def train(args, run_opts, background_process_handler):
""" The main function for training.
Args:
args: a Namespace object with the required parameters
obtained from the function process_args()
run_opts: RunOpts object obtained from the process_args()
"""
arg_string = pprint.pformat(vars(args))
logger.info("Arguments for the experiment\n{0}".format(arg_string))
# Set some variables.
feat_dim = common_lib.get_feat_dim(args.feat_dir)
ivector_dim = common_lib.get_ivector_dim(args.online_ivector_dir)
ivector_id = common_lib.get_ivector_extractor_id(args.online_ivector_dir)
config_dir = '{0}/configs'.format(args.dir)
var_file = '{0}/vars'.format(config_dir)
variables = common_train_lib.parse_generic_config_vars_file(var_file)
# Set some variables.
try:
model_left_context = variables['model_left_context']
model_right_context = variables['model_right_context']
# this is really the number of times we add layers to the network for
# discriminative pretraining
num_hidden_layers = variables['num_hidden_layers']
add_lda = common_lib.str_to_bool(variables['add_lda'])
include_log_softmax = common_lib.str_to_bool(
variables['include_log_softmax'])
except KeyError as e:
raise Exception("KeyError {0}: Variables need to be defined in "
"{1}".format(str(e), '{0}/configs'.format(args.dir)))
left_context = args.chunk_left_context + model_left_context
right_context = args.chunk_right_context + model_right_context
left_context_initial = (args.chunk_left_context_initial + model_left_context if
args.chunk_left_context_initial >= 0 else -1)
right_context_final = (args.chunk_right_context_final + model_right_context if
args.chunk_right_context_final >= 0 else -1)
# Initialize as "raw" nnet, prior to training the LDA-like preconditioning
# matrix. This first config just does any initial splicing that we do;
# we do this as it's a convenient way to get the stats for the 'lda-like'
# transform.
if (args.stage <= -4):
logger.info("Initializing a basic network")
common_lib.run_job(
"""{command} {dir}/log/nnet_init.log \
nnet3-init --srand=-2 {dir}/configs/init.config \
{dir}/init.raw""".format(command=run_opts.command,
dir=args.dir))
default_egs_dir = '{0}/egs'.format(args.dir)
if (args.stage <= -3) and args.egs_dir is None:
logger.info("Generating egs")
if args.use_dense_targets:
target_type = "dense"
try:
num_targets = int(variables['num_targets'])
if (common_lib.get_feat_dim_from_scp(args.targets_scp)
!= num_targets):
raise Exception("Mismatch between num-targets provided to "
"script vs configs")
except KeyError as e:
num_targets = -1
else:
target_type = "sparse"
try:
num_targets = int(variables['num_targets'])
except KeyError as e:
raise Exception("KeyError {0}: Variables need to be defined "
"in {1}".format(
str(e), '{0}/configs'.format(args.dir)))
train_lib.raw_model.generate_egs_using_targets(
data=args.feat_dir, targets_scp=args.targets_scp,
egs_dir=default_egs_dir,
left_context=left_context,
right_context=right_context,
left_context_initial=left_context_initial,
right_context_final=right_context_final,
run_opts=run_opts,
frames_per_eg_str=args.chunk_width,
srand=args.srand,
egs_opts=args.egs_opts,
cmvn_opts=args.cmvn_opts,
online_ivector_dir=args.online_ivector_dir,
samples_per_iter=args.samples_per_iter,
transform_dir=args.transform_dir,
stage=args.egs_stage,
target_type=target_type,
num_targets=num_targets)
if args.egs_dir is None:
egs_dir = default_egs_dir
else:
egs_dir = args.egs_dir
[egs_left_context, egs_right_context,
frames_per_eg_str, num_archives] = (
common_train_lib.verify_egs_dir(egs_dir, feat_dim,
ivector_dim, ivector_id,
left_context, right_context))
if args.chunk_width != frames_per_eg_str:
raise Exception("mismatch between --egs.chunk-width and the frames_per_eg "
"in the egs dir {0} vs {1}".format(args.chunk_width,
frames_per_eg_str))
if (args.num_jobs_final > num_archives):
raise Exception('num_jobs_final cannot exceed the number of archives '
'in the egs directory')
# copy the properties of the egs to dir for
# use during decoding
common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir)
if (add_lda and args.stage <= -2):
logger.info('Computing the preconditioning matrix for input features')
train_lib.common.compute_preconditioning_matrix(
args.dir, egs_dir, num_archives, run_opts,
max_lda_jobs=args.max_lda_jobs,
rand_prune=args.rand_prune)
if (args.stage <= -1):
logger.info("Preparing the initial network.")
