def init_parameters_from_cfg(self):
"""
Sets initial parameters in the ParameterSet from the configuration.
"""
prefix = 'initval_'
for name in dir(self.cfg):
if name.startswith(prefix):
varname = name[len(prefix):]
value = getattr(self.cfg, name)
print "Parameter initialization: %015s = %s" % (varname,
repr(value))
if isinstance(value, (float, int)):
self.ps[varname] = value
else:
self.ps[varname] = post(value)
prefix = 'initvar_'
for name in dir(self.cfg):
if name.startswith(prefix):
varname = name[len(prefix):]
variance = getattr(self.cfg, name)
meanname = 'initmean_' + varname
mean = getattr(self.cfg, meanname) if meanname in dir(
self.cfg) else 0.0
print "Random parameter initialization: %015s ~ N(%.3f, %.3f)" % (
varname, mean, variance)
self.ps[varname] = post(
np.random.normal(mean,
variance,
size=self.ps[varname].shape))
def init_parameters(self, var=None, seed=None):
"""Initializes the parameteres of the ParameterSet using a zero-mean normal distribution.
:param var: Variance of the normal distribution.
If None, then the 'initialization_variance' parameter from the configuration is used.
:param seed: random seed for initialization
"""
if var is None:
if self.cfg.has('initialization_variance'):
var = self.cfg.initialization_variance
elif self.cfg.has('initvar' ):
var = self.cfg.initvar
else:
var = 0.01
if seed is None:
if self.cfg.has('initialization_seed'):
seed = self.cfg.initialization_seed
else:
seed = 1
print "Random parameter initialization: ALL ~ N(0, %.3f) with seed %d" % (var, seed)
np.random.seed(seed)
self.ps.data[:] = post(np.random.normal(0, var, size=self.ps.data.shape))
if self.cfg.get('positive_weights_init'):
print "Ensuring positive initialization weights."
self.ps.data[:] = xp.abs(self.ps.data)
self.init_parameters_from_cfg()
self.ps.restore_constants()
def init_parameters(self, var=None, seed=None):
"""Initializes the parameteres of the ParameterSet using a zero-mean normal distribution.
:param var: Variance of the normal distribution.
If None, then the 'initialization_variance' parameter from the configuration is used.
:param seed: random seed for initialization
"""
if var is None:
if self.cfg.has('initialization_variance'):
var = self.cfg.initialization_variance
elif self.cfg.has('initvar'):
var = self.cfg.initvar
else:
var = 0.01
if seed is None:
if self.cfg.has('initialization_seed'):
seed = self.cfg.initialization_seed
else:
seed = 1
print "Random parameter initialization: ALL ~ N(0, %.3f) with seed %d" % (
var, seed)
np.random.seed(seed)
self.ps.data[:] = post(
np.random.normal(0, var, size=self.ps.data.shape))
if self.cfg.get('positive_weights_init'):
print "Ensuring positive initialization weights."
self.ps.data[:] = xp.abs(self.ps.data)
self.init_parameters_from_cfg()
self.ps.restore_constants()
def constants(self, value):
self._constants = dict(value)
# build constant selector and zero array for nullifying gradient
idxs = [np.arange(*self.extents_of_var(var)) for var in self._constants.iterkeys()]
sel = reduce(lambda a, b: np.concatenate((a, b)), idxs, [])
sel = np.sort(sel)
self._constants_selector = post(sel)
self._constants_zeros = xp.zeros((len(sel),))
def constants(self, value):
self._constants = dict(value)
# build constant selector and zero array for nullifying gradient
idxs = [
np.arange(*self.extents_of_var(var))
for var in self._constants.iterkeys()
]
sel = reduce(lambda a, b: np.concatenate((a, b)), idxs, [])
sel = np.sort(sel)
self._constants_selector = post(sel)
self._constants_zeros = xp.zeros((len(sel), ))
def set_constant_parameters_from_cfg(self):
"""
Sets constant parameters in the ParameterSet from the configuration.
"""
prefix = 'const_'
constants = self.ps.constants
for name in dir(self.cfg):
if name.startswith(prefix):
varname = name[len(prefix):]
value = getattr(self.cfg, name)
print "Constant parameter: %015s = %s" % (varname, repr(value))
constants[varname] = post(value)
self.ps.constants = constants
def set_constant_parameters_from_cfg(self):
"""
Sets constant parameters in the ParameterSet from the configuration.
