3KGQkwHDFxjzWgohhINx/kQP4OFtjNjY711jHJov7zIG/CrMlYHKEg7A4G9kOF8hhMOyKtErpfoqpQ4qpWKVUpPyWf+hUmqn+SdGKXXeYl2oUmqlUipaKbVfKVXHduHfBKWg43ij73tWOszsAXsXFVx+xcvGQGX934EGPUovTiGEsLEiE71SyhWYBvQDmgLDlFJNLctorZ/VWrfWWrcGPgV+slg9G3hXa90E6ACcs1XwtyS0Ezy6Hmq0hIWPGAk9N+f6Mltnwt/TzQOVjbVPnEIIYSPWXNF3AGK11ke01lnAfCCikPLDgHkA5g8EN631KgCtdZrW2oYd229R5Row8lfo8Kgxdvx3AyEtwVgXuxqWTYSGfaDPm/aNUwghbMCaRB8EnLR4HmdedgOlVG2gLrDGvKgRcF4p9ZNSaodS6l3zN4S8241XSkUppaISEhJu7hXcKjcPo1pm0Ayj++QXd8KuH+DHUcbgYvd/VXZmiBJCiGKwdWPsUGCh1jrX/NwN6AI8D7QH6gGj8m6ktZ6htQ7XWocHBgbaOKQitBoCY1Yag4QtHm8eqGz+zQ9UJoQQZZQ1if4UEGLxPNi8LD9DMVfbmMUBO83VPjnAz0DbWwm0RNVsafS3v+1J4yYr31B7RySEEDZjzTDFW4GGSqm6GAl+KDA8byGlVBjgB2zKs62vUipQa50AdAfK5mDz3lWlTl4I4ZSKvKI3X4k/CawAooEFWut9SqnXlVL3WhQdCszXFuMem6twngdWK6X2AAr40pYvQAghROGcZzx6IYQox8rHePRCCCHyJYleCCGcnCR6IYRwcpLohRDCyUmiF0IIJyeJXgghnFyZ616plEoAjhdjFwFAoo3CKQkSX/FIfMUj8RVPWY6vttY63zFkylyiLy6lVFRBfUnLAomveCS+4pH4iqesx1cQqboRQggnJ4leCCGcnDMm+hn2DqAIEl/xSHzFI/EVT1mPL19OV0cvhBDies54RS+EEMKCJHohhHByDpnolVJ9lVIHlVKxSqlJ+az3VEr9YF7/t1KqTinGFqKUWquU2q+U2qeUejqfMt2UUqlKqZ3mn8mlFZ9FDMeUUnvMx79hXGhl+MR8DncrpUptZjClVGOLc7NTKXVBKfVMnjKleg6VUrOUUueUUnstllVVSq1SSh0y//YrYNuR5jKHlFIjSzG+d5VSB8x/v8VKKd8Cti30vVCC8U1RSp2y+Bv2L2DbQv/fSzC+HyxiO6aU2lnAtiV+/opNa+1QP4ArcBhj/lkPYBfQNE+Zx4HPzY+HAj+UYnw1gbbmx5WBmHzi6wb8ZufzeAwIKGR9f2A5xmQxnYC/7fj3PotxM4jdziFwJ8Y0mHstlr0DTDI/ngT8N5/tqgJHzL/9zI/9Sim+3oCb+fF/84vPmvdCCcY3BXjeir9/of/vJRVfnvXvA5Ptdf6K++OIV/QdgFhtzEObBcwHIvKUiQC+NT9eCPRQSqnSCE5rfUZrvd38+CLGrFxBpXFsG4sAZmvDZowpIWvaIY4ewGGtdXHuli42rfV6IDnPYsv32bfAwHw27QOs0lona61TgFVA39KIT2u9UhszxAFsxpjv2S4KOH/WsOb/vdgKi8+cOx7g+vmwHYojJvog4KTF8zhuTKRXy5jf6KmAf6lEZ8FcZdQG+Duf1bcppXYppZYrpZqVamAGDaxUSm1TSo3PZ70157k05J1w3pK9z2F1rfUZ8+OzQPV8ypSV8/gIxje0/BT1XihJT5qrlmYVUPVVFs5fFyBea32ogPX2PH9WccRE7xCUUpWARcAzWusLeVZvx6iKaAV8Cvxc2vEBd2it2wL9gCeUUnfaIYZCKaU8gHuBH/NZXRbO4VXa+A5fJvsqK6VeBnKAOQUUsdd7YTpQH2gNnMGoHimLhlH41XyZ/19yxER/CgixeB5sXpZvGaWUG+ADJJVKdMYx3TGS/Byt9U9512utL2it08yPlwHuSqmA0orPfNxT5t/ngMUYX5EtWXOeS1o/YLvWOj7virJwDoH4K9VZ5t/n8ilj1/OolBoFDAAeNH8Y3cCK90KJ0FrHa61ztdYm4MsCjmvv8+cG3Af8UFAZe52/m+GIiX4r0FApVdd8xTcUWJKnzBLgSu+G+4E1Bb3Jbc1cn/cVEK21/qCAMjWutBkopTpg/B1K84OoolKq8pXHGI12e/MUWwI8bO590wlItaimKC0FXknZ+xyaWb7PRgK/5FNmBdBbKeVnrprobV5W4pRSfYGJwL1a60sFlLHmvVBS8Vm2+Qwq4LjW/L+XpJ7AAa11XH4r7Xn+boq9W4Nv5QejR0gMRmv8y+Zlr2O8oQG8ML7uxwJbgHqlGNsdGF/hdwM7zT/9gceAx8xlngT2YfQg2Ax0LuXzV8987F3mOK6cQ8sYFTDNfI73AOGlHGNFjMTtY7HMbucQ4wPnDJCNUU88BqPdZzVwCIgEqprLhgMzLbZ9xPxejAVGl2J8sRj121feh1d6otUClhX2Xiil+L4zv7d2YyTvmnnjMz+/4f+9NOIzL//mynvOomypn7/i/sgQCEII4eQcsepGCCHETZBEL4QQTk4SvRBCODlJ9EII4eQk0QshhJOTRC+EEE5OEr0QQji5/wdvVq4iV8gIvwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metrics[[\"accuracy\",\"val_accuracy\"]].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hrkJC-P-RINA",
+    "outputId": "707305a6-2141-49f6-ce05-cf49e6ef42d7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4/4 [==============================] - 23s 6s/step - loss: 1.4655 - accuracy: 0.4215\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[1.4654699563980103, 0.42149999737739563]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.evaluate(test_image_gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0URE2Jq8ELba",
+    "outputId": "8032a906-cbb2-41a6-b5e6-f0a59c8b1c80"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12/12 [==============================] - 15s 1s/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = tl_model.predict(test_image_gen, verbose=1)\n",
+    "predictions = predictions.argmax(axis=1)\n",
+    "\n",
+    "test_labels = test_image_gen.classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "id": "8_MFqgkGEP_g"
+   },
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "cm = confusion_matrix(test_labels, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "mqFY3QfYE2cj"
+   },
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "\n",
+    "def plot_confusion_matrix(cm, classes,\n",
+    "                          normalize=True,\n",
+    "                          title='Confusion matrix',\n",
+    "                          cmap=plt.cm.Blues):\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",
+    "    plt.figure(figsize=(7,7))\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+    "    plt.title(title)\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(len(classes))\n",
+    "    plt.xticks(tick_marks, classes, rotation=45)\n",
+    "    plt.yticks(tick_marks, classes)\n",
