detecting-brain-tumors-vgg16-accuracy-98.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/jarredpriester/detecting-brain-tumors-vgg16-accuracy-98?scriptVersionId=105530416\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","id":"a12db8e1","metadata":{"editable":false,"papermill":{"duration":0.016461,"end_time":"2022-09-13T01:59:50.19337","exception":false,"start_time":"2022-09-13T01:59:50.176909","status":"completed"},"tags":[]},"source":["# Detecting Brain Tumors in MRIs using a Convolutional Neural Network with Transfer Learning\n","### Jarred Priester\n","### 6/17/22"]},{"cell_type":"markdown","id":"0f7f142d","metadata":{"editable":false,"papermill":{"duration":0.014291,"end_time":"2022-09-13T01:59:50.222822","exception":false,"start_time":"2022-09-13T01:59:50.208531","status":"completed"},"tags":[]},"source":["1. Introduction\n","    * 1.1 Brief Overview of Task\n","    * 1.2 Purpose of the Project\n","    * 1.3 Brief Description of Dataset\n","\n","2. Downloading the Data\n","    * 2.1 Downloading the Libraries\n","    * 2.2 Downloading the Images\n","    \n","3. Processing the Images\n","    * 3.1 Changing Pixel Value\n","    * 3.2 Visualization\n","    \n","4. Convolutional Neural Network\n","    * 4.1 Split Data into Train, Validation, Test\n","    * 4.2 Creating our Baseline Model\n","    * 4.3 Adding Data Agumentation\n","    * 4.4 Adding a Rretrained Model\n","\n","5. Results\n","    * 5.1 Results Table\n","   \n","6. Conclusion\n","    * 6.1 Summary\n","    * 6.2 Brief Thoughts on Results\n","    * 6.3 Futrue Work"]},{"cell_type":"markdown","id":"c75ac913","metadata":{"editable":false,"papermill":{"duration":0.014603,"end_time":"2022-09-13T01:59:50.252029","exception":false,"start_time":"2022-09-13T01:59:50.237426","status":"completed"},"tags":[]},"source":["# 1. Introduction"]},{"cell_type":"markdown","id":"dc72bb1c","metadata":{"editable":false,"papermill":{"duration":0.01439,"end_time":"2022-09-13T01:59:50.280769","exception":false,"start_time":"2022-09-13T01:59:50.266379","status":"completed"},"tags":[]},"source":["## 1.1 Brief Overview of Task\n","\n","The brain is one of, if not the most important part of our body. Any type of disease involving the brain is very serious. It is very important to detect a tumor not only accurately but as early as possible. Our task will be to take MRI images and accurately predict if the image of the brain has a tumor."]},{"cell_type":"markdown","id":"0025bffb","metadata":{"editable":false,"papermill":{"duration":0.015293,"end_time":"2022-09-13T01:59:50.31046","exception":false,"start_time":"2022-09-13T01:59:50.295167","status":"completed"},"tags":[]},"source":["To better understand our task below is a brief explanation of brain tumors from the Mayo Clinic:\n","\n","*A brain tumor is a mass or growth of abnormal cells in your brain.*\n","\n","*Many different types of brain tumors exist. Some brain tumors are noncancerous (benign), and some brain tumors are cancerous (malignant). Brain tumors can begin in your brain (primary brain tumors), or cancer can begin in other parts of your body and spread to your brain as secondary (metastatic) brain tumors.*\n","\n","*How quickly a brain tumor grows can vary greatly. The growth rate as well as the location of a brain tumor determines how it will affect the function of your nervous system.*\n","\n","*Brain tumor treatment options depend on the type of brain tumor you have, as well as its size and location.*"]},{"cell_type":"markdown","id":"8b8bfaaa","metadata":{"editable":false,"papermill":{"duration":0.014188,"end_time":"2022-09-13T01:59:50.339022","exception":false,"start_time":"2022-09-13T01:59:50.324834","status":"completed"},"tags":[]},"source":["## 1.2 Purpose of the Project\n","\n","The purpose of this project will be to create a convolutional neural network that can classify a MRI of the brain into two catagories: tumor, no tumor. We will start with a baseline model. From there we use data agumentation to add data. Then we will apply transfer learning to improve the accuracy. The idea is that the accuracy will improve with each step."]},{"cell_type":"markdown","id":"53c0234f","metadata":{"editable":false,"papermill":{"duration":0.014186,"end_time":"2022-09-13T01:59:50.367647","exception":false,"start_time":"2022-09-13T01:59:50.353461","status":"completed"},"tags":[]},"source":["## 1.3 Brief Description of Dataset\n","\n","Unfornately we do not have much information about this dataset and where it comes from. This is a limitation to the project but we will still be able to use this data to give us a good idea of how to detect tumors using a convolutional neural network. Furture studies will involve more rigorous and larger dataset. "]},{"cell_type":"markdown","id":"f4ad2286","metadata":{"editable":false,"papermill":{"duration":0.014342,"end_time":"2022-09-13T01:59:50.39664","exception":false,"start_time":"2022-09-13T01:59:50.382298","status":"completed"},"tags":[]},"source":["# 2. Downloading the Data"]},{"cell_type":"markdown","id":"24755b17","metadata":{"editable":false,"papermill":{"duration":0.014102,"end_time":"2022-09-13T01:59:50.425312","exception":false,"start_time":"2022-09-13T01:59:50.41121","status":"completed"},"tags":[]},"source":["## 2.1 Downloading the Libraries"]},{"cell_type":"code","execution_count":1,"id":"2fceb965","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T01:59:50.456435Z","iopub.status.busy":"2022-09-13T01:59:50.455659Z","iopub.status.idle":"2022-09-13T01:59:56.828202Z","shell.execute_reply":"2022-09-13T01:59:56.827349Z"},"papermill":{"duration":6.390851,"end_time":"2022-09-13T01:59:56.830563","exception":false,"start_time":"2022-09-13T01:59:50.439712","status":"completed"},"tags":[]},"outputs":[],"source":["#importing libraries\n","import numpy as np \n","import pandas as pd\n","import random as rd\n","import os\n","\n","#data visualization\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","import seaborn as sns\n","from PIL import Image\n","\n","#CNN model\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import classification_report\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","from tensorflow.keras.layers.experimental import preprocessing\n","from keras.metrics import accuracy\n","from keras.applications.vgg16 import VGG16\n","\n","#setting seed for reproducability\n","from numpy.random import seed\n","seed(25)\n","tf.random.set_seed(50)"]},{"cell_type":"markdown","id":"5ae84518","metadata":{"editable":false,"papermill":{"duration":0.010145,"end_time":"2022-09-13T01:59:56.850729","exception":false,"start_time":"2022-09-13T01:59:56.840584","status":"completed"},"tags":[]},"source":["## 2.2 Downloading the Images"]},{"cell_type":"code","execution_count":2,"id":"fbd807d5","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T01:59:56.871722Z","iopub.status.busy":"2022-09-13T01:59:56.871201Z","iopub.status.idle":"2022-09-13T01:59:58.597987Z","shell.execute_reply":"2022-09-13T01:59:58.595715Z"},"papermill":{"duration":1.73963,"end_time":"2022-09-13T01:59:58.599969","exception":false,"start_time":"2022-09-13T01:59:56.860339","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["data shape is: (139, 128, 128, 3)\n","labels shape is: (139, 1)\n"]}],"source":["# 0 - Normal\n","# 1 - Tumor\n","\n","data = [] #creating a list for images\n","paths = [] #creating a list for paths\n","labels = [] #creating a list to put our 0 or 1 labels\n","\n","#staring with the images that have tumors\n","for r, d, f in os.walk(r'../input/brain-mri-images-for-brain-tumor-detection/yes'):\n","    for file in f:\n","        if '.jpg' in file:\n","            paths.append(os.path.join(r, file))\n","\n","for path in paths:\n","    img = Image.open(path)\n","    img = img.resize((128,128))\n","    img = np.array(img)\n","    if(img.shape == (128,128,3)):\n","        data.append(np.array(img))\n","        labels.append(1)\n","\n","#now working with the images with no tumors        \n","paths = []\n","for r, d, f in os.walk(r\"../input/brain-mri-images-for-brain-tumor-detection/no\"):\n","    for file in f:\n","        if '.jpg' in file:\n","            paths.append(os.path.join(r, file))\n","\n","for path in paths:\n","    img = Image.open(path)\n","    img = img.resize((128,128))\n","    img = np.array(img)\n","    if(img.shape == (128,128,3)):\n","        data.append(np.array(img))\n","        labels.append(0)\n","        \n","data = np.array(data)\n","data.shape\n","\n","labels = np.array(labels)\n","labels = labels.reshape(139,1)\n","\n","print('data shape is:', data.shape)\n","print('labels shape is:', labels.shape)"]},{"cell_type":"markdown","id":"688b5f3d","metadata":{"editable":false,"papermill":{"duration":0.009588,"end_time":"2022-09-13T01:59:58.619891","exception":false,"start_time":"2022-09-13T01:59:58.610303","status":"completed"},"tags":[]},"source":["# 3. Processing the Images"]},{"cell_type":"code","execution_count":3,"id":"c776fdf9","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T01:59:58.641313Z","iopub.status.busy":"2022-09-13T01:59:58.640527Z","iopub.status.idle":"2022-09-13T01:59:58.644972Z","shell.execute_reply":"2022-09-13T01:59:58.64421Z"},"papermill":{"duration":0.017799,"end_time":"2022-09-13T01:59:58.647593","exception":false,"start_time":"2022-09-13T01:59:58.629794","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["(139, 128, 128, 3)\n"]}],"source":["#we have 139 images with 128 height, 128 weight, RGB color channel\n","print(data.shape)"]},{"cell_type":"markdown","id":"59385588","metadata":{"editable":false,"papermill":{"duration":0.00998,"end_time":"2022-09-13T01:59:58.668588","exception":false,"start_time":"2022-09-13T01:59:58.658608","status":"completed"},"tags":[]},"source":["## 3.1 Changing Pixel Values"]},{"cell_type":"markdown","id":"2f7e57c1","metadata":{"editable":false,"papermill":{"duration":0.010393,"end_time":"2022-09-13T01:59:58.6887","exception":false,"start_time":"2022-09-13T01:59:58.678307","status":"completed"},"tags":[]},"source":["Each pixel has a value between 255 and 0. We will reduce this down to 1 and 0 in order to help the neural network converge quicker."]},{"cell_type":"code","execution_count":4,"id":"d00bf7a1","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T01:59:58.710005Z","iopub.status.busy":"2022-09-13T01:59:58.70968Z","iopub.status.idle":"2022-09-13T01:59:58.72717Z","shell.execute_reply":"2022-09-13T01:59:58.725953Z"},"papermill":{"duration":0.030808,"end_time":"2022-09-13T01:59:58.729445","exception":false,"start_time":"2022-09-13T01:59:58.698637","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["255\n","0\n"]}],"source":["#getting the max of the array\n","print(np.max(data))\n","#getting the min of the array\n","print(np.min(data))"]},{"cell_type":"code","execution_count":5,"id":"f38469f0","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T01:59:58.750906Z","iopub.status.busy":"2022-09-13T01:59:58.75063Z","iopub.status.idle":"2022-09-13T01:59:58.781809Z","shell.execute_reply":"2022-09-13T01:59:58.780347Z"},"papermill":{"duration":0.043975,"end_time":"2022-09-13T01:59:58.783776","exception":false,"start_time":"2022-09-13T01:59:58.739801","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["1.0\n","0.0\n"]}],"source":["#reducing the data to between 1 and 0\n","data = data / 255.00\n","#getting the max of the array\n","print(np.max(data))\n","#getting the min of the array\n","print(np.min(data))"]},{"cell_type":"markdown","id":"1c790341","metadata":{"editable":false,"papermill":{"duration":0.010676,"end_time":"2022-09-13T01:59:58.804606","exception":false,"start_time":"2022-09-13T01:59:58.79393","status":"completed"},"tags":[]},"source":["## 3.2 Visualizing the Images"]},{"cell_type":"code","execution_count":6,"id":"51b30e17","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T01:59:58.826568Z","iopub.status.busy":"2022-09-13T01:59:58.826195Z","iopub.status.idle":"2022-09-13T02:00:00.423226Z","shell.execute_reply":"2022-09-13T02:00:00.422422Z"},"papermill":{"duration":1.61107,"end_time":"2022-09-13T02:00:00.425603","exception":false,"start_time":"2022-09-13T01:59:58.814533","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 5040x5040 with 1 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\n","text/plain":["<Figure 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size 5040x5040 with 1 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 5040x5040 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["for i in range(5):\n","    fig = plt.figure(figsize=(70,70))\n","    plt.subplot(5,5,i+1)\n","    image = plt.imshow(data[i])\n","    plt.show(image)"]},{"cell_type":"markdown","id":"ddba53d3","metadata":{"editable":false,"papermill":{"duration":0.01332,"end_time":"2022-09-13T02:00:00.452454","exception":false,"start_time":"2022-09-13T02:00:00.439134","status":"completed"},"tags":[]},"source":["# 4 Model"]},{"cell_type":"markdown","id":"f39ac525","metadata":{"editable":false,"papermill":{"duration":0.013233,"end_time":"2022-09-13T02:00:00.479004","exception":false,"start_time":"2022-09-13T02:00:00.465771","status":"completed"},"tags":[]},"source":["## 4.1 Creating Training and Test Sets"]},{"cell_type":"markdown","id":"e07e8caa","metadata":{"editable":false,"papermill":{"duration":0.013393,"end_time":"2022-09-13T02:00:00.50557","exception":false,"start_time":"2022-09-13T02:00:00.492177","status":"completed"},"tags":[]},"source":["Before we create our model we will split up the data into traing sets and test sets with 70% going to training and 30% going to testing."]