def main(options):
print 'Load data stream'
train_datastream = get_datastream(path=options['data_path'],
which_set='train_si84',
batch_size=options['batch_size'])
valid_datastream = get_datastream(path=options['data_path'],
which_set='test_dev93',
batch_size=options['batch_size'])
print 'Build and compile network'
input_data = T.ftensor3('input_data')
input_mask = T.fmatrix('input_mask')
target_data = T.imatrix('target_data')
target_mask = T.fmatrix('target_mask')
network = build_network(
input_data=input_data,
input_mask=input_mask,
num_inputs=options['num_inputs'],
num_inner_units_list=options['num_inner_units_list'],
num_factor_units_list=options['num_factor_units_list'],
num_outer_units_list=options['num_outer_units_list'],
num_outputs=options['num_outputs'],
dropout_ratio=options['dropout_ratio'],
use_layer_norm=options['use_layer_norm'],
learn_init=options['learn_init'],
grad_clipping=options['grad_clipping'])
network_params = get_all_params(network, trainable=True)
if options['reload_model']:
print('Loading Parameters...')
pretrain_network_params_val, pretrain_update_params_val, pretrain_total_batch_cnt = pickle.load(
open(options['reload_model'], 'rb'))
print('Applying Parameters...')
set_model_param_value(network_params, pretrain_network_params_val)
else:
pretrain_update_params_val = None
pretrain_total_batch_cnt = 0
print 'Build network trainer'
training_fn, trainer_params = set_network_trainer(
input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
network=network,
updater=options['updater'],
learning_rate=options['lr'],
grad_max_norm=options['grad_norm'],
l2_lambda=options['l2_lambda'],
load_updater_params=pretrain_update_params_val)
print 'Build network predictor'
predict_fn = set_network_predictor(input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
network=network)
evaluation_history = [[[10.0, 10.0, 1.0], [10.0, 10.0, 1.0]]]
check_early_stop = 0
total_batch_cnt = 0
print 'Start training'
try:
# for each epoch
for e_idx in range(options['num_epochs']):
# for each batch
for b_idx, data in enumerate(
train_datastream.get_epoch_iterator()):
total_batch_cnt += 1
if pretrain_total_batch_cnt >= total_batch_cnt:
continue
# get input, target data
train_input = data
# get output
train_output = training_fn(*train_input)
train_predict_cost = train_output[2]
network_grads_norm = train_output[3]
# show intermediate result
if total_batch_cnt % options[
'train_disp_freq'] == 0 and total_batch_cnt != 0:
print '============================================================================================'
print 'Model Name: ', options['save_path'].split('/')[-1]
print '============================================================================================'
print 'Epoch: ', str(e_idx), ', Update: ', str(
total_batch_cnt)
print '--------------------------------------------------------------------------------------------'
print 'Prediction Cost: ', str(train_predict_cost)
print 'Gradient Norm: ', str(network_grads_norm)
print '--------------------------------------------------------------------------------------------'
print 'Train NLL: ', str(
evaluation_history[-1][0][0]), ', BPC: ', str(
evaluation_history[-1][0][1]), ', FER: ', str(
evaluation_history[-1][0][2])
print 'Valid NLL: ', str(
evaluation_history[-1][1][0]), ', BPC: ', str(
evaluation_history[-1][1][1]), ', FER: ', str(
evaluation_history[-1][1][2])
# evaluation
train_nll, train_bpc, train_per = network_evaluation(
predict_fn, train_datastream)
valid_nll, valid_bpc, valid_per = network_evaluation(
predict_fn, valid_datastream)
# check over-fitting
if valid_per > evaluation_history[-1][1][2]:
check_early_stop += 1.
else:
check_early_stop = 0.