common_train_lib.prepare_initial_network(args.dir, run_opts)
# set num_iters so that as close as possible, we process the data
# $num_epochs times, i.e. $num_iters*$avg_num_jobs) ==
# $num_epochs*$num_archives, where
# avg_num_jobs=(num_jobs_initial+num_jobs_final)/2.
num_archives_to_process = int(args.num_epochs * num_archives)
num_archives_processed = 0
num_iters = ((num_archives_to_process * 2)
/ (args.num_jobs_initial + args.num_jobs_final))
models_to_combine = common_train_lib.verify_iterations(
num_iters, args.num_epochs,
num_hidden_layers, num_archives,
args.max_models_combine, args.add_layers_period,
args.num_jobs_final)
def learning_rate(iter, current_num_jobs, num_archives_processed):
return common_train_lib.get_learning_rate(iter, current_num_jobs,
num_iters,
num_archives_processed,
num_archives_to_process,
args.initial_effective_lrate,
args.final_effective_lrate)
min_deriv_time = None
max_deriv_time_relative = None
if args.deriv_truncate_margin is not None:
min_deriv_time = -args.deriv_truncate_margin - model_left_context
max_deriv_time_relative = \
args.deriv_truncate_margin + model_right_context
logger.info("Training will run for {0} epochs = "
"{1} iterations".format(args.num_epochs, num_iters))
for iter in range(num_iters):
if (args.exit_stage is not None) and (iter == args.exit_stage):
logger.info("Exiting early due to --exit-stage {0}".format(iter))
return
current_num_jobs = int(0.5 + args.num_jobs_initial
+ (args.num_jobs_final - args.num_jobs_initial)
* float(iter) / num_iters)
if args.stage <= iter:
model_file = "{dir}/{iter}.raw".format(dir=args.dir, iter=iter)
shrinkage_value = 1.0
if args.shrink_value != 1.0:
shrinkage_value = (args.shrink_value
if common_train_lib.do_shrinkage(
iter, model_file,
args.shrink_saturation_threshold,
get_raw_nnet_from_am=False)
else 1
)
train_lib.common.train_one_iteration(
dir=args.dir,
iter=iter,
srand=args.srand,
egs_dir=egs_dir,
num_jobs=current_num_jobs,
num_archives_processed=num_archives_processed,
num_archives=num_archives,
learning_rate=learning_rate(iter, current_num_jobs,
num_archives_processed),
dropout_edit_string=common_train_lib.get_dropout_edit_string(
args.dropout_schedule,
float(num_archives_processed) / num_archives_to_process,
iter),
shrinkage_value=shrinkage_value,
minibatch_size_str=args.num_chunk_per_minibatch,
num_hidden_layers=num_hidden_layers,
add_layers_period=args.add_layers_period,
left_context=left_context,
right_context=right_context,
min_deriv_time=min_deriv_time,
max_deriv_time_relative=max_deriv_time_relative,
momentum=args.momentum,
max_param_change=args.max_param_change,
shuffle_buffer_size=args.shuffle_buffer_size,
run_opts=run_opts,
get_raw_nnet_from_am=False,
background_process_handler=background_process_handler)
if args.cleanup:
# do a clean up everythin but the last 2 models, under certain
# conditions
common_train_lib.remove_model(
args.dir, iter-2, num_iters, models_to_combine,
args.preserve_model_interval,
get_raw_nnet_from_am=False)
if args.email is not None:
reporting_iter_interval = num_iters * args.reporting_interval
if iter % reporting_iter_interval == 0:
# lets do some reporting
[report, times, data] = (
nnet3_log_parse.generate_acc_logprob_report(args.dir))
message = report
subject = ("Update : Expt {dir} : "
"Iter {iter}".format(dir=args.dir, iter=iter))
common_lib.send_mail(message, subject, args.email)
num_archives_processed = num_archives_processed + current_num_jobs
if args.stage <= num_iters:
logger.info("Doing final combination to produce final.raw")
train_lib.common.combine_models(
dir=args.dir, num_iters=num_iters,
models_to_combine=models_to_combine, egs_dir=egs_dir,
left_context=left_context, right_context=right_context,
minibatch_size_str=args.num_chunk_per_minibatch,
run_opts=run_opts, chunk_width=args.chunk_width,
background_process_handler=background_process_handler,
get_raw_nnet_from_am=False,
sum_to_one_penalty=args.combine_sum_to_one_penalty)
if include_log_softmax and args.stage <= num_iters + 1:
logger.info("Getting average posterior for purposes of "
"adjusting the priors.")
