commit da38f8048fcc47e0b2e06ad6be2471db2d87715f
parent 2b9e1c3af675c6ebd4c30a3ac037752a6f4b5e90
Author: William Lindholm <85635561+LindholmLabs@users.noreply.github.com>
Date: Fri, 18 Oct 2024 16:21:36 +0100
Skapades med Colab
Diffstat:
1 file changed, 4273 insertions(+), 0 deletions(-)
diff --git a/CS4287_Prj1_24293059_24273759_id3.ipynb b/CS4287_Prj1_24293059_24273759_id3.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/LindholmLabs/ContactBridge/blob/master/CS4287_Prj1_24293059_24273759_id3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gNbyHNt__G-S"
+ },
+ "source": [
+ "Lars Jacobs (24293059)\n",
+ "William Lindholm (24273759)\n",
+ "Patrick Vorreiter (24284335)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZLO-lC_K8YiA"
+ },
+ "source": [
+ "# Project 1 notes\n",
+ "-----------------\n",
+ "\n",
+ "## Data Set\n",
+ "\n",
+ "Skin cancer dataset. This dataset contains 1800 pictures of benign cell pictures and 1500 pictures of malignant cell pictures\n",
+ "\n",
+ "## Pre-processing\n",
+ "\n",
+ "This will be resizing and flipping the pictures randomly to improve the models generalization. Maybe grayscaling and exposure settings that can also be changed\n",
+ "\n",
+ "## Network and hyperparameters\n",
+ "\n",
+ "Probably this will be most work. Figuring out which network setup works best and makes most sense. Lots of research needed here to know what kind of layers and how big / how much of them to use.\n",
+ "\n",
+ "## Loss Function\n",
+ "\n",
+ "Try different loss functions and try to explain why some work better than others:\n",
+ "- MSE\n",
+ "- Cross entropy\n",
+ "- DICE\n",
+ "\n",
+ "\n",
+ "## Optimiser\n",
+ "\n",
+ "Try different optimisers and try to explain why some work better than others:\n",
+ "- SGD\n",
+ "- ADAM\n",
+ "\n",
+ "\n",
+ "## Cross Fold Validation\n",
+ "\n",
+ "Basically just use it, maybe 5 or 10 fold if needed\n",
+ "\n",
+ "## Results\n",
+ "\n",
+ "Display different kinds of metrics. For classification of our dataset we can use terms like FPR, TPR, precision, recall, accuracy, etc. Make a confusion matrix, stuff like that\n",
+ "\n",
+ "## Evaluation\n",
+ "\n",
+ "Try to see if we overfitted or underfitted the data. Not sure how to do this yet but we can figure it out.\n",
+ "\n",
+ "## Experiments\n",
+ "\n",
+ "Experiments with number of layers and what kind of layers for example. It will be crucial here to keep our notebook clean and make use of the code blocks in the notebook so we dont have to rerun all the code for different experiments."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "D30oqzQgCkdt"
+ },
+ "source": [
+ "**Loading the dataset**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "collapsed": true,
+ "id": "Lv9XC-tFCocp",
+ "outputId": "6bcab507-fb1b-4f9c-f980-9710fd693cae"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
+ "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
+ "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.8.30)\n",
+ "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.5)\n",
+ "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
+ "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.2.3)\n",
+ "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
+ "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
+ "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4.0)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.10)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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+ " style=\"border:none\" />\n",
+ " <output id=\"result-aa107e7e-5040-4b13-9767-ed34774334a9\">\n",
+ " Upload widget is only available when the cell has been executed in the\n",
+ " current browser session. Please rerun this cell to enable.\n",
+ " </output>\n",
+ " <script>// Copyright 2017 Google LLC\n",
+ "//\n",
+ "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "// you may not use this file except in compliance with the License.\n",
+ "// You may obtain a copy of the License at\n",
+ "//\n",
+ "// http://www.apache.org/licenses/LICENSE-2.0\n",
+ "//\n",
+ "// Unless required by applicable law or agreed to in writing, software\n",
+ "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "// See the License for the specific language governing permissions and\n",
+ "// limitations under the License.\n",
+ "\n",
+ "/**\n",
+ " * @fileoverview Helpers for google.colab Python module.\n",
+ " */\n",
+ "(function(scope) {\n",
+ "function span(text, styleAttributes = {}) {\n",
+ " const element = document.createElement('span');\n",
+ " element.textContent = text;\n",
+ " for (const key of Object.keys(styleAttributes)) {\n",
+ " element.style[key] = styleAttributes[key];\n",
+ " }\n",
+ " return element;\n",
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+ "\n",
+ "// Max number of bytes which will be uploaded at a time.\n",
+ "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+ "\n",
+ "function _uploadFiles(inputId, outputId) {\n",
+ " const steps = uploadFilesStep(inputId, outputId);\n",
+ " const outputElement = document.getElementById(outputId);\n",
+ " // Cache steps on the outputElement to make it available for the next call\n",
+ " // to uploadFilesContinue from Python.\n",
+ " outputElement.steps = steps;\n",
+ "\n",
+ " return _uploadFilesContinue(outputId);\n",
+ "}\n",
+ "\n",
+ "// This is roughly an async generator (not supported in the browser yet),\n",
+ "// where there are multiple asynchronous steps and the Python side is going\n",
+ "// to poll for completion of each step.\n",
+ "// This uses a Promise to block the python side on completion of each step,\n",
+ "// then passes the result of the previous step as the input to the next step.\n",
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+ " outputElement.lastPromiseValue = value;\n",
+ " return next.value.response;\n",
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+ "}\n",
+ "\n",
+ "/**\n",
+ " * Generator function which is called between each async step of the upload\n",
+ " * process.\n",
+ " * @param {string} inputId Element ID of the input file picker element.\n",
+ " * @param {string} outputId Element ID of the output display.\n",
+ " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+ " */\n",
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+ "\n",
+ " cancel.remove();\n",
+ "\n",
+ " // Disable the input element since further picks are not allowed.\n",
+ " inputElement.disabled = true;\n",
+ "\n",
+ " if (!files) {\n",
+ " return {\n",
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+ " };\n",
+ "\n",
+ " // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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+ " const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+ " const chunk = new Uint8Array(fileData, position, length);\n",
