{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":45867,"databundleVersionId":6924515,"sourceType":"competition"}],"dockerImageVersionId":30558,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"
\n

Application for Ovarian Cancer Subtype Classification

\n\n## Actuality\nOvarian carcinoma is the most lethal cancer of the female reproductive system. There are five common subtypes of ovarian cancer: high-grade serous carcinoma, clear-cell ovarian carcinoma, endometrioid, low-grade serous, and mucinous carcinoma. Recently histological diagnosis have several challenges, including disagreements between observers and the reproducibility of diagnostics. Furthermore, underserved communities often lack access to specialist pathologists, and even well-developed communities face a shortage of pathologists with expertise in gynecologic malignancies.\n\n## The aim of the project\nto improve accuracy in identifying ovarian cancer subtypes with histological images using Deep Learning algorithms \n","metadata":{}},{"cell_type":"markdown","source":"
\n

Quick navigation

\n\n* [0. Installation of libraries](#0)\n* [1. Basic Data Overview](#1)\n* [2. Transformation of data](#2)\n* [3. Convolutional networks](#3)\n* [4. Conclusions](#4)\n ","metadata":{}},{"cell_type":"markdown","source":"\n

0. Installation of libraries

","metadata":{}},{"cell_type":"code","source":"#pip install opencv-python","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:29.891312Z","iopub.execute_input":"2024-01-08T17:24:29.891575Z","iopub.status.idle":"2024-01-08T17:24:29.896591Z","shell.execute_reply.started":"2024-01-08T17:24:29.89155Z","shell.execute_reply":"2024-01-08T17:24:29.895634Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#pip install rembg","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:29.898263Z","iopub.execute_input":"2024-01-08T17:24:29.89853Z","iopub.status.idle":"2024-01-08T17:24:29.906333Z","shell.execute_reply.started":"2024-01-08T17:24:29.898507Z","shell.execute_reply":"2024-01-08T17:24:29.905554Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport os\nimport pandas as pd\nimport sklearn\nimport matplotlib.pyplot as plt\nimport imageio\nimport plotly.express as px\nfrom skimage import io \nfrom PIL import Image\nimport cv2\n\nimport tensorflow as tf\nimport tensorflow.keras as keras\nimport tensorflow.keras.models as M\nimport tensorflow.keras.layers as L\nimport tensorflow.keras.backend as K\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Activation,Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.optimizers import Adam","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:29.907196Z","iopub.execute_input":"2024-01-08T17:24:29.907453Z","iopub.status.idle":"2024-01-08T17:24:42.098504Z","shell.execute_reply.started":"2024-01-08T17:24:29.90743Z","shell.execute_reply":"2024-01-08T17:24:42.09678Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

1. Basic Data Overview

","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/UBC-OCEAN/train.csv')\ndisplay(train_df.head(),\n train_df.info(), \n 'Missing values',\n train_df.isna().sum())","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:42.102564Z","iopub.execute_input":"2024-01-08T17:24:42.103943Z","iopub.status.idle":"2024-01-08T17:24:42.176228Z","shell.execute_reply.started":"2024-01-08T17:24:42.103866Z","shell.execute_reply":"2024-01-08T17:24:42.175123Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig=px.histogram(train_df.groupby(['label','is_tma'],as_index=False)['is_tma'].value_counts(),\n x='label', \n y='count',\n color='is_tma',\n text_auto=True,\n title='Distribution of histological types'\n )\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:42.177547Z","iopub.execute_input":"2024-01-08T17:24:42.177917Z","iopub.status.idle":"2024-01-08T17:24:43.780874Z","shell.execute_reply.started":"2024-01-08T17:24:42.177876Z","shell.execute_reply":"2024-01-08T17:24:43.779931Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Because of the limit of memory the work will be proceeded with thumbnail files","metadata":{}},{"cell_type":"code","source":"train_df['filename'] = train_df['image_id'].astype(str)+'_thumbnail.png'\nfile_paths = {}\nfor root, _, files in os.walk('/kaggle/input/UBC-OCEAN/train_thumbnails'):\n for filename in files:\n path = os.path.join(root, filename) \n file_paths[filename]=path\n \ntrain_df['path'] = train_df['filename'].map(file_paths)\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:43.782127Z","iopub.execute_input":"2024-01-08T17:24:43.78276Z","iopub.status.idle":"2024-01-08T17:24:43.97948Z","shell.execute_reply.started":"2024-01-08T17:24:43.782729Z","shell.execute_reply":"2024-01-08T17:24:43.978526Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.dropna(axis=0, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:43.980508Z","iopub.execute_input":"2024-01-08T17:24:43.9808Z","iopub.status.idle":"2024-01-08T17:24:43.987658Z","shell.execute_reply.started":"2024-01-08T17:24:43.980775Z","shell.execute_reply":"2024-01-08T17:24:43.986688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig=px.histogram(train_df.groupby(['label','is_tma'],as_index=False)['is_tma'].value_counts(),\n x='label', \n y='count',\n color='is_tma',\n text_auto=True,\n title='Distribution of histological types'\n )\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:43.988584Z","iopub.execute_input":"2024-01-08T17:24:43.988893Z","iopub.status.idle":"2024-01-08T17:24:44.065421Z","shell.execute_reply.started":"2024-01-08T17:24:43.988853Z","shell.execute_reply":"2024-01-08T17:24:44.064527Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that the data is imbalanced, which can be a problem for further modelling","metadata":{}},{"cell_type":"code","source":"import matplotlib.image as mpimg\n\nlabels = train_df['label'].unique()\nimage_paths = []\n\nfor label in labels:\n path = train_df[train_df['label'] == label].sample(n=1, random_state=42)['path'].values[0]\n image_paths.append(path)\nfig, axes = plt.subplots(1, len(image_paths), figsize=(20, 5))\n\nfor i, path in enumerate(image_paths):\n img = mpimg.imread(path)\n axes[i].imshow(img)\n axes[i].set_title(labels[i])\n axes[i].axis('off')\n\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:44.068639Z","iopub.execute_input":"2024-01-08T17:24:44.068929Z","iopub.status.idle":"2024-01-08T17:24:52.621948Z","shell.execute_reply.started":"2024-01-08T17:24:44.068906Z","shell.execute_reply":"2024-01-08T17:24:52.621077Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We see that the format of pictures and of slices are variable, which can be also a cause of bad predictions.","metadata":{}},{"cell_type":"markdown","source":"## First experiments (werent included)\nThe first modelling with basic augmentation, artificial networks with different parameters (activation (sigmoid/relu) and optimizing (adam/sgd) functions) showed very low accuracy - just 43% were detected correctly:\n\naccuracy: 0.4208\n\nval_accuracy: 0.4297\n\nThe data and the model should be improved","metadata":{}},{"cell_type":"markdown","source":"\n

2. Transformation of data

","metadata":{}},{"cell_type":"markdown","source":"# 0.Decrease the images size","metadata":{}},{"cell_type":"code","source":"def small(image,n):\n height, width, _ = image.shape\n new_width = int(width/n) # specify the new width\n new_height = int(height * (new_width / width)) # calculate the new height to maintain the aspect ratio\n resized_img = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)\n return resized_img","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:52.623161Z","iopub.execute_input":"2024-01-08T17:24:52.623512Z","iopub.status.idle":"2024-01-08T17:24:52.629618Z","shell.execute_reply.started":"2024-01-08T17:24:52.623479Z","shell.execute_reply":"2024-01-08T17:24:52.628688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output_folder = '/kaggle/working/train_small/'\nos.makedirs(output_folder, exist_ok=True)\ntrain_df.reset_index(inplace=True)\n\nfor i in range(train_df.shape[0]):\n input_path = train_df.loc[i, 'path']\n image = cv2.imread(input_path)\n resized_img = small(image,2)\n output_path = os.path.join(output_folder, train_df.loc[i, 'filename'])\n cv2.imwrite(output_path, resized_img)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:24:52.630865Z","iopub.execute_input":"2024-01-08T17:24:52.631159Z","iopub.status.idle":"2024-01-08T17:27:12.279069Z","shell.execute_reply.started":"2024-01-08T17:24:52.631133Z","shell.execute_reply":"2024-01-08T17:27:12.278085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_small=train_df.copy()\nfile_paths = {}\nfor root, _, files in os.walk('/kaggle/working/train_small/'):\n for filename in files:\n path = os.path.join(root, filename) \n file_paths[filename]=path\n \ntrain_small['path'] = train_small['filename'].map(file_paths)\ntrain_small.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:27:12.280387Z","iopub.execute_input":"2024-01-08T17:27:12.281087Z","iopub.status.idle":"2024-01-08T17:27:12.300181Z","shell.execute_reply.started":"2024-01-08T17:27:12.281051Z","shell.execute_reply":"2024-01-08T17:27:12.299368Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1. Transformtion of doubled pictures","metadata":{}},{"cell_type":"markdown","source":"## 1.1. Selection of doubled images in dataset","metadata":{}},{"cell_type":"code","source":"def plot40(image_paths,image_ids):\n num_rows = 5\n num_cols = 8\n fig, axes = plt.subplots(num_rows, num_cols, figsize=(20, 10))\n for i, ax in enumerate(axes.flat):\n if i < len(image_paths):\n img_path = image_paths[i]\n img_id = image_ids[i]\n img = mpimg.imread(img_path)\n ax.imshow(img)\n ax.set_title(f\"Image ID: {img_id}\")\n ax.axis('off')\n else:\n ax.axis('off')\n plt.tight_layout()\n plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:27:12.301401Z","iopub.execute_input":"2024-01-08T17:27:12.302116Z","iopub.status.idle":"2024-01-08T17:27:12.308431Z","shell.execute_reply.started":"2024-01-08T17:27:12.302084Z","shell.execute_reply":"2024-01-08T17:27:12.307486Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mc_df = train_small[train_small['label'] == 'MC']\nimage_paths = mc_df['path'].unique()\nimage_ids = mc_df['image_id'].unique()\nplot40(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:27:12.309518Z","iopub.execute_input":"2024-01-08T17:27:12.309778Z","iopub.status.idle":"2024-01-08T17:27:26.529483Z","shell.execute_reply.started":"2024-01-08T17:27:12.309756Z","shell.execute_reply":"2024-01-08T17:27:26.528334Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"lgsc_df = train_small[train_small['label'] == 'LGSC']\nimage_paths = lgsc_df['path'].unique()\nimage_ids = lgsc_df['image_id'].unique()\nplot40(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:27:26.530811Z","iopub.execute_input":"2024-01-08T17:27:26.531128Z","iopub.status.idle":"2024-01-08T17:27:39.128641Z","shell.execute_reply.started":"2024-01-08T17:27:26.5311Z","shell.execute_reply":"2024-01-08T17:27:39.127656Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hgsc_df = train_small[train_small['label'] == 'HGSC']\nimage_paths = hgsc_df['path'].unique()\nimage_ids = hgsc_df['image_id'].unique()\nplot40(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:27:39.129976Z","iopub.execute_input":"2024-01-08T17:27:39.13027Z","iopub.status.idle":"2024-01-08T17:27:51.986411Z","shell.execute_reply.started":"2024-01-08T17:27:39.130245Z","shell.execute_reply":"2024-01-08T17:27:51.985449Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cc_df = train_small[train_small['label'] == 'CC']\nimage_paths = cc_df['path'].unique()\nimage_ids = cc_df['image_id'].unique()\nplot40(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:27:51.987578Z","iopub.execute_input":"2024-01-08T17:27:51.987883Z","iopub.status.idle":"2024-01-08T17:28:04.197278Z","shell.execute_reply.started":"2024-01-08T17:27:51.987857Z","shell.execute_reply":"2024-01-08T17:28:04.196322Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ec_df = train_small[train_small['label'] == 'EC']\nimage_paths = ec_df['path'].unique()\nimage_ids = ec_df['image_id'].unique()\nplot40(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:28:04.198529Z","iopub.execute_input":"2024-01-08T17:28:04.198908Z","iopub.status.idle":"2024-01-08T17:28:18.143125Z","shell.execute_reply.started":"2024-01-08T17:28:04.19888Z","shell.execute_reply":"2024-01-08T17:28:18.141928Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#The images with double slices:\ndoubled=[14312,23523,28562,30868,35792,37190,\n 39208,48502,281,2391,3092,3672,8130,\n 8985,9183,25256,26862,34277,38585,\n 38959,52612,54473,56500,\n 1252,1295,2706,3055,5264,5307,5992,6175,6843,9341,\n 1943,4827,8279,12222,12442,13526,13987,14051,15231,15470,15486,17067,18014,22290,\n 2906,6793,15912,18810,21232,21910,22425,6363, 6449, 3098\n ]\nfour_pics = [5251,61689]\nthree_pics = [5852,21260,706,1666,1660,7955,10548]","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:28:18.144395Z","iopub.execute_input":"2024-01-08T17:28:18.144719Z","iopub.status.idle":"2024-01-08T17:28:18.151593Z","shell.execute_reply.started":"2024-01-08T17:28:18.14469Z","shell.execute_reply":"2024-01-08T17:28:18.150767Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.concat([mc_df.iloc[:40],lgsc_df.iloc[:40]])\ndf = pd.concat([df,hgsc_df.iloc[:40]])\ndf = pd.concat([df,cc_df.iloc[:40]])\ndf = pd.concat([df,ec_df.iloc[:40]])\n\ndf['doubled'] = df['image_id'].apply(lambda x: '2' if x in doubled \n else '3' if x in three_pics\n else '4' if x in four_pics\n else '1')\n\ndisplay(df.head(),\n df.info())","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:28:18.152893Z","iopub.execute_input":"2024-01-08T17:28:18.153159Z","iopub.status.idle":"2024-01-08T17:28:18.193337Z","shell.execute_reply.started":"2024-01-08T17:28:18.153136Z","shell.execute_reply":"2024-01-08T17:28:18.192256Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.doubled.