wookayin

# use the following snippet in your ipython notebook shell
import argparse
import tensorflow as tf

tf.app.flags.FLAGS = tf.python.platform.flags._FlagValues()
tf.app.flags._global_parser = argparse.ArgumentParser()

ckinsey

import json
import markovify
import re
import time

from slackclient import SlackClient



BOT_TOKEN = "insert bot token here"

volkancirik

"""
TreeLSTM[1] implementation in Pytorch

Based on dynet benchmarks : 
  https://github.com/neulab/dynet-benchmark/blob/master/dynet-py/treenn.py
  https://github.com/neulab/dynet-benchmark/blob/master/chainer/treenn.py
Other References:
  https://github.com/pytorch/examples/tree/master/word_language_model
  https://github.com/pfnet/chainer/blob/29c67fe1f2140fa8637201505b4c5e8556fad809/chainer/functions/activation/slstm.py
  https://github.com/stanfordnlp/treelstm

monikkinom

#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
import numpy as np
import random
from random import shuffle
import tensorflow as tf

# from tensorflow.models.rnn import rnn_cell
# from tensorflow.models.rnn import rnn

NUM_EXAMPLES = 10000

ririw

# An implementation of "Machine Learning on Sequential Data Using a Recurrent Weighted Average" using pytorch 
# https://arxiv.org/pdf/1703.01253.pdf
#
# 
# This is a RNN (recurrent neural network) type that uses a weighted average of values seen in the past, rather
# than a separate running state.
# 
# Check the test code at the bottom for an example of usage, where you can compare it's performance
# against LSTM and GRU, at a classification task from the paper. It handily beats both the LSTM and 
# GRU :)

lampts

# required tensorflow 0.12
# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

from gensim.models import Word2Vec
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

# loading your gensim
model = Word2Vec.load("YOUR-MODEL")

odashi

#!/usr/bin/python3

import datetime
import sys
import math
import numpy as np
from argparse import ArgumentParser
from collections import defaultdict

from chainer import FunctionSet, Variable, functions, optimizers

alvations

NLTK API to Stanford NLP Tools compiled on 2015-12-09

Stanford NER

With NLTK version 3.1 and Stanford NER tool 2015-12-09, it is possible to hack the StanfordNERTagger._stanford_jar to include other .jar files that are necessary for the new tagger.

First set up the environment variables as per instructed at https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software

vadimkantorov

# References:
# [1] Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, Fukui et al., https://arxiv.org/abs/1606.01847
# [2] Compact Bilinear Pooling, Gao et al., https://arxiv.org/abs/1511.06062
# [3] Fast and Scalable Polynomial Kernels via Explicit Feature Maps, Pham and Pagh, https://chbrown.github.io/kdd-2013-usb/kdd/p239.pdf
# [4] Fastfood — Approximating Kernel Expansions in Loglinear Time, Le et al., https://arxiv.org/abs/1408.3060
# [5] Original implementation in Caffe: https://github.com/gy20073/compact_bilinear_pooling

# TODO: migrate to use of new native complex64 types
# TODO: change strided x coo matmul to torch.matmul(): M[sparse_coo] @ M[strided] -> M[strided]

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__