def saveModel(dnn,cfg):
log("> Start saveModel")
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.layers, path=cfg.param_output_file, input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor)
log('> ... the best PDNN model param so far is ' + cfg.param_output_file)
if cfg.cfg_output_file != '':
_cfg2file(dnn.cfg, filename=cfg.cfg_output_file)
log('> ... the best PDNN model config so far is ' + cfg.cfg_output_file)
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.write_model_to_kaldi(cfg.kaldi_output_file)
log('> ... the best Kaldi model so far is ' + cfg.kaldi_output_file)
log("< End SaveModel")
for batch_index in xrange(cfg.train_sets.cur_frame_num / cfg.batch_size): # loop over mini-batches
[reconstruction_cost, free_energy_cost] = pretraining_fns[i](index=batch_index, lr=pretrain_lr,
momentum=momentum)
r_c.append(reconstruction_cost)
fe_c.append(free_energy_cost)
cfg.train_sets.initialize_read()
log('> pre-training layer %i, epoch %d, r_cost %f, fe_cost %f' % (i, epoch, numpy.mean(r_c), numpy.mean(fe_c)))
# output nnet parameters and training state, for training resume
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
save_two_integers((i, epoch+1), wdir + '/training_state.tmp')
start_epoch_index = 0
save_two_integers((i+1, 0), wdir + '/training_state.tmp')
# save the pretrained nnet to file
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.layers, filename=cfg.param_output_file)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file)
if cfg.cfg_output_file != '':
_cfg2file(dnn.cfg, filename=cfg.cfg_output_file)
log('> ... the final PDNN model config is ' + cfg.cfg_output_file)
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.write_model_to_kaldi(cfg.kaldi_output_file)
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file)
# finally remove the training-resuming files
os.remove(wdir + '/nnet.tmp')
os.remove(wdir + '/training_state.tmp')
log('> ... learning the adaptation network')
cfg = cfg_adapt
while (cfg.lrate.get_rate() != 0):
# one epoch of sgd training
# train_error = train_sgd_verbose(train_fn, cfg_si.train_sets, cfg_si.train_xy,
# cfg.batch_size, cfg.lrate.get_rate(), cfg.momentum)
# log('> epoch %d, training error %f ' % (cfg.lrate.epoch, 100*numpy.mean(train_error)) + '(%)')
# validation
valid_error = validate_by_minibatch_verbose(valid_fn, cfg_si.valid_sets, cfg_si.valid_xy, cfg.batch_size)
log('> epoch %d, lrate %f, validation error %f ' % (cfg.lrate.epoch, cfg.lrate.get_rate(), 100*numpy.mean(valid_error)) + '(%)')
cfg.lrate.get_next_rate(current_error = 100 * numpy.mean(valid_error))
cfg.lrate.rate = 0
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.dnn_adapt.layers, filename = cfg.param_output_file + '.adapt',
input_factor = cfg_adapt.input_dropout_factor, factor = cfg_adapt.dropout_factor)
_nnet2file(dnn.cnn_si.layers, filename = cfg.param_output_file + '.si',
input_factor = cfg_si.input_dropout_factor, factor = cfg_si.dropout_factor)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file + ' (.si, .adapt)')
if cfg.cfg_output_file != '':
_cfg2file(cfg_adapt, filename=cfg.cfg_output_file + '.adapt')
_cfg2file(cfg_si, filename=cfg.cfg_output_file + '.si')
log('> ... the final PDNN model config is ' + cfg.cfg_output_file + ' (.si, .adapt)')
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.cnn_si.fc_dnn.write_model_to_kaldi(cfg.kaldi_output_file + '.si')
dnn.dnn_adapt.write_model_to_kaldi(cfg.kaldi_output_file + '.adapt', with_softmax = False)
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file + ' (.si, .adapt)')
cfg.lrate.get_next_rate(current_error = 100 * numpy.mean(valid_error))
# output nnet parameters and lrate, for training resume
if cfg.lrate.epoch % cfg.model_save_step == 0:
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
_lrate2file(cfg.lrate, wdir + '/training_state.tmp')
# save the model and network configuration
for i in range(len(cfg.hidden_layers_sizes)):
if i==0:
n_params = (cfg.n_ins + 1) * cfg.hidden_layers_sizes[i]
else:
n_params += (cfg.hidden_layers_sizes[i-1] + 1) * cfg.hidden_layers_sizes[i]
n_params += cfg.n_outs * (cfg.hidden_layers_sizes[i-1] + 1)
ratio = float(n_params) / float(n_data_train)
log('-->> Ratio Parameters / Data : ' + str(ratio))
if cfg.param_output_file != '':
_nnet2file(dnn.layers, filename=cfg.param_output_file, input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file)
if cfg.cfg_output_file != '':
_cfg2file(dnn.cfg, filename=cfg.cfg_output_file)
log('> ... the final PDNN model config is ' + cfg.cfg_output_file)
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.write_model_to_kaldi(cfg.kaldi_output_file)
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file)
# remove the tmp files (which have been generated from resuming training)
os.remove(wdir + '/nnet.tmp')
os.remove(wdir + '/training_state.tmp')
for batch_index in xrange(cfg.train_sets.cur_frame_num / cfg.batch_size): # loop over mini-batches
[reconstruction_cost, free_energy_cost] = pretraining_fns[i](index=batch_index, lr=pretrain_lr,
momentum=momentum)
r_c.append(reconstruction_cost)
fe_c.append(free_energy_cost)
cfg.train_sets.initialize_read()
log('> pre-training layer %i, epoch %d, r_cost %f, fe_cost %f' % (i, epoch, numpy.mean(r_c), numpy.mean(fe_c)))
# output nnet parameters and training state, for training resume
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
save_two_integers((i, epoch+1), wdir + '/training_state.tmp')
start_epoch_index = 0
save_two_integers((i+1, 0), wdir + '/training_state.tmp')
# save the pretrained nnet to file
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.layers, filename=cfg.param_output_file)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file)
if cfg.cfg_output_file != '':
_cfg2file(srbm.cfg, filename=cfg.cfg_output_file)
log('> ... the final PDNN model config is ' + cfg.cfg_output_file)
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.write_model_to_kaldi(cfg.kaldi_output_file)
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file)
# finally remove the training-resuming files
os.remove(wdir + '/nnet.tmp')
os.remove(wdir + '/training_state.tmp')
else:
log('> task %d, epoch %d, training error %f ' % (n, cfg.lrate.epoch, numpy.mean(train_error_array[n])) + '(%)')
train_error_array[n] = []
# perform validation, output valid error rate, and adjust learning rate based on the learning rate
valid_error = validate_by_minibatch(valid_fn_array[n], cfg)
log('> task %d, epoch %d, lrate %f, validation error %f ' % (n, cfg.lrate.epoch, cfg.lrate.get_rate(), numpy.mean(valid_error)) + '(%)')
cfg.lrate.get_next_rate(current_error = numpy.mean(valid_error))
# output nnet parameters and lrate, for training resume
_nnet2file(dnn_array[n].layers, filename=wdir + '/nnet.tmp.task' + str(n))
_lrate2file(cfg.lrate, wdir + '/training_state.tmp.task' + str(n))
# if the lrate of a task decays to 0, training on this task terminates; it will be excluded from future training
if cfg.lrate.get_rate() == 0:
active_tasks_new.remove(n)
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn_array[n].layers, filename=cfg.param_output_file + '.task' + str(n),
input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file + '.task' + str(n))
if cfg.cfg_output_file != '':
_cfg2file(dnn_array[n].cfg, filename=cfg.cfg_output_file + '.task' + str(n))
log('> ... the final PDNN model config is ' + cfg.cfg_output_file + '.task' + str(n))