+    "\n",
+    "    fmt = '.1f' if normalize else 'd'\n",
+    "    thresh = cm.max() / 2.\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        plt.text(j, i, format(cm[i, j], fmt),\n",
+    "                 horizontalalignment=\"center\",\n",
+    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')\n",
+    "    plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 759
+    },
+    "id": "JlxJ29AXFBRh",
+    "outputId": "a972f5c8-257d-45c9-9c6e-6984561c99be"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized confusion matrix\n",
+      "[[0.44897959 0.20408163 0.20408163 0.         0.04081633 0.10204082\n",
+      "  0.        ]\n",
+      " [0.1038961  0.64935065 0.07792208 0.03896104 0.03896104 0.07792208\n",
+      "  0.01298701]\n",
+      " [0.02439024 0.03658537 0.59146341 0.01829268 0.13414634 0.19512195\n",
+      "  0.        ]\n",
+      " [0.11764706 0.05882353 0.11764706 0.35294118 0.         0.35294118\n",
+      "  0.        ]\n",
+      " [0.03614458 0.03614458 0.13253012 0.         0.51204819 0.28313253\n",
+      "  0.        ]\n",
+      " [0.0039801  0.01691542 0.02985075 0.0039801  0.04676617 0.89651741\n",
+      "  0.00199005]\n",
+      " [0.         0.04761905 0.         0.         0.         0.33333333\n",
+      "  0.61904762]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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f+PVLzrnfAO2BtZUNwMyuAK4AOLFByWzoUO13MPPd7Vx1ZhNqmPH21t3kfZ3PwLYn89nufVGDsF8FAgF+c8cDXDt+OPv3Bxk84nJantaWv0/+I207dOHH5w3gsXsnsu/bb7ntmtBFGI1S03noyekeRx4tEdoRCASY/OjjDB7Yj2AwyLjxE2iXkcGkOyfStVt3Bg0ewvgJP2PC+DFktGlFcnI9pkz1T/wHBAIB7n/oUUYMHUAwGOSyseNp2y6De+66gy5du3PBwMFcPm4Cv/r5OLp1aE1ycjJPPfeC12FHCQQCTLp3MmNHDia4P8jFl47jtDbtePhPk+jQuSvnXzCI9evW8Mtxo9izZzevLF/C5Pvu5qW31nkdepFE6U/VgcXqijYzWwQ86px7ycyuBZoQGnBfBb4EpjrnJkeUfxZY5JybbWYrCWW2a4rV+SXQ3Dl3yNPLp7Rq70beP/Ow2+MH4zsfXVNfiaxDkzpeh1Al9hUEvQ7hiO2NON1ztGpYxz+now7XmT27s3btmipPI49p0NLVHXxPVVcLwM5nR691znWPSeXliEnGa2b1gHOADmbmgJqAA54AvgMGAG+Y2efOuamHUPUGoBuhwVtERKqBo2E6/FDE6hzvCGCKc66pc66Zc64xsAVoDOCc+xzoD9xjZv0Ood4/AQ+YWSMAM0sys59XcewiIiIxE6tzvJcAxZ8bN4fQxVEAOOe2mNkQYImZXVRKHZHneHc6585zzi0xs4bAyxb6E8gBz8QgfhER8YvESnhjM/A65/qWsu0x4LFi29YDB05crorYfnY5df8T+GeVBCoiIhJn+lpAERHxL9M5XhERETkCynhFRMTXlPGKiIjIYVPGKyIivqaMV0RERA6bMl4REfEtw9svNIgFDbwiIuJviTXuaqpZRESkLGbW38w+MrNMM7u1lP2Tzezd8PKxme2uqE5lvCIi4l8ePkDDzGoS+nKf84FsYLWZLXDObTxQxjl3Q0T5a4AuFdWrjFdERKR0pwOZzrnNzrkCYDqh75ovyyXAtIoqVcYrIiK+FsOMt4GZRX7v+5POuScj1tOAbRHr2UDP0ioys6ZAcyrxtbUaeEVEpLra6ZzrXkV1jQZmO+eCFRXUwCsiIr7m4e1EOYS/Rz4sPbytNKOBqypTqc7xioiIlG41cKqZNTezJEKD64LihcysDZAMvF2ZSjXwioiIv1mMlgo45wqBq4HlwCZgpnNug5lNMrMhEUVHA9Odc64yzdFUs4iI+JqXT65yzi0BlhTbNrHY+p2HUqcyXhERkTiqNhlvoxOP5f/6tPA6jCPS74HXvQ6hSrx6a1+vQzhi+woqvHBR4uSYmkd//hDcX6kZSl+LVQvMEu9ZzUd/jxURETmKVJuMV0REjk7KeEVEROSwKeMVERFfU8YrIiIih00Zr4iI+FtiJbzKeEVEROJJGa+IiPhaop3j1cArIiL+ZYk38GqqWUREJI6U8YqIiG8ZkGAJrzJeERGReFLGKyIiPqYvSRAREZEjoIxXRER8LcESXmW8IiIi8aSMV0REfE3neKu5la+s4JyeHenTI4O/PPpAif3/+8+bDOzbi5YNT2TJgrkeRFixs05rwNKbzmL5zb35RZ/mpZbp36Ehi244k4U3nMmDozvGOcLKee3l5fTu0Z4zu7bl8cklj8V/33qDfn160qTB8Sya789j8fKKZZzeuR3dOrTmkQfvK7E/Pz+fCWMvoVuH1pzXpxefbc2Kf5CVkAjtSIT+9NLyZXRp34aObU/loQfuLbE/Pz+fsZeNpmPbUzn7rB+xNSsr/kGKBt5DEQwGmXjL9Tw7Yz4vvfUOC+bO4pOPNkWVSU1vzIOPP8nQ4aM8irJ8NQwmDm3LL/65lkGT32Rg5xRannJCVJmm9Y/nir4tuPRv/2Pw5Le4Z+GHHkVbtmAwyG9/cx3Pz1rAa/9dz4tzZvDxh9HHIq1xYyY/8RQXjhjtUZTlCwaD/N+N1zJz3iLeXvs+c2bN4MNNG6PKPP/cM9Stm8za9z/iyquv587f3+ZRtGVLhHYkSn+68bqrmbtgCWvWb2DWjOlsKnYcnvvn09StW5f3Nn3CVddez+9/e6tH0R4CC53jjcXiFQ28h+Dddatp2rwlTZo1JykpicEXjWTF0kVRZRo3aUrbjA5YDX/+13ZsXIfPdn1H9pf7+CHoWLI+j3PbnRJVZuTp6bzw9mfs3VcIwJffFngRarneWbuaZi1a0rRZC5KSkhg67GKWL1kYVaZxk2a0a9+BGj49FmvXrKJ5i5Y0ax5qw7ARF7N00YKoMksWLWD0ZWMAGHrRcP698lWcc16EW6ZEaEci9Kc1q1fRomUrmrcItWHExaNYvHB+VJnFCxdw2ZhxAFw0bAQrX3vFV8ehNAbUqGExWbzizx7kUzvycklNTS9aT0lNY0dejocRHbqGtY8jb8/3Revb93xPw9rHRZVp1uB4mjU4gRd+dTrTf92Ts05rEO8wK7Q9L5fUtMZF6ympaWw/yo5FXm4uaekH25Calk5eXm6ZZQKBALVr1+HLXbviGmdFEqEdidCfcnNzSG988PdTWlo6uTk5JctEHIc6teuwy0fHobrwdOA1s6CZvWtm681snZmdEbHvdDP7t5l9ZGbvmNlTZnZ8eN8FZrbGzDaG9z3kXSsST6CG0bTB8Yx9cjU3TXuPu4a146TjdB2eiHhDU81Va59zrrNzrhNwG/AnADNrCMwCbnHOtXbOdQGWASeZWXvgceBy51w7oDuQGY9gG6akkpubXbSel5tDw5S0eHx0ldmx93tS6hzMcBvVOY4de7+PKrN9Tz6vbfycwv2OnK/2kbXzO5o2OD7eoZarUUoquTnbitbzcnNodJQdi5TUVHKyD7YhNyeblJTUMssUFhayd+8e6tWvH9c4K5II7UiE/pSamkb2toO/n3JysklNSytZJuI47Nm7h/o+Og7VhdcDb6TawFfh11cBzznn3j6w0zk32zm3A/g/4I/OuQ/D24POub/GI8BOXbqTtTmTbVuzKCgoYOG8WZzff2A8PrrKvJ+9l6b1jyctuRbH1DQGdErh1Y2fR5V5eePnnN6iHgB1jz+GZg2OJ/vLfV6EW6bOXbuz5dNMPtu6hYKCAubPnclPLhjkdViHpGu3Hmz+NJOtWaE2zJ09k/4DB0eVuWDgYKZPnQLA/Hlz6N2nr+9urUiEdiRCf+rWvQefZn5C1pZQG2bPnMGAQUOiygwYNJipU54DYN7c2fQ5+xxfHYeymFlMFq94PfDWCk81fwg8BdwV3t4eWFvGe8rbF8XMrghPSa/5ctcXRxxsIBBg0r2TGTtyMOed0ZlBQ4dzWpt2PPynSbwUvshq/bo1/KhDS5YsmMvtN13D+Wd2PeLPrUrB/Y67Fmzi6QndWHzjWSx9bzuZn3/LNee3om/bkwF48+Od7P7uBxbdcCb/uqIHDyz5mN3f/eBx5NECgQB33/8Ilw4fxNk9OzL4whG0btuOB+75AyvCF8W8u24N3TJasGj+HG654Sr69urscdTRAoEA9z/0KCOGDuBHXdtz4fARtG2XwT133cHSxaE2XD5uAl9+uYtuHVrz1z9P5o5J93gcdUmJ0I5E6U8PPfJnLhzUn24d2zFsxEjatcvgrj9MZPHC0MVu4376M7788ks6tj2Vxx+dzKS7/+Rx1NWTeXlFm5l945w7Mfy6F6HBtz0wh1DGO7+U96wDfuqcW38on9Wxcze38JW3qiBq7/R74HWvQ6gSr97a1+sQjlitpJpehyBh+wqCXodwxOocf4zXIRyx3r16sG7tmipPI2ulnuZa/eyJqq4WgA/u/sla51z3mFReDq8z3iLhaeUGwMnABqBbGUXL2yciIuJrvhl4zawNUBPYRejiqXFm1jNi/7DwRVcPALeb2Wnh7TXM7FdexCwiIrFlJN45Xq/vEallZu+GXxswzjkXBHaY2WjgQTM7BdgP/BtY5pzbYWbXA9PCtxc5YFFplYuIiPiNpwOvc67ME2XhqefeZexbhAZbEZFqwNvsNBa8znhFRETKlWDjrn/O8YqIiFQHynhFRMTXEm2qWRmviIhIHCnjFRER//L4Cw1iQRmviIhIHCnjFRER3zrwAI1EooxXREQkjpTxioiIryVYwquBV0RE/E1TzSIiInLYNPCKiIivmcVmqdxnW38z+8jMMs3s1jLKXGxmG81sg5m9UFGdmmoWEREphZnVBJ4AzgeygdVmtsA5tzGizKnAbcCZzrmvwt+oVy4NvCIi4l/m6Tne04FM59xmADObDgwFNkaU+QXwhHPuKwDn3OcVVaqpZhERqa4amNmaiOWKYvvTgG0R69nhbZFOA04zs7fM7L9m1r+iD1XGKyIivhV6gEbMqt/pnOt+hHUEgFOBs4F04N9m1sE5t7usNyjjFRERKV0O0DhiPT28LVI2sMA594NzbgvwMaGBuEwaeEVExMcMs9gslbAaONXMmptZEjAaWFCszIuEsl3MrAGhqefN5VWqgVdERKQUzrlC4GpgObAJmOmc22Bmk8xsSLjYcmCXmW0EXgN+45zbVV691eYcb6CmUe+EJK/DOCL/nXie1yFUiQGPv+V1CEdsxXVneR1Clcj96nuvQzhiX39f6HUIR6zeiUf37yYInYuNWd0ePrjKObcEWFJs28SI1w64MbxUSrUZeEVE5OikR0aKiIjIYVPGKyIi/nUIj3c8WijjFRERiSNlvCIi4luhB2gkVsqrjFdERCSOlPGKiIivKeMVERGRw6aMV0REfC3BEl5lvCIiIvGkjFdERHwt0c7xauAVERH/0gM0RERE5Ego4xUREd8yKv3duUcNZbwiIiJxpIxXRER8LcESXmW8IiIi8aSB9xC9tGIZXTu2pVPGaTz8wH0l9ufn5zP+8tF0yjiNvr17sXVrVvyDrMDLK5bRo3M7unZozeQHS2/DhLGX0LVDa87r04vPfNgGgJ7Nk5n28+7M/EUPxvRsXGqZc1o3YOqEbjw/oRt3DmoT5wgr9tKKZXTp0JZO7U7joTL607jLR9OpXbg/ZWXFP8gK/PvVFfQ7qzPn9+rAk39+sMT+1W+/yUXnn0G79NosWzTPgwgr5z+vv8ywc7px4dmdefavD5fY//xTjzPy/NMZ3f8MrrxsMHnZn3kQZflWLF9Gx4zWZLRpxQP331tif35+PpdfOoqMNq3ofUZPX/an0tQwi8niWXs8++SjUDAY5Kbrr2HO/MWsfucDZs+azoebNkaV+dezz1A3OZn1Gz7mqmuu447f3upRtKULBoP85sZrmTVvEf9d+z5zZs0o0YYpzz1DnbrJrHv/I668+nru/P1tHkVbthoGN5/XiptmfcClT6/hvLYn06z+8VFl0pOPY+yPmvCrqeu5/Jm1PPrqpx5FW7pgMMhN113D3PmLWf3uB8yeWUZ/qpvM+o2h/jTxd/7rT5Nuv5Gnps5j8etrWfTiLDI/2hRVJiW9MX969O8Muuhij6KsWDAY5L6JN/HYs7OZtWIVyxfMYfMnH0aVaZPRkSkLVjJ92X8494KhPHbvRI+iLV0wGOT6a69i/sKlvPPeRmZNn8amjdH96dlnnia5bjIbPszkmutu4Le33+JRtNWbBt5DsGb1Klq0bEnz5i1ISkpi+MhRLF60IKrM4kXzueSysQBcOGwEK1e+inPOi3BLtXbNKlq0aEmzcBuGjbiYJcXasHTRAi65bAwAQy8azus+awNAu5STyN69j9w931O43/Hypi/o3ap+VJkhHVOY804uX+cXAvDVdz94EWqZivpTi4P9adHCYv1p4XwuvTyiP73mr2Px3jtraNqsBY2bNicpKYmBQ0fwyvJFUWXSGzelTbsO1Kjh3183G9avpXHTFqQ3ac4xSUn8ZPAwXn9pcVSZ7r1+zHG1Qn/cte/Sgx3bc70ItUyrV62iZctWRf1p5KjRLFo4P6rMooXzuWzMOACGDR/Byldf8VV/KotZbBav+PcnwYfycnNITz84pZmalkZuTk6xMrlFZQKBALVr1+HLXbviGmd58nJzSYtqQzp5edG/QHIjyvixDQAnn3gsO77OL1r/4ut8Tj4pKapMk3q1aJxci79d2oknL+9Mz+bJ8Q6zXHm5OVHHIi0tjbzc6P6UW6w/1aldh10+OpPoa5wAACAASURBVBY7tufSKC29aL1hSho7tud5GNHh+Xx7Lg1T0orWT2mUxufltGP+jCmc0ef8eIRWabnFfj+lpaWTk1O8P+WQ3jjiZ7uOv/pTdeGLgdfMgmb2rpmtN7N1ZnZGeHszM/sgotwvzGytmSWb2bNmNsK7qMXvatYwGifX4qrp73HHwg+5td9pnHhsTa/DkqPcknkz2PT+O4y94lqvQ6kWQtmpxWTxii8GXmCfc66zc64TcBvwp+IFzGwMcA3Qzzn3VbwDBEhJTSM7e1vRem5ODqlpacXKpBaVKSwsZO/ePdSrHz0F6qWU1FRyotqQTUpKalSZ1IgyfmwDwBff5NPwpGOL1k8+6Vi++LogqsznX+fzZuYugvsdeXu+Z9tX39E4uVa8Qy1TSmpa1LHIyckhJTW6P6UW60979u6hvo+ORcNGqWzPyS5a35GXQ8NGKR5GdHhOaZTKjryD2eHn23M4pZR2/O/N13jmiQd5+B/TSTr22BL7vZRa7PdTTk42aWnF+1Ma2dsifrb3+Ks/laWGxWbxrD3efXSZagNRA6uZXQzcCvzEObfTk6iAbt17sDkzk6ysLRQUFDBn1gwGDBwcVWbAwCFMm/ovAF6cO5s+ffr66qkrXbv14NNPM9kabsPc2TO5oFgb+g8czLSpUwCYP28OP/ZZGwA25X1NenItUuocR6CGcV7bk3kzM3rK7N+f7KJLk7oA1KkVoHHy8eTs/t6LcEvVrXsPPs3MJGvLwf40cFCx/jRoCC88H9GfzvbXsejQuRtZWz5l22dZFBQUsHj+bM7pN9DrsA5Zu45d2Zb1KTnbsvihoIAVC+fy4/MGRJX5cMN67vnt9Tz8j+nUa3CyR5GWrXuPHmRmflLUn2bNmM7AQUOiygwcNISpU54DYO6c2fTpe46v+lN14ZcHaNQys3eB44AU4JyIfU2Bx4Euzrnth1KpmV0BXAHQuHGTIw4yEAjwwOTHuGjwBQSDQcaM+ylt22Vw96Q76Nq1GwMGDWHs+AlcMWEsnTJOIzm5Hv+c8sIRf25VCgQC3P/QowwfOoBgMMhlY8fTtl0G99x1B527dmfAwMGMGTeBX/18HF07tCY5OZmnn/NXGwCCDh5+OZPJI9tT04xF729ny67v+PlZTflw+9e8mfkl/9vyFT2bJTN1Qjf2O3hi5Wb2fl/odehFAoEADz7yGBcOvoD9kf3pD3fQpVs3Bob70y8mjKVTu9NIrlePf/7LX8ciEAgw8Z6H+PklQwkGgwwfPZZTW7fj0fvvon2nrpzbbyDvvbuWqyeMZu/u3bz