def train():
model_name = available_models[0]
language_model = ModelFactory.make_model(model_name)
hyperparams = net_conf.get_hyperparams(model_name)
corpus_name = available_corpus[2]
corpus_params = params.get_corpus_params(corpus_name)
tools.train_model(language_model, hyperparams, corpus_params)
def train():
run_this_model = available_models[2]
text_match_model = ModelFactory.make_model(run_this_model)
hyperparams = net_conf.get_hyperparams(run_this_model)
dataset_name = available_datasets[0]
dataset_params = params.get_dataset_params(dataset_name)
tools.train_model(text_match_model, hyperparams, dataset_params)
def main():
run_this_model = available_models[0]
language_model = ModelFactory.make_model(run_this_model)
hyperparams = net_conf.get_hyperparams(run_this_model)
hyperparams.batch_size = 1
corpus_name = available_corpus[0]
corpus_params = params.get_corpus_params(corpus_name)
tools.train_model(language_model, hyperparams, corpus_params)
def main():
run_this_model = available_models[2]
text_match_model = ModelFactory.make_model(run_this_model)
hyperparams = net_conf.get_hyperparams(run_this_model)
hyperparams.batch_size = 1
dataset_name = available_datasets[2]
dataset_params = params.get_dataset_params(dataset_name)
tools.train_model(text_match_model, hyperparams, dataset_params)
def apply():
model_name = available_models[1]
text_match_model = ModelFactory.make_model(model_name)
model_url = ''
text_match_model.load(model_url)
hyperparams = net_conf.get_hyperparams(model_name)
dataset_name = available_datasets[0]
dataset_params = params.get_dataset_params(dataset_name)
text_match_model.setup(hyperparams, dataset_params)
text_match_model.evaluate_generator()
def tune_enc_layer_num_TEBLDModel():
run_this_model = available_models[2]
model_full_name = model_name_abbr_full[run_this_model]
print('============ ' + model_full_name + ' tune enc layer num ============')
enc_layer_nums = [1, 2, 3, 4, 5, 6]
for layer_num in enc_layer_nums:
text_match_model = ModelFactory.make_model(run_this_model)
hyperparams = net_conf.get_hyperparams(run_this_model)
hyperparams.layers_num = layer_num
dataset_name = available_datasets[0]
dataset_params = params.get_dataset_params(dataset_name)
tools.train_model(text_match_model, hyperparams, dataset_params)
def tune_dropout_rate_REBLDModel():
model_name = available_models[3]
model_full_name = model_name_abbr_full[model_name]
print('============ ' + model_full_name + ' tune dropout rate ============')
p_dropouts = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
for p_dropout in p_dropouts:
text_match_model = ModelFactory.make_model(model_name)
hyperparams = net_conf.get_hyperparams(model_name)
hyperparams.lstm_p_dropout = p_dropout
hyperparams.dense_p_dropout = p_dropout
dataset_name = available_datasets[0]
dataset_params = params.get_dataset_params(dataset_name)
tools.train_model(text_match_model, hyperparams, dataset_params)
def tune_layer_num_SBLDModel():
run_this_model = available_models[1]
model_full_name = model_name_abbr_full[run_this_model]
print('============ ' + model_full_name + ' tune layer num ============')
# RNMTPlusEncoderBiLSTMDenseModel | StackedBiLSTMDenseModel
layer_nums = [0, 1, 2, 3]
for num in layer_nums:
text_match_model = ModelFactory.make_model(run_this_model)
hyperparams = net_conf.get_hyperparams(run_this_model)
hyperparams.bilstm_retseq_layer_num = num
dataset_name = available_datasets[0]
dataset_params = params.get_dataset_params(dataset_name)
tools.train_model(text_match_model, hyperparams, dataset_params)
def tune_dropout_rate_DULBModel():
model_name = available_models[0]
model_full_name = model_name_abbr_full[model_name]
print('============ ' + model_full_name +
' tune dropout rate ============')