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn_array[n].write_model_to_kaldi(cfg.kaldi_output_file + '.task' + str(n))
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file + '.task' + str(n))
# remove the tmp files (which have been generated from resuming training)
os.remove(wdir + '/nnet.tmp.task' + str(n)); os.remove(wdir + '/training_state.tmp.task' + str(n))
active_tasks = active_tasks_new
(cfg.lrate.epoch, cfg.lrate.get_rate(),
100 * numpy.mean(valid_error)) + '(%)')
cfg.lrate.get_next_rate(current_error=100 * numpy.mean(valid_error))
cfg.lrate.rate = 0
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.dnn_adapt.layers,
filename=cfg.param_output_file + '.adapt',
input_factor=cfg_adapt.input_dropout_factor,
factor=cfg_adapt.dropout_factor)
_nnet2file(dnn.cnn_si.layers,
filename=cfg.param_output_file + '.si',
input_factor=cfg_si.input_dropout_factor,
factor=cfg_si.dropout_factor)
log('> ... the final PDNN model parameter is ' +
cfg.param_output_file + ' (.si, .adapt)')
if cfg.cfg_output_file != '':
_cfg2file(cfg_adapt, filename=cfg.cfg_output_file + '.adapt')
_cfg2file(cfg_si, filename=cfg.cfg_output_file + '.si')
log('> ... the final PDNN model config is ' + cfg.cfg_output_file +
' (.si, .adapt)')
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.cnn_si.fc_dnn.write_model_to_kaldi(cfg.kaldi_output_file + '.si')
dnn.dnn_adapt.write_model_to_kaldi(cfg.kaldi_output_file + '.adapt',
with_softmax=False)
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file +
' (.si, .adapt)')
# output nnet parameters and lrate, for training resume
if cfg.lrate.epoch % cfg.model_save_step == 0:
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
_lrate2file(cfg.lrate, wdir + '/training_state.tmp')
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.dnn.layers, filename = cfg.param_output_file,
input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor)
_nnet2file(dnn.dnn_tower1.layers, filename = cfg.param_output_file + '.tower1',
input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor)
_nnet2file(dnn.dnn_tower2.layers, filename = cfg.param_output_file + '.tower2',
input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file + '(, .tower1, .tower2)')
if cfg.cfg_output_file != '':
_cfg2file(cfg, filename=cfg.cfg_output_file)
_cfg2file(cfg_tower1, filename=cfg.cfg_output_file + '.tower1')
_cfg2file(cfg_tower2, filename=cfg.cfg_output_file + '.tower2')
log('> ... the final PDNN model config is ' + cfg.cfg_output_file + '(, .tower1, .tower2)')
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.dnn.write_model_to_kaldi(cfg.kaldi_output_file)
dnn.dnn_tower1.write_model_to_kaldi(cfg.kaldi_output_file + '.tower1', with_softmax = False)
dnn.dnn_tower2.write_model_to_kaldi(cfg.kaldi_output_file + '.tower2', with_softmax = False)
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file + '(, .tower1, .tower2)')
# remove the tmp files (which have been generated from resuming training)
os.remove(wdir + '/nnet.tmp')
os.remove(wdir + '/training_state.tmp')
+ "(%)"
)
cfg.lrate.get_next_rate(current_error=100 * numpy.mean(valid_error))
cfg.lrate.rate = 0
# save the model and network configuration
if cfg.param_output_file != "":
_nnet2file(
dnn.dnn_adapt.layers,
filename=cfg.param_output_file + ".adapt",
input_factor=cfg_adapt.input_dropout_factor,
factor=cfg_adapt.dropout_factor,
)
_nnet2file(
dnn.cnn_si.layers,
filename=cfg.param_output_file + ".si",
input_factor=cfg_si.input_dropout_factor,
factor=cfg_si.dropout_factor,
)
log("> ... the final PDNN model parameter is " + cfg.param_output_file + " (.si, .adapt)")
if cfg.cfg_output_file != "":
_cfg2file(cfg_adapt, filename=cfg.cfg_output_file + ".adapt")