best_network_params_vals = get_model_param_values(
network_params)
pickle.dump(
best_network_params_vals,
open(options['save_path'] + '_best_model.pkl', 'wb'))
if check_early_stop > 10:
print('Training Early Stopped')
break
# save results
evaluation_history.append([[train_nll, train_bpc, train_per],
[valid_nll, valid_bpc, valid_per]])
numpy.savez(options['save_path'] + '_eval_history',
eval_history=evaluation_history)
cur_network_params_val = get_model_param_values(network_params)
cur_trainer_params_val = get_update_params_values(trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([
cur_network_params_val, cur_trainer_params_val,
cur_total_batch_cnt
], open(options['save_path'] + '_last_model.pkl', 'wb'))
except KeyboardInterrupt:
print('Training Interrupted')
cur_network_params_val = get_model_param_values(network_params)
cur_trainer_params_val = get_update_params_values(trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([
cur_network_params_val, cur_trainer_params_val, cur_total_batch_cnt
], open(options['save_path'] + '_last_model.pkl', 'wb'))
def main(options):
#################
# build network #
#################
print 'Build and compile network'
# input data
input_data = T.ftensor3('input_data')
input_mask = T.fmatrix('input_mask')
# target data
target_data = T.imatrix('target_data')
target_mask = T.fmatrix('target_mask')
# network
network = build_network(input_data=input_data,
input_mask=input_mask,
num_inputs=options['num_inputs'],
num_units_list=options['num_units_list'],
num_outputs=options['num_outputs'],
dropout_ratio=options['dropout_ratio'],
use_layer_norm=options['use_layer_norm'],
weight_noise=options['weight_noise'],
learn_init=options['learn_init'],
grad_clipping=options['grad_clipping'])
network_params = get_all_params(network, trainable=True)
###################
# Load Parameters #
###################
if options['reload_model']:
print('Loading Parameters...')
pretrain_network_params_val, pretrain_update_params_val, pretrain_total_batch_cnt = pickle.load(open(options['reload_model'], 'rb'))
print('Applying Parameters...')
set_model_param_value(network_params, pretrain_network_params_val)
else:
pretrain_update_params_val = None
pretrain_total_batch_cnt = 0
#########################
# build network trainer #
#########################
print 'Build network trainer'
training_fn, trainer_params = set_network_trainer(input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
network=network,
updater=options['updater'],
learning_rate=options['lr'],
grad_max_norm=options['grad_norm'],
l2_lambda=options['l2_lambda'],
load_updater_params=pretrain_update_params_val)
###########################
# build network predictor #
###########################
print 'Build network predictor'
predict_fn = set_network_predictor(input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
network=network)
################
# load dataset #
################
print 'Load data stream'
train_dataset, train_datastream = timit_datastream(path=options['data_path'],
which_set='train',
pool_size=options['pool_size'],
maximum_frames=options['max_total_frames'],
local_copy=False)
valid_dataset, valid_datastream = timit_datastream(path=options['data_path'],
which_set='dev',
pool_size=options['pool_size'],
maximum_frames=options['max_total_frames'],
local_copy=False)
phone_dict = train_dataset.get_phoneme_dict()
phoneme_dict = {k: phone_to_phoneme_dict[v] if v in phone_to_phoneme_dict else v for k, v in phone_dict.iteritems()}
black_list = ['<START>', '<STOP>', 'q', '<END>']
##################
# start training #
##################
evaluation_history =[[[1000.0, 1.0], [1000.0, 1.0]]]
check_early_stop = 0
total_batch_cnt = 0
print 'Start training'
try:
# for each epoch
for e_idx in range(options['num_epochs']):
# for each batch
for b_idx, data in enumerate(train_datastream.get_epoch_iterator()):
total_batch_cnt += 1
if pretrain_total_batch_cnt>=total_batch_cnt:
continue
# get input, target data
train_input = data
# get output
train_output = training_fn(*train_input)
train_ctc_cost = train_output[0]
train_cost_per_char = train_output[1]
train_regularizer_cost = train_output[2]