train_lib.common.compute_average_posterior(
dir=args.dir, iter='final', egs_dir=egs_dir,
num_archives=num_archives,
left_context=left_context, right_context=right_context,
prior_subset_size=args.prior_subset_size, run_opts=run_opts,
get_raw_nnet_from_am=False)
if args.cleanup:
logger.info("Cleaning up the experiment directory "
"{0}".format(args.dir))
remove_egs = args.remove_egs
if args.egs_dir is not None:
# this egs_dir was not created by this experiment so we will not
# delete it
remove_egs = False
common_train_lib.clean_nnet_dir(
nnet_dir=args.dir, num_iters=num_iters, egs_dir=egs_dir,
preserve_model_interval=args.preserve_model_interval,
remove_egs=remove_egs,
get_raw_nnet_from_am=False)
# do some reporting
[report, times, data] = nnet3_log_parse.generate_acc_logprob_report(args.dir)
if args.email is not None:
common_lib.send_mail(report, "Update : Expt {0} : "
"complete".format(args.dir), args.email)
with open("{dir}/accuracy.report".format(dir=args.dir), "w") as f:
f.write(report)
common_lib.run_job("steps/info/nnet3_dir_info.pl "
"{0}".format(args.dir))
def train(args, run_opts):
""" The main function for training.
Args:
args: a Namespace object with the required parameters
obtained from the function process_args()
run_opts: RunOpts object obtained from the process_args()
"""
arg_string = pprint.pformat(vars(args))
logger.info("Arguments for the experiment\n{0}".format(arg_string))
# Set some variables.
# note, feat_dim gets set to 0 if args.feat_dir is unset (None).
feat_dim = common_lib.get_feat_dim(args.feat_dir)
ivector_dim = common_lib.get_ivector_dim(args.online_ivector_dir)
ivector_id = common_lib.get_ivector_extractor_id(args.online_ivector_dir)
config_dir = '{0}/configs'.format(args.dir)
var_file = '{0}/vars'.format(config_dir)
variables = common_train_lib.parse_generic_config_vars_file(var_file)
# Set some variables.
try:
model_left_context = variables['model_left_context']
model_right_context = variables['model_right_context']
if 'include_log_softmax' in variables:
include_log_softmax = common_lib.str_to_bool(
variables['include_log_softmax'])
else:
include_log_softmax = False
except KeyError as e:
raise Exception("KeyError {0}: Variables need to be defined in "
"{1}".format(str(e), '{0}/configs'.format(args.dir)))
left_context = model_left_context
right_context = model_right_context
# Initialize as "raw" nnet, prior to training the LDA-like preconditioning
# matrix. This first config just does any initial splicing that we do;
# we do this as it's a convenient way to get the stats for the 'lda-like'
# transform.
if (args.stage <= -5) and os.path.exists(args.dir+"/configs/init.config"):
logger.info("Initializing the network for computing the LDA stats")
common_lib.execute_command(
"""{command} {dir}/log/nnet_init.log \
nnet3-init --srand=-2 {dir}/configs/init.config \
{dir}/init.raw""".format(command=run_opts.command,
dir=args.dir))
default_egs_dir = '{0}/egs'.format(args.dir)
if (args.stage <= -4) and args.egs_dir is None:
if args.targets_scp is None or args.feat_dir is None:
raise Exception("If you don't supply the --egs-dir option, the "
"--targets-scp and --feat-dir options are required.")