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+ " const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+ " yield {\n",
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+ " Math.round((position / fileData.byteLength) * 100);\n",
+ " percent.textContent = `${percentDone}% done`;\n",
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+ " // All done.\n",
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+ "\n",
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+ "text": [
+ "Saving kaggle.json to kaggle.json\n",
+ "ref title size lastUpdated downloadCount voteCount usabilityRating \n",
+ "------------------------------------------------------------ -------------------------------------------------- ----- ------------------- ------------- --------- --------------- \n",
+ "lainguyn123/student-performance-factors Student Performance Factors 94KB 2024-09-02 10:53:57 35194 622 1.0 \n",
+ "valakhorasani/mobile-device-usage-and-user-behavior-dataset Mobile Device Usage and User Behavior Dataset 11KB 2024-09-28 20:21:12 7041 125 1.0 \n",
+ "mohamedyosef101/2024-olympics-medals-and-economic-status 2024 Olympics Medals and Economic status 2KB 2024-10-13 12:39:58 1470 22 1.0 \n",
+ "abdulszz/spotify-most-streamed-songs Spotify Most Streamed Songs 60KB 2024-09-07 18:23:14 14059 179 1.0 \n",
+ "valakhorasani/gym-members-exercise-dataset Gym Members Exercise Dataset 22KB 2024-10-06 11:27:38 3511 72 1.0 \n",
+ "octopusteam/imdb-top-1000-movies IMDb Top 1000 Movies 24KB 2024-10-18 08:00:05 841 23 1.0 \n",
+ "ankulsharma150/marketing-analytics-project Marketing Analytics Project 38KB 2024-10-01 09:27:22 1722 30 1.0 \n",
+ "valakhorasani/best-books-of-the-decade-2020s Best Books of the Decade: 2020's 3MB 2024-10-18 07:24:43 561 28 1.0 \n",
+ "mafzal19/electric-vehicle-sales-by-state-in-india Electric Vehicle Sales by State in India 453KB 2024-10-11 18:59:45 1508 28 1.0 \n",
+ "prajwaldongre/top-100-healthiest-food-in-the-world 100 Healthiest Foods:Nutrition and Origin dataset 3KB 2024-10-11 09:40:41 1393 34 1.0 \n",
+ "ka66ledata/gym-membership-dataset Gym Membership Dataset 24KB 2024-10-14 06:48:29 1610 27 1.0 \n",
+ "waqi786/remote-work-and-mental-health Remote Work & Mental Health 🌍🧠 93KB 2024-09-22 11:44:29 6425 90 1.0 \n",
+ "delfinaoliva/league-of-legends-champspopularity-winrate-kda League of Legends Champs(Popularity, WinRate, KDA) 76KB 2024-09-28 01:48:52 730 27 1.0 \n",
+ "waqi786/youth-smoking-and-drug-dataset Youth Smoking and Drug Dataset 🚭💊 152KB 2024-10-08 15:22:23 1978 36 1.0 \n",
+ "zafarali27/house-price-prediction-dataset House Price Prediction Dataset 29KB 2024-09-21 19:19:08 2002 32 1.0 \n",
+ "umerhaddii/walmart-stock-data-2024 Walmart Stock Data 2024 304KB 2024-10-15 07:25:18 505 36 1.0 \n",
+ "valakhorasani/electric-vehicle-charging-patterns Electric Vehicle Charging Patterns 130KB 2024-10-02 10:52:27 2268 59 1.0 \n",
+ "syedfaizanalii/predict-students-dropout-and-academic-success Predict Students Dropout and Academic Success 105KB 2024-09-28 09:41:55 2465 59 1.0 \n",
+ "sachinkumar62/phone-price-prediction-dataset Phone Price Prediction Dataset 13MB 2024-10-06 17:39:10 849 30 1.0 \n",
+ "zafarali27/car-price-prediction Car Price Prediction 45KB 2024-09-21 20:30:22 2419 33 1.0 \n",
+ "Dataset URL: https://www.kaggle.com/datasets/rm1000/skin-cancer-isic-images\n",
+ "License(s): CC0-1.0\n",
+ "Downloading skin-cancer-isic-images.zip to /content\n",
+ " 97% 49.0M/50.6M [00:03<00:00, 22.1MB/s]\n",
+ "100% 50.6M/50.6M [00:03<00:00, 14.4MB/s]\n",
+ "Archive: skin-cancer-isic-images.zip\n",
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+ " inflating: malignant/1421.jpg \n",
+ " inflating: malignant/1422.jpg \n",
+ " inflating: malignant/1423.jpg \n",
+ " inflating: malignant/1424.jpg \n",
+ " inflating: malignant/1425.jpg \n",
+ " inflating: malignant/1426.jpg \n",
+ " inflating: malignant/1427.jpg \n",
+ " inflating: malignant/1428.jpg \n",
+ " inflating: malignant/1429.jpg \n",
+ " inflating: malignant/1430.jpg \n",
+ " inflating: malignant/1431.jpg \n",
+ " inflating: malignant/1432.jpg \n",
+ " inflating: malignant/1433.jpg \n",
+ " inflating: malignant/1434.jpg \n",
+ " inflating: malignant/1435.jpg \n",
+ " inflating: malignant/1436.jpg \n",
+ " inflating: malignant/1437.jpg \n",
+ " inflating: malignant/1438.jpg \n",
+ " inflating: malignant/1439.jpg \n",
+ " inflating: malignant/1440.jpg \n",
+ " inflating: malignant/1441.jpg \n",
+ " inflating: malignant/1442.jpg \n",
+ " inflating: malignant/1443.jpg \n",
+ " inflating: malignant/1444.jpg \n",
+ " inflating: malignant/1445.jpg \n",
+ " inflating: malignant/1446.jpg \n",
+ " inflating: malignant/1447.jpg \n",
+ " inflating: malignant/1448.jpg \n",
+ " inflating: malignant/1449.jpg \n",
+ " inflating: malignant/1450.jpg \n",
+ " inflating: malignant/1451.jpg \n",
+ " inflating: malignant/1452.jpg \n",
+ " inflating: malignant/1453.jpg \n",
+ " inflating: malignant/1454.jpg \n",
+ " inflating: malignant/1455.jpg \n",
+ " inflating: malignant/1456.jpg \n",
+ " inflating: malignant/1457.jpg \n",
+ " inflating: malignant/1458.jpg \n",
+ " inflating: malignant/1459.jpg \n",
+ " inflating: malignant/1460.jpg \n",
+ " inflating: malignant/1461.jpg \n",
+ " inflating: malignant/1462.jpg \n",
+ " inflating: malignant/1463.jpg \n",
+ " inflating: malignant/1464.jpg \n",
+ " inflating: malignant/1465.jpg \n",
+ " inflating: malignant/1466.jpg \n",
+ " inflating: malignant/1467.jpg \n",
+ " inflating: malignant/1468.jpg \n",
+ " inflating: malignant/1469.jpg \n",
+ " inflating: malignant/1470.jpg \n",
+ " inflating: malignant/1471.jpg \n",
+ " inflating: malignant/1472.jpg \n",
+ " inflating: malignant/1473.jpg \n",
+ " inflating: malignant/1474.jpg \n",
+ " inflating: malignant/1475.jpg \n",
+ " inflating: malignant/1476.jpg \n",
+ " inflating: malignant/1477.jpg \n",
+ " inflating: malignant/1478.jpg \n",
+ " inflating: malignant/1479.jpg \n",
+ " inflating: malignant/1480.jpg \n",
+ " inflating: malignant/1481.jpg \n",
+ " inflating: malignant/1482.jpg \n",
+ " inflating: malignant/1483.jpg \n",
+ " inflating: malignant/1484.jpg \n",
+ " inflating: malignant/1485.jpg \n",
+ " inflating: malignant/1486.jpg \n",
+ " inflating: malignant/1487.jpg \n",
+ " inflating: malignant/1488.jpg \n",
+ " inflating: malignant/1489.jpg \n",
+ " inflating: malignant/1490.jpg \n",
+ " inflating: malignant/1491.jpg \n",
+ " inflating: malignant/1492.jpg \n",
+ " inflating: malignant/1493.jpg \n",
+ " inflating: malignant/1494.jpg \n",
+ " inflating: malignant/1495.jpg \n",
+ " inflating: malignant/1496.jpg \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Needed libraries\n",
+ "!pip install kaggle;\n",
+ "\n",