network_grads_norm = train_output[3]
# show intermediate result
if total_batch_cnt%options['train_disp_freq'] == 0 and total_batch_cnt!=0:
print '============================================================================================'
print 'Model Name: ', options['save_path'].split('/')[-1]
print '============================================================================================'
print 'Epoch: ', str(e_idx), ', Update: ', str(total_batch_cnt)
print 'CTC Cost: ', str(train_ctc_cost)
print 'Per Char Cost: ', str(train_cost_per_char)
print 'Regularizer Cost: ', str(train_regularizer_cost)
print 'Gradient Norm: ', str(network_grads_norm)
print '============================================================================================'
print 'Train CTC Cost: ', str(evaluation_history[-1][0][0]), ', PER: ', str(evaluation_history[-1][0][-1])
print 'Valid CTC Cost: ', str(evaluation_history[-1][1][0]), ', PER: ', str(evaluation_history[-1][1][-1])
# evaluation
train_ctc_cost, train_per = network_evaluation(predict_fn=predict_fn,
data_stream=train_datastream,
phoneme_dict=phoneme_dict,
black_list=black_list)
valid_ctc_cost, valid_per = network_evaluation(predict_fn=predict_fn,
data_stream=valid_datastream,
phoneme_dict=phoneme_dict,
black_list=black_list)
# check over-fitting
if valid_per>evaluation_history[-1][1][-1]:
check_early_stop += 1.
else:
check_early_stop = 0.
best_network_params_vals = get_model_param_values(network_params)
pickle.dump(best_network_params_vals,
open(options['save_path'] + '_best_model.pkl', 'wb'))
if check_early_stop>10:
print('Training Early Stopped')
break
# save results
evaluation_history.append([[train_ctc_cost, train_per],
[valid_ctc_cost, valid_per]])
numpy.savez(options['save_path'] + '_eval_history',
eval_history=evaluation_history)
cur_network_params_val = get_model_param_values(network_params)
cur_trainer_params_val = get_update_params_values(trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([cur_network_params_val, cur_trainer_params_val, cur_total_batch_cnt],
open(options['save_path'] + '_last_model.pkl', 'wb'))
except KeyboardInterrupt:
print('Training Interrupted')
cur_network_params_val = get_model_param_values(network_params)
cur_trainer_params_val = get_update_params_values(trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([cur_network_params_val, cur_trainer_params_val, cur_total_batch_cnt],
open(options['save_path'] + '_last_model.pkl', 'wb'))
def main(options):
#################
# build network #
#################
print 'Build and compile network'
# input data
input_data = T.ftensor3('input_data')
input_mask = T.fmatrix('input_mask')
# target data
target_data = T.imatrix('target_data')
target_mask = T.fmatrix('target_mask')
# network
network = build_network(
input_data=input_data,
input_mask=input_mask,
num_inputs=options['num_inputs'],
num_inner_units_list=options['num_inner_units_list'],
num_factor_units_list=options['num_factor_units_list'],
num_outer_units_list=options['num_outer_units_list'],
num_outputs=options['num_outputs'],
dropout_ratio=options['dropout_ratio'],
use_layer_norm=options['use_layer_norm'],
weight_noise=options['weight_noise'],
learn_init=True,
grad_clipping=0.0)
network_params = get_all_params(network, trainable=True)
###################
# load parameters #
###################
if options['load_params']:
print 'Load parameters into network'
#########################
# build network trainer #
#########################
print 'Build network trainer'
training_fn, trainer_params = set_network_trainer(
input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
network=network,
updater=options['updater'],
learning_rate=options['lr'],
grad_max_norm=options['grad_norm'],
l2_lambda=options['l2_lambda'],
load_updater_params=options['updater_params'])
###########################
# build network predictor #
###########################
print 'Build network predictor'
predict_fn = set_network_predictor(input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
network=network)
################
# load dataset #
################
print 'Load data stream'
