def main():
args = argumentparser.ArgumentParser()
ta_csv = args.data_dir + "train1.csv"
ts_csv = args.data_dir + "test1.csv"
train_pair(args, ta_csv, ts_csv)
def main():
args = argumentparser.ArgumentParser()
train(args)
lst = [(t['misc']['vals'], -t['result']['loss']) for t in trials.trials]
new = []
for dict, val in lst:
dict['val'] = val
new.append(dict)
keys = new[0].keys()
with open(csvfile, 'wb') as output_file:
dict_writer = csv.DictWriter(output_file, keys)
dict_writer.writeheader()
dict_writer.writerows(new)
if __name__ == '__main__':
args = argumentparser.ArgumentParser()
if (args.dataset == 'pun'):
x_train, y_train, x_test, y_test, embedding_matrix, nb_classes = pun(
args)
space = {
'optimizer': hp.choice('optimizer', ['adadelta', 'rmsprop']),
'batch_size': hp.choice('batch_size', [32, 64]),
'filter_size': hp.choice('filter_size', [3, 4, 5]),
'nb_filter': hp.choice('nb_filter', [75, 100]),
'dropout1': hp.uniform('dropout1', 0.25, 0.75),
'dropout2': hp.uniform('dropout2', 0.25, 0.75),
'use_embeddings': True,
'embeddings_trainable': False,
'lstm_hs': hp.choice('lstm_hs', [32, 50, 64])
}