logger.info("Generating egs")
if args.use_dense_targets:
target_type = "dense"
try:
num_targets = int(variables['num_targets'])
if (common_lib.get_feat_dim_from_scp(args.targets_scp)
!= num_targets):
raise Exception("Mismatch between num-targets provided to "
"script vs configs")
except KeyError as e:
num_targets = -1
else:
target_type = "sparse"
try:
num_targets = int(variables['num_targets'])
except KeyError as e:
raise Exception("KeyError {0}: Variables need to be defined "
"in {1}".format(
str(e), '{0}/configs'.format(args.dir)))
train_lib.raw_model.generate_egs_using_targets(
data=args.feat_dir, targets_scp=args.targets_scp,
egs_dir=default_egs_dir,
left_context=left_context, right_context=right_context,
run_opts=run_opts,
frames_per_eg_str=str(args.frames_per_eg),
srand=args.srand,
egs_opts=args.egs_opts,
cmvn_opts=args.cmvn_opts,
online_ivector_dir=args.online_ivector_dir,
samples_per_iter=args.samples_per_iter,
transform_dir=args.transform_dir,
stage=args.egs_stage,
target_type=target_type,
num_targets=num_targets)
if args.egs_dir is None:
egs_dir = default_egs_dir
else:
egs_dir = args.egs_dir
[egs_left_context, egs_right_context,
frames_per_eg_str, num_archives] = (
common_train_lib.verify_egs_dir(egs_dir, feat_dim,
ivector_dim, ivector_id,
left_context, right_context))
assert(str(args.frames_per_eg) == frames_per_eg_str)
if (args.num_jobs_final > num_archives):
raise Exception('num_jobs_final cannot exceed the number of archives '
'in the egs directory')
# copy the properties of the egs to dir for
# use during decoding
common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir)
if (args.stage <= -3) and os.path.exists(args.dir+"/configs/init.config"):
logger.info('Computing the preconditioning matrix for input features')
train_lib.common.compute_preconditioning_matrix(
args.dir, egs_dir, num_archives, run_opts,
max_lda_jobs=args.max_lda_jobs,
rand_prune=args.rand_prune)
if (args.stage <= -1):
logger.info("Preparing the initial network.")
common_train_lib.prepare_initial_network(args.dir, run_opts)
# set num_iters so that as close as possible, we process the data
# $num_epochs times, i.e. $num_iters*$avg_num_jobs) ==
# $num_epochs*$num_archives, where
# avg_num_jobs=(num_jobs_initial+num_jobs_final)/2.
num_archives_expanded = num_archives * args.frames_per_eg
num_archives_to_process = int(args.num_epochs * num_archives_expanded)
num_archives_processed = 0
num_iters = ((num_archives_to_process * 2)
/ (args.num_jobs_initial + args.num_jobs_final))
models_to_combine = common_train_lib.get_model_combine_iters(
num_iters, args.num_epochs,
num_archives_expanded, args.max_models_combine,
args.num_jobs_final)
def learning_rate(iter, current_num_jobs, num_archives_processed):
return common_train_lib.get_learning_rate(iter, current_num_jobs,
num_iters,
num_archives_processed,
num_archives_to_process,
args.initial_effective_lrate,
args.final_effective_lrate)
logger.info("Training will run for {0} epochs = "
"{1} iterations".format(args.num_epochs, num_iters))
for iter in range(num_iters):
if (args.exit_stage is not None) and (iter == args.exit_stage):
logger.info("Exiting early due to --exit-stage {0}".format(iter))
return
current_num_jobs = int(0.5 + args.num_jobs_initial
+ (args.num_jobs_final - args.num_jobs_initial)
* float(iter) / num_iters)
lrate = learning_rate(iter, current_num_jobs,
num_archives_processed)
shrink_value = 1.0
if args.proportional_shrink != 0.0:
shrink_value = 1.0 - (args.proportional_shrink * lrate)
if shrink_value <= 0.5:
raise Exception("proportional-shrink={0} is too large, it gives "
"shrink-value={1}".format(args.proportional_shrink,
shrink_value))
if args.stage <= iter:
train_lib.common.train_one_iteration(
dir=args.dir,
iter=iter,
srand=args.srand,
egs_dir=egs_dir,
num_jobs=current_num_jobs,
num_archives_processed=num_archives_processed,
num_archives=num_archives,
learning_rate=lrate,
dropout_edit_string=common_train_lib.get_dropout_edit_string(
