brainstormot

import os
import csv

searchKeywords = ["P2P","금융"]

def main():
    for dirname, dirnames, filenames in os.walk('.'):
        for subdirname in dirnames:
            if subdirname == "news":
                print("in " + os.path.join(dirname, subdirname))

brainstormot

[Solarized Dark]
text(bold)=839496
magenta(bold)=6c71c4
text=839496
white(bold)=fdf6e3
green=859900
red(bold)=cb4b16
green(bold)=586e75
black(bold)=073642
red=dc322f

brainstormot

import tensorflow as tf
import numpy as np
import os

import zconfig
import utils


class RBM(object):

brainstormot

import tensorflow as tf
import numpy as np
import input_data
import Image
from util import tile_raster_images


def sample_prob(probs):
    return tf.nn.relu(
        tf.sign(

brainstormot

""" 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