},{"cell_type":"code","execution_count":7,"id":"6b76aee6","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:00.533577Z","iopub.status.busy":"2022-09-13T02:00:00.532937Z","iopub.status.idle":"2022-09-13T02:00:00.556142Z","shell.execute_reply":"2022-09-13T02:00:00.555333Z"},"papermill":{"duration":0.039697,"end_time":"2022-09-13T02:00:00.558109","exception":false,"start_time":"2022-09-13T02:00:00.518412","status":"completed"},"tags":[]},"outputs":[],"source":["x_train,x_test,y_train,y_test = train_test_split(data, labels, test_size=0.3, shuffle=True, random_state=7)"]},{"cell_type":"code","execution_count":8,"id":"a1d99fbe","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:00.586035Z","iopub.status.busy":"2022-09-13T02:00:00.585728Z","iopub.status.idle":"2022-09-13T02:00:00.591553Z","shell.execute_reply":"2022-09-13T02:00:00.590737Z"},"papermill":{"duration":0.02205,"end_time":"2022-09-13T02:00:00.593694","exception":false,"start_time":"2022-09-13T02:00:00.571644","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["shape of our training data: (97, 128, 128, 3)\n","shape of our training labels: (97, 1)\n","shape of our test data: (42, 128, 128, 3)\n","shape of our test labels: (42, 1)\n"]}],"source":["print(\"shape of our training data:\",x_train.shape)\n","print(\"shape of our training labels:\",y_train.shape)\n","print(\"shape of our test data:\",x_test.shape)\n","print(\"shape of our test labels:\",y_test.shape)"]},{"cell_type":"markdown","id":"4d6725ff","metadata":{"editable":false,"papermill":{"duration":0.013006,"end_time":"2022-09-13T02:00:00.619787","exception":false,"start_time":"2022-09-13T02:00:00.606781","status":"completed"},"tags":[]},"source":["## 4.2 Creating our Baseline Model"]},{"cell_type":"markdown","id":"8849f2f0","metadata":{"editable":false,"papermill":{"duration":0.013083,"end_time":"2022-09-13T02:00:00.646221","exception":false,"start_time":"2022-09-13T02:00:00.633138","status":"completed"},"tags":[]},"source":["We will start with creating a sequential model set up for binary classification using the keras libary."]},{"cell_type":"code","execution_count":9,"id":"126f3b29","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:00.67413Z","iopub.status.busy":"2022-09-13T02:00:00.673783Z","iopub.status.idle":"2022-09-13T02:00:03.543977Z","shell.execute_reply":"2022-09-13T02:00:03.542995Z"},"papermill":{"duration":2.888154,"end_time":"2022-09-13T02:00:03.547354","exception":false,"start_time":"2022-09-13T02:00:00.6592","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["2022-09-13 02:00:00.770666: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-09-13 02:00:00.905537: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-09-13 02:00:00.906381: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-09-13 02:00:00.908534: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n","To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"]},{"name":"stdout","output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 128, 128, 32)      2432      \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 64, 64, 32)        0         \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 64, 64, 64)        18496     \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 32, 32, 64)        0         \n","_________________________________________________________________\n","conv2d_2 (Conv2D)            (None, 32, 32, 128)       73856     \n","_________________________________________________________________\n","max_pooling2d_2 (MaxPooling2 (None, 16, 16, 128)       0         \n","_________________________________________________________________\n","conv2d_3 (Conv2D)            (None, 16, 16, 128)       147584    \n","_________________________________________________________________\n","max_pooling2d_3 (MaxPooling2 (None, 8, 8, 128)         0         \n","_________________________________________________________________\n","flatten (Flatten)            (None, 8192)              0         \n","_________________________________________________________________\n","dropout (Dropout)            (None, 8192)              0         \n","_________________________________________________________________\n","dense (Dense)                (None, 256)               2097408   \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 1)                 257       \n","=================================================================\n","Total params: 2,340,033\n","Trainable params: 2,340,033\n","Non-trainable params: 0\n","_________________________________________________________________\n"]},{"name":"stderr","output_type":"stream","text":["2022-09-13 02:00:00.909007: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-09-13 02:00:00.910089: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-09-13 02:00:00.911153: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-09-13 02:00:03.074241: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-09-13 02:00:03.075116: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-09-13 02:00:03.075831: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2022-09-13 02:00:03.076448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n"]}],"source":["model = keras.Sequential([\n","    \n","    layers.Conv2D(filters=32, kernel_size=(5,5), activation=\"relu\", padding='same', input_shape=[128, 128, 3]),\n","    layers.MaxPool2D(),\n","    \n","    layers.Conv2D(filters=64, kernel_size=(3,3), activation=\"relu\", padding='same'),\n","    layers.MaxPool2D(),\n","    \n","    layers.Conv2D(filters=128, kernel_size=(3,3), activation=\"relu\", padding='same'),\n","    layers.MaxPool2D(),\n","    \n","    layers.Conv2D(filters=128, kernel_size=(3,3), activation=\"relu\", padding='same'),\n","    layers.MaxPool2D(),\n","    \n","    layers.Flatten(),\n","    layers.Dropout(.25),\n","    layers.Dense(units=256, activation=\"relu\"),\n","    layers.Dense(units=1, activation=\"sigmoid\"),\n","])\n","\n","model.summary()"]},{"cell_type":"code","execution_count":10,"id":"a4efd829","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:03.575824Z","iopub.status.busy":"2022-09-13T02:00:03.575502Z","iopub.status.idle":"2022-09-13T02:00:03.588249Z","shell.execute_reply":"2022-09-13T02:00:03.58749Z"},"papermill":{"duration":0.028929,"end_time":"2022-09-13T02:00:03.590027","exception":false,"start_time":"2022-09-13T02:00:03.561098","status":"completed"},"tags":[]},"outputs":[],"source":["model.compile(\n","    optimizer=tf.keras.optimizers.Adam(epsilon=0.01),\n","    loss='binary_crossentropy',\n","    metrics=['accuracy']\n",")\n","\n","#including early stopping to prevent overfitting\n","early_stopping = keras.callbacks.EarlyStopping(\n","    patience=10,\n","    min_delta=0.001,\n","    restore_best_weights=True,\n",")"]},{"cell_type":"code","execution_count":11,"id":"adb4b63a","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:03.617424Z","iopub.status.busy":"2022-09-13T02:00:03.617111Z","iopub.status.idle":"2022-09-13T02:00:20.03596Z","shell.execute_reply":"2022-09-13T02:00:20.035126Z"},"papermill":{"duration":16.435107,"end_time":"2022-09-13T02:00:20.038305","exception":false,"start_time":"2022-09-13T02:00:03.603198","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["2022-09-13 02:00:03.724359: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"]},{"name":"stdout","output_type":"stream","text":["Epoch 1/200\n"]},{"name":"stderr","output_type":"stream","text":["2022-09-13 02:00:05.357421: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"]},{"name":"stdout","output_type":"stream","text":["2/2 - 8s - loss: 0.6920 - accuracy: 0.5155 - val_loss: 0.6946 - val_accuracy: 0.4286\n","Epoch 2/200\n","2/2 - 0s - loss: 0.6908 - accuracy: 0.5361 - val_loss: 0.6942 - val_accuracy: 0.4286\n","Epoch 3/200\n","2/2 - 0s - loss: 0.6909 - accuracy: 0.5361 - val_loss: 0.6938 - val_accuracy: 0.4286\n","Epoch 4/200\n","2/2 - 0s - loss: 0.6881 - accuracy: 0.5258 - val_loss: 0.6932 - val_accuracy: 0.4286\n","Epoch 5/200\n","2/2 - 0s - loss: 0.6872 - accuracy: 0.5361 - val_loss: 0.6926 - val_accuracy: 0.4286\n","Epoch 6/200\n","2/2 - 0s - loss: 0.6873 - accuracy: 0.5361 - val_loss: 0.6920 - val_accuracy: 0.4286\n","Epoch 7/200\n","2/2 - 0s - loss: 0.6872 - accuracy: 0.5361 - val_loss: 0.6914 - val_accuracy: 0.4286\n","Epoch 8/200\n","2/2 - 0s - loss: 0.6861 - accuracy: 0.5361 - val_loss: 0.6907 - val_accuracy: 0.4286\n","Epoch 9/200\n","2/2 - 0s - loss: 0.6830 - accuracy: 0.5361 - val_loss: 0.6900 - val_accuracy: 0.4286\n","Epoch 10/200\n","2/2 - 0s - loss: 0.6831 - accuracy: 0.5361 - val_loss: 0.6892 - val_accuracy: 0.4286\n","Epoch 11/200\n","2/2 - 0s - loss: 0.6813 - accuracy: 0.5361 - val_loss: 0.6885 - val_accuracy: 0.4286\n","Epoch 12/200\n","2/2 - 0s - loss: 0.6806 - accuracy: 0.5361 - val_loss: 0.6878 - val_accuracy: 0.4286\n","Epoch 13/200\n","2/2 - 0s - loss: 0.6797 - accuracy: 0.5361 - val_loss: 0.6871 - val_accuracy: 0.4286\n","Epoch 14/200\n","2/2 - 0s - loss: 0.6762 - accuracy: 0.5464 - val_loss: 0.6866 - val_accuracy: 0.4286\n","Epoch 15/200\n","2/2 - 0s - loss: 0.6756 - accuracy: 0.5361 - val_loss: 0.6861 - val_accuracy: 0.4286\n","Epoch 16/200\n","2/2 - 0s - loss: 0.6760 - accuracy: 0.5361 - val_loss: 0.6854 - val_accuracy: 0.4286\n","Epoch 17/200\n","2/2 - 0s - loss: 0.6748 - accuracy: 0.5361 - val_loss: 0.6848 - val_accuracy: 0.4286\n","Epoch 18/200\n","2/2 - 0s - loss: 0.6719 - accuracy: 0.5361 - val_loss: 0.6840 - val_accuracy: 0.4286\n","Epoch 19/200\n","2/2 - 0s - loss: 0.6709 - accuracy: 0.5361 - val_loss: 0.6833 - val_accuracy: 0.4286\n","Epoch 20/200\n","2/2 - 0s - loss: 0.6733 - accuracy: 0.5361 - val_loss: 0.6828 - val_accuracy: 0.4286\n","Epoch 21/200\n","2/2 - 0s - loss: 0.6680 - accuracy: 0.5361 - val_loss: 0.6820 - val_accuracy: 0.4286\n","Epoch 22/200\n","2/2 - 0s - loss: 0.6675 - accuracy: 0.5361 - val_loss: 0.6806 - val_accuracy: 0.4286\n","Epoch 23/200\n","2/2 - 0s - loss: 0.6631 - accuracy: 0.5361 - val_loss: 0.6786 - val_accuracy: 0.4286\n","Epoch 24/200\n","2/2 - 0s - loss: 0.6640 - accuracy: 0.5361 - val_loss: 0.6762 - val_accuracy: 0.4286\n","Epoch 25/200\n","2/2 - 0s - loss: 0.6638 - accuracy: 0.5361 - val_loss: 0.6733 - val_accuracy: 0.4286\n","Epoch 26/200\n","2/2 - 0s - loss: 0.6616 - accuracy: 0.5567 - val_loss: 0.6702 - val_accuracy: 0.4762\n","Epoch 27/200\n","2/2 - 0s - loss: 0.6624 - accuracy: 0.5567 - val_loss: 0.6665 - val_accuracy: 0.5476\n","Epoch 28/200\n","2/2 - 0s - loss: 0.6563 - accuracy: 0.5773 - val_loss: 0.6631 - val_accuracy: 0.5476\n","Epoch 29/200\n","2/2 - 0s - loss: 0.6546 - accuracy: 0.5876 - val_loss: 0.6600 - val_accuracy: 0.5476\n","Epoch 30/200\n","2/2 - 0s - loss: 0.6517 - accuracy: 0.6289 - val_loss: 0.6567 - val_accuracy: 0.5714\n","Epoch 31/200\n","2/2 - 0s - loss: 0.6534 - accuracy: 0.6082 - val_loss: 0.6531 - val_accuracy: 0.5714\n","Epoch 32/200\n","2/2 - 0s - loss: 0.6543 - accuracy: 0.6289 - val_loss: 0.6496 - val_accuracy: 0.5952\n","Epoch 33/200\n","2/2 - 0s - loss: 0.6464 - accuracy: 0.6495 - val_loss: 0.6463 - val_accuracy: 0.6190\n","Epoch 34/200\n","2/2 - 0s - loss: 0.6448 - accuracy: 0.6804 - val_loss: 0.6435 - val_accuracy: 0.6190\n","Epoch 35/200\n","2/2 - 0s - loss: 0.6396 - accuracy: 0.7216 - val_loss: 0.6404 - val_accuracy: 0.6190\n","Epoch 36/200\n","2/2 - 0s - loss: 0.6421 - accuracy: 0.7010 - val_loss: 0.6375 - val_accuracy: 0.6429\n","Epoch 37/200\n","2/2 - 0s - loss: 0.6392 - accuracy: 0.7113 - val_loss: 0.6347 - val_accuracy: 0.6667\n","Epoch 38/200\n","2/2 - 0s - loss: 0.6352 - accuracy: 0.6907 - val_loss: 0.6303 - val_accuracy: 0.6667\n","Epoch 39/200\n","2/2 - 0s - loss: 0.6309 - accuracy: 0.6598 - val_loss: 0.6260 - val_accuracy: 0.7143\n","Epoch 40/200\n","2/2 - 0s - loss: 0.6303 - accuracy: 0.6907 - val_loss: 0.6208 - val_accuracy: 0.7143\n","Epoch 41/200\n","2/2 - 0s - loss: 0.6240 - accuracy: 0.6598 - val_loss: 0.6154 - val_accuracy: 0.7143\n","Epoch 42/200\n","2/2 - 0s - loss: 0.6266 - accuracy: 0.6804 - val_loss: 0.6088 - val_accuracy: 0.6905\n","Epoch 43/200\n","2/2 - 0s - loss: 0.6196 - accuracy: 0.6804 - val_loss: 0.6017 - val_accuracy: 0.7143\n","Epoch 44/200\n","2/2 - 0s - loss: 0.6218 - accuracy: 0.6701 - val_loss: 0.5956 - val_accuracy: 0.7143\n","Epoch 45/200\n","2/2 - 0s - loss: 0.6139 - accuracy: 0.6804 - val_loss: 0.5898 - val_accuracy: 0.7143\n","Epoch 46/200\n","2/2 - 0s - loss: 0.6109 - accuracy: 0.6701 - val_loss: 0.5855 - val_accuracy: 0.7143\n","Epoch 47/200\n","2/2 - 0s - loss: 0.6070 - accuracy: 0.6804 - val_loss: 0.5815 - val_accuracy: 0.7143\n","Epoch 48/200\n","2/2 - 0s - loss: 0.6001 - accuracy: 0.6701 - val_loss: 0.5750 - val_accuracy: 0.6905\n","Epoch 49/200\n","2/2 - 0s - loss: 0.6062 - accuracy: 0.6804 - val_loss: 0.5691 - val_accuracy: 0.6905\n","Epoch 50/200\n","2/2 - 0s - loss: 0.6023 - accuracy: 0.7113 - val_loss: 0.5664 - val_accuracy: 0.6905\n","Epoch 51/200\n","2/2 - 0s - loss: 0.5881 - accuracy: 0.6804 - val_loss: 0.5663 - val_accuracy: 0.6905\n","Epoch 52/200\n","2/2 - 0s - loss: 0.5920 - accuracy: 0.6804 - val_loss: 0.5719 - val_accuracy: 0.7143\n","Epoch 53/200\n","2/2 - 0s - loss: 0.5856 - accuracy: 0.6907 - val_loss: 0.5740 - val_accuracy: 0.7143\n","Epoch 54/200\n","2/2 - 0s - loss: 0.5873 - accuracy: 0.6804 - val_loss: 0.5613 - val_accuracy: 0.7143\n","Epoch 55/200\n","2/2 - 0s - loss: 0.5886 - accuracy: 0.6701 - val_loss: 0.5502 - val_accuracy: 0.6905\n","Epoch 56/200\n","2/2 - 0s - loss: 0.5837 - accuracy: 0.6701 - val_loss: 0.5418 - val_accuracy: 0.7143\n","Epoch 57/200\n","2/2 - 0s - loss: 0.5841 - accuracy: 0.6701 - val_loss: 0.5371 - val_accuracy: 0.7381\n","Epoch 58/200\n","2/2 - 0s - loss: 0.5877 - accuracy: 0.6907 - val_loss: 0.5343 - val_accuracy: 0.7381\n","Epoch 59/200\n","2/2 - 