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#!/usr/bin/env python | |
# encoding: utf-8 | |
import tweepy #https://github.com/tweepy/tweepy | |
import csv | |
#Twitter API credentials | |
consumer_key = "" | |
consumer_secret = "" | |
access_key = "" |
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""" | |
Check that the gradient of the logistic regression is correct | |
""" | |
import numpy as np | |
BIG = 1e12 | |
def phi(t): | |
""" |
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import numpy as np | |
import theano | |
import theano.tensor as T | |
from theano.printing import Print | |
from collections import OrderedDict | |
def yellow_fin(loss, params, beta=0.99, | |
learning_rate_init=0.01, momentum_init=0.0, | |
t=None, window_width=20, debug=False): |
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""" | |
PyTorch implementation of a sequence labeler (POS taggger). | |
Basic architecture: | |
- take words | |
- run though bidirectional GRU | |
- predict labels one word at a time (left to right), using a recurrent neural network "decoder" | |
The decoder updates hidden state based on: | |
- most recent word |
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