def run_comparison_by_trained(start = 0,times = 5 ):
PATH = 'ablation_comparisoned.txt'
ld_train = read_ld_train()
ld_test = read_ld_test() # NOTE: 必须是test
# 5 * (2 + 2 + 2 + 1) * 25 = 875(min) = 14.58(hour)
for model_idx in range(times):
model_idx = model_idx + start
SEED = SEEDS_FOR_TRAIN_PLUS[model_idx]
m = load_model(f'SEED_{SEED}_LEFTAUXE2')
dic['LEFT_AUX0'].append(test_left_aux(m, ld_test))
save_dic(PATH)
m = load_model(f'SEED_{SEED}_RIGHTAUXE2')
dic['RIGHT_AUX0'].append(test_right_aux(m, ld_test))
save_dic(PATH)
m = load_model(f'SEED_{SEED}_NOAUXE2')
dic['NO_AUX0'].append(test_no_aux(m, ld_test))
save_dic(PATH)
m = load_model(f'SEED_{SEED}_COUNTERAUXE2')
dic['COUTER_AUX0'].append(test_counter_aux(m, ld_test))
save_dic(PATH)
m = load_model(f'SEED_{SEED}_STAND')
dic['STAND0'].append(test_chain_baseline(m, ld_test)) # NOTE: 参数顺序不同
save_dic(PATH)
m = load_model(f'SEED_{SEED}_AUX00')
dic['AUX000'].append(test_chain(m, ld_test)) # DD
save_dic(PATH)
def train_and_plot(times = 3, start = 0):
epochs = 3
ld_train = read_ld_train()
ld_dev = read_ld_dev()
for model_idx_org in range(times):
model_idx = model_idx_org + start
SEED = RANDOM_SEEDs[model_idx]
# COUNTER AUX
m = create_model_with_seed(SEED)
cb = create_iteration_callback_shell(f'COUTER_AUX{model_idx}', m, ld_dev, test_counter_aux, intensively_log_interval = 20)
for i in range(epochs):
train_counter_aux(m, ld_train, fl_rate = 0, iteration_callback = cb)
# Baseline:
m = create_model_with_seed(SEED)
cb = create_iteration_callback_shell(f'STAND{model_idx}', m, ld_dev, test_chain_baseline, intensively_log_interval = 20)
for i in range(epochs):
train_baseline(m, ld_train, fl_rate = 0, iteration_callback = cb)
# LEFT
m = create_model_with_seed(SEED)
cb = create_iteration_callback_shell(f'LEFT_AUX{model_idx}', m, ld_dev, test_left_aux, intensively_log_interval = 20)
for i in range(epochs):
train_left_aux(m, ld_train, fl_rate = 0, aux_rate = 0.0, iteration_callback = cb)
# RIGHT
m = create_model_with_seed(SEED)
cb = create_iteration_callback_shell(f'RIGHT_AUX{model_idx}', m, ld_dev, test_right_aux, intensively_log_interval = 20)
for i in range(epochs):
train_right_aux(m, ld_train, fl_rate = 0, aux_rate = 0.0, iteration_callback = cb)
# NO AUX
m = create_model_with_seed(SEED)
cb = create_iteration_callback_shell(f'NO_AUX{model_idx}', m, ld_dev, test_no_aux, intensively_log_interval = 20)
for i in range(epochs):
train_no_aux(m, ld_train, fl_rate = 0.0, iteration_callback = cb)
def train_and_plot(times = 3, start = 0):
epochs = 3
ld_train = read_ld_train()
ld_dev = read_ld_dev()
for model_idx_org in range(times):
model_idx = model_idx_org + start
print(f'random seed {RANDOM_SEEDs[model_idx]}')
t.manual_seed(RANDOM_SEEDs[model_idx])
m = Sector_2022()
cb = create_iteration_callback(f'AUXFL_FS_DEV{model_idx}', m, ld_dev, intensively_log_interval = 20)
for i in range(epochs):
train(m, ld_train, fl_rate = 5.0, aux_rate = 0.1, iteration_callback = cb)
t.manual_seed(RANDOM_SEEDs[model_idx])
m = Sector_2022()
cb = create_iteration_callback(f'AUX_FS_DEV{model_idx}', m, ld_dev, intensively_log_interval = 20)
for i in range(epochs):
train(m, ld_train, fl_rate = 0, aux_rate = 0.2, iteration_callback = cb)
