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,