0s - loss: 0.5765 - accuracy: 0.6701 - val_loss: 0.5389 - val_accuracy: 0.7143\n","Epoch 60/200\n","2/2 - 0s - loss: 0.5699 - accuracy: 0.6907 - val_loss: 0.5466 - val_accuracy: 0.6905\n","Epoch 61/200\n","2/2 - 0s - loss: 0.5676 - accuracy: 0.6804 - val_loss: 0.5605 - val_accuracy: 0.6905\n","Epoch 62/200\n","2/2 - 0s - loss: 0.5623 - accuracy: 0.7010 - val_loss: 0.5665 - val_accuracy: 0.7143\n","Epoch 63/200\n","2/2 - 0s - loss: 0.5690 - accuracy: 0.6907 - val_loss: 0.5564 - val_accuracy: 0.6905\n","Epoch 64/200\n","2/2 - 0s - loss: 0.5560 - accuracy: 0.7320 - val_loss: 0.5367 - val_accuracy: 0.6905\n","Epoch 65/200\n","2/2 - 0s - loss: 0.5573 - accuracy: 0.7113 - val_loss: 0.5277 - val_accuracy: 0.7381\n","Epoch 66/200\n","2/2 - 0s - loss: 0.5649 - accuracy: 0.6907 - val_loss: 0.5310 - val_accuracy: 0.7381\n","Epoch 67/200\n","2/2 - 0s - loss: 0.5611 - accuracy: 0.6804 - val_loss: 0.5556 - val_accuracy: 0.6905\n","Epoch 68/200\n","2/2 - 0s - loss: 0.5476 - accuracy: 0.7010 - val_loss: 0.5690 - val_accuracy: 0.6429\n","Epoch 69/200\n","2/2 - 0s - loss: 0.5504 - accuracy: 0.7010 - val_loss: 0.5681 - val_accuracy: 0.6429\n","Epoch 70/200\n","2/2 - 0s - loss: 0.5298 - accuracy: 0.7113 - val_loss: 0.5424 - val_accuracy: 0.6905\n","Epoch 71/200\n","2/2 - 0s - loss: 0.5339 - accuracy: 0.7010 - val_loss: 0.5171 - val_accuracy: 0.7381\n","Epoch 72/200\n","2/2 - 0s - loss: 0.5331 - accuracy: 0.7216 - val_loss: 0.5108 - val_accuracy: 0.7143\n","Epoch 73/200\n","2/2 - 0s - loss: 0.5320 - accuracy: 0.7320 - val_loss: 0.5226 - val_accuracy: 0.7381\n","Epoch 74/200\n","2/2 - 0s - loss: 0.5245 - accuracy: 0.7010 - val_loss: 0.5631 - val_accuracy: 0.6429\n","Epoch 75/200\n","2/2 - 0s - loss: 0.5210 - accuracy: 0.7010 - val_loss: 0.5770 - val_accuracy: 0.6667\n","Epoch 76/200\n","2/2 - 0s - loss: 0.5112 - accuracy: 0.7320 - val_loss: 0.5154 - val_accuracy: 0.7381\n","Epoch 77/200\n","2/2 - 0s - loss: 0.5141 - accuracy: 0.7320 - val_loss: 0.4971 - val_accuracy: 0.6667\n","Epoch 78/200\n","2/2 - 0s - loss: 0.5215 - accuracy: 0.7732 - val_loss: 0.5023 - val_accuracy: 0.7143\n","Epoch 79/200\n","2/2 - 0s - loss: 0.4855 - accuracy: 0.7629 - val_loss: 0.5678 - val_accuracy: 0.6905\n","Epoch 80/200\n","2/2 - 0s - loss: 0.4969 - accuracy: 0.7320 - val_loss: 0.5865 - val_accuracy: 0.6667\n","Epoch 81/200\n","2/2 - 0s - loss: 0.4947 - accuracy: 0.7629 - val_loss: 0.4976 - val_accuracy: 0.6905\n","Epoch 82/200\n","2/2 - 0s - loss: 0.4873 - accuracy: 0.7423 - val_loss: 0.4928 - val_accuracy: 0.6905\n","Epoch 83/200\n","2/2 - 0s - loss: 0.5063 - accuracy: 0.7216 - val_loss: 0.5316 - val_accuracy: 0.6905\n","Epoch 84/200\n","2/2 - 0s - loss: 0.4926 - accuracy: 0.7732 - val_loss: 0.5202 - val_accuracy: 0.7143\n","Epoch 85/200\n","2/2 - 0s - loss: 0.4940 - accuracy: 0.7732 - val_loss: 0.5227 - val_accuracy: 0.7143\n","Epoch 86/200\n","2/2 - 0s - loss: 0.4485 - accuracy: 0.7938 - val_loss: 0.5099 - val_accuracy: 0.6905\n","Epoch 87/200\n","2/2 - 0s - loss: 0.4418 - accuracy: 0.8041 - val_loss: 0.4984 - val_accuracy: 0.6905\n","Epoch 88/200\n","2/2 - 0s - loss: 0.4461 - accuracy: 0.7938 - val_loss: 0.5404 - val_accuracy: 0.6667\n","Epoch 89/200\n","2/2 - 0s - loss: 0.4804 - accuracy: 0.8041 - val_loss: 0.5006 - val_accuracy: 0.6905\n","Epoch 90/200\n","2/2 - 0s - loss: 0.4479 - accuracy: 0.7938 - val_loss: 0.4846 - val_accuracy: 0.6905\n","Epoch 91/200\n","2/2 - 0s - loss: 0.4184 - accuracy: 0.8041 - val_loss: 0.6663 - val_accuracy: 0.6429\n","Epoch 92/200\n","2/2 - 0s - loss: 0.4987 - accuracy: 0.7732 - val_loss: 0.4858 - val_accuracy: 0.7619\n","Epoch 93/200\n","2/2 - 0s - loss: 0.4700 - accuracy: 0.7938 - val_loss: 0.4837 - val_accuracy: 0.7619\n","Epoch 94/200\n","2/2 - 0s - loss: 0.4125 - accuracy: 0.8454 - val_loss: 0.6436 - val_accuracy: 0.6667\n","Epoch 95/200\n","2/2 - 0s - loss: 0.4573 - accuracy: 0.7732 - val_loss: 0.4800 - val_accuracy: 0.7381\n","Epoch 96/200\n","2/2 - 0s - loss: 0.4211 - accuracy: 0.8041 - val_loss: 0.4859 - val_accuracy: 0.7143\n","Epoch 97/200\n","2/2 - 0s - loss: 0.3873 - accuracy: 0.8247 - val_loss: 0.5243 - val_accuracy: 0.6905\n","Epoch 98/200\n","2/2 - 0s - loss: 0.3829 - accuracy: 0.8144 - val_loss: 0.4638 - val_accuracy: 0.7619\n","Epoch 99/200\n","2/2 - 0s - loss: 0.4066 - accuracy: 0.7938 - val_loss: 0.5081 - val_accuracy: 0.6905\n","Epoch 100/200\n","2/2 - 0s - loss: 0.3864 - accuracy: 0.8247 - val_loss: 0.5831 - val_accuracy: 0.6667\n","Epoch 101/200\n","2/2 - 0s - loss: 0.3645 - accuracy: 0.8041 - val_loss: 0.4604 - val_accuracy: 0.7381\n","Epoch 102/200\n","2/2 - 0s - loss: 0.3875 - accuracy: 0.8557 - val_loss: 0.5469 - val_accuracy: 0.6905\n","Epoch 103/200\n","2/2 - 0s - loss: 0.3871 - accuracy: 0.8144 - val_loss: 0.5735 - val_accuracy: 0.6905\n","Epoch 104/200\n","2/2 - 0s - loss: 0.4100 - accuracy: 0.8041 - val_loss: 0.4578 - val_accuracy: 0.7381\n","Epoch 105/200\n","2/2 - 0s - loss: 0.3417 - accuracy: 0.8557 - val_loss: 0.5709 - val_accuracy: 0.6905\n","Epoch 106/200\n","2/2 - 0s - loss: 0.3150 - accuracy: 0.8763 - val_loss: 0.4402 - val_accuracy: 0.7619\n","Epoch 107/200\n","2/2 - 0s - loss: 0.3483 - accuracy: 0.8557 - val_loss: 0.4721 - val_accuracy: 0.7381\n","Epoch 108/200\n","2/2 - 0s - loss: 0.3086 - accuracy: 0.9175 - val_loss: 0.4904 - val_accuracy: 0.6905\n","Epoch 109/200\n","2/2 - 0s - loss: 0.3492 - accuracy: 0.8660 - val_loss: 0.4772 - val_accuracy: 0.7381\n","Epoch 110/200\n","2/2 - 0s - loss: 0.3019 - accuracy: 0.8969 - val_loss: 0.4640 - val_accuracy: 0.7381\n","Epoch 111/200\n","2/2 - 0s - loss: 0.3100 - accuracy: 0.8763 - val_loss: 0.5901 - val_accuracy: 0.6667\n","Epoch 112/200\n","2/2 - 0s - loss: 0.3500 - accuracy: 0.8351 - val_loss: 0.5547 - val_accuracy: 0.7381\n","Epoch 113/200\n","2/2 - 0s - loss: 0.4413 - accuracy: 0.7732 - val_loss: 0.5155 - val_accuracy: 0.6905\n","Epoch 114/200\n","2/2 - 0s - loss: 0.2715 - accuracy: 0.9072 - val_loss: 0.4389 - val_accuracy: 0.7381\n","Epoch 115/200\n","2/2 - 0s - loss: 0.2748 - accuracy: 0.9175 - val_loss: 0.4823 - val_accuracy: 0.6905\n","Epoch 116/200\n","2/2 - 0s - loss: 0.2405 - accuracy: 0.9175 - val_loss: 0.4148 - val_accuracy: 0.8333\n","Epoch 117/200\n","2/2 - 0s - loss: 0.2730 - accuracy: 0.8969 - val_loss: 0.4238 - val_accuracy: 0.7619\n","Epoch 118/200\n","2/2 - 0s - loss: 0.2516 - accuracy: 0.8969 - val_loss: 0.4549 - val_accuracy: 0.7143\n","Epoch 119/200\n","2/2 - 0s - loss: 0.2301 - accuracy: 0.9381 - val_loss: 0.4239 - val_accuracy: 0.8095\n","Epoch 120/200\n","2/2 - 0s - loss: 0.2417 - accuracy: 0.9485 - val_loss: 0.5109 - val_accuracy: 0.7143\n","Epoch 121/200\n","2/2 - 0s - loss: 0.2321 - accuracy: 0.8969 - val_loss: 0.4513 - val_accuracy: 0.7619\n","Epoch 122/200\n","2/2 - 0s - loss: 0.1813 - accuracy: 0.9794 - val_loss: 0.4576 - val_accuracy: 0.7619\n","Epoch 123/200\n","2/2 - 0s - loss: 0.2405 - accuracy: 0.9278 - val_loss: 0.5130 - val_accuracy: 0.7381\n","Epoch 124/200\n","2/2 - 0s - loss: 0.2200 - accuracy: 0.9175 - val_loss: 0.4565 - val_accuracy: 0.8333\n","Epoch 125/200\n","2/2 - 0s - loss: 0.2220 - accuracy: 0.8969 - val_loss: 0.5904 - val_accuracy: 0.6905\n","Epoch 126/200\n","2/2 - 0s - loss: 0.2481 - accuracy: 0.9072 - val_loss: 0.4358 - val_accuracy: 0.8810\n"]}],"source":["history = model.fit(\n","    x = x_train,\n","    y = y_train,\n","    validation_data= (x_test,y_test),\n","    batch_size = 64,\n","    epochs=200,\n","    callbacks=[early_stopping],\n","    verbose=(2),\n",")"]},{"cell_type":"code","execution_count":12,"id":"ad721886","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:20.091746Z","iopub.status.busy":"2022-09-13T02:00:20.091398Z","iopub.status.idle":"2022-09-13T02:00:25.354233Z","shell.execute_reply":"2022-09-13T02:00:25.353439Z"},"papermill":{"duration":5.291378,"end_time":"2022-09-13T02:00:25.356319","exception":false,"start_time":"2022-09-13T02:00:20.064941","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["history_frame = pd.DataFrame(history.history)\n","history_frame.loc[:, ['loss','val_loss']].plot()\n","history_frame.loc[:, ['accuracy','val_accuracy']].plot();"]},{"cell_type":"code","execution_count":13,"id":"3012bc39","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:25.410186Z","iopub.status.busy":"2022-09-13T02:00:25.409855Z","iopub.status.idle":"2022-09-13T02:00:25.694097Z","shell.execute_reply":"2022-09-13T02:00:25.693224Z"},"papermill":{"duration":0.313332,"end_time":"2022-09-13T02:00:25.696276","exception":false,"start_time":"2022-09-13T02:00:25.382944","status":"completed"},"tags":[]},"outputs":[],"source":["pred = model.predict(x_test)\n","\n","for i in range(len(pred)):\n","    if pred[i] > 0.5:\n","        pred[i] = 1\n","    else:\n","        pred[i] = 0\n","     \n","pred = pred.astype(int)"]},{"cell_type":"code","execution_count":14,"id":"50db111c","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:25.756745Z","iopub.status.busy":"2022-09-13T02:00:25.756385Z","iopub.status.idle":"2022-09-13T02:00:25.76601Z","shell.execute_reply":"2022-09-13T02:00:25.765131Z"},"papermill":{"duration":0.046246,"end_time":"2022-09-13T02:00:25.768466","exception":false,"start_time":"2022-09-13T02:00:25.72222","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.87      0.83      0.85        24\n","           1       0.79      0.83      0.81        18\n","\n","    accuracy                           0.83        42\n","   macro avg       0.83      0.83      0.83        42\n","weighted avg       0.84      0.83      0.83        42\n","\n"]}],"source":["#creating a classification report\n","print(classification_report(y_test, pred))"]},{"cell_type":"markdown","id":"5464faed","metadata":{"editable":false,"papermill":{"duration":0.028772,"end_time":"2022-09-13T02:00:25.825737","exception":false,"start_time":"2022-09-13T02:00:25.796965","status":"completed"},"tags":[]},"source":["## 4.3 Adding Data Agumentation"]},{"cell_type":"markdown","id":"117e56a7","metadata":{"editable":false,"papermill":{"duration":0.056398,"end_time":"2022-09-13T02:00:25.939856","exception":false,"start_time":"2022-09-13T02:00:25.883458","status":"completed"},"tags":[]},"source":["Here we will add a technique called data agumentation. We will be adding a layer that flips our images horizontaly creating more data for our model to train on."]},{"cell_type":"code","execution_count":15,"id":"91f09ab5","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:26.042241Z","iopub.status.busy":"2022-09-13T02:00:26.041854Z","iopub.status.idle":"2022-09-13T02:00:26.073359Z","shell.execute_reply":"2022-09-13T02:00:26.072461Z"},"papermill":{"duration":0.079634,"end_time":"2022-09-13T02:00:26.07522","exception":false,"start_time":"2022-09-13T02:00:25.995586","status":"completed"},"tags":[]},"outputs":[],"source":["model = keras.Sequential([\n","    \n","    preprocessing.RandomFlip('horizontal'), # flip left-to-right\n","    \n","    layers.Conv2D(filters=32, kernel_size=(5,5), activation=\"relu\", padding='same', input_shape=[128, 128, 3]),\n","    layers.MaxPool2D(),\n","    \n","    layers.Conv2D(filters=64, kernel_size=(3,3), activation=\"relu\", padding='same'),\n","    layers.MaxPool2D(),\n","    \n","    layers.Conv2D(filters=128, kernel_size=(3,3), activation=\"relu\", padding='same'),\n","    layers.MaxPool2D(),\n","    \n","    layers.Conv2D(filters=128, kernel_size=(3,3), activation=\"relu\", padding='same'),\n","    layers.MaxPool2D(),\n","    \n","    layers.Flatten(),\n","    layers.Dropout(.25),\n","    layers.Dense(units=256, activation=\"relu\"),\n","    layers.Dense(units=1, activation=\"sigmoid\"),\n","])"]},{"cell_type":"code","execution_count":16,"id":"28652387","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:26.156504Z","iopub.status.busy":"2022-09-13T02:00:26.155983Z","iopub.status.idle":"2022-09-13T02:00:26.168097Z","shell.execute_reply":"2022-09-13T02:00:26.167396Z"},"papermill":{"duration":0.054991,"end_time":"2022-09-13T02:00:26.170157","exception":false,"start_time":"2022-09-13T02:00:26.115166","status":"completed"},"tags":[]},"outputs":[],"source":["model.compile(\n","    optimizer=tf.keras.optimizers.Adam(epsilon=0.01),\n","    loss='binary_crossentropy',\n","    metrics=['accuracy']\n",")\n","\n","#including early stopping to revent overfitting\n","early_stopping = keras.callbacks.EarlyStopping(\n","    patience=30,\n","    min_delta=0.001,\n","    restore_best_weights=True,\n",")"]},{"cell_type":"code","execution_count":17,"id":"c435fe39","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:26.25123Z","iopub.status.busy":"2022-09-13T02:00:26.250709Z","iopub.status.idle":"2022-09-13T02:00:38.27267Z","shell.execute_reply":"2022-09-13T02:00:38.271781Z"},"papermill":{"duration":12.064659,"end_time":"2022-09-13T02:00:38.275044","exception":false,"start_time":"2022-09-13T02:00:26.210385","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/200\n","2/2 - 1s - loss: 0.6981 - accuracy: 0.4948 - val_loss: 0.6926 - val_accuracy: 0.5952\n","Epoch 2/200\n","2/2 - 0s - loss: 0.6956 - accuracy: 0.5361 - val_loss: 0.6917 - val_accuracy: 0.4524\n","Epoch 3/200\n","2/2 - 0s - loss: 0.6942 - accuracy: 0.5052 - val_loss: 0.6907 - val_accuracy: 0.5000\n","Epoch 4/200\n","2/2 - 0s - loss: 0.6924 - accuracy: 0.4742 - val_loss: 0.6903 - val_accuracy: 0.4524\n","Epoch 5/200\n","2/2 - 0s - loss: 0.6921 - accuracy: 0.4845 - val_loss: 0.6902 - val_accuracy: 0.4286\n","Epoch 6/200\n","2/2 - 0s - loss: 0.6866 - accuracy: 0.5567 - val_loss: 0.6902 - val_accuracy: 0.4286\n","Epoch 7/200\n","2/2 - 0s - loss: 0.6812 - accuracy: 0.5464 - val_loss: 0.6908 - val_accuracy: 0.4286\n","Epoch 8/200\n","2/2 - 0s - loss: 0.6882 - accuracy: 0.5361 - val_loss: 0.6913 - val_accuracy: 0.4286\n","Epoch 9/200\n","2/2 - 0s - loss: 0.6832 - accuracy: 0.5361 - val_loss: 0.6911 - val_accuracy: 0.4286\n","Epoch 10/200\n","2/2 - 0s - loss: 0.6737 - accuracy: 0.5361 - val_loss: 0.6908 - val_accuracy: 0.4286\n","Epoch 11/200\n","2/2 - 0s - loss: 0.6729 - accuracy: 0.5361 - val_loss: 0.6902 - val_accuracy: 0.4286\n","Epoch 12/200\n","2/2 - 0s - loss: 0.6743 - accuracy: 0.5361 - val_loss: 0.6890 - val_accuracy: 0.4286\n","Epoch 13/200\n","2/2 - 0s - loss: 0.6759 - accuracy: 0.5361 - val_loss: 0.6869 - val_accuracy: 0.4286\n","Epoch 14/200\n","2/2 - 0s - loss: 0.6735 - accuracy: 0.5361 - val_loss: 0.6851 - val_accuracy: 0.4286\n","Epoch 15/200\n","2/2 - 0s - loss: 0.6695 - accuracy: 0.5361 - val_loss: 0.6824 - val_accuracy: 0.4286\n","Epoch 16/200\n","2/2 - 0s - loss: 0.6649 - accuracy: 0.5361 - val_loss: 0.6783 - val_accuracy: 0.4524\n","Epoch 17/200\n","2/2 - 0s - loss: 0.6617 - accuracy: 0.5361 - val_loss: 0.6740 - val_accuracy: 0.4762\n","Epoch 18/200\n","2/2 - 0s - loss: 0.6622 - accuracy: 0.5464 - val_loss: 0.6692 - val_accuracy: 0.4762\n","Epoch 19/200\n","2/2 - 0s - loss: 0.6636 - accuracy: 0.5670 - val_loss: 0.6645 - val_accuracy: 0.5476\n","Epoch 20/200\n","2/2 - 0s - loss: 0.6578 - accuracy: 0.5979 - val_loss: 0.6608 - val_accuracy: 0.5476\n","Epoch 21/200\n","2/2 - 0s - loss: 0.6646 - accuracy: 0.6186 - val_loss: 0.6569 - val_accuracy: 0.5714\n","Epoch 22/200\n","2/2 - 0s - loss: 0.6573 - accuracy: 0.6392 - val_loss: 0.6517 - val_accuracy: 0.5714\n","Epoch 23/200\n","2/2 - 0s - loss: 0.6482 - accuracy: 0.6289 - val_loss: 0.6459 - val_accuracy: 0.6190\n","Epoch 24/200\n","2/2 - 0s - loss: 0.6596 - accuracy: 0.6082 - val_loss: 0.6399 - val_accuracy: 0.6190\n","Epoch 25/200\n","2/2 - 0s - loss: 0.6405 - accuracy: 0.7010 - val_loss: 0.6341 - val_accuracy: 0.6905\n","Epoch 26/200\n","2/2 - 0s - loss: 0.6417 - accuracy: 0.6907 - val_loss: 0.6284 - val_accuracy: 0.7143\n","Epoch 27/200\n","2/2 - 0s - loss: 0.6394 - accuracy: 0.6907 - val_loss: 0.6222 - val_accuracy: 0.7143\n","Epoch 28/200\n","2/2 - 0s - loss: 0.6340 - accuracy: 0.7010 - val_loss: 0.6172 - val_accuracy: 0.7143\n","Epoch 29/200\n","2/2 - 0s - loss: 0.6422 - accuracy: 0.6804 - val_loss: 0.6142 - val_accuracy: 0.7143\n","Epoch 30/200\n","2/2 - 0s - loss: 0.6390 - accuracy: 0.6598 - val_loss: 0.6119 - val_accuracy: 0.7143\n","Epoch 31/200\n","2/2 - 0s - loss: 0.6320 - accuracy: 0.6804 - val_loss: 0.6085 - val_accuracy: 0.7143\n","Epoch 32/200\n","2/2 - 0s - loss: 0.6321 - accuracy: 0.6701 - val_loss: 0.6046 - val_accuracy: 0.7143\n","Epoch 33/200\n","2/2 - 0s - loss: 0.6298 - accuracy: 0.6804 - val_loss: 0.6008 - val_accuracy: 0.6905\n","Epoch 34/200\n","2/2 - 0s - loss: 0.6237 - accuracy: 0.6701 - val_loss: 0.5987 - val_accuracy: 0.7143\n","Epoch 35/200\n","2/2 - 0s - loss: 0.6273 - accuracy: 0.6701 - val_loss: 0.5937 - val_accuracy: 0.6905\n","Epoch 36/200\n","2/2 - 0s - loss: 0.6056 - accuracy: 0.6907 - val_loss: 0.5901 - val_accuracy: 0.6905\n","Epoch 37/200\n","2/2 - 0s - loss: 0.6135 - accuracy: 0.6598 - val_loss: 0.5870 - val_accuracy: 0.7143\n","Epoch 38/200\n","2/2 - 0s - loss: 0.6155 - accuracy: 0.6907 - val_loss: 0.5807 - val_accuracy: 0.7143\n","Epoch 39/200\n","2/2 - 0s - loss: 0.6132 - accuracy: 0.6701 - val_loss: 0.5783 - val_accuracy: 0.7143\n","Epoch 40/200\n","2/2 - 0s - loss: 0.6092 - accuracy: 0.6804 - val_loss: 0.5751 - val_accuracy: 0.7143\n","Epoch 41/200\n","2/2 - 0s - loss: 0.6104 - accuracy: 0.6701 - val_loss: 0.5724 - val_accuracy: 0.7143\n","Epoch 42/200\n","2/2 - 0s - loss: 0.6037 - accuracy: 0.6598 - val_loss: 0.5644 - val_accuracy: 0.6905\n","Epoch 43/200\n","2/2 - 0s - loss: 0.5994 - accuracy: 0.6701 - val_loss: 0.5588 - val_accuracy: 0.6905\n","Epoch 44/200\n","2/2 - 0s - loss: 0.6075 - accuracy: 0.6907 - val_loss: 0.5598 - val_accuracy: 0.6905\n","Epoch 45/200\n","2/2 - 0s - loss: 0.5959 - accuracy: 0.6907 - val_loss: 0.5627 - val_accuracy: 0.6905\n","Epoch 46/200\n","2/2 - 0s - loss: 0.5912 - accuracy: 0.6907 - val_loss: 0.5678 - val_accuracy: 0.6905\n","Epoch 47/200\n","2/2 - 0s - loss: 0.5855 - accuracy: 0.6598 - val_loss: 0.5632 - val_accuracy: 0.6667\n","Epoch 48/200\n","2/2 - 0s - loss: 0.5931 - accuracy: 0.6804 - val_loss: 0.5460 - val_accuracy: 0.6905\n","Epoch 49/200\n","2/2 - 0s - loss: 0.5873 - accuracy: 0.7113 - val_loss: 0.5389 - val_accuracy: 0.6905\n","Epoch 50/200\n","2/2 - 0s - loss: 0.6034 - accuracy: 0.6804 - val_loss: 0.5487 - val_accuracy: 0.6905\n","Epoch 51/200\n","2/2 - 0s - loss: 0.5788 - accuracy: 0.7113 - val_loss: 0.5728 - val_accuracy: 0.6905\n","Epoch 52/200\n","2/2 - 0s - loss: 0.5856 - accuracy: 0.7113 - val_loss: 0.6015 - val_accuracy: 0.6905\n","Epoch 53/200\n","2/2 - 0s - loss: 0.5854 - accuracy: 0.7320 - val_loss: 0.5813 - val_accuracy: 0.6905\n","Epoch 54/200\n","2/2 - 0s - loss: 0.5777 - accuracy: 0.7010 - val_loss: 0.5386 - val_accuracy: 0.6667\n","Epoch 55/200\n","2/2 - 0s - loss: 0.5764 - accuracy: 0.6804 - val_loss: 0.5313 - val_accuracy: 0.6667\n","Epoch 56/200\n","2/2 - 0s - loss: 0.5950 - accuracy: 0.6701 - val_loss: 0.5382 - val_accuracy: 0.6667\n","Epoch 57/200\n","2/2 - 0s - loss: 0.5818 - accuracy: 0.6907 - val_loss: 0.5619 - val_accuracy: 0.6905\n","Epoch 58/200\n","2/2 - 0s - loss: 0.5474 - accuracy: 0.7320 - val_loss: 0.5705 - val_accuracy: 0.6905\n","Epoch 59/200\n","2/2 - 0s - loss: 0.5525 - accuracy: 0.7423 - val_loss: 0.5611 - val_accuracy: 0.6905\n","Epoch 60/200\n","2/2 - 0s - loss: 0.5661 - accuracy: 0.7216 - val_loss: 0.5449 - val_accuracy: 0.6905\n","Epoch 61/200\n","2/2 - 0s - loss: 0.5604 - accuracy: 0.7113 - val_loss: 0.5552 - val_accuracy: 0.6905\n","Epoch 62/200\n","2/2 - 0s - loss: 0.5565 - accuracy: 0.6804 - val_loss: 0.5771 - val_accuracy: 0.6905\n","Epoch 63/200\n","2/2 - 0s - loss: 0.5459 - accuracy: 0.7835 - val_loss: 0.5674 - val_accuracy: 0.6905\n","Epoch 64/200\n","2/2 - 0s - loss: 0.5447 - accuracy: 0.7423 - val_loss: 0.5336 - val_accuracy: 0.6905\n","Epoch 65/200\n","2/2 - 0s - loss: 0.5634 - accuracy: 0.6907 - val_loss: 0.5408 - val_accuracy: 0.6905\n","Epoch 66/200\n","2/2 - 0s - loss: 0.5230 - accuracy: 0.7113 - val_loss: 0.5855 - val_accuracy: 0.6905\n","Epoch 67/200\n","2/2 - 0s - loss: 0.5383 - accuracy: 0.7423 - val_loss: 0.6086 - val_accuracy: 0.6905\n","Epoch 68/200\n","2/2 - 0s - loss: 0.5609 - accuracy: 0.7113 - val_loss: 0.5288 - val_accuracy: 0.6905\n","Epoch 69/200\n","2/2 - 0s - loss: 0.5356 - accuracy: 0.7526 - val_loss: 0.5345 - val_accuracy: 0.6667\n","Epoch 70/200\n","2/2 - 0s - loss: 0.5147 - accuracy: 0.7423 - val_loss: 0.5758 - val_accuracy: 0.6905\n","Epoch 71/200\n","2/2 - 0s - loss: 0.5075 - accuracy: 0.7216 - val_loss: 0.5627 - val_accuracy: 0.6905\n","Epoch 72/200\n","2/2 - 0s - loss: 0.5176 - accuracy: 0.7938 - val_loss: 0.5276 - val_accuracy: 0.6667\n","Epoch 73/200\n","2/2 - 0s - loss: 0.5104 - accuracy: 0.6907 - val_loss: 0.5493 - val_accuracy: 0.6667\n","Epoch 74/200\n","2/2 - 0s - loss: 0.4838 - accuracy: 0.7732 - val_loss: 0.6422 - val_accuracy: 0.6667\n","Epoch 75/200\n","2/2 - 0s - loss: 0.5260 - accuracy: 0.7423 - val_loss: 0.5418 - val_accuracy: 0.6667\n","Epoch 76/200\n","2/2 - 0s - loss: 0.5238 - accuracy: 0.7320 - val_loss: 0.5165 - val_accuracy: 0.7143\n","Epoch 77/200\n","2/2 - 0s - loss: 0.5341 - accuracy: 0.7216 - val_loss: 0.6244 - val_accuracy: 0.6667\n","Epoch 78/200\n","2/2 - 0s - loss: 0.5192 - accuracy: 0.7938 - val_loss: 0.5251 - val_accuracy: 0.7143\n","Epoch 79/200\n","2/2 - 0s - loss: 0.4648 - accuracy: 0.7526 - val_loss: 0.5233 - val_accuracy: 0.6905\n","Epoch 80/200\n","2/2 - 0s - loss: 0.4806 - accuracy: 0.7629 - val_loss: 0.5767 - val_accuracy: 0.6905\n","Epoch 81/200\n","2/2 - 0s - loss: 0.4721 - accuracy: 0.7629 - val_loss: 0.5209 - val_accuracy: 0.6667\n","Epoch 82/200\n","2/2 - 0s - loss: 0.4677 - accuracy: 0.7629 - val_loss: 0.5208 - val_accuracy: 0.6667\n","Epoch 83/200\n","2/2 - 0s - loss: 0.4594 - accuracy: 0.7732 - val_loss: 0.5046 - val_accuracy: 0.7143\n","Epoch 84/200\n","2/2 - 0s - loss: 0.4588 - accuracy: 0.7938 - val_loss: 0.5276 - val_accuracy: 0.6667\n","Epoch 85/200\n","2/2 - 0s - loss: 0.4342 - accuracy: 0.8144 - val_loss: 0.5596 - val_accuracy: 0.6667\n","Epoch 86/200\n","2/2 - 0s - loss: 0.4551 - accuracy: 0.7732 - val_loss: 0.4984 - val_accuracy: 0.7619\n","Epoch 87/200\n","2/2 - 0s - loss: 0.4447 - accuracy: 0.7835 - val_loss: 0.5349 - val_accuracy: 0.6667\n","Epoch 88/200\n","2/2 - 0s - loss: 0.4198 - accuracy: 0.8247 - val_loss: 0.6049 - val_accuracy: 0.6667\n","Epoch 89/200\n","2/2 - 0s - loss: 0.4611 - accuracy: 0.8041 - val_loss: 0.4921 - val_accuracy: 0.7381\n","Epoch 90/200\n","2/2 - 0s - loss: 0.5142 - accuracy: 0.7423 - val_loss: 0.5568 - val_accuracy: 0.6667\n","Epoch 91/200\n","2/2 - 0s - loss: 0.4651 - accuracy: 0.7732 - val_loss: 0.6836 - val_accuracy: 0.6190\n","Epoch 92/200\n","2/2 - 0s - loss: 0.4717 - accuracy: 0.7320 - val_loss: 0.4904 - val_accuracy: 0.7381\n","Epoch 93/200\n","2/2 - 0s - loss: 0.5073 - accuracy: 0.7423 - val_loss: 0.5338 - val_accuracy: 0.6905\n","Epoch 94/200\n","2/2 - 0s - loss: 0.4484 - accuracy: 0.7938 - val_loss: 0.6107 - val_accuracy: 0.6429\n","Epoch 95/200\n","2/2 - 0s - loss: 0.4644 - accuracy: 0.7732 - val_loss: 0.4500 - val_accuracy: 0.8333\n","Epoch 96/200\n","2/2 - 0s - loss: 0.4490 - accuracy: 0.7938 - val_loss: 0.4718 - val_accuracy: 0.7381\n","Epoch 97/200\n","2/2 - 0s - loss: 0.4027 - accuracy: 0.8351 - val_loss: 0.5568 - val_accuracy: 0.7143\n","Epoch 98/200\n","2/2 - 0s - loss: 0.4474 - accuracy: 0.8041 - val_loss: 0.4573 - val_accuracy: 0.7381\n","Epoch 99/200\n","2/2 - 0s - loss: 0.4397 - accuracy: 0.7526 - val_loss: 0.4691 - val_accuracy: 0.7381\n","Epoch 100/200\n","2/2 - 0s - loss: 0.3909 - accuracy: 0.8351 - val_loss: 0.6095 - val_accuracy: 0.6667\n","Epoch 101/200\n","2/2 - 0s - loss: 0.4148 - accuracy: 0.8247 - val_loss: 0.4646 - val_accuracy: 0.7143\n","Epoch 102/200\n","2/2 - 0s - loss: 0.4333 - accuracy: 0.7835 - val_loss: 0.4446 - val_accuracy: 0.8333\n","Epoch 103/200\n","2/2 - 0s - loss: 0.3727 - accuracy: 0.8351 - val_loss: 0.6583 - val_accuracy: 0.6429\n","Epoch 104/200\n","2/2 - 0s - loss: 0.4455 - accuracy: 0.7938 - val_loss: 0.5692 - val_accuracy: 0.6905\n","Epoch 105/200\n","2/2 - 0s - loss: 0.3878 - accuracy: 0.8041 - val_loss: 0.4301 - val_accuracy: 0.8333\n","Epoch 106/200\n","2/2 - 0s - loss: 0.3850 - accuracy: 0.7835 - val_loss: 0.4717 - val_accuracy: 0.6905\n","Epoch 107/200\n","2/2 - 0s - loss: 0.3669 - accuracy: 0.8144 - val_loss: 0.4674 - val_accuracy: 0.6905\n","Epoch 108/200\n","2/2 - 0s - loss: 0.3756 - accuracy: 0.8041 - val_loss: 0.4242 - val_accuracy: 0.7857\n","Epoch 109/200\n","2/2 - 0s - loss: 0.3486 - accuracy: 0.8247 - val_loss: 0.5153 - val_accuracy: 0.7143\n","Epoch 110/200\n","2/2 - 0s - loss: 0.3263 - accuracy: 0.8763 - val_loss: 0.4471 - val_accuracy: 0.7857\n","Epoch 111/200\n","2/2 - 0s - loss: 0.4073 - accuracy: 0.7938 - val_loss: 0.4635 - val_accuracy: 0.7619\n","Epoch 112/200\n","2/2 - 0s - loss: 0.4023 - accuracy: 0.8144 - val_loss: 0.5055 - val_accuracy: 0.7381\n","Epoch 113/200\n","2/2 - 0s - loss: 0.2994 - accuracy: 0.8763 - val_loss: 0.4741 - val_accuracy: 0.7143\n","Epoch 114/200\n","2/2 - 0s - loss: 0.3623 - accuracy: 0.8247 - val_loss: 0.6266 - val_accuracy: 0.6667\n","Epoch 115/200\n","2/2 - 0s - loss: 0.3796 - accuracy: 0.7835 - val_loss: 0.5509 - val_accuracy: 0.6905\n","Epoch 116/200\n","2/2 - 0s - loss: 0.3090 - accuracy: 0.8866 - val_loss: 0.3924 - val_accuracy: 0.7857\n","Epoch 117/200\n","2/2 - 0s - loss: 0.3538 - accuracy: 0.8041 - val_loss: 0.4557 - val_accuracy: 0.7143\n","Epoch 118/200\n","2/2 - 0s - loss: 0.3127 - accuracy: 0.8763 - val_loss: 0.4504 - val_accuracy: 0.7143\n","Epoch 119/200\n","2/2 - 0s - loss: 0.2901 - accuracy: 0.8969 - val_loss: 0.3868 - val_accuracy: 0.8333\n","Epoch 120/200\n","2/2 - 0s - loss: 0.3134 - accuracy: 0.8041 - val_loss: 0.4747 - val_accuracy: 0.7143\n","Epoch 121/200\n","2/2 - 0s - loss: 0.2557 - accuracy: 0.9278 - val_loss: 0.5309 - val_accuracy: 0.6905\n","Epoch 122/200\n","2/2 - 0s - loss: 0.2887 - accuracy: 0.9175 - val_loss: 0.3974 - val_accuracy: 0.8571\n","Epoch 123/200\n","2/2 - 0s - loss: 0.2687 - accuracy: 0.9072 - val_loss: 0.4745 - val_accuracy: 0.7619\n","Epoch 124/200\n","2/2 - 0s - loss: 0.3118 - accuracy: 0.8866 - val_loss: 0.4056 - val_accuracy: 0.8095\n","Epoch 125/200\n","2/2 - 0s - loss: 0.2823 - accuracy: 0.8660 - val_loss: 0.3871 - val_accuracy: 0.8571\n","Epoch 126/200\n","2/2 - 0s - loss: 0.2740 - accuracy: 0.8660 - val_loss: 0.7152 - val_accuracy: 0.6905\n","Epoch 127/200\n","2/2 - 0s - loss: 0.3122 - accuracy: 0.8557 - val_loss: 0.3664 - val_accuracy: 0.8095\n","Epoch 128/200\n","2/2 - 0s - loss: 0.2850 - accuracy: 0.8660 - val_loss: 0.3999 - val_accuracy: 0.7857\n","Epoch 129/200\n","2/2 - 0s - loss: 0.2325 - accuracy: 0.9072 - val_loss: 0.6718 - val_accuracy: 0.6905\n","Epoch 130/200\n","2/2 - 0s - loss: 0.2742 - accuracy: 0.9072 - val_loss: 0.3510 - val_accuracy: 0.8571\n","Epoch 131/200\n","2/2 - 0s - loss: 0.2746 - accuracy: 0.8660 - val_loss: 0.4200 - val_accuracy: 0.8333\n","Epoch 132/200\n","2/2 - 0s - loss: 0.2071 - accuracy: 0.9278 - val_loss: 0.6323 - val_accuracy: 0.6667\n","Epoch 133/200\n","2/2 - 0s - loss: 0.2569 - accuracy: 0.8763 - val_loss: 0.3964 - val_accuracy: 0.8571\n","Epoch 134/200\n","2/2 - 0s - loss: 0.2629 - accuracy: 0.9175 - val_loss: 0.4619 - val_accuracy: 0.7857\n","Epoch 135/200\n","2/2 - 0s - loss: 0.1991 - accuracy: 0.9278 - val_loss: 0.6352 - val_accuracy: 0.6905\n","Epoch 136/200\n","2/2 - 0s - loss: 0.2261 - accuracy: 0.8866 - val_loss: 0.3490 - val_accuracy: 0.8333\n","Epoch 