def evaluate(test_data_path, test_label_path, model_path, output_path, pad_index=0, max_length=256): test_data = dp.create_data(test_data_path) test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=pad_index, padding="post", maxlen=max_length) test_labl = dp.create_label(test_label_path) model = keras.models.load_model(model_path) result = model.evaluate(test_data, test_labl) with open(output_path, 'w+', encoding='UTF-8') as f: f.write(str(result[1])) f.close()
def predict(test_data_path, model_path, output_path, pad_index=0, max_length=256): test_data = dp.create_data(test_data_path) test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=pad_index, padding="post", maxlen=max_length) model = keras.models.load_model(model_path) results = model.predict(test_data) with open(output_path, 'w+', encoding='UTF-8') as f: for result in results: f.write("{}\n".format(result)) f.close()
import sys train_data_path = sys.argv[1] train_label_path = sys.argv[2] dev_data_path = sys.argv[3] dev_label_path = sys.argv[4] model_path = sys.argv[5] #dict_path="dict.txt" pad_index = 0 max_length = 256 train_data = dp.create_data(train_data_path) train_label = dp.create_label(train_label_path) dev_data = dp.create_data(dev_data_path) dev_label = dp.create_label(dev_label_path) #word_index_dict=dp.create_dict(dict_path) #index_word_dict=dict([(value, key) for (key,value) in word_index_dict.items()]) vocab_size = 50000 train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=pad_index, padding='post', maxlen=max_length) dev_data = keras.preprocessing.sequence.pad_sequences(dev_data,