deanmark

def tensordot_pytorch(a, b, axes=2):
    # code adapted from numpy
    try:
        iter(axes)
    except Exception:
        axes_a = list(range(-axes, 0))
        axes_b = list(range(0, axes))
    else:
        axes_a, axes_b = axes
    try:

karpathy

""" 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

obfusk

import code; code.interact(local=dict(globals(), **locals()))

tylerneylon

-- copy.lua
--
-- Lua functions of varying complexity to deep copy tables.
--


-- 1. The Problem.
--
-- Here's an example to see why deep copies are useful. Let's
-- say function f receives a table parameter t, and it wants to

kinopyo

require 'net/https'
require 'nokogiri'

url = "https://example.com"
url = URI.parse( url )
http = Net::HTTP.new( url.host, url.port )
http.use_ssl = true if url.port == 443
http.verify_mode = OpenSSL::SSL::VERIFY_NONE if url.port == 443
path = url.path
path += "?" + url.query unless url.query.nil?