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[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_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 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)
for in_out_pair in generate_in_out_pair_file(fname, tokenizer):
# 每次生成一个批的数据，每次返回固定相同数目的样本
if batch_samples_count < batch_size - 1:
in_out_pairs.append(in_out_pair)
batch_samples_count += 1
else:
in_out_pairs.append(in_out_pair)
X, y = process_format_model_in(in_out_pairs, max_len, batch_size, pad, cut)
yield X, y
in_out_pairs = list()
batch_samples_count = 0
if __name__ == '__main__':
cikm_en = available_datasets[0]
cikm_en = params.get_dataset_params(cikm_en)
# ========== test _load_vectors() function ==========
needed_word2vec = _load_vectors(cikm_en.fastText_en_pretrained_wiki_word_vecs_url,
head_n=50)
for word, vector in needed_word2vec.items():
print(word, end=' ')
for value in vector:
print(value, end=' ')
print()
# ========== test _read_words() function ==========
all_distinct_words = _read_words(cikm_en.processed_en_train_url)
for word in all_distinct_words:
print(word, end=' | ')
print('\ntotal distinct words number:', len(all_distinct_words))
elif label == 0:
negative_count += 1
total_count += 1
tmp = fname.split(os.path.sep)
fname = tmp[len(tmp) - 1]
print('==========', fname, '==========')
print('total count:', total_count)
print('positive count:', positive_count)
print('negative count:', negative_count)
if __name__ == '__main__':
# ========== test _remove_symbols() func ==========
# str1 = "'Random Number' is what I don't like at all."
# str1 = "I don't like 'Random Number'."
# str1 = "I don't like 'Random Number' at all"
# print(remove_symbols(str1, params.MATCH_SINGLE_QUOTE_STR))
# ========== test data_statistic() func ==========
cikm_en = available_datasets[0]
cikm_en = params.get_dataset_params(cikm_en)
fname = cikm_en.raw_url
data_statistic(fname)
fname = cikm_en.train_url
data_statistic(fname)
fname = cikm_en.val_url
data_statistic(fname)
fname = cikm_en.test_url
data_statistic(fname)