t.manual_seed(RANDOM_SEEDs[model_idx])
m = Sector_2022()
cb = create_iteration_callback_baseline(f'FL_FS_DEV{model_idx}', m, ld_dev, intensively_log_interval = 20)
for i in range(epochs):
train_baseline(m, ld_train, fl_rate = 2.0, iteration_callback = cb)
t.manual_seed(RANDOM_SEEDs[model_idx])
m = Sector_2022()
cb = create_iteration_callback_baseline(f'STAND_FS_DEV{model_idx}', m, ld_dev, intensively_log_interval = 20)
for i in range(epochs):
train_baseline(m, ld_train, fl_rate = 0, iteration_callback = cb)
def run_comparison_by_trained(start = 0,times = 10 ):
PATH = 'news_comparisoned.txt'
ld_train = read_ld_train()
ld_test = read_combined_test() # NOTE: 约7万个句子, 好像没有什么必要
# 5 * (2 + 2 + 2 + 1) * 25 = 875(min) = 14.58(hour)
for model_idx in range(times):
model_idx = model_idx + start
SEED = SEEDS_FOR_TRAIN[model_idx]
m = load_model(f'SEED{SEED}_AUX01FL50E2')
dic['AUXFL_FS_TEST0'].append(test_chain(m, ld_test))
save_dic(PATH)
m = load_model(f'SEED{SEED}_AUX02E2')
dic['AUX_FS_TEST0'].append(test_chain(m, ld_test))
save_dic(PATH)
m = load_model(f'SEED{SEED}_FL20E3')
dic['FL_FS_TEST0'].append(test_chain_baseline(m, ld_test))
save_dic(PATH)
m = load_model(f'SEED{SEED}_STDE3')
dic['STAND_FS_TEST0'].append(test_chain_baseline(m, ld_test))
save_dic(PATH)
def train_and_save(start = 0, times = 10):
ld_train = read_ld_train()
for model_idx_org in range(times):
model_idx = model_idx_org + start
SEED = SEEDS_FOR_TRAIN[model_idx]
m = create_model_with_seed(SEED)
for i in range(2): # AUX_FL
train(m, ld_train, fl_rate = 5.0, aux_rate = 0.1)
save_model(m, f'SEED{SEED}_AUX01FL50E2.tch')
m = create_model_with_seed(SEED)
for i in range(2): # AUX
train(m, ld_train, fl_rate = 0, aux_rate = 0.2)
save_model(m, f'SEED{SEED}_AUX02E2.tch')
m = create_model_with_seed(SEED)
for i in range(3): # FL
train_baseline(m, ld_train, fl_rate = 2.0)
save_model(m, f'SEED{SEED}_FL20E3.tch')
m = create_model_with_seed(SEED)
for i in range(2): # STAND
train_baseline(m, ld_train, fl_rate = 0)
save_model(m, f'SEED{SEED}_STDE3.tch') # NOTE: 名字错了，应该是E2的，但是将就用把
def train_and_save(start = 0, times = 5):
epochs = 2
ld_train = read_ld_train()
for model_idx_org in range(times):
model_idx = model_idx_org + start
SEED = SEEDS_FOR_TRAIN_PLUS[model_idx]
# COUNTER AUX
m = create_model_with_seed(SEED)
for i in range(epochs):
train_counter_aux(m, ld_train, fl_rate = 0)
save_model(m, f'SEED_{SEED}_COUNTERAUXE2')
# Baseline:
m = create_model_with_seed(SEED)
for i in range(epochs):
train_baseline(m, ld_train, fl_rate = 0)
save_model(m, f'SEED_{SEED}_STAND')
# AUX00:
m = create_model_with_seed(SEED)
for i in range(epochs):
train(m, ld_train, fl_rate = 0, aux_rate = 0)
save_model(m, f'SEED_{SEED}_AUX00')
# LEFT
m = create_model_with_seed(SEED)
for i in range(epochs):
train_left_aux(m, ld_train, fl_rate = 0, aux_rate = 0.0)
save_model(m, f'SEED_{SEED}_LEFTAUXE2')
# # RIGHT
m = create_model_with_seed(SEED)
for i in range(epochs):
train_right_aux(m, ld_train, fl_rate = 0, aux_rate = 0.0)
save_model(m, f'SEED_{SEED}_RIGHTAUXE2')
# # NO AUX
m = create_model_with_seed(SEED)
for i in range(epochs):
train_no_aux(m, ld_train, fl_rate = 0.0)
save_model(m, f'SEED_{SEED}_NOAUXE2')