_cfg2file(cfg_si, filename=cfg.cfg_output_file + ".si")
log("> ... the final PDNN model config is " + cfg.cfg_output_file + " (.si, .adapt)")
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != "":
dnn.cnn_si.fc_dnn.write_model_to_kaldi(cfg.kaldi_output_file + ".si")
dnn.dnn_adapt.write_model_to_kaldi(cfg.kaldi_output_file + ".adapt", with_softmax=False)
log("> ... the final Kaldi model is " + cfg.kaldi_output_file + " (.si, .adapt)")
def dnn_run(arguments):
required_arguments = ['train_data', 'valid_data', 'nnet_spec', 'wdir']
for arg in required_arguments:
if arguments.has_key(arg) == False:
print "Error: the argument %s has to be specified" % (arg); exit(1)
train_data_spec = arguments['train_data']
valid_data_spec = arguments['valid_data']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
cfg = NetworkConfig()
cfg.parse_config_dnn(arguments, nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0; ptr_file = ''
if arguments.has_key('ptr_file') and arguments.has_key('ptr_layer_number'):
ptr_file = arguments['ptr_file']
ptr_layer_number = int(arguments['ptr_layer_number'])
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir + '/nnet.tmp') and os.path.exists(wdir + '/training_state.tmp'):
resume_training = True
cfg.lrate = _file2lrate(wdir + '/training_state.tmp')
log('> ... found nnet.tmp and training_state.tmp, now resume training from epoch ' + str(cfg.lrate.epoch))
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
log('> ... building the model')
# setup model
if cfg.do_dropout:
dnn = DNN_Dropout(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg)
else:
dnn = DNN(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg)
# initialize model parameters
# if not resuming training, initialized from the specified pre-training file
# if resuming training, initialized from the tmp model file
if (ptr_layer_number > 0) and (resume_training is False):
_file2nnet(dnn.layers, set_layer_num = ptr_layer_number, filename = ptr_file)
if resume_training:
_file2nnet(dnn.layers, filename = wdir + '/nnet.tmp')
# get the training, validation and testing function for the model
log('> ... getting the finetuning functions')
train_fn, valid_fn = dnn.build_finetune_functions((cfg.train_x, cfg.train_y), (cfg.valid_x, cfg.valid_y),
batch_size=cfg.batch_size)
log('> ... finetuning the model')
while (cfg.lrate.get_rate() != 0):
# one epoch of sgd training
train_error = train_sgd(train_fn, cfg)
log('> epoch %d, training error %f ' % (cfg.lrate.epoch, 100*numpy.mean(train_error)) + '(%)')
# validation
valid_error = validate_by_minibatch(valid_fn, cfg)
log('> epoch %d, lrate %f, validation error %f ' % (cfg.lrate.epoch, cfg.lrate.get_rate(), 100*numpy.mean(valid_error)) + '(%)')
cfg.lrate.get_next_rate(current_error = 100*numpy.mean(valid_error))
# output nnet parameters and lrate, for training resume
if cfg.lrate.epoch % cfg.model_save_step == 0:
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
_lrate2file(cfg.lrate, wdir + '/training_state.tmp')
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.layers, filename=cfg.param_output_file, input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file)
if cfg.cfg_output_file != '':
_cfg2file(dnn.cfg, filename=cfg.cfg_output_file)
log('> ... the final PDNN model config is ' + cfg.cfg_output_file)
def dnn_run(arguments):
required_arguments = ['train_data', 'valid_data', 'nnet_spec', 'wdir']
for arg in required_arguments:
if arguments.has_key(arg) == False:
print "Error: the argument %s has to be specified" % (arg)
exit(1)
train_data_spec = arguments['train_data']
valid_data_spec = arguments['valid_data']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
cfg = NetworkConfig()
