import pickle
import keras
import numpy as np
from sklearn_crfsuite.metrics import flat_classification_report
import process_data
import cnn_bilsm_crf_model_dp
EPOCHS = 10
model, (train_x, chars_x, train_y, word_len), (
test_x, test_chars_x, test_y, length), (vocab, chunk_tags) = cnn_bilsm_crf_model_dp.create_model(0.9)
dev_x, dev_chars_x, dev_y, _, _, dev_length = process_data.load_cnn_data(use_dev=True)
# train model
# split = 7000
chars_x = np.array([[[ch] for ch in s] for s in chars_x])
test_chars_x = np.array([[[ch] for ch in s] for s in test_chars_x])
dev_chars_x = np.array([[[ch] for ch in s] for s in dev_chars_x])
#
# train_x = train_x[:100]
# chars_x = chars_x[:100]
# train_y = train_y[:100]
# test_x = test_x[:100]
# test_chars_x = test_chars_x[:100]
# test_y = test_y[:100]
history = model.fit([train_x, chars_x], train_y, batch_size=16, epochs=EPOCHS,
validation_data=[[test_x, test_chars_x], test_y],
callbacks=[
import pickle
import keras
import numpy as np
from sklearn_crfsuite.metrics import flat_classification_report
import process_data
import cnn_bilsm_crf_model_dp
EPOCHS = 10
model, (train_x, chars_x, train_y,
word_len), (test_x, test_chars_x, test_y, length), (
vocab, chunk_tags) = cnn_bilsm_crf_model_dp.create_model(0.7)
dev_x, dev_chars_x, dev_y, _, _, dev_length = process_data.load_cnn_data(
use_dev=True)
# train model
# split = 7000
chars_x = np.array([[[ch] for ch in s] for s in chars_x])
test_chars_x = np.array([[[ch] for ch in s] for s in test_chars_x])
dev_chars_x = np.array([[[ch] for ch in s] for s in dev_chars_x])
#
# train_x = train_x[:100]
# chars_x = chars_x[:100]
# train_y = train_y[:100]
# test_x = test_x[:100]
# test_chars_x = test_chars_x[:100]
# test_y = test_y[:100]
history = model.fit(
[train_x, chars_x],