+ "from google.colab import files\n",
+ "\n",
+ "files.upload()\n",
+ "\n",
+ "! mkdir ~/.kaggle;\n",
+ "! cp kaggle.json ~/.kaggle/;\n",
+ "! chmod 600 ~/.kaggle/kaggle.json;\n",
+ "! kaggle datasets list\n",
+ "!kaggle datasets download -d rm1000/skin-cancer-isic-images;\n",
+ "!unzip skin-cancer-isic-images.zip;\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Importing Data**"
+ ],
+ "metadata": {
+ "id": "bsi1Gr-w3N0I"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "1jpf8RG8FbPf"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import matplotlib.pyplot as plt\n",
+ "from PIL import Image\n",
+ "\n",
+ "# Set the path for your dataset\n",
+ "benign_path = './benign'\n",
+ "malignant_path = './malignant'\n",
+ "\n",
+ "# Function to load images from a folder\n",
+ "def load_images_from_folder(folder):\n",
+ " images = []\n",
+ " for filename in os.listdir(folder):\n",
+ " img_path = os.path.join(folder, filename)\n",
+ " img = Image.open(img_path)\n",
+ " images.append(img)\n",
+ " return images\n",
+ "\n",
+ "# Load benign and malignant images\n",
+ "benign_images = load_images_from_folder(benign_path)\n",
+ "malignant_images = load_images_from_folder(malignant_path)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Visualization**"
+ ],
+ "metadata": {
+ "id": "AdWFPfZr3TDj"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "def plot_pixel_intensity_distribution(images, title):\n",
+ " intensities = []\n",
+ " for img in images:\n",
+ " img_array = np.array(img.convert('L')) # Convert to grayscale\n",
+ " intensities.extend(img_array.flatten())\n",
+ "\n",
+ " plt.hist(intensities, bins=50, color='purple', alpha=0.7)\n",
+ " plt.title(f'{title} Image Pixel Intensity Distribution')\n",
+ " plt.xlabel('Pixel Intensity (0-255)')\n",
+ " plt.ylabel('Frequency')\n",
+ " plt.show()\n",
+ "\n",
+ "# Plot pixel intensity distribution for benign and malignant images\n",
+ "plot_pixel_intensity_distribution(benign_images, \"Benign\")\n",
+ "plot_pixel_intensity_distribution(malignant_images, \"Malignant\")\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "cYB6fsMIDS-b"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "r-9GxnJHk_IH"
+ },
+ "source": [
+ "# Our model class\n",
+ "\n",
+ "--network structure\n",
+ "--Opimtizer\n",
+ "--Loss function\n",
+ "--Cross fold validation\n",
+ "--Explain what things we can change and which methods we will use for them"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hzkED5MihiXy"
+ },
+ "outputs": [],
+ "source": [
+ "from re import X\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers, models\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "import seaborn as sns\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.model_selection import KFold\n",
+ "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+ "\n",
+ "class ImageClassificationModel:\n",
+ " def __init__(self, optimizer='adam', loss_function='binary_crossentropy', activation_function='relu', output_activation_function='sigmoid', num_output_neurons=1, batch_size=64, epochs=20, strides=(2, 2), dilation=(1, 1), data_augmentation=None):\n",
+ " # Fixed parameters\n",
+ " self.img_size = (228, 228)\n",
+ " self.num_classes = 2\n",
+ " self.test_size = 0.2\n",
+ " self.val_size = 0.1\n",
+ "\n",
+ " # Customizable parameters\n",
+ " self.optimizer = optimizer\n",
+ " self.loss_function = loss_function\n",
+ " self.activation_function = activation_function\n",
+ " self.batch_size = batch_size\n",
+ " self.epochs = epochs\n",
+ " self.output_activation_function = output_activation_function\n",
+ " self.data_augmentation = data_augmentation\n",
+ " self.model_summary = False\n",
+ " self.training_progress = 0\n",
+ " self.num_output_neurons = num_output_neurons\n",
+ " self.strides = strides\n",
+ " self.dilation = dilation\n",
+ "\n",
+ " def show_model_summary(self):\n",
+ " self.model_summary = True\n",
+ "\n",
+ " def show_training_progress(self):\n",
+ " self.training_progress = 1\n",
+ "\n",
+ " # Function to resize and normalize images\n",
+ " def preprocess_images(self, images):\n",
+ " images_resized = np.array([tf.image.resize(img, self.img_size).numpy() for img in images])\n",
+ " images_resized = images_resized.astype('float32') / 255.0\n",
+ " return images_resized\n",
+ "\n",
+ " # Function to load data, preprocess, and split into train, validation, and test sets\n",
+ " def load_data(self, benign_images, malignant_images):\n",
+ " benign_images_array = self.preprocess_images(benign_images)\n",
+ " malignant_images_array = self.preprocess_images(malignant_images)\n",
+ "\n",
+ " benign_labels = np.zeros(len(benign_images_array))\n",
+ " malignant_labels = np.ones(len(malignant_images_array))\n",
+ "\n",
+ " X_data = np.concatenate((benign_images_array, malignant_images_array), axis=0)\n",
+ " y_data = np.concatenate((benign_labels, malignant_labels), axis=0)\n",
+ "\n",
+ " indices = np.arange(X_data.shape[0])\n",
+ " np.random.shuffle(indices)\n",
+ " X_data = X_data[indices]\n",
+ " y_data = y_data[indices]\n",
+ "\n",
+ " X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=self.test_size, random_state=42)\n",
+ " X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=self.val_size, random_state=42)\n",
+ "\n",
+ " if self.num_output_neurons != 1:\n",
+ " y_train = tf.keras.utils.to_categorical(y_train, self.num_classes)\n",
+ " y_val = tf.keras.utils.to_categorical(y_val, self.num_classes)\n",
+ " y_test = tf.keras.utils.to_categorical(y_test, self.num_classes)\n",
+ "\n",
+ " return X_train, y_train, X_val, y_val, X_test, y_test\n",
+ "\n",
+ " # Function to build the model\n",
+ " def build_model(self, input_shape):\n",
+ " model = models.Sequential()\n",
+ "\n",
+ " # Input layer\n",
+ " model.add(layers.Input(shape=input_shape))\n",
+ "\n",
+ " # 1st block\n",
+ " model.add(layers.Conv2D(32, (3, 3), strides=self.stride, dilation_rate=self.dilation, padding='same', activation=self.activation_function))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
+ " model.add(layers.Dropout(0.2))\n",
+ "\n",
+ " # 2nd block\n",
+ " model.add(layers.Conv2D(64, (3, 3), strides=self.stride, dilation_rate=self.dilation, padding='same', activation=self.activation_function))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.Conv2D(64, (3, 3), strides=self.stride, dilation_rate=self.dilation, padding='same', activation=self.activation_function))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
+ " model.add(layers.Dropout(0.3))\n",
+ "\n",
+ " # 3rd block\n",
+ " model.add(layers.Conv2D(128, (3, 3), strides=self.stride, dilation_rate=self.dilation, padding='same', activation=self.activation_function))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.Conv2D(128, (3, 3), strides=self.stride, dilation_rate=self.dilation, padding='same', activation=self.activation_function))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