137/200\n","2/2 - 0s - loss: 0.1980 - accuracy: 0.9485 - val_loss: 0.3479 - val_accuracy: 0.8333\n","Epoch 138/200\n","2/2 - 0s - loss: 0.1858 - accuracy: 0.9588 - val_loss: 0.5963 - val_accuracy: 0.7143\n","Epoch 139/200\n","2/2 - 0s - loss: 0.2048 - accuracy: 0.8866 - val_loss: 0.4157 - val_accuracy: 0.8095\n","Epoch 140/200\n","2/2 - 0s - loss: 0.1537 - accuracy: 0.9588 - val_loss: 0.3608 - val_accuracy: 0.8333\n","Epoch 141/200\n","2/2 - 0s - loss: 0.1544 - accuracy: 0.9794 - val_loss: 0.5568 - val_accuracy: 0.7381\n","Epoch 142/200\n","2/2 - 0s - loss: 0.1775 - accuracy: 0.9278 - val_loss: 0.3974 - val_accuracy: 0.8333\n","Epoch 143/200\n","2/2 - 0s - loss: 0.1358 - accuracy: 0.9381 - val_loss: 0.3388 - val_accuracy: 0.8333\n","Epoch 144/200\n","2/2 - 0s - loss: 0.1557 - accuracy: 0.9485 - val_loss: 0.5916 - val_accuracy: 0.7381\n","Epoch 145/200\n","2/2 - 0s - loss: 0.1399 - accuracy: 0.9485 - val_loss: 0.6014 - val_accuracy: 0.7381\n","Epoch 146/200\n","2/2 - 0s - loss: 0.1454 - accuracy: 0.9485 - val_loss: 0.3986 - val_accuracy: 0.8333\n","Epoch 147/200\n","2/2 - 0s - loss: 0.1335 - accuracy: 0.9588 - val_loss: 0.4475 - val_accuracy: 0.8095\n","Epoch 148/200\n","2/2 - 0s - loss: 0.1060 - accuracy: 0.9588 - val_loss: 0.5336 - val_accuracy: 0.8095\n","Epoch 149/200\n","2/2 - 0s - loss: 0.1196 - accuracy: 0.9485 - val_loss: 0.3687 - val_accuracy: 0.8333\n","Epoch 150/200\n","2/2 - 0s - loss: 0.1301 - accuracy: 0.9794 - val_loss: 0.4908 - val_accuracy: 0.8095\n","Epoch 151/200\n","2/2 - 0s - loss: 0.1124 - accuracy: 0.9485 - val_loss: 0.5588 - val_accuracy: 0.7857\n","Epoch 152/200\n","2/2 - 0s - loss: 0.1335 - accuracy: 0.9691 - val_loss: 0.3635 - val_accuracy: 0.8571\n","Epoch 153/200\n","2/2 - 0s - loss: 0.0750 - accuracy: 0.9897 - val_loss: 0.4557 - val_accuracy: 0.8095\n","Epoch 154/200\n","2/2 - 0s - loss: 0.0947 - accuracy: 0.9691 - val_loss: 0.5051 - val_accuracy: 0.7857\n","Epoch 155/200\n","2/2 - 0s - loss: 0.0856 - accuracy: 0.9794 - val_loss: 0.4541 - val_accuracy: 0.8095\n","Epoch 156/200\n","2/2 - 0s - loss: 0.0764 - accuracy: 0.9691 - val_loss: 0.4143 - val_accuracy: 0.8571\n","Epoch 157/200\n","2/2 - 0s - loss: 0.0630 - accuracy: 1.0000 - val_loss: 0.5574 - val_accuracy: 0.7857\n","Epoch 158/200\n","2/2 - 0s - loss: 0.1035 - accuracy: 0.9485 - val_loss: 0.3684 - val_accuracy: 0.8810\n","Epoch 159/200\n","2/2 - 0s - loss: 0.1029 - accuracy: 0.9485 - val_loss: 0.4809 - val_accuracy: 0.8333\n","Epoch 160/200\n","2/2 - 0s - loss: 0.0663 - accuracy: 0.9897 - val_loss: 0.5371 - val_accuracy: 0.8095\n","Epoch 161/200\n","2/2 - 0s - loss: 0.0635 - accuracy: 0.9897 - val_loss: 0.4392 - val_accuracy: 0.8571\n","Epoch 162/200\n","2/2 - 0s - loss: 0.0488 - accuracy: 1.0000 - val_loss: 0.7006 - val_accuracy: 0.8095\n","Epoch 163/200\n","2/2 - 0s - loss: 0.0950 - accuracy: 0.9794 - val_loss: 0.4621 - val_accuracy: 0.8571\n","Epoch 164/200\n","2/2 - 0s - loss: 0.0558 - accuracy: 0.9897 - val_loss: 0.5415 - val_accuracy: 0.8095\n","Epoch 165/200\n","2/2 - 0s - loss: 0.0607 - accuracy: 0.9794 - val_loss: 0.3624 - val_accuracy: 0.8810\n","Epoch 166/200\n","2/2 - 0s - loss: 0.1109 - accuracy: 0.9485 - val_loss: 0.6252 - val_accuracy: 0.8095\n","Epoch 167/200\n","2/2 - 0s - loss: 0.0742 - accuracy: 0.9691 - val_loss: 0.7791 - val_accuracy: 0.8333\n","Epoch 168/200\n","2/2 - 0s - loss: 0.0583 - accuracy: 1.0000 - val_loss: 0.3775 - val_accuracy: 0.8571\n","Epoch 169/200\n","2/2 - 0s - loss: 0.1327 - accuracy: 0.9485 - val_loss: 0.7503 - val_accuracy: 0.7857\n","Epoch 170/200\n","2/2 - 0s - loss: 0.1012 - accuracy: 0.9381 - val_loss: 0.5856 - val_accuracy: 0.8095\n","Epoch 171/200\n","2/2 - 0s - loss: 0.1271 - accuracy: 0.9691 - val_loss: 0.4447 - val_accuracy: 0.8810\n","Epoch 172/200\n","2/2 - 0s - loss: 0.0707 - accuracy: 0.9794 - val_loss: 0.9549 - val_accuracy: 0.7143\n","Epoch 173/200\n","2/2 - 0s - loss: 0.1424 - accuracy: 0.9278 - val_loss: 0.3716 - val_accuracy: 0.8333\n"]}],"source":["history = model.fit(\n","    x = x_train,\n","    y = y_train,\n","    validation_data= (x_test,y_test),\n","    batch_size = 64,\n","    epochs=200,\n","    callbacks=[early_stopping],\n","    verbose=(2),\n",")"]},{"cell_type":"code","execution_count":18,"id":"bc094615","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:38.363816Z","iopub.status.busy":"2022-09-13T02:00:38.362923Z","iopub.status.idle":"2022-09-13T02:00:47.823726Z","shell.execute_reply":"2022-09-13T02:00:47.822913Z"},"papermill":{"duration":9.51046,"end_time":"2022-09-13T02:00:47.828448","exception":false,"start_time":"2022-09-13T02:00:38.317988","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABdi0lEQVR4nO2dd3gc1dm377NNq967ZFu25W5jg21MsemdAKGZFgIhISFACoSEvKSQhLz5Et6EhARCCC0QakKJEwi9GAM2Lrj3KqtYzerSaqXd+f44c3ZmV6tirO5zX5eu3Z2ZnTk72v3NM7/znOcIwzDQaDQazcjHMdQN0Gg0Gk3/oAVdo9FoRgla0DUajWaUoAVdo9FoRgla0DUajWaUoAVdo9FoRgm9CroQ4jEhRJUQYmM364UQ4n4hxE4hxHohxNH930yNRqPR9EZfIvQngLN7WH8OUGz+3Qj8+fCbpdFoNJpDpVdBNwxjKXCwh00uBJ40JMuBFCFEbn81UKPRaDR9w9UP+8gH9ttel5rLKiI3FELciIziiY+PP2bKlCn9cHiNRqM5cli9enWNYRiZ0db1h6D3GcMwHgYeBpg7d66xatWqwTy8RqPRjHiEEPu6W9cfWS5lQKHtdYG5TKPRaDSDSH8I+hLgWjPbZQHQYBhGF7tFo9FoNANLr5aLEOJZ4GQgQwhRCvwUcAMYhvEQ8BpwLrATaAWuH6jGajQajaZ7ehV0wzCu7GW9AdzcH43p6OigtLQUn8/XH7sbtXi9XgoKCnC73UPdFI1GM4wY1E7R3igtLSUxMZFx48YhhBjq5gxLDMOgtraW0tJSioqKhro5Go1mGDGshv77fD7S09O1mPeAEIL09HR9F6PRaLowrAQd0GLeB/Q50mg00Rh2gq7RaDSjkr3LoHr7gB5CC3oECQkJQ90EjUYzGllyKyy7b0APoQVdo9FoBoPOdgi0D+ghtKB3g2EY3HHHHcyYMYOZM2fy/PPPA1BRUcGiRYuYPXs2M2bM4MMPPyQQCHDdddeFtr3vvoG9Cms0mhFIsFP+DSDDKm3Rzs/+vYnN5Y39us9peUn89AvT+7TtSy+9xNq1a1m3bh01NTXMmzePRYsW8cwzz3DWWWdx1113EQgEaG1tZe3atZSVlbFxoywZX19f36/t1mg0o4BgJwQDA3oIHaF3w7Jly7jyyitxOp1kZ2dz0kknsXLlSubNm8fjjz/O3XffzYYNG0hMTGT8+PHs3r2bW2+9lddff52kpKShbr5GoxluDIKgD9sIva+R9GCzaNEili5dyquvvsp1113HbbfdxrXXXsu6det44403eOihh3jhhRd47LHHhrqpGo1mOBEMgKEj9CFh4cKFPP/88wQCAaqrq1m6dCnz589n3759ZGdn87WvfY2vfvWrrFmzhpqaGoLBIJdccgn33HMPa9asGermazSa4caR7KEPNV/84hf55JNPOOqooxBC8Jvf/IacnBz+9re/ce+99+J2u0lISODJJ5+krKyM66+/nmAwCMCvfvWrIW69RqMZdgyCoAtZW2vwiTbBxZYtW5g6deqQtGekoc+VRjOCMAz4WQqMPRGuf/WwdiWEWG0Yxtxo67TlotFoNAONIe/eBzpC14Ku0Wg0A40Sct0pqtFoNCMcJeg6QtdoNJoRTkjQdYSu0Wg0I5uAFnSNRqMZHWjLRaPRaEYJulN0+NNT7fS9e/cyY8aMQWyNRqMZtugIXaPRaEYJIUEPDuhhhu/Q///eCQc29O8+c2bCOf+v29V33nknhYWF3HzzzQDcfffduFwu3nvvPerq6ujo6OCee+7hwgsvPKTD+nw+brrpJlatWoXL5eJ3v/sdp5xyCps2beL666/H7/cTDAZ58cUXycvL4/LLL6e0tJRAIMCPf/xjFi9efFgfW6PRDDGqM1TXchk8Fi9ezHe+852QoL/wwgu88cYbfOtb3yIpKYmamhoWLFjABRdccEgTNT/wwAMIIdiwYQNbt27lzDPPZPv27Tz00EN8+9vf5uqrr8bv9xMIBHjttdfIy8vj1Vfl8OCGhoYB+awajWYQGSQPffgKeg+R9EAxZ84cqqqqKC8vp7q6mtTUVHJycvjud7/L0qVLcTgclJWVUVlZSU5OTp/3u2zZMm699VYApkyZwtixY9m+fTvHHXccv/zlLyktLeXiiy+muLiYmTNncvvtt/ODH/yA888/n4ULFw7Ux9VoNIOF9tCHhssuu4x//vOfPP/88yxevJinn36a6upqVq9ezdq1a8nOzsbn8/XLsa666iqWLFlCbGws5557Lu+++y6TJk1izZo1zJw5kx/96Ef8/Oc/75djaTSaIWSQBhYN3wh9iFi8eDFf+9rXqKmp4YMPPuCFF14gKysLt9vNe++9x759+w55nwsXLuTpp5/m1FNPZfv27ZSUlDB58mR2797N+PHj+da3vkVJSQnr169nypQppKWlcc0115CSksIjjzwyAJ9So9EMKiEPXQv6oDJ9+nSamprIz88nNzeXq6++mi984QvMnDmTuXPnMmXKlEPe5ze/+U1uuukmZs6cicvl4oknniAmJoYXXniBp556CrfbTU5ODv/zP//DypUrueOOO3A4HLjdbv785z8PwKfUaDSDyiBZLroe+ghFnyuNZgSx50P42/ng9MCPqw9rV7oeukaj0QwlgxSha8vlMNmwYQNf+tKXwpbFxMSwYsWKIWqRRqMZdijv3AjK2YsOIe35UBh2gm4YxiHleA81M2fOZO3atYN6zKGyyTQazeck2GF7HgDnwEjvsLJcvF4vtbW1WrB6wDAMamtr8Xq9Q90UjUbTV+xWywAOLhpWEXpBQQGlpaVUVx9ep8Fox+v1UlBQMNTN0Gg0fcUu6MFOIGZADjOsBN3tdlNUVDTUzdBoNJr+xZ5/PoAdo32yXIQQZwshtgkhdgoh7oyyfowQ4j0hxGdCiPVCiHP7v6kajUYzQgmL0AfOculV0IUQTuAB4BxgGnClEGJaxGY/Al4wDGMOcAXwYH83VKPRaEYsw0XQgfnATsMwdhuG4QeeAyLrxxpAkvk8GSjvvyZqNBrNCGeQOkX7Iuj5wH7b61JzmZ27gWuEEKXAa8Ct0XYkhLhRCLFKCLFKd3xqNJojhi6dogNDf6UtXgk8YRhGAXAu8JQQosu+DcN42DCMuYZhzM3MzOynQ2s0Gs0wJ6xTdGgj9DKg0Pa6wFxm5wbgBQDDMD4BvEBGfzRQo9FoRjzDKEJfCRQLIYqEEB5kp+eSiG1KgNMAhBBTkYKuPRWNRnNk0OmHzvbu14d56AM3r2ivgm4YRidwC/AGsAWZzbJJCPFzIcQF5ma3A18TQqwDngWuM/RwT41Gc6Tw72/DC1/ufv0gReh9GlhkGMZryM5O+7Kf2J5vBk7o36ZpNBrNCOHgbuho6X79IA0sGlYjRTUajWZE4m/uubNzGOWhazQajaYn2hvDKypGEoiotjhAaEHXaDSaw6W9OVy0IxlGA4s0Go1G0xP+5p698eFUnEuj0Wg03dDZDgG//ANob4InL5IdpQrtoWs0Gs0IoL1ZPirL5eAe2P0e7F9pbTOMBhZpNBqNpjvaG+WjEmol7P4maxsdoWs0Gs0IwB8RoatsF78tL90u4rpTVKPRaIYp7WYkroQ85KU3W9toy0Wj0WhGAEq4jaCMxAPRInRtuWg0Gs3wR3noIMW8Vw9dR+gajWYoadbFU7vFb7dWOrq3XJwe+Xwoqy1qNJojnIr18H8ToWrrULdkeNJui8QDHd13ijpjzOc6QtdoNENF0wH52Fw5tO0YrkRG4iHLJWK5Swm69tA1Gs1QoSyEnmqVHMlERui9CrqO0ENsKG3gLx/sYm9ND7WHNRpN/xESdP/QtmOoWPUYPH5u9+vDOj978NAHQdBHXD30dRvWsOzD5bzwejqu1EImFmQzIy+ZaXlJpMV5SE/wkJcSO9TN1GhGD0e6oB/YCKWrul/f5wjdK58PYKfoiBP0axI+4xrP/5MvWqF+eyL1W+Pw4cGHmzLc+JNiKUx24fQ1QFsdQV8DQjgQLq+8SrpjITYVkvIhYyLkzoaxx0NizpB+No1mWHKkWy6d7RBoh2AQHFFMDXsk3m2nqI7Qo3P0tTBmATSUQcN+UhpKiWupp7mliaDfR2NzMwca26hpNSgqnMReh5tPGwPEe5wsKkxiXLITOlqh9SAc3AU737K+sKlFMO4EmHYRFJ0ELs+QflTNMKRiPeTMBCGGuiWDh5r8+EiN0Dt98jHQDo4od//t3Vgu/mbrImCP0AewU3TkCXp8hvyz4QHSzOcZQOWuWr774jr2b2sD4LJjCthU3shPNjby8wunc+1x46w3Bzrkj7TkYyhZDpv/DZ/9HRLzYNH3YM6XtLBrJFVb4S8L4dolMP6koW7N4BGIGNJ+pKEEvaNN3t1H4m8Gh9sU8075p+hohZiE8Dx0HaEfGsdNSOf1by/igfd2UpgWx5Xzx9AZCPKNv6/m7iWbyEyI4eTJWTT5Othc0ciWihRKDp7MUeMv5NQzk8mq+gg++j28eht89Ac4+U6YtRgczqH+aJqhpKlcPrYcYYNstOViPvqir29vlBZuS1V4hA5S7GMSZFQe8tB1hH7IxMe4+P7ZU0KvXU4Hf7hiDpf/5RNuenpNl+0TY1w8+2kJTofgotn53PyFFxnfsBze/QW8chMs/zOce6+0ezRHJr4G+djROrTtGGy66xR9//9Ja3LscYPfpsFECXm3gt4MCVlS0O0eulqXiGm5qAhdC3q/EB/j4pmvLuCtLZVUNvqIdTuZmpvE1NxEkmPdbKts4vmV+3lmRQkvf1bKebPyuOWC/zC55k146yfw2Fkw83I442eQlAdAfauf5Fg34kjyVI9U2urlo18LOgAf/lZe5Ea7oKvP3dGdoDdB5hRrW/udjMp0CXYOykjRI0rQAZLj3Fx6TEHUdVNykvjpF6bzzZMn8siy3fz9k338e105p08dS96YJzi29AnO2PA8bHiZ5qmL+TDzSm5/u5FTp2Rx/5Vz8LqlJWMYBoYBDocW+VFFKEI/wsZARLNcDEMuV3bEaCYUobdFWdcuI/LYVPk62NnVclHLnW4QDj1SdLDJTIzhh+dMZdkPTuVbpxWzrrSB17Y2cl9wMT8f8wSvipOI3/wc539wPo8lPMjOrWu55pEVNPk66AgEufKvy7nl2a62jmaEowS9vyP0N+6C3R/07z77k84oEfqRlPkS8tCjXLxUhkucKej2PHSwUhqDAdkH53DpCH2oSI33cNsZk7jtjElhyw+2nMX/vvwh57S+wsKal3nLu4LflV3MN58ymJqfxvLdBwHYUdlEcXbiUDRdMxD46uVjf3rohiH7Zzpah2/mTDTLJZTKdwR0lNqzXCJRgh5r5tkFO7q3XBwuEE49Y9FwIy3ew8+uOY0FN/4RcesqnJPP5g7Xc3x7/3f474efcO7MHDwuB3/7ZO9QN1XTnwxEp2igQ/7Am6v6b5/9TTTLJbTsSLBceshyCUXopqAHOqWoK788UtAdLm25DGsSc2DxU3DJo8z0lPOm9y7um7yFC2bl8tKaMqoafZTUthIIGmFv21nVzIGGbjpZND1jGFC5afCPOxCdouriMCIEXUfoXVCCHfLQzbRF9VqNFg0JulNnuYwIZl5KTOF8ePkb8J+b+dH4L/Cm/wvM/993AEiNc3PqlGy+f/ZkNpc38vWnVtMZDHLqlCzuOm8aRRnxQ/wBRhD7PoInzoNvLJOjNgeLgYjQlVi0jDRBN58fEZ2iffDQleUS8MsoPTYVmg9EeOhK0LWHPjJIGQNf/jd89HtS3vtfPk5ZyZvFd9NWcDxr9tXz6oZy3tp8AF9HkEk5CSwqzuTpFSVc/OBHPHD10UzLTSLW4yTGpQcw9UjdPvlYXzI0gu7vxyyXUIQ+jAcrhUaK2qLxUIQe0SkaDAJG+CC8xnJ495dw/u+seiYjBcPoOcslJOj2TlG/HFHq8lqVGAMdVqeoHlg0gnA4YeHtMP4UEl78KhdvuAmSv8PVF/8Pt5w6kf95aQO+zgCPXzePlDgPi+cVct3jK7nqrysA8LgcHFuUxjULxnLWdF0sLCoqmm2tHdzjhjpFo/ywPy9qXx0tMpqLSei/ffcX0TJaustyefunUP4ZXPcfa9mepbD277DgJsiZMbBt7W+CnVZ1xGh56JEeukpbdLrBk9DVchE6Qh+Z5B8N3/gQXv8hLLsPdr1L0SWP8uyN4SNNx6bH8/I3j+ff68rpDBqU1rXxzpZKvv7Uaq6cX8jBFj8f76rl5W8ez8QsnTEDQEtN+KNi/6eQNgHi0/v/mIYxMHnodpFoqTp8Qd/xNqx6FK54pv8KiCnRto+ADHQj6Ad3yz87ymceiR2o9o7QaJ2iIQ9dWS4dVt0WT7y8SBuGjModbt0pOqLxxMMF98Piv0P9fnhooSyWb4R3kKbEefjSceO4/oQifnz+NN787knccGIRz366n+W7D9LmD/Dcp/uH6EMMQ1QtFXuEbhjwtwvgkz8NzDE72mxV9AagUxT6x3bZuxS2vSarifYXPVkunRGC3tHW9Q5GRamR244E7L55VEE3/3/eZPmoOkUdLohJlJ9dCbjDZVZe1II+spn6BbjpY1kH5j/fhSfOh/UvhJfdtOFxOfjx+dP48Pun8MkPT+W0qVm8sraMjkCQYNBg64FGnvu0hPe3VeHrGLgvx7ClOYrl4muQHqea/7K/UdE5on87Re3i1x8do+o7pQqJ9QfRovHuLJeOtq6dh0rQR3qEHs1q6/TJyFsV3gqYc4qqCN3fZFksw2VgkRDibOAPgBN4xDCM/xdlm8uBuwEDWGcYxlX92M6RT1IuXPMSrHwEPv4jvPQ1+UUYswCO/jJMv0j6bjYK0+IAuPSYQt7YVMmSteX8fcU+PiupD23jdTu4fG4hl88tpKa5nbyUWCaN9sFM0SwXJe6tNV237w+UoMdndhX0ivVyeVJu7/vxt4InznodFqH3wyTMKquisaL/Ooyj5aGHBD1CpDta5YXVMCzLJxShj0RB7yVC72yXYu5wyGH9qpaL8tDb6myC7hr6TlEhhBN4ADgDKAVWCiGWGIax2bZNMfBD4ATDMOqEEFkD1eARjcMBx94I874K+5fD9tdh66vw0lfh7btlp9HR14I3KextJ0/OJD3ew/f+uQ63w8HPLpjOwuIMSg628ur6Cp5ZUcKTn8jMD5dD8IuLZnDl/DEArNtfz/Or9vPDc6aQ6HVHtmh48N6voKEULnqgb9uHLBebeLfVmev6SdCDQfkDVuKrOkSTcqFyc/i2z14BxWfCF37f8z4PbIC/nAS3rIT0CXKZXST6w3JREXpj2eHvSxGtHnpI0CPy0DvaZCdioMOqLqh85hEp6L146J0+63OqmuhBU9AdTvm9tgu6GPo89PnATsMwdgMIIZ4DLgTs3+qvAQ8YhlEHYBjGME6qHQY4HHLKu7HHw2l3y1mTPv4jvHkXfPAbmPcVGbWnFQHgdjpYPK+QR5ft4eFr53LSpEwAxmcmcPLkLL59ejEr9x4kNzmWP7+/ix++tIHlu2s5Z0Yud/xjHU3tnbR3BPnt5Uf12Kz739mB0yG4+ZSJMsLa8RZMOBWcA9h33tkuh773NZ0tGIzuoSvPuL8i9JWPwIf/B7dtlf8vFaEn5kHFOisK6/RL8Wzrg2ddu0tGZ3V7LEEPReiifywXJZ5NFYe/L0U0eyXQTW62Su3rbLMJurJcDtNDr9snz3PenMPbz6EQZrn0EKGD/D4EVJaLRwq4vznCQx96Qc8H7D1ypcCxEdtMAhBCfIS0Ze42DOP1fmnhaMfhgElnyb+yNfDx/XJSjWX3ybz2426FeTdw+5mT+fqiCSTHdY2yC1LjKEiVkeTcsan84Z0dPLx0N/9aW05hWiwXzM7j6RUlnD41i3NmRrcF9tS08Pu3txPrdnLDiUV4970Hz1wG5/0O5t0wcJ9/x5vQ3gDtwhLJnvDVS1F0eqDFJuhKUFv6KZWxYp20QPxNssNLjRJVtoq/BWJT5OARCJ9XsjvaG81HW9+J8mWT8vpntKg6xoBE6NEslygROkjxUx2F3VkuB/fIz92Xi3nTAXjsbOlL39rDhM39Ta+Wi89qv9Nt1XJxuE0PvTnCQx/YtMX+6hR1AcXAycCVwF+FECmRGwkhbhRCrBJCrKquHsYDKYaK/KPhsifgW5/BOfdC8hj47x3w11Nwbn6ZZI/R6y5cTge3nzmZD+44hTvOmszzNx7H3RdMZ1ZBMt9+fi2/eX0rze3yC1XV5OOP7+yguqmdP767g6ABLf4AS7dXy/Q3gNWPd8nKqWz0sfA37/LW5n7wfDf8w3xi9E3QVHSeMUmmDyoBURG6fdnh0LA/fL/2CB2sYzSakbC/L4JuCrmv0Vqm9pMytp8EXVku/Rih9zj0P9JDbwtfDzbLxW5f+OHPJ8Dqv/V+/A4fPHe17OgNdU4PEn0SdDNCd7itaotOt5W2qNI9Q7VchlbQy4BC2+sCc5mdUmCJYRgdhmHsAbYjBT4MwzAeNgxjrmEYczMzMz9vm0c/qeOk137df+CSR6G1Dv55Pdw3Hd75hRx51ws5yV5uPmUieSmxuJ0OHv3yPM6fmcuD7+/irPuW8u915Vz20Cf89q3tnP37pbzyWRnXnzCOlDg3/914QNpALq/0fcvCSwE/+cle9h9s466XN9DkO4xaHr5G2Pa6/LzQtwwVJXqZk+VjqDPUFpn3h4+uIlzlzSshURG6skpUNklfInRfNxG6cEByfj9luQyA5dLXLBfDsM5LmKBHsVw6WuRfcx/+5xtfhLJV5kW8Hwd19QX1Od1xPVgukRG6ObAoJkHeTarPP0yqLa4EioUQRUIID3AFsCRim1eQ0TlCiAykBRMxukBzyAgBMy+Fb6+Fq/4B+cfAst/Bg8dJf/sQyEyM4XeLZ/PiTcfhdAhuffYz6ls7+P3i2WQmxhDvcXHzKRM5c1o2Wzevh9qdPMzFtOLlrad+xZ4a+aVs8wd4ZkUJU3OTqG5u57dvbv/8n2/bf6VYHHeLfN0XEVIRetZU87Up3nYP+3B9dMOQnVlgE/R6+aOOtBFCEXr0FNQwQpZLRITujoP4rBHQKRrFcjGC1qTIAb9tVKVNeKNZLup5X3L61f+g6CR5wTB6v1PtN9SFyZsSfeh/wOahO1wRA4vMbDNl1w