tanemaki

basic_rl (v.0.0.3)

A basic_rl.py provides a simple implementation of SARSA/Q-learning algorithms (specified by -a flag) with epsilon-greedy/softmax policies (specified by -p flag). You can also select the environment other than Roulette-v0 using -e flag. It also generates a graphical summary of your simulation.

Type the following command in your console to run the simulation using the default setting.

chmod +x basic_rl.py
./basic_rl.py

korymath

# Safari Reader Night Theme
# by @LogicaEns

# background = 39 40 34 (#272822)
defaults write TeXShop background_R 0.05
defaults write TeXShop background_G 0.06
defaults write TeXShop background_B 0.03

# commands = 102 217 239 (#66D9EF)
defaults write TeXShop commandred 0.3

kukuruza

from math import sqrt

def put_kernels_on_grid (kernel, pad = 1):

  '''Visualize conv. filters as an image (mostly for the 1st layer).
  Arranges filters into a grid, with some paddings between adjacent filters.

  Args:
    kernel:            tensor of shape [Y, X, NumChannels, NumKernels]
    pad:               number of black pixels around each filter (between them)

danijar

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf


class GRU(tf.contrib.rnn.RNNCell):

grantslatton

import urllib2
import re
import sys
from collections import defaultdict
from random import random

"""
PLEASE DO NOT RUN THIS QUOTED CODE FOR THE SAKE OF daemonology's SERVER, IT IS 
NOT MY SERVER AND I FEEL BAD FOR ABUSING IT. JUST GET THE RESULTS OF THE 
CRAWL HERE: http://pastebin.com/raw.php?i=nqpsnTtW AND SAVE THEM TO "archive.txt"

danijar

import functools

import tensorflow as tf


class share_variables(object):

  def __init__(self, callable_):
    self._callable = callable_
    self._wrappers = {}

danijar

# Example for my blog post at:
# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf


def lazy_property(function):
    attribute = '_' + function.__name__

josephernest

# From "A simple unix/linux daemon in Python" by Sander Marechal 
# See http://stackoverflow.com/a/473702/1422096 and http://web.archive.org/web/20131017130434/http://www.jejik.com/articles/2007/02/a_simple_unix_linux_daemon_in_python/
#
# Modified to add quit() that allows to run some code before closing the daemon
# See http://stackoverflow.com/a/40423758/1422096
#
# Modified for Python 3 (see also: http://web.archive.org/web/20131017130434/http://www.jejik.com/files/examples/daemon3x.py)
#
# Joseph Ernest, 20200507_1220

spitis

"""adapted from https://github.com/OlavHN/bnlstm to store separate population statistics per state"""
import tensorflow as tf, numpy as np
RNNCell = tf.nn.rnn_cell.RNNCell

class BNLSTMCell(RNNCell):
    '''Batch normalized LSTM as described in arxiv.org/abs/1603.09025'''
    def __init__(self, num_units, is_training_tensor, max_bn_steps, initial_scale=0.1, activation=tf.tanh, decay=0.95):
        """
        * max bn steps is the maximum number of steps for which to store separate population stats
        """

woollsta

/**
 * Chunkify
 * Google Chrome Speech Synthesis Chunking Pattern
 * Fixes inconsistencies with speaking long texts in speechUtterance objects 
 * Licensed under the MIT License
 *
 * Peter Woolley and Brett Zamir
 */
 
var speechUtteranceChunker = function (utt, settings, callback) {