Junki Ohmura jojonki

Sony Researcher. Previously, visiting researcher at Language Technologies Institute, Carnegie Mellon University. Email: junki dot ohmura at gmail.

jojonki / gym-CartPole-v0-test.py

Created January 2, 2017 14:33

CartPole-v0 which is OpenAI's gym.

	# -- coding: utf-8 --

	import gym
	from gym import wrappers

	env = gym.make("CartPole-v0")
	env = wrappers.Monitor(env, './cartpole-experiment-1')
	for i_episode in range(20):
	observation = env.reset()
	for t in range(100):

jojonki / FrozenLake-V0-QLearning.py

Last active May 19, 2022 17:47

FrozenLake-v0 with Q learning

	# -- coding: utf-8 --
	# ref: https://gym.openai.com/evaluations/eval_1lfzNKEHS9GA7nNWE73w

	import numpy as np
	import gym
	from gym import wrappers

	# Q learning params
	ALPHA = 0.1 # learning rate
	GAMMA = 0.99 # reward discount

jojonki / FrozenLake-V0-QLearning-epsilon-greedy.py

Last active October 14, 2022 11:39

	# -- coding: utf-8 --
	# ref: http://taotao54321.hatenablog.com/entry/2016/11/08/180245

	import numpy as np
	import gym
	from gym import wrappers

	# Q learning params
	ALPHA = 0.1 # learning rate
	GAMMA = 0.99 # reward discount

jojonki / pg-pong.py

Last active January 6, 2017 08:47 — forked from karpathy/pg-pong.py

Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

jojonki / variational_autoencoder.py

Created May 5, 2017 15:34

fix vae's x shape; not using batch

	'''This script demonstrates how to build a variational autoencoder with Keras.
	Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
	'''
	import numpy as np
	import matplotlib.pyplot as plt
	from scipy.stats import norm

	from keras.layers import Input, Dense, Lambda, Layer
	from keras.models import Model
	from keras import backend as K

jojonki / CustomTrainingParams.py

Created November 10, 2017 22:16

Add custom trainable parameters in PyTorch

	import random
	import torch
	import torch.nn as nn
	from torch.autograd import Variable
	from torch import optim
	import torch.nn.functional as F

	seq_len = 10
	n_class = 5
	class SimpleNet(nn.Module):

jojonki / return_indices_by_value.py

Last active November 14, 2017 22:07

return indices by value

	# https://stackoverflow.com/questions/7851077/how-to-return-index-of-a-sorted-list
	s = [2, 3, 1, 4, 5]
	sorted(range(len(s)), key=lambda k: s[k])
	# [2, 0, 1, 3, 4]

	sorted(range(len(s)), key=lambda k: s[k], reverse=True)
	# [4, 3, 1, 0, 2]

jojonki / heatmap.ipynb

Last active November 21, 2017 21:54

Word alignment heatmap in matplotlib

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jojonki / auto_load_extenton.ipynb-dummy

Last active December 18, 2017 21:40

Load libraries dynamically in IPython

	%load_ext autoreload
	%autoreload 2

jojonki / python_memory_print.py

Created November 24, 2017 03:25

print memory usage in python

	# https://discuss.pytorch.org/t/how-pytorch-releases-variable-garbage/7277/2
	def memReport():
	for obj in gc.get_objects():
	if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
	print(type(obj), obj.size())

	def cpuStats():
	print(sys.version)
	print(psutil.cpu_percent())
	print(psutil.virtual_memory()) # physical memory usage