1CtcVePXTDMDqFELcAbyD98ccMw9gkhPg5sMowjCXmujOFEJuBAHCHYRiDPC57FONwwqQz5V/tLnjhy/D0ZTD5XJh8Nkw53xp63AvHjE3jtW8v5G8f7+XMadkUZydy3qxcmnydpMV7OGdmLp7PHgc3fBp3Eke7mjjx4Buc/uf3+f2XjmNrRSN1rR08dM0xvLqhgr99spevLRpPfkqU2dAVqrh/JGWrZWrXlPPhte/1UdBNsc40BV2Jt30gzeH66K211g/ZLujeZCm+0I8Requs+5GQdfjD/wOdUnTccfKOIjJF8vPSk+Wiljtd4SLeW4Su1vfFqlLbxqXJ6NaeQTPQqAtPbEr0LJ3Odvn/A7NTNGJgEVgZUspDH8Bsnz556IZhvGYYxiTDMCYYhvFLc9lPTDHHkNxmGMY0wzBmGobx3IC1+EgnfQLc8CYcf4vsuFtyK/x2Mjx/DWz5T59G4yXEyGhcTb7hdjpIi5c/kBMmZHCudyPV7jz+ePOlzD1jMbH4mSJKuOyhT/jxvzYxPS+J+UVpXDl/DIYBn+7pQUCX/1laRbW7uq6r3AhZ06SYCWe45VK9DZ69qqs4t1RJiyLDdPSUkLcdlLngEG6/vP9r+OdXej0nYajoHMItF2+K9EUhSoTeFw/dtG3sEXqnzxJ0ODzbRd0lqHPTH7ZLMGh5vmFZLlEyXuyCHhahR/HQlX3Rl0Fane2AsO6OBnMKwFCEntz9wCK7h646RZ0eS9DVd3QQqi3qkaIjEU8cnHkPfHcj3Pi+zGsvWQ7PXw0Pndhzh1igs8dbVo/RznGOzWTOPpfYGBfkzALgDyc7uG/xUdxyykR++cWZCCGYlJ1IotdF7fo34eM/dd1vMAjLH5TC8sxi69YTzJrmG2WxJocTErLDBX3zEtj2Krz9k/B9tlRDXLol3i22CD3DnFnKbrnseEP69Idym24X9NAFo96M0M1oTP24lWgG/L1fTKN2iraCK1ZaLnB4tovaf4bZv9AftkuYcEcZ+m9fbhdnexQa1XLxha/ric42KZqhu6NB9NFVm70pPQwssnnoHT7AkIKuxpO02QV9YAcWaUEfyQghc3LP/pXMl778SfkjfuK8cFFSbH0N/jALHlwAu96Nvs8dbyE6WmS5ApCpk95kEuq28MU5BXzvrMnMLkwBwOkQXJu1m2v33CFz6Jf+Hx2BIL/67xbe31YFJR/LErdzb4C6vdJWMfEfLJFRb/Z0uSAxJzyirFgrHz/7O+z72FreUiPFz5sio3q75ZI6Tv5glMgbBtTslBHdoZQEUOdOOMIj9NgUcJsRuhIvewd1b1F6d52i7liIzzA/3+EIunn8TPPC1h+ZLkrQXd7o9dDt24RZLm3WdlEtG+Wh90XQTdEcEkE3RTw2pfcI3em2tnG6IMYUdHXHOAjVFrWgjxacLph2oSwv0FwlI/X1L8D+lXKQzGNnw3NXyi9ZZzs89cXoKWObXoK4DBh7onwthIzSD6zvum3lZr5Tcze7gnn4p3wR3ruHZS//hb98sJvrHl/J6iUPYngS5N3E7Cth+5sQDLCxrIFb73tK7iPbHJ6emEtj9X42lZu2RMV6mHSOmbr5A+uYLdVS/BwO6amqH0vbQRm5x6VbIt9SbdkcB6NYPormKku4ARpL5Y80qaCrh648aX+LvGA0VViV9rqpzROiuzx0e2er3Y45VAYyQvfESyEKmp2eYSmIUaZoC1kqLV23A0vw+yTopmiG7o76YNP0V8dpKEJP7t5DVxG6w2193rAI3fwOhSyXYP+0LQpa0EcbY46Fr70LaeNlvZhHT4dXb5cR7Jn3wNeXws0roGAeLL03YkLbFtj+hrww2EeH5sySU74FIiKLVY/iEHCt/06WTv8FHbnHMGPD/+PUCYl847gcJte+w0vt8/jt+/sxxp4gxbVyE3/9cDcTDXOSiuxpAPhis+hsKOeXr26RbW0ogbHHwfyvyYtJk5nz3lxl2S1xGTIa97fKH31cmrnMFPmaHVZbo3n4iicvgv/cZr1uKIWkfLk/dbvsa+jaKdpWJ4+rrB57hF66ClY+Gn6cUIQemeXitQTd1w+CnpgDMcn946HbBR2snGq7uEW1XKIIdrQsl0hx3v4GbHolfFlnuzxHnj5G6IEO+N1UWPd8z9sBVG2Bl27s+t0OHdsno2pPglWjJnK902a5qKwdh9sWoZvfIaf20DWfh8xJ8JU34fKn4Mrn4ZZVUsSPv1VmB7hiYNEdcvBMaGAP8sfU0QozLg7fX+4s+cWt3WktC3TKH96ks2hwpvHOjnruM64iU9Tzm6LV3Bn7CgnCx77CL/LHd3fyXJUcytC4fSmvrq9gqmMf1a7cUMfR1uY40kQzn+2ppHGPORIw9ygYc5x8vn+5fGypsToQ4zNkhK6i9Ng0WQtdRei1NkHvLkJvrICqTVC91VrWUArJBXIWmrY6KSC+BnkhsQu6EsyMifLRnumy6jF480fhx+opQlcdaIcToatO0ZhEOQKzD+MVeiUk6Anhr+0DiqJ1ikazVOzv6c5D/+RP8OFvw5d1RHjovUX1DaXyf2P//3fHrndh/fPdX/xCdwdeq0ZN2Hp7hO6yLlBOm6BHeuha0DWHjNMF0y6QaY0ZxV0nOyg+E7JnyBIDwaD8W/2E7JxUIqpQVfvstsveD6G1BufMS5lVkMyzn5bw0L5cylPnkfHp/8kSBnNv4Ls3XMuZ07L58XsNtMXlU7b2bQKGwWxPGZuD1ni1ZZVyyH+6UcfeDaZnnnuU/HN5oWSF7Jj0N1l+s7Jc1A8mFKGbgl6zQ743fWL3EfreZfKxbq8VfTWUQnKhuf+DlqeeMkZeEB0uGYkpjzpahN50QP64VcTW6bdELFraotMtO0cPZySk2q8nARKz+6d6Y2dEhB7oY4SuxN1+Tuy+e0c3gu5v7doXEfLQIzqku0P9v/qS467uiLqzcdSxlU8e2THaxUO3CbrLI9fZLZdhMLBIMxoRAhbeBjXb4eWvw7s/hz0fwMLvdc0Zz5gkbyvtgr7xRTlwovgMLp9XyPET0nnl5hPIu/Bu+YPMnwtn/wohBPdedhQFqbH8t6mIjINrOGdSInmBcj7zF1Db3M6+2hZW1sooZ3piK76SNXIofGyq/FHkHS0j9M3/kscef7J8VOKtbmlj08I99NqdcgajjEk9CPpS+djRKj33QIcU4+R8K0KvN+2hFFnBEne8GaGXW+cHwoUosma7fe5Je8EmlbYI0nM9LA/dPH5MovzrS258b0RaLup1p092GoPNPomSh36oEXpHa9d2h6LkiDEA3aHKNvTFa1f/l+6iflWrxS7o1dvkxSJgTk8XEnSPdRFxmnnyMYlypDcMj4FFmlHM9IvldGHv3iNfz/mS9Kwjcbql112yXEby/ibYsgSmnAfu2FA9dsmJcPWLsi6NeSuaHOvmX7ecSNnbG8hcvYx7457CQZCNwSI2lDWwdn89VchJdk8vCJK1axudhfOsL+eYY2U1ylWPyQ6/vKPl8uR8GZ3XmKNV49Jl9O5rkMJcs0OmRSYXylvrYFB2ptrZ86G8MPmbZJTe0QYY0nIxgrIztG6v3FYJuidOCoCK0NOjWC5qSHtrDaQUWp2zyWZHa3uTmTlhpi2CvEXvDw/dkyA/U2+dtH2hO8uls12Kla8hepZLKEI3hdIVG91DD7RLYVR9Nh09Reh99NBVhN4nQVfTCkZsu/9T2XcUeXfQVg9/WQSn/URWRIVwy0X1HTjMzxOTZNk5oeJcOkLXDARCSC/90sdkLvt5v+t+HsoZl0DpSnjhS/DomfKHesx10bctPr3LyNXkWDfTjjsHgPgtL+CfeRXvBufwye5anvxkH0VFUhRPdqxjnDjAdsd4682FC6TvWLEWZl9ltbH4TPn4mcyYuX95Lft85g+v6YAU4vRiORir09c166OhVJaynXmJfF2314rukvJlxG8E4cBG2cmVaNZxccdKUWkql3cJKsslNHdmp20SDjNCV0KdVCAfldiqtEXohwi9UYqe0yVHm/alHEFvdBF0m+USkxy+jRJa4ewagcelRc9ygfBMGH+rFFe76HX65AUhWoTua4SP7g/PHKkvCT92T4QidNs+G8rkd3zDC9bdgYrC6/bKZS3V1uexWy4KFaF7k6z26oFFmkFhxiVw3m97Hk59/K0yS2brf2TEcc1LMgvlUEifKPPbT/kRnosfZGxGIo8t28PBFj9fP2suONxk7nyBatJ4pM5W87pwvnwUDpi12FqePUPmnh/YAMD9H9fw6BrzB1q2WnqVGcVWBB3ZMar889nXyMe6vTJdEuQI1lh510DFOhlZKytKWS61uyB1rDVUX4lDSzVy4i4s+0cJdXK+9ToYkGKohOpwI3R/s9W56kmwJijuCV8j3D8H9n4UfX13lkug3TpWpKDHpnYd2h+bFmG52Ace9ea9+yI8dNv2W1+Ft34sO7YVoQi9D/nqIQ/dJv412wBDdipHeuh1e8z2tVif0Z62qAhZLrbJaobDjEUaTYjjb5XeeFKeFLJDRQg5YbbJjPxk9tS0cPb0HI4akyotHIeLl+K/wUsfVPHdg61yGr64NMidLSNk2zRvS9ZX0NI0myvZS6MRy5xxmWwviQEP0qIBGaEn5sjntbss/x1g59tSaPKPkfuu2yd/2MmF8jjqLqNyk5wqUKEslwMb5NSBrhj5Y1YiZO+MVJG6EvtkW4SuBMdtioU36fByx9ubrEhaVfqze/TRqNosbbcD62HcCV3XK7GOiRKhJ+RYz0EKrcO8O4js9IxLCy8PHK14l2FYwtrebKVyRkbJ9vdGq0f/uTx027aqv6Wlxiq+pf5HB3dbbQ4JuorQbXKqnquLHuiBRZphyNjjPp+YR2H+uFRcDsHtZ5qdipf/DS59lPOPm4EQ8I9V1rwqHde8gnHJI2Hvf2PTAZb4jwGgzZXM3796LInj5rCCmRiVm6S4ZRRLsXbFwvv/Dx4+RZYD9rfIkbPTLpC+euo4GaHvXylz9MGK0APtln8OMqKu2S79dZUBFJNgiYpd0FWEHs1yCQl6P0Xo7U2WgKjIsLeOUSVe3WXXdHZnufhsEbrKcjFTMF2xXfPQ49KiD0YCS8Q7261qjWERuhklOxxmGVub+KqLhNreXimzT4IeJctFiXZrjS1CNy+KB+0RurJceojQ1UUJbJNEaw9dMwq5cv4YPvzBKaEiYYr8lFgWFWfyj9Wl+DuDNLR1cO5f1nPhX9dS1WSJwubyRpInLsBIyCErO5cYl5Mbzzyaxb4f8tr5K+C2zTLqdTjg1LukdXNwN7z9U1m6t6MFZl4md5Y6Tnr0jaVdBR2ocmXT0GYKlzvO6ugya93IjtUIQRcOW4SuBN2cIMPXYIlIyENP7tlDb6uHZb/vfqRhe4TlAr376MqG6u5C0m2Wi98m6GqkqNkf4PbaIvRmmSHlju++WqMSfbuotkdYLuocuWPDo2l1rtW5b6mx2T2fM8vFHqFH89DV9l0idJtlqcS9i+Xi0IKuGZ24nA5yk6PbAdcdP46KBh83/G0l337uM/bUtLCjspmLH/yY/QdbafJ1sKemhWn5qYjz70Ms+j4AM/NlRLS71h8eHR1/K1zxNJz0A9izFN79hZx9aMzxcn3qOEsUlGevOjuB367w8eB75sCqUElaIb12MCN0UxyUyKRNsNIWfZEeepMlCEqsYswOtMjBK4qt/5EXowProq8Pi9CVr3+YEbpqS5csl24idJfXjNBtlosnXvbPdDf7jxJeu6DbL0QdPisKdsdFWC5V4Z9T2S2xqX2L0KPloR+0C7rKcokQ9I5WW4RuCnmY5WIKujeKh64tF82RxilTsrj30ll8vKuW97dVc/cF03n+6wuobmrn0WV72FIhf/Az8pNgyrnyD/C6neSnxIYm5OjC3Ovl4Km6vTK7RaUxqlmTnDFW1G27IOzqSGNzhfnjNy2Shrgx/PJtM6PCk2CboLlSFg9LyrPloTdIsYszB0W1N1oiom7n1Y+/u3RDdaGILLy2/Q2Zi+9vihKh9yLoSrzauxN0U7TsA4sCndKfV8eye+juOCl+YYKeEKW4l13QzTbahbrdZqHYB++4YyMsl4gIXQl6xuSugl5fEj4eIRiw7J5QzfZOS7Rbo0To6nz4m7tG6GGWi4rQIzx0XW1Rc6Ry2dxCnrphPvdcNIOrjx3DrIIUFhZn8tbmylARr+l5yV3eV5QRz+7uBN0dCyfeBojwjBkl6HmzwyMuU9RLjUx2VJqiYYrb2o5Cnl9pCkikh56QLfPi7Z2iMUnme0WEh26L0KH7aFmV1q23zdneXAXPXC5nsgrrFFWlBHoQdMOA2t09HzNaHroSNdVee5aLO1YKnN1y8cRLOyLSQ1f7VMJrtz1CKaAdgGGL0GMjIvRIQTcvdpmTulouS74VXhs/rKaOuW1DiYygE3PlxbijLTzLJdS+FlslSlstF0V3WS7CKfsJBmjWJS3ommHN8RMyuGbBWISZe37GtCzK6tt4cU0pGQkeshJjurynKCOe3dXNGN39aI79Oty62urQBEvQlX+uiE0lIFxUksqBRh+Nvo6QAK9oy6fR10l9qz88Qm+ulEPv4zPCO0W9STLTJyYpeqdoKELvxs9Wk1/YI/QK037ZszTcclFi2ZMn31xpRai9WS4xNkFXEXm0tEV3nBQ/e6eoJ16KXsBvCVlHm5VFFNVDN+9SIqNglTIK8qKh2q0uXPX75WdPypcXHrtfXblR1uxRy+x3Qkr81QWucL4U3uaq8EqP9u37EqGHWS5Oa8DRAPnoWtA1I4pTp2QjBGwsa2RaXnJI6O0UZcTT6OvkYEs3E04IIQcb2UnMgXPulWJvJzaVOncOhvlT2VHZHBLgzUGZ7bOvtjV8qH0oQreNWm1vtKI1r5nNEpm2GIrQuxN0M0JvKLGWqbrxFeulYEZ66D1ZLsp+iEvv/pidUSwXu/cvnF0jdHdseNqiitAhfKRpXLq1DUR46M3hx49mudizidQ+GvbLtNPIQUgtteZgIJ818ChsohHz/cqCKjxWPhqBcMsFABGR5RIlbbGnTlEYMB9dC7pmRJGZGBOaYGN6XlLUbYoypfh066N3x7E3hqcnAow/heWueeQkyR/tzipZHCyIk03BcQDsrTVFy28O5GkyBT3eFKzWWikeIbFNND30Q4zQleUSGaE7XIQGMnWJ0HsQdCVeeXMOzXKxC5krJryWi9sbJUJPsNVCURkxPtnPoMQRwi0S1W61nzBBN5fZ89rVBaCxTPZdqIha7bNmm7WtKhURNULfJdurJiGHrpZLcqG8AKh2hCwXW5ZLVMvFbUXoA+Sja0HXjDhOn5oNwIwo/jnAhAwpPrtrWjAMo3vrpU8H+yl3+69mYXEGXrdDRuizruBHWX8iMUNmrJTUtlqWS3ujFCEVoYP00dsbLcGOMeusRKYtxvTSKaosF7uHXrEeJp1tzaR0KJ2itbukyGRPl+2Ldp6U5aIuOmGC7rEmRgZbp6itbovy0JXo2QXdHReeVx51HtLICD0uSoQurHPWVicjf3VHoSJve3nkkKCrTm6bjXNwl5xLQNXcV8d2OCyRTp8gI2x1zD5bLqaHDjpC12gUlx5TwPmzcjlxYkbU9fmpsbidgj01LfzolY2c/8dl1Da3R922NxpaO6hp9jMxK4EJmQlsr2qm0+HhXwfSOGFiBtlJMew72CotjmCnJbYJ2VaZ39Ya0982L0AxZs2WkHWhIvQeJrkIdMpMFpdX7q+jzaoEmX+MVYZBCbnTJffbU4Gug2bpgtg02fZoaX6BdilUoSyPDqtT1OWVWUH2eujuWCneHREeeshysQu617qzAUt8hdMWoUcMr/fY0haVoCcXWFF+W705VWBEIa/qbVK44zLkc7BNCJJtvf/gHinocbbvljq2ykZSdp3KYApF6FHSFkMRupAXBe2hazThZCd5+dNVR5Mc54663ukQjE2P550tlTzzaQmbyhv50qOfWgODDoFdNVJYxmcmUJyVwM7KJrZVNtHiD3DM2FTGpsWbEboZGatJQBKzLY9YWS7dRehKLEOZKQ0yWrZHzK21gGGlVDaUhmrYkDsLihaF7wPCO2qjUbtb5sqHLiQNsO8T+OgP1jZqBnslUGEReoxcZx89qkaKGgG53N4pCrYI3RxS74mz7A71mJBl5aFHi9DVds1VgLDGEASD5sxSKbbJMMxtq7dC5mT5pyJ0ZTMl5lr/i1ZzAhX1vws7tvmYZgq6qsMfLUJ3REToSshVPSAt6BpN3ynKiGd7ZTMJHhd/uGI2O6qaOOu+pbywcj/BYN8tmN3VMnIbnxlPcXYi5Q0+7ntLCsIxY1MZkx4nPXTVCVm5UT4m5FhRXnO1mSMeKegR/rArRka8vkZ4/Yfwm/Hw8Z9kB6OyW/JlqQMa9lsZLjlHwbSLZEeemnQbwlMpo9FYKkv7em2dsWuehHd+bglOwC/F3N6pqaJmZ4y0XUKdoq3WSFGQYt5h2lFK0O0dqK4YuS7SconPtNod2XGsOkUNQ0bocenyguRvMS0UQ0bonohO0eptkDlF1q6v3ibfryL0hGwp/MGAORl4mvxc6kIXitBj5IU7UVp+tNbK0cBKrJ1RLBd3nLzj6CLoA2O56OJcmlHJ+Azpod6wsIgLZ+dTkBrHL/6zme+/uJ7le2r5v0uPwuHoplSwjd3VzbgcgjFpcRRnSdF+Z2sVd54zhcK0OMamxVHV1I7PEYsXCK75O47kMeakFwYg4NOH5c4yiuVjTKKV5eKKDa/RrkroliyX69+8S5YZmHCqXJ9v1oKv3y/986R8SDD93hveDG98TxF6Z7sUr4Sc8Ai9qVyKTXOl7FxUIyVDEXqHVd/F5TXzy9vl8mCnlYcOliXhibfm3VQXg852qySu3XJxuKxJQNR26lgg96+i/+YqKcYqw8hXb57DlIi5X+vlOcycLNvrq7f6NYRTWmMdLXI7sEo+qCyl0AU3FpK8Vn9Fa538XCrTSom2cFrCLYRsn7pADnCnqBZ0zajkpMmZrN5Xx1dOLAJkNP3yN4/nD+/s4Pdv7yAYNDhreg5TcpMoMsU/GrurWxiTFofb6eDYonROmZzJ1ceO5fRpMkobky6F4z9bm7gUcDRXyBrzSqRjU6VXXXSSnFAEpEXQ2SZL/KrIUxGTJLNkarbL/ZR8Isv8qpz5nFkyKqzdIWeYisybD9tXD7MWqQyRhEzL229vtCbtaCiVgq4sF4fTSlG0+9rKcrGPelWdvHZBV4O11MWgU0Xo8ba0xTYpljGJ1oCsSA9diWlHq6y0mJht7qPJJsgp4fOPKs88c4rlc9dss/L2PfEyQldTxan8+PgM+b8L+ffx1vYgLRe1DqyLnjPCClRpqjDgnaJa0DWjkuMnZHD8TeGdpkIIvnP6JIIG3P/ODl5ZW05yrJtP7zqNGJeT97ZVMTM/mYwE60e69UAjk3OkL50c5+bx6+eH7XNcuvxxP7eujkvN33FJ4QWEkh/jM2V0dtGDlsjPuATeuAv2fSQjbDveJCniRlCWDO5ohRUPWxM+J+XKGjSrnpAiNv/G7k+CJ0FGpoFOOfny5HNklAqWhZOQHRGhK0HfLwfXKMsFTPGOJujt4aNeVUSrKhMm5lgRupqhKNhpeujxtoqJLdIq8dgm5+gysMi8WHS0yvdlTDK3bwmP0EOWS5uV4ZI52YqQa7abqaRJ8iIR7LA6We0ROlhtP+fX8jMrMW6tDU9nVLaUPX0R5DGUl687RTWa/uW2Mybx6V2n8ZtLZtHQ1sFHO2vYXN7I9Y+v5PrHV+LrkD+2Rl8He2tbQwW/ojHWjNAbgvKHvcaYxK9X2qKvs/4XrnrOqoMOMgKc/kX5PHIEYkySJUy5R8mIPNAO+z6WwhKTZGZ1NMnJvFVnaDRiTMtl/wpZ1OuhE6068aEIPcvy0BvLrVQ+leseaLcETaUo2oe8hyJ0W069EjnV+ZgyxpaHbisdoLJcVHaL8uDt3n+0TlGQAtlcKdvvSZBtUnn6sSnhkXz9PhkZp4yRF1B3PFRtlRG6N8kSf1WLPtYWoavPCfICl3uUlUnUGhGhq45QR0ScHJOkO0U1moEkK9HLRXPySfS6eG3DAZ5fWYLLIdhQ1sDP/i1nv9lUJsVtRg+CnhLnISXOTXJmAYYzhoriq3l1fQWldTIie+TAeP68J7trLvxcs6aITdCX767FUB2ncRnS8lBWy95lUryEkB2ZACd9v/spA8GatUhNcp0zE978kbRVVDQan2VF6PZc7ZCgd4RbCWERuteqomiP0JWNpAbzJBfaLBefNYpUTfwcabnYvf9oA4tAev0Bv+mhmwLbaLbZm2IbWNQiL17xGebQe4fMCqpYa47eTbQuEuozx6aY50YJeoQtpiyXYEdEhK46RyMidG80QdeWi0bTr3hcDs6Yls2bmw4AcO7MXPJTY/nz+7u4+OiCUAGwngQd4P4r5pCX4kUk7yK/KgAbP2JjWSP5KbE88N5O6lo72F/Xyi8unIFTdcQWzofsmaFocFN5A1c8vJyPprjJBxkJCiFnXFIjL9Vgl5mXS8Eff0rPHzDGrNFetw8w54999go5jZqKZhOyTI/c3Y2g+8OthIDf8sGdHnNZfUSEbopp9Xbpz8emyDleQUbndstG+dcgxVdF6Oo4kZNIqGi6crN8TBljeecNKsJOsVkzbdKPj8+yPlve0XLC8fQJMmJXAq0idOWhx3Uj6OoCYG8XWBF6pIeemGudT90pqtEMHOfNzOWlNfKHvHheIUcVpvDYsj28ur6CulY/ucneME89GosmWaMKJ2V3IgRsO9DEnDEp1LV2MCUnkWdWlDAjL5mrjjXddSHg6n+EZugpqZWiVtPptQQdZNSXNQ3K10jxBZh0pvzrDZUSWLdHRvvpZpZNfYmM0L0pliB5k63Ow7QJVhnaTn94NUF7LReV5RLw20a9esOna8ucYm5r6xQNvT/WslyCQSm+njgrpz9aiVolpuVr5GPmVDl9HkjRdLjNVEFhjSptqbKibZClDjrboGqLbF9YhC6sTmJ1vj02AVfnVREWoXcj6Kf/1LoL0SNFNZqB48TiDBJjXBSmxXLc+HQSYlycNCmT1zceYENpQ9TyvD0R53ExJi2ObZWNofrpP79wBhkJHtburwvb9sUdAf65U1ox5Q1SuKraTfFUgg6W7WIXpb6gBhlVbYaUsZZVU7fP8p8V3uTwCT7UiNeonaKRA4v80T30YId1THs98cgIHaTAdrSaWS62SbcjI3QVeZetkcdOHWd9zoZSGZ0rG0rZOS3V4Z81z5yA3AiEe+gNZfL9qvN6ynlw4YPWRUnh8liRdliErmyVCEGPTbX6UEKdot3MOnWYaEHXHNHEuJzce9lR/PqSWaG89HNm5nCg0cfumhY5gcYhMjk7ka0HmthqTsIxOSeR4qxEtqt66sgO15/8ayMPfSALZB1okIJY2m4KX1RBt4lSnz6cKYzV28yOyRh5+1+/zxS5bGvb0CjWJJkN4quXghrNcgm0m5UDnWZxLr/ldduzXMAqdhbKQ28Pj7rt6YXKcrHXoVGzINlFGmTaZsYkeQcTskxKzYJfJh4zQm+uDq/NkjY+fJCX6kBt2B82SxXuWJhzdfR+CnXMvmS52NHVFjWageXsGTkcP8GKfk+bmo3bKX/EPWW4dMeUnET21rSwdn8d+SmxJMe6Kc5OYGeVVaP9hZX7afEH2H+wlWDQCEXoz7QdB5c/BWlF1g7VcP+EQxR0e4VENbF3yljLcrGLnOoYTcyVnZggI9YwQXdLIVLD9tWysAg9NqyjNyToIcul3Yq63V6beLdYlot9+jw1sElh969VCqbaR1ud1aGptm2pDu9/ACmqebPl8xhbhO6rD5tHtkfUMfuSh25HV1vUaAaXJK+bhcVSAHrrEI3G5Jwkgga8t62aqbnSDijOTqS5vZOKBh+BoMETH+/FIaC9M0h1czsHTEHf0eSibeJ54TvMmw2zr4GJZxxaQ+x1XVJMQU8da1ouERG6iliTci17oKG0m05Rn22ZWZzLPrCopwjdnrOuarmAVSbAbffQm8Knn4MIQTdL3KpoGcIjdHec2SFM14uhsl1iksL3GZdGn1DviWa59CTo2kPXaAafm0+ZyI2LxkedEak31EAkf2eQKTlSKFXZgB1Vzby1+QCldW1cMV+KXcnBVirq24jzOEOvw3DFwEUPQMbEQ2uIvfMuFKGPkdaCv6mrhw5y0FJI0PdHEfSOiAjdXKZGdsalhUfoKtp3ugERkbnitcTYLug9Rui2fasI3X7hskfonnirc9ceoYMl6N6k8AtCnyP0aJaLra+hO4bDwCIhxNlCiG1CiJ1CiDt72O4SIYQhhJjbf03UaAafY8am8j/nTo06I1JvjEuPw+OSP60pZoQ+KVs+7qhs4p+ry8hJ8vKVE6Stsqe6hcqmduaOk9Hh3tpDnJijO2Jsgp5is1zUZBjRBD0pV9Z3EU4ZoXf6LbsklIduE1mVh96wX0b8qsBY6Li2rB5XjIy47R56Yq58XrdHRq1qpCiYWS5t4aJpf64mobBfuMIi9FhrEFSkoI9bKEfi5s0Jv0jE9jFCDwl6HwYW2RnqgUVCCCfwAHAOMA24UggxLcp2icC3gRX93UiNZiThcjpCEfnUXBmhp8V7SI/3sHpfHUu3V3PerFzGpMUhBKzeV0cgaLBgvBSTfX0U9FueWcP5f/yQ1fvqom+ghM7hkmmLED4jU1inqM1Dd5rbR43QzU5R+yw9wQ6ZFRPK5HBIUfckhke8zpiupQPSxsvnqhSwOy58gmv73YDatyvWzHAx+xncsbK+DXT10BWRgh6fAV//QEb57sGM0IfecpkP7DQMY7dhGH7gOeDCKNv9Avg14OvH9mk0I5KpuUnEup2hWi8AxdkJvL7pAP5AkPNn5eJxOchLjmXFHlnEanJ2ImnxHvbWRploIgqr9taxsayRS/78Me9urey6gRLG5AJLSJT1AuEReshDN4U/bw7sfFv63VEtF5ugg8w5t5c3cHtlyqL9DkdF8/Y5Sd2xkFRg5ZK77RF6FA8dZBSfXmyNzBTCek9YlotNqCMF3Y7TZX2Ovnro0SL0EdIpmg/Y5ryi1FwWQghxNFBoGMarPe1ICHGjEGKVEGJVdXX1ITdWoxkpfOf0Yh67bp41MhQozkrEMKAgNTY0L+qYtLiQgOcmxzI2Pa5PEXogaFDd3M71J4zD5RDRo3Qlcik2EU8qsDrm4qN56KYFMv9GWXyq0y7opuXSXGltr9apyZkVrtiu87O6vGZGTEQFxfQJshQwmHns5ghUlYfuiujHiE2DnBkRn9UU2LAI3bRSvCmWbdQdKprva4TujhKhdzdS1M5w7xQVQjiA3wG397atYRgPG4Yx1zCMuZmZPVwxNZoRTkFqHMdNSA9bNilbCux5s3JD3vyYNMsWyE32Mi49nr01vUfoB1v8BIIGRRnxpMR5ONgSZTYmd6wUEHtU7nSZFR5F+EClyWfDqT+28t/HnShLE0B4hN5WJ8W38Fi5TImtEQyP0GdfBTMvC2+P09PVQwdIn2gVJFNZL4k5slhYtAj9imfgzHvCl0WL0JVI9xSdh94f5YLQl+2jRujDu1O0DLBdeikwlykSgRnA+0KIvcACYInuGNVowplflE5avIdLj7aET9VT97odpMS5GZseR3lDG49/tKfHeVArG6UoZiV6SYt3U9fi77qREHDqXTDn2vDlqWPlTD/2SDI2FRZ9L3xihgU3yef2CL2lWtoFY4+3linsgn76T2HmpeHHdcWE56HbBV2hRDhtvCy/2+HrWjM+c1LXNETVARzNQ+9L/n4oQu+r5aLSFu0RejcjRe0MdacosBIoFkIUCSE8wBXAErXSMIwGwzAyDMMYZxjGOGA5cIFhGKsGpMUazQhlck4ia358BsXZVpqditBzk2MRQnD2jBym5CTxs39v5tKHPqEzEH2IeHWTFMXspBhS4zwcjCboAAtvh8KISTCmnCf/emPGJVB8Jowxo3El7MIJBWZdeHtGi13QoxHKY28Ln+knmqCnFsnMl2gRejSieugqQu9DyQTPIVou0SJ0IaSo9+ihm4I+VB66YRidwC3AG8AW4AXDMDYJIX4uhLhgQFql0RwhWIIuRWtKThL//fZC/nDFbPbUtPDGJtnZ2d4ZLgChCD3JS1q8h4Ot4YJ+xz/WceeL66MfdMFNcMH9vTfO7ZUFxFTNdSXoeXOsiNhuL9g99Gi4vFaEbhfp9AnWcyWsaUXSw2+p6eqhR8PTQ4Tel5IJyhPvc6eoGikacbFxuIfUQ+9TtUXDMF4DXotY9pNutj358Jul0RwZKEHPSQ4XhvNn5XHfW9t5ZNluYlwObn5mDb9fPJtzZspOy8pGGaFnJsSQFu8Js1x2VDbxj9WlYf58v6CEStktYHU2urzSxukJe5aL3UZJGSMj22BnuOUCMtOlTxG6Kcif20OPk2Ib08faPdFGigJMPA3ye3Cbh4GHrtFoBoiUODdzxqSwoChcDJ0OwVdOLOKzknpueno17Z1BXv7M6rqqavKRFu/B43JIQW/1EwzKAUMPfbAbgIqGttCyfkFF42NP6LosuaDnyTbAVibA17UzMXWcfG63XBR9EfSYBCmW9lRFFe0n9EHQ3XHSbunrQDJ1HGeEoF/xNBy1uPv3hQRdT3Ch0Yw6hBC8/M0Toq679JgCfv/2DlLj3EzJTeLdLVX4OgJ43U4qG9tDZQlS4zwEDVnBscUf4F9ry0iNc1PX2kFNSztZiX0QxL4QkygFSXnqEC7ovRGqzBjFF0+fCLU7rVTDtEMU9AmnyrlK7YKsbJS+ROjjFvY9OofoA4v6QmwqXLsEMooP7X19RAu6RjNMifO4eO1bC0n0uvh0z0FeXV/BJ7trOWVyFlVNPrKTpJikxUtRrW3x898NFQQMg++cPomfLtlERb2v/wR97ldg/MkRoz8PUdADUTx0sDpGlVB64uVI1ubKvonmtAvln52MYplXr8oP98SxN/a+jR2Vr9+X6N+OywPjTzq09xwC2nLRaIYxOcle4mNcHDchnVi3k3e3yMmdq+wRuinodS1+dle3kJvk5ZixUnTL62Vlw+W7awkcrv3iTbaKWilCgt5LhyhIe6KzvWt9FpAXi/N+G27FKB+9L52i0Ugrgtu3WnZOf5I3G761Nrxu/TBAC7pGMwLwup2cWJzBO1sqQ6NEQxF6nBTVgy1+9te1UpAWR16KtC7KG3ysL63nioeX8/rGA/3fMNehROgeWXCrtbarSKdPgHlfDV+mfPRDtTUGC7stNEzQgq7RjBDOmJpNeYOPD7ZXEQgaZCdJUUxLMCP0Vj/7D7ZRmBpHapwbr9tBRX0b60vlZNfrSuv7v1GZU81a7af3vm18lhxpemBD3/xqJZiRA4s03aI9dI1mhHDW9Bx+9MrGUBZLZmJ4hH6goZ3KJh+FaXKQUl5yLBUNPnxmDvsGU9j7FbdX1mrvCyd9X6b11e3tObVPEbJctKD3FS3oGs0IITnOzalTsnh9k7ROVIQe63HidTvYUNaAYUBhqkzXy0uJpay+LTQIaWN5A4ZhfK4a7/2C0w1jFsi/vqAyQQ4l++QIR1suGs0I4uKjrUKnWUlW5JoW5wlZKoW20afl9W1sPdBEotdFk6+z62xIw5nco+Cal/pm52gALegazYji5MlZpMbJEZuZCVbHYmq8J1TfpTBNdojmpcRS1dROc3snXzhK1jnfUDYAtstAMvE0q+65ple0oGs0IwiPy8Hl8wqZkBkfmuYOrFx0j9NBtumt56VYEfyFR+Xhdgo2ljUOboM1g4q+9Gk0I4zvnzWF28+YHLZMCXp+aiwOc1KN3GQZqQsBM/KTmZyTyMaRFqFrDgkdoWs0IwynQ4RF5yCH/4OcDUmhctHHpsURH+NiRl5yqGMU4I1NB9hcHj1ib2jroLk9vN5Imz/Azqrmfvscmv5HC7pGMwpQEXqhrcKislzURNVzxqRQ39rBloomGto6uPWZz/jTezui7u8rT6zktufXhi176INdnPuHD6lv7ab2umbI0YKu0YwC1PB/lbIIshbMWdOzOdcsuXvmtBxcDsG/1pbxhjlZdbTp7vydQdaX1rN0RzW+DqvM66p9B/EHgnywXc8HPFzRgq7RjALU4CKV4aL4y5fmhjJcUuM9nDw5k3+tLecVsxTvvtqWkAWj2FHVREfAwNcRZNVeOfl0MGiwfr/0398x68lohh9a0DWaUcD0vCQKUmOZXZjS43YXzcnnQKOPj3fVkpEQQ4s/QE1zuIVi99U/3CGj8d01zTS1d5LkdfH+tio6upkaTzO0aEHXaEYB4zLiWfaDUylI7XmWotOnZpMQI5Pbvr5IDq1XUbqa9WhzRSOxbifHFqWF7JW1ZnT+tYXjafR1snpf3UB9FM1hoAVdozmC8LqdXDm/kBMmpnP6tGwA9ta28vaWKub98m22VDSyubyRKbmJnDw5i60Hmqhq9LFufz0JMS6+fMI43E7Bu1u17TIc0XnoGs0Rxl3nTQNk56fTIdhX20Jti5/OoMFTy/exuaKRC47KY2FxBr9+HV7bUMG60npmFSST5HUzd2waH++qGeJPoYmGjtA1miMUj8tBfkose2tbWbG7FoB/rNpPk6+TaXlJTMtNYv64NO55dQubyhs5yvTnp+QmsquqJTRfaSBo0NDWQat/YObJ1PQdLegazRHM2PQ41uyrY1d1C2dOy6YjIEV6Wm4SDofgkevmMj0viUDQ4KiCFADGZybQ1hHgQKOP8vo2Zt39Bkf97E3m3fM2Tb6OIfw0Gi3oGs0RTFFGPGXmNHXfOHkCU3IScQiYkiMHIyV53Tz5lWP53y/O5LSpWQBMyJTzfu6ubmHl3oO0+AOcOS2bFn+AfbUjqJrjKEQLukZzBDM2XYpznMfJzPxkfnL+NG4/czKxHmdom+Q4N1cdOwa3U8rFhMwEQKYybipvxONycNPJEwBCFweAVn8nT32yl4Y2HbUPFrpTVKM5ghmXLtMcjxmbitvp4PiJGRw/MaPH92QlxpAQ42JXVTM7qpqZkpMYujCU1UlB317ZxDefXsPOqmY6AgZfOXH4zb85GtERukZzBDMuQwrxsUVpfX6PEILxmfHsqm5hY1kD0/OSQ3OYhuybp1ZT3+onxuVgf522YQYLLegazRHM+Ix4fn3JTL60YNwhvW9CZgKr9h2k0dfJjPwkhBDkp8RSVtdGk6+D3TUtXH9CEePS4ymta+t9h5p+QVsuGs0RjBCCxfPGHPL7JmTG4+uQw/9n5CUDkJ8aR3lDG7uqWwCYmJVAYVos+0fStHcjHB2hazSaQ2a82THqdAgm5yQCkJ/ipayuLVQzvTgrgYLUOErr2roUANMMDFrQNRrNIaMyXYqzEvC6ZUZMfkostS1+NpTW43E6GJMWR0FqLM3tnTrTZZDQgq7RaA6ZselxOARMy0sKLcs3Z0tauqOG8ZnxuJyOULGw/Qe1jz4YaEHXaDSHjNft5NeXzOKmkyaEluWnSPHeU9PCxCwZwav67KV1reytaeGtzZWD39gjCN0pqtFoPheXzS0Me62mvANCgh6K0OtaeWVtGe9sqWLNT84gyesevIYeQegIXaPR9As5SV6cDgFAcZbsKE2OdZPkdbGrqoWl22voDBp8tENXahwotKBrNJp+weV0kJMko/Ti7ITQ8oLUOF7bUEGbOT/pe9t0LfWBok+CLoQ4WwixTQixUwhxZ5T1twkhNgsh1gsh3hFCjO3/pmo0muFOfkosTodgnFkKAKSP3tTeSZzHyelTs3lvW7VOYxwgehV0IYQTeAA4B5gGXCmEmBax2WfAXMMwZgH/BH7T3w3VaDTDnxn5ycwuTMHjsqRF+egnTszg7Bk5VDe1s8mct3RfbQuL//IJlY2+IWnvaKMvEfp8YKdhGLsNw/ADzwEX2jcwDOM9wzDUcLDlQEH/NlOj0YwE7jpvKs/duCBsWaGZznja1CxOmpQJwPum7fLw0t2s2HOQ5eYEG5rDoy+Cng/st70uNZd1xw3Af6OtEELcKIRYJYRYVV1d3fdWajSaEYHTIUJldhUnFmdwbFEaZ07LITMxhmPGpvL0ihJK61p5aU0ZALvM0aWaw6NfO0WFENcAc4F7o603DONhwzDmGoYxNzMzsz8PrdFohikTsxJ5/uvHkRrvAeDH50+jstHHZQ99QltHgHiPk53VWtD7g74IehlgTzgtMJeFIYQ4HbgLuMAwjPb+aZ5GoxltzC5M4cZFE6ho8DF3bCrHTUhnV1XLUDdrVNCXgUUrgWIhRBFSyK8ArrJvIISYA/wFONswDJ2TpNFoeuQ7pxdT1+Jn8fxC3th4QOaoB4K4nDqT+nDo9ewZhtEJ3AK8AWwBXjAMY5MQ4udCiAvMze4FEoB/CCHWCiGWDFiLNRrNiMfrdvLrS2dx9JhUJmQl4A8E2W/WTQ8GDa5//FNe+ayLEaDphT4N/TcM4zXgtYhlP7E9P72f26XRaI4QVJmAXVXNFGXEs6akjve2VeN0OLhoTk/5F5pI9P2NRqMZUlQpXtUxumRdOQCbyhuGrE0jFS3oGo1mSEmOdZOZGGNOKB3k1fUVuJ2CigYftc06v+JQ0IKu0WiGnAmZ8eyqbuajnTXUtvi5ZoGsHrKpvBHDMGho1RNk9AUt6BqNZsiZmJXAxrIG7vjnepK8Lr558kQANpY38PcVJcz737fZpXPVe0XXQ9doNEPOF2blsauqhaRYF2fPkCNKx6TFsbGsga0HmvB3BvnD2zu4/8o5Q93UYY0WdI1GM+QcOz6dZ29MD1s2PS+JtzdX4Q8EmZSdwL/Xl3P2jByeWVHCgvFp3HJq8RC19vMTDBp8uLOGqbmJZCV6e3/DIaItF41GMyyZkZ+MPxAkOdbNk185lgSPi28+vYaPd9Xw27e28+meg0PdxEOmurmdLz/2KW9sPDAg+9eCrtFohiXTzQmoLz2mgJxkLz+9YDqL5xby3vdOpjA1ju/9Yx0t7Z3dvt8wDNburx+S2us+czKPSMrr5eCp3OTYATmuFnSNRjMsWTA+nS8fN5YbF40HpLD/+tJZjE2P5/8uO4qSg608+2lJ2Hve2VLJCrMU7383HuCiBz5i1b66QW13eX0bs+5+kw93dK0oW9Eg677npvS/3QJa0DUazTDF63byswtnkJ3UVfzmF6UxKTuBd7dapaM+3lnD155cxc3PfIavIxAS+x2Vg5sds7OqGX8gyFubKwF4/KM9/OCf6wErQs/TEbpGo9FYnDolm0/3HKTR18GBBh+3PvsZafEeaprbefC9nSzbKSej3lc7uJUcD5hR+Me7ajEMg0c+3MMra8sIBg0qGnx43Q5S4twDcmwt6BqNZkRy2tQsOoMGS7dX88OX1tPWEeC5GxcwPS+J+9/diWFAWryHfbWtve+sH1G2ys6qZj7YXk1ZfRvtnUEONPqoaGgjLzkWIcSAHFsLukajGZHMKUwhJc7Nb9/cznvbqrntjElMzErk6ydNAOCEienMKUxh7wBG6I2+DlbvC8+2OdDYFnp+z6tbQs/31rRQXu8bMP8ctKBrNJoRisvp4KRJmeypaaE4K4EvHz8OgHNn5HDx0fl869RixqTHUXKwdcAyXR5btofLHvqEuhZ/aFlFg4+puUkkeV3srGpmTJqcJHtPbQsVDW0DluECWtA1Gs0I5pwZuQgBP7twemguU5fTwe8un82x49MZlx5Pqz9AdS9Fvv69rpyr/rqcm/6+mmU7avp8/K0VTQQNWLu/PrTsQIOP/JRYFoyXA6WuWTCGGJeDnVXNVDW1k5esI3SNRqPpwlnTs1nxP6dx/ISMqOvHpMvouKQXH/2pT/axoayBD3fU8OD7O/t8fFXy97MSKzWyosFHbrKXkyZn4nIIzpmRy9j0OD7dcxDDgNyUgYvQ9dB/jUYzYhFC9DiEflx6PAB7a1tZu7+e8nofPzpvKg6H1SkZDBpsKm/g0mMKMIAXV5cSCBo4HeEdl/f8ZzNJsW6+dZosOdARCLK3Rvrza0rqAWjzB2ho6yAn2csV88awqDiTwrQ4xqXH89YWmcaYqyN0jUajOXTyU2JxCNhY1sD/vbmNxz7awy9e3Rzmqe+uaaHFH2BmQQpHj0mlxR9ge2VT2H4Mw+DFNaX8c3VpaNm+2hY6gwapcW7W7q8nEDQ40GgOHEr24nQICk3/vCgjHnXIvAGM0LWgazSaUYvH5SA/NZZnPi3B1xHkjGnZPP7RXh75cE9omw1l9QDMzE9mzpgUANaUhI8urW5qp661g5KDraEO0J1V0m65cHY+ze2d7KpupqJBZrjkREThY807BdARukaj0XxuxqbF4+8McvSYFP5yzTGcNzOXX762hX+tlZNQbyhtxOt2MCEznjFpcaTHe1izrz5sH1sPWBH7+jI5NZ4agXrpMQUArNlXFxpUFJnJMi5DRuqJMS4SvQMzqAi0oGs0mlHOWLNj9EvHjcXhEPz28qOYX5TG9/6xjs9K6thY1sD0vGRcTgdCCOaMSeWz/eER+ja7oJsZLTurm8lPiWV6XhKpcW4+K6kPDSrKiShXUJQhI/SBzEEHLegajWaUs2hSJkePSeGcGbmArBHz1y/NJT0+hrte3sjG8gZm5ieHtp8zJoXd1S3Ut1q55dsqm8hIiGFCZjzrSq0IfWJWAkII5o1L452tVeypaSElzk2sxxnWhuxEL163Y0Bz0EELukajGeWcNT2Hl755Al63JbLJcW5+fP40Nlc00uoPMMMm6EePSQXgkQ/3hCap3nagiSk5icwqSGF9aT3BoMHuGinoAF9dOJ6a5nZe+aysS3QO4HAIFs8t5OwZOQP5UbWgazSaI5NzZ+awsFjmr0dG6EePSeFP7+3kxF+/x+byRrZXNjE5J5FZBclUNbWzYs9BfB1Bik1Bn1+UxsLiDDqDRrednj+7cAZXzh8zoJ9JC7pGozkiEUJw76VH8ZPzpzEpOyG03Ot28uJNx/Pqt07E43Lwnec/o70zaAp6CgDXPf4pTofgmLGpoffddsYkoGuGy2CiBV2j0Ryx5CR7+cqJRV2qHwohmJ6XzC2nTGS7mc0yJSeR6XlJpMV7mJKTyMvfPJ7i7MTQe+aMSeV/vziTa48bN5gfIQw9UlSj0Wi64drjx/K3T/ZSVt9GcVYiXreTj+88FY/TETbaVHHVsQNrqfSGFnSNRqPphhiXk/sWz2bNvrpQ5oq9c3W4oQVdo9FoemDeuDTmjUsb6mb0Ce2hazQazShBC7pGo9GMErSgazQazShBC7pGo9GMErSgazQazShBC7pGo9GMErSgazQazShBC7pGo9GMEoR9br1BPbAQ1cC+z/n2DKCmH5szGIy0Nuv2Diy6vQPPSGtzX9s71jCMzGgrhkzQDwchxCrDMOYOdTsOhZHWZt3egUW3d+AZaW3uj/Zqy0Wj0WhGCVrQNRqNZpQwUgX94aFuwOdgpLVZt3dg0e0deEZamw+7vSPSQ9doNBpNV0ZqhK7RaDSaCLSgazQazShhxAm6EOJsIcQ2IcROIcSdQ92eSIQQhUKI94QQm4UQm4QQ3zaX3y2EKBNCrDX/zh3qtiqEEHuFEBvMdq0yl6UJId4SQuwwH1N7289gIISYbDuHa4UQjUKI7wy38yuEeEwIUSWE2GhbFvWcCsn95nd6vRDi6GHS3nuFEFvNNr0shEgxl48TQrTZzvVDw6S93X4HhBA/NM/vNiHEWYPd3h7a/LytvXuFEGvN5Z/vHBuGMWL+ACewCxgPeIB1wLShbldEG3OBo83nicB2YBpwN/C9oW5fN23eC2RELPsNcKf5/E7g10Pdzm6+DweAscPt/AKLgKOBjb2dU+Bc4L+AABYAK4ZJe88EXObzX9vaO86+3TA6v1G/A+bvbx0QAxSZGuIcDm2OWP9b4CeHc45HWoQ+H9hpGMZuwzD8wHPAhUPcpjAMw6gwDGON+bwJ2ALkD22rPhcXAn8zn/8NuGjomtItpwG7DMP4vCOOBwzDMJYCByMWd3dOLwSeNCTLgRQhRO6gNNQkWnsNw3jTMIxO8+VyoGAw29QT3Zzf7rgQeM4wjHbDMPYAO5FaMqj01GYhhAAuB549nGOMNEHPB/bbXpcyjMVSCDEOmAOsMBfdYt6+PjZcLAwTA3hTCLFaCHGjuSzbMIwK8/kBIHtomtYjVxD+Axiu51fR3TkdCd/rryDvIhRFQojPhBAfCCEWDlWjohDtOzASzu9CoNIwjB22ZYd8jkeaoI8YhBAJwIvAdwzDaAT+DEwAZgMVyNur4cKJhmEcDZwD3CyEWGRfach7wGGV3yqE8AAXAP8wFw3n89uF4XhOu0MIcRfQCTxtLqoAxhiGMQe4DXhGCJE0VO2zMaK+AxFcSXhw8rnO8UgT9DKg0Pa6wFw2rBBCuJFi/rRhGC8BGIZRaRhGwDCMIPBXhuCWrzsMwygzH6uAl5Ftq1S3/eZj1dC1MCrnAGsMw6iE4X1+bXR3Toft91oIcR1wPnC1eRHCtC5qzeerkZ70pCFrpEkP34Fhe34BhBAu4GLgebXs857jkSboK4FiIUSRGaFdASwZ4jaFYXphjwJbDMP4nW253RP9IrAx8r1DgRAiXgiRqJ4jO8I2Is/rl83Nvgz8a2ha2C1hEc1wPb8RdHdOlwDXmtkuC4AGmzUzZAghzga+D1xgGEarbXmmEMJpPh8PFAO7h6aVFj18B5YAVwghYoQQRcj2fjrY7euB04GthmGUqgWf+xwPdk9vP/QUn4vMHNkF3DXU7YnSvhORt9LrgbXm37nAU8AGc/kSIHeo22q2dzwyA2AdsEmdUyAdeAfYAbwNpA11W21tjgdqgWTbsmF1fpEXmwqgA+nZ3tDdOUVmtzxgfqc3AHOHSXt3Ir1n9T1+yNz2EvO7shZYA3xhmLS32+8AcJd5frcB5wyX74S5/AngGxHbfq5zrIf+azQazShhpFkuGo1Go+kGLegajUYzStCCrtFoNKMELegajUYzStCCrtFoNKMELegajUYzStCCrtFoNKOE/w/dADJc1wZQBAAAAABJRU5ErkJggg==\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["history_frame = pd.DataFrame(history.history)\n","history_frame.loc[:, ['loss','val_loss']].plot()\n","history_frame.loc[:, ['accuracy','val_accuracy']].plot();"]},{"cell_type":"code","execution_count":19,"id":"f6f8288e","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:48.010423Z","iopub.status.busy":"2022-09-13T02:00:48.009855Z","iopub.status.idle":"2022-09-13T02:00:48.43634Z","shell.execute_reply":"2022-09-13T02:00:48.435511Z"},"papermill":{"duration":0.516457,"end_time":"2022-09-13T02:00:48.438499","exception":false,"start_time":"2022-09-13T02:00:47.922042","status":"completed"},"tags":[]},"outputs":[],"source":["pred = model.predict(x_test)\n","\n","for i in range(len(pred)):\n","    if pred[i] > 0.5:\n","        pred[i] = 1\n","    else:\n","        pred[i] = 0\n","\n","pred = pred.astype(int)"]},{"cell_type":"code","execution_count":20,"id":"df86a602","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:48.528266Z","iopub.status.busy":"2022-09-13T02:00:48.527404Z","iopub.status.idle":"2022-09-13T02:00:48.537991Z","shell.execute_reply":"2022-09-13T02:00:48.536495Z"},"papermill":{"duration":0.058847,"end_time":"2022-09-13T02:00:48.540291","exception":false,"start_time":"2022-09-13T02:00:48.481444","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.90      0.79      0.84        24\n","           1       0.76      0.89      0.82        18\n","\n","    accuracy                           0.83        42\n","   macro avg       0.83      0.84      0.83        42\n","weighted avg       0.84      0.83      0.83        42\n","\n"]}],"source":["#creating a classification report\n","print(classification_report(y_test, pred))"]},{"cell_type":"markdown","id":"bd338b11","metadata":{"editable":false,"papermill":{"duration":0.055146,"end_time":"2022-09-13T02:00:48.649679","exception":false,"start_time":"2022-09-13T02:00:48.594533","status":"completed"},"tags":[]},"source":["## 4.4 Adding a Pretrained Model"]},{"cell_type":"markdown","id":"02489b95","metadata":{"editable":false,"papermill":{"duration":0.043511,"end_time":"2022-09-13T02:00:48.744097","exception":false,"start_time":"2022-09-13T02:00:48.700586","status":"completed"},"tags":[]},"source":["First we will load a pretrained model named vgg16."]},{"cell_type":"code","execution_count":21,"id":"b0bf1c94","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:48.831602Z","iopub.status.busy":"2022-09-13T02:00:48.830531Z","iopub.status.idle":"2022-09-13T02:00:50.203234Z","shell.execute_reply":"2022-09-13T02:00:50.202381Z"},"papermill":{"duration":1.418942,"end_time":"2022-09-13T02:00:50.20537","exception":false,"start_time":"2022-09-13T02:00:48.786428","status":"completed"},"tags":[]},"outputs":[],"source":["# load base model\n","vgg16_weight_path = '../input/keras-pretrained-models/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n","base_model = VGG16(\n","    weights=vgg16_weight_path,\n","    include_top=False, \n","    input_shape=(128,128,3)\n",")"]},{"cell_type":"markdown","id":"cc91f550","metadata":{"editable":false,"papermill":{"duration":0.0444,"end_time":"2022-09-13T02:00:50.293667","exception":false,"start_time":"2022-09-13T02:00:50.249267","status":"completed"},"tags":[]},"source":["Now we will replace our convolutional blocks with the vgg16 model."]