+ " model.add(layers.Dropout(0.4))\n",
+ "\n",
+ " # Dense layer\n",
+ " model.add(layers.Flatten())\n",
+ " model.add(layers.Dense(128, activation=self.activation_function))\n",
+ " model.add(layers.Dropout(0.5))\n",
+ " model.add(layers.Dense(self.num_output_neurons, activation=self.output_activation_function))\n",
+ "\n",
+ " return model\n",
+ "\n",
+ " # Function to compile and train the model using the preset parameters\n",
+ " def train_model(self, X_train, y_train, X_val, y_val):\n",
+ " model = self.build_model(X_train.shape[1:])\n",
+ "\n",
+ " model.compile(optimizer=self.optimizer, loss=self.loss_function, metrics=['accuracy'])\n",
+ "\n",
+ " if self.model_summary:\n",
+ " model.summary()\n",
+ "\n",
+ " if self.data_augmentation:\n",
+ " history = model.fit(self.data_augmentation.flow(X_train, y_train, batch_size=self.batch_size), epochs=self.epochs, validation_data=(X_val, y_val), verbose=self.training_progress)\n",
+ " else:\n",
+ " history = model.fit(X_train, y_train, batch_size=self.batch_size, epochs=self.epochs, validation_data=(X_val, y_val), verbose=self.training_progress)\n",
+ "\n",
+ " self.model = model\n",
+ " return model, history\n",
+ "\n",
+ " # Function to evaluate the model on test data\n",
+ " def evaluate_model(self, model, X_test, y_test):\n",
+ " \"\"\"Evaluates the model and prints performance metrics.\"\"\"\n",
+ "\n",
+ " # For binary classification with sigmoid output:\n",
+ " if self.num_output_neurons == 1:\n",
+ " y_pred_prob = model.predict(X_test)\n",
+ " y_pred = (y_pred_prob > 0.5).astype(int) # Binary predictions (0 or 1)\n",
+ " y_true = y_test # No need to use argmax for binary classification\n",
+ " # For multi-class classification with softmax output:\n",
+ " else:\n",
+ " y_pred_prob = model.predict(X_test)\n",
+ " y_pred = np.argmax(y_pred_prob, axis=1)\n",
+ " y_true = np.argmax(y_test, axis=1)\n",
+ "\n",
+ " # Calculate the metrics\n",
+ " accuracy = accuracy_score(y_true, y_pred)\n",
+ " precision = precision_score(y_true, y_pred)\n",
+ " recall = recall_score(y_true, y_pred)\n",
+ " f1 = f1_score(y_true, y_pred)\n",
+ "\n",
+ " # Print the results\n",
+ " print(f\"\\nTest loss: {model.evaluate(X_test, y_test, verbose=0)[0]}\") # Keep original loss printing\n",
+ " print(f\"Test accuracy: {accuracy}\")\n",
+ " print(f\"Test precision: {precision}\")\n",
+ " print(f\"Test recall: {recall}\")\n",
+ " print(f\"Test F1 score: {f1}\")\n",
+ "\n",
+ " return accuracy, precision, recall, f1 # Return all metrics\n",
+ "\n",
+ " def run(self, evaluate_model=True, plot_history=True, print_confusion_matrix=True):\n",
+ " X_train, y_train, X_val, y_val, X_test, y_test = self.load_data(benign_images, malignant_images)\n",
+ "\n",
+ " # Train the model\n",
+ " model, history = self.train_model(X_train, y_train, X_val, y_val)\n",
+ "\n",
+ " if evaluate_model:\n",
+ " # Evaluate the model on the test data\n",
+ " self.evaluate_model(model, X_test, y_test)\n",
+ "\n",
+ " if plot_history:\n",
+ " # Plot the training history\n",
+ " self.plot_history(history)\n",
+ "\n",
+ " if print_confusion_matrix:\n",
+ " # Print confusion matrix\n",
+ " self.print_confusion_matrix(model, X_test, y_test)\n",
+ "\n",
+ "\n",
+ " # Function to plot training and validation accuracy and loss\n",
+ " def plot_history(self, history):\n",
+ " acc = history.history['accuracy']\n",
+ " val_acc = history.history['val_accuracy']\n",
+ " loss = history.history['loss']\n",
+ " val_loss = history.history['val_loss']\n",
+ "\n",
+ " epochs_range = range(len(acc))\n",
+ "\n",
+ " plt.figure(figsize=(12, 6))\n",
+ " plt.subplot(1, 2, 1)\n",
+ " plt.plot(epochs_range, acc, label='Training Accuracy')\n",
+ " plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
+ " plt.legend(loc='lower right')\n",
+ " plt.title('Training and Validation Accuracy')\n",
+ "\n",
+ " plt.subplot(1, 2, 2)\n",
+ " plt.plot(epochs_range, loss, label='Training Loss')\n",
+ " plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
+ " plt.legend(loc='upper right')\n",
+ " plt.title('Training and Validation Loss')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "\n",
+ " def print_confusion_matrix(self, model, X_test, y_test):\n",
+ " \"\"\"Prints the confusion matrix.\"\"\"\n",
+ " if self.num_output_neurons == 1: # Check if binary classification\n",
+ " y_pred_prob = model.predict(X_test)\n",
+ " y_pred = (y_pred_prob > 0.5).astype(int) # Binary predictions (0 or 1)\n",
+ " y_true = y_test # No need to use argmax for binary classification\n",
+ " else:\n",
+ " y_pred_prob = model.predict(X_test)\n",
+ " y_pred = np.argmax(y_pred_prob, axis=1)\n",
+ " y_true = np.argmax(y_test, axis=1)\n",
+ "\n",
+ " conf_matrix = confusion_matrix(y_true, y_pred)\n",
+ "\n",
+ " plt.figure(figsize=(8, 6))\n",
+ " sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
+ " xticklabels=['Benign', 'Malignant'],\n",
+ " yticklabels=['Benign', 'Malignant'])\n",
+ " plt.xlabel(\"Predicted\")\n",
+ " plt.ylabel(\"Actual\")\n",
+ " plt.title(\"Confusion Matrix\")\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "180yUGXuOCX5"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**Results**\n",
+ "\n",
+ "-Include results of varying paramters"
+ ],
+ "metadata": {
+ "id": "cNmBJN2K88E6"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ORjee3Qd8f7S"
+ },
+ "source": [
+ "Here is the default configuration that we got from the lab."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "zegpbfKC8fTH",
+ "outputId": "6bae85db-3d1e-4a1f-8c16-466756ec64da"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 79ms/step - accuracy: 0.7156 - loss: 0.8960 - val_accuracy: 0.5871 - val_loss: 0.6352\n",
+ "Epoch 2/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.7573 - loss: 0.5656 - val_accuracy: 0.6174 - val_loss: 0.6345\n",
+ "Epoch 3/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.7547 - loss: 0.5219 - val_accuracy: 0.4773 - val_loss: 0.7720\n",
+ "Epoch 4/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.7737 - loss: 0.4243 - val_accuracy: 0.4545 - val_loss: 1.0895\n",
+ "Epoch 5/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.7957 - loss: 0.4040 - val_accuracy: 0.8068 - val_loss: 0.4109\n",
+ "Epoch 6/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8021 - loss: 0.3925 - val_accuracy: 0.7311 - val_loss: 0.5411\n",
+ "Epoch 7/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8051 - loss: 0.3926 - val_accuracy: 0.8220 - val_loss: 0.3631\n",
+ "Epoch 8/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8232 - loss: 0.3664 - val_accuracy: 0.8068 - val_loss: 0.3974\n",