args.dropout_schedule,
float(num_archives_processed) / num_archives_to_process,
iter),
minibatch_size_str=args.minibatch_size,
frames_per_eg=args.frames_per_eg,
momentum=args.momentum,
max_param_change=args.max_param_change,
shrinkage_value=shrink_value,
shuffle_buffer_size=args.shuffle_buffer_size,
run_opts=run_opts,
get_raw_nnet_from_am=False,
image_augmentation_opts=args.image_augmentation_opts)
if args.cleanup:
# do a clean up everythin but the last 2 models, under certain
# conditions
common_train_lib.remove_model(
args.dir, iter-2, num_iters, models_to_combine,
args.preserve_model_interval,
get_raw_nnet_from_am=False)
if args.email is not None:
reporting_iter_interval = num_iters * args.reporting_interval
if iter % reporting_iter_interval == 0:
# lets do some reporting
[report, times, data] = (
nnet3_log_parse.generate_acc_logprob_report(args.dir))
message = report
subject = ("Update : Expt {dir} : "
"Iter {iter}".format(dir=args.dir, iter=iter))
common_lib.send_mail(message, subject, args.email)
num_archives_processed = num_archives_processed + current_num_jobs
if args.stage <= num_iters:
if args.final_combination:
logger.info("Doing final combination to produce final.raw")
train_lib.common.combine_models(
dir=args.dir, num_iters=num_iters,
models_to_combine=models_to_combine, egs_dir=egs_dir,
minibatch_size_str=args.minibatch_size, run_opts=run_opts,
get_raw_nnet_from_am=False,
sum_to_one_penalty=args.combine_sum_to_one_penalty)
else:
common_lib.force_symlink("{0}.raw".format(num_iters),
"{0}/final.raw".format(args.dir))
if include_log_softmax and args.stage <= num_iters + 1:
logger.info("Getting average posterior for purposes of "
"adjusting the priors.")
train_lib.common.compute_average_posterior(
dir=args.dir, iter='final', egs_dir=egs_dir,
num_archives=num_archives,
prior_subset_size=args.prior_subset_size, run_opts=run_opts,
get_raw_nnet_from_am=False)
if args.cleanup:
logger.info("Cleaning up the experiment directory "
"{0}".format(args.dir))
remove_egs = args.remove_egs
if args.egs_dir is not None:
# this egs_dir was not created by this experiment so we will not
# delete it
remove_egs = False
common_train_lib.clean_nnet_dir(
nnet_dir=args.dir, num_iters=num_iters, egs_dir=egs_dir,
preserve_model_interval=args.preserve_model_interval,
remove_egs=remove_egs,
get_raw_nnet_from_am=False)
# do some reporting
[report, times, data] = nnet3_log_parse.generate_acc_logprob_report(args.dir)
if args.email is not None:
common_lib.send_mail(report, "Update : Expt {0} : "
"complete".format(args.dir), args.email)
with open("{dir}/accuracy.report".format(dir=args.dir), "w") as f:
f.write(report)
common_lib.execute_command("steps/info/nnet3_dir_info.pl "
"{0}".format(args.dir))
def train(args, run_opts, background_process_handler):
""" The main function for training.
Args:
args: a Namespace object with the required parameters
obtained from the function process_args()
run_opts: RunOpts object obtained from the process_args()
"""
arg_string = pprint.pformat(vars(args))
logger.info("Arguments for the experiment\n{0}".format(arg_string))
# Set some variables.
feat_dim = common_lib.get_feat_dim(args.feat_dir)
ivector_dim = common_lib.get_ivector_dim(args.online_ivector_dir)
config_dir = '{0}/configs'.format(args.dir)
var_file = '{0}/vars'.format(config_dir)
variables = common_train_lib.parse_generic_config_vars_file(var_file)
# Set some variables.
try:
model_left_context = variables['model_left_context']
model_right_context = variables['model_right_context']
# this is really the number of times we add layers to the network for
# discriminative pretraining
num_hidden_layers = variables['num_hidden_layers']
add_lda = common_lib.str_to_bool(variables['add_lda'])
include_log_softmax = common_lib.str_to_bool(
variables['include_log_softmax'])
except KeyError as e:
raise Exception("KeyError {0}: Variables need to be defined in "
"{1}".format(str(e), '{0}/configs'.format(args.dir)))
