SoloSynth1

SoloSynth1

# This is a short script to use Monte Carlo method to estimate
# the win rate of different strategies taken in the famous Monty Hall Problem -
# Because my wife had a hard time understanding the rationale behind it ;)

import random

rounds = 1_000_000


def main():

SoloSynth1

# train-test partition
python3 ../../scripts/preprocessing/partition_dataset.py -i images -o images -x

# compile TFRecords - Train
python3 ../../scripts/preprocessing/generate_tfrecord.py --xml_dir=images/train --labels_path=annotations/label_map.pbtxt --output_path=annotations/train.record --csv_path=annotations/train.csv

# compile TFRecords - Test
python3 ../../scripts/preprocessing/generate_tfrecord.py --xml_dir=images/test --labels_path=annotations/label_map.pbtxt --output_path=annotations/test.record --csv_path=annotations/test.csv

# actual training

SoloSynth1

import argparse
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np

import ray
from ray import tune
from ray.util.sgd.tf.tf_trainer import TFTrainer, TFTrainable

SoloSynth1

curl -X POST http://localhost:5555/api/experimental/dags/{your-dag-name-here}/dag_runs -H 'Cache-Control: no-cache' -H 'Content-Type: application/json' -d '{"conf": ""}'