+ "Epoch 9/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8199 - loss: 0.3736 - val_accuracy: 0.8068 - val_loss: 0.5795\n",
+ "Epoch 10/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8290 - loss: 0.3690 - val_accuracy: 0.6212 - val_loss: 1.0786\n",
+ "Epoch 11/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8256 - loss: 0.3554 - val_accuracy: 0.7917 - val_loss: 0.4041\n",
+ "Epoch 12/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8365 - loss: 0.3587 - val_accuracy: 0.7955 - val_loss: 0.6913\n",
+ "Epoch 13/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8499 - loss: 0.3257 - val_accuracy: 0.7879 - val_loss: 0.3982\n",
+ "Epoch 14/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8650 - loss: 0.3261 - val_accuracy: 0.8258 - val_loss: 0.4838\n",
+ "Epoch 15/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8610 - loss: 0.3089 - val_accuracy: 0.7917 - val_loss: 0.9966\n",
+ "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step\n",
+ "\n",
+ "Test loss: 0.9525874257087708\n",
+ "Test accuracy: 0.806060606060606\n",
+ "Test precision: 0.7985611510791367\n",
+ "Test recall: 0.7551020408163265\n",
+ "Test F1 score: 0.7762237762237763\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1200x600 with 2 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 800x600 with 2 Axes>"
+ ],
+ "image/png": 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inp6eqlatmiSpYcOGWr9+vV577TX16tVLZ86c0bFjx+zSxEOHDik8PFySFB4erp9++snufBfufr5wTFEx3AwAAOAiS+BcSmFhofLz89WwYUOVLVtWK1assO3buXOn0tPTFRcXJ0mKi4vTli1blJWVZTsmOTlZgYGBqlWrVrGuS5IIAADgIiZMmKAOHTqocuXKOnHihObPn6/Vq1dr+fLlCgoK0sCBAzVmzBiFhoYqMDBQI0aMUFxcnO644w5JUtu2bVWrVi09+OCDmjp1qjIzM/XUU08pISGhWGmmRJMIAADgMl/Ll5WVpYceekgZGRkKCgpS3bp1tXz5crVp00aSNG3aNFmtVvXs2VP5+flq166d3nrrLdvrPTw8tGTJEg0bNkxxcXHy8/NTfHy8pkyZUuxaWCcRwHWFdRKBG5dT10lsPslh5z6V4rhzOxJJIgAAgIskia6ETwQAAAAmJIkAAADWv38X8o2GJBEAAAAmJIkAAADMSTShSQQAACiBRa9vNLTNAAAAMCFJBAAAYLjZhE8EAAAAJiSJAAAAzEk0IUkEAACACUkiAAAAcxJN+EQAAABgQpIIAADAnEQTmkQAAACGm034RAAAAGBCkggAAMBwswlJIgAAAExIEgEAAJiTaMInAgAAABOSRAAAAOYkmpAkAgAAwIQkEQAAgDmJJjSJAAAANIkmfCIAAAAwIUkEAADgxhUTkkQAAACYkCQCAAAwJ9GETwQAAAAmJIkAAADMSTQhSQQAAIAJSSIAAABzEk1oEgEAABhuNqFtBgAAgAlJIgAAcHsWkkQTkkQAAACYkCQCAAC3R5JoRpIIAAAAE5JEAAAAgkQTkkQAAACYkCQCAAC3x5xEM5pEAADg9mgSzRhuBgAAgAlJIgAAcHskiWYkiQAAADAhSQQAAG6PJNGMJBEAAAAmJIkAAAAEiSYkiQAAADAhSQQAAG6POYlmJIkAAAAwIUkEAABujyTRjCYRAAC4PZpEM4abAQAAYEKSCAAA3B5JohlJIgAAAExIEgEAAAgSTUgSAQAAYEKSCAAA3B5zEs1IEgEAAGBCkggAANweSaIZTSIAAHB7NIlmDDcDAADAhCQRAACAINGEJBEAAAAmJIkAAMDtMSfRjCQRAAAAJiSJAADA7ZEkmpEkAgAAwIQkEQAAuD2SRDOXaRILCwu1e/duZWVlqbCw0G5f8+bNnVQVAABwBzSJZi7RJP7444/q06ePfv/9dxmGYbfPYrGooKDASZUBAAC4J5doEocOHapGjRrpq6++UkREBN08AAAoXbQeJi7RJO7atUuffvqpqlWr5uxSAAAAIBe5u7lx48bavXu3s8sAAABuymKxOOxxvXKJJHHEiBEaO3asMjMzVadOHZUtW9Zuf926dZ1UGQAAgHtyiSaxZ8+ekqQBAwbYtlksFhmGwY0rAADA4a7nxM9RXKJJ3Ldvn7NLAAAAwF+4RJMYFRXl7BIAAIAbI0k0c4km8YsvvrjkdovFIm9vb1WrVk0xMTGlXBUAAHAb9IgmLtEkduvWzTYH8a/+Oi+xadOmWrx4sUJCQpxUJQAAgGMlJiZq4cKF2rFjh3x8fHTnnXfqxRdfVGxsrO2Yli1bas2aNXave/jhhzVr1izb8/T0dA0bNkyrVq2Sv7+/4uPjlZiYqDJlit76ucQSOMnJybr99tuVnJys48eP6/jx40pOTlbjxo21ZMkSpaSk6PDhwxo3bpyzSwUAADcgV1kCZ82aNUpISNCPP/6o5ORknT17Vm3btlVeXp7dcYMHD1ZGRobtMXXqVNu+goICderUSWfOnNEPP/yguXPnKikpSRMnTixWLS6RJI4aNUqzZ8/WnXfeadt21113ydvbW0OGDNHWrVs1ffp0u7ufAQAAbjTLli2ze56UlKSwsDBt2LBBzZs3t2339fVVeHj4Jc/xzTffaNu2bfr2229VsWJF1a9fX88++6wef/xxTZo0SZ6enkWqxSWSxD179igwMNC0PTAwUHv37pUkVa9eXX/++WdplwYAANyAI5PE/Px85eTk2D3y8/OLVNfx48clSaGhoXbbP/jgA5UvX161a9fWhAkTdPLkSdu+1NRU1alTRxUrVrRta9eunXJycrR169YifyYu0SQ2bNhQ48ePV3Z2tm1bdna2HnvsMd1+++2Szn91X6VKlZxVIgAAwDVJTExUUFCQ3SMxMfGqryssLNSjjz6qJk2aqHbt2rbtffr00fvvv69Vq1ZpwoQJmjdvnh544AHb/szMTLsGUZLteWZmZpHrdonh5vfee09du3bVzTffbGsEDxw4oCpVqujzzz+XJOXm5uqpp55yZpkoJYPvbarB9zRTVOT5/2vavjdTz89eqm++3yZJGtCjiXp1aKT6NW5WoL+PwpuN1/HcU3bnCAn01auP36uOzWur0DC0eEWaxk39VHmnzpT6+wFwZTPffF2z3nrDblt0TIw+X/J/w26b0jbq9demacuWzfKwWhVbo6Zmzn5P3t7epV0ublCOXAJnwoQJGjNmjN02Ly+vq74uISFBv/76q9auXWu3fciQIbaf69Spo4iICN11113as2ePqlatWjJFy0WaxNjYWG3btk3ffPONfvvtN9u2Nm3ayGo9H3Z269bNiRWiNP3v0DE9/frn2p2eLYsseqBLY30ybYju6P2Ctu/NlK93WSX/sE3JP2zTsyO7XvIcc56PV3j5IHUe9obKlvHQ25Mf0JtP91G/J5NK980AKJKq1apr9rtzbM89ynjYft6UtlGPPDxIAwY9rCf+9bTKeHho584dtr8fAFfn5eVVpKbwr4YPH267effmm2++4rGNGzeWJO3evVtVq1ZVeHi4fvrpJ7tjDh06JEmXncd4KS7RJEqS1WpV+/bt1b59e2eXAif7OuVXu+eT3vxSg+9tqn/UjdH2vZl6Y/5qSVKzhtUv+frYmIpq1+RWNek7Vb9sS5ckjXnxEy1+fZgmTFukjOzjDq0fQPGV8fBQ+QoVLrnvpRcTdX/fBzVw8P+lJ9ExVUqrNLgJV1lM2zAMjRgxQosWLdLq1auLtE50WlqaJCkiIkKSFBcXp+eee05ZWVkKCwuTdH4lmcDAQNWqVavItTitSZwxY4aGDBkib29vzZgx44rHjhw5spSqgquxWi3q2eY2+fl4at3mon19Y+O6MTqac9LWIErSynU7VVho6PbaUfpi1WZHlQvgGv2e/rtat2wqTy8v1atXXyMfHauIyEgdPnxYWzZvUsfOXfRQ3946cCBdMTFVNHzko7qtYSNnl40biWv0iEpISND8+fP1+eefKyAgwDaHMCgoSD4+PtqzZ4/mz5+vjh07qly5ctq8ebNGjx6t5s2bq27dupKktm3bqlatWnrwwQc1depUZWZm6qmnnlJCQkKxEk2nNYnTpk1T37595e3trWnTpl32OIvFcsUmMT8/33SHkFFYIIvV4zKvwPXg1mqRWj13rLw9yyj3VL56jX1HO/YWbbJtxXKByj5ywm5bQUGhjuScVMXy5rvoAThXnbp19exziYqOjlF2drbenvmm+j/UV599/qX+98cBSdKsN9/QmPGPKbZGTS35fLGGDOynzz5foqioaOcWD5SwmTNnSjq/YPZfzZkzR/369ZOnp6e+/fZbTZ8+XXl5eapUqZJ69uxpd9+Gh4eHlixZomHDhikuLk5+fn6Kj4/XlClTilWL05rEffv2XfLn4kpMTNTkyZPttnlUvF1lI/5xzeeE8/22/5Aa905UkL+PurduoHemPKi2g14rcqMI4PrRtFkL28+3xNZQnbr11KFNKy1ftlRVqpyfhH/Pfb3UrXtPSVLNmrW0bl2qFi/8TKNGj3VKzbjxuNJw85VUqlTJ9G0rlxIVFaWvv/76b9Vy3c/6nTBhgu1bWi48ylRs6Oyy8DedPVegvQf+1MbtBzTx9S+05bf/KeH+lkV67aHDOaoQGmC3zcPDqtBAXx36M8cB1QIoSYGBgYqKitaB9HTbPMUqF92xGVOlqjIzDjqjPMBtuMSNKwUFBUpKStKKFSuUlZWlwsJCu/0rV6687GsvdccQQ803HqvFIi/Pov3num7zPoUE+qpBzUrauP38UFXL22+R1WrR+l9/d2SZAErAybw8HThwQJ3urqCbbrpZFcLCtP+iEaff9+9X02bNL3MGoPhcJUl0JS7RJI4aNUpJSUnq1KmTateuzb8oNzdlxN1a/v1WHcg4qgA/b/Xq0EjNG1VXl0fekiRVLBegiuUCVbVyeUlS7eqROpF3Wgcyj+pozknt3HdIy7/fqjef7qORzy1Q2TIemvbEffpk+S/c2Qy4oFdeelEtWrZSRGSksrOyNPPN1+XhYVWHjp1lsVjUr/9AzXzzdcXG1lBsjZr64vNF2r9vr16ZduWbHgH8PS7RJC5YsEAff/yxOnbs6OxS4AIqhPrrvWcfUnj5QB3PPa1fd/1PXR55SyvX7ZAkDbqnmZ4a+n//rXz7n9GSpMET5+n9L9dJkvo/OVfTnrhPX789QoWF5xfTHjv1k9J/MwCu6tChTD0xfoyOHTumkNBQNbitoebN/9j2NWQPPNRP+fln9NLURB0/flyxsTU0653/qFLlyk6uHDcS8ikzi3G1GZKlIDIyUqtXr9Ytt9xSIufzaTC8RM4DwPUcXf/G1Q8CcF3ydmJ0VW3cUoede/fLHRx2bkdyiRtXxo4dq9dee+2qd/QAAAA4gsVicdjjeuUSw81r167VqlWrtHTpUt16660qW7as3f6FCxc6qTIAAOAOruNezmFcokkMDg5W9+7dnV0GAAAA/j+XaBLnzJlz9YMAAAAc5HoeFnYUl5iTKEnnzp3Tt99+q7ffflsnTpz/SrWDBw8qNzfXyZUBAAC4H5dIEn///Xe1b99e6enpys/PV5s2bRQQEKAXX3xR+fn5mjVrlrNLBAAANzCCRDOXSBJHjRqlRo0a6ejRo/Lx8bFt7969u1asWOHEygAAANyTSySJ3333nX744Qd5enrabY+Ojtb//vc/J1UFAADchdVKlHgxl0gSCwsLVVBQYNr+xx9/KCAgwAkVAQAAuDeXaBLbtm2r6dOn255bLBbl5ubqmWee4av6AACAw1ksjntcr1xiuPmVV15Ru3btVKtWLZ0+fVp9+vTRrl27VK5cOX344YfOLg8AANzgWALHzCWaxJtvvlmbNm3SggULtHnzZuXm5mrgwIHq27ev3Y0sAAAAKB0uMdx8+PBhlSlTRg888IBGjBih8uXLa+fOnfr555+dXRoAAHADDDebObVJ3LJli6KjoxUWFqYaNWooLS1Nt99+u6ZNm6bZs2erVatWWrx4sTNLBAAAcEtObRIfe+wx1alTRykpKWrZsqU6d+6sTp066fjx4zp69KgefvhhvfDCC84sEQAAuAGLxeKwx/XKqXMS169fr5UrV6pu3bqqV6+eZs+erUceeURW6/nedcSIEbrjjjucWSIAAIBbcmqTeOTIEYWHh0uS/P395efnp5CQENv+kJAQ2/c4AwAAOMr1nPg5itNvXLn4Xwr/kgAAAJzP6Uvg9OvXT15eXpKk06dPa+jQofLz85Mk5efnO7M0AADgJsiozJzaJMbHx9s9f+CBB0zHPPTQQ6VVDgAAcFOMZJo5tUmcM2eOMy8PAACAy3D6cDMAAICzESSaOf3GFQAAALgekkQAAOD2mJNoRpIIAAAAE5JEAADg9ggSzUgSAQAAYEKSCAAA3B5zEs1IEgEAAGBCkggAANweQaIZTSIAAHB7DDebMdwMAAAAE5JEAADg9ggSzUgSAQAAYEKSCAAA3B5zEs1IEgEAAGBCkggAANweQaIZSSIAAABMSBIBAIDbY06iGU0iAABwe/SIZgw3AwAAwIQkEQAAuD2Gm81IEgEAAGBCkggAANweSaIZSSIAAABMSBIBAIDbI0g0I0kEAACACUkiAABwe8xJNKNJBAAAbo8e0YzhZgAAAJiQJAIAALfHcLMZSSIAAABMSBIBAIDbI0g0I0kEAACACUkiAABwe1aiRBOSRAAAAJiQJAIAALdHkGhGkwgAANweS+CYMdwMAAAAE5JEAADg9qwEiSYkiQAAADAhSQQAAG6POYlmJIkAAAAwIUkEAABujyDRjCQRAAAAJiSJAADA7VlElHgxmkQAAOD2WALHjOFmAAAAmJAkAgAAt8cSOGYkiQAAADAhSQQAAG6PINGMJBEAAAAmJIkAAMDtWYkSTUgSAQAAYEKSCAAA3B5BohlJIgAAcHsWi8Vhj+JITEzU7bffroCAAIWFhalbt27auXOn3TGnT59WQkKCypUrJ39/f/Xs2VOHDh2yOyY9PV2dOnWSr6+vwsLCNH78eJ07d65YtdAkAgAAuIg1a9YoISFBP/74o5KTk3X27Fm1bdtWeXl5tmNGjx6tL7/8Up988onWrFmjgwcPqkePHrb9BQUF6tSpk86cOaMffvhBc+fOVVJSkiZOnFisWiyGYRgl9s5chE+D4c4uAYCDHF3/hrNLAOAg3k6cBHdv0i8OO/cn/W675tdmZ2crLCxMa9asUfPmzXX8+HFVqFBB8+fP1z333CNJ2rFjh2rWrKnU1FTdcccdWrp0qTp37qyDBw+qYsWKkqRZs2bp8ccfV3Z2tjw9PYt0bZJEAAAAB8rPz1dOTo7dIz8/v0ivPX78uCQpNDRUkrRhwwadPXtWrVu3th1To0YNVa5cWampqZKk1NRU1alTx9YgSlK7du2Uk5OjrVu3FrlumkQAAOD2rBaLwx6JiYkKCgqyeyQmJl61psLCQj366KNq0qSJateuLUnKzMyUp6engoOD7Y6tWLGiMjMzbcf8tUG8sP/CvqLi7mYAAAAHmjBhgsaMGWO3zcvL66qvS0hI0K+//qq1a9c6qrQrokkEAABuz5Er4Hh5eRWpKfyr4cOHa8mSJUpJSdHNN99s2x4eHq4zZ87o2LFjdmnioUOHFB4ebjvmp59+sjvfhbufLxxTFAw3AwAAuAjDMDR8+HAtWrRIK1euVExMjN3+hg0bqmzZslqxYoVt286dO5Wenq64uDhJUlxcnLZs2aKsrCzbMcnJyQoMDFStWrWKXAtJIgAAcHvFXc/QURISEjR//nx9/vnnCggIsM0hDAoKko+Pj4KCgjRw4ECNGTNGoaGhCgwM1IgRIxQXF6c77rhDktS2bVvVqlVLDz74oKZOnarMzEw99dRTSkhIKFaiSZMIAADcntU1ekTNnDlTktSyZUu77XPmzFG/fv0kSdOmTZPValXPnj2Vn5+vdu3a6a233rId6+HhoSVLlmjYsGGKi4uTn5+f4uPjNWXKlGLVwjqJAK4rrJMI3LicuU5i33lpDjv3Bw/Wd9i5HYkkEQAAuD1XGW52Jdy4AgAAABOSRAAA4PYIEs1IEgEAAGBCkggAANwecxLNitQkfvHFF0U+4d13333NxQAAAMA1FKlJ7NatW5FOZrFYVFBQ8HfqAQAAKHWusk6iKylSk1hYWOjoOgAAAJyG4WYzblwBAACAyTXduJKXl6c1a9YoPT1dZ86csds3cuTIEikMAACgtJAjmhW7Sdy4caM6duyokydPKi8vT6Ghofrzzz/l6+ursLAwmkQAAIAbQLGHm0ePHq0uXbro6NGj8vHx0Y8//qjff/9dDRs21Msvv+yIGgEAABzKarE47HG9KnaTmJaWprFjx8pqtcrDw0P5+fmqVKmSpk6dqieffNIRNQIAAKCUFbtJLFu2rKzW8y8LCwtTenq6JCkoKEgHDhwo2eoAAABKgcXiuMf1qthzEhs0aKD169erevXqatGihSZOnKg///xT8+bNU+3atR1RIwAAAEpZsZPE559/XhEREZKk5557TiEhIRo2bJiys7M1e/bsEi8QAADA0SwWi8Me16tiJ4mNGjWy/RwWFqZly5aVaEEAAABwvmtaJxEAAOBGch0Hfg5T7CYxJibmitHp3r17/1ZBAAAApe16XqrGUYrdJD766KN2z8+ePauNGzdq2bJlGj9+fEnVBQAAACcqdpM4atSoS25/88039fPPP//tggAAAEobQaJZse9uvpwOHTros88+K6nTAQAAwIlK7MaVTz/9VKGhoSV1OgAAgFJzPS9V4yjXtJj2Xz9IwzCUmZmp7OxsvfXWWyVaHAAAAJyj2E1i165d7ZpEq9WqChUqqGXLlqpRo0aJFnet0r+b7uwSADjIXdO+c3YJABzk+/HNnHbtEpt/dwMpdpM4adIkB5QBAAAAV1LsxtnDw0NZWVmm7YcPH5aHh0eJFAUAAFCa+Fo+s2IniYZhXHJ7fn6+PD09/3ZBAAAApc16/fZyDlPkJnHGjBmSznfa7777rvz9/W37CgoKlJKS4jJzEgEAAPD3FLlJnDZtmqTzSeKsWbPshpY9PT0VHR2tWbNmlXyFAAAADkaSaFbkJnHfvn2SpFatWmnhwoUKCQlxWFEAAABwrmLPSVy1apUj6gAAAHCa6/kGE0cp9t3NPXv21IsvvmjaPnXqVN17770lUhQAAACcq9hNYkpKijp27Gja3qFDB6WkpJRIUQAAAKXJanHc43pV7CYxNzf3kkvdlC1bVjk5OSVSFAAAAJyr2E1inTp19NFHH5m2L1iwQLVq1SqRogAAAEqTxeK4x/Wq2DeuPP300+rRo4f27Nmjf/7zn5KkFStWaP78+fr0009LvEAAAABHs17P3ZyDFLtJ7NKlixYvXqznn39en376qXx8fFSvXj2tXLlSoaGhjqgRAAAApazYTaIkderUSZ06dZIk5eTk6MMPP9S4ceO0YcMGFRQUlGiBAAAAjlbs+Xdu4Jo/k5SUFMXHxysyMlKvvPKK/vnPf+rHH38sydoAAADgJMVKEjMzM5WUlKT33ntPOTk5uu+++5Sfn6/Fixdz0woAALhuMSXRrMhJYpcuXRQbG6vNmzdr+vTpOnjwoF5//XVH1gYAAAAnKXKSuHTpUo0cOVLDhg1T9erVHVkTAABAqeLuZrMiJ4lr167ViRMn1LBhQzVu3FhvvPGG/vzzT0fWBgAAACcpcpN4xx136J133lFGRoYefvhhLViwQJGRkSosLFRycrJOnDjhyDoBAAAchsW0zYp9d7Ofn58GDBigtWvXasuWLRo7dqxeeOEFhYWF6e6773ZEjQAAAA7Fdzeb/a1lgWJjYzV16lT98ccf+vDDD0uqJgAAADjZNS2mfTEPDw9169ZN3bp1K4nTAQAAlCpuXDFjgXEAAACYlEiSCAAAcD0jSDQjSQQAAIAJSSIAAHB71/NdyI5CkggAAAATkkQAAOD2LCJKvBhNIgAAcHsMN5sx3AwAAAATkkQAAOD2SBLNSBIBAABgQpIIAADcnoXVtE1IEgEAAGBCkggAANwecxLNSBIBAABgQpIIAADcHlMSzWgSAQCA27PSJZow3AwAAAATkkQAAOD2uHHFjCQRAAAAJiSJAADA7TEl0YwkEQAAACYkiQAAwO1ZRZR4MZJEAAAAmJAkAgAAt8ecRDOaRAAA4PZYAseM4WYAAACYkCQCAAC3x9fymZEkAgAAwIQkEQAAuD2CRDOSRAAAAJiQJAIAALfHnEQzkkQAAACY0CQCAAC3Z7E47lFcKSkp6tKliyIjI2WxWLR48WK7/f369ZPFYrF7tG/f3u6YI0eOqG/fvgoMDFRwcLAGDhyo3NzcYtVBkwgAANye1YGP4srLy1O9evX05ptvXvaY9u3bKyMjw/b48MMP7fb37dtXW7duVXJyspYsWaKUlBQNGTKkWHUwJxEAAMCFdOjQQR06dLjiMV5eXgoPD7/kvu3bt2vZsmVav369GjVqJEl6/fXX1bFjR7388suKjIwsUh0kiQAAwO1dPHxbko/8/Hzl5OTYPfLz8/9WvatXr1ZYWJhiY2M1bNgwHT582LYvNTVVwcHBtgZRklq3bi2r1ap169YV+Ro0iQAAAA6UmJiooKAgu0diYuI1n699+/b673//qxUrVujFF1/UmjVr1KFDBxUUFEiSMjMzFRYWZveaMmXKKDQ0VJmZmUW+DsPNAADA7TlyAZwJEyZozJgxdtu8vLyu+Xy9e/e2/VynTh3VrVtXVatW1erVq3XXXXdd83kvRpIIAADgQF5eXgoMDLR7/J0m8WJVqlRR+fLltXv3bklSeHi4srKy7I45d+6cjhw5ctl5jJdCkwgAANye1WJx2MPR/vjjDx0+fFgRERGSpLi4OB07dkwbNmywHbNy5UoVFhaqcePGRT4vw80AAAAuJDc315YKStK+ffuUlpam0NBQhYaGavLkyerZs6fCw8O1Z88ePfbYY6pWrZratWsnSapZs6bat2+vwYMHa9asWTp79qyGDx+u3r17F/nOZokkEQAAQBYHPorr559/VoMGDdSgQQNJ0pgxY9SgQQNNnDhRHh4e2rx5s+6++27dcsstGjhwoBo2bKjvvvvObgj7gw8+UI0aNXTXXXepY8eOatq0qWbPnl2sOkgSAQCA23Olr25u2bKlDMO47P7ly5df9RyhoaGaP3/+36qDJBEAAAAmJIkAAMDtWVwpSnQRJIkAAAAwIUkEAABuj9TMjM8EAAAAJiSJAADA7TEn0YwkEQAAACYkiQAAwO2RI5qRJAIAAMCEJBEAALg95iSa0SQCAAC3x9CqGZ8JAAAATEgSAQCA22O42YwkEQAAACYkiQAAwO2RI5qRJAIAAMCEJBEAALg9piSauUSSOGXKFJ08edK0/dSpU5oyZYoTKgIAAHBvLtEkTp48Wbm5uabtJ0+e1OTJk51QEQAAcCdWWRz2uF65xHCzYRiXvPV806ZNCg0NdUJFAADAnTDcbObUJjEkJEQWi0UWi0W33HKLXaNYUFCg3NxcDR061IkVAgAAuCenNonTp0+XYRgaMGCAJk+erKCgINs+T09PRUdHKy4uzokVAgAAd2C5joeFHcWpTWJ8fLwkKSYmRnfeeafKli3rzHIAAADw/7nEnMQWLVqosLBQv/32m7KyslRYWGi3v3nz5k6qDAAAuAPmJJq5RJP4448/qk+fPvr9999lGIbdPovFooKCAidVBgAA4J5cokkcOnSoGjVqpK+++koRERF8yTYAAChV1/NSNY7iEk3irl279Omnn6patWrOLgUAAABykcW0GzdurN27dzu7DAAA4KYsFsc9rlcukSSOGDFCY8eOVWZmpurUqWO6y7lu3bpOqgwAALiD67mZcxSXaBJ79uwpSRowYIBtm8VisX0TCzeuAAAAlC6XaBL37dvn7BIAAIAbYzFtM5doEqOiopxdAgAAAP7CJZrEC7Zt26b09HSdOXPGbvvdd9/tpIoAAIA7sBIkmrhEk7h37151795dW7Zssc1FlGRbL5E5iQAAAKXLJZbAGTVqlGJiYpSVlSVfX19t3bpVKSkpatSokVavXu3s8gAAwA3O4sB/rlcukSSmpqZq5cqVKl++vKxWq6xWq5o2barExESNHDlSGzdudHaJAAAAbsUlksSCggIFBARIksqXL6+DBw9KOn9Dy86dO51ZGgAAcAMspm3mEkli7dq1tWnTJsXExKhx48aaOnWqPD09NXv2bFWpUsXZ5QEAgBvc9Tws7Cgu0SQ+9dRTysvLkyRNmTJFnTt3VrNmzVSuXDl99NFHTq4OAADA/bhEk9iuXTvbz9WqVdOOHTt05MgRhYSE2O5wBgAAcBSWwDFziSbxUkJDQ51dAgAAgNtyiSYxLy9PL7zwglasWKGsrCwVFhba7d+7d6+TKgMAAO6AOYlmLtEkDho0SGvWrNGDDz6oiIgIhpgBAACczCWaxKVLl+qrr75SkyZNnF0KXNQ9ndsoM+OgaXv3e3tr8LAReu/tN/XTjz/oUGaGgoND1LzlXRo0bIT8///SSgBcw4ONb1aL6uUVVc5H+WcLteVgjmau2a/0o6ckSQHeZTSoSZT+ER2sigFeOnrqrL7bdVjvrP1deWfOf/tWtQp+eqDxzap7U5CCfcooIydfi9My9Mkv5j8jgKIinzJziSYxJCSEOYi4onfmfaTCv3w94949uzX6kUFq1bqd/szO1p/ZWUp4dJxiYqoqM+OgXkqcoj//zNK/p053XtEATOpXCtLCjQe1PTNXHlaLHm4WrWn31lbfORt0+myhyvt7qry/p95YvU/7D59UxUAvjW9TTeX9vfTUF9slSbEV/XX05FlN+Wqnsk7kq/ZNAXq8bXUVGoY+25jh5HcI3DgsxoUvSnai999/X59//rnmzp0rX