def testing1(best_model_path):
sentences1, sentences2, class1, train_pair = make_new_train_data()
test_pair = make_test_data()
#print('sentences1' , sentences1)
tokenizer, embedding_matrix = word_embed_meta_data(
sentences1 + sentences2, siamese_config['EMBEDDING_DIM'])
embedding_meta_data = {
'tokenizer': tokenizer,
'embedding_matrix': embedding_matrix
}
class Configuration(object):
"""Dump stuff here"""
CONFIG = Configuration()
CONFIG.embedding_dim = siamese_config['EMBEDDING_DIM']
CONFIG.max_sequence_length = siamese_config['MAX_SEQUENCE_LENGTH']
CONFIG.number_lstm_units = siamese_config['NUMBER_LSTM']
CONFIG.rate_drop_lstm = siamese_config['RATE_DROP_LSTM']
CONFIG.number_dense_units = siamese_config['NUMBER_DENSE_UNITS']
CONFIG.activation_function = siamese_config['ACTIVATION_FUNCTION']
CONFIG.rate_drop_dense = siamese_config['RATE_DROP_DENSE']
CONFIG.validation_split_ratio = siamese_config['VALIDATION_SPLIT']
#print('go to siamese')
siamese = SiameseBiLSTM(CONFIG.embedding_dim, CONFIG.max_sequence_length,
CONFIG.number_lstm_units,
CONFIG.number_dense_units, CONFIG.rate_drop_lstm,
CONFIG.rate_drop_dense, CONFIG.activation_function,
CONFIG.validation_split_ratio)
best_model_path = siamese.update_model(best_model_path, train_pair, class1,
embedding_meta_data)
#print(best_model_path)
from operator import itemgetter
from keras.models import load_model
model = load_model(best_model_path)
test_data_x1, test_data_x2, leaks_test = create_test_data(
tokenizer, test_pair, siamese_config['MAX_SEQUENCE_LENGTH'])
preds = list(
model.predict([test_data_x1, test_data_x2, leaks_test],
verbose=1).ravel())
results = [(x, y, z) for (x, y), z in zip(test_pair, preds)]
results.sort(key=itemgetter(2), reverse=True)
#print(results)
#print(preds)
return results, preds
def testing1(best_model_path):
#making the training data
text1, text2, class1, train_pair = make_new_train_data()
#making test data
test_pair = make_test_data()
#making tokenizer and emedding matrix
tokenizer, embedding_matrix = word_embed_meta_data(
text1 + text2, siamese_config['EMBEDDING_DIM'])
embedding_meta_data = {
'tokenizer': tokenizer,
'embedding_matrix': embedding_matrix
}
class Configuration(object):
"""Dump stuff here"""
CONFIG = Configuration()
# setting configuration for the model
CONFIG.embedding_dim = siamese_config['EMBEDDING_DIM']
CONFIG.max_sequence_length = siamese_config['MAX_SEQUENCE_LENGTH']
CONFIG.number_lstm_units = siamese_config['NUMBER_LSTM']
CONFIG.rate_drop_lstm = siamese_config['RATE_DROP_LSTM']
CONFIG.number_dense_units = siamese_config['NUMBER_DENSE_UNITS']
CONFIG.activation_function = siamese_config['ACTIVATION_FUNCTION']
CONFIG.rate_drop_dense = siamese_config['RATE_DROP_DENSE']
CONFIG.validation_split_ratio = siamese_config['VALIDATION_SPLIT']
# making siamese network
siamese = SiameseBiLSTM(CONFIG.embedding_dim, CONFIG.max_sequence_length,
CONFIG.number_lstm_units,
CONFIG.number_dense_units, CONFIG.rate_drop_lstm,
CONFIG.rate_drop_dense, CONFIG.activation_function,
CONFIG.validation_split_ratio)
#updating the pretrained model and saving it into the model.
best_model_path = siamese.update_model(best_model_path, train_pair, class1,
embedding_meta_data)
# loading the best updated model
model = load_model(best_model_path)
# creatng text data as per requirement
test_data_x1, test_data_x2, leaks_test = create_test_data(
tokenizer, test_pair, siamese_config['MAX_SEQUENCE_LENGTH'])
# storing results of test data in the preds varibale
preds = list(
model.predict([test_data_x1, test_data_x2, leaks_test],
verbose=1).ravel())
'''
storing the results in the following form:
known1, unknown, result2
known2, unknown, result2
known3, unknown , result3
'''
results = [(x, y, z) for (x, y), z in zip(test_pair, preds)]
results.sort(key=itemgetter(2), reverse=True)
return results, preds