20lL+/MAfWfz6Gq9DjxIIBPjNHx7kmrHDCO4PMmTk5bQ8rS1/e/iPtO3QhT7nD+CxP/2efd9+y61XhS5OapiazuSnpnsc+UGBQIDJjz7O4IH9CAaDjBs/gXYZGUy6cyJdu3Vn0OAhjJ/wMyaMH0NGm1YkJ9djylT/xF+eRPvjwPxwRZuZfeOcOzH8uhfwFNCe0KD7KvAlMNU5NzniPc8Ci5xzsyvzGV27dXevv7WqqkOPq+B+749VVRjw+Fteh3DEVlx3ltchVIncr/wzA3C4vvbRH1OHKyO9ttchHLEze3Zn7do1VT5C1mna1p1523NVXS0AS6/sudY51z0mlZfDLxlvEefc22bWADgwl/MdMAB4w8w+d85N9S46ERGJtwRLeP038JpZG6AmsAs4HsA597mZ9QdWmtlO59xyL2MUERE5XH4ZeA+c4wUwYJxzLhg5r++c22JmQ4AlZnZRePPfzeyR8Ottzrle8QtZRERizQh9NWAi8cXA65wr9eZK51wWoXO9B9bXAweujz+6T9iKiEi15IuBV0REpCxe3nMbC368j1dERCRhKeMVERH/8vjxjrGggVdERHwtwcZdTTWLiIjEkzJeERHxLQNqJFjKq4xXREQkjpTxioiIryVYwquMV0REJJ6U8YqIiK8l2u1EynhFRETiSBmviIj4llninePVwCsiIr6m24lERESqCTPrb2YfmVmmmd1ayv7xZvaFmb0bXn5eUZ3KeEVExNe8ynfNrCbwBHA+kA2sNrMFzrmNxYrOcM5dXdl6lfGKiIiU7nQg0zm32TlXAEwHhh5ppRp4RUTE1yz8DUVVvQANzGxNxHJFsY9OA7ZFrGeHtxU33MzeM7PZZta4ovZoqllERKqrnc657kdYx0JgmnMu38x+CTwHnFPeG6rNwPtDcD9ffJ3vdRhH5KTjEuNwPf/THl6HcMSeX/eZ1yFUifE9mnkdwhF7dnWW1yEcsYz02l6H4FuhL0nw7ONzgMgMNj28rYhzblfE6lPA/RVVqqlmERGR0q0GTjWz5maWBIwGFkQWMLOUiNUhwKaKKk2MFEpERBLTwfOxceecKzSzq4HlQE3gGefcBjObBKxxzi0ArjWzIUAh8CUwvqJ6yxx4zezPgCsnoGsPrQkiIiJHF+fcEmBJsW0TI17fBtx2KHWWl/GuOaToREREYiDBHlxV9sDrnHsuct3MjnfOfRf7kERERA6qdt9OZGa9zGwj8GF4vZOZ/SXmkYmIiCSgylxc9QjQj/CVXM659Wb245hGJSIigue3E8VEpW4ncs5tK7YpGINYREREEl5lMt5tZnYG4MzsGOA6KnGfkoiISFWodud4gV8BVxF6PmUu0Dm8LiIiIoeowozXObcTuCwOsYiIiJSQWPlu5a5qbmFmC8Nf9Pu5mc03sxbxCE5ERCTRVGaq+QVgJpACpAKzgGmxDEpERARCD8+oYRaTxSuVGXiPd85Ncc4VhpfngeNiHZiIiEgiKu9ZzfXCL5ea2a3AdELPbh5FsedWioiIxEqCXdRc7sVVawkNtAea/MuIfY5DfCi0iIjI4Ui024nKe1Zz83gGIiIiUh1U6slVZtbezC42s7EHllgH5levv7qC83p1ou/p7fnbYw+W2L/q7TcZcm4vTks5iaUL53kQYcVefWk5vbpmcHqntjz28P0l9r/91huc2/t0UpJrsfDFOR5EWDmJcCw2/Pd17hh9DhNHns3yf/21zHLrXlvKlWc0Z+um9+IYXeWtWL6MjhmtyWjTigfuv7fE/vz8fC6/dBQZbVrR+4yebM3Kin+QFUiEY5EIx6E0ZrFZvFKZ24nuAP4cXvoC9wNDYhyXLwWDQe685QaemfYiy99cx8K5s/jko+iHeKWmNeb+x55k8LBRHkVZvmAwyC03Xce0OQt5c/V65s6ewUcfbowqk5bemMf++hTDRo72KMqKJcKx2B8MMv3BiVz90LNMfGEFq19eQN6WT0qU+/7bb3ht5j9pltHZgygrFgwGuf7aq5i/cCnvvLeRWdOnsWljdJ969pmnSa6bzIYPM7nmuhv47e23eBRt6RLhWCTCcaguKpPxjgDOBbY7534KdALqxDQqn1q/bg1Nm7ekSbPmJCUlMeiiEby8bFFUmfQmTWmT0YEaNSo1mRB369aspnmLljRr3oKkpCQuGn4xyxYvjCrTpGkzMtp39G0bIDGORdbG9Zyc3pST05oQOCaJ7ucNZv0bL5Uot+AfD/OTy3/FMUnHehBlxVavWkXLlq1o3iLUp0aOGs2ihfOjyixaOJ/LxowDYNjwEax89RWcc16EW6pEOBaJcBxKY8TmViK/3060zzm3Hyg0s9rA50Dj2IblTzu255KSlla03igljR15uR5GdOi25+WQlp5etJ6SmkZe7tHVBkiMY7H7i+0kN0wpWk8+uRG7v9geVeazjz7gq8/z6HDmOfEOr9Jyc3NITz/4KyEtLZ2cnJySZRqHygQCAWrXqcOuXbviGmd5EuFYJMJxqC4q8yUJa8ysLvAPQlc6fwO8HdOoADMLAu8DxwCFwL+Ayc65/WZ2NjAf2BIuvtM5d16sYxKJp/379zP7sbsZ97uS568lvnQsPOTx+dhYqMyzmn8dfvk3M1sG1HbOxeOqgn3Ouc4AZnYKoSdo1QbuCO9/wzk3KA5xFGnYKJW8iL8gt+fl0DAlNZ4hHLFGKWnkZGcXrefl5pCSenS1ARLjWNQ9uRFf7cgrWv/qi+3UPblR0Xr+d9+Qu/ljHr4qdK5975df8NdbfsGV9/2Dpm07xj3esqSmppGdffCbQ3NyskmLmI0oKrNtG+np6RQWFrJ3zx7q168f71DLlAjHIhGOQ3VR5lSzmXUtvgD1gED4ddw45z4HrgCuNg9v6OrYpRtZmzPZtjWLgoICFs2bzbn9BnoVzmHp0q07mzdnsjVrCwUFBcybM5N+A+L690uVSIRj0bRtRz7PzmJn7jYKfyhgzcsL6XjWwYmbWifW5sGl6/jj3Df549w3aZ7RxVe/6A/o3qMHmZmfkLUl1KdmzZjOwEHR118OHDSEqVOeA2DunNn06XuOr+7NTIRjkQjHoSxmFpPFK+VlvA+Vs88BcT3R4ZzbbGY1gVPCm3qb2bvh17Occ38s/h4zu4LQgE1q+pGflg4EAtxx78OMHzWE/cEgIy4dy2lt2jH53kl06NyV8/oP4r131nDl+NHs2bObV1cs4dH772bZG2uP+LOrSiAQ4N4HHmHURQMJBvdz6ZhxtGmbwb1330nnrt3oP2Aw76xdw/jLRrJn91esWLqY+++ZxBur1nsdepREOBY1AwFG3/gH/nzDWPYH93PGoJGktjiNhf94mCZtOtCp9/leh1gpgUCAyY8+zuCB/QgGg4wbP4F2GRlMunMiXbt1Z9DgIYyf8DMmjB9DRptWJCfXY8rU6V6HHSURjkUiHIfqwvx6RZuZfeOcO7HYtt1Aa6AtcPOhTDV36NzVzX/prSqOMr5OOq4yp+T97+vvC70O4YityNzhdQhVYnyPZl6HcMSeXZ3ldQhHLBGOw5k9u7N27ZoqTyNPadXejXpgVlVXC8Djw9qtdc51j0nl5ThqfpOHv4owSOiq6rYehyMiInFgJN4jI/15g2MxZnYy8DfgcefXFF1ERKQS/Jzx1gqfwz1wO9EU4GFvQxIRkXirkVgJb8UDb/gq4suAFs65SWbWBGjknFsVy8CcczXL2bcSWBnLzxcREYmFykw1/wXoBVwSXv8aeCJmEYmIiESoYbFZvFKZqeaezrmuZvYOgHPuKzNLinFcIiIiCakyA+8P4ftnHRRd6LQ/plGJiIhw4Cv8Euskb2Wmmh8D5gGnmNkfgTeBe2IalYiISIKqzLOap5rZWkJfDWjAhc65TRW8TUREpEpUx6uamwDfAQsjtznnPotlYCIiIlANv50IWEzo/K4BxwHNgY+AjBjGJSIikpAqM9XcIXI9/M1Evy6juIiISJUxoEaCpbyH/MhI59w6oGcMYhEREUl4lTnHe2PEag2gK5Abs4hEREQiHBVfKnAIKnOO96SI14WEzvnOiU04IiIiia3cgTf84IyTnHM3xykeERGRKAl2irfsDN7MAs65IHBmHOMRERFJaOVlvKsInc9918wWALOAbw/sdM7NjXFsIiJSzZlZwl3VXJlzvMcBu4BzOHg/rwM08IqIiByi8gbeU8JXNH/AwQH3ABfTqERERMISLOEt9yrtmsCJ4eWkiNcHFhERkZjz8vt4zay/mX1kZplmdms55YabmTOz7hXVWV7Gm+ecm1S50ERERBJL+M6eJ4DzgWxgtZktcM5tLFbuJOA64H+Vqbe8jDfBknsRETnaHHhkZCyWSjgdyHTObXbOFQDTgaGllLsLuA/4vjKVlpfxnluZCo4WgRo1qHdCktdhCBDcf/RfIjC+RzOvQ6gSl/9rrdchHLGxPdK8DkGOXg3MbE3E+pPOuScj1tOAbRHr2RR7ZHL4+wsaO+cWm9lvKvOhZQ68zrkvK1OBiIhILMXw4qqdzrkKz8mWxcxqAA8D4w/lfYn2CEwREZGqkgM0jlhPD2874CSgPbDSzLKAHwELKrrAqjL38YqIiHjjEK5AjoHVwKlm1pzQgDsauPTATufcHqDBgXUzWwnc7JxbQzmU8YqIiJTCOVcIXA0sBzYBM51zG8xskpkNOdx6lfGKiIivmYc32TjnlgBLim2bWEbZsytTpzJeERGROFLGKyIivhW6j9frKKqWBl4REfG1RBt4NdUsIiISR8p4RUTE1yzBvp5IGa+IiEgcKeMVERHfSsSLq5TxioiIxJEyXhER8S+L6ZckeEIZr4iISBwp4xUREV+r5JfWHzWU8R6il1cso0fndnTt0JrJD95XYn9+fj4Txl5C1w6tOa9PLz7bmhX/ICuQCG0A+PerK+h3ZmfO+1EH/v7nB0vsX/32m1x4/hm0TavNsoXzPIiwYiuWL6NjRmsy2rTigfvvLbE/Pz+fyy8dRUabVvQ+oydbs7LiH2QFOqfV5tHhGfx5RAYXdmxYYv/Zrerz9CUdeWBoWx4Y2pZzT6vvQZQVW/fmq1w5+Cx+ObAXs5/+c4n9S2c+x7XD+nL9yPO4ddwQPvv0Iw+iLF8i9KfqQAPvIQgGg/zmxmuZNW8R/137PnNmzeDDTRujykx57hnq1E1m3fsfceXV13Pn72/zKNrSJUIbINSOP9x2I/94YR5L/r2WRfNmkfnRpqgyKWmNuffRvzPooos9irJ8wWCQ66+9ivkLl/LOexuZNX0amzZGH4tnn3ma5LrJbPgwk2uuu4Hf3n6LR9GWrobBz3s14Y8rPuGGuRs5q0U90useV6Lcf7Z8xW/mb+I38zfxyse7PIi0fMFgkL/fczt3/HUqj7/4Om8sfbHEwNpnwDAem/saj8x6mYvGX8UzD9zpTbBlSIT+VJoDVzXHYvGKBt5DsHbNKlq0aEmz5i1ISkpi2IiLWbJoQVSZpYsWcMllYwAYetFwXl/5Ks45L8ItVSK0AeC9d9bQtHkLmjRtTlJSEgMvHMHLyxdFlUlv0pQ27TpQo4Y/u/nqVato2bIVzVuEjsXIUaNZtHB+VJlFC+dz2ZhxAAwbPoKVr77iq2PRqsEJbN/7PZ9/XUDhfsdbm7+iR5O6Xod1yD754B0aNWlGo/SmHHNMEr37D2XVa8ujyhx/4klFr/P3fee7hzokQn8qi1lsFq/48zeST+Xl5pKW3rhoPTUtnby83KgyuRFlAoEAtWvX4ctd/vkLPxHaALAjL5dGqelF641S0tiRl+dhRIcuNzeH9IhjkZaWTk5OTskyjSOORZ067PLRsah3wjHs/PaHovVd3xZQ7/hjSpT7UbNkHrqwLTf1bUH9E0ru99quHdtp0DCtaL1+wxR2fb69RLnF0//JLwf8iGcn380vbr07niFWKBH6U3Xh6cBrZs7Mno9YD5jZF2a2KLw+Prz+bsTSzsyamdkH3kUuIpW1Ztturpz5Pje9uIn3cvdyde9mXod02AaO/il/X/Jfxl3/W2Y++YjX4VQTRo0YLV7xOuP9FmhvZrXC6+cDOcXKzHDOdY5YNuKRlNRUcrK3Fa3n5mSTkpIaVSY1okxhYSF79+6hXn3/XEySCG0AaJiSyvbc7KL17Xk5NExJ8TCiQ5eamkZ2xLHIyckmLS2tZJltEcdizx7q++hYfPntDzSIyGDrn5DEl9/9EFXmm/wghftD05mvfLyTFg1OiGuMlVG/YSN27jj4q2fXjjzqn9KozPK9L7iQ/722LB6hVVoi9KfqwuuBF2AJMDD8+hJgmoexlKtrtx58+mkmW7O2UFBQwNzZM7lg4OCoMv0HDmba1CkAzJ83hx/36eurc0GJ0AaADp27kbX5U7ZtzaKgoIDFL87m3J8MrPiNPtK9Rw8yMz8ha0voWMyaMZ2Bg4ZElRk4aAhTpzwHwNw5s+nT9xxfHYvMnd+SUuc4TjkxiUAN48wWyaz+bHdUmbq1Dt612L1JXXJ274t3mBU6NaMzeVu3sCP7M374oYA3ls3n9LP7RZXJ3bq56PWaf79MSpPm8Q6zXInQn0pjJN45Xj/cxzsdmBieXu4IPAP0jtg/yszOiljvVdmKzewK4AqA9MZNjjjQQCDA/Q89yvChAwgGg1w2djxt22Vwz1130LlrdwYMHMyYcRP41c/H0bVDa5KTk3n6uReO+HOrUiK0AULtmHjPQ/zskqEEg0FGXDKWU9u049H77qJ9566c228g772zlqsmjGbv7t289tJSHnvgjyz59xqvQy8SCASY/OjjDB7Yj2AwyLjxE2iXkcGkOyfStVt3Bg0ewvgJP2PC+DFktGlFcnI9pkyd7nXYUfY7eOrtz/hdv1OpYcarn+wke/f3jOqSwqc7v2PNtj0MaHcKPZrUJegc3+QHefyNLK/DLqFmIMAVt9/DnVdewv5gkHMvHE2TVq2Z+sT9tGrXiZ59+7F42jOs/98bBALHcELtOlx/92Nehx0lEfpTdWFeXtFmZt845040szXAE8CpwArgZufcIDMbD3R3zl1d7H3NgEXOufaV/awuXbu71978X5XFLofvi6/zvQ7hiDWuf7zXIVSJy/+11usQjtjYHmkVF/K5n7Qte1r7aHFmz+6sXbumyvPIpm07utueWVBxwcNw5RnN1zrnusek8nL4IeMFWAA8CJwN6ISDiIgkLL8MvM8Au51z75vZ2V4HIyIi/pFoj4z0xcDrnMsGyjphUvwc76+BXKC1mWVHbL/BOTcrVjGKiEj8Hbi4KpF4OvA6504sZdtKYGX49bPAs2W83X934YuIiFTAFxmviIhIWRJtqtkP9/GKiIhUG8p4RUTE1xIs4VXGKyIiEk/KeEVExLeMxMsQE609IiIivqaMV0RE/Mvw/Rc5HCplvCIiInGkjFdERHwtsfJdDbwiIuJjhh6gISIiIkdAGa+IiPhaYuW7ynhFRETiShmviIj4WoKd4lXGKyIiEk/KeEVExMdMD9AQERGRw6eMV0REfEtfkiAiIiJHpNpkvGZw7DFH998Zwf3O6xCqxCm1j/U6hCPmXGIci7+P6uR1CEcs/azrvQ7hiH21+nGvQ/A1L8/xmll/4FGgJvCUc+7eYvt/BVwFBIFvgCuccxvLq/PoHolERCThWYyWCj/XrCbwBHAB0A64xMzaFSv2gnOug3OuM3A/8HBF9WrgFRERKd3pQKZzbrNzrgCYDgyNLOCc2xuxegJQ4XRYtZlqFhGRo1Bsv4+3gZmtiVh/0jn3ZMR6GrAtYj0b6Fm8EjO7CrgRSALOqehDNfCKiEh1tdM51/1IK3HOPQE8YWaXAr8DxpVXXgOviIj4lse3E+UAjSPW08PbyjId+GtFleocr4iISOlWA6eaWXMzSwJGAwsiC5jZqRGrA4FPKqpUGa+IiPiaV7cTOecKzexqYDmh24mecc5tMLNJwBrn3ALgajM7D/gB+IoKpplBA6+IiEiZnHNLgCXFtk2MeH3dodapgVdERHwtsb4iQed4RURE4koZr4iI+FqCfSugBl4REfGv0O1EiTXyaqpZREQkjpTxioiIryXaVLMyXhERkThSxisiIj5mmM7xioiIyOHSwHuIVixfRqeMNrRveyoP3n9vif35+fmMuXQ07dueyo/P/BFbs7LiH2QFXlqxjC4d2tKp3Wk89MB9Jfbn5+cz7vLRdGp3Gn179/JlGwBeXrGM7p3a0aV9ayY/WHo7fjrmErq0b825P+7F1q1Z8Q+yAonQnwBeeWk5Pbtk0KNjGx596P4S+/Pz8/nZ2Evp0bENPzn7DD7z4bE4/4y2rJ/3ez6Yfwc3//T8EvubpCSz5G/XsGrGbSz/x3WknVLXgyjLt2L5MjpmtCajTSseKKM/XX7pKDLatKL3GT1925+KM4vN4hUNvIcgGAxyw3VX8+LCJaxbv4FZM6azaePGqDLP/vNp6ibX5YNNn3DNtdfzu9tv9Sja0gWDQW667hrmzl/M6nc/YPbM6Xy4KboN/3r2GerWTWb9xo+56prrmPg7f7UBQu24+YZrmf3iIv637n1mz5pRoh1Twu1454OP+PU113Pn727zKNrSJUJ/glA7brnxWmbMXchba95j7qzpfFTsWEx97hnq1q3L6vc+5FdXXccffn+7R9GWrkYN45FbL2bo1X+hy/C7Gdm/G21aNIoq86cbLmLq4lWcPupP3PPkUiZdM8SjaEsXDAa5/tqrmL9wKe+8t5FZ06eV7E/PPE1y3WQ2fJjJNdfdwG9vv8WjaKs3DbyHYM3qVbRs2YrmLVqQlJTEiItHsWjh/Kgyixcu4PIxoWdkXzR8BCtfewXnnBfhlmrN6lW0aNmyqA3DR45i0cKoL9tg8cL5XHr5WAAuHDaCla+96qs2AKxdE2pHs+bhdoy4mCWLotuxZPECLrl8DABDLxrO6yv91Y5E6E8A69asonmLg8fiohGjWLp4YVSZpYsXMvqy0LEYctFw3vDZsejRvhmfbttJVs4ufigMMmv5Ogad3TGqTJsWKby+6iMAXl/9MYPO7uBFqGVavSq6P40cNbpEf1q0cD6XhfvTsOEjWPmq//pTcQfu443F4hUNvIcgNyeHtPT0ovW0tHRyc3NKKRP6+sZAIEDtOnXYtWtXXOMsT17uwfgA0tLSyCvehtxc0iPaUKe2v9oAkJebS1rawXakpqWTl5tbZplAIEDt2nX40kftSIT+BKH/59SIdqSW0qfycnNLtMNPxyL1lDpk7/iqaD1nx1eknVwnqsz7H+cw9JzOAAw9pxO1T6xFvTonxDXO8uTm5hT93EKoP+XkFP/ZziG9sb/7UwkxmmbWVHMxZubM7KGI9ZvN7E4z62NmbxcrGzCzHWaWGv9IRaS6uG3yPHp3a8Xb026hd7dW5Oz4imBwv9dhyVHIlwMvkA8MM7MGxba/AaSbWdOIbecBG5xzucRYaloaOdnZRes5OdmkpqaVUmYbAIWFhezds4f69evHOrRKS0k9GB9ATk4OKcXbkJpKdkQb9uz1VxsAUlJTyck52I7cnGxSUlPLLFNYWMjevXuo56N2JEJ/gtD/c25EO3JL6VMpqakl2uGnY5H7+R7SGyYXrac1TCbniz1RZfK+2MPom5+i1yX3ccfjoan0Pd/si2uc5UlNTSv6uYVQf0pLK/6znUb2Nn/3p9Io442Pfl4DuwAAIABJREFUQuBJ4IbIjc65/cBMYHTE5tHAtHgE1a17DzIzPyFryxYKCgqYPXMGAwdFX2AxYNBgnp/yHADz5symz9nnePYlzqXp1r0Hn2ZmFrVhzqwZDBw0OKrMgEFDeOH5fwHw4tzZ9Dm7r6/aANC1W7gdWeF2zJ7JBQOj23HBgMFMe34KAPPnzeHHffzVjkToTwBduvVg86eZbA0fi3mzZ9B/wKCoMv0HDGL61NCxWDBvDr19dizWbNhKqyYn0zS1PscEajKyX1cWr3wvqkz9uicUxfybCf14bv5/vQi1TN17RPenWTOml+hPAwcNYWq4P82dM5s+ff3Xn6oDPz9A4wngPTMrfm/CNOAfwH1mdiwwALixtArM7ArgCoDGTZoccUCBQICHH/kzQwb2J7g/yP+3d99xUtVXH8c/X1hAFJEFQQVEsQXBBykL2FssIEWNEEVFEOwtGpMnJsZGYk3sJY8NRSWggAaIvWJJVASVCIgSsAAWFAuKgizn+eP3W5ktsAvszL0znDevfTFz5+6dc3d277nnV+49fvAJtO/QgeGXXESXriX06duPIScMY9iQ49ll5x0pLm7Kvffn5JygxoqKivjr9TdyeN9erCwtZdDgE9i5fQf+fOnFdO7ald59+nH8kKGcNPR4dm2/E8VNm3L3vX9POuxKioqK+Mu1N3Bkv0MpLS3luOOHsHP7Dlw2/GI6dynh0D59GTRkKKcMG0znXX5GcXExI1K2H4Xw+wRhP6685gYGHN6blaWlHDNoCO3ad+CKP11Cpy5d6dW7L8cOHsrpJw6hW8d2NCku5o57RiUddjmlpSs596oHmXTrGdStI0ZOeIVZcz/hwtN6M23mhzwy+T/sU7Ijw8/qhxm8NG0O51zxYNJhl1NUVMR1N9xM396HUFpayuAhQyv/Pg0dxtAhg+jQbgeKi5ty36gxSYddI4V2AQ2lcUSbpG/NrJGk4cCPwPdAIzO7JL7+HtAH2BkYZmZ9V7uxqEvXEnv5lSlZjDr7Slem77NaF4WwH/WL0tpYtHaWLi9NOoT11nqvc5IOYb19OeXmpENYb3v2KGHq1NdrPUPutEsnu2Xs07W9WQAObt98qpmVZGXja5DmihfgemAacHeF5aMJTcw7k6NmZuecc7knoE5hFbyp7eMFwMwWE/p0h1V4aTRwHHAAMKHi9znnnHNplerEG10DlBvdbGazgO+AZ83su0Sics45lxPK0r+kpLKp2cwaZTz+FNi4inU65TQo55xzrhakMvE655xzZQptxpMnXuecc6lWaNOJ8qGP1znnnCsYXvE655xLLZ9O5Jxzzrn14hWvc865FEt26k82eMXrnHPO5ZBXvM4559Ir4Vv4ZYNXvM4551wOecXrnHMu1Qqs4PWK1znnnMslr3idc86lVpjHW1g1ryde55xzqVZYadebmp1zzrmc8orXOedcuhVYyesVr3POOZdDXvE655xLNb9kpHPOOefW2QZT8QpQng9JX7aiNOkQasUmDTaYX7vUK4TP4tbb/zfpENbb7pc9m3QI6232x0uytu08P3RX4hWvc845l0P5f7rrnHOuoBVYwesVr3POObc6knpKmi1pjqTzq3j915JmSpou6RlJ21S3TU+8zjnn0k1Z+qrubaW6wC1AL6A9MFBS+wqrvQGUmFlHYBxwdXXb9cTrnHMutUKOzM6/GugOzDGzuWa2HBgDHJa5gpk9Z2ZL49NXgNbVbdQTr3POuQ3V5pJez/g6ucLrrYCPMp7Pj8tWZxjwWHVv6oOrnHPOpZeyOp3oczMrqY0NSToOKAH2rW5dT7zOOedc1RYAW2c8bx2XlSPpQOACYF8zW1bdRr2p2TnnXKolNLYKYAqwo6S2kuoDRwMTy8UmdQZuA/qZ2Wc12agnXuecc64KZrYCOBN4ApgFPGhmMyQNl9QvrvYXoBEwVtKbkiauZnM/8aZm55xz6ZbgFTTM7FHg0QrLLsp4fODabtMrXueccy6HvOJ1zjmXYjWec5s3PPE655xLNb87kXPOOefWmVe8zjnnUmstpv7kDa94nXPOuRzyxLuWnnzicTp2+Bkd2u3AX66+stLry5Yt47hjjqJDux3Ye48efPD++7kPshrPPPUEPTp3oFvHdtxwTeUbaSxbtoxhxx9Dt47tOHi/Pfjwg/dzH2QNFMJnUQj7AIWxH//59/P8vv/+nP+LfXhk5K2VXn9u/P1cOPBgLj62F5efdCQL5r6bQJRrtsf2TXn4jB5MOGs3Ttiz6rvTHdS+BeNP78G407pz+S8q3mgnpRK8gkY2eOJdC6WlpZxz9hlMmPQYb0yfydgxo5k1c2a5de4ZcRfFTYqZ8c4czvrVuVzwh98lFG3VSktL+d2vz+aBhybx8uvTeWjsGGbPKr8Po0aOoEmTJkyZ/g6nnvErLr3wDwlFu3qF8lnk+z5AYezHytJS7r/6Qs69YSR/fuBpXn1iYqXEutshh/Gn0U9y6ajH6DXoVB64/s8JRVu1OoLzD/0ZZ456iyNveZWeu7Rgu803LrdOm6YNGbrXNgwZMZX+f3uNvzz+XkLRbtg88a6FKa+9xvbb70Db7bajfv36DDjqaP45aUK5df45aQLHDhoMwC+O7M/zzz6DmSURbpWmvf4abbfbnm3bhn04ov9RPPbIpHLrPPbIJI4+dhAA/Y44kheffzZV+wCF8VkUwj5AYezH3Blv0qL1trRo1YaievXpcXBf3nzhqXLrNGy06U+Pl32/NHUdj7u0asxHi5ey4KsfWLHSeGLGZ+zXrnm5dY7o0pIHp8xnyQ8rAPhy6Y9JhLrWErwtYFZ44l0LCxcuoHXrVdfLbtWqNQsWLKi8ztZhnaKiIhpvthlffPFFTuNck48XLqRl61W3i2zZqhUfL1xQaZ1Wrcvvw+IU7QMUxmdRCPsAhbEfXy36hKZbbPXT8+IWW/Hlok8qrffM2JH87oi9GXvTFRx73qW5DLFaLTZtwKffrLo+/6ffLKP5pg3KrbNNs41p02xj7j6hCyOHdWWP7ZvmOkxHDhKvpOckHVJh2TmS/iZpc0k/Sjq1wutDJf1H0nRJb0s6LOO130h6J14Tc4qk47O9D845B/DzAYO56uEXGXDm+UwacVPS4ay1unVEm6Ybc9LIN/j9+Blc2LcdjRqkf3KLlJ2vpOSi4h1NuKNDpqPj8gHAK8DAshcktSbcXmkvM+sI7AZMj6+dChwEdDezTsDPyWGDT8uWrZg/f9U9kRcsmE+rVq0qr/NRWGfFihV88/XXNGvWLFchVmurli1ZOH/+T88XLljAVi1bVVpnwfzy+9A0RfsAhfFZFMI+QGHsR5PmW7L4049/ev7lZx9T3HzL1a7f/eB+vDH5yVyEVmOfLVnGFo1XVbhbNG7AoiXl71D32TfLmPzu56xYaSz86gc++GIpbZo1zHWoG7xcJN5xQO94SyUkbQu0BF4kJNzzgFYx4QK0AJYA3wKY2bdmNi++9gfgNDP7Jr72jZmNzME+AFDSrRtz5rzH+/PmsXz5csY+MIbeffqVW6d3n36Mui+E9ND4cey7/wEoRZdd6dy1G3P/O4cP3g/78PC4B+h5aJ9y6/Q8tA9jRt0HwMSHx7P3vvunah+gMD6LQtgHKIz9aNt+Vz79