train_datastream = framewise_timit_datastream(
path=options['data_path'],
which_set='train',
batch_size=options['batch_size'],
local_copy=False)
valid_datastream = framewise_timit_datastream(
path=options['data_path'],
which_set='test',
batch_size=options['batch_size'],
local_copy=False)
##################
# start training #
##################
evaluation_history = [[[1000.0, 1000.0, 1.0], [1000.0, 1000.0, 1.0]]]
check_early_stop = 0
total_batch_cnt = 0
print 'Start training'
try:
# for each epoch
for e_idx in range(options['num_epochs']):
# for each batch
for b_idx, data in enumerate(
train_datastream.get_epoch_iterator()):
# get input, target data
train_input = data
# get output
train_output = training_fn(*train_input)
train_predict_cost = train_output[2]
train_regularizer_cost = train_output[3]
network_grads_norm = train_output[4]
# count batch
total_batch_cnt += 1
# show intermediate result
if total_batch_cnt % options[
'train_disp_freq'] == 0 and total_batch_cnt != 0:
print '============================================================================================'
print 'Model Name: ', options['save_path'].split('/')[-1]
print '============================================================================================'
print 'Epoch: ', str(e_idx), ', Update: ', str(
total_batch_cnt)
print 'Prediction Cost: ', str(train_predict_cost)
print 'Regularizer Cost: ', str(train_regularizer_cost)
print 'Gradient Norm: ', str(network_grads_norm)
print '============================================================================================'
print 'Train NLL: ', str(
evaluation_history[-1][0][0]), ', BPC: ', str(
evaluation_history[-1][0][1]), ', PER: ', str(
evaluation_history[-1][0][2])
print 'Valid NLL: ', str(
evaluation_history[-1][1][0]), ', BPC: ', str(
evaluation_history[-1][1][1]), ', PER: ', str(
evaluation_history[-1][1][2])
# evaluation
train_nll, train_bpc, train_per = network_evaluation(
predict_fn, train_datastream)
valid_nll, valid_bpc, valid_per = network_evaluation(
predict_fn, valid_datastream)
# check over-fitting
if valid_per > evaluation_history[-1][1][2]:
check_early_stop += 1.
else:
check_early_stop = 0.
best_network_params_vals = get_model_param_values(
network_params)
pickle.dump(
best_network_params_vals,
open(options['save_path'] + '_best_model.pkl', 'wb'))
if check_early_stop > 3:
print('Training Early Stopped')
break
# save results
evaluation_history.append([[train_nll, train_bpc, train_per],
[valid_nll, valid_bpc, valid_per]])
numpy.savez(options['save_path'] + '_eval_history',
eval_history=evaluation_history)
cur_network_params_val = get_model_param_values(network_params)
cur_trainer_params_val = get_update_params_values(trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([
cur_network_params_val, cur_trainer_params_val,
cur_total_batch_cnt
], open(options['save_path'] + '_last_model.pkl', 'wb'))
except KeyboardInterrupt:
print('Training Interrupted')
cur_network_params_val = get_model_param_values(network_params)
cur_trainer_params_val = get_update_params_values(trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([
cur_network_params_val, cur_trainer_params_val, cur_total_batch_cnt
], open(options['save_path'] + '_last_model.pkl', 'wb'))
def main(options):
print 'Build and compile network'
input_data = T.ftensor3('input_data')
input_mask = T.fmatrix('input_mask')
target_data = T.imatrix('target_data')
target_mask = T.fmatrix('target_mask')
network_outputs = build_network(
input_data=input_data,
input_mask=input_mask,
num_inputs=options['num_inputs'],
num_outputs=options['num_outputs'],
num_inner_units_list=options['num_inner_units_list'],
num_outer_units_list=options['num_outer_units_list'],
use_peepholes=options['use_peepholes'],
use_layer_norm=options['use_layer_norm'],
learn_init=options['learn_init'],
grad_clipping=options['grad_clip'])
network = network_outputs[-1]
inner_loop_layers = network_outputs[:-1]
network_params = get_all_params(network, trainable=True)
print("number of parameters in model: %d" %
count_params(network, trainable=True))
if options['reload_model']:
print('Loading Parameters...')
