akrylysov

import asyncio
import random

q = asyncio.Queue()


async def producer(num):
    while True:
        await q.put(num + random.random())
        await asyncio.sleep(random.random())

kylemcdonald

autosquid

# construct node
def opencv_matrix(loader, node):
    mapping = loader.construct_mapping(node, deep=True)
    mat = np.array(mapping["data"])
    mat.resize(mapping["rows"], mapping["cols"])
    return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix)

# loading

jnlaia

import keras
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.wrappers.scikit_learn import KerasClassifier

import numpy as np


def target2classes(y):

andreaskoepf

local SpatialUnpooling, parent = torch.class('nn.SpatialUnpooling', 'nn.Module')

function SpatialUnpooling:__init(kW, kH, dW, dH, padW, padH)
   parent.__init(self)
   self.dW = dW or kW
   self.dH = dH or kH
   self.padW = padW or 0
   self.padH = padH or 0
   self.indices = torch.LongTensor()
   self._indexTensor = torch.LongTensor()

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

Newmu

import os
import numpy as np
from matplotlib import pyplot as plt
from time import time

from foxhound import activations
from foxhound import updates
from foxhound import inits
from foxhound.theano_utils import floatX, sharedX

craffel

import matplotlib.pyplot as plt

def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])
    

bravegnu

""" 
Usage: head [options] [FILE]...

  -c K, --bytes=K        print the first K bytes of each file;
                         with the leading `-', print all but the last
                         K bytes of each file
  -n K, --lines=K        print the first K lines instead of the first 10;
                         with the leading `-', print all but the last
                         K lines of each file [default: 10]
  -q, --quiet, --silent  never print headers giving file names

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