def main():
"""Main function that trains and/or evaluates a model."""
params = interpret_args()
# Prepare the dataset into the proper form.
data = atis_data.ATISDataset(params)
# Construct the model object.
model_type = InteractionATISModel if params.interaction_level else ATISModel
model = model_type(
params,
data.input_vocabulary,
data.output_vocabulary,
data.anonymizer if params.anonymize and params.anonymization_scoring else None)
last_save_file = ""
if params.train:
last_save_file = train(model, data, params)
if params.evaluate:
evaluate(model, data, params, last_save_file)
if params.interactive:
interact(model, params, data.anonymizer, last_save_file)
if params.attention:
evaluate_attention(model, data, params, params.save_file)
def main():
"""Main function that trains and/or evaluates a model."""
params = interpret_args()
# Prepare the dataset into the proper form.
data = atis_data.ATISDataset(params)
if params.new_version:
#my_vocab = Vocabulary(params.interaction_train, params.interaction_valid)
#pickle.dump(my_vocab, open("new_vocab_train", "wb"))
my_vocab = pickle.load(open("new_vocab_train", "rb"))
print(my_vocab.id2label)
print(len(my_vocab))
data.output_vocabulary = my_vocab
#new_interaction_train = pickle.load(open("interactions_new_train", "rb"))
#new_interaction_valid = pickle.load(open("interactions_new_valid", "rb"))
#data.train_data.examples = new_interaction_train
#data.valid_data.examples = new_interaction_valid
transfer_dataset(data.valid_data, name="valid", maximum=params.train_maximum_sql_length)
transfer_dataset(data.train_data, name="train", maximum=params.train_maximum_sql_length)
# Construct the model object.
model_type = InteractionATISModel if params.interaction_level else ATISModel
model = model_type(
params,
data.input_vocabulary,
data.output_vocabulary,
data.anonymizer if params.anonymize and params.anonymization_scoring else None)
last_save_file = params.save_file
if params.train:
last_save_file = train(model, data, params, last_save_file)
if params.evaluate:
evaluate(model, data, params, last_save_file)
if params.interactive:
interact(model, params, data.anonymizer, last_save_file)
if params.attention:
evaluate_attention(model, data, params, params.save_file)
def main():
params = interpret_args()
data = atis_data.ATISDataset(params)