[
pretrain_network_params_val, pretrain_update_params_val,
pretrain_total_batch_cnt
] = pickle.load(open(options['reload_model'], 'rb'))
print('Applying Parameters...')
set_model_param_value(network_params, pretrain_network_params_val)
else:
pretrain_update_params_val = None
pretrain_total_batch_cnt = 0
print 'Build network trainer'
train_lr = theano.shared(convert_to_floatX(options['lr']))
training_fn, trainer_params = set_network_trainer(
input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
num_outputs=options['num_outputs'],
network=network,
inner_loop_layers=inner_loop_layers,
updater=options['updater'],
learning_rate=train_lr,
grad_max_norm=options['grad_norm'],
l2_lambda=options['l2_lambda'],
load_updater_params=pretrain_update_params_val)
print 'Build network predictor'
predict_fn = set_network_predictor(input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
num_outputs=options['num_outputs'],
network=network)
# evaluation
if options['reload_model']:
train_eval_datastream = get_datastream(
path=options['data_path'],
norm_path=options['norm_data_path'],
which_set='train_si84',
batch_size=options['eval_batch_size'])
valid_eval_datastream = get_datastream(
path=options['data_path'],
norm_path=options['norm_data_path'],
which_set='test_dev93',
batch_size=options['eval_batch_size'])
train_nll, train_bpc, train_fer = network_evaluation(
predict_fn, train_eval_datastream)
valid_nll, valid_bpc, valid_fer = network_evaluation(
predict_fn, valid_eval_datastream)
print '======================================================='
print 'Train NLL: ', str(train_nll), ', FER: ', str(train_fer)
print 'Valid NLL: ', str(valid_nll), ', FER: ', str(valid_fer)
print '======================================================='
print 'Load data stream'
train_datastream = get_datastream(path=options['data_path'],
norm_path=options['norm_data_path'],
which_set='train_si84',
batch_size=options['batch_size'])
print 'Start training'
if os.path.exists(options['save_path'] + '_eval_history.npz'):
evaluation_history = numpy.load(
options['save_path'] +
'_eval_history.npz')['eval_history'].tolist()
else:
evaluation_history = [[[100.0, 100.0, 1.0], [100.0, 100.0, 1.0]]]
total_batch_cnt = 0
start_time = time.time()
try:
# for each epoch
for e_idx in range(options['num_epochs']):
# for each batch
for b_idx, data in enumerate(
train_datastream.get_epoch_iterator()):
total_batch_cnt += 1
if pretrain_total_batch_cnt >= total_batch_cnt:
continue
# get input, target data
input_data = data[0].astype(floatX)
input_mask = data[1].astype(floatX)
# get target data
target_data = data[2]
target_mask = data[3].astype(floatX)
# get output
train_output = training_fn(input_data, input_mask, target_data,
target_mask)
train_predict_cost = train_output[0]
network_grads_norm = train_output[1]
train_sf_cost0 = train_output[2]
train_sf_cost1 = train_output[3]
train_sf_cost2 = train_output[4]
print('=====================================================')
print(total_batch_cnt, train_predict_cost, network_grads_norm)
print(train_sf_cost0, train_sf_cost1, train_sf_cost2)
if numpy.isnan(train_predict_cost) or numpy.isnan(
network_grads_norm):
print('update cnt: ', total_batch_cnt)
print('NaN detected: ', train_predict_cost,
network_grads_norm)
raw_input()
# show intermediate result
if total_batch_cnt % options[
'train_disp_freq'] == 0 and total_batch_cnt != 0:
best_idx = numpy.asarray(evaluation_history)[:, 1,
2].argmin()
print '============================================================================================'
print 'Model Name: ', options['save_path'].split('/')[-1]
print '============================================================================================'
print 'Epoch: ', str(e_idx), ', Update: ', str(
total_batch_cnt), ', Time: ', str(time.time() -
start_time)
print '--------------------------------------------------------------------------------------------'