# Don't set dropout rate too large, because it will cause information loss.
p_dropouts = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
for p_dropout in p_dropouts:
language_model = ModelFactory.make_model(model_name)
hyperparams = net_conf.get_hyperparams(model_name)
hyperparams.p_dropout = p_dropout
corpus_name = available_corpus[2]
corpus_params = params.get_corpus_params(corpus_name)
tools.train_model(language_model, hyperparams, corpus_params)
def tune_state_dim_SBLDModel():
run_this_model = available_models[1]
model_full_name = model_name_abbr_full[run_this_model]
print('============ ' + model_full_name + ' tune hidden state dim num ============')
# RNMTPlusEncoderBiLSTMDenseModel | StackedBiLSTMDenseModel
# The hidden state dim of LSTM should have a certain relationship with the word emb dim.
# Information will be lost if dim is set to small.
state_dims = [100, 200, 300, 400, 500, 600, 700]
for state_dim in state_dims:
text_match_model = ModelFactory.make_model(run_this_model)
hyperparams = net_conf.get_hyperparams(run_this_model)
hyperparams.state_dim = state_dim
dataset_name = available_datasets[0]
dataset_params = params.get_dataset_params(dataset_name)
tools.train_model(text_match_model, hyperparams, dataset_params)
def apply():
model_name = available_models[1]
language_model = ModelFactory.make_model(model_name)
hyperparams = net_conf.get_hyperparams(model_name)
corpus_name = available_corpus[0]
corpus_params = params.get_corpus_params(corpus_name)
model_url = 'E:\PyCharmProjects\yangzl-lm-keras\\result' \
'\REBModel_just_for_test_2018-10-19 00_38_16\epoch_0014-0.99687.h5'
hyperparams.batch_size = 1
language_model.setup(hyperparams, corpus_params)
language_model.build()
language_model.load(model_url)
language_model.compile()
language_model.evaluate_generator()
def tune_dropout_rate_SBLDModel():
model_name = available_models[1]
model_full_name = model_name_abbr_full[model_name]
print('============ ' + model_full_name + ' tune dropout rate ============')
# Don't set dropout rate too large, because it will cause information loss.
# According to previous experiment: lstm rate >= 0.5, 0 <= dense rate <= 0.2
lstm_p_dropouts = [0.5, 0.6, 0.7]
dense_p_dropouts = [0, 0.1, 0.2]
for lstm_rate in lstm_p_dropouts:
for dense_rate in dense_p_dropouts:
text_match_model = ModelFactory.make_model(model_name)
hyperparams = net_conf.get_hyperparams(model_name)
hyperparams.lstm_p_dropout = lstm_rate
hyperparams.dense_p_dropout = dense_rate
dataset_name = available_datasets[0]
dataset_params = params.get_dataset_params(dataset_name)
tools.train_model(text_match_model, hyperparams, dataset_params)
def tune_l2_lambda_SBLDModel():
run_this_model = available_models[1]
model_full_name = model_name_abbr_full[run_this_model]
print('============ ' + model_full_name + ' tune l2 lambda ============')
# RNMTPlusEncoderBiLSTMDenseModel | StackedBiLSTMDenseModel
kernel_l2_lambdas = [1e-5, 1e-4]
recurrent_l2_lambdas = [1e-5, 1e-4]
bias_l2_lambdas = [1e-5, 1e-4]
activity_l2_lambdas = [0, 1e-5, 1e-4]
for kernel_l2_lambda in kernel_l2_lambdas:
for recurrent_l2_lambda in recurrent_l2_lambdas:
for bias_l2_lambda in bias_l2_lambdas:
for activity_l2_lambda in activity_l2_lambdas:
text_match_model = ModelFactory.make_model(run_this_model)
hyperparams = net_conf.get_hyperparams(run_this_model)
hyperparams.kernel_l2_lambda = kernel_l2_lambda
hyperparams.recurrent_l2_lambda = recurrent_l2_lambda
hyperparams.bias_l2_lambda = bias_l2_lambda
hyperparams.activity_l2_lambda = activity_l2_lambda
dataset_name = available_datasets[0]
dataset_params = params.get_dataset_params(dataset_name)
tools.train_model(text_match_model, hyperparams, dataset_params)
