JuhaKiili

# This code works for:
# 1. Single image
# 2. List of images
# 3. An archive with images
# 4. List of archives
# 5. Mix of everything above

# Defined by list of:
# 1. Public URIs

JuhaKiili

import valohai


def main(old_config):
    pipeline = valohai.Pipeline(name="train-superbai", config=old_config)

    # Define nodes
    convert = papi.execution("convert-superbai")
    weights = papi.execution("weights")
    train = papi.execution("train")

JuhaKiili

import numpy as np
from yolov3_tf2.models import YoloV3
from yolov3_tf2.utils import load_darknet_weights
import tensorflow as tf
import valohai

params = {
    "weights_num_classes": 80,
}

JuhaKiili

import os
from PIL import Image
import valohai

parameters = {
    "width": 640,
    "height": 480,
}
inputs = {
    "images": [

JuhaKiili

import glob
import os
import argparse
import json
import shutil
import tempfile
import zipfile
from PIL import Image

parser = argparse.ArgumentParser()

JuhaKiili

from enums import *
import random
import tensorflow as tf
import numpy as np


class DeepGambler:
    def __init__(self, learning_rate=0.5, discount=0.95, exploration_rate=1.0, iterations=10000):
        self.learning_rate = learning_rate
        self.discount = discount # How much we appreciate future reward over current

JuhaKiili

from enums import *
import random
import tensorflow as tf
import numpy as np

class DeepGambler:
    def __init__(self, learning_rate=0.1, discount=0.95, exploration_rate=1.0, iterations=10000):
        self.learning_rate = learning_rate
        self.discount = discount # How much we appreciate future reward over current
        self.exploration_rate = 1.0 # Initial exploration rate

JuhaKiili

from enums import *
import random
import tensorflow as tf
import numpy as np

class DeepGambler:
    def __init__(self, learning_rate=0.1, discount=0.95, exploration_rate=1.0, iterations=10000):
        self.learning_rate = learning_rate
        self.discount = discount # How much we appreciate future reward over current
        self.exploration_rate = 1.0 # Initial exploration rate