print 'Prediction Cost: ', str(train_predict_cost)
print 'Gradient Norm: ', str(network_grads_norm)
print '--------------------------------------------------------------------------------------------'
print 'Learn Rate: ', str(train_lr.get_value())
print '--------------------------------------------------------------------------------------------'
print 'Train NLL: ', str(
evaluation_history[-1][0][0]), ', BPC: ', str(
evaluation_history[-1][0][1]), ', FER: ', str(
evaluation_history[-1][0][2])
print 'Valid NLL: ', str(
evaluation_history[-1][1][0]), ', BPC: ', str(
evaluation_history[-1][1][1]), ', FER: ', str(
evaluation_history[-1][1][2])
print '--------------------------------------------------------------------------------------------'
print 'Best NLL: ', str(
evaluation_history[best_idx][1][0]), ', BPC: ', str(
evaluation_history[best_idx][1]
[1]), ', FER: ', str(
evaluation_history[best_idx][1][2])
start_time = time.time()
# # evaluation
# if total_batch_cnt%options['train_eval_freq'] == 0 and total_batch_cnt!=0:
# train_eval_datastream = get_datastream(path=options['data_path'],
# norm_path=options['norm_data_path'],
# which_set='train_si84',
# batch_size=options['eval_batch_size'])
# valid_eval_datastream = get_datastream(path=options['data_path'],
# norm_path=options['norm_data_path'],
# which_set='test_dev93',
# batch_size=options['eval_batch_size'])
# train_nll, train_bpc, train_fer = network_evaluation(predict_fn,
# train_eval_datastream)
# valid_nll, valid_bpc, valid_fer = network_evaluation(predict_fn,
# valid_eval_datastream)
#
# # check over-fitting
# if valid_fer<numpy.asarray(evaluation_history)[:, 1, 2].min():
# best_network_params_vals = get_model_param_values(network_params)
# pickle.dump(best_network_params_vals,
# open(options['save_path'] + '_best_model.pkl', 'wb'))
#
# # save results
# evaluation_history.append([[train_nll, train_bpc, train_fer],
# [valid_nll, valid_bpc, valid_fer]])
# numpy.savez(options['save_path'] + '_eval_history',
# eval_history=evaluation_history)
# save network
if total_batch_cnt % options[
'train_save_freq'] == 0 and total_batch_cnt != 0:
cur_network_params_val = get_model_param_values(
network_params)
cur_trainer_params_val = get_update_params_values(
trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([
cur_network_params_val, cur_trainer_params_val,
cur_total_batch_cnt
],
open(
options['save_path'] +
str(total_batch_cnt).zfill(10) +
'_model.pkl', 'wb'))
except KeyboardInterrupt:
print 'Training Interrupted'
cur_network_params_val = get_model_param_values(network_params)
cur_trainer_params_val = get_update_params_values(trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([
cur_network_params_val, cur_trainer_params_val, cur_total_batch_cnt
], open(options['save_path'] + '_last_model.pkl', 'wb'))
def main(options):
print 'Build and compile network'
input_data = T.ftensor3('input_data')
input_mask = T.fmatrix('input_mask')
target_data = T.imatrix('target_data')
target_mask = T.fmatrix('target_mask')
skip_scale = theano.shared(convert_to_floatX(options['skip_scale']))
network, rand_layer_list = build_network(
input_data=input_data,
input_mask=input_mask,
num_inputs=options['num_inputs'],
num_units_list=options['num_units_list'],
num_outputs=options['num_outputs'],
skip_scale=skip_scale,
dropout_ratio=options['dropout_ratio'],
weight_noise=options['weight_noise'],
use_layer_norm=options['use_layer_norm'],
peepholes=options['peepholes'],
learn_init=options['learn_init'],
grad_clipping=options['grad_clipping'],
gradient_steps=options['gradient_steps'],
use_projection=options['use_projection'])
network_params = get_all_params(network, trainable=True)
print("number of parameters in model: %d" %
count_params(network, trainable=True))
if options['reload_model']:
print('Loading Parameters...')
