danijar

import random
import time
import threading
import matplotlib.pyplot as plt


class Plot:

    def __init__(self):
        plt.ion()

danijar

import flask_restful


class Api(flask_restful.Api):
    """
    Patch Flask-style custom error handling into the Flask-RESTful api class.
    """

    def __init__(self, *args, **kwargs):
        super(Api, self).__init__(*args, **kwargs)

danijar

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

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__

danijar

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


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

danijar

import argparse
import collections
import re

TOKEN_REGEX = re.compile(r'[A-Za-z]+')
BLACKLIST = set([
    'pdf', 'and', 'the', 'proceedings', 'conference', 'ieee', 'for',
    'about', 'details', 'data', 'with', 'arxiv', 'preprint', 'advances'])

def tokenize(line):

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):

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):

danijar

keygen
uprises
non-infected
counter-weight
pipetted
quispel
rooster-whistles
moisturisers
enfeoffing
pre-adolescents

danijar

Char-RNN Sample

Parameters

dataset = ArxivAbstracts(
    categories='stat.ML cs.NE cs.LG math.OC',
    keywords='neural network deep')
max_length = 50
sampling_temperature = 0.5