left_context = args.chunk_left_context + model_left_context
right_context = args.chunk_right_context + model_right_context
# Initialize as "raw" nnet, prior to training the LDA-like preconditioning
# matrix. This first config just does any initial splicing that we do;
# we do this as it's a convenient way to get the stats for the 'lda-like'
# transform.
if (args.stage <= -4):
logger.info("Initializing a basic network")
common_lib.run_job("""{command} {dir}/log/nnet_init.log \
nnet3-init --srand=-2 {dir}/configs/init.config \
{dir}/init.raw""".format(command=run_opts.command,
dir=args.dir))
default_egs_dir = '{0}/egs'.format(args.dir)
if (args.stage <= -3) and args.egs_dir is None:
logger.info("Generating egs")
if args.use_dense_targets:
target_type = "dense"
try:
num_targets = int(variables['num_targets'])
if (common_lib.get_feat_dim_from_scp(args.targets_scp) !=
num_targets):
raise Exception("Mismatch between num-targets provided to "
"script vs configs")
except KeyError as e:
num_targets = -1
else:
target_type = "sparse"
try:
num_targets = int(variables['num_targets'])
except KeyError as e:
raise Exception("KeyError {0}: Variables need to be defined "
"in {1}".format(str(e), '{0}/configs'.format(
args.dir)))
train_lib.raw_model.generate_egs_using_targets(
data=args.feat_dir,
targets_scp=args.targets_scp,
egs_dir=default_egs_dir,
left_context=left_context,
right_context=right_context,
valid_left_context=left_context + args.chunk_width,
valid_right_context=right_context + args.chunk_width,
run_opts=run_opts,
frames_per_eg=args.chunk_width,
srand=args.srand,
egs_opts=args.egs_opts,
cmvn_opts=args.cmvn_opts,
online_ivector_dir=args.online_ivector_dir,
samples_per_iter=args.samples_per_iter,
transform_dir=args.transform_dir,
stage=args.egs_stage,
target_type=target_type,
num_targets=num_targets)
if args.egs_dir is None:
egs_dir = default_egs_dir
else:
egs_dir = args.egs_dir
[egs_left_context, egs_right_context, frames_per_eg, num_archives
] = (common_train_lib.verify_egs_dir(egs_dir, feat_dim, ivector_dim,
left_context, right_context))
assert (args.chunk_width == frames_per_eg)
if (args.num_jobs_final > num_archives):
raise Exception('num_jobs_final cannot exceed the number of archives '
'in the egs directory')
# copy the properties of the egs to dir for
# use during decoding
common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir)
if (add_lda and args.stage <= -2):
logger.info('Computing the preconditioning matrix for input features')
train_lib.common.compute_preconditioning_matrix(
args.dir,
egs_dir,
num_archives,
run_opts,
max_lda_jobs=args.max_lda_jobs,
rand_prune=args.rand_prune)
if (args.stage <= -1):
logger.info("Preparing the initial network.")
common_train_lib.prepare_initial_network(args.dir, run_opts)