"""
prefix = 'const_'
constants = self.ps.constants
for name in dir(self.cfg):
if name.startswith(prefix):
varname = name[len(prefix):]
value = getattr(self.cfg, name)
print "Constant parameter: %015s = %s" % (varname, repr(value))
constants[varname] = post(value)
self.ps.constants = constants
def __init__(self, ds, idx, minibatch_size, pad_data, force_cpu):
self._keys = [k for k in ds.keys() if not k.startswith('meta_')]
self._minibatch_size = minibatch_size
self.n_samples = len(idx)
self.n_bytes = 0
for key in self._keys:
data = ds[key][idx, ...]
if pad_data:
data = self._pad(data, minibatch_size)
self.n_bytes += data.nbytes
if not force_cpu:
data = post(data)
setattr(self, key, data)
def init_parameters_from_cfg(self):
"""
Sets initial parameters in the ParameterSet from the configuration.
"""
prefix = 'initval_'
for name in dir(self.cfg):
if name.startswith(prefix):
varname = name[len(prefix):]
value = getattr(self.cfg, name)
print "Parameter initialization: %015s = %s" % (varname, repr(value))
if isinstance(value, (float, int)):
self.ps[varname] = value
else:
self.ps[varname] = post(value)
prefix = 'initvar_'
for name in dir(self.cfg):
if name.startswith(prefix):
varname = name[len(prefix):]
variance = getattr(self.cfg, name)
meanname = 'initmean_' + varname
mean = getattr(self.cfg, meanname) if meanname in dir(self.cfg) else 0.0
print "Random parameter initialization: %015s ~ N(%.3f, %.3f)" % (varname, mean, variance)
self.ps[varname] = post(np.random.normal(mean, variance, size=self.ps[varname].shape))
def load(cls, state_dir):
"""
Loads parameters history and best results from the state directory.
:param state_dir: directory to load from
:return: a ParameterHistory object loaded from the state directory
"""
history_filename, results_filename, best_pars_filename = cls._get_result_filenames(state_dir)
self = cls()
self.history = np.load(history_filename)['history']
self.best_pars = post(np.load(best_pars_filename)['best_pars'])
with open(results_filename, 'rb') as results_file:
data = json.load(results_file)
self.best_iter = data['best_iter']
self.best_val_loss = data['best_val_loss']
self.best_tst_loss = data['best_tst_loss']
self.start_time = data['start_time']
self.end_time = data['end_time']
self.termination_reason = data['termination_reason']
if 'cfg' in data:
self.cfg = data['cfg']
else:
self.cfg = {}
return self
def load(cls, state_dir):
"""
Loads parameters history and best results from the state directory.
:param state_dir: directory to load from
:return: a ParameterHistory object loaded from the state directory
"""
history_filename, results_filename, best_pars_filename = cls._get_result_filenames(
state_dir)
self = cls()
self.history = np.load(history_filename)['history']
self.best_pars = post(np.load(best_pars_filename)['best_pars'])
with open(results_filename, 'rb') as results_file:
data = json.load(results_file)
self.best_iter = data['best_iter']
self.best_val_loss = data['best_val_loss']
self.best_tst_loss = data['best_tst_loss']
self.start_time = data['start_time']
self.end_time = data['end_time']
self.termination_reason = data['termination_reason']
if 'cfg' in data:
self.cfg = data['cfg']
else:
self.cfg = {}
return self
def load_parameters(self, filename):
"""Loads the parameters of the ParameterSet of the model from the given file."""
self.ps.data[:] = post(np.load(filename)['ps'])
def __setstate__(self, state):
state['best_pars'] = post(state['best_pars'])
self.__dict__ = state
def generic_training(self,
cfg_dir,
checkpoint=None,
checkpoint_handler=None,
loss_record_interval=10,
max_missed_val_improvements=200,
iteration_gain=1.25,
min_improvement=1e-7,
reset_termination_criteria=False,
desired_loss=None,
initialize=True,
large_gradient_threshold=0.0,
print_gradient_info=False,
print_gradient=False,
print_parameters=False,
log_parameters=[],
plot_logged_parameters=True,
print_logged_parameters=False,
check_gradient_finite=False,
log_modules=None):
"""
Generic training procedure.
:param cfg_dir: configuration directory
:param checkpoint: checkpoint
:param checkpoint_handler: checkpoint handler
:param loss_record_interval: number of iterations between calculating and recording losses
:param max_missed_val_improvements: maximum iterations without improvement of loss before training ist stopped
:param iteration_gain: If not set to 0, then training is performed up to iteration
(iteration_gain * iteration_of_last_improvement).
:param min_improvement: minimum loss change to count as improvement
:param reset_termination_criteria: resets the termination criteria after loading a checkpoint
:param desired_loss: if specified, training is terminated with this loss is reached
:param initialize: if True, the model parameters are initialized using the init_parameters method.