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:28:18.194673Z","iopub.execute_input":"2024-01-08T17:28:18.195023Z","iopub.status.idle":"2024-01-08T17:28:18.203694Z","shell.execute_reply.started":"2024-01-08T17:28:18.194988Z","shell.execute_reply":"2024-01-08T17:28:18.20264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# dataset with unknown number of doubled slices\nind = df.index\npredict_df = train_small[~train_small.index.isin(ind)]\n\npredict_df.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:28:18.204967Z","iopub.execute_input":"2024-01-08T17:28:18.205273Z","iopub.status.idle":"2024-01-08T17:28:18.220993Z","shell.execute_reply.started":"2024-01-08T17:28:18.205248Z","shell.execute_reply":"2024-01-08T17:28:18.220016Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 1.2. Prediction of the doubled images","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n\ndatagen = ImageDataGenerator(\n rescale=1./255.,\n validation_split=0.25,\n rotation_range=40,\n horizontal_flip=True,\n fill_mode='nearest')\n\ntrain_generator = datagen.flow_from_dataframe(\n dataframe=df,\n directory=\"/kaggle/working/train_small\",\n x_col='path',\n y_col=\"doubled\",\n subset=\"training\",\n batch_size=32,\n seed=42,\n shuffle=True,\n class_mode=\"categorical\",\n target_size=(100,100))\n\nvalidation_generator = datagen.flow_from_dataframe(\n dataframe=df,\n directory=\"/kaggle/working/train_small\",\n x_col='path',\n y_col=\"doubled\",\n subset=\"validation\",\n batch_size=32,\n seed=42,\n shuffle=False,\n class_mode=\"categorical\",\n target_size=(100,100))\n","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:28:18.222127Z","iopub.execute_input":"2024-01-08T17:28:18.222527Z","iopub.status.idle":"2024-01-08T17:28:18.246157Z","shell.execute_reply.started":"2024-01-08T17:28:18.222483Z","shell.execute_reply":"2024-01-08T17:28:18.24538Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_doubled = Sequential([\n Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)),\n MaxPooling2D((2, 2)),\n Conv2D(64, (3, 3), activation='relu'),\n MaxPooling2D((2, 2)),\n Flatten(),\n Dense(64, activation='relu'),\n Dense(4, activation='softmax') # num_classes is the number of output classes\n])\nmodel_doubled.compile(optimizer='adam',\n loss='categorical_crossentropy', \n metrics=['accuracy'])\nmodel_doubled.fit(train_generator, epochs=5, validation_data=validation_generator)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:28:18.25244Z","iopub.execute_input":"2024-01-08T17:28:18.252709Z","iopub.status.idle":"2024-01-08T17:29:04.535457Z","shell.execute_reply.started":"2024-01-08T17:28:18.252686Z","shell.execute_reply":"2024-01-08T17:29:04.534428Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_datagen = ImageDataGenerator(rescale=1./255.)\ntest_generator = test_datagen.flow_from_dataframe(\n dataframe=predict_df, \n directory=\"/kaggle/working/train_small\", \n batch_size=32, \n seed=42, \n shuffle=False, # Set shuffle to False for test data\n target_size=(100, 100), \n class_mode=None, # Set class_mode to None because there are no labels for test data\n x_col='path' \n)\npredictions = model_doubled.predict(test_generator)\npredicted_classes = []\nclasses = ['1', '2', '3', '4']\n\nfor prediction in predictions:\n predicted_class_index = np.argmax(prediction)\n predicted_class = classes[predicted_class_index] \n predicted_classes.append(predicted_class) \npredict_df_copy = predict_df.copy()\npredict_df_copy.loc[:, 'doubled'] = predicted_classes","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:04.536854Z","iopub.execute_input":"2024-01-08T17:29:04.537218Z","iopub.status.idle":"2024-01-08T17:29:17.08519Z","shell.execute_reply.started":"2024-01-08T17:29:04.537188Z","shell.execute_reply":"2024-01-08T17:29:17.084305Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predict_df_copy.doubled.value_counts(normalize=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:17.086399Z","iopub.execute_input":"2024-01-08T17:29:17.086732Z","iopub.status.idle":"2024-01-08T17:29:17.095782Z","shell.execute_reply.started":"2024-01-08T17:29:17.086704Z","shell.execute_reply":"2024-01-08T17:29:17.094725Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.concat([predict_df_copy,df])\ndisplay(train.info(),\n train.head())","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:17.097027Z","iopub.execute_input":"2024-01-08T17:29:17.097319Z","iopub.status.idle":"2024-01-08T17:29:17.124872Z","shell.execute_reply.started":"2024-01-08T17:29:17.097294Z","shell.execute_reply":"2024-01-08T17:29:17.123717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# check if we marked doubled slices correctly\ndoub_df = train[train['doubled'] == '2'].sample(n=40)\nimage_paths = doub_df['path'].unique()\nimage_ids = doub_df['image_id'].unique()\nplot40(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:17.126083Z","iopub.execute_input":"2024-01-08T17:29:17.126335Z","iopub.status.idle":"2024-01-08T17:29:26.621475Z","shell.execute_reply.started":"2024-01-08T17:29:17.126312Z","shell.execute_reply":"2024-01-08T17:29:26.620546Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot10(image_paths,image_ids):\n num_rows = 2\n num_cols = 5\n fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 5))\n for i, ax in enumerate(axes.flat):\n if i < len(image_paths):\n img_path = image_paths[i]\n img_id = image_ids[i]\n img = mpimg.imread(img_path)\n ax.imshow(img)\n ax.set_title(f\"Image ID: {img_id}\")\n ax.axis('off')\n else:\n ax.axis('off')\n plt.tight_layout()\n plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:26.622858Z","iopub.execute_input":"2024-01-08T17:29:26.623221Z","iopub.status.idle":"2024-01-08T17:29:26.631591Z","shell.execute_reply.started":"2024-01-08T17:29:26.623189Z","shell.execute_reply":"2024-01-08T17:29:26.630624Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"doub_df = train[train['doubled'] == '3'].sample(n=10)\nimage_paths = doub_df['path'].unique()\nimage_ids = doub_df['image_id'].unique()\nplot10(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:26.632954Z","iopub.execute_input":"2024-01-08T17:29:26.633291Z","iopub.status.idle":"2024-01-08T17:29:28.308585Z","shell.execute_reply.started":"2024-01-08T17:29:26.633264Z","shell.execute_reply":"2024-01-08T17:29:28.307703Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#in case the 4-pics werent distinguished\ntrain.loc[train['image_id'] == 61689, 'doubled'] = '4'","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:28.309867Z","iopub.execute_input":"2024-01-08T17:29:28.310152Z","iopub.status.idle":"2024-01-08T17:29:28.315553Z","shell.execute_reply.started":"2024-01-08T17:29:28.310126Z","shell.execute_reply":"2024-01-08T17:29:28.314545Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"doub_list = train[train['doubled'] == '4']['image_id'].to_list()\nimage_paths = []\nfor pic in doub_list:\n path = train[train['image_id'] == pic]['path'].values[0]\n image_paths.append(path)\n\nfig, axes = plt.subplots(1, len(doub_list), figsize=(20, 5))\n\nfor i, path in enumerate(image_paths):\n img = mpimg.imread(path)\n axes[i].imshow(img)\n axes[i].set_title(doub_list[i])\n axes[i].axis('off')\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:28.316722Z","iopub.execute_input":"2024-01-08T17:29:28.316996Z","iopub.status.idle":"2024-01-08T17:29:28.760745Z","shell.execute_reply.started":"2024-01-08T17:29:28.316971Z","shell.execute_reply":"2024-01-08T17:29:28.759789Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Pictures which were classified incorrectly:\n\nas 3:61100 (6), 35909(1)\n\nas 2:30515(3)\n\nas 1:3264(2)\n\nas 4: 52308(?)","metadata":{}},{"cell_type":"code","source":"# Problematic pictures:\npics=[61100, 35909, 30515, 3264, 52308, 49587]\nmask = train['image_id'].isin(pics)\ntrain_filt = train.loc[~mask]\ntrain_filt.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:28.761838Z","iopub.execute_input":"2024-01-08T17:29:28.762106Z","iopub.status.idle":"2024-01-08T17:29:28.775704Z","shell.execute_reply.started":"2024-01-08T17:29:28.762083Z","shell.execute_reply":"2024-01-08T17:29:28.774815Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 1.3. Crop doubled slices to keep only one picture","metadata":{}},{"cell_type":"code","source":"def crop_d(image,n):\n width, height = image.size\n width = width // n\n cropped_image = image.crop((0, 0, width, height))\n return cropped_image","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:28.776818Z","iopub.execute_input":"2024-01-08T17:29:28.777131Z","iopub.status.idle":"2024-01-08T17:29:28.786494Z","shell.execute_reply.started":"2024-01-08T17:29:28.777081Z","shell.execute_reply":"2024-01-08T17:29:28.785559Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import shutil\n\noutput_folder = '/kaggle/working/train_crop/'\nos.makedirs(output_folder, exist_ok=True)\ntrain_filt.reset_index(drop=True, inplace=True)\n\nfor i in range(train_filt.shape[0]):\n if train_filt.loc[i, 'doubled'] == '1':\n input_path = train_filt.loc[i, 'path']\n output_path = os.path.join(output_folder, train_filt.loc[i, 'filename'])\n shutil.copyfile(input_path, output_path)\n elif train_filt.loc[i, 'doubled'] == '2':\n input_path = train_filt.loc[i, 'path']\n image = Image.open(input_path)\n cropped_image = crop_d(image, 2.5) \n output_path = os.path.join(output_folder, train_filt.loc[i, 'filename'])\n cropped_image.save(output_path)\n elif train_filt.loc[i, 'doubled'] == '3':\n input_path = train_filt.loc[i, 'path']\n image = Image.open(input_path)\n cropped_image = crop_d(image, 4.5) \n output_path = os.path.join(output_folder, train_filt.loc[i, 'filename'])\n cropped_image.save(output_path)\n elif train_filt.loc[i, 'doubled'] == '4':\n input_path = train_filt.loc[i, 'path']\n image = Image.open(input_path)\n cropped_image = crop_d(image, 5.5) \n output_path = os.path.join(output_folder, train_filt.loc[i, 'filename'])\n cropped_image.save(output_path)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:28.787453Z","iopub.execute_input":"2024-01-08T17:29:28.787708Z","iopub.status.idle":"2024-01-08T17:29:45.275278Z","shell.execute_reply.started":"2024-01-08T17:29:28.787685Z","shell.execute_reply":"2024-01-08T17:29:45.27447Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_crop=train_filt.copy()\nfile_paths = {}\nfor root, _, files in os.walk('/kaggle/working/train_crop'):\n for filename in files:\n path = os.path.join(root, filename) \n file_paths[filename]=path\n \ntrain_crop['path'] = train_crop['filename'].map(file_paths)\ntrain_crop.