# set num_iters so that as close as possible, we process the data
# $num_epochs times, i.e. $num_iters*$avg_num_jobs) ==
# $num_epochs*$num_archives, where
# avg_num_jobs=(num_jobs_initial+num_jobs_final)/2.
num_archives_to_process = args.num_epochs * num_archives
num_archives_processed = 0
num_iters = ((num_archives_to_process * 2) /
(args.num_jobs_initial + args.num_jobs_final))
models_to_combine = common_train_lib.verify_iterations(
num_iters, args.num_epochs, num_hidden_layers, num_archives,
args.max_models_combine, args.add_layers_period, args.num_jobs_final)
def learning_rate(iter, current_num_jobs, num_archives_processed):
return common_train_lib.get_learning_rate(iter, current_num_jobs,
num_iters,
num_archives_processed,
num_archives_to_process,
args.initial_effective_lrate,
args.final_effective_lrate)
min_deriv_time = None
max_deriv_time = None
if args.deriv_truncate_margin is not None:
min_deriv_time = -args.deriv_truncate_margin - model_left_context
max_deriv_time = (args.chunk_width - 1 + args.deriv_truncate_margin +
model_right_context)
logger.info("Training will run for {0} epochs = "
"{1} iterations".format(args.num_epochs, num_iters))
for iter in range(num_iters):
if (args.exit_stage is not None) and (iter == args.exit_stage):
logger.info("Exiting early due to --exit-stage {0}".format(iter))
return
current_num_jobs = int(0.5 + args.num_jobs_initial +
(args.num_jobs_final - args.num_jobs_initial) *
float(iter) / num_iters)
if args.stage <= iter:
model_file = "{dir}/{iter}.raw".format(dir=args.dir, iter=iter)
shrinkage_value = 1.0
if args.shrink_value != 1.0:
shrinkage_value = (args.shrink_value
if common_train_lib.do_shrinkage(
iter,
model_file,
args.shrink_saturation_threshold,
get_raw_nnet_from_am=False) else 1)
logger.info("On iteration {0}, learning rate is {1} and "
"shrink value is {2}.".format(
iter,
learning_rate(iter, current_num_jobs,
num_archives_processed),
shrinkage_value))
train_lib.common.train_one_iteration(
dir=args.dir,
iter=iter,
srand=args.srand,
egs_dir=egs_dir,
num_jobs=current_num_jobs,
num_archives_processed=num_archives_processed,
num_archives=num_archives,
learning_rate=learning_rate(iter, current_num_jobs,
num_archives_processed),
shrinkage_value=shrinkage_value,
minibatch_size=args.num_chunk_per_minibatch,
num_hidden_layers=num_hidden_layers,
add_layers_period=args.add_layers_period,
left_context=left_context,
right_context=right_context,
min_deriv_time=min_deriv_time,
max_deriv_time=max_deriv_time,
momentum=args.momentum,
max_param_change=args.max_param_change,
shuffle_buffer_size=args.shuffle_buffer_size,
cv_minibatch_size=args.cv_minibatch_size,
run_opts=run_opts,
get_raw_nnet_from_am=False,
background_process_handler=background_process_handler)
if args.cleanup:
# do a clean up everythin but the last 2 models, under certain
# conditions
common_train_lib.remove_model(args.dir,
iter - 2,
num_iters,
models_to_combine,
args.preserve_model_interval,
get_raw_nnet_from_am=False)
if args.email is not None:
reporting_iter_interval = num_iters * args.reporting_interval
if iter % reporting_iter_interval == 0:
# lets do some reporting
[report, times, data
] = (nnet3_log_parse.generate_accuracy_report(args.dir))
message = report
subject = ("Update : Expt {dir} : "
"Iter {iter}".format(dir=args.dir, iter=iter))
common_lib.send_mail(message, subject, args.email)
num_archives_processed = num_archives_processed + current_num_jobs
if args.stage <= num_iters:
logger.info("Doing final combination to produce final.raw")
train_lib.common.combine_models(
dir=args.dir,
num_iters=num_iters,
models_to_combine=models_to_combine,
egs_dir=egs_dir,
left_context=left_context,
right_context=right_context,
run_opts=run_opts,
chunk_width=args.chunk_width,
background_process_handler=background_process_handler,
get_raw_nnet_from_am=False)
if include_log_softmax and args.stage <= num_iters + 1:
logger.info("Getting average posterior for purposes of "
"adjusting the priors.")
train_lib.common.compute_average_posterior(
dir=args.dir,
iter='final',
egs_dir=egs_dir,
num_archives=num_archives,
left_context=left_context,
right_context=right_context,
prior_subset_size=args.prior_subset_size,
run_opts=run_opts,
get_raw_nnet_from_am=False)
if args.cleanup:
logger.info("Cleaning up the experiment directory "
"{0}".format(args.dir))
remove_egs = args.remove_egs
if args.egs_dir is not None:
# this egs_dir was not created by this experiment so we will not
# delete it
remove_egs = False
common_train_lib.clean_nnet_dir(
nnet_dir=args.dir,
num_iters=num_iters,
egs_dir=egs_dir,
preserve_model_interval=args.preserve_model_interval,
remove_egs=remove_egs,
get_raw_nnet_from_am=False)
# do some reporting
[report, times, data] = nnet3_log_parse.generate_accuracy_report(args.dir)
if args.email is not None:
common_lib.send_mail(
report, "Update : Expt {0} : "
"complete".format(args.dir), args.email)
with open("{dir}/accuracy.report".format(dir=args.dir), "w") as f:
f.write(report)
common_lib.run_job("steps/info/nnet3_dir_info.pl " "{0}".format(args.dir))