train( saveto='./{}.npz'.format(model_name), reload_=True, dim_word=100, # or 300 glove dim=300, patience=10, n_words=22671, #s p500 46174 #ding 33976 40325 clip_c=10., lrate=0.0004, optimizer='adam', maxlen=None, batch_size=32, valid_batch_size=32, dispFreq=20, validFreq=int(1411 / 32 + 1), #1411 #1379 saveFreq=int(1411 / 32 + 1), use_dropout=True, verbose=False, delay1=3, delay2=7, types='title', cut_word=False, cut_news=70, last_layer='LSTM', CNN_filter=128, CNN_kernel=3, keep_prob=0.5, datasets=[ '../data/ding_new_10/train.csv', '../data/ding_new_10/train_label.csv' ], valid_datasets=[ '../data/ding_new_10/validate.csv', '../data/ding_new_10/validate_label.csv' ], test_datasets=[ '../data/ding_new_10/test.csv', '../data/ding_new_10/test_label.csv' ], dictionary='../data/ding_new_10/vocab_cased_title.pickle', embedding='../emb/vectors_latest.txt', wait_N=1, )
train( saveto = './{}.npz'.format(model_name), reload_ = True, dim_word = 100, # or 300 glove dim = 300, patience = 20, n_words = 154000, #s p500 46174 #ding 33976 40325 clip_c = 10., lrate = 0.0004, optimizer = 'adam', maxlen = None, batch_size = 8, valid_batch_size = 8, dispFreq = 100, validFreq = int(1411/8+1),#s p500 1321 # ding 1426 saveFreq = int(1411/8+1), use_dropout = True, verbose = False, delay1 = 3, delay2 = 7, types = 'article', cut_word = 30, cut_sentence = 30, cut_news = 30, last_layer = 'LSTM', CNN_filter = 64, CNN_kernel = 3, keep_prob = 0.5, datasets = ['../data/ding_new_4/train.csv', '../data/ding_new_4/train_label.csv'], valid_datasets = ['../data/ding_new_4/validate.csv', '../data/ding_new_4/validate_label.csv'], test_datasets = ['../data/ding_new_4/test.csv', '../data/ding_new_4/test_label.csv'], dictionary = '../data/ding_new_4/vocab_cased_article.pickle', embedding = '../emb/yunke_latest_medium.txt', wait_N = 1, )