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:45.276326Z","iopub.execute_input":"2024-01-08T17:29:45.276581Z","iopub.status.idle":"2024-01-08T17:29:45.296036Z","shell.execute_reply.started":"2024-01-08T17:29:45.276558Z","shell.execute_reply":"2024-01-08T17:29:45.295169Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"doub_df = train_crop[train_crop['doubled'] == '2'].sample(n=40)\nimage_paths = doub_df['path'].unique()\nimage_ids = doub_df['image_id'].unique()\nplot40(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:45.297011Z","iopub.execute_input":"2024-01-08T17:29:45.297282Z","iopub.status.idle":"2024-01-08T17:29:50.811775Z","shell.execute_reply.started":"2024-01-08T17:29:45.297258Z","shell.execute_reply":"2024-01-08T17:29:50.810861Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"doub_df = train_crop[train_crop['doubled'] == '3'].sample(n=10)\nimage_paths = doub_df['path'].unique()\nimage_ids = doub_df['image_id'].unique()\nplot10(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:50.813236Z","iopub.execute_input":"2024-01-08T17:29:50.813575Z","iopub.status.idle":"2024-01-08T17:29:51.967136Z","shell.execute_reply.started":"2024-01-08T17:29:50.813544Z","shell.execute_reply":"2024-01-08T17:29:51.966258Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"doub_list = train_crop[train_crop['doubled'] == '4']['image_id'].to_list()\nimage_paths = []\nfor pic in doub_list:\n path = train_crop[train_crop['image_id'] == pic]['path'].values[0]\n image_paths.append(path)\n\nfig, axes = plt.subplots(1, len(doub_list), figsize=(20, 5))\n\nfor i, path in enumerate(image_paths):\n img = mpimg.imread(path)\n axes[i].imshow(img)\n axes[i].set_title(doub_list[i])\n axes[i].axis('off')\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:51.96834Z","iopub.execute_input":"2024-01-08T17:29:51.96862Z","iopub.status.idle":"2024-01-08T17:29:52.39204Z","shell.execute_reply.started":"2024-01-08T17:29:51.968595Z","shell.execute_reply":"2024-01-08T17:29:52.391067Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Problematic pictures\nimage_paths = []\npics=[59031, 16494, 48973,45254]\nmask = train_crop['image_id'].isin(pics)\ntrain_crop_filt = train_crop.loc[~mask]\ntrain_crop_filt.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:52.393283Z","iopub.execute_input":"2024-01-08T17:29:52.393584Z","iopub.status.idle":"2024-01-08T17:29:52.407662Z","shell.execute_reply.started":"2024-01-08T17:29:52.393557Z","shell.execute_reply":"2024-01-08T17:29:52.406603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#pip install histolab","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:52.409095Z","iopub.execute_input":"2024-01-08T17:29:52.409376Z","iopub.status.idle":"2024-01-08T17:29:52.414283Z","shell.execute_reply.started":"2024-01-08T17:29:52.409351Z","shell.execute_reply":"2024-01-08T17:29:52.413211Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 2.Zooming the slices","metadata":{}},{"cell_type":"code","source":"def zoom1(image, w=100, h=100):\n # Convert the image to RGBA mode (if not already in RGBA mode)\n image = image.convert('RGBA')\n\n # Get the image data as a list of RGBA tuples\n image_data = list(image.getdata())\n\n # Define the black color threshold (adjust as needed)\n black_threshold = 50\n\n # Create a mask to identify non-black pixels\n mask = [(0, 0, 0, 0) if all(item < black_threshold for item in pixel[:3]) else pixel for pixel in image_data]\n\n # Create a new image with the non-black pixels\n image.putdata(mask)\n\n # Find the bounding box of the non-black pixels\n bbox = image.getbbox()\n\n # Calculate the center of the bounding box\n center_x = (bbox[0] + bbox[2]) // 2\n center_y = (bbox[1] + bbox[3]) // 2\n \n crop_width = w\n crop_height = h\n left = center_x - crop_width\n upper = center_y - crop_height\n right = center_x + crop_width\n lower = center_y + crop_height\n \n crop_image = image.crop((left, upper, right, lower))\n return crop_image\n\n# Example usage:\nimage_path = '/kaggle/working/train_crop/61689_thumbnail.png' \nimage = Image.open(image_path)\nzoom1(image, w=70, h=70)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:52.415839Z","iopub.execute_input":"2024-01-08T17:29:52.416118Z","iopub.status.idle":"2024-01-08T17:29:52.609058Z","shell.execute_reply.started":"2024-01-08T17:29:52.416093Z","shell.execute_reply":"2024-01-08T17:29:52.608135Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_path = '/kaggle/working/train_crop/41361_thumbnail.png' \nimage = Image.open(image_path)\nzoom1(image, w=70, h=70)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:52.610197Z","iopub.execute_input":"2024-01-08T17:29:52.610456Z","iopub.status.idle":"2024-01-08T17:29:52.823327Z","shell.execute_reply.started":"2024-01-08T17:29:52.610432Z","shell.execute_reply":"2024-01-08T17:29:52.822289Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def zoom2(image, w=100, h=100):\n # Convert the image to grayscale\n grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n # Apply thresholding to create a binary mask\n _, binary_mask = cv2.threshold(grayscale_image, 1, 255, cv2.THRESH_BINARY)\n\n # Find contours in the binary mask\n contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n # Select the largest contour\n largest_contour = max(contours, key=cv2.contourArea)\n\n # Calculate the center of the largest contour\n M = cv2.moments(largest_contour)\n center_x = int(M[\"m10\"] / M[\"m00\"])\n center_y = int(M[\"m01\"] / M[\"m00\"])\n \n crop_width = w\n crop_height = h\n left = max(center_x - crop_width, 0)\n upper = max(center_y - crop_height, 0)\n right = min(center_x + crop_width, image.shape[1])\n lower = min(center_y + crop_height, image.shape[0])\n \n crop_image = Image.fromarray(image[upper:lower, left:right])\n return crop_image\n\n# Example usage:\nimage_path = '/kaggle/working/train_crop/41361_thumbnail.png' \nimage = cv2.imread(image_path)\nzoom2(image, w=70, h=70)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:52.824792Z","iopub.execute_input":"2024-01-08T17:29:52.825692Z","iopub.status.idle":"2024-01-08T17:29:52.864078Z","shell.execute_reply.started":"2024-01-08T17:29:52.825634Z","shell.execute_reply":"2024-01-08T17:29:52.863222Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_path = '/kaggle/working/train_crop/61689_thumbnail.png' \nimage = cv2.imread(image_path)\nzoom2(image, w=70, h=70)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:52.865256Z","iopub.execute_input":"2024-01-08T17:29:52.865525Z","iopub.status.idle":"2024-01-08T17:29:52.88089Z","shell.execute_reply.started":"2024-01-08T17:29:52.865502Z","shell.execute_reply":"2024-01-08T17:29:52.880053Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#one of the zoom options\n\n#output_folder = '/kaggle/working/train_crop_zoom_white/'\n#os.makedirs(output_folder, exist_ok=True)\n#train_crop_filt.reset_index(drop=True, inplace=True)\n#for i in range(train_crop_filt.shape[0]):\n #input_path = train_crop_filt.loc[i, 'path']\n #image = cv2.imread(input_path)\n #image = zoom2(image, w=70, h=70) \n #output_path = os.path.join(output_folder, train_crop_filt.loc[i, 'filename'])\n #image.save(output_path)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:52.882105Z","iopub.execute_input":"2024-01-08T17:29:52.882363Z","iopub.status.idle":"2024-01-08T17:29:52.886429Z","shell.execute_reply.started":"2024-01-08T17:29:52.882333Z","shell.execute_reply":"2024-01-08T17:29:52.885374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output_folder = '/kaggle/working/train_crop_zoom_white/'\nos.makedirs(output_folder, exist_ok=True)\ntrain_crop_filt.reset_index(drop=True, inplace=True)\nfor i in range(train_crop_filt.shape[0]):\n input_path = train_crop_filt.loc[i, 'path']\n image = Image.open(input_path)\n image = zoom1(image, w=100, h=100) \n output_path = os.path.join(output_folder, train_crop_filt.loc[i, 'filename'])\n image.save(output_path)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:29:52.887753Z","iopub.execute_input":"2024-01-08T17:29:52.88809Z","iopub.status.idle":"2024-01-08T17:44:19.647561Z","shell.execute_reply.started":"2024-01-08T17:29:52.888054Z","shell.execute_reply":"2024-01-08T17:44:19.646777Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_zoom=train_crop_filt.copy()\nfile_paths = {}\nfor root, _, files in os.walk('/kaggle/working/train_crop_zoom_white'):\n for filename in files:\n path = os.path.join(root, filename) \n file_paths[filename]=path\n \ntrain_zoom['path'] = train_zoom['filename'].map(file_paths)\ntrain_zoom.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:19.648687Z","iopub.execute_input":"2024-01-08T17:44:19.648979Z","iopub.status.idle":"2024-01-08T17:44:19.668058Z","shell.execute_reply.started":"2024-01-08T17:44:19.648954Z","shell.execute_reply":"2024-01-08T17:44:19.667166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample = train_zoom.sample(n=40)\nimage_paths = sample['path'].unique()\nimage_ids = sample['image_id'].unique()\nplot40(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:19.6691Z","iopub.execute_input":"2024-01-08T17:44:19.669353Z","iopub.status.idle":"2024-01-08T17:44:23.539486Z","shell.execute_reply.started":"2024-01-08T17:44:19.66933Z","shell.execute_reply":"2024-01-08T17:44:23.538306Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Problematic pictures - delete all containing white background:\npics_much=[60928,1666,52612,35592,60685,30738,29331,30868,3092,2706,8985,\n 32035,15912,10548,6582,3511,1080,25792,706,50246,30539,39252,\n 1252,39144,54506,62828,26862,21232,31473,25331,59515,65022,\n 34688,15583,7955,46815,18896,34845,7955,54825,57100,47431]\npics_few = [60928,64824,52420,39365,11559,2666,15470,14542,30738,17738,\n 18196,19512,48861,60287,26644,45104,36008,\n 14051,56843,36008,57265,45104,61689,43998]\npics = pics_much+pics_few\nmask = train_zoom['image_id'].isin(pics)\ntrain_zoom_filt = train_zoom.loc[~mask]\ntrain_zoom_filt.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:23.540635Z","iopub.execute_input":"2024-01-08T17:44:23.540939Z","iopub.status.idle":"2024-01-08T17:44:23.557595Z","shell.execute_reply.started":"2024-01-08T17:44:23.540913Z","shell.execute_reply":"2024-01-08T17:44:23.556585Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample = train_zoom_filt.sample(n=40)\nimage_paths = sample['path'].unique()\nimage_ids = sample['image_id'].unique()\nplot40(image_paths,image_ids)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:23.558853Z","iopub.execute_input":"2024-01-08T17:44:23.559202Z","iopub.status.idle":"2024-01-08T17:44:27.591196Z","shell.execute_reply.started":"2024-01-08T17:44:23.559163Z","shell.execute_reply":"2024-01-08T17:44:27.590308Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

3. Convolutional network model

","metadata":{}},{"cell_type":"markdown","source":"# Image Augmentation:\n\n***rotation_range***: Randomly rotates the images by up to x degrees. This helps the model generalize better by learning features from various angles.\n\n***width_shift_range and height_shift_range***: Randomly shifts the images horizontally and vertically by up to x% of the image width or height. This can help the model learn position invariance.\n\n***shear_range***: Applies shear mapping, which distorts the shape of the object. It randomly distorts the image by shearing it with a shear intensity of up to x%.\n\n***zoom_range***: Randomly zooms into the images by up to x%. This helps the model learn to focus on specific parts of the image.\n\n***horizontal_flip=True***: Randomly flips half of the images horizontally. This helps in situations where the orientation of the object in the image doesn't affect its classification.\n\n***fill_mode='nearest'***: This determines the strategy used for filling in newly created pixels. 'nearest' means that the nearest existing pixel will be used to fill the new pixel values.","metadata":{}},{"cell_type":"code","source":"#The target classes are imbalanced, \n#therefore split on train and validation groups will be done with stratifier\n\nfrom sklearn.model_selection import StratifiedShuffleSplit\nstratified_splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\nfor train_index, val_index in stratified_splitter.split(train_zoom_filt['path'], train_zoom_filt['label']):\n train_df = train_zoom_filt.iloc[train_index]\n val_df = train_zoom_filt.iloc[val_index]\n\n#Because we work with histologic images its crucial to save details, \n#therefore augmentation should be done carefully\ntrain_datagen = ImageDataGenerator(\n rescale=1./255.,\n horizontal_flip=True\n)\n\nval_datagen = ImageDataGenerator(rescale=1./255.)