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# 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, |
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""" | |
An example to check the AUC score on a validation set for each 10 epochs. | |
I hope it will be helpful for optimizing number of epochs. | |
""" | |
# -*- coding: utf-8 -*- | |
import logging | |
from sklearn.metrics import roc_auc_score | |
from keras.callbacks import Callback |
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""" | |
Implementation of pairwise ranking using scikit-learn LinearSVC | |
Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich, | |
T. Graepel, K. Obermayer. | |
Authors: Fabian Pedregosa <[email protected]> | |
Alexandre Gramfort <[email protected]> | |
""" |
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import random | |
class Markov(object): | |
def __init__(self, open_file): | |
self.cache = {} | |
self.open_file = open_file | |
self.words = self.file_to_words() | |
self.word_size = len(self.words) | |
self.database() |