Masanori Yamada MasanoriYamada

## linear_sum_assignment_pytorch.py
import torch
import random
from scipy.optimize import linear_sum_assignment as linear_sum_assignment_scipy
import time


def augmenting_path(cost, u, v, path, row4col, i):
    device = cost.device

## lr_scheduler.ipynb

      
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                MasanoriYamada
                / lr_scheduler.ipynb
            
            
              Last active
              May 6, 2023 16:50
            
              
                torchopt lr scheduler
              
          
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## torch_jacobian.py
def get_jacobian(net, x, noutputs):
    x = x.squeeze()
    n = x.size()[0]
    x = x.repeat(noutputs, 1)
    x.requires_grad_(True)
    y = net(x)
    y.backward(torch.eye(noutputs))
    return x.grad.data

## run_batch2.py
from __future__ import print_function
import threading
from joblib import Parallel, delayed
import Queue
import os

# Fix print
_print = print
_rlock = threading.RLock()
def print(*args, **kwargs):

## pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as 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

## joke.coffee
# Description:
#   Joke commands.
#
# Commands:
#   ぬるぽ - You reply with, "ｶﾞｯ" When you post a "ぬるぽ" word.
#
# Notes:
#   ネタ/ジョーク系のbot全般

module.exports = (robot) ->
	import torch
	import random
	from scipy.optimize import linear_sum_assignment as linear_sum_assignment_scipy
	import time




	def augmenting_path(cost, u, v, path, row4col, i):
	device = cost.device
	def get_jacobian(net, x, noutputs):
	x = x.squeeze()
	n = x.size()[0]
	x = x.repeat(noutputs, 1)
	x.requires_grad_(True)
	y = net(x)
	y.backward(torch.eye(noutputs))
	return x.grad.data
	from __future__ import print_function
	import threading
	from joblib import Parallel, delayed
	import Queue
	import os

	# Fix print
	_print = print
	_rlock = threading.RLock()
	def print(args, *kwargs):
	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as 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
	# Description:
	# Joke commands.
	#
	# Commands:
	# ぬるぽ - You reply with, "ｶﾞｯ" When you post a "ぬるぽ" word.
	#
	# Notes:
	# ネタ/ジョーク系のbot全般

	module.exports = (robot) ->