\n\ntrain_generator = train_datagen.flow_from_dataframe(\n dataframe=train_df,\n directory=\"/kaggle/working/train_crop_zoom_white\",\n x_col='path',\n y_col=\"label\",\n batch_size=40,\n seed=42,\n shuffle=True,\n class_mode=\"categorical\"\n)\n\nvalidation_generator = val_datagen.flow_from_dataframe(\n dataframe=val_df,\n directory=\"/kaggle/working/train_crop_zoom_white\",\n x_col='path',\n y_col=\"label\",\n batch_size=20,\n seed=42,\n shuffle=False,\n class_mode=\"categorical\"\n)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:27.592372Z","iopub.execute_input":"2024-01-08T17:44:27.592711Z","iopub.status.idle":"2024-01-08T17:44:27.761886Z","shell.execute_reply.started":"2024-01-08T17:44:27.592681Z","shell.execute_reply":"2024-01-08T17:44:27.761103Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Check the class indices and the class distribution in generators\n\n#train\ntrain_class_indices = train_generator.class_indices\nprint(\"Class Indices for Training Generator:\", train_class_indices)\ntrain_class_counts = train_generator.classes\nunique_classes, counts = np.unique(train_class_counts, return_counts=True)\nclass_distribution_train = dict(zip(unique_classes, counts))\nprint(\"Class Distribution in Training Generator:\", class_distribution_train)\n\n#validation\nval_class_indices = validation_generator.class_indices\nprint(\"\\nClass Indices for Validation Generator:\", val_class_indices)\nval_class_counts = validation_generator.classes\nunique_classes_val, counts_val = np.unique(val_class_counts, return_counts=True)\nclass_distribution_val = dict(zip(unique_classes_val, counts_val))\nprint(\"Class Distribution in Validation Generator:\", class_distribution_val)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:27.762901Z","iopub.execute_input":"2024-01-08T17:44:27.763158Z","iopub.status.idle":"2024-01-08T17:44:27.772087Z","shell.execute_reply.started":"2024-01-08T17:44:27.763135Z","shell.execute_reply":"2024-01-08T17:44:27.770964Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Visualization of augmented pictures\ndef plotImages(images_arr):\n fig, axes = plt.subplots(4, 4, figsize=(10,10))\n axes = axes.flatten()\n for img, ax in zip( images_arr, axes):\n ax.imshow(img)\n ax.axis('off')\n plt.tight_layout()\n plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:27.773399Z","iopub.execute_input":"2024-01-08T17:44:27.773799Z","iopub.status.idle":"2024-01-08T17:44:27.788006Z","shell.execute_reply.started":"2024-01-08T17:44:27.773767Z","shell.execute_reply":"2024-01-08T17:44:27.787011Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_training_images, _ = next(train_generator)\n# Plot 16 random images from training data \nplotImages(sample_training_images[:16])","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:27.789173Z","iopub.execute_input":"2024-01-08T17:44:27.789515Z","iopub.status.idle":"2024-01-08T17:44:29.200546Z","shell.execute_reply.started":"2024-01-08T17:44:27.789489Z","shell.execute_reply":"2024-01-08T17:44:29.199564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Test your model on unknown samples (testing data)\n# If the test_df contains doubled images, its needed to be cut\n# The image was zoomed\n\npath = '/kaggle/input/UBC-OCEAN/test_thumbnails/41_thumbnail.png'\nimage = Image.open(path)\nimage = zoom1(image,h=100,w=100)\noutput_dir = '/kaggle/working/test'\nos.makedirs(output_dir, exist_ok=True)\n\noutput_path = '/kaggle/working/test/41_thumbnail_cropped.png'\nimage.save(output_path)\nimage","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:29.201757Z","iopub.execute_input":"2024-01-08T17:44:29.202059Z","iopub.status.idle":"2024-01-08T17:44:36.479154Z","shell.execute_reply.started":"2024-01-08T17:44:29.202033Z","shell.execute_reply":"2024-01-08T17:44:36.478249Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df = pd.read_csv('/kaggle/input/UBC-OCEAN/test.csv')\ntest_df['filename'] = test_df['image_id'].astype(str)+'_thumbnail.png'\n#file_paths = {}\n#for root, _, files in os.walk('/kaggle/working/test/test_thumbnails/'):\n #for filename in files:\n #path = os.path.join(root, filename) \n #file_paths[filename]=path\n \ntest_df['path'] = '/kaggle/working/test/41_thumbnail_cropped.png'\n\ncorrect_type = pd.read_csv('/kaggle/input/UBC-OCEAN/sample_submission.csv')\ndisplay('The correct type of ovarian cancer on test image',\n correct_type)\n\ntest_datagen = ImageDataGenerator(rescale=1./255.)\ntest_generator = test_datagen.flow_from_dataframe(\n dataframe=test_df, \n directory=\"/kaggle/input/UBC-OCEAN/test_thumbnails\", \n batch_size=32, \n seed=42, \n shuffle=False, \n class_mode=None, # Set class_mode to None because there are no labels for test data\n x_col='path' \n)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:36.480422Z","iopub.execute_input":"2024-01-08T17:44:36.480847Z","iopub.status.idle":"2024-01-08T17:44:36.510761Z","shell.execute_reply.started":"2024-01-08T17:44:36.480814Z","shell.execute_reply":"2024-01-08T17:44:36.509746Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Plot for accuracy and val_loss\ndef plot_accur(history, epochs=20):\n acc = history.history['accuracy']\n val_acc = history.history['val_accuracy']\n loss = history.history['loss']\n val_loss = history.history['val_loss']\n epochs_range = range(epochs)\n plt.figure(figsize=(12, 4))\n plt.subplot(1, 2, 1)\n plt.plot(epochs_range, acc, label='Training Accuracy')\n plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n plt.legend(loc='lower right')\n plt.title('Training and Validation Accuracy')\n \n plt.subplot(1, 2, 2)\n plt.plot(epochs_range, loss, label='Training Loss')\n plt.plot(epochs_range, val_loss, label='Validation Loss')\n plt.legend(loc='upper right')\n plt.title('Training and Validation Loss')\n plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:36.511985Z","iopub.execute_input":"2024-01-08T17:44:36.512268Z","iopub.status.idle":"2024-01-08T17:44:36.520369Z","shell.execute_reply.started":"2024-01-08T17:44:36.512243Z","shell.execute_reply":"2024-01-08T17:44:36.519362Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Plot for accuracy and val_loss\ndef plot_lr(history, epochs=20):\n loss = history.history['loss']\n val_loss = history.history['val_loss']\n lr = history.history['lr']\n epochs_range = range(epochs)\n plt.figure(figsize=(12, 6))\n plt.subplot(2, 1, 1)\n plt.plot(epochs_range, loss, label='Training Loss')\n plt.plot(epochs_range, val_loss, label='Validation Loss')\n plt.legend(loc='lower right')\n plt.title('Training and Validation Loss')\n \n plt.subplot(2, 1, 2)\n plt.plot(epochs_range, lr, label='Learning rate')\n \n plt.legend(loc='upper right')\n plt.title('Learning rate')\n plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:36.521458Z","iopub.execute_input":"2024-01-08T17:44:36.521767Z","iopub.status.idle":"2024-01-08T17:44:36.530713Z","shell.execute_reply.started":"2024-01-08T17:44:36.521742Z","shell.execute_reply":"2024-01-08T17:44:36.529842Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# The basic 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HYVqdm+FpWDsAeVWWeV2WeV2WeV2WeV2WeV2WeV2WeV2WeV2WAp5fJ8ZNZHlO8hiZ5DEzyGJnkMTPIYmeQxMZSRWOROyfwkBtIQrNDtYMRe/hdffzfswHQqEj6nikanY4bH5cyeLXGaqOiG4LWRvy2S7Mh42FR7mAFBc4qoo4aaDnwYmCaUAztYxo2+q2A4zFuXrG/WYZhCvJGuCDiVlgWXI+FLByQN6dRMbQMTG0QUxKgGJVRkxIEdMSMFMnjY0elP7JwXNx36Tgl3Uvma5syOiRDA3MCfdwommbuPi4e10PtpfmM4u8Z1egBYkd23c1rn10K/wBdba2vKgxIpWjEBgfhy5rPMJdnDAJsuOYiT4qKGxiyFbYwtMeUKU3+DZSBNwZGGH8BXHmzS23GxLlVIt8JBTLIvZLZrhxrcjgPu9gMSesiR6HuPLnmtVip51+q+9CwVhZmYDzRmmNbGKiXfZkOyWSbwspJABIR9aiXKq9t6HafcqxK2xc4obcsqXHuHPiuvW/CVO+OiM12DJm7JbIJHE9pWGBuYA+74HrH7h4wIFeOKMjocQcwyDhqQsffeei2Ura/iJ8SRUCKq1ZHwjVbpDSUWtX0u8jql5sjReOX+D1ZTtthNajG/ANAQhyUoiu8sDp8ljYGtBJRKYSGHTAY1aMThsiMZK9B4CFkFphlxasLmup4pHsqwSkdUDUzagLXMpAtaOCMZvCVGHMC6s3GBqxBItNGcw9dHdJ8nEmBqBR1SiCwbqmOo/ivasZ7XI9pw7uAPrXOqTf64dsr2pJk4rWxlCFAN+Nah35zrJ4TJZTRRzm7ubYFGJJ0tTfwLqa6vix7N/EW9EjVvGIRbkSHpjkkQPgSpQ4YRnBYjgRw15nWAGjRe6fAkSGRADkhnpEigrpHNCqjI0o/X5tG351eDfWWEeDkDKX4ckihjx7cxwSrgUYY7pj5XnMXQCYNH++GAhskFK8pYhph6gLWCRXgaAO0n8DsnhpTOyfwp8PmjPRITBVKtc+o1xxUuxMr4nBjfAuwEk18qBKbkYT4B5DDqBqaW/AuBPPVzIEtro4Hw3vUonxhbMb18CQkiPXSNsj3lHWse4/w5At4HDaQDOm11eUlI4PT+2EUbblp/pOwsIJndSkSLTQG7TAA2/8Ano82PGt406VOeWykyB1zpJpWK1HIxViPNYCC/qA3PsYkXjt/584UOEQmBF4vY0jTxCjLXwWx/VYkcGviTnCbMsSFOC3Ucb/nTEUaeghGiZHnClKcLZIJEAZwtpozcbWd7Ab2la52lIx2So/oGTW7+hkXUCKTmRs5kbOZGzmRs5kbOZGzmRs5kbOZGzmRs5kbOZGzmRs5kbOZGx81zrYd0Ep5V6rIiWKbjbsLnQ7NsstzPNBI2yO+98+C1hLpgESS1A8+JnPiZz4mc+JnPiZz4mc+JnPiZz4mc+JnPiZz4mc+JnPiZJt4UUMSxiT0+CcSlZok5ok5ok5ok5ZxpJ4M6YsuKgCGOGGV5iil7cUasmNjFDGaAjUqWyRVPeVneVneVneVgBvZ/RSK2JKdsjzZHmyPNkebI82R5sjzZHmyPNkebI82R5sjzZHmyPAxEFLFRtDjaFNk1YUrx0wmigQFh5JgMlSPIGcaZSlZE8uMr4EFsGJobmhuaG5obmhuaG5obmhuaG5obmhuaG5obmhuSIgJQhCYFv8AAkyHinyrNkOS68jME3qAKtYqub/0cyEskr6NCmDUpJM2q/L/APAZD1GD+9e/SXGvVT/xRl1vO9Rp8OSRQx/RM+1/vS/cYP7v+LG+tL+X4c/7L0FGhR7D82H5sPzYfmw/Nh+bD82H5sPzYfiBfmy7ttOzadmy7Np2bLkzadmy7Nh+bD82H5sPzYfmw/Nh+bD82H5sPzYfmw/Nh+bD82H5sPzYfmw/Nh+bD82H5sPzYfmw/Nh+bD82H5sPzYfmw/Nh+bD82H5sPzYfmw/Nh+bD82H5sPzYfmw/Gg7Px0fuTYfmw/Nh+bD82H5sPzYfmw/Nh+bD82H5sPzYfmw/Nh+bD82H5sPzYfmw/Nh+BDtYYW83Yfmw/AShSX7D8B/sMN+hkkjYg2IpMX8M2H5sPx8VSM/nXcl8cb7sMJpL0DHRrcrskXLQv/yGPyrGU9tkto1swV4ZI7rdg50m4N5W+12DkudhsSZyX/ziP2xwWyJEcN5w6nzdN5tk46VVs8YHzzDjjuSyZMW0cQfnalwBmyA/BSqN5UaEZpQRJAnpClPbIgzCR5MSX2bCMOVUiUQv6CTFbJc+C1801K0xpFcMwvK2Koq7YOeA8krypiy21LUclM1JHlY/LlqWFcOsZrHGQUn+fGrVi47psKx31u5KBUDC9lFHQD6lxGip2BVtO0KAqgxljASLH+CKZKHDi224SBcFlRwXJGt8ybgrl6BdbKMkOVyxf9AGrjgVtYFqxqwEVPI4unywCyEp4ullYBrwgZHT/rf/xAAsEQACAgEDBAEDBAIDAAAAAAABAgADERASMQQTIUEgIlBRIzNhcTBAcICB/9oACAEDAQE/Af8Aj5mC8zn7ndSLgBFUIAo+59QLSP04mdo3fcAMmOoGl9xpAOIjb1B/OnU3GhN2Mymzu1h/z9tXmWcjSx0QZaA5GlrpWuXiMHAK6WWLUu5ojrYu5f8ANiY0PGiczYJsmybI6YGm0zaZtMI0HmbDNhhU6BSYVI+K8yz1pbUtow0VQoAHrS2pbl2tK0FahF9aXUrcu1pXWtSbV0DA8Tp6kZMkRq6UIGOYlVTA+JZRWEJx8RD4mZka8g6Jz8X4Oi+piYmI4AMGmDMGFSImjL8F5lnrTqK2tXCxAVUAnOnVVNbXtQ4lCGusK3k6dVW91e1Z09bV1hW0WsIcidKM1QgQD8GW57Z/o/ERvJ+PrROfi/B0X1POr8waetG4MVsGczmMuq8yzTqLHrXKiISVBIxp1Nj1V7kGZQ7PWGYYOnVWPVXuSdO7WVhn0rdmYgidL+1GUsQR6ijaOJb+2f6PxHMYfQIiFoaj6mDK1IjJgHROfi/B0X18H5g509aNwdAxE5E5jrg6LzLPWltq1DzFYMAR70uuWhdzStxYodfel1y0pvaVWLcm5dMgzpf2v/YzMpGOIp8fVLSO2f6PxHMcYSVgbRpcMERR4EfG06Jz8X4Oievg/OimecaNwdQcQYMt9aLzLNLFR1+qAADS1EsXDxFCjC6Woli4ceJWiouF40SrYczpSvb8zcn5E3qPYlrLsPkcH4jmEAiAACeBGKkczeg9x7UIPnQeJ3BO4J3BO4IzgjQHBnd/id3+J3f4hOTrkzJmT8ASJknReZZyNL6e8MZiLsULp1NPfTbnEpr7VYT8adTT369mZRV2Kwn+Te35mSf91eY5B06ju4/TiZ2jdp1JtFf6XMp39sb+fuF1wpAMRg4DD7nZsx9fEHH3O6kXDzFUIMf9Sf/EADMRAAEEAQMCBAUCBQUAAAAAAAEAAgMREBIhMRNBBCAyUSJQYXHwIzAUM0CBsUJDcIDB/9oACAECAQE/Af8Aj4Anj5pHIYzacS4k/M4tAPxp1EmvmDjQUbi4Yij6hq04UaxBEJn6bUrOm8t+Wu4UXBw1rnH4V3wxjnmmpzS0kHDI3SHS1PY6N2l37lq1atWgd8OulrK1laytZTH2axqC1BagtQOda1oOBwXAFBwPldwouDhjzGdkSScRyuiNtT3mQlx5xHK6J2pqkkMjtTsFhAsheJmkZJQKbLM4HfhPllaQLUc8peLPm5XIxxytkNzh+f4iMXztfY9ud6XKj9Qw7a1qWpakwkhO4xatNfakw19+R3Ci4OInhhso0TiCRsb7cLUrmveSBWPDyNikDni1M9skhc0Vh0rniivFmpVZRPuFER1B5Sgub/PzfOy7jD8/qNYWaCbJ3sdySO/1TRQCj9Qw/uqGYvSncY7qR4jF1fA2+prvXuoJNb6qqrmu9+1+xT22FuPz6rhNdeXcKLjETGvNEp1A4gY2R9PNKVoY8hpvHh42SSU80pmNZIWsN4kY1rQQV4wfqpjg0EHunHUefz83UQHUG/lPCHqKc4NQlBO6M8LD8TgPuR/6UZWSeg2AmvsgYf5Y/UMP7+SL0o8Y7qRnUFXXB2+hvvfsoI9D7skmua7X7Ae5w5tqqK4THahh3Ci4OGMMh2RBB3xHG6U01PYY3Fp5xFE6Z2lqkjMTtLsUvGfzf7JjWkGzunN32+iiB1jynhMIL1ITqKteFJLn/wBv8L/fd9h/kqO9Q++H8eWP1DD9rx4ijpsE771Z7H2+tKCgXUCBe12Ow91H6cOatrwz1Zc20bCi4OHcKLjDXOafhW+GOcw21Elxs4Y57HWzlOc57rdzh8utoC8U1xk2WmT6rQ89iomOEg8ruECQUSSVuV0A51lgv7WmwFt6W8+wpMieHAkYItdIrpFdIrpFMYQcOaHBdIe66Q910h7prdIzpC0haQO3k0gqgMO4UXBxFJ0zdJx1En3xDL0naqtSP6jy73x4eXov1UpperIX1+5ob7KgP6124UVgYi0X8adVmsQdMv8A1OFLp1nRx8wjjMhpOBaSPmbdX+n5pHIYzYTiXGz/ANSf/8QAUhAAAgECAwMFCgoHBwMDBAMAAQIDABEEEiETIjEyQVFhcQUQFCMzNIGisdEgMEJSYnKRkqHBc4KTssLh8CRAQ1BTYJRjo/EV0uIGJXCzZIDy/9oACAEBAAY/Av8A8E6fEaH+7Wv/ALJ7n9x5mYYJk2jqptmO8f4a7o4qKXN3MEeZMPmJYNXc5O6kMCxd0UzQmIm6dRvU2NTB4Y4XDz7KS7HM3ZXdOLExKPB4BPBr5Qf+SKfB4GDD+EYbDibE7S9s1uSPtr/6XxaLkz4k3XoNxXdqbYx3wE2zQa72vPXckwJENpBtI49qQrXXW9f/AFHivBrPFIoO+fGNr9lQYPutFAr4qHawmG+n0TesPj2weH8DM+xk3jmPZ3sLJgTafD4UzD9Vq7p91OMYwKOyjmI4rWDXFeBmLGNlCwuTJCebNXdXGbGLPg8TsVGtm3gKg7n4LCxyGSFJSxPJHPWIOTAxRwtZYpJryyjpAB09NYKYCwk7m5rfb3sZ3QbzJ8Y+Hn6ug1AYgjyz4qRIy7WQa8o9Vd1IYvBnxWGjzbWFzkZLcR113NxGKjjkgklWMvmJbKec13WXYrbCj+z/APU3svtrFxYCDDnwCEPidoTvG18q13LbuZhVmbHq1kZuDV3bxONy7XYxWjB5T5RoKjkxmzErLdtnwFd1O68Zticbidmr/NTgK7nYzuXiNjjFytM7sbSdNY3uouuGTEiGcdTCpliYWl7rkK3bXcTAYw2xmAxbRODzrbQ13U7nL5FwMTGOi/K/2Lh8dgcR4Jj8Por5cwIrHSd2MX4VisZHsy6rlCL1VgJO6WLSaPuemWBUTLfrNYzuf4SubEYja58vDUe6u5+JMuXwcBZFt5QA3AqfHdysSkEmJi2UwkTN6RXcdMPNZe58u0bMvL1runHBjkjwuOfaMuzub13MxGAxUcc+Ch2W+lwdK7rjET3XHsrLZeQRUOM7q4pJ5MNDsoRGmX9Y9dR9zfClzLidtnyfh3oO6O0GSOAxZLcb13T8ZfC4xCgjAsUFYSObHIcJheSI4srP0Zq7oYaPHomExU22y7K5vel7obYFRhRBky/jXdOJMZEIca5e5iu978L9FYTGmZSIMGMOVy8evvd1cPiZA646YyqQOR0VhcKMSBisLPtopcmnYRWPXulixI+KTIFiTKkYtzVL3KxuIiddNgypye2u5Lbcf2NQJd3ymub21jZ+5mKSBcdHknV0zekV3HbDzWj7n5rgjV713RxeLKyLi4sirl1TQC/4VFhcVKJ2jGUOBbSu6fcPHKyqkxMbW4qdVIrCQ91O6W3wWFYFI1jyk24XruuuJcPF3QYG1uTpUWA8JUlMWMRmy/hWG7qwyCNkttEy8q1d0+6LoyR6YeG4te3E/wD9osrHXqoMhuD/ALmEUOsx9UdJo5wXv8q3GocLjBaDExl0POjDj/uURxDPM3AdHWaOuZ21ZjznvYDFrxgxAB7G09tqGbyPP9D+Xs/ybaTuI0uN4/A1/wA5CRjPK3JX8z1USTnkblN097xDDZ83PesXFptlTNl6xqPZSPyhl3h0rz/kfSaCk5sM3k3vyer/ACWX68X/AOxa7r7LGyQLhEV4lVV45L63HCoIRinwscmAWdhGFvmv1iu58UsmZc+JidgLbTIbA04fEyKIu7CxJky6Dd6qVS7SEC2ZuJ/zdVQZ5X5K/wBc1FmOeVuW39c3e181H/c/lRdHy35rV3NxKcHDQOfxH50yxJtTG+iAjNfX0HS/20YUIkgcXjv0c62pYZSTET4tz+6f7gYIoZsTMozOsQ5I6ya2kBNsxVgRYqw4g00s75I14nvptWy53CL9Y/HmOdA6Eg2PVrU52YvOLS/S5qWZ1XwdMMIUUMQw191QwmBRHD5MLu5Oy1Phxh0WJ3zso03unt0pVXgBbj/mm+2tr256w4SB326swzblrW6e2nkOF0QX8pRdznlflN+Q6u9//E//AG//AB9vtqzuqnrNOycqFllHoOv4XonDWWVgpUtwJ+b9tvto7K6ON+B7WaPq+320RiY9snJfLyh2j3UIy+0U6RydPUeuryuF7aDIbg84+N7or3RlXD7eQSxySHKrLlAtf0V/6ic0WDk7ppJci25ky5j1E13fkj8fENiyHiOsj8awolljHc84M5d/xROfn/Gu5s+IlZMCJsQsUkraZDyLn0aU+Ins8Cd1rln4BLj8KTY22eXdtwt/kckcqykRqHkdUuEB6fsqwIvxpze+QXIpHRXUPEkm8PnVmuLdNafCiQpJJJJfKsa34VHNE25Jyb6Va4vW6jjxkiXtpuG1aGt0g/AkjdZbRlQ7hN1b8KtcX6KxE7bwgjLsBx0F6bdZbW1YcdL1muMvT8TvEDm1qRo43co1rZSP/NYhGk2ULLaNk5QpZcgacII9qRvEdtYH6kv8NYj9G3s7275sOf8A1P5e3vBVG0lbggp9ry760mAliGzniJRyePMRaoBmjVtpsg+uja5T7ftrwuBFWWG7mO/FTe6fbf8AChiotlsZNJOOnXUjS7MOq7xjJuO2oMRjBvSJdesVFtImbDTyZMwPJa1eLbhxHOPjxitc+y2Vua17/G3NaxSfZWquP1a1a3bXlFrRx9tcf7ni2xBkGGeKNbK1g/KuDz0EGFIxXhm18L0ts89+PHk7uWsKowjRYmLP4RiNPG7pHHnuSD6KVJIDYxYBWBPzG36ly4XPghjVk8HWwDrsraDhy9bdVY3JHsk8LfKnRw+Fg2RnjRFkzOh4XtWJijwLYkPCqYV7+SPadRrresRG2GaXGSOpixmm5oPSLa6V5I+Uxh4/O5NRjBxbHNgMk1jlzPmTQnptn1rF7HCeBoMNFeOyjXM/zfgY18SZNg7xkKGsr2HPUCLhjFjI5WaXGaWYa8/E3005vRRigwLQTpg5Y5nuPHMUta/ytdbmm2kOaNsTCxv80R2Na4XbYNMU7DDi2oKixAOnG+lOVTZr4RLZb3tvn4OtZUDOcubQafbUGbdzEhkRc/rc1DaHKUkzKc+c27Tw9FMY0Cl9WIHHv4APfkS8Db5tTne8m3y2qw825z/qfy7xjwltNGkPBfeaOW5Y8pjxNXdAx6xWFxSjXDS6/VbT3U2clM7KdoDrroR1nn9F6SecDnE4j5r89vrCpWLqsAFzGjeUj7ekcNKbDRaZrLEOo6a/S6f51DAUVliQKLipIkWzLvx2+cNRUZl0HBZAbFD0dn/ivHDbx/PQa+ke6s0TBl6v70Prr8DUVrGv3a8mK+UP1jWkkv3q0mevLepXLT7tZ5FUrcDQ0D8DjXGuNca41xrjXGuPwNniYkmj+a63FLHEixoo0VRYD4opIodDoQeesmFhjgT5qLl+IyYqGOZL3s65qCxqFUcAO+upbM2XdW9PbLBZtGbe0qQBWn2iZt7yfV1VGZHy5RqicmgkSBFHMot8HAWVn3JeH6tT+Jk8m3R0dtXYhFUVrmiw/wBjP7hQVAFUcAO8URM1ue9Orm21Urltc3qM4pMkB8XMOJy9PVbX8K2OM4YlZIp2toW6e3RqTENdJ4TZRl8pzZmHo/CsIxTa5JNq2Xg+XW/V/wCKjbCgSq65r3om2Vl4ipUy5kFzl6UPEf10NSxuc8TeSk/hNZ0Jil+evP29NWxS6f6iDT0jmpY4EE7kZuVoKbc2cqcpf7uPrD4z9ZfbS9neNPZ3L3Y6y5QADXlh/wAqvLD/AJVeWH/Kryw/5VeWH/Kryw/5VeWH/Kryw/5VeLxCBr8+JrZyOu1XTQ/Ha1yhXKFcoVyhXKHwSztw16a8WllKXzMefsqN8TNqvQcqk08eDhEWU6ZlyrTmUmRWFsjcn7PiMD9SX+GsR+jb2UsmK1I1RByV9572QXkk+YnGvHts0+ZGf4qOxAKc2vCsHiL3WUGGTt4r+dSPJCpjU7XMo3Svyh1c/wB2p8I/EoxgPzmXVW/rooTSJlaRLw2HK/1Kxc+y2Wwi3Rz68L/Zb0VhNnvOYw0gv8o6mo8XfxsL3ZRzpz++o59gjAaXVflc1x+H61bMRo8Eq54ezo9FCLGKpJ8k1tX6u2ndYVgstx87tPRWGeHflZLzKx1zHWvC2kKzqwJVD8jn/rqoNOM0R4SqNPSOarqbj4EYhyhnPFhe1eVj/Zfzrysf7L+deVj/AGX868rH+y/nXlY/2X868rH+y/nUaylHRzlNlt8V+sK/nX8+9/Pv/wA6/n3v51/Ov1hz9dDs7xrEfUl/eoaCuSK5IrkiuSK5IrkiuSK5IrCW6D7R3jT5Z2GrfKCgAGvOv+6vurzr/ur7q86/7q+6vOv+6vurzr/ur7q86/7q+6g00yyxE28pc0JIjcHvwi7ZLMTla19KszgHjbwh65Y/5D1yx/yHrlj/AJD1yx/yHrZ4pw0bfKuTagyG4Pe3GK5nAJU256bNJIsjcbObmvLT/tGqTwmQlM10MmtqDIbg/E4EB2Tcl1X9Wp/7RKfFt83o7KCatJ8xRc141tjH81OV9tZYlCDq78ifKG8vaKixNrxDin+t2fYV+lerYHc2MRmQ/OVbre3WptR2aAOWzQvm+SOOvpAt1VFGZNzFYgB3I5a35Xpt/V+8VbUHQ1LEGVJE0QtwbnA9hHpotgP7GzHbI5+d8oFe321bExEzyabTNzjpPybfZUWGmbaFnzNJbyijify/894q2oPGijm658jdTc32j8aLYQ7I86fINZJlMMvQeDdhp4Y4HmCGzMDb7KTEYdrxvWH7T7O+4y8GslhXjBx5OnAd/D/pBTSztkjXiaWEt41lLAdQ/wDPxH6w715mCDrNDayML6XCGkK4hbPyb6Xq66jvyTPIGWPjlNzUfgcE+JiPLdVtlqVsPgZGsdxmcKpHTrReaGSHKl2vbLfoBvSyqpCsw9tDs7xrEfUl/eofE4TsPtHeNP2t+/XinctqTyQBvVxf7y1xf7y1xf7y1xf7y1xf7y1xf7y0iSi8DnKN4UCOfvRfVb2UwzMgZluV7DQPjwG1F5UrjN+1SuM37VK4zftUohtqQf8AqpQgxB8W/JuwNAqbih+kX21im6EX86WXbogbW2SmRsVHZtORUWHkkSRCOYWrdN/iMAXvbJLwF/m1OBn1jb/Dboq0S5b8evveMdU7TXig8v1F/OmJLLY8OiosE4Nimcubb4vw4ipMuHl2ynPEzFOPELx57W0+bQxEMZGY58py2yOpHTwGn3aaJQRpkZ935HDXrv6tFWjkhWFG0LCwJ6Nf6tWHeYkHJ976XppInVnzmyyHgvVSYgwTvIdNoShsfkkb39XozQYSUTCTMYrrv/OHHjz/AGUJ0jkPha+Mkum6Oka+innSGXCgaINyyejN2cOuo+Xh47cBpm1400OJzyx3AVzwTtNDMkSpOMjbxPZ/XZTQzYi0kfOqcodOtDDYeaSR5W2e0J+21uinTDwNPEW3HDc3XXg80kgkfecxykWN+asO21ktYrlvpw49tIPCHJU6kqN7qqS0ia8i6cO3XWkuUKBdeN79VJnjUtfeAl0A+ypPEOQvJsRv1GNlIM4vw5PUaw7kSKNrbWNr1iocMsrSsmg2TD8qZ8PJicQ3gcoDNbQ3XQddSr3OjxUWFbYhhvKc203rX15PE069zkaRzAyRllYbDc3bNwIvapGzvbIoaNoXTe6d46n4J7RVzlygUR3QhywE7hHyabCYneNt1vnCsTDxaI519FYbEwsQG3TY8xoxuqSJkBF+NybU7RJEurgbt+TTO6R7Z1D5sg1pg54xblPG+jqCpqNWa6NFr18KUJGY9m+Uj+dDs7xrEfUl/eofABLAXrRwaClWB7KG+N7QX72E7D7R3jUn6379P9T+Jvh4b9L+RpOzvRfVb2UfrL+6a1C3YxrmK3tpWaONmXp2K15F/wBiteRf9iteRf8AYrRXZSC/REtCHFrJsmayMw6qU/TX21i/qL+dYFDyGOtMkOFaXLxs/wDOr/8Ap73/AEg/91N4TBIIy2m+DbXTnpXXgfh4H6kv8NYj9G3srWVEH0U1rxsksna9vZV44kU9IHezMNeqopoFs+FNxb5vPURwzKjKcqR/JkbpHRY6HouallnjISRZMO4t8phfT07vbeiiKwB2Zm3PlA/+7N9lTrCxaTGnZq54tzA9nH+hUEOXSJAgI6BTQIMvOD0N000bkwyPdbAaSHot87/zUeP7ojZuG2WJ+YB1fhxqdY4Ts5RnlBX/AAugD8KTDxvtZX3dvx2l/ljq9tRRPHYRLlQg62poMOuQN8rnv006wgbujRfMbpX8DakxUByRxje1sW+cnVXhAFkVbIvzer7P3u/h+0+zv6irlTbv4f8ASD4pqWPNZpWC6GpcNiH2qFbr00V4TYdiEamvwkS/sqWP5cDeytu2iySKqDqFG/BMR+DVs+TLCbxt9GhG14MXHyaGcZWbckX6XMa2UmYx5cu5x1vb8qlj03WXh00OzvGsR9SX96h8CPs/Ol7aPbWutNu5c3Ky7t6w1mYXUjs4VGM6sBy7rqa3oQxv8h+bp1qW4Itm5vp0wuLlL2/WPw8N+l/I1H2d6L6reyj9Zf3TQ+vF7Ki7Pg4X9L/CaURC7DKbVi9smzOReftrAfXrGsOYio5ZsZs86g8BWV+6OnHgKWGTFrJDYams0LBl+FgNmwU5JeIv82p7zKRs2/w+rt+Dla5PPbmpdmczTaJ+dPh4uWxCxS8yN8m3T+ZBposS5XLh3c5/9Zb69nKPaDTyE3S95PGbt25PXxvULwAiXB3k2Z5rcw6vZUUgbOJUDrl6DRmVt0caOIyZs+h13Yuhr9PZWw7oHbBtH03Wa2lh2cfRQOfNFh/LEndeNTzenWjitmY2j4xdHSF9H41Gzvn2i5kCa3HTTSwvurys2mWvCHUrheDr89ek9VTBDkyrtWt8r6B6eb+jQjxAyNKbuPp9I6qMDu7ups2RbhaWWBg8bC4IrD9p9nfccczbmtKpNy2p/Lv4f9IKLyaKOOl6dIJMzx2zCxFr0kBPjHRnUdQtf2j4bUrf4eGT1qXEYc5mi4jpHRQYeRxHP9Kkvy8MfvR1Mg5MsWY13Pt/qD8KseTOCl+gi9qR4/OYTZh01HiIQRLEd5OevC8Mcy9A6ayRahNfui3tqTfuMy6fmaHZ3jWI+pL+9Q+AAUBtWiAUDtCenQa0vjToega03jTrw3RpWG3/AJJtpw4VHea9uVu8qrbYXve+Tm6Kl3vnW04b9PqLZOj6RqLkDIb6JanCYgqxe4OW9urWmubjm0+Bhv0v5Go+zvRfVb2UfrL+6aH14vZUXZ32Zhe1EIuVraEm9Ye6XUSaWOp3TSIWyuUzZTxtTTRSFDl5jasDAc2dHsbrWO9Fdzv0X8NSbCEMqNl1a1ebr9+kjxEIysctw9Bhz/BwBdgoyS8T9WpxtYz4tvl9Vecy+r7q85m9X3V5zN6vurzmX1fdTbrPc8bcaw+Ml3oyuzyn/DPT/XVSDbSo3k7MRz/LHTl0+9TYeHxhVmnzH5qjh+t/EankVxmzKQmbXU307PzrGvIXljdbqZOe3/nqrCiQFrxg6Dk/RpJ7sBG19gRy6CGRw825GpIyv6bUO5iYiQyI99sTr05u3p/nUh5LxL5EHRyu7k7P5VkWSTEIg5bcJOcEdP8AWtQllecFdbb2Q35NPO+aMXGWI6Z6LLLMzXy7M2vm6KSAyyO0B8ILfOX+vYK8Hw8jy7Q2EhOg6x2VJG0M0u9uuELZ+uhDNPLDI+9YW8X+FYdjNIRYrlNrcOPDjUY28u6b303u3SpPHyDPw4bvZpSttX0W2XSx66VfCJdGvm3bnq4VJ46Tf4cNzs0pDtXOUWtpvdtQKZ5SdrfNpf2VJiM2IxJRdIxrmP2V4l5HmeZXxkmykF/ztw9FYE4t55sPssRvosgtdksp56jlmXFS7LCsyRlmUucxyg/StWJHjPBzHGy5lewbW9s3o+C9Mx1LSN7aMvBTy/fTwMcgbWI0jZLyodnKvUaN42zuojzdA5zSFRuQ7saj5TUsSJt2XVjwUHtppdph1ZuK56DZdlLzOu8prKyWZtXAHPWWNRv87c3vp82cgEAM3PQ7O8axH1Jf3qHxOE7D7R3jUn6379P9T+Jvh4b9L+RqPs70X1W9lH6y/umh9eL2VF2d9lZrXosGzm3ADjWGurC0l72+iaiUsPGLuq2l/RSbHdVNAqHd+yonwyxySpJmJItpWO8KGya4Fdz/ANF/DWNMeh2/8Irfxjj0ClzY5tDfmp4JsQHjXgT2VdTcfAwN9dyX+GsRoPJtzdVWjdWPRfX4MrS8kCpMPPyJGyTMf8O+unbq30fYWwru5t4MNP8ACy3Nv1d7tryh4+LQx30Y9HZlrBeEKcqShZl+YL6r2a/1bvySQC8LaOLXyL88dZ6OjXt23cgmbK2xgTiNeO9/55NQ3kYth1EqB0/xOfTnutJJiAFGbK68QjHUDs5++2LDiPZbqE8H6fdUmKk8Rl39mdDl6+oimYixGiA/Jj5h/XR38P2t7O/drdHHhSk/K4d/D/pB8VJS/rfK66XLEJFIswz60ERhNA2mR9ClZY115zm41z/erUWe1synmox5WWJP8KPiRXie5JA6ZaaWfBrBk1umlcRf69b65rcLtSKBp20OzvGsR9SX96h8ThOw+0d41J+t+/T/AFP4m+Hhv0v5Go+zvRtYkANew6qP1l/dND68XsqLs+DhP0v8JpeysuzW182g56kKvIpf6fDsppi2Zsmt0GtYWIw5ZMmV2G5zdIrH2uQJeHoFRrPFfxLNwuBwoNEkTK3DcqU7GO+Q/IFC/wADAZACckvE2+bU90jA2bf4h6OyhtY1ftF68TLLH+tmH41qI5h1bhrxqSRfWXT7RpR2LAJzddYXCDTINtL/AA0NrIGjiSzoBu5OLdvp+dT4m2fPAUT/AKbSckf1000THOVbLHZt1QnK/DLrWPjz7TMm5mFmt+fNWGKHK4S0htxYaGo8J/jTNbP9Hn9NCMy7zmyovC/SfnUs4P8AYcOu4vzOa47fd00DNlOIxBz4dDwSXj/XprYwymQICq5tHtx9PvqBYPFSBbTG3ygbG32U+Hn8sTYSDdGXnueakVJFlkbcXIdI157ejnoLFuCD5VuLfN7P5UhkGzEhyOOYE8COon29dbLEYqNJOcX4UGQ3B4EVh+1vZ333/KNfhwqPNIg3jxPPUg2i3i5YB1WowLnaarZTUDrFL5a1imU/jUs0mHmyxrfgK2E8T4aUqXUPbeHPYineHERSInKZXBArNhpo5l6Ua/wpaKEaq7e2o1lleP6nPUkufaBrb1+P860Fddc9Yl0AD7C1z9YVf+0zH52e1Rje5Lcr6pq1r9tCyAZVsOqo7XUX+dTZZ2u2ouBu0xDJly7oy89LmVLW3iG4VNeM3yy8403qJIbd+gaK5wCBf0VoQb/DwnYfaO8ak/W/fp/qfxN8BN1dRzim0C210prSvr2aVh9/TPwt1GoN9LZd/c49mtf4ZbN1jSpY5Ikdfk73H8KBK/2djctxtpSbhF3j6NKQsGAX6NOrNYouZuylMbhgwzDrHfwv6X+E0nZ8BHw52TrzisbFPINptuc6nRaT9E3tHel+oaHwMD9SX+GsR+jb2V/aUzL/AKkY/LjQaNgynnHfLq+W/EWoNyXhuwemedi0ZcbcHl9OX6X/APmpDi7FIkmmZOg2tkt+sf6FbOe7SZVXNe2Rl1Iv9K/5VhYQbSyloGFsoVv5e6okw52QjQLwoxtv5uVenVHLwRqQ2duC/K3vw1+lSvLuqDthERaw+Rf+uantmeI6Q5T5HXj1X91HDzvba3EgTkq3G3Xxv6ajMDeDsi5RZdLU2VzI78prUx2aO/k4sy3+s39dFBY8626HIpoIpZJP+qQLIeo21qePF32uc3J+V10iSy7JjrHdNUF6w7GdyLFctlte3HhSK00zZTe+fU9tSZgx2nKu5pZBGudVyq1tQKypGqjjYL38P+kFYiGAZpGXdFOXQYUCCSONWa5LN025vfUuIkjiwQSKNcjuLPlcNqejS3prujiAiKj7MbjZgSB0/Clrkn7tKYpBEOh0o7qFs1zk4VyT92uB+wVqp+ysToR4ro+kKaNWlxcq8QGyqKi0sch/dNDQ3+rQ6+HXWY8x1rLvXC35JqOySb/0Dp21IRBIcptbTX8axG78iT96k3F+lv8ACm0S/wAnWg1o7KNNzUUi58uVrnJu3/GnO2c5jfm3fg4TsPtHeNOkoy8bde9T/U/ib4C3e1uqjlOcnSmyot/k3fjUHJtn/GxqC7pbLvjJx/GrGaxzXuq83RUl5pLN2bvZWWXM27l1c0FcEw7VSHZzpUe4hDanTjXAfAwn6X+E0nZ8GaSNbyUvi2TxTcq3SO9L9Q0PgYASKGGSXj+rU5EKA7Nubq7xeImGTnZOft6a/tKZl/1I/wAxQaNg6ngQatI4U9ZqKBTvYp8v6vE/110HALTYZM+zA9vWdOy9JDiD5O+1boy3J9ZhQ3Ayy789kG5bUL6b+ysTLEc/gzLJEff16fj1VFOZFCSqGW5p8QpDabuvE81ATm5k1yDi3S3Zx9tLHiPF7TxjqmhVeYZvw0668EwqrLHbWyi8a+w0JcGSwgIZfnaa5W6/fUc8kyJHILqWNr0+IidHUDQg6XoZlZ8Qw0jHED8umv7WRl/0l5Pp6a0oFlBI4ad6FlVmAY3sL20rkv8AcNcl/uGuS/3DXJf7hrkv9w1yX+4ahyo+jXO6Rb4FhoPhTdlcmhtY81qbZR8a4f19tcmuFXiJVrcVoWxOIXeHBuutm8srqdDnfjV8gv2VounZS6EUOzvGsQs4CbkmU346ih8ThOw+0d880g4GpI8YwiYJxdrX3jXnEX3xXnEX3xXnEX3xXnEX3xXnEX3xXnEX3xWHEUqSHafJa/Maj7PgFJReskGIZYxwGnurzl/V91ecv6vurzl/V91ecv6vuqMzylwpvragOj4STYM5GAsdKQTPs2fQaAj7bUVMhsdOC1ssQLcN/mPwMBlQybkug/Vqe+GcDZt8peir4cZ4uePo+r7qzRm4720iJik+cvP29NPteVeocY5vFGhVFHFb8WoSYfYsGnGRAnE/JH9dVPDhmDSYm8RfLyk3iT6fzqWWEosFwrjL8oHc5+ObU9oox4kpmkjZZMg+Vx99YaLGXzLHu9Q5hUd5dnh4pM+XLy2oouzyRi72Q73Dr48kek1kR4Tipd59w7v41lWSI85JQ6n7aMbNHLLltaOM3t1m/tqDD4l9oiJaMjmF+FNmdo8Mjq+g5TDhVo1t09fxFuf46Xsr5P218m/bXN9tcx9Nbq+2riKtV/Gk3beMX21yK5J+yuQ1BrH0mgO+ykb9tDXlfVFeV9UV5X1RXlfVFeV9UV5X1RXlfVFeV9UV5X1RUcmIfNl+BmkXerk1ya5NcmuTXJq4WrD+52w7bMZ8xHEfypxjVEcd8u+RlambelQkWRVF1rTvYH6kv8NYj9G3sogjJIvKToraQnZy9PT21sphs5vm349ne30Vu0VKy8ptxbdJ0pVgsMQiKUPRc2B+3Lbsp9iu0bIUwuXVtecjsUUHVx4PAvjFVvKnrPTb3VhZ1A2MEyNY/Nvz/Stx9HTW+qsOsUzHdRBc9lbXLmlkYsqHp6ewX/GrMTJM+ptynrxp2MfzFO8e0+6ssa5R1VlmjWQdDC9ZUAVegfDiKGI7N82ylayyaHQ+2nmwuSFIcPHKkLamTNzflU8qYgRs/dEIzZPk2GlYvDiR/wD7hPIiH5uVyr+qK7m7M5rphlkU2+Xp03qaOVktFopH+LvcodWlu34t4/nVyhWr1yjXyj6a5FeTFeTX7Khso8vHzfSrh/mUjYdssjfPuy/ZTFbYcXzyOQMrdNXysvUawAuRuS8Db5tTnPL5Nv8AEPRQeM5JV5LfkeqirDJKvKTorLILj2VbEHPF/q9H1vfRWJQwHPXc7CxfJvPILX6h+deKYx7TEAKflX1Gbo/oUcXl2eGgQvGvbwPblFLiGvHiXa8ajnc87f1oKnjZczspJdeDt09RrBsq55DENp1Go8NECJ8TKIyB83if666sviQeLW3j2dFHZrqeJ5z8XBhJcOJY25TtwRjfL+6amn2bT+Cgm6RE2tzBuFGGQLlcrlVlNySL6gjThUdjozWVtmct7248ONRMsSxjaPFeVCh3bnTT6NM4fIIwOMZXdPC3VTGFicpysCpUg9YP9yhRpFV9qjWJ5s3GlkiYPG4upHOPicTFHO2Giw+VdwC7MRfn5uFTnYySwYUhZ5rjo1Nufrq3gz7Lwg4faZhy/dUUgjkOaMuVFrqcwXL25jb0GhCyNhsQs+HuM1wUaUDj9tROsTeMxLYcC/OM3/trCT44bAPtC2SxBCgmjJicLJEDC00e8DmA5uo1JBLA0Eiosgub3Vr+74M0Ec7YaGBVuUAzOx7eapxspMRHg1HhE1wOa/Dn01p/7O+wTEDDtJmHKNradG8KWR0caSl1502Zs342+2nieNsJiLxMu+DddooP9ddZtmfOvB/Te16w7YtdhmxUke4QQQufj92lklwzxxSxs8LZhvWF7Ho0oRSYdoC8W1S7A3X399BBYSzSrGpPyb89KGlmxsszhI0bKNfQBQhXCv4TtGjaPMN0hc3HotTPJG6AQvLbpyGzL7PtqSQ4Z9hCwWZ8w3Tpzc9r14PNmd5MRicrdASS1vxrDpFDaCWGRyS2oysBURhhlxTLho5pTdVO8PbUrpA74eIpnkuODAHh+t8VrSyJoyLZBzCsA08DTyJHIJGgXS5t7qlRcFirshA8XQZDdTQdDklXkt+XZRV1ySpyl/rm7xMNsh/Cp55oo3lVeUV+yosOFjWyDOcouAePs9U1GzQJHCPGlDGNb8m/u6q2zwx2OkKCO5I+datlHBFhyRzIpZR0mgMAoEfzaLSWMp/D43ESySDwiSQSJJ8zLaw/D8axWD26+DzLJl3N5c3t41iC0+SaXIQ6ryGXnqIJN4qMRBQy5iMhvp21kllvFtZXsF1s4YEetV8TiAzhURSsdtAwb8qxcma/hEu07N0L/D/csFpdkxKg/UPGgqiwGg+JM8M8uGlYZWKW3vQakzyzbOUgzRXGWQjp+yst3t4R4Rx+Ve9YwHP/AGpszb3J7OjXXtppHmmxEhlW8jWB8U+g4cL0r7WXIkxnWO4yhze/tNIrNJJHGXyo1rANxH400c008y7JoVzMNxT0fzpsQCc7RrH6Bf3/AAfCIpZMPNlys0dt4dd6kzTTZZlAnUEWl5tdOjoqRbvaTELiDr8oEf8AtrGNIrN4YuWQE6ejoqQyzz4hriPO5Fxka9uHSKz7WYJthPswRlz0pzyMqztMqG1gWBv6N40FaWaSNI2jiVmHiwdNP51FKC2aOLZDXm/od8xTXsdbg2IPSKAmxc8jqwaNzluh+yklzO8quzl2Orki2voqBTn8TMZl3ucm9j1VkaWVVxLbR4Qdxytv5UpillikWSSQOtr75uw4cKwxhklXYKycb5lY3INJHDPPCBAsD5WG+q8L6cesViY95UnK5gObKABb7PjjLELxnyiD94UGU3U8DQKnJIvIboopIMkq8V/MdXewuFXlYjELw6F1/IVKcU6+C5rfWt7Ra9bEjwfbb85+WF6Oro+2tlhEBnccT0dJqwuSdWY8Sfj8AoSGTymkq3HCjh86LIuLjjWO3+GQvN9tRzbTbNLBK+XZ8CvDhXc14u6W0jOJs7grbkHT+ummXaphFSJ5ItweMfO+7+A4a71Suz5BHiYYtll5mVL/AL39xjmRsg28YY2vulta8OxG8uJYDDQJa9ub09PRSWgmaZpTDsRlLB8ubptwpgcNiNkk2xklsMqt9t+etnkk2e12O20y5+jjfjpSyTtnfPIL/rn4kyzkhAQNBfibCpYxn3TaRTdGHPQgzNt5trIu+xGXN1/WFYqQscuFuJdOGl6BHxLzTXCILmwvUsXjAy22iMCjC9BGZ9riHkMe+zC3GsUA2uG8ppw3c3spJE1VhcfEGIlwRLssxQ5c/RehjJS4jiV2k8a/RzfZWHBY+POWPr0vU0aHeiIDadIv8ZLIvFEJrDyjEYSbaSRKyxDkZunWsSSkm0hiaUIUIzgdFQQNDKm1gM2Zl5NiKZ2MiKqh96Mi6k2uKlMQdYlbxsbKQYyfldlaUpBySLyWHNR2p3hppwpMdiXQYbBwta+pJ56EsqsTbM55zrw9LW+w1awfFzG9ub/wKNzndtXbp/ufAVw/uUaZ8mSVJOHzWvUoSRVjaUTRxtHmVX+V2g9FYNnaBGw8rSEQQbNWupXp66xMO18tidtfLw1Bt+FGWPwYxGQyb+GvICddHvQhz57Mxva3Fifz+JMcIYuZYuTxG+NamQh54vCg8jtHnMseTnXns3NWBkyYiaFYpxpARlzMhC5eYaGu6uHTDSs+ON4Tl03kUa9FqA6PicVHECXZLDLxrFKNpiFZ4maRlzF49bpbS9jrbrrBSqk8kKySnKuHK7MFeAXorumVws8nhyhobJz5MuU9HDn6ahjOpRAvxEkx2rx/+oZtgbZSum/6OPoqfaq4xng8iy2w9tqSOd773VWAkTDYi+EkVpFMdjbKRp01jcS8bRpPIMgcWNgoF/jJI72zqVvWGwyTLmwjRl7fRpzPOjZ4JYWcRWd89tWN9TWHaaZDbDPh5cqWzKbcNdOFPH/ZVbdAkjgysQCDrr1ViJ819sFFui1WPmx4f9P+Xs72Z116jTxxWXausQ9J1/C9RnJmYchPnN7h7SaLOc8r8pvy7P8Ab3dJJ5kjeSSPKGaxbcFTLgZmBkwzSIrSZijhhYNpu9BFR4pZNlhMRLYZ32QVQvO1ja7X/CsOMRi9oq4fP4prq+8ba26O8QwuDQikN4W0jbo+iaym7HntXcnCYfe1aY26tPfW9q9rdnUP9wPHmdAwtdDlNLFGoWNRYD4BRxdTxFEBXccx40ZmTx7ixbny9HwsXJEcrpC7Keu1YXaeFSHEyLH4+wtulrj7tARFohH4Shs3Eqo1pVkjLzhIMm95TPz/AG3+z/bykc7Afj8FnkYIii5J5qIh2nTdomUH7RUsMnIkUq1ug1FGSy7Eho3U7ykV/iE3kNy3z+VWGlZAIMHFki3rsx6+z86vGwYXtcUSeaop4dY5UDr2H4MotyGy/h/kSxy4qLas2URq12v2V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98V5xF98VPA2MaFFEeRUjBvfrtSRhGyyOyRvcbzC/uNY8xQWxOFh2mRmVvYaljkheOSKATMDbgSf8A21yJAiwLiJJDyUQgn8qMWzaJ8ucBiDdfRWEMZAiuXnv/AKY4+29Nh1scKInFramRct/36xBljdXw+TOgIY7xsOFTeEwSwyRhTlNtQxsNeHGkkxNsPm5nYV51D+0FedQ/tBXnUP7QV51D+0FedQ/tBXnUP7QV51D+0FedQ/tBXnUP7QV51D+0FedQ/tBXnUP7QV51D+0FedQ/tBTSy4iMovHK2ar4LExT245Hvb4qytlIYG9r15aL9l/8q8tF+y/+VeWi/Zf/ACry0X7L/wCVSKCszBkcIq5c2VgSvHntVsOmKsroZ12To2zvrb+VIIExKYBsWtlOZdMjZusLe1QQyrP4OmIn0LNyfk36qwyzNKsCrKmsUkpvn3b5Tfk8DRHdPwid7RiCTI+W2UX4ck5r8aijiSSKNMTL4QGid+c5NAdR2UiY1sViovByIGRHXezHiOINsouawCbNQVw0YIckEHLXIi++fdXIi++fdXIi++fdXIi++fdUpktd2zbp4aD/ACJWnw8bOpzK1tQe2vJr9leTX7K8mv2V5Nfsrya/ZXk1+yvJr9leTX7K8mv2V5Nfsrya/ZXk1+yvJr9leTX7K8mv2ViJ819sFFui1SJG6LE+e3ihtFzfT9NTxyTbsuG8H8XEEsOn8aMnhWSWSDYzFY+UNeHQdTU0UjF45sMmHYcNFBH50czRPpYFYQh9NJJKd1YpIynTmt7qhi8IkukUsbPztn4msRsnzyyLDGBHGEyhXvcfbU0smIDzyIIwTFuhfq+mkw4OcKSeHSb6dWtckVyRXJFckVyRXJFckVyRXJFckVyRXJFckVyRRixEavGeKnnrLEiovQot/ccFEtsk2fN6BUUM0b5ZCFEmZbXPVe/4UruHW+0zC2qZOVf2emm8TLtVZF2YKMd7hwNuagWUoSNVPN/uSCVJmhkhvYqAeI66MkmJkYs8bvurqU4ejqruhLio2iTE7ipm4Dnbqv8AkKUSy5ssqyAiNV5PZ/8AgJ3Xio/z6NfnX70icyqp9v8Adphbyb5fwB/Oky87gfGSyDUohPwZfq/59B+t7O9P9RPz/u2L/S/wLUf6Vfb8Zif0bez4LIbgMLaV5zL6vurzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91ecSn7vury8h+77q8tJ+Hury0n4e6vLyer7qHj5PV91ecSer7q8tJ+Hury8nq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6gzSu+Xhe3eLrK6Eixy2rzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91ecy+r7q85l9X3V5zL6vurzmX1fdXnMvq+6vOZfV91PvFy5uSasSV1uCK85l9X3V5zL6vurJDjJSxFxdAL9m7rXnMvq+6s8WJmy5ivBeY26KTaYqbfYINF4n0Uzz4qYKqM53QdBx5qbJiZt3jujov0ddW8LlO9kNgpsfsrzmX1fdXnMvq+6mR55SrCxG77v7/htnJLEHnCOYkztax4CxpIjJJiHybQnEFYWtfoOX2VhuGyxGTI20W+9w3ePPUvhWHyAYrYJYjpqSNMPLLIs4gCrbeYpmoQmwG12N9qt831ePHShD4Ri4Y9hm/s0G01zc+61eB7NziL6DpS3L7ObtoSYjBuXfFPBGqMNSC/X9Go8LOgRpDYeMUm9r8ONYnHYXCnZrCZIndhvDptUvhCyKVgRtlunVnKi3WalGIw0qTR5PFgg5gzWFqmjkiaGWEgMpN+PA/39nPBReocZPipM0ibTZJlyC44cL1gZsZaTNh1eRjKM56bLz1jU2RIwgJax3zpfReisFIg8s7ZRHIrK+4TxqPEd0NrebCtiC20zCwtwXm5VGafDpBwttJwPtNYDwWEGHELJnu2oKkCokgjfEzsGc5mC2UNah4LhZJTsNsQWC21Iy9ulRzR8iRQy9h+Kuxm8JETJGlwMlzzWqZYYpC2aPwWQPpGul/z7aUxYaRZlxEzs5fRkOaw9lHaYeTZ58O+zXc1BOe292VjFiikEMkWJEMRbk3RQB97N9tYlgjuRMrQxneR/FKLHXTW9Ypo4WRpMbHLmB0KZVv+INWnhdMVbxsja5z2/wCQwFiRsZNoLf110uKDFXy5HWws4/o1I+3kVJJUlZABxW3P0aCmRCYSZBLmT5973qJmdjKk+2Z7DfaxGvoP4UzwzMsTOXMWUce2vCIcS8D7PIbKDz9dDFmWQ4kPcP8ARtyPq/nSeOcrHijiUWw0JzXHrGhJt5MgnM4Sw5Rvz+mjgM7+DmJorfRPup3xLtLI8SRluHJYsCOu5/CpJMW7YqR8urgaZTcWA69axE4JvNluOi3+QKkGKmGGXkw7pAHRe17UYFxE6Rth1ge2XeVeHNXhDYmXaKrCPdXxd/Rr6aSTayPIszTEmwuxXLwqGEtI8cWGbD2J4qbfjpUO0xs7vA+eNyqXGhHR11hjHNKHgd2zG29nNyDpUfguImgkRWXOtjdSb21FXiL+Q2Opvpcm/brUUCElYkCAnq+KwuMkn2ollVHjKgcp8un21BDYs8kecM5C5tTw6eFYIyYYiXEwmUlSMqjTX8akxDpJNhkwsMzNujKCCTWiN5xsPT01DNi0MeaKRyiWN8pHvrZSYZ/CMyDIGHBr2N/1TWfIUYOyMp5iDb/cMdtowjN0DOSF9FQcvLCbombS/T+NIIs1o0KJduCnm/Csg2gTZrEy7Q2ZV4A1tt++12uXOcua1r2rKys65XWzNwDcR+FB2zvJmDZ3e504e00wj+U5c9p/3d//xAAtEAEAAgIBAgUEAwADAQEBAAABESEAMUFRYXGBofDxEJGxwSAw0UBQYOFwgP/aAAgBAQABPyH/APCFglrAMoR5/oSAKb/4wyAU3/4lI6aYiyPB4S41kjQOOdEs34dMtj2dNmkyJrrjPpw+wKcbLveqw3+5EK6G9wPvgkd2ZQWN31ayq700xinfW8k3XhAVn/4wpAyBMsJyXHgYVBCxpEE8WeMYeCClFRldNlZGtpCtOzo85lyZyuMDmWRY8vvgXu/WLk71lzhGg6tM1qN4cN3MCw/fGE6qJm++kxe43rC0JBpv1lpN4EUAVxK4+iXa262U8H8JzhxxYI7p0ZPPlTlUvMh6LvFNeG+vOIcSspVLc+5Y1itZNcQoGou2dY3eXINCJqh32OMidTdRHZU88ZEkElM103Pjjd4wbNgHsSeeSk1nWgiO76bnJtByt5vgRXcMQ64QJo+F5JuUFh/8Xl3ySHiNx8lp/wCFSuJ5YKR8X7tYkyF9wENbB8vHG2cTqkCq6A+xiGrmWDJETgIhmqx25qR++TFdMEUETuDA73LBaF5qxyXykbuYXpL6GT6eF9dMT45GZJqlqnN2mayOYBCMrcHisU3nZod6TPPXAgxqSqd5FmfPBaM+STMDNxx5ZOR6sfBdDtv1xNd5OTQs6o104vE9CbgqJtPbD0SsVUhT9ukuBPAKJE0Zov6QrRwqb5XCYjOVjNZZOv8APLHIzmBQkWzzeCJQFyrJ3Xk6YSUte91O53hZZM4VE87ia7vkEo4k03e1354N2DrNQ6nhkx/cZca7FYED6optaUd4IE77gy6RW3nIpGAxjAe6MOJh9i4Qjq7YhXU0p0GesV5YSsjmRvE8SEPP/wDUU67zBMYBTWT/ANMeIWQWvwXTrEdwZSS+I7UPbAO8qIUe0I9vwIJGR1/6TYDPEPwfnjLoI5b3cfRp0OBUBhkKmhDm2PZ7761r/pXgKCsAqB6p9/4Bsif+5R9ZZ9+gOvkWmEHEy5b/AEdDjFAl0YsShsB4s9MtlqLe3je8BBqbC3694kZth5nI6UmwT03o8Pl0/wCmsSlAjE1tFkFSbb1Fqs8b4rQX6GuZqsaRGXVc6i54yg1ClSNrSsT1vAeDUFPLAEv/AG8S1UfPVXgTb+0MgTRleegHA4Pyq5MbxOM3v734bvRcqlfXNtuKlk3F+X15tw4KRTiZAYZqUJPCzbEIsWKyJ0mntkZFViHhPw8xDe/7xhJCszUgCY1M9slfGS6KXCe6w9xbzUsfvJN/RgMyA2lH97RmGpUD1DF3QSdAQnycko+Fo2kiDSm8cMUzEtWgmFzZplTcsosupwkEEJTUdf8AtJQAD5oG0NuDN8nkaJb7MbipYHg8Mahwo+3QHTz2v0v3PZ7RgrVGMChogymEx+d7HOCmP+2ll0Is3AiIEZRDbpNvlVlkEy+HsC02Dvx2qsCEuszTfb+v3wwDWlxhUNZJ/tgMXRRcqUXXc65MMGOIftwReIcegebMIEjUVbxwSPHXCRtHZP5jDhJonVDiFJ4jIMhSmXI30JeoR1iVjBfNFR5f9GuEZEqhW+WhjBAkiBNxkUCUjcGCd4sAgkJ5SL8TPyVVgCwnj/KTruQRBXfcwFKE7j5Nj24xEslom8ghU2wKJXgUrJMBTd4d9kZ/hd1JxY2d87iDnLZY9V5O2t8p+gY3APECWh53eIS1JtWCaX/QQM6wlFvGEJCbnXxU+IHIChIQ/L/99MJkAiS8O3f6Xh0fTFEka9PCe/k2Eax2oJ3fF6Hf91luYVFx6+nbFtFcNGgJ5GtzrImQSSvocQsh2AcZxoVDcUkSTnhwcmucNxnwziNPk8ZZaSGQ0rRdk+V5F8PF1WHt1rrnSWjbybimOvHfElzRaTomz+xDsnEneRjaYnr1eieMQo3iCQm/6w7oAdZx1l4B/efmc8jqT2YJRJ45szgbQfPJ/wCEgA6SG/cBOhnBlPs9K28g9Ly84tmJYwGd9a2hDEmCI3LHfA+ubJqKJ1KijaiZNsZN5uASoBKk2OtIn8jl3FDolzuHRxkIxSLAMyks5mVnqBgig+wAMzJKKC57udF9I03q5wKkmBgJGuUHRLeBlJGQNiEhUV2/gCjUcHwvZrTFjh5DHpLe21aadmLC51uCB24yPFcJHMiERIk3CR/8wILRzyRVCfxSEmRuBDACMJJ6cdP4gyoDecHDSS/G8mkEhCL3FQ5k7T1OWdkm8DtyaaHEmVQFd/qDCJbPRcCRMQliQeFybZo1+Ofm8+GwCAgMbRK2jdD8H36ZdgZXL9V9xxgAU0g5MCg6TyTwlb2MCI6ykFb4BioyQXhz1ntJ8skMLZDK4Lh0oloI16EAuksIhA3LB5bxLVdTgHzeQIIwfwQKwtHmR55OhQyW1LreNCS0ZqL4E/l8fswnIUSp/wCU47P5MvfXPt8slxOKgHxB/ObW7uE5Pw+BH4yOwk6F+8i0PCf5Mh0ziQf1gGyR3D+HITb4o/eRhdEw29EwhNJ9YNp987T752n3ztPvnaffO0++dp987T752n3ztPv/AAnSiKKY7OQYiEA7B/UeoYeQdzFCgyjHjB/QKOoDgesOC8eBwB2PqlwQGi94GPFzzzsR2wJHa/E4xWMEJsOA/pL6ZNYkkQ18SazUA6IPL+Kq4TjP3uDKMyKMJV1jZMRaAw+wK/avF7ch9EAQB9CulRUNYyCADyIJZN5Ugu02txo1KC2jGhBIAAFk60mXgTkCpl5oStgWjJ0hKSSG0OrYwuLaUm2ETLGFAIOqGz3xhcwKa7aZJeg7TRHBzih4Fk+Z+HnW9o3XcI/YjzZRG6zn8z1nfJBhzQHVk5xitkkOkedf8dxyf7/wn3GS85MZM+WT69snx+30njLg85DBdn0qx0wFcPUhUKHr+cj3P4/8z5f/AJny/wDzPl/+Z8v/AMz5f/mfL/8AM+X/AOYCEgLdIGxI6ZF0sEs3/wDP4TlZWVlfyRsDPkM+Qz5DPkM+QwGjP11vAEQZAKjWi8ByhsaFo5+OshxLrPHab7SuOlF2CdoFvXieuBAUoER7bwAgID+m8ZkR8l5v7OOI5yNjSin/ACHdTPVtJfHb7R45NGWmFS1EJN2uuBxxZwgWASZTKbkExIKLvL9owwq9CvjJSzIDZ7WAU6mFAO0CUuIedxwSchkE0EttEpAwTK3WPFwKOK7ItBLGOxiKHS0XHHTYX8TImSVEYEksIrq7Va6D2y22RZQnAtJwb31CbbZIMCdEh+69oYVspIyFbrmI7cy4sodn6K6W5sTARgStD1n3s6xrDZFJEa/hMCQoAE6q/qef8nz8nz8n+k+r+IrNw7eT1/qUD33k2JIqtMWa3xvkydWlzP7wWm/u/eWgWZnT/wDcJda7zk+vB0ZLp+WU3Vs3kpjg9ucjHlzTJIO++x6V9PROe8dGLk1HBnwJnwJnwJnwJnwJnwJnwJnwJgICOziaxwx0wtbFdTJaPucjr+WGGGGGD6ghAd9HTBsk4fqvjgSTGkjk19CxQdcZ75/We+f1nvn9Z75/WWqNAipYti48ZwswqfpErIwQgbL1gQAxcINSjLqL+nAQPaLqgRddK79cgTKn+lDDwk/eOMLCkHa1VPEpKK+EHdgzri5pXjx8vvlntlBt6vfv9Th5g+/H+eeR+ngzZLEIylAuBogWZo8pM6HB71TN5SyKgWypXDBRayN5HQwoqJbxIOK4ngAQUYMIdB5MAKKhEVteSeikFDlkJj3BjlYcKwxErAk05kw10yFc6cbMok4lOO8CkTpKMfQBkGB2yXAJF6avOHxd8FqiqFvLHD3OtjgJwob9E+G+2JC2dEjDbeRKpJ1GYR+qwETQt+GaRzHhjIC5giv1vaujkSwy/BkSyG+4BfR/R7DvnZNHXeFG9ASBY1uXXjksKUESfEKyavAms6EhdemTBudIznvecv8AcG4VT2AkTbLmyVVR2A7fMrrh9ntdpqHnUdL6PiyaZciKY3cVeQShJTw99XrPSvp6Jz2zoz0h/UFNZ6Bziuvx8TJRGICQFkus9o/rPaP6z2j+s9o/rPaP6z2j+saKbKiMTx4YGnCfp73vwNkmJMSRYnpkSaAAGkkYyc3ObnNpGVCN5iUpmawV05lyeM6TPdenEM+XlGB8EmDxnIZMVfnzxJ9kVRccr1wqQHb+ju9G+iY40gFFq5ivHIJdnJXVdr4/QYSHSRgGegyp+nrldJCcflgISFBBCM0db5Nyy5vLLjPQqag2KmsmKWISKWk0zk5NLcgSQhCzZWQJjzJKuHcO6ATItEkXrSmQG0GKESP5DJlvqEwTJUuCrObrKqBN6iAQCZIIkcrjbFH2qiJwhlFzKoieAEnCMQmoDvyjsZJd4pGiughFOVhnBGgN6DER5iq6erbpJCa6dkGK3zm2k9FSVUHEmzEoshIx4d24RrZ4YC5uDgu4AKJZiBq2YWhiEtNWS48stIfKKRFI6VE84xG0BfsPunLs0v0TtB05LrnBTeqvU62eSMBqSMQY8NR5zmwvF56iWz29cuuwn+QmvPGRhs0QupNOVTnE0oeInzwq83I2anCA0mTrlABAx4GsFdpiZBLU2gp3LDh2iBiHJLoCblXrEENwEBMtToqbgvX8WjEVs8clabEiwG/fbAvIz2hpnryxDbjDJipBPp8SOOk4ZnBXcqfU344ibR2JsGm4bwZMYMIIJPHUdccQm3EFbet6xZslfCEp5mLsWCzJSz+TACWrLEK9Msg+MxJLfZb7uDqCQSKNpAGZmtSZ6V9PROe2dGekP4Br+YmZpjg7ZNAs63vprKo+K3QTdkh98RQRMiFTZDDPbO/H0Cms9A56r8eevfzhPaxU8P8AT3vf9OsyFgxsNt+eKCMsG6GJ9P4ZZZPYQhhJ3njKGqYmbcL0ftgEaX9GHuuuTgGhlJLqTwMRQ7OGF0WemTig03HniyrxMIvqPMZoQZP6L6eytgHwsz9yx6Z6d0H2gOKlHgz9/pKec1ROOnG60qj9rvpeLu8+itHNYyJkSTE8MxmEkeoDA4kd4W3KHnITnqTNpnchegK3JAikqOrKrgDIrpDAB+MBGVN9DUvEwUshnW59fJEhxkCzDgOilcWNooiaDD3ZBk6aStrsmVFd1E2JrO/AXVDYeBlmgUCAKJzZpFpj14iDAmC+yISbRoVa1zDE2t4jwFQvUOCUUDcKIuo8Jt3Rx/KZIKAlVgEB1R9fauj/AFKG7ma8CEbbLZeifLKgBdABN2cjzjieHRrX3K8McqdeakT/ABHlgD7UeCk9D0yCD3GKZ80yNjmPh/pfTJIs9C2tD1OE7ZIiquZJi75GNdMK8tZUkCfASvLKaUOkhkXUVLmJxkYmu2yzyAz0r6eic9s6M9Ifw9Wx6P8AnPXsQhwNSTfXECWZSUusiM+eNpRrEU3hN+O8qCVvssMH2cYNVxJ6T7YEWleSM9sri4c0ShjfDeb1f88vQfp73v8Ap1906M9e/L/H1LE+bRXMQ8YlB04Jm+sM9C/eJtMH7OSt1mIlJi3LsJFNoic9TDcWKAhl79jO4ah/kPbH+izgVCSBCSUk6ZofweMDgnFyaSI6Sr1B1rrgoTccc3rITbEJMzAkqkKSCqAIPBcrRIMKWoW0AGirdrxLgyrIC25714C6KxLCLhBBYJH1xVWulj0jreRZCh6LIbs8VlFEkKtH8yQJ2QttKO8YgqMjcGLApScVSAziheMpMGGBsIKDs0Ei+NwFLh8Dhiz7MTfTCNQOpkrpBuORZ6YJcDxIcGgstyTFYn2Mxqe15bXaCS8cUJZpyL25iYyQypqT6zQBOpvJJVU9EYoXZmYOH19q6OK8h0XoMiVROwyjZzDkT+IDai385xGTZgcVUz3l12IPFemDyTQxHkhsYswLQBHCBX3PUwdAEvUafKYfPB4pLiRt99cUghZKjRn/AHBYFH1r9j9jIexb6rsnv+soJOqCDKR16dZyTpwIS1gewTfSMhCho3SQr8T9nKQSyAjyzAtq64gz0r6eic9s6M9IfwBXswqm2ePHIYlhpF313kiBAwbDyoU+EHbAAVZFuPTVeJeKAAqUpHpEX5y4iCMnTab9/BwNolBE/wDjJvlL8JPrjoYCxR4e94qR5xGcu/fp65FBmFUtmga77nBytVp9ia+3YMBgVSFEIu5u+x/HL0H6e97/AK9TPWvz9ZCqIuNuAMpKUBE8fGOlo3ltIQD7s+mKjCinyMpsYjyDrHjgQEjMWDNpDn5/wcFj5cikwaamB10hz4njfYIZuOm+MHXBP8VxY5IyAmIABZVBvCD9OPw2Pw2PweJLCkDLbmt5YIK55klCAxbDryEkfSnkpEWIRpRI49vQGVkICRRLMSdo4AGKC2DIQgBZhNog0o4qQADaDLLBeG5WJcnoCXb6qJjyxnSDpCIlOpNTPNc4bvpMZkgFuVKYGsNeRFYDIQD0rudGN1tkAiUFNmHVJPTkBuAItokVu/EZ2yX5GFlJ0uCYHx1kJpxwebRGIdSbuOc23UoXVIrfhF6vDmekClSa3NBZ41GMEA3fjJXqIhqZrHLq0YDqRSs2bJxZT7tkzBK3zM8xGdYioUu9+HbANRnbxOvZ44ASUKfb/wBJwLXJqs9UG/trWQztm6Hq0jyPHFsTiU+9/tOLM090XWk/ZPDDLQZIwhqoR5T3yXZEYfAQS8+N5EQ9yDZmAhlBBo3U4BswShVuRpi7g4jIPnF6gLTu53klUbGytOWiXfiZ/i4fp2xSZLVieYeWBlggoqHXkanovTAIQM1C24Hskh0TEEhIBuYfKb8nOct8ozyJljvjFEeuJfkG3rPTJC+QtjO20ek6ygiAnA9J798nFEQg7VTZ4hHDh8SZykiHi9RhaAZBSCNQ7EQTQRWAk4QAcy0AAaz0r6eic9s6M9If1BTWegc9V+PPXv6LL0H6e97/AK9TPWvz9U5xhEi6e2GkJQwU1tj9ZGQmVgmmpXwjJF+oSMdV4KaisBg8IrPwyKljb9sMDUhEcMO2nviGG0Mk6yiUKh3HLiwZ3Uj+suDiMG4eHfI2bkY4P5yP5uT+ABB1ZmgGnExVElmjxNn8bO+thni+GYw8QwZKpTPQFnkxxg4wKg7pQmo8kpJrGilChAskE8Sys23VrowmCDUJg6dRR0zw+ldsQXmwiGt2ocowAQGiXlEFbLK3QNF5VHVW4UJw7EFtkSKQDM744TzLflXpH1J2+XbCR4yOdxMxivYgrLGh7JjHdnUYnwQGB2RcOx5mDo/kJ16JRKXfyww4BNuPr7V0f6vYcBiKmSQ7qxCNrhnmRp7Y0ZJWrm7iOlYBEUnAuNqN5NCHKb68cjULzV5HaeXxyARrAqJLY4ug6mKkI6P9QuBEN0hIZmiA7/rCCEAOiw6YnXf84JYUFSQ9Z43hRVMobzu956V9PROe2dGekP6gprPQOeq/Hnr39Fl6D9LXOpJtfXqZ61+f4+q4+l41D1ofF44oLYSJ8hkPthEUQDFnM/5XbGwrSEkIzIqnePLJVpUgMu+d+I+XOC+EyZueuPxzkp6JAFjdJNc4UBiBg48MaWn+C61/x6XOe8ERiU1H85HBmo/yzd4J+BOPKMkvMRA8LH7mOxPWQeMnqyHZNgGGEnB10KMCWamXTCG8nKaXlVpyCL5KiXN+RYbc88IDE8VmAwnGysQ6uVIhaxQYZaifcnEcs1CsTYw/JingHaRyBDewgCepoPF4cZLLPkm13SW4K1kQdqY7Vq1Fo7jbI5GMxOtZyky8EBu2kQIwKlxbTDFFnFRKQ2WaERt54dEkQgW2hEU9Wh5mJk4CNNxucleNYEUj1psEcnaeRkcmH3b0crkB4Km8RHPbbxdMGCaUSP1EUBLhw8dRenkQrBIgqK6YyDlMx4hhWPKKI6rEHnGIggJHTuOHfDDZkbuxffJ16DYKCQlSSVvETWjY90aypIDDPazw/ko8LJy1AssQyPRyAjVi0wiVQdcXhlrRADOYNQIeHpxmhsk077x85xkpMMxb74yZEBnapUGXpTWkDl8NxiCEVsL0SH2MgQTJSdg8G+YySZBAOwkRzM5OTnYlOlcemGhGTIbN6tZO2cKsrw9CiTxl74zTKCKTrWbO0ZoyznbdiL+5khOiQTRczZ4S9sjSkJZJ7kDJ3MhRKkWE6rwMTBSMide+Qm/5hTWegc9V+PPXv4SIDKKyMyn6yQ64BTDIjE99PZxUIFyMPwkkfeXvklLIiLDukifDC2IGzwdDznKfjx+d4ikB1+qeHrk+kU5bFUzLb0xUbRFWlaee2S0YGzN7gFTNoOISOqYjK8E1nueF/X1LH0n8fVBIbxGfbpKTuYBl6goG6CJs4Pp5QUUlJhiyd57D0z2/D+N42EdruyjrNpPPV6+Oa8iLI/W3FqksElBLfeYuK4vXTB8FtMWRFSBjSkLmi0nS0b4jMqPgDyTyHuewkQwPVY5GGiGMFFkC5VEtxEYVhZ4jtIFPj3w5LhR35Y3u5iVSO6jtViMHjdw62HsTyHixFwHFYLCdBAk6AR0FDKWgbFbSITUFQYmAQIpINESYYwHJDXQ4Ly/1sQCIbODXjDnIx87nnlYYV21kSZx3WBJeDHCmbGPo2Zh1GZrrksSoz8Ei3PSo1k1fSJc709pjtgSBv4L0hs7Y0oxjBekEweUYchPR0M+bg0FYAL6wHrms3ntXRyo2SISyctZE28IyC6wBFL9mcScyUhURQXrk4xkgmigzSlsmJ8d/yEwdMkWvhG/MPTBwv3Il4uTg9MfOw0Ipq7sXk65oQb4BddumSJmmao/GV24GkvB0MCJnEukacVCDckkuID1wgqsYJJuJi46zLh7qC1Ax5mMghIpBKGHi8k1AYkGHpVnhgYMg1YjxiJ7bxUQKCQ8h1g0HA2nqTCPGHEToN6m4dHFOBPJSOpBb9sTEOWyROZoiuCcWVKZ2Z3fB2C+uMJBBBDnUFPBUxTQsjKA2Fc8+kfyCmsMA3GK51lXdKGujnr38KLMhEU8v+4taUiERLbL0LzTWdgh1kGPC8TMDpZV6oj9zkuCKC4VDp65evSU+5P3xmAEgIehP3nJ0XRQ1uNT3wpjXiJUXNedYvR1aKOadW98iVAUuizNa19fUcfQfx/FYKkgO3GkQ+8eCv09h6Z67+Gks8TkV0QY0lHNDHbn/AOC15r8M0u2Cn5TykwqxbYcLImiDCH4pTot8II9jAOUAipbDWkiF6SSZSFQOIubLazGIEQhNTCSCpGqmCSoQkGmhVXtXpUhDojbqBlcJqSBJEn94TmNNHoeamSHXHd1SaBImLtbSA4Kf4ddhQ4NXcfAVcgdqluCu84thoVstm5SgtsxsFCQQ6AkmJVZerQJO8Y9dLo9/3Nl5YuDNlnUP3P3g7YBAgNZsyYdPpAq5OsVwZ7/+Oe//AI57/wDjnv8A+Oe//jnv/wCOVUJ9AAblA7db8fqgIbHIwgcB/JR43IQL64j8T7jI/ClSm3neTiExZZ8GJ/zAkhrPY8o5Tf5yHl3NQUvG63kiBXpE8a3/APMUPgFEUYY3OMcGKI6QNy9fLJ2DGASQzN85CVdLBY8RxIEgQebdInhGISgmtf7npX0E+FgjFZIXaLC/vnpD+oKa+kBEKR6mEESAgck3HUnxz2t+89rfvPa37z2t+89rfvPa37wN