mrocklin

import theano
from theano.tensor.io import send, recv, mpi_cmps
import theano.sandbox.linalg as linalg
from theano.gof.sched import sort_schedule_fn
from time import time

dot = theano.tensor.dot
dtype = 'float32'
n = 500
run = False

r-a-y

/**
 * Disables BuddyPress' registration process and fallsback to WordPress' one.
 */
function my_disable_bp_registration() {
  remove_action( 'bp_init',    'bp_core_wpsignup_redirect' );
  remove_action( 'bp_screens', 'bp_core_screen_signup' );
}
add_action( 'bp_loaded', 'my_disable_bp_registration' );

arngarden

import theano
from pylearn2.models import mlp
from pylearn2.training_algorithms import sgd
from pylearn2.termination_criteria import EpochCounter
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
import numpy as np
from random import randint


class XOR(DenseDesignMatrix):

syhw

"""
A deep neural network with or w/o dropout in one file.

License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""

import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

allieus

#!/usr/bin/env python2
# coding: utf-8
from __future__ import print_function
import sys
import re
import json
import urllib
from colorama import init, Fore, Back, Style

URL = 'http://csearch.naver.com/dcontent/spellchecker.nhn'

koorukuroo

import networkx as nx
from IPython.display import display, HTML
def d3_graph(G):
    text_edges = ''
    edges = G.edges()
    length = len(edges)
    for index, edge in enumerate(edges):
        text = '{"source":"'+edge[0]+'","target":"'+edge[1]+'"}'
        if index == length-1:
            text_edges = text_edges + text

danijar

# Working example for my blog post at:
# http://danijar.com/variable-sequence-lengths-in-tensorflow/
import functools
import sets
import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn


def lazy_property(function):

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