(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py | |
def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen): | |
"""for every sample, calculate probability for every possible label | |
you need to supply your RNN model and maxlen - the length of sequences it can handle | |
""" | |
data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty) | |
return rnn_model.predict(data, verbose=0) | |
def beamsearch(predict=keras_rnn_predict, |
import SocketServer, subprocess, sys | |
from threading import Thread | |
from TwitterAPI import TwitterAPI | |
import json, unidecode | |
api = TwitterAPI(consumer_key, consumer_secret, access_token_key, access_token_secret) | |
HOST = 'localhost' | |
PORT = 9999 |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.