1mgKN0DrPQf4HlDrA+AFMCXqnnPgsPgsPgsPgsIaqxFeuA652fR/FAQ3gSE4hYwxZ2Mu6pAcekIHaYXIhRVBUnhjOLBDQZqOzMb61P8ABUY3gP3uGKCRWrlA5vDrtnu/aukaQvDmoR6Jw9vo8Q90+VrzXhEud1R6cVjfT1Nidg6jUNUOX4YWI1VJqyWqhOR12hkMmrsBHSHCSYhXzEoF4FJOkZMQfH3J2CCDYAZkJCgyLIMDvnO4MYldUdAJuSA8LY1GKkIAyTKOpOxs6GC+JNITqXoNBzHik7QrLHWff7GP8KjRXFEN93fK6tpUpvymuNFY0rQOZbMhG3uHTIhCsrldV5f6I7Dsn+7jk2yTFpiW1HXV+mSSEQLJDEeGRFEYjlv7fvJaVhLUemD7Yh0SjrrJ42B2oh9t/fKRGIKisc2H5/OLTBDCssmNMZPWFXrHpOOQqNWa+95oxEuj8uNI7JQj7Rnag+sSKrCCg1rPs/f2zs/f2zs/f2zs/f2zs/f2zs/f2zs/f2zs/f2xhfSUECk6L0Zr6xJ02Z3GdxncZ3GdxncYAZTgFoP+HGbWZMcgdW6543ktt3yXX31cYm8rmC8knMb3vWDukn638D3rGQUWbf7Hh5zxg5KPQ8/k4yFgxOw618n2eofR0UDUDlnK40sv+p8siUudbxpkSAlkqly0il2psO6okmQ4rBmULuyQ/DBySSIGZWC0gR9p0RyWDOdEDkDbcHAMGsdyY25jRzcNhnFuclusdOOhmyvhI9H2nI0t8Db18c7EVI+zgAa0MH82cjON4lL1NoQTXQTgxoYULPUik3dm4g03M0hs6KIwUy/GawuqEiLMjNItBLJiyNzQ630ueUHSRdwsL24if6hVYhE4tZdWG8gpWNVObNfAwe8nW/i5r89IGIBv6jANDI/7CJ3lxTG0g8VSdMYcEY64iTJD37G8FykUhifDhO51wXOfusySoSFNLhbxKWuoP5PYhwghuW/NyPD+5M69IQwrqJp75WVxgvwv0rrHOhNJtORZUtZEEdbJfs43ghQeyIECbERMFSAQTBIwWHBjyAE8J1yMdILtDmCiSR66j6Ceeon3pkiCaMhPl5UhITxkcW7mTQWlagjU4lNEBpEFA0ehfMwq4GIrWZXqrb/WGCa3qBiNvk11xTSOCqjAtNN1DOnAt4YS2uSjn0yTAfzV8cJkF3xM5VRVYDu2NtkeNZGhamhYUgVIWSYYZfYDQARhG+p/wonpD4xJtx3wzAW5EJE+/wDTtYye81KAHQ3N8ZcIeERghwAjCOYGMoIu2gwVuSR48c49FnMCF3E4F/Yw6FgBVyOwEQ/ePwscKcT4fvk9iHS1FmwI46dMUOL5QRVFQpV83gSJAS4DWmXJ4W/xWsI3kXZQAcEq9rBEjJxVJSQpQBdTrEAIdzI5sys4ynmmhDzJmDrByejDJPdnN2fc50J0yf4mPWrYYLckjouQ4ckM2yRpnNEm9XDWBKrfIxcaEknfb9YlQPkSuHMA1yxgW9E11WQBAq3qidr4OqQknrIROustFWEVKa7R80MiQ19naNhFLWmJjKyao1HPsEeGRW1En9Fnzk6Y/wAS8RKEULhYIO94BD3EoFJswCe3X+oCgkciZNcvjpx5RP2xskEZMjSyTL/cR8JoRKIc98E+SRMm3D5fN1XTz3Djd3bmOiPK4fvDJ9E3kUJjCJNsBl4S8S5IRnGSY54YEdJclTwUOgV0BXYXYDB801JXAcvPQHlzb6zxLliDVBMxsvBLsRJi+s9953jyGnT+0TRdETEbsM+8srDYzdruYArie/OaaS7NMC2Tx08ciVOecUpprLE1vB010KzCeFsga1j1RxEeSS2p7GceLCIpG9uf+FtOkNyx+r5YEoYHQ/pM9FVBJiRSSdl984tG6QJpJMJhBjxwIiI9Hb61OSxEENJFz53xlzq3QWWkFLNXO9YVeqTENTa0LzUYw9NsBSklLbvF+270OSBbAXJre8kBaC1Kjzv/ABnh4aozAAjCsO7cThyIYS3EohpJkViCeSgdpHrhCEXsCIfE57hhWe4QRigFhLEvXWabyFDk6mJJiYtcYiMyDJ1LYhyAPgYeWkrFDJCuubER9Lj9/qMKUCETInCJOPFG3DySQSxRkRMg9qxB2wDgBEaMrBvCyt9SbrsdMZw8m2xakmJAgxrcrX9g3NhEntNGR8qpMIkS7Bmmu+KQrjJok0s6G/DBbMqQIEp0P9zbmyS/9Opz4kJ4ySFiZHXnom3hyPJ+KRPT1Xqd30S3RI22fX7mCKueaAtQcIBcyL3Nm2shQQCKVAWZig4EmUVWBU+0ODnRFo9RHzxK+4AP70RS/Ta465FP2ANhHRLbtimAcxDyhbmyWYqMN4fm3Ljgi0An7mMpcwIIqjuLSCds0Q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"}}},{"cell_type":"code","source":"batch = next(train_generator)\nfirst_image = batch[0][0] \nimage_size = first_image.shape\nimage_size","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:36.53196Z","iopub.execute_input":"2024-01-08T17:44:36.532291Z","iopub.status.idle":"2024-01-08T17:44:36.668538Z","shell.execute_reply.started":"2024-01-08T17:44:36.532259Z","shell.execute_reply":"2024-01-08T17:44:36.667683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = Sequential()\n\n# Add convolutional/pooling layers\n\nmodel.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=image_size, activation='relu',))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_size, activation='relu',))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(filters=128, kernel_size=(3,3),input_shape=image_size, activation='relu',))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\n# Add flatten layer\nmodel.add(Flatten())\n\n# Add dense/dropout layers + activation functions\n\nmodel.add(Dense(128))\nmodel.add(Activation('relu'))# relu activation function for deep layer\nmodel.add(Dropout(0.2))\n\nmodel.add(Dense(5))\nmodel.add(Activation('softmax'))\n\n# Print summary of your model\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:36.669576Z","iopub.execute_input":"2024-01-08T17:44:36.669857Z","iopub.status.idle":"2024-01-08T17:44:36.789772Z","shell.execute_reply.started":"2024-01-08T17:44:36.669833Z","shell.execute_reply":"2024-01-08T17:44:36.788987Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.callbacks import LearningRateScheduler\n\nK.clear_session()\n\n#find a lr\ndef lr_schedule(epoch):\n lr = 0.001 * 0.9**epoch\n return lr\n\nlr_scheduler = LearningRateScheduler(lr_schedule)\n\nmodel.compile(optimizer=Adam(lr=0.0001),\n loss='categorical_crossentropy', \n metrics=['accuracy'])\n\nhistory=model.fit(train_generator,\n epochs=20,\n validation_data=validation_generator,\n callbacks=[lr_scheduler])\nhistory","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:44:36.790962Z","iopub.execute_input":"2024-01-08T17:44:36.791287Z","iopub.status.idle":"2024-01-08T17:45:17.00349Z","shell.execute_reply.started":"2024-01-08T17:44:36.791255Z","shell.execute_reply":"2024-01-08T17:45:17.002452Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_accur(history, epochs=20)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:45:17.004941Z","iopub.execute_input":"2024-01-08T17:45:17.00525Z","iopub.status.idle":"2024-01-08T17:45:17.53313Z","shell.execute_reply.started":"2024-01-08T17:45:17.005222Z","shell.execute_reply":"2024-01-08T17:45:17.53215Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_lr(history, epochs=20)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:45:17.534277Z","iopub.execute_input":"2024-01-08T17:45:17.534548Z","iopub.status.idle":"2024-01-08T17:45:17.942191Z","shell.execute_reply.started":"2024-01-08T17:45:17.534523Z","shell.execute_reply":"2024-01-08T17:45:17.941217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions = model.predict(test_generator)\npredicted_classes = []\nclasses = train_crop.label.unique()\n\nfor prediction in predictions:\n predicted_class_index = np.argmax(prediction)\n predicted_class = classes[predicted_class_index] \n predicted_classes.append(predicted_class) \nsubmission = test_df.copy()\nsubmission.loc[:, 'label'] = predicted_classes\ncols=['image_id', 'label']\nsubmission = submission[cols]\nsubmission","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:45:17.943458Z","iopub.execute_input":"2024-01-08T17:45:17.943841Z","iopub.status.idle":"2024-01-08T17:45:18.212011Z","shell.execute_reply.started":"2024-01-08T17:45:17.943805Z","shell.execute_reply":"2024-01-08T17:45:18.211162Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Add regularization and learning rate=0.001\n\nfrom tensorflow.keras import regularizers\nmodel = Sequential()\n\n# Add convolutional/pooling layers\nmodel.add(Conv2D(filters=32, kernel_size=(3, 3), input_shape=image_size, activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(filters=64, kernel_size=(3, 3), input_shape=image_size, activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(filters=128, kernel_size=(3, 3), input_shape=image_size, activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\n# Add flatten layer\nmodel.add(Flatten())\n\n# Add dense/dropout layers + activation functions with L2 regularization\nmodel.add(Dense(128, kernel_regularizer=regularizers.l2(0.01)))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.2))\n\nmodel.add(Dense(5, kernel_regularizer=regularizers.l2(0.01)))\nmodel.add(Activation('softmax'))\n\n# Compile the model\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Display model summary\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:45:18.213156Z","iopub.execute_input":"2024-01-08T17:45:18.21344Z","iopub.status.idle":"2024-01-08T17:45:18.34877Z","shell.execute_reply.started":"2024-01-08T17:45:18.213414Z","shell.execute_reply":"2024-01-08T17:45:18.34784Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"K.clear_session()\nmodel.compile(optimizer=Adam(lr=0.001),\n loss='categorical_crossentropy', \n metrics=['accuracy'])\nhistory=model.fit(train_generator,\n epochs=20,\n validation_data=validation_generator)\nhistory","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:45:18.34993Z","iopub.execute_input":"2024-01-08T17:45:18.350219Z","iopub.status.idle":"2024-01-08T17:45:53.993556Z","shell.execute_reply.started":"2024-01-08T17:45:18.350194Z","shell.execute_reply":"2024-01-08T17:45:53.992292Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_accur(history, epochs=20)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:45:53.995145Z","iopub.execute_input":"2024-01-08T17:45:53.996141Z","iopub.status.idle":"2024-01-08T17:45:54.498237Z","shell.execute_reply.started":"2024-01-08T17:45:53.996102Z","shell.execute_reply":"2024-01-08T17:45:54.497521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions = model.predict(test_generator)\npredicted_classes = []\nclasses = train_crop.label.unique()\n\nfor prediction in predictions:\n predicted_class_index = np.argmax(prediction)\n predicted_class = classes[predicted_class_index] \n predicted_classes.append(predicted_class) \nsubmission = test_df.copy()\nsubmission.loc[:, 'label'] = predicted_classes\ncols=['image_id', 'label']\nsubmission = submission[cols]\nsubmission","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:45:54.507101Z","iopub.execute_input":"2024-01-08T17:45:54.507368Z","iopub.status.idle":"2024-01-08T17:45:54.646322Z","shell.execute_reply.started":"2024-01-08T17:45:54.507344Z","shell.execute_reply":"2024-01-08T17:45:54.64542Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# SPECIAL NETWORK MODELS","metadata":{}},{"cell_type":"markdown","source":"- **ResNet:** Leveraging its depth and performance to identify subtle patterns in cancer cell images.\n\n- **Inception:** Efficiently utilizing computational resources to detect unique features indicative of different cancer types.\n\n- **MobileNet:** Balancing speed and accuracy, making it ideal for analyzing large volumes of cell tissue samples.\n\n- **EfficientNet:** Offering state-of-the-art performance, which could be beneficial in achieving high classification accuracy for ovarian cancer types.\n\n- **VGGNet:** Providing a simple yet effective approach, making it a good starting point for educational purposes or as a baseline model.","metadata":{}},{"cell_type":"markdown","source":"## ResNet","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:3503a530-ad9f-48b7-be49-b1320505c921.png)","metadata":{},"attachments":{"3503a530-ad9f-48b7-be49-b1320505c921.png":{"image/png":"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"}}},{"cell_type":"code","source":"from tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras import layers, models\nfrom tensorflow.keras.optimizers import Adam,Adamax\n\n# Load pre-trained ResNet50 model without the top (classification) layer\nbase_model = ResNet50(weights=None, include_top=False, input_shape=image_size)\n\n# Add your own classification layers on top of the base model\nx = base_model.output\nx = layers.GlobalAveragePooling2D()(x) # Global average pooling layer\nx = layers.Dense(256, activation='relu')(x) # Dense layer with 256 units and ReLU activation\npredictions = layers.Dense(5, activation='softmax')(x) # Output layer with softmax activation for num_classes classes\n\n# Create the final model\nmodel_rn = models.Model(inputs=base_model.input, outputs=predictions)\nK.clear_session()\n# Compile the model\nmodel_rn.compile(optimizer=Adamax(lr=0.0001), # You can adjust the learning rate\n loss='categorical_crossentropy', # Use categorical_crossentropy for multi-class classification\n metrics=['accuracy'])\nhistory_rn = model_rn.fit(\n train_generator,\n epochs=20,\n validation_data=validation_generator,\n)\nhistory_rn","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:45:54.64737Z","iopub.execute_input":"2024-01-08T17:45:54.647616Z","iopub.status.idle":"2024-01-08T17:48:23.755326Z","shell.execute_reply.started":"2024-01-08T17:45:54.647595Z","shell.execute_reply":"2024-01-08T17:48:23.754505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_accur(history_rn, epochs=20)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:48:23.756387Z","iopub.execute_input":"2024-01-08T17:48:23.756671Z","iopub.status.idle":"2024-01-08T17:48:24.241967Z","shell.execute_reply.started":"2024-01-08T17:48:23.756635Z","shell.execute_reply":"2024-01-08T17:48:24.241102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions = model_rn.predict(test_generator)\npredicted_classes = []\nclasses = train_crop.label.unique()\n\nfor prediction in predictions:\n predicted_class_index = np.argmax(prediction)\n predicted_class = classes[predicted_class_index] \n predicted_classes.append(predicted_class) \nsubmission = test_df.copy()\nsubmission.loc[:, 'label'] = predicted_classes\ncols=['image_id', 'label']\nsubmission = submission[cols]\nsubmission","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:48:24.243308Z","iopub.execute_input":"2024-01-08T17:48:24.243578Z","iopub.status.idle":"2024-01-08T17:48:25.640171Z","shell.execute_reply.started":"2024-01-08T17:48:24.243552Z","shell.execute_reply":"2024-01-08T17:48:25.639223Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Flatten the predictions array\nflat_predictions = predictions.flatten()\n\n# Create a DataFrame\ndf = pd.DataFrame({'class': classes, 'probability': flat_predictions})\ndf.probability = round(df.probability*100)\ndf","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:48:25.641371Z","iopub.execute_input":"2024-01-08T17:48:25.6417Z","iopub.status.idle":"2024-01-08T17:48:25.653409Z","shell.execute_reply.started":"2024-01-08T17:48:25.641662Z","shell.execute_reply":"2024-01-08T17:48:25.652505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = px.bar(df, \n x='class',\n y='probability',\n orientation='v',\n text = 'probability')\n\nfig.