},{"cell_type":"code","execution_count":22,"id":"e5cb37b7","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:50.383385Z","iopub.status.busy":"2022-09-13T02:00:50.382949Z","iopub.status.idle":"2022-09-13T02:00:50.396834Z","shell.execute_reply":"2022-09-13T02:00:50.39613Z"},"papermill":{"duration":0.061482,"end_time":"2022-09-13T02:00:50.398733","exception":false,"start_time":"2022-09-13T02:00:50.337251","status":"completed"},"tags":[]},"outputs":[],"source":["model = keras.Sequential([\n","    \n","    preprocessing.RandomFlip('horizontal'), # flip left-to-right\n","    \n","    base_model, #vgg16\n","    \n","    layers.Flatten(),\n","    layers.Dropout(.25),\n","    layers.Dense(units=256, activation=\"relu\"),\n","    layers.Dense(units=1, activation=\"sigmoid\"),\n","])\n"]},{"cell_type":"code","execution_count":23,"id":"8085e1f5","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:50.495134Z","iopub.status.busy":"2022-09-13T02:00:50.494728Z","iopub.status.idle":"2022-09-13T02:00:50.506017Z","shell.execute_reply":"2022-09-13T02:00:50.505229Z"},"papermill":{"duration":0.06423,"end_time":"2022-09-13T02:00:50.507904","exception":false,"start_time":"2022-09-13T02:00:50.443674","status":"completed"},"tags":[]},"outputs":[],"source":["model.compile(\n","    optimizer=tf.keras.optimizers.Adam(epsilon=0.01),\n","    loss='binary_crossentropy',\n","    metrics=['accuracy']\n",")\n","\n","#including early stopping to revent overfitting\n","early_stopping = keras.callbacks.EarlyStopping(\n","    patience=40,\n","    min_delta=0.001,\n","    restore_best_weights=True,\n",")"]},{"cell_type":"code","execution_count":24,"id":"fc1dce4f","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:00:50.597121Z","iopub.status.busy":"2022-09-13T02:00:50.596747Z","iopub.status.idle":"2022-09-13T02:01:13.636856Z","shell.execute_reply":"2022-09-13T02:01:13.635831Z"},"papermill":{"duration":23.086925,"end_time":"2022-09-13T02:01:13.63926","exception":false,"start_time":"2022-09-13T02:00:50.552335","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/200\n","2/2 - 8s - loss: 0.8057 - accuracy: 0.4639 - val_loss: 0.7853 - val_accuracy: 0.4286\n","Epoch 2/200\n","2/2 - 0s - loss: 0.6503 - accuracy: 0.6082 - val_loss: 0.5912 - val_accuracy: 0.6667\n","Epoch 3/200\n","2/2 - 0s - loss: 0.6208 - accuracy: 0.5979 - val_loss: 0.5446 - val_accuracy: 0.7619\n","Epoch 4/200\n","2/2 - 0s - loss: 0.5454 - accuracy: 0.7835 - val_loss: 0.7038 - val_accuracy: 0.5476\n","Epoch 5/200\n","2/2 - 0s - loss: 0.5606 - accuracy: 0.7216 - val_loss: 0.4818 - val_accuracy: 0.7619\n","Epoch 6/200\n","2/2 - 0s - loss: 0.5187 - accuracy: 0.7423 - val_loss: 0.4623 - val_accuracy: 0.8095\n","Epoch 7/200\n","2/2 - 0s - loss: 0.4317 - accuracy: 0.7938 - val_loss: 0.5594 - val_accuracy: 0.6667\n","Epoch 8/200\n","2/2 - 0s - loss: 0.3944 - accuracy: 0.8247 - val_loss: 0.3472 - val_accuracy: 0.8333\n","Epoch 9/200\n","2/2 - 0s - loss: 0.4456 - accuracy: 0.7732 - val_loss: 0.4977 - val_accuracy: 0.6667\n","Epoch 10/200\n","2/2 - 0s - loss: 0.4178 - accuracy: 0.7938 - val_loss: 0.2775 - val_accuracy: 0.9524\n","Epoch 11/200\n","2/2 - 0s - loss: 0.3848 - accuracy: 0.8454 - val_loss: 0.2904 - val_accuracy: 0.9524\n","Epoch 12/200\n","2/2 - 0s - loss: 0.2976 - accuracy: 0.8763 - val_loss: 0.3894 - val_accuracy: 0.7143\n","Epoch 13/200\n","2/2 - 0s - loss: 0.3186 - accuracy: 0.8247 - val_loss: 0.2342 - val_accuracy: 0.9286\n","Epoch 14/200\n","2/2 - 0s - loss: 0.1932 - accuracy: 0.9485 - val_loss: 0.4408 - val_accuracy: 0.7381\n","Epoch 15/200\n","2/2 - 0s - loss: 0.1970 - accuracy: 0.9072 - val_loss: 0.1619 - val_accuracy: 0.9762\n","Epoch 16/200\n","2/2 - 0s - loss: 0.1616 - accuracy: 0.9485 - val_loss: 0.3159 - val_accuracy: 0.8571\n","Epoch 17/200\n","2/2 - 0s - loss: 0.1101 - accuracy: 0.9588 - val_loss: 0.1252 - val_accuracy: 0.9762\n","Epoch 18/200\n","2/2 - 0s - loss: 0.0921 - accuracy: 0.9691 - val_loss: 0.1281 - val_accuracy: 0.9286\n","Epoch 19/200\n","2/2 - 0s - loss: 0.0425 - accuracy: 0.9794 - val_loss: 0.2536 - val_accuracy: 0.8333\n","Epoch 20/200\n","2/2 - 0s - loss: 0.0569 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9286\n","Epoch 21/200\n","2/2 - 0s - loss: 0.0911 - accuracy: 0.9588 - val_loss: 1.1437 - val_accuracy: 0.6190\n","Epoch 22/200\n","2/2 - 0s - loss: 0.6227 - accuracy: 0.7320 - val_loss: 0.1300 - val_accuracy: 0.9524\n","Epoch 23/200\n","2/2 - 0s - loss: 0.1627 - accuracy: 0.9588 - val_loss: 0.8335 - val_accuracy: 0.6429\n","Epoch 24/200\n","2/2 - 0s - loss: 0.3715 - accuracy: 0.8041 - val_loss: 0.2796 - val_accuracy: 0.8571\n","Epoch 25/200\n","2/2 - 0s - loss: 0.3298 - accuracy: 0.8351 - val_loss: 0.2512 - val_accuracy: 0.9524\n","Epoch 26/200\n","2/2 - 0s - loss: 0.1971 - accuracy: 0.9381 - val_loss: 0.5573 - val_accuracy: 0.6190\n","Epoch 27/200\n","2/2 - 0s - loss: 0.2631 - accuracy: 0.8660 - val_loss: 0.2451 - val_accuracy: 0.9524\n","Epoch 28/200\n","2/2 - 0s - loss: 0.1338 - accuracy: 0.9691 - val_loss: 0.2269 - val_accuracy: 0.9286\n","Epoch 29/200\n","2/2 - 0s - loss: 0.1156 - accuracy: 0.9485 - val_loss: 0.3017 - val_accuracy: 0.8333\n","Epoch 30/200\n","2/2 - 0s - loss: 0.0981 - accuracy: 0.9485 - val_loss: 0.2009 - val_accuracy: 0.9048\n","Epoch 31/200\n","2/2 - 0s - loss: 0.0318 - accuracy: 0.9897 - val_loss: 0.1879 - val_accuracy: 0.9286\n","Epoch 32/200\n","2/2 - 0s - loss: 0.0713 - accuracy: 0.9691 - val_loss: 0.1471 - val_accuracy: 0.9286\n","Epoch 33/200\n","2/2 - 0s - loss: 0.0313 - accuracy: 0.9897 - val_loss: 0.2871 - val_accuracy: 0.8095\n","Epoch 34/200\n","2/2 - 0s - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.5007 - val_accuracy: 0.7857\n","Epoch 35/200\n","2/2 - 0s - loss: 0.0304 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9048\n","Epoch 36/200\n","2/2 - 0s - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.1340 - val_accuracy: 0.9286\n","Epoch 37/200\n","2/2 - 0s - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.1456 - val_accuracy: 0.9286\n","Epoch 38/200\n","2/2 - 0s - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.1310 - val_accuracy: 0.9286\n","Epoch 39/200\n","2/2 - 0s - loss: 0.0119 - accuracy: 0.9897 - val_loss: 0.1703 - val_accuracy: 0.9048\n","Epoch 40/200\n","2/2 - 0s - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.5175 - val_accuracy: 0.7857\n","Epoch 41/200\n","2/2 - 0s - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.5270 - val_accuracy: 0.8333\n","Epoch 42/200\n","2/2 - 0s - loss: 0.0052 - accuracy: 1.0000 - val_loss: 0.2932 - val_accuracy: 0.8571\n","Epoch 43/200\n","2/2 - 0s - loss: 3.8922e-04 - accuracy: 1.0000 - val_loss: 0.1743 - val_accuracy: 0.8810\n","Epoch 44/200\n","2/2 - 0s - loss: 3.7856e-04 - accuracy: 1.0000 - val_loss: 0.1319 - val_accuracy: 0.9286\n","Epoch 45/200\n","2/2 - 0s - loss: 5.7842e-04 - accuracy: 1.0000 - val_loss: 0.1255 - val_accuracy: 0.9524\n","Epoch 46/200\n","2/2 - 0s - loss: 4.0785e-04 - accuracy: 1.0000 - val_loss: 0.1267 - val_accuracy: 0.9524\n","Epoch 47/200\n","2/2 - 0s - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9524\n","Epoch 48/200\n","2/2 - 0s - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1349 - val_accuracy: 0.9286\n","Epoch 49/200\n","2/2 - 0s - loss: 3.1155e-04 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9286\n","Epoch 50/200\n","2/2 - 0s - loss: 2.7951e-04 - accuracy: 1.0000 - val_loss: 0.1635 - val_accuracy: 0.9286\n","Epoch 51/200\n","2/2 - 0s - loss: 1.3571e-04 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9286\n","Epoch 52/200\n","2/2 - 0s - loss: 2.4342e-04 - accuracy: 1.0000 - val_loss: 0.1836 - val_accuracy: 0.9286\n","Epoch 53/200\n","2/2 - 0s - loss: 3.6312e-04 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9286\n","Epoch 54/200\n","2/2 - 0s - loss: 1.8249e-04 - accuracy: 1.0000 - val_loss: 0.1816 - val_accuracy: 0.9286\n","Epoch 55/200\n","2/2 - 0s - loss: 1.1598e-04 - accuracy: 1.0000 - val_loss: 0.1785 - val_accuracy: 0.9286\n","Epoch 56/200\n","2/2 - 0s - loss: 1.2506e-04 - accuracy: 1.0000 - val_loss: 0.1748 - val_accuracy: 0.9286\n","Epoch 57/200\n","2/2 - 0s - loss: 9.3754e-05 - accuracy: 1.0000 - val_loss: 0.1705 - val_accuracy: 0.9524\n"]}],"source":["history = model.fit(\n","    x = x_train,\n","    y = y_train,\n","    validation_data= (x_test,y_test),\n","    batch_size = 64,\n","    epochs=200,\n","    callbacks=[early_stopping],\n","    verbose=(2),\n",")"]},{"cell_type":"code","execution_count":25,"id":"680f4f94","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:01:13.750879Z","iopub.status.busy":"2022-09-13T02:01:13.750297Z","iopub.status.idle":"2022-09-13T02:01:32.92545Z","shell.execute_reply":"2022-09-13T02:01:32.924635Z"},"papermill":{"duration":19.233683,"end_time":"2022-09-13T02:01:32.927434","exception":false,"start_time":"2022-09-13T02:01:13.693751","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["history_frame = pd.DataFrame(history.history)\n","history_frame.loc[:, ['loss','val_loss']].plot()\n","history_frame.loc[:, ['accuracy','val_accuracy']].plot();"]},{"cell_type":"code","execution_count":26,"id":"15fd1559","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:01:33.028863Z","iopub.status.busy":"2022-09-13T02:01:33.028009Z","iopub.status.idle":"2022-09-13T02:01:34.723574Z","shell.execute_reply":"2022-09-13T02:01:34.722711Z"},"papermill":{"duration":1.748947,"end_time":"2022-09-13T02:01:34.726039","exception":false,"start_time":"2022-09-13T02:01:32.977092","status":"completed"},"tags":[]},"outputs":[],"source":["pred = model.predict(x_test)\n","\n","for i in range(len(pred)):\n","    if pred[i] > 0.5:\n","        pred[i] = 1\n","    else:\n","        pred[i] = 0\n","\n","pred = pred.astype(int)"]},{"cell_type":"code","execution_count":27,"id":"e26497d6","metadata":{"editable":false,"execution":{"iopub.execute_input":"2022-09-13T02:01:34.825265Z","iopub.status.busy":"2022-09-13T02:01:34.824882Z","iopub.status.idle":"2022-09-13T02:01:34.834335Z","shell.execute_reply":"2022-09-13T02:01:34.833148Z"},"papermill":{"duration":0.061958,"end_time":"2022-09-13T02:01:34.837087","exception":false,"start_time":"2022-09-13T02:01:34.775129","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.96      1.00      0.98        24\n","           1       1.00      0.94      0.97        18\n","\n","    accuracy                           0.98        42\n","   macro avg       0.98      0.97      0.98        42\n","weighted avg       0.98      0.98      0.98        42\n","\n"]}],"source":["#creating a classification report\n","print(classification_report(y_test, pred))"]},{"cell_type":"markdown","id":"8f2920f7","metadata":{"editable":false,"papermill":{"duration":0.049483,"end_time":"2022-09-13T02:01:34.936507","exception":false,"start_time":"2022-09-13T02:01:34.887024","status":"completed"},"tags":[]},"source":["# 5. Results"]},{"cell_type":"markdown","id":"86053b4a","metadata":{"editable":false,"papermill":{"duration":0.048661,"end_time":"2022-09-13T02:01:35.034473","exception":false,"start_time":"2022-09-13T02:01:34.985812","status":"completed"},"tags":[]},"source":["## 5.1 Results Table"]},{"cell_type":"markdown","id":"da34b5e5","metadata":{"editable":false,"papermill":{"duration":0.048621,"end_time":"2022-09-13T02:01:35.131229","exception":false,"start_time":"2022-09-13T02:01:35.082608","status":"completed"},"tags":[]},"source":["The idea we set out with was to start with a baseline model and add to that model and improve the accuracy with each step. Here is the results of each step:\n","\n","Model                      |     Accuracy\n","---------------------------|---------------\n","Baseline                   |     86%\n","Adding Data Agumentation   |     89%\n","Adding Pretrained Model    |     98%"]},{"cell_type":"markdown","id":"25b5ef40","metadata":{"editable":false,"papermill":{"duration":0.049177,"end_time":"2022-09-13T02:01:35.228893","exception":false,"start_time":"2022-09-13T02:01:35.179716","status":"completed"},"tags":[]},"source":["# 6. Conclusion"]},{"cell_type":"markdown","id":"b4c5c8a2","metadata":{"editable":false,"papermill":{"duration":0.048778,"end_time":"2022-09-13T02:01:35.32591","exception":false,"start_time":"2022-09-13T02:01:35.277132","status":"completed"},"tags":[]},"source":["## 6.1 Summary"]},{"cell_type":"markdown","id":"ecf0bcb7","metadata":{"editable":false,"papermill":{"duration":0.049022,"end_time":"2022-09-13T02:01:35.424245","exception":false,"start_time":"2022-09-13T02:01:35.375223","status":"completed"},"tags":[]},"source":["We set out with the task of coming up with a model that could predict if a brain MRI image has a tumor or not. We used the keras libary to create a convolutional neural network. We used that as a baseline. From there we added data agumentation to add data. We then added a pretrained model to get our final results. "]},{"cell_type":"markdown","id":"21a66019","metadata":{"editable":false,"papermill":{"duration":0.049194,"end_time":"2022-09-13T02:01:35.522238","exception":false,"start_time":"2022-09-13T02:01:35.473044","status":"completed"},"tags":[]},"source":["## 6.2 Brief Thoughts on Results"]},{"cell_type":"markdown","id":"7726aa27","metadata":{"editable":false,"papermill":{"duration":0.049008,"end_time":"2022-09-13T02:01:35.672029","exception":false,"start_time":"2022-09-13T02:01:35.623021","status":"completed"},"tags":[]},"source":["I was pleasantly surprised by the results. I was not sure if we would be able to get an accuracy above 90% with such a small dataset. The transfer learning worked really well!"]},{"cell_type":"markdown","id":"009b53c6","metadata":{"editable":false,"papermill":{"duration":0.049007,"end_time":"2022-09-13T02:01:35.769917","exception":false,"start_time":"2022-09-13T02:01:35.72091","status":"completed"},"tags":[]},"source":["## 6.3 Future Work"]},{"cell_type":"markdown","id":"0d200a33","metadata":{"editable":false,"papermill":{"duration":0.048785,"end_time":"2022-09-13T02:01:35.867435","exception":false,"start_time":"2022-09-13T02:01:35.81865","status":"completed"},"tags":[]},"source":["Next step would be to apply this model to a much larger dataset. A much larger data set would allow the model to train off of more cases allowing the model to generalize better. Another step would be to include the different types of tumors and their stages, and to focus on early detection."]}],"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":"3.7.12"},"papermill":{"default_parameters":{},"duration":117.432233,"end_time":"2022-09-13T02:01:39.741731","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-09-13T01:59:42.309498","version":"2.3.4"}},"nbformat":4,"nbformat_minor":5}