Kaggle competition: Histopathologic Cancer Detection
<SOS> metric: AUC Kaggle data source: histopathologic-cancer-detection<save_to_csv>
!pip install livelossplot
print('all folder score is:%s'%str(oof_score))
print('OOF mean score is: %f'%(sum(oof_score)/len(oof_score)))
 print('Generate submission.............')
 submission_csv_path = '/kaggle/input/liverpool-ion-switching/sample_submission.csv'
ss = pd.read_csv(submission_csv_path, dtype={'time': str})
 test_preds_all = test_preds_all / np.sum(test_preds_all, axis=1)[:, None]
test_pred_frame = pd.DataFrame({'time': ss['time'].astype(str),
'open_channels': np.argmax(test_preds_all, axis=1)})
 test_pred_frame.to_csv("./gru_preds.csv", index=False)
 print('over' )<import_modules>
TRAINING_LOGS_FILE = "training_logs.csv"
MODEL_FILE = "histopathologic.h5"
ROC_PLOT_FILE = "roc.png"
KAGGLE_SUBMISSION_FILE = "kaggle_submission.csv"
INPUT_DIR = '.. /input/'
import os
import gc
import numpy as np
import pandas as pd
from sklearn.metrics import f1_score
from sklearn import model_selection<import_modules>
SAMPLE_COUNT = 50000
TRAINING_RATIO = 0.9
IMAGE_SIZE = 96
EPOCHS = 12
BATCH_SIZE = 128
VERBOSITY = 1
TESTING_BATCH_SIZE = 3000