def main(model_name):
print('model name', model_name)
if model_name == 'bimpm':
model = bimpm()
if model_name == 'drmmt':
model = drmm_tks()
if model_name == 'cnn':
model = model_conv1D_()
if model_name == 'slstm':
model = Siamese_LSTM()
if model_name == 'esim':
model = esim()
if model_name == 'dam':
model = decomposable_attention()
if model_name == 'abcnn':
model = ABCNN(
left_seq_len=config.word_maxlen, right_seq_len=config.word_maxlen, depth=3,
nb_filter=100, filter_widths=[5, 4, 3],
collect_sentence_representations=True, abcnn_1=True, abcnn_2=True,
# mode="euclidean",
mode="cos",
# mode='dot'
)
do_train_cv(model_name, model, epoch_nums=1, kfolds=5)
def get_model(model_name):
lr = 0.001
if model_name == 'bimpm': #3,no
model = bimpm()
if model_name == 'drmmt': #3, yes, but all 1
model = drmm_tks(num_layer=3, hidden_sizes=[100, 80, 1], topk=20)
if model_name == 'msrnn':
model = MATCHSRNN()
if model_name == 'dssm':
model = dssm() #5
if model_name == 'arc2':
model = arc2()
if model_name == 'test':
model = test()
if model_name == 'cnn':
lr = 0.01
model = model_conv1D_()
if model_name == 'rnn':
model = rnn_v1()
if model_name == 'rnn0': #3,yes
model = my_rnn()
if model_name == 'slstm':
model = Siamese_LSTM() #5,no
if model_name == 'scnn':
model = Siamese_CNN() #not exit
if model_name == 'esim': #5,no
lr = 0.01
model = esim()
if model_name == 'dam': #3, yes
model = decomposable_attention()
if model_name == 'abcnn':
model = ABCNN(
left_seq_len=config.word_maxlen,
right_seq_len=config.word_maxlen,
depth=2,
nb_filter=100,
filter_widths=[5, 3],
collect_sentence_representations=False,
abcnn_1=True,
abcnn_2=True,
# mode="euclidean",
# mode="cos",
mode='dot')
return model, lr
def main(model_name):
print('model name', model_name)
x_train, y_train, x_dev, y_dev = load_data()
if model_name == 'bimpm':
model = bimpm()
if model_name == 'cnn':
model = model_conv1D_()
if model_name == 'slstm':
model = Siamese_LSTM()
if model_name == 'esim':
model = esim()
if model_name == 'dam':
model = decomposable_attention()
if model_name == 'abcnn':
model = ABCNN(
left_seq_len=config.word_maxlen,
right_seq_len=config.word_maxlen,
depth=3,
nb_filter=100,
filter_widths=[5, 4, 3],
collect_sentence_representations=True,
abcnn_1=True,
abcnn_2=True,
#mode="euclidean",
mode="cos",
#mode='dot'
)
train(x_train, y_train, x_dev, y_dev, model_name, model)