joannapurosto

def create_tf_example(example, path_root):
    # import image
    f_image = Image.open(path_root + example["image_name"])

    # get width and height of image
    width, height = f_image.size

    # crop image randomly around bouding box within a 0.15 * bbox extra range
    if FLAGS.evaluation_status != "test":

joannapurosto

from IPython.core.magic import Magics, magics_class, cell_magic
import os

@magics_class
class HelloMagics(Magics):
    @cell_magic
    def hello(self, line='', cell=None):
        print('We executed this instead')
        
ip = get_ipython()

joannapurosto

from IPython.core.magic import Magics, magics_class, cell_magic
import os

@magics_class
class ValohaiMagics(Magics):
    @cell_magic
    def valohai(self, line='', cell=None):
        path = os.path.expanduser('~/%s/execute.py' % project)
        
        with open(path, 'w+') as f:

joannapurosto

from enums import *
import random

class DungeonSimulator:
    def __init__(self, length=5, slip=0.1, small=2, large=10):
        self.length = length # Length of the dungeon
        self.slip = slip  # probability of 'slipping' an action
        self.small = small  # payout for BACKWARD action
        self.large = large  # payout at end of chain for FORWARD action
        self.state = 0  # Start at beginning of the dungeon

joannapurosto

from enums import *
import random

class Drunkard:
    def __init__(self):
        self.q_table = None

    def get_next_action(self, state):
        # Random walk
        return FORWARD if random.random() < 0.5 else BACKWARD

joannapurosto

import random
import json
import argparse
import time
from drunkard import Drunkard
from accountant import Accountant
from gambler import Gambler
from dungeon_simulator import DungeonSimulator

def main():

joannapurosto

from enums import *
import random

class Accountant:
    def __init__(self):
        # Spreadsheet (Q-table) for rewards accounting
        self.q_table = [[0,0,0,0,0], [0,0,0,0,0]]

    def get_next_action(self, state):
        # Is FORWARD reward is bigger?

joannapurosto

from enums import *
import random

class Gambler:
    def __init__(self, learning_rate=0.1, discount=0.95, exploration_rate=1.0, iterations=10000):
        self.q_table = [[0,0,0,0,0], [0,0,0,0,0]] # Spreadsheet (Q-table) for rewards accounting
        self.learning_rate = learning_rate # How much we appreciate new q-value over current
        self.discount = discount # How much we appreciate future reward over current
        self.exploration_rate = 1.0 # Initial exploration rate
        self.exploration_delta = 1.0 / iterations # Shift from exploration to explotation

joannapurosto

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

joannapurosto

import tensorflow as tf
import time

tf.reset_default_graph()
myvar = tf.get_variable('myvar', shape=[])
myvar_summary = tf.summary.scalar(name='Myvar', tensor=myvar)
init = tf.global_variables_initializer()

with tf.Session() as sess:
    writer = tf.summary.FileWriter('./logs/run1', sess.graph)