pretrain_network_params_val, pretrain_update_params_val, pretrain_total_batch_cnt = pickle.load(
open(options['reload_model'], 'rb'))
print('Applying Parameters...')
set_model_param_value(network_params, pretrain_network_params_val)
else:
pretrain_update_params_val = None
pretrain_total_batch_cnt = 0
print 'Build network trainer'
training_fn, trainer_params = set_network_trainer(
input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
num_outputs=options['num_outputs'],
network=network,
rand_layer_list=rand_layer_list,
updater=options['updater'],
learning_rate=options['lr'],
grad_max_norm=options['grad_norm'],
l2_lambda=options['l2_lambda'],
load_updater_params=pretrain_update_params_val)
print 'Build network predictor'
predict_fn = set_network_predictor(input_data=input_data,
input_mask=input_mask,
target_data=target_data,
target_mask=target_mask,
num_outputs=options['num_outputs'],
network=network)
print 'Load data stream'
train_datastream = get_datastream(path=options['data_path'],
norm_path=options['norm_data_path'],
which_set='train_si84',
batch_size=options['batch_size'])
print 'Start training'
if os.path.exists(options['save_path'] + '_eval_history.npz'):
evaluation_history = numpy.load(
options['save_path'] +
'_eval_history.npz')['eval_history'].tolist()
else:
evaluation_history = [[[10.0, 10.0, 1.0], [10.0, 10.0, 1.0]]]
early_stop_flag = False
early_stop_cnt = 0
total_batch_cnt = 0
try:
# for each epoch
for e_idx in range(options['num_epochs']):
# for each batch
for b_idx, data in enumerate(
train_datastream.get_epoch_iterator()):
total_batch_cnt += 1
if pretrain_total_batch_cnt >= total_batch_cnt:
continue
# get input, target data
input_data = data[0].astype(floatX)
input_mask = data[1].astype(floatX)
# get target data
target_data = data[2]
target_mask = data[3].astype(floatX)
# get output
train_output = training_fn(input_data, input_mask, target_data,
target_mask)
train_predict_cost = train_output[0]
network_grads_norm = train_output[1]
skip_means = train_output[2:]
# show intermediate result
if total_batch_cnt % options[
'train_disp_freq'] == 0 and total_batch_cnt != 0:
# pdb.set_trace()
best_idx = numpy.asarray(evaluation_history)[:, 1,
2].argmin()
print '============================================================================================'
print 'Model Name: ', options['save_path'].split('/')[-1]
print '============================================================================================'
print 'Epoch: ', str(e_idx), ', Update: ', str(
total_batch_cnt)
print '--------------------------------------------------------------------------------------------'
print 'Prediction Cost: ', str(train_predict_cost)
print 'Gradient Norm: ', str(network_grads_norm)
print '--------------------------------------------------------------------------------------------'
print 'Skip Ratio: ', skip_means
print 'Skip Scale: ', str(skip_scale.get_value())
print '--------------------------------------------------------------------------------------------'
print 'Train NLL: ', str(
evaluation_history[-1][0][0]), ', BPC: ', str(
evaluation_history[-1][0][1]), ', FER: ', str(
evaluation_history[-1][0][2])
print 'Valid NLL: ', str(
evaluation_history[-1][1][0]), ', BPC: ', str(
evaluation_history[-1][1][1]), ', FER: ', str(
evaluation_history[-1][1][2])
print '--------------------------------------------------------------------------------------------'
print 'Best NLL: ', str(
evaluation_history[best_idx][1][0]), ', BPC: ', str(
evaluation_history[best_idx][1]
[1]), ', FER: ', str(
evaluation_history[best_idx][1][2])
# evaluation
if total_batch_cnt % options[
'train_eval_freq'] == 0 and total_batch_cnt != 0:
train_eval_datastream = get_datastream(
path=options['data_path'],
norm_path=options['norm_data_path'],
which_set='train_si84',
batch_size=options['eval_batch_size'])
valid_eval_datastream = get_datastream(
path=options['data_path'],
norm_path=options['norm_data_path'],
which_set='test_dev93',
batch_size=options['eval_batch_size'])
train_nll, train_bpc, train_fer = network_evaluation(
predict_fn, train_eval_datastream)
valid_nll, valid_bpc, valid_fer = network_evaluation(
predict_fn, valid_eval_datastream)
# check over-fitting
if valid_fer > numpy.asarray(evaluation_history)[:, 1,
2].min():
early_stop_cnt += 1.
