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@funktor / Secret
Last active Nov 6, 2018

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from keras.models import Model, Input
from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional
from keras_contrib.layers import CRF
from keras.models import load_model
import utils
import numpy as np
def pred2label(pred, tag_inverse_transformer):
out = []
for pred_i in pred:
out_i = []
for p in pred_i:
p_i = np.argmax(p)
out_i.append(tag_inverse_transformer[p_i].replace("PAD", "O"))
return out
class BiLSTM(object):
def __init__(self, vocab_size, max_words, num_tags, embedding_size, model_file_path):
self.vocab_size = vocab_size
self.max_words = max_words
self.num_tags = num_tags
self.embedding_size = embedding_size
self.model = None
self.model_file_path = model_file_path
def build_model(self):
print "Building model..."
input = Input(shape=(self.max_words,))
embed = Embedding(input_dim=self.vocab_size + 1, output_dim=self.embedding_size, input_length=self.max_words, mask_zero=True)(input)
bilstm = Bidirectional(LSTM(units=50, return_sequences=True, recurrent_dropout=0.1))(embed)
dense = TimeDistributed(Dense(50, activation="relu"))(bilstm)
out = TimeDistributed(Dense(self.num_tags, activation="softmax"))(dense)
self.model = Model(input, out)
self.model.compile(optimizer="adam", loss='categorical_crossentropy', metrics=['accuracy'])
def fit(self, X, y):
print "Fitting model...", np.array(y), batch_size=32, epochs=15, validation_split=0.1, verbose=1)
def save(self):
def load(self):
def predict(self, X):
print "Predicting..."
return self.model.predict(X, verbose=1)
def score(self, X, y, tag_inverse_transformer):
test_pred = self.predict(X)
pred_labels, test_labels = pred2label(test_pred, tag_inverse_transformer), pred2label(y, tag_inverse_transformer)
print utils.get_accuracy(test_labels, pred_labels)
return utils.get_classification_score(test_labels, pred_labels)
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