<jupyter_start><jupyter_text>Run the following code snippets to setup your Repository in Google drive to be able to run this code successfully.Make sure to follow along steps for a tailor made approach.<jupyter_code>#CHECKS FOR GPU FUNCTIONING
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
#Mounting Google Drive

from google.colab import drive
drive.mount('/content/gdrive')
# Navigating to your base folder
# Create this folder in your drive before running 

%cd gdrive/My Drive/APM_FINAL #Could be any folder name
# Cloning git repo
! git clone https://github.com/skyler14/ICIAR-histology/ICIAR2018.git
#Navigating into the drive
%cd gdrive/My Drive/APM_FINAL/ICIAR2018<jupyter_output>Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly

Enter your authorization code:
··········
Mounted at /content/gdrive
/content/gdrive/My Drive/APM_FINAL/ICIAR2018<jupyter_text>Place the competition microscopy images into data\train|test directories. Please note the competition rules disallow us to redistribute the data.1. To extract features run this. For less powerful GPU decrease BATCH_SIZE in feature_extractor.py<jupyter_code>python feature_extractor.py --images <directory/containing/images/> --features <directory/to/store/features/><jupyter_output><empty_output><jupyter_text>By default preprocessed feature files are contained in directory data/preprocessed/[test|train]/model_name/2. To train LightGBM models using cross-validation and to generate predictions for all models, crop sizes, seeds, augmentations and folds run this.<jupyter_code>python train_lgbm.py<jupyter_output><empty_output><jupyter_text>3. To combine predictions across all models, seeds and augmentations, and crossvalidate across all folds run:<jupyter_code>python crossvalidate_blending.py<jupyter_output><empty_output>
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