else:
early_stop_cnt = 0.
best_network_params_vals = get_model_param_values(
network_params)
pickle.dump(
best_network_params_vals,
open(options['save_path'] + '_best_model.pkl',
'wb'))
if early_stop_cnt > 10:
early_stop_flag = True
break
# save results
evaluation_history.append(
[[train_nll, train_bpc, train_fer],
[valid_nll, valid_bpc, valid_fer]])
numpy.savez(options['save_path'] + '_eval_history',
eval_history=evaluation_history)
# save network
if total_batch_cnt % options[
'train_save_freq'] == 0 and total_batch_cnt != 0:
cur_network_params_val = get_model_param_values(
network_params)
cur_trainer_params_val = get_update_params_values(
trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([
cur_network_params_val, cur_trainer_params_val,
cur_total_batch_cnt
], open(options['save_path'] + '_last_model.pkl', 'wb'))
if total_batch_cnt % 1000 == 0 and total_batch_cnt != 0:
skip_scale.set_value(
convert_to_floatX(skip_scale.get_value() * 1.01))
if early_stop_flag:
break
except KeyboardInterrupt:
print 'Training Interrupted'
cur_network_params_val = get_model_param_values(network_params)
cur_trainer_params_val = get_update_params_values(trainer_params)
cur_total_batch_cnt = total_batch_cnt
pickle.dump([
cur_network_params_val, cur_trainer_params_val, cur_total_batch_cnt
], open(options['save_path'] + '_last_model.pkl', 'wb'))
print '--------------------------------------------------------------------------------------------'
print 'Train NLL: ', str(evaluation_history[-1][0][0]), ', BPC: ', str(evaluation_history[-1][0][1]), ', FER: ', str(evaluation_history[-1][0][2])
print 'Valid NLL: ', str(evaluation_history[-1][1][0]), ', BPC: ', str(evaluation_history[-1][1][1]), ', FER: ', str(evaluation_history[-1][1][2])
# evaluation
train_nll, train_bpc, train_per = network_evaluation(predict_fn,
train_datastream)
valid_nll, valid_bpc, valid_per = network_evaluation(predict_fn,
valid_datastream)
# check over-fitting
if valid_per>evaluation_history[-1][1][2]:
check_early_stop += 1.
else:
check_early_stop = 0.
best_network_params_vals = get_model_param_values(network_params)
pickle.dump(best_network_params_vals,
open(options['save_path'] + '_best_model.pkl', 'wb'))
if check_early_stop>10:
print('Training Early Stopped')
break
# save results
evaluation_history.append([[train_nll, train_bpc, train_per],
[valid_nll, valid_bpc, valid_per]])
numpy.savez(options['save_path'] + '_eval_history',
eval_history=evaluation_history)
cur_network_params_val = get_model_param_values(network_params)
cur_trainer_params_val = get_update_params_values(trainer_params)