cfg.parse_config_dnn(arguments, nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0
ptr_file = ''
if arguments.has_key('ptr_file') and arguments.has_key('ptr_layer_number'):
ptr_file = arguments['ptr_file']
ptr_layer_number = int(arguments['ptr_layer_number'])
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir +
'/nnet.tmp') and os.path.exists(wdir +
'/training_state.tmp'):
resume_training = True
cfg.lrate = _file2lrate(wdir + '/training_state.tmp')
log('> ... found nnet.tmp and training_state.tmp, now resume training from epoch '
+ str(cfg.lrate.epoch))
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2**30))
log('> ... building the model')
# setup model
if cfg.do_dropout:
dnn = DNN_Dropout(numpy_rng=numpy_rng, theano_rng=theano_rng, cfg=cfg)
else:
dnn = DNN(numpy_rng=numpy_rng, theano_rng=theano_rng, cfg=cfg)
# initialize model parameters
# if not resuming training, initialized from the specified pre-training file
# if resuming training, initialized from the tmp model file
if (ptr_layer_number > 0) and (resume_training is False):
_file2nnet(dnn.layers,
set_layer_num=ptr_layer_number,
filename=ptr_file)
if resume_training:
_file2nnet(dnn.layers, filename=wdir + '/nnet.tmp')
# get the training, validation and testing function for the model
log('> ... getting the finetuning functions')
train_fn, valid_fn = dnn.build_finetune_functions(
(cfg.train_x, cfg.train_y), (cfg.valid_x, cfg.valid_y),
batch_size=cfg.batch_size)
log('> ... finetuning the model')
while (cfg.lrate.get_rate() != 0):
# one epoch of sgd training
train_error = train_sgd(train_fn, cfg)
log('> epoch %d, training error %f ' %
(cfg.lrate.epoch, 100 * numpy.mean(train_error)) + '(%)')
# validation
valid_error = validate_by_minibatch(valid_fn, cfg)
log('> epoch %d, lrate %f, validation error %f ' %
(cfg.lrate.epoch, cfg.lrate.get_rate(),
100 * numpy.mean(valid_error)) + '(%)')
cfg.lrate.get_next_rate(current_error=100 * numpy.mean(valid_error))
# output nnet parameters and lrate, for training resume
if cfg.lrate.epoch % cfg.model_save_step == 0:
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
_lrate2file(cfg.lrate, wdir + '/training_state.tmp')
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.layers,
filename=cfg.param_output_file,
input_factor=cfg.input_dropout_factor,
factor=cfg.dropout_factor)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file)
if cfg.cfg_output_file != '':
_cfg2file(dnn.cfg, filename=cfg.cfg_output_file)
log('> ... the final PDNN model config is ' + cfg.cfg_output_file)
c.append(pretraining_fns[i](index = batch_index,
corruption = cfg.corruption_levels[i],
lr = cfg.learning_rates[i],
momentum = cfg.momentum))
cfg.train_sets.initialize_read()
log('> layer %i, epoch %d, reconstruction cost %f' % (i, epoch, numpy.mean(c)))
# output nnet parameters and training state, for training resume
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
save_two_integers((i, epoch+1), wdir + '/training_state.tmp')
# output nnet parameters and training state, for training resume
start_epoch_index = 0
save_two_integers((i+1, 0), wdir + '/training_state.tmp')
# save the pretrained nnet to file
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.layers, filename=cfg.param_output_file)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file)
if cfg.cfg_output_file != '':
_cfg2file(sda.cfg, filename=cfg.cfg_output_file)
log('> ... the final PDNN model config is ' + cfg.cfg_output_file)
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.write_model_to_kaldi(cfg.kaldi_output_file)
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file)
# finally remove the training-resuming files
os.remove(wdir + '/nnet.tmp')
os.remove(wdir + '/training_state.tmp')