1/fv32+7NxzJVAVXNlrLyfqh+/WaMHipZecnrAyebmeffpxJa/9WWXKuMT/C6GEdJuZ6uwSUIKCfcrqq+F36JEPN2nTHzmXPKbVLeU1sVOsWk//XgWX+RtrTOuqig711ciPtziwWjja9+ObOe/au4467NxNqoc47NyO5BJ/e77yyivas2ePKlasqOjoaJUtW9Zu/y+//OKkyuCKzp49o2++XqJeD8Rfdv5qXu4J+fn50yACLs7Py0OSlHP68v9z7+/lobwzBZdtEM8fU+aK5wCuxsp4s4lL/A3arVu3a35tfn6+8vPz7bed9ZCXl9ffrAquKmXVSuXmnlDHLt0uuf/Y0aNKeneWuvS4t3QLA1AsFkmj/llFm/44rn1/nrzkMUE+ZdQvrrK+2HT5YeTakQG6K7a8xi/c6qBKAffkEk3iM888c82vTUxM1OTJk+22jZvwtB57cuLfLQsu6qvPP1PjO5uqfIUw07683FyNHzVM0VWqauCQR5xQHYCiGtummqqU99Ow+Zsuud/X00Mv9bhV+w6f1Hs/pF/ymJjyvnqh+636zw/p+mn/MQdWixsdOaKZSzSJf8eECRM0ZswYu205Zz2cVA0cLTPjoH7+6Uc999Jrpn0n8/I0dsTD8vXz0/Mvz1CZi6YtAHAdY+6qqjurhCphwSZl554x7fct66FX76mtk2cL9OTibSooNI81R5fz1Yz76uiLTRma++OB0igbcCsu0SRe7uv3LBaLvL29Va1aNfXr10/9+/c3HePl5WUaWs7nxpUb1ldfLFJISKjimja3256Xm6sxw4eorKenXnz1DaYbAC5szF1V1bx6OQ1fsFkZx/NN+309PTTt3to6c65Qjy/cpjOXmIwYU85XM3rV0dKthzR77e+lUTZudESJJi7RJE6cOFHPPfecOnTooH/84x+SpJ9++knLli1TQkKC9u3bp2HDhuncuXMaPHiwk6uFsxQWFurrLxapfeeudjek5OXmanTCYOWfPq2Jz76gvLxc5eXlSpKCQ0Ll4UGyDLiKsa2rqk3NMD2xaJtOni1QqN/5xD83v0BnzhXK19ND0++tLa+yHpry1U75eXnYbm45dvKsCo3zQ8yv31dH6/Yf1YKf/2c7R2GhdOzUWae9N+BG4xJN4tq1a/Xvf/9bQ4cOtdv+9ttv65tvvtFnn32munXrasaMGTSJbuzndak6lJmhTl172G3fuWObtv26WZLUq1sHu32ffPmNIiJvKrUaAVxZjwaRkqQ3769rt/25r3fq661Ziq3or1sjAyVJHw++3e6Ynm//pMycfLW6pbxC/DzV/taKan9rRdv+jOOndc/s9Q5+B7hR8bV8Zi6xTqK/v7/S0tJUrVo1u+27d+9W/fr1lZubqz179qhu3brKy8u76vlYJxG4cbFOInDjcuY6iev2HHfYuRtXDXLYuR3J6uwCJCk0NFRffvmlafuXX35p+yaWvLw8BfAVawAAwAEsFsc9rlcuMdz89NNPa9iwYVq1apVtTuL69ev19ddfa9asWZKk5ORktWjRwpllAgCAG9R13Ms5jEs0iYMHD1atWrX0xhtvaOHChZKk2NhYrVmzRnfeeackaezYsc4sEQAAwK24RJMoSU2aNFGTJk2cXQYAAHBHRIkmTmsSc3JyFBgYaPv5Si4cBwAAgNLhtCYxJCREGRkZCgsLU3Bw8CUX0zYMQxaLRQUFBU6oEAAAuAuWwDFzWpO4cuVK253Lq1atclYZAAAAuASnNYl/vVOZu5YBAIAzXc9L1TiK05rEzZs3F/nYunXrXv0gAAAAlBinNYn169eXxWLR1b7whTmJAADA0QgSzZzWJO7bt89ZlwYAALBHl2jitCYxKirKWZcGAADAVbjMYtqStG3bNqWnp+vMmTN22++++24nVQQAANwBS+CYuUSTuHfvXnXv3l1btmyxm6d4Ye1E5iQCAACULquzC5CkUaNGKSYmRllZWfL19dXWrVuVkpKiRo0aafXq1c4uDwAA3OAsFsc9rlcukSSmpqZq5cqVKl++vKxWq6xWq5o2barExESNHDlSGzdudHaJAAAAbsUlksSCggIFBARIksqXL6+DBw9KOn9zy86dO51ZGgAAcAMWBz6uVy6RJNauXVubNm1STEyMGjdurKlTp8rT01OzZ89WlSpVnF0eAACA23GJJvGpp55SXl6eJGny5Mnq0qWLmjVrpnLlymnBggVOrg4AANzwrufIz0Fcokls166d7efq1atrx44dOnLkiEJCQmx3OAMAADgKS+CYObVJHDBgQJGO+89//uPgSgAAAPBXTm0Sk5KSFBUVpQYNGlz1O5wBAAAchYFLM6c2icOGDdOHH36offv2qX///nrggQcUGhrqzJIAAAAgJy+B8+abbyojI0OPPfaYvvzyS1WqVEn33Xefli9fTrIIAABKDUvgmDl9nUQvLy/df//9Sk5O1rZt23TrrbfqkUceUXR0tHJzc51dHgAAgFtyibubL7Barbbvbub7mgEAQKm5niM/B3F6kpifn68PP/xQbdq00S233KItW7bojTfeUHp6uvz9/Z1dHgAAQKlKSUlRly5dFBkZKYvFosWLF9vtNwxDEydOVEREhHx8fNS6dWvt2rXL7pgjR46ob9++CgwMVHBwsAYOHFjsEVqnNomPPPKIIiIi9MILL6hz5846cOCAPvnkE3Xs2FFWq9P7VwAA4CYsDvynuPLy8lSvXj29+eabl9w/depUzZgxQ7NmzdK6devk5+endu3a6fTp07Zj+vbtq61btyo5OVlLlixRSkqKhgwZUrzPxHDiHSJWq1WVK1dWgwYNrrho9sKFC4t13uzcc3+3NAAuqtvMVGeXAMBBvh/fzGnX3vq/PIed+9ab/K75tRaLRYsWLVK3bt0knU8RIyMjNXbsWI0bN06SdPz4cVWsWFFJSUnq3bu3tm/frlq1amn9+vVq1KiRJGnZsmXq2LGj/vjjD0VGRhbp2k6dk/jQQw/xjSoAAMDpHNmO5OfnKz8/326bl5eXvLy8in2uffv2KTMzU61bt7ZtCwoKUuPGjZWamqrevXsrNTVVwcHBtgZRklq3bi2r1ap169ape/fuRbqW0xfTBgAAcDZHRlaJiYmaPHmy3bZnnnlGkyZNKva5MjMzJUkVK1a0216xYkXbvszMTIWFhdntL1OmjEJDQ23HFIVL3d0MAABwo5kwYYLGjBljt+1aUsTSRpMIAADgwCjxWoeWLyU8PFySdOjQIUVERNi2Hzp0SPXr17cdk5WVZfe6c+fO6ciRI7bXFwW3EAMAAFwnYmJiFB4erhUrVti25eTkaN26dYqLi5MkxcXF6dixY9qwYYPtmJUrV6qwsFCNGzcu8rVIEgEAgNu7lqVqHCU3N1e7d++2Pd+3b5/S0tIUGhqqypUr69FHH9W///1vVa9eXTExMXr66acVGRlpuwO6Zs2aat++vQYPHqxZs2bp7NmzGj58uHr37l3kO5slmkQAAACX8vPPP6tVq1a25xfmM8bHxyspKUmPPfaY8vLyNGTIEB07dkxNmzbVsmXL5O3tbXvNBx98oOHDh+uuu+6S1WpVz549NWPGjGLV4dR1Eh2FdRKBGxfrJAI3Lmeuk7gz86TDzh0b7uuwczsScxIBAABgwnAzAABwe64zI9F10CQCAADQJZow3AwAAAATkkQAAOD2XGkJHFdBkggAAAATkkQAAOD2LASJJiSJAAAAMCFJBAAAbo8g0YwkEQAAACYkiQAAAESJJjSJAADA7bEEjhnDzQAAADAhSQQAAG6PJXDMSBIBAABgQpIIAADcHkGiGUkiAAAATEgSAQAAiBJNSBIBAABgQpIIAADcHuskmtEkAgAAt8cSOGYMNwMAAMCEJBEAALg9gkQzkkQAAACYkCQCAAC3x5xEM5JEAAAAmJAkAgAAMCvRhCQRAAAAJiSJAADA7TEn0YwmEQAAuD16RDOGmwEAAGBCkggAANwew81mJIkAAAAwIUkEAABuz8KsRBOSRAAAAJiQJAIAABAkmpAkAgAAwIQkEQAAuD2CRDOaRAAA4PZYAseM4WYAAACYkCQCAAC3xxI4ZiSJAAAAMCFJBAAAIEg0IUkEAACACUkiAABwewSJZiSJAAAAMCFJBAAAbo91Es1oEgEAgNtjCRwzhpsBAABgQpIIAADcHsPNZiSJAAAAMKFJBAAAgAlNIgAAAEyYkwgAANwecxLNSBIBAABgQpIIAADcHuskmtEkAgAAt8dwsxnDzQAAADAhSQQAAG6PINGMJBEAAAAmJIkAAABEiSYkiQAAADAhSQQAAG6PJXDMSBIBAABgQpIIAADcHuskmpEkAgAAwIQkEQAAuD2CRDOaRAAAALpEE4abAQAAYEKSCAAA3B5L4JiRJAIAAMCEJBEAALg9lsAxI0kEAACAicUwDMPZRQDXKj8/X4mJiZowYYK8vLycXQ6AEsTvN+BcNIm4ruXk5CgoKEjHjx9XYGCgs8sBUIL4/Qaci+FmAAAAmNAkAgAAwIQmEQAAACY0ibiueXl56ZlnnmFSO3AD4vcbcC5uXAEAAIAJSSIAAABMaBIBAABgQpMIAAAAE5pE3FCio6M1ffp0Z5cB4CL79++XxWJRWlqaJGn16tWyWCw6duyYU+sCcHk0iSgV/fr1k8VisT3KlSun9u3ba/PmzSV6nfXr12vIkCElek7AXV34vR06dKhpX0JCgiwWi/r163dN577zzjuVkZGhoKCgv1llyUtKSlJwcLCzywCcjiYRpaZ9+/bKyMhQRkaGVqxYoTJlyqhz584leo0KFSrI19e3RM8JuLNKlSppwYIFOnXqlG3b6dOnNX/+fFWuXPmaz+vp6anw8HBZLJaSKBOAA9AkotR4eXkpPDxc4eHhql+/vp544gkdOHBA2dnZkqQDBw7ovvvuU3BwsEJDQ9W1a1ft37/f9vp+/fqpW7duevnllxUREaFy5copISFBZ8+etR1z8XDzjh071LRpU3l7e6tWrVr69ttvZbFYtHjxYkn/NwS2cOFCtWrVSr6+vqpXr55SU1NL4yMBXN5tt92mSpUqaeHChbZtCxcuVOXKldWgQQPbtmXLlqlp06YKDg5WuXLl1LlzZ+3Zs+ey573UcPM777yjSpUqydfXV927d9err75ql+hNmjRJ9evX17x58xQdHa2goCD17t1bJ06cKHIdV/udX716tfr376/jx4/bRj4mTZr0Nz5B4PpFkwinyM3N1fvvv69q1aqpXLlyOnv2rNq1a6eAgAB99913+v777+Xv76/27dvrzJkzttetWrVKe/bs0apVqzR37lwlJSUpKSnpktcoKChQt27d5Ovrq3Xr1mn27Nn617/+dclj//Wvf2ncuHFKS0vTLbfcovvvv1/nzp1zxFsHrjsDBgzQnDlzbM//85//qH///nbH5OXlacyYMfr555+1YsUKWa1Wde/eXYWFhUW6xvfff6+hQ4dq1KhRSktLU5s2bfTcc8+ZjtuzZ48WL16sJUuWaMmSJVqzZo1eeOGFYtdxud/5O++8U9OnT1dgYKBt5GPcuHHF+biAG4cBlIL4+HjDw8PD8PPzM/z8/AxJRkREhLFhwwbDMAxj3rx5RmxsrFFYWGh7TX5+vuHj42MsX77cdo6oqCjj3LlztmPuvfdeo1evXrbnUVFRxrRp0wzDMIylS5caZcqUMTIyMmz7k5OTDUnGokWLDMMwjH379hmSjHfffdd2zNatWw1Jxvbt20v8cwCuJ/Hx8UbXrl2NrKwsw8vLy9i/f7+xf/9+w9vb28jOzja6du1qxMfHX/K12dnZhiRjy5YthmH83+/axo0bDcMwjFWrVhmSjKNHjxqGYRi9evUyOnXqZHeOvn37GkFBQbbnzzzzjOHr62vk5OTYto0fP95o3LjxZd/D5eq40u/8nDlz7K4LuCuSRJSaVq1aKS0tTWlpafrpp5/Url07dejQQb///rs2bdqk3bt3KyAgQP7+/vL391doaKhOnz5tN1R06623ysPDw/Y8IiJCWVlZl7zezp07ValSJYWHh9u2/eMf/7jksXXr1rU7p6TLnhdwNxUqVFCnTp2UlJSkOXPmqFOnTipfvrzdMbt27dL999+vKlWqKDAwUNHR0ZKk9PT0Il1j586dpt/PS/2+RkdHKyAgwPb84j8DiloHv/PA1ZVxdgFwH35+fqpWrZrt+bvvvqugoCC98847ys3NVcOGDfXBBx+YXlehQgXbz2XLlrXbZ7FYijycdSV/Pe+FifQlcV7gRjFgwAANHz5ckvTmm2+a9nfp0kVRUVF65513FBkZqcLCQtWuXdtuukhJuNqfAUWtg9954OpoEuE0FotFVqtVp06d0m233aaPPvpIYWFhCgwMLJHzx8bG6sCBAzp06JAqVqwo6fwSOQCK78L8YIvFonbt2tntO3z4sHbu3Kl33nlHzZo1kyStXbu2WOePjY01/X4W9/e1JOqQzt95XVBQUOzXATcahptRavLz85WZmanMzExt375dI0aMUG5urrp06aK+ffuqfPny6tq1q7777jvt27dPq1ev1siRI/XHH39c0/XatGmjqlWrKj4+Xps3b9b333+vp556SpJYdgMoJg8PD23fvl3btm2zm/IhSSEhISpXrpxmz56t3bt3a+XKlRozZkyxzj9ixAh9/fXXevXVV7Vr1y69/fbbWrp0abF+V0uiDun8kHZubq5WrFihP//8UydPniz2OYAbAU0iSs2yZcsUERGhiIgINW7cWOvXr9cnn3yili1bytfXVykpKapcubJ69OihmjVrauDAgTp9+vQ1J4seHh5avHixcnNzdfvtt2vQoEG2u5u9vb1L8q0BbiEwMPCSv49Wq1ULFizQhg0bVLt2bY0ePVovvfRSsc7dpEkTzZo1S6+++qrq1aunZcuWafTo0cX6XS2JOqTzC30PHTpUvXr1UoUKFTR16tRinwO4EVgMwzCcXQRQWr7//ns1bdpUu3fvVtWqVZ1dDoArGDx4sHbs2KHvvvvO2aUAbok5ibihLVq0SP7+/qpevbp2796tUaNGqUmTJjSIgAt6+eWX1aZNG/n5+Wnp0qWaO3eu3nrrLWeXBbgtmkTc0E6cOKHHH39c6enpKl++vFq3bq1XXnnF2WUBuISffvpJU6dO1YkTJ1SlShXNmDFDgwYNcnZZgNtiuBkAAAAm3LgCAAAAE5pEAAAAmNAkAgAAwIQmEQAAACY0iQAAADChSQTgsvr166du3brZnrds2VKPPvpoqdexevVqWSwWHTt2rNSvDQDOQpMIoNj69esni8Uii8UiT09PVatWTVOmTNG5c+ccet2FCxfq2WefLdKxNHYA8PewmDaAa9K+fXvNmTNH+fn5+vrrr5WQkKCyZctqwoQJdsedOXNGnp6eJXLN0NDQEjkPAODqSBIBXBMvLy+Fh4crKipKw4YNU+vWrfXFF1/Yhoife+45RUZGKjY2VpJ04MAB3XfffQoODlZoaKi6du2q/fv3285XUFCgMWPGKDg4WOXKldNjjz2mi9f6v3i4OT8/X48//rgqVaokLy8vVatWTe+9957279+vVq1aSZJCQkJksVjUr18/SVJhYaESExMVExMjHx8f1atXT59++qnddb7++mvdcsst8vHxUatWrezqBAB3QZMIoET4+PjozJkzkqQVK1Zo586dSk5O1pIlS3T27Fm1a9dOAQEB+u677/T999/L399f7du3t73mlVdeUVJSkv7zn/9o7dq1OnLkiBYtWnTFaz700EP68MMPNWPGDG3fvl1vv/22/P39ValSJX322WeSpJ07dyojI0OvvfaaJCkxMVH//e9/NWvWLG3dulWjR4/WAw88oDVr1kg638z26NFDXbp0UVpamgYNGqQnnnjCUR8bALgshpsB/C2GYWjFihVavny5RowYoezsbPn5+endd9+1DTO///77Kiws1LvvviuLxSJJmjNnjoKDg7V69Wq1bdtW06dP14QJE9SjRw9J0qxZs7R8+fLLXve3337Txx9/rOTkZLVu3VqSVKVKFdv+C0PTYWFhCg4OlnQ+eXz++ef17bffKi4uzvaatWvX6u2331aLFi00c+ZMVa1a1fYd37GxsdqyZYtefPHFEvzUAMD10SQCuCZLliyRv7+/zp49q8LCQvXp00eTJk1SQkKC6tSpYzcPcdOmTdq9e7cCAgLsznH69Gnt2bNHx48fV0ZGhho3bmzbV6ZMGTVq1Mg05HxBWlqaPDw81KJFiyLXvHv3bp08eVJt2rSx237mzBk1aNBAkrR9+3a7OiTZGkoAcCc0iQCuSatWrTRz5kx5enoqMjJSZcr83x8nfn5+dsfm5uaqYcOG+uCDD0znqVChwjVd38fHp9ivyc3NlSR99dVXuummm+z2eXl5XVMdAHCjokkEcE38/PxUrVq1Ih1722236aOPPlJYWJgCAwMveUxERITWrVun5s2bS5LOnTunDRs26Lbbbrvk8XXq1FFhYaHWrFljG27+qwtJZkFBgW1brVq15OXlpfT09MsmkDVr1tQXX3xht+3HH3+8+psEgBsMN64AcLi+ffuqfPny6tq1q7777jvt27dPq1ev1siRI/XHH39IkkaNGqUXXnhBixcv1o4dO/TII49ccY3D6OhoxcfHa8CAAVq8eLHtnB9//LEkKSoqShaLRUuWLFF2drZyc3MVEBCgcePGafTo0Zo7d6727NmjX375Ra+//rrmzp0rSRo6dKh27dql8ePHa+fOnZo/f76SkpIc/REBgMuhSQTgcL6+vkpJSVHlypXVo0cP1axZUwMHDtTp06dtyeLYsWP14IMPKj4+XnFxcQoICFD37t2veN6ZM2fqnnvu0SOPPKIaNWpo8ODBysvLkyTddNNNmjx5sp544glVrFhRw4cPlyQ9++yzevrpp5WYmKiaNWuqffv2+uqrrxQTEyNJqly5sj777DMtXrxY9erV06xZs/T888878NMBANdkMS43KxwAAABuiyQRAAAAJjSJAAAAMKFJBAAAgAlNIgAAAExoEgEAAGBCkwgAAAATmkQAAACY0CQCAADAhCYRAAAAJjSJAAAAMKFJBAAAgMn/A21an8T5D8vKAAAAAElFTkSuQmCC\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "model_obj = ImageClassificationModel(optimizer='RMSprop', loss_function='categorical_crossentropy', activation_function='relu', output_activation_function='softmax', batch_size=32, epochs=15, num_output_neurons=2)\n",
+ "\n",
+ "model_obj.show_training_progress()\n",
+ "model_obj.run()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GZP80_YWyEsn"
+ },
+ "source": [
+ "**Pre Processing Testing**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "mS3XmLaux4A7",
+ "outputId": "1480df8c-e2a8-4753-83a3-f96d30e8ff23"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/15\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+ " self._warn_if_super_not_called()\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 309ms/step - accuracy: 0.6746 - loss: 0.7990 - val_accuracy: 0.7235 - val_loss: 0.6676\n",
+ "Epoch 2/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 254ms/step - accuracy: 0.7517 - loss: 0.5030 - val_accuracy: 0.6174 - val_loss: 0.6556\n",
+ "Epoch 3/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 251ms/step - accuracy: 0.7597 - loss: 0.4789 - val_accuracy: 0.4886 - val_loss: 0.6921\n",
+ "Epoch 4/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 250ms/step - accuracy: 0.7779 - loss: 0.4482 - val_accuracy: 0.7159 - val_loss: 0.5731\n",
+ "Epoch 5/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 251ms/step - accuracy: 0.8047 - loss: 0.4120 - val_accuracy: 0.6932 - val_loss: 0.5282\n",
+ "Epoch 6/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 257ms/step - accuracy: 0.8007 - loss: 0.3968 - val_accuracy: 0.7992 - val_loss: 0.4039\n",
+ "Epoch 7/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 250ms/step - accuracy: 0.8116 - loss: 0.3837 - val_accuracy: 0.8106 - val_loss: 0.4346\n",
+ "Epoch 8/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 249ms/step - accuracy: 0.8267 - loss: 0.3559 - val_accuracy: 0.7879 - val_loss: 0.3967\n",
+ "Epoch 9/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 250ms/step - accuracy: 0.8187 - loss: 0.3873 - val_accuracy: 0.8068 - val_loss: 0.4188\n",
+ "Epoch 10/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 251ms/step - accuracy: 0.8159 - loss: 0.3685 - val_accuracy: 0.8409 - val_loss: 0.3797\n",
+ "Epoch 11/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 253ms/step - accuracy: 0.8286 - loss: 0.3600 - val_accuracy: 0.7159 - val_loss: 0.6100\n",
+ "Epoch 12/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 254ms/step - accuracy: 0.8325 - loss: 0.3442 - val_accuracy: 0.8447 - val_loss: 0.3360\n",
+ "Epoch 13/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 248ms/step - accuracy: 0.8474 - loss: 0.3251 - val_accuracy: 0.8447 - val_loss: 0.3458\n",
+ "Epoch 14/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 250ms/step - accuracy: 0.8400 - loss: 0.3370 - val_accuracy: 0.7879 - val_loss: 0.4263\n",
+ "Epoch 15/15\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 249ms/step - accuracy: 0.8301 - loss: 0.3447 - val_accuracy: 0.8561 - val_loss: 0.3481\n",
+ "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step\n",
+ "\n",
+ "Test loss: 0.3715205490589142\n",
+ "Test accuracy: 0.8378787878787879\n",
+ "Test precision: 0.8175675675675675\n",
+ "Test recall: 0.8203389830508474\n",
+ "Test F1 score: 0.8189509306260575\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1200x600 with 2 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 800x600 with 2 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "datagen = ImageDataGenerator(\n",
+ " horizontal_flip=True,\n",
+ " vertical_flip=True,\n",
+ " zoom_range=0.2\n",
+ ")\n",
+ "\n",
+ "model_obj = ImageClassificationModel(\n",
+ " optimizer='adam',\n",
+ " loss_function='binary_crossentropy',\n",
+ " activation_function='relu',\n",
+ " output_activation_function='sigmoid',\n",
+ " batch_size=32, epochs=15,\n",
+ " data_augmentation=datagen,\n",
+ " num_output_neurons=1)\n",
+ "\n",
+ "model_obj.show_training_progress()\n",
+ "model_obj.run()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Okv5mQkN-4gY"
+ },
+ "source": [
+ "#"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**Evaluation of results**"
+ ],
+ "metadata": {
+ "id": "6sngJcRd9Ewf"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
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+ },
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+}
+\ No newline at end of file