aB6LFnzIih+X8+qTk+i090Hl1vn0w3k/PZ7+8rO02HrbHEe5ZjMWLKFNs41p2WQjiuqIQzq04PnZn5db57l3FlGyTRMAmjSsxzbNNmbBl98nEe5aKbBBzdm/gIaZLZb0GtALmECodh8k3FB4KzN7TdKDwFHANcBbwKfAPEnPAA+Z2SRJjYFNzWxuTd9b0snAyQBbt2mz3vtSVFTEdTfcTN/eh1BaWsrgIUNp36EDwy+5iC5dS+jTtx9Dhg5j6JBBdGi3A8XFTblv1Jj1ft/aVFRUxJXX3MCAw3uzsrSUYwYNoV37Dlzxp0vo1KUrvXr35djBQzn9xCF069iOJsXF3HHPqKTDrqRQPot83wcojP2oW1TEcb8dzrVnH8/KlaXs1feXtNp+Jx6+7Rq23bkjnfc5iGfGjmTmay9Rt6gemzRuzIkXX5t02OWUmnHVo+9y63GdqCMx4c2FzF30Haft15aZC5cw+d3P+dd/F7P79k0Zf3oPSlca1z81h6+/X5F06GuWdJbMAuViZKGkY4E+ZjZQ0pvAMGB/oNjMLpDUERhhZiVxfQHdCE3Jw4D7gWuBD8yseF1i6Nq1xF5+9fVa2JvkfLcs5X8gNbRJHvQpufwx+o0Pkw5hvd346JykQ1hvs287jaULZ9d6iuywaxd74NEXanuzAPxP602nluWdXMrVqOYJwM8ldQE2NrOphGbmIZLeByYCHSXtCGDBa2Z2BaFCPjI2L38rabscxeyccy4FfDrROjCzb4HngBHAaEk7AY3MrJWZbWtm2wJXAAMltYwJukwn4IP4+ArgltjsjKRGPqrZOedcPsllm99o4GFCBTswPs40HngAGAn8VVJL4AdgEVA23ehvQCNgiqQfgR8J/cLOOecKkCi82wLmLPGa2T9Y1UVeaea5mU0Hdo5PD1jNNgy4On4555xzecdHuTjnnEu1Ait4/ZKRzjnnXC55xeuccy7dCqzk9YrXOeecyyGveJ1zzqVaknNus8ETr3POuVQrtOlE3tTsnHPO5ZBXvM4551KtwAper3idc865XPKK1znnXLoVWMnrFa9zzjmXQ17xOuecSy1ReNOJvOJ1zjnncsgrXuecc+kln8frnHPOufXgFa9zzrlUK7CC1xOvc865lCuwzOtNzc4551wOecXrnHMuxVRw04k2mMQ7bdrUzxvW0wdZfIvNgc+zuP1cKYT98H1Ij0LYj0LYB8j+fmyTxW0XlA0m8ZpZ82xuX9LrZlaSzffIhULYD9+H9CiE/SiEfYD83g+fTuScc865dbbBVLzOOefyjyi4Qc1e8dai25MOoJYUwn74PqRHIexHIewDFM5+5D2ZWdIxOOecc1Xq2KmrTXzm5axsu+3mDacm0e/tFa9zzjm3GpJ6SpotaY6k86t4fR9J0yStkNS/Jtv0xOuccy7VlKV/1b6vVBe4BegFtAcGSmpfYbUPgSHA32u6Pz64Kosk1TWzUkkyb9N3zqVMvhybEpxO1B2YY2ZzQxwaAxwGzCxbwczej6+trOlGveLNEkl7AidLapEPv9gVSdpFUqek49hQSTpE0oCk43A/NSXuGB/n/QBbSR0l7SupTj4em2rZ5pJez/g6ucLrrYCPMp7Pj8vWiyfeLJDUE7gN+J48vJqLpEOBu4ABklomHc+GRtLBwNXAoqRjqS2S8vlY0x+4sxASlaRDgHuBnYFdEg6nxpSlL+BzMyvJ+MrJyG9vaq5lsUq8CRhqZi8mHc/aknQQcA1wvJlNSTqetSVpB6Chmf0n6VjWRUy6dwJ9zGy6pBbAVmb2VsKhrRNJewA/mNm0mLhq3ByXtIxm2MuAPwM9gH/n236UkbQvcCNwYj4emxKyANg643nruGy95PNZaKpkNEG1Bp4ysxdX1yyV8uaqHsClZjZFUhHkT7UiqT7wv8BxkjokHc/aklQP6AgsBt6XtAkwjvJ/+PmmK/CgpE5mtjJffpcAMqrbr4CVwMC4PK+SbsbxZjfg1nhsqlPhtYrrpodCH282vmpgCrCjpLbx+HI0MHF9dylv/gjyQKP4/3dAizgaTrAqcUnaPw/6fFsDnQDMbEX8fyWApG0Ti6oakg4EjgGuBBoDv5S0S8brZZ9FvWQirJ6Z/QjcQWjmfwh4A7jbzP6ZaGDrQNLekjqb2U3AtcA98fnKshO6uN7myUVZNUkdJI2SVCypgZktAy4F9pHUJ+n41lbG8eZHYLP4uG7mOpJ2y5eBVrkUj4FnAk8As4AHzWyGpOGS+gFI6iZpPjAAuE3SjOq264m3FsQ+3TslbUw4O94G6FJ2hp9xhlwC9EzbWWX8xekTTxCeBkpjtVX2etnvyVlVDKVPVMbPshvQOI4+vBrYitBH3RHCwUfSKcCItFVdknaUtIek/Qmh3kT4Q/8BeCGuU3dN20iT2Fx+H1AfwMxuBe4B7pbUteyELn4eF0naKKlYK5K0DbApIUmNI8S3n5ktBEYC28f18uLzkNRV0u7x6ZeEaTGY2Y+S6mck2r0Jf0MplcVe3mqY2aNmtpOZbW9ml8VlF5nZxPh4ipm1NrNNzKyZmVXb2paqA1A+ioMVLgNuM7OlZvYGoWr5p6RuwEZxvUHACcDLaTqrjAOpbiUMtmhBqLL2B06TtCmEilfSUcBehD/e1Mj4WTYCmsRl84DLCcn3SElNJQ0Ffgtck6amQkm9gQcIsV0AvC1pV0Jf3F3A9ZJ2M7PSBMOssbg/lwHHmtmrkraU1MTMrif0Xd8Vlx0G/BEYYWY/JBlzGUlbAr8CupvZEOBCwu/7vZJOB1oCgyVtkw+fRywIHgA2jZX73cAiSS8AmNnyuN4g4Dhqoe/S1YwPrloPkn5OaEY708yei02xQ83solgxXg8skfQJoe+0v5n9N7GAK5C0N3AdcFzmQKp4kLkZ2CJW8XOAYcBAM/s4kWCrEJuSLzezfoR+0c3j8jpm9r6kKwkJ7T5CX+PBZjY9sYAriAfGC4FzzWxyXHYxMAk41MxukGTA1ZLONbOpCYZbLUlbEZLu82b2ckxkLwAXAWPM7Oa4P3MJXTIHpGwQ3GeEE89ukk4D7jGzf0n6F6FSbELohjlV0gVpOoGrSFIP4AbgpHhsKiuyDiOcSEwBXgKWAkcCR5pZKhOvqHF/bN7wxLt+jgA+jL/YbQhnl/cCmNl1kh4l9Kk0BP5oZvOTC7VKbYEb40CqerH5qZ6ZvRnPgn8G7AcYMMDMZiUZbBXeB1ZKGgX8i9AHA0A8w58r6QbCScN5ZvZOMmFWJqkp8CjQz8wmS9rIzH4ws0tj8/mEWPneT5iW9lmS8VZH0mZm9rGkG4EOkn4NHE5oYRhTtp6Z3SJpKTDFzN5OKt5MCnN065jZbEn3E7qLehLm4Zcl37cIx8uvgDvTnHSjHYBx8djUDNgtnmh/Q2h52w/YjtDX29fM3kss0hoosLzrN0lYF/EPdRnwMaH5TEAHwkCYm8v6dSU1M7Mvkox1TSRdAuxiZv0zlin2h7aNTbapEwfklJrZl5IaEJrKTwCWA+MJJxQCPiVU639MS3NmptgseyWwn5l9kTGQB0nPEU4WpileAS3RYNcgdrdcDpwTR8wOAs4B3jOzozPWOwz42syeTybSymJSWgR8ThhAVUq4i88xwLaEyvw2M1uaVIzrQuECPn8nfC5HEY5VGxGazrckVLjLkouw5nbt3NUee+7fWdl2q+IGidwkwSvetaQwpPy3wBLCfNcTCc3NXxP65Mr6RAcDR0k6kjCPMRVnOPFAY2a2GBgDnBsrq+kxRhEq3KGSJpvZ0wmGW0nsk76EMN3mPTO7QNI5hIFIBwK/ATYhnM1/RTjQpy7pApjZIwqXmXtNUkk8kagXRzd/QxjgQ5qTbrQT4cTzIknXmNl9cczbHpIGxecDCM3QvZMMtKJ4wnMgYVBhHWBXQsvVt4QTuSbAj5LuSHuiyjxxA/4NnE84Pr1KGBj2LqE75ioqjGpOO29q3oDFP9DWhCrlt8AZhCtU/ZqQdK+VdC6hz+Q0wkT17xMKt5IKSWsW8CfC78AgwtnxtHjScDTQh1DNp0bsE/0D4QD+AXCepI3NbImk8wjx/hUYZGZzEgy1xszsMUlnAq9nJN/jCVXJpwmHV1OjCSc6HwGnxJOHsuTbPVbEOwCHp7FJ08yejTHeSEi8WwAHEOZsdiecVNxHaOVKpRj/yZImmtnI2BQ+Oj7/LmO9QwmfRX1C/65LgI9qroHY5waVp6w0B04hjAYeRmjKeZqQlIempQ8LKiWty4Ed4rSOcwhxnybp8Ti454+EK1d9kFjAFWT0iV5jZhMIB44Dgb9Kui1WtUMJU0HuTS7StWdmjxHmCr4QB/WcCgwzs9T26ypc77djfLqYUB22B/5GSACHmtl9wFuE6XUnmtnMqreWPDN7itBa8jbwnZmNJDQ370y4itjXSca3JpLKriGwF/AbSWMl/VJSq7KkK6lZ7Hf/FXCKmX2VVLzrIqm7E2WLV7w1UGHKSr24bJ6kywnJ7HTCraNOJlSR96R0IM+RZjZBUnfgAEm3Efp8LiAkrAGESnJU2ipGM1ssqS/wZ0lzCScQtxOq3HGSxpjZ0ZIGsuoiAXkjVr51CRfO6Gxm1U7CT0rsrngTWBBbeD4g/A7dQOiqGEUY+VvfzO6U9KCZfZNcxDWT0fT/iqTd0zw+o4zCvOPhhBOe8YTPYE9CVXuppF8RBiGuAPYgtAal9gRoQ+GJtxqqfsrKVcB5hEsVXmNmlW6UnLQ1JK27CH+sW5vZsYSpRakVD4ylhCkffzCzK+GnaV0TMgazfbem7aSVmf1TYc5rqpsAK/SLdiRUhecS5oE2N7P7JTUk3Lv0qXxIumXiCVB94GmFi32kffRyI0LT+JfAPOB6M+ulcI3sSwktco0JU9SOsTh3N+8UWB+vNzVX7wPClZxGEQbwVJyy8l9CtWvx9VQys0eA3xOS1jNmdrGZfUi4WMYWSuGl+6piZo8DhwAnSGoSFw8gTNnKz4NKhrQn3TJm9ixwEDCYMKp8MqE/tGdMXOMIc0jz7iQodmXsnQdJl9gy8hKh5ecOYJakywjT0HoT+qkvAR7N26RbgLziXQ1JW5rZJ3HgzkDCweUmYHkcyNA2rKZPgdnABWk/aJrZ4zH2myX9X+znGQBsTIoHjlRkZk/FkcwvSbqVcHA52cyWJBzaBsXMnol90s8Du5vZbXEa2nLy/CTIzL5NOobViV1HyzNivA5oQ7hIzyLgd4QLsPwrtsy9nFCotabACl5PvFWR1A6YqXDxhVlmdruks4EvCBfNqGrKSqqTbplCSVr51CdayMzs0Tj2cIqkPcvmfkt+wf1siK0844A3JL0Qq/OlrDo2nQX8EiiG/LuT0obCE2/VviVcCekToL+kvYAHCU3KRYQzzGPSNgCppgolaeVLn2ihi8m3HqFftCQs8qSbDWb2laSTCAOlbo0DJZ8nXHr0aULz/12EOdRPp33ucU2sxS388oZfuWo1JF0LtAKOJTTHHk0YLXsGMIJwybszk4tw/cU5sJ60XK2Q1CjNTbSFRtJOhGsG9AAaAB8SKt/RwOeWouuqr49OXbraU5Nfzcq2WzSul8iVq3xwVQUZc3bPJwyY2pxwubX/Ad4jXPD9v4T+3rzmSdfVJk+6uWVm7wJXm9nhwFTCMeokYG6hJN1C5U3NFZiZZSTf9wiXhewK/NrM/qFwnebPzSxVt8dzzm2QVgKY2R8ltSC0YubdSPJqFVhTsyfeKsT+qeUKdyqZDNxiZv+Ir6XuknfOuQ1TWaFgQWqvdObK86bmNTCz2YQm57oK96V1zrlU2RAGsilLX0nxxFu9V4AuSQfhnHOuMHhTczXM7B1JR/tAJOecS0ahTSfyircGPOk655yrLV7xOuecS7Fkb+GXDV7xOueccznkFa9zzrnUEt7H65xzzrn14InXOUBSqaQ3Jb0taez6zNuWdI+k/vHxnZLar2Hd/eJNy9f2Pd6v6h7Kq1teYZ21urSjpEsk/WZtY3TOVc0Tr3PB92bWycx2IdxL9tTMFyWtU7eMmZ1oZjPXsMp+hDvNOOdWo+wORbX9lRRPvM5V9iKwQ6xGX5Q0kXB/5rqS/iJpiqTpkk6BcGMNSTdLmi3paaBF2YYkPR9vlYeknpKmSXpL0jOStiUk+HNjtb23pOaSxsf3mCJpz/i9zSQ9KWmGpDupwYV3JP1D0tT4PSdXeO26uPwZSc3jsu0lPR6/58V4X2rnXC3zwVXOZYiVbS/g8bioC7CLmc2LyetrM+smqQHwsqQngc7Az4D2wBbATMKtIzO32xy4A9gnbqupmS2W9H/At2b217je34HrzOwlSW2AJ4CdgYuBl8xsuKTewLAa7M7Q+B4NCTeqH29mXwCbAK+b2bmSLorbPhO4HTjVzN6T1AO4FThgHX6MztWqQptO5InXuaChpDfj4xeJNxMHXjOzeXH5wUDHsv5bwv2ZdwT2AUabWSmwUNKzVWx/N+CFsm2Z2eLVxHEg0H7VDbJoLKlRfI9fxO99RFJN7o51tqQj4uOtY6xfEO5o80Bcfj/wUHyPPYCxGe/doAbv4ZxbS554nQu+N7NOmQtiAsq8xZqAs8zsiQrrHVqLcdQBdjOzH6qIpcYk7UdI4rub2VJJzwMbrWZ1i+/7VcWfgXOJS7g/Nhu8j9e5mnsCOE1SPQBJO0naBHgBOCr2AW8F7F/F974C7COpbfzepnH5EmDTjPWeBM4qeyKpLBG+ABwTl/UCiquJdTPgy5h02xEq7jJ1gLKq/RhCE/Y3wDxJA+J7SNKu1byHc24deOJ1rubuJPTfTpP0NnAbodXoYeC9+Nq9wL8rfqOZLQJOJjTrvsWqpt5JwBFlg6uAs4GSOHhrJqtGV19KSNwzCE3OH1YT6+NAkaRZwJWExF/mO6B73IcDgOFx+bHAsBjfDOCwGvxMnMuqbN0SMMkiWhvArRydc87lqS5dS2zyy69lZduNG9adamYlWdn4Gngfr3POuXQrsD5eT7zOOedSrdCmE3kfr3POOZdDXvE655xLNZ9O5Jxzzrl15hWvc865VCuwgtcrXueccy6XvOJ1zjmXbgVW8nrF65xzzuWQJ17nnHOppiz9q9F7h/toz5Y0R9L5VbzeQNID8fVX432218gTr3POudQSYTpRNr6qfW+pLnAL4R7d7YGBktpXWG0Y4YYkOwDXAVdVt11PvM4551zVugNzzGyumS0HxlD55iGHASPj43HAz1XNfTx9cJVzzrnUmjZt6hMN62nzLG1+I0mvZzy/3cxuz3jeCvgo4/l8oEeFbfy0jpmtkPQ10Az4fHVv6onXOedcaplZz6RjqG3e1Oycc85VbQGwdcY+5LF4AAAAmklEQVTz1nFZletIKgI2A75Y00Y98TrnnHNVmwLsKKmtpPrA0cDECutMBAbHx/2BZ62aG917U7NzzjlXhdhneybwBFAXGGFmMyQNB143s4nAXcB9kuYAiwnJeY1UTWJ2zjnnXC3ypmbnnHMuhzzxOueccznkidc555zLIU+8zjnnXA554nXOOedyyBOvc845l0OeeJ1zzrkc+n+348vLX4lQBgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 504x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm_plot_labels = list(test_image_gen.class_indices.keys())\n",
+    "\n",
+    "plot_confusion_matrix(cm, cm_plot_labels, title='Confusion Matrix', normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "he-0IpYlFVYm",
+    "outputId": "8803b112-b01d-48a1-c2e3-0057726d4a06"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.48      0.45      0.46        49\n",
+      "           1       0.55      0.65      0.60        77\n",
+      "           2       0.58      0.59      0.59       164\n",
+      "           3       0.38      0.35      0.36        17\n",
+      "           4       0.53      0.51      0.52       166\n",
+      "           5       0.90      0.90      0.90      1005\n",
+      "           6       0.81      0.62      0.70        21\n",
+      "\n",
+      "    accuracy                           0.78      1499\n",
+      "   macro avg       0.60      0.58      0.59      1499\n",
+      "weighted avg       0.78      0.78      0.78      1499\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(test_image_gen.classes, predictions, zero_division=0))"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Skin_VGG85_78.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}