Andrew andrewliao11

## gist:ba460909bd07548a30336a53da5461ea
# inspired by the post: https://tanelp.github.io/posts/a-bug-that-plagues-thousands-of-open-source-ml-projects/
# tl;dr
# If you are using numpy random generator with `torch.utils.data.Dataset`,
# you might get identical results either across different workers or epochs

# disclaimer: this might not be the best choice since setting worker to be persistent requires additional RAM.
# Welcome for any idea
# Here's a simple fix with torch>=1.7.0
# See the original example here: https://tanelp.github.io/posts/a-bug-that-plagues-thousands-of-open-source-ml-projects/#a-minimal-example

## imagenet_id_to_label.json
{"0": "tench", "1": "goldfish", "2": "great white shark", "3": "tiger shark", "4": "hammerhead", "5": "electric ray", "6": "stingray,", "7": "cock,", "8": "hen,", "9": "ostrich", "10": "brambling", "11": "goldfinch", "12": "house finch", "13": "junco", "14": "indigo bunting", "15": "robin", "16": "bulbul,", "17": "jay,", "18": "magpie,", "19": "chickadee,", "20": "water ouzel", "21": "kite,", "22": "bald eagle", "23": "vulture,", "24": "great grey owl", "25": "European fire salamander", "26": "common newt", "27": "eft,", "28": "spotted salamander", "29": "axolotl", "30": "bullfrog", "31": "tree frog", "32": "tailed frog", "33": "loggerhead", "34": "leatherback turtle", "35": "mud turtle,", "36": "terrapin,", "37": "box turtle", "38": "banded gecko,", "39": "common iguana", "40": "American chameleon", "41": "whiptail", "42": "agama,", "43": "frilled lizard", "44": "alligator lizard,", "45": "Gila monster", "46": "green lizard", "47": "African chameleon", "48": "Komodo dragon", "49": "African crocodile", "50":

## natural_gradient.py
import imageio
import numpy as np
from utils import *

mode = 'sgd'    # sgd, fisher, or dig_fisher


X_train, X_test, t_train, t_test = get_data()
W = get_model()

## fim.py
import torch
import imageio
import numpy as np
import seaborn
import matplotlib.pyplot as plt
import matplotlib
torch.manual_seed(1)


# data generation: y = ax + b

## layernorm.py
# using pytorch==0.4.0

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.rnn import RNNCellBase
from torch.nn._functions.thnn import rnnFusedPointwise as fusedBackend

## pytorch-policy-gradient.py
import argparse
import gym
import numpy as np
from itertools import count
from collections import namedtuple, deque

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

## baselines_sample.py
# code locate in baselines/gail

def sample(algo, load_model_path, policy_fn):

  assert algo in ['trpo', 'ppo', 'acktr', 'ddpg', 'a2c']
  if algo in ['trpo', 'ppo']:
    with tf.Session() as sess:
      # manually build graph
      policy = policy_fn()
      # load model

## baselines_model.py
# for model in `trpo_mpi`, `ppo`

class CnnPolicy():
  def __init__():
    # build graph
    _ = conv2d()
    _ = conv2d()

  def step():
    sess.run(act, feed_dict)

## noisy-cem-rl.py
# modified from https://gist.github.com/sorenbouma/6502fbf55ecdf988aa247ef7f60a9546
import gym
import numpy as np
import matplotlib.pyplot as plt

env = gym.make('CartPole-v0')
env.render(close=True)
#vector of means(mu) and standard dev(sigma) for each paramater
mu=np.random.uniform(size=env.observation_space.shape)
sigma=np.random.uniform(low=0.001,size=env.observation_space.shape)
	# inspired by the post: https://tanelp.github.io/posts/a-bug-that-plagues-thousands-of-open-source-ml-projects/
	# tl;dr
	# If you are using numpy random generator with `torch.utils.data.Dataset`,
	# you might get identical results either across different workers or epochs

	# disclaimer: this might not be the best choice since setting worker to be persistent requires additional RAM.
	# Welcome for any idea
	# Here's a simple fix with torch>=1.7.0
	# See the original example here: https://tanelp.github.io/posts/a-bug-that-plagues-thousands-of-open-source-ml-projects/#a-minimal-example
	import imageio
	import numpy as np
	from utils import *

	mode = 'sgd' # sgd, fisher, or dig_fisher


	X_train, X_test, t_train, t_test = get_data()
	W = get_model()
	import torch
	import imageio
	import numpy as np
	import seaborn
	import matplotlib.pyplot as plt
	import matplotlib
	torch.manual_seed(1)


	# data generation: y = ax + b
	# using pytorch==0.4.0

	import math
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch.nn.parameter import Parameter
	from torch.nn.modules.rnn import RNNCellBase
	from torch.nn._functions.thnn import rnnFusedPointwise as fusedBackend
	import argparse
	import gym
	import numpy as np
	from itertools import count
	from collections import namedtuple, deque

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torch.optim as optim
	# code locate in baselines/gail

	def sample(algo, load_model_path, policy_fn):

	assert algo in ['trpo', 'ppo', 'acktr', 'ddpg', 'a2c']
	if algo in ['trpo', 'ppo']:
	with tf.Session() as sess:
	# manually build graph
	policy = policy_fn()
	# load model
	# for model in `trpo_mpi`, `ppo`

	class CnnPolicy():
	def __init__():
	# build graph
	_ = conv2d()
	_ = conv2d()

	def step():
	sess.run(act, feed_dict)
	# modified from https://gist.github.com/sorenbouma/6502fbf55ecdf988aa247ef7f60a9546
	import gym
	import numpy as np
	import matplotlib.pyplot as plt

	env = gym.make('CartPole-v0')
	env.render(close=True)
	#vector of means(mu) and standard dev(sigma) for each paramater
	mu=np.random.uniform(size=env.observation_space.shape)
	sigma=np.random.uniform(low=0.001,size=env.observation_space.shape)