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:48:25.654988Z","iopub.execute_input":"2024-01-08T17:48:25.655261Z","iopub.status.idle":"2024-01-08T17:48:25.735876Z","shell.execute_reply.started":"2024-01-08T17:48:25.655236Z","shell.execute_reply":"2024-01-08T17:48:25.735033Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission","metadata":{"execution":{"iopub.status.busy":"2024-01-08T18:08:20.296012Z","iopub.execute_input":"2024-01-08T18:08:20.296494Z","iopub.status.idle":"2024-01-08T18:08:20.307586Z","shell.execute_reply.started":"2024-01-08T18:08:20.296453Z","shell.execute_reply":"2024-01-08T18:08:20.306437Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('/kaggle/working/submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T18:16:54.270599Z","iopub.execute_input":"2024-01-08T18:16:54.27153Z","iopub.status.idle":"2024-01-08T18:16:54.277116Z","shell.execute_reply.started":"2024-01-08T18:16:54.271494Z","shell.execute_reply":"2024-01-08T18:16:54.276198Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 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"}}},{"cell_type":"markdown","source":"due to the issues with memory occupation it is excluded from final notebook\n\n from tensorflow.keras.applications import EfficientNetB7\n\n # Load pre-trained EfficientNetB0 model without the top (classification) layer\n base_model = EfficientNetB7(weights=None, include_top=False, input_shape=image_size)\n\n # Add your own classification layers on top of the base model\n x = base_model.output\n x = tf.keras.layers.GlobalAveragePooling2D()(x) \n x = Dense(256, activation='relu')(x) \n predictions = Dense(5, activation='softmax')(x) \n\n # Create the final model\n model_en = models.Model(inputs=base_model.input, outputs=predictions)\n K.clear_session()\n # Compile the model\n model_en.compile(optimizer=Adam(lr=0.001), \n loss='categorical_crossentropy', \n metrics=['accuracy'])\n history_en = model_en.fit(\n train_generator,\n epochs=20,\n validation_data=validation_generator,\n )\n plot_accur(history_en, epochs=20)\n \n predictions = model_en.predict(test_generator)\n predicted_classes = []\n classes = train_crop.label.unique()\n\n for prediction in predictions:\n predicted_class_index = np.argmax(prediction)\n predicted_class = classes[predicted_class_index] \n predicted_classes.append(predicted_class) \n submission = test_df.copy()\n submission.loc[:, 'label'] = predicted_classes\n cols=['image_id', 'label']\n submission = submission[cols]\n submission\n","metadata":{}},{"cell_type":"markdown","source":"\n

4. Conclusions

","metadata":{}},{"cell_type":"markdown","source":"After several efforts of image transformation, different augmentation and models architecture, the quality of predictions is still low - like random guess (and most cases the test image was classified as CC type). The maximum accuracy was achieved for test data - 0.7337. The model could classify the test image (HGSC) as CC with 47% of accuracy and as HGSC with 33%.\n\n**The possible reasons:**\n\n1. Small sample of training data - just 500 pictures\n\n2. The histologic samples were given in very variable formats, and it should be highly zoomed to get important cellular details. This rocedure is memory exhasting and impossible in resent conditions.\n\n3. Possible options to improve predictions:\n - use library histolabs, which can provide detailed slices of images\n - simplification of models with Network Auto-Reduction (NAR) and ResRep - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197439/\n - Inception module","metadata":{}},{"cell_type":"markdown","source":"## Inception","metadata":{}},{"cell_type":"markdown","source":"**Multi-scale Processing**:\nInception modules use filters of different sizes (1x1, 3x3, 5x5) simultaneously. This allows the network to capture patterns at different scales in the input data.\n\n**Parameter Efficiency**:\n1x1 convolutions are used to reduce the number of input channels before applying larger convolutions. This helps in reducing the number of parameters and computational cost.\n\n**Computational Efficiency**:\nBy using parallel convolutions and concatenating their outputs, the network can process information in a computationally efficient manner.\n\n**Network Depth**:\nThe architecture allows for effective training of very deep networks. This was crucial as deeper networks were found to perform better on certain tasks.\n\n**Global Average Pooling**:\nThe architecture uses global average pooling instead of fully connected layers in the final layers. This reduces the risk of overfitting and decreases the number of parameters.\n\n**Feature Reuse**:\nThe network encourages feature reuse by using 1x1 convolutions. This enables the network to learn a variety of low and high-level features.\n\n**High Accuracy**:\nInception architectures have achieved state-of-the-art performance on various image classification tasks, demonstrating their effectiveness.\n\n**Versatility**:\nThe Inception architecture has been used as a base architecture for other tasks beyond image classification, such as object detection (in the context of the Inception V2 and V3 models).","metadata":{}},{"cell_type":"markdown","source":"Inception runs the memory out - therefore its complicated to apply here","metadata":{}},{"cell_type":"markdown","source":"def inception_module(x, filters):\n conv1x1 = Conv2D(filters[0], (1, 1), padding='same', activation='relu')(x)\n \n conv3x3_reduce = Conv2D(filters[1], (1, 1), padding='same', activation='relu')(x)\n conv3x3 = Conv2D(filters[2], (3, 3), padding='same', activation='relu')(conv3x3_reduce)\n \n conv5x5_reduce = Conv2D(filters[3], (1, 1), padding='same', activation='relu')(x)\n conv5x5 = Conv2D(filters[4], (5, 5), padding='same', activation='relu')(conv5x5_reduce)\n \n maxpool = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(x)\n maxpool_proj = Conv2D(filters[5], (1, 1), padding='same', activation='relu')(maxpool)\n \n inception = concatenate([conv1x1, conv3x3, conv5x5, maxpool_proj], axis=-1)\n \n return inception\n\ninput_shape = (100, 100, 3)\ninput_layer = Input(shape=input_shape)\n\ninception1 = inception_module(input_layer, [64, 128, 128, 32, 32, 32])\ninception2 = inception_module(inception1, [128, 192, 192, 64, 64, 64])\n\nflatten = Flatten()(inception2)\ndense1 = Dense(256, activation='relu')(flatten)\noutput_layer = Dense(5, activation='softmax')(dense1) # Adjust num_classes based on your task\n\ninception_model = Model(inputs=input_layer, outputs=output_layer)\n\ninception_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\nhistory = inception_model.fit(\n train_generator,\n epochs=10,\n validation_data=validation_generator,\n)\n\nhistory","metadata":{}},{"cell_type":"markdown","source":"# Production","metadata":{}},{"cell_type":"code","source":"import os\nimport pickle\nfrom sklearn.base import TransformerMixin, BaseEstimator\nfrom sklearn.pipeline import Pipeline\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras import layers, models\nfrom tensorflow.keras.optimizers import Adamax\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom PIL import Image\n\nclass MyTransformer(TransformerMixin, BaseEstimator):\n def __init__(self):\n pass\n\n def transform(self, input_path, output_folder, d):\n def crop_d(image, n):\n width, height = image.size\n width = width // n\n cropped_image = image.crop((0, 0, width, height))\n return cropped_image\n\n def zoom1(image, w=100, h=100):\n image = image.convert('RGBA')\n image_data = list(image.getdata())\n black_threshold = 50\n mask = [(0, 0, 0, 0) if all(item < black_threshold for item in pixel[:3]) else pixel for pixel in image_data]\n image.putdata(mask)\n bbox = image.getbbox()\n center_x = (bbox[0] + bbox[2]) // 2\n center_y = (bbox[1] + bbox[3]) // 2\n crop_width = w\n crop_height = h\n left = center_x - crop_width\n upper = center_y - crop_height\n right = center_x + crop_width\n lower = center_y + crop_height\n crop_image = image.crop((left, upper, right, lower))\n return crop_image\n\n if d == '1':\n image = Image.open(input_path)\n image = zoom1(image, w=100, h=100)\n output_path = os.path.join(output_folder, 'processed_img_1.png') # Unique name for each processed image\n image.save(output_path)\n\n elif d == '2':\n image = Image.open(input_path)\n image = crop_d(image, 2.5)\n image = zoom1(image, w=100, h=100)\n output_path = os.path.join(output_folder, 'processed_img_2.png')\n image.save(output_path)\n\n elif d == '3':\n image = Image.open(input_path)\n image = crop_d(image, 4.5)\n image = zoom1(image, w=100, h=100)\n output_path = os.path.join(output_folder, 'processed_img_3.png')\n image.save(output_path)\n\n elif d == '4':\n image = Image.open(input_path)\n image = crop_d(image, 5.5)\n image = zoom1(image, w=100, h=100)\n output_path = os.path.join(output_folder, 'processed_img_4.png')\n image.save(output_path)\n\n return output_path\n\n# Load pre-trained ResNet50 model without the top (classification) layer\nbase_model = ResNet50(weights=None, include_top=False, input_shape=(224, 224, 3))\n\n# Add your own classification layers on top of the base model\nx = base_model.output\nx = layers.GlobalAveragePooling2D()(x)\nx = layers.Dense(256, activation='relu')(x)\npredictions = layers.Dense(5, activation='softmax')(x)\n\n# Create the final model\nmodel_rn = models.Model(inputs=base_model.input, outputs=predictions)\n# Compile the model\nmodel_rn.compile(optimizer=Adamax(lr=0.0001), \n loss='categorical_crossentropy', \n metrics=['accuracy'])\nmodel_rn.fit(\n train_generator,\n epochs=20,\n validation_data=validation_generator,\n)\nmodel_rn.save_weights('/kaggle/working/weights.h5')\n\n# Create the pipeline\npipe = Pipeline([\n ('FeatureEngineering', MyTransformer()),\n ('ResNet', model_rn)\n])\n\n\n# Save the entire pipeline\nwith open('/kaggle/working/my_new_pipeline.pkl', 'wb') as output:\n pickle.dump(pipe, output)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:48:25.746805Z","iopub.execute_input":"2024-01-08T17:48:25.747175Z","iopub.status.idle":"2024-01-08T17:50:45.249574Z","shell.execute_reply.started":"2024-01-08T17:48:25.747144Z","shell.execute_reply":"2024-01-08T17:50:45.248624Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Example usage\ninput_path = '/kaggle/input/UBC-OCEAN/test_thumbnails/41_thumbnail.png'\noutput_folder = '/kaggle/working/pipe_output'\nos.makedirs(output_folder, exist_ok=True)\n\nd = '1' # or '2', '3', '4'\n\n# Transform input image\nprocessed_image_path = pipe.named_steps['FeatureEngineering'].transform(input_path, output_folder, d)\n\n# Load the model and make predictions\nloaded_model = pipe.named_steps['ResNet']\nloaded_model.load_weights('/kaggle/working/weights.h5') # Load your saved model weights\n\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.resnet50 import preprocess_input\n\n# Load and preprocess a single image\nimg = image.load_img(processed_image_path, target_size=(224, 224))\nimg_array = image.img_to_array(img)\nimg_array = preprocess_input(img_array)\nimg_array = np.expand_dims(img_array, axis=0) # Add batch dimension\n\n# Create ImageDataGenerator for testing\ntest_datagen = ImageDataGenerator(rescale=1./255.)\n\n# Use flow method to generate batches\nprocessed_image = test_datagen.flow(\n x=img_array,\n batch_size=1,\n shuffle=False\n)\n\n# Now you can make predictions\npredictions = loaded_model.predict(processed_image)\npredicted_classes = []\nclasses = ['HGSC', 'EC', 'CC', 'LGSC', 'MC']\nfor prediction in predictions:\n predicted_class_index = np.argmax(prediction)\n predicted_class = classes[predicted_class_index] \n predicted_classes.append(predicted_class) \n\nprint(predictions,\n predicted_classes)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T17:50:45.250973Z","iopub.execute_input":"2024-01-08T17:50:45.251703Z","iopub.status.idle":"2024-01-08T17:50:54.389602Z","shell.execute_reply.started":"2024-01-08T17:50:45.251665Z","shell.execute_reply":"2024-01-08T17:50:54.388708Z"},"trusted":true},"execution_count":null,"outputs":[]}]}