:param large_gradient_threshold: if specified, a check for large gradient elements that exceed the
given threshold is performed every iteration and they are printed.
:param print_gradient_info: if True, this function prints diagnostic gradient information
:param print_gradient: if True, this function prints the full gradient every minibatch
:param log_parameters: list of parameter names (from self.ps) that should be logged every iteration
to file params.out
:param plot_logged_parameters: if True, logged parameters are plotted to params.png
:param print_logged_parameters: if True, logged parameters are also printed to standard output
:param check_gradient_finite: if True, gradient is checked for infs and nans in every iteration
:param log_modules: list of python modules for which version or latest git commit should be logged
:return: ParameterHistory object of training
"""
max_iters = self.cfg.max_iters if self.cfg.has('max_iters') else None
# build gradient preprocessing chain
grad_func = self.mb_loss_grad
# gradient magnitude cap
if self.cfg.has('gradient_cap'):
grad_with_cap_orig_grad = grad_func
def grad_with_cap(pv):
g = grad_with_cap_orig_grad(pv)
g_mag = xp.sqrt(xp.sum(g**2))
if g_mag > self.cfg.gradient_cap:
print "gradient magnitude %f is being rescaled" % g_mag
g *= self.cfg.gradient_cap / g_mag
return g
grad_func = grad_with_cap
# gradient element cap
if self.cfg.has('gradient_element_cap'):
grad_with_element_cap_orig_grad = grad_func
def grad_with_element_cap(pv):
g = grad_with_element_cap_orig_grad(pv)
elems = xp.where(xp.abs(g) > self.cfg.gradient_element_cap)
g[elems] = xp.sign(g[elems]) * self.cfg.gradient_element_cap
return g
grad_func = grad_with_element_cap
# gradient of constants set to zero
grad_without_const_orig_grad = grad_func
def grad_without_const(pv):
g = grad_without_const_orig_grad(pv)
return self.ps.nullify_gradient_of_constants(g)
grad_func = grad_without_const
# initialize or restore checkpoint, if available
if not checkpoint:
itr = 0
if initialize:
self.init_parameters()
his = ParameterHistory(
cfg=self.cfg,
state_dir=cfg_dir,
max_iters=max_iters,
max_missed_val_improvements=max_missed_val_improvements,
min_improvement=min_improvement,
desired_loss=desired_loss,
iteration_gain=iteration_gain)
logger = ParameterLogger(out_dir=cfg_dir,
parameters=log_parameters,
plot=plot_logged_parameters,
print_stdout=print_logged_parameters)
git_log(modules=log_modules, log_dir=cfg_dir)
# Record initial loss and parameters
self.record_loss(his, itr)
logger.log(itr, self.ps)
else:
itr = checkpoint['iter']
self.ps.data[:] = post(checkpoint['data'])
if 'optimizer_step_rate' in checkpoint:
self.cfg.optimizer_step_rate = checkpoint[
'optimizer_step_rate']
his = checkpoint['his']
his.state_dir = cfg_dir
his.max_missed_val_improvements = max_missed_val_improvements
his.desired_loss = desired_loss
his.iteration_gain = iteration_gain
# start and endtimes in his should have the same length, this is
# not the case in explicit auto-save checkpoints, therefore set the
# end to the saved
if len(his.start_time) != len(his.end_time):
his.end_time.append(checkpoint['save_time'])
his.start()
logger = checkpoint['logger']
git_log(modules=log_modules, log_dir=cfg_dir, check=True)
# reset termination criteria if requested
second_chance_file = join(cfg_dir, "2nd_chance")
if exists(second_chance_file):
print "Resetting termination criteria because %s is present" % second_chance_file
reset_termination_criteria = True
unlink(second_chance_file)
if self.cfg.continue_training:
print "Resetting termination criteria because --continue flag was specified"
reset_termination_criteria = True
if reset_termination_criteria:
his.reset_best()
if 'step_element_cap' in dir(self.cfg):
step_element_cap_orig = self.cfg.step_element_cap
step_element_cap_decrease_iteration = None
restart = True
while restart and (max_iters is None or max_iters > 0):
# create optimizer
if isinstance(self.cfg.optimizer, dict):
def wrt_fprime_for_part(partition):
wrt_for_part = self.ps.num_partition(partition)
def fprime_for_part(pv_part):
# we assume that the optimizer updates the ParameterSet inplace and
# evaluates the gradient at the current values of the parameters
start, stop = self.ps.extents_of_partition(partition)
return grad_func(self.ps.num_data)[start:stop]
return wrt_for_part, fprime_for_part
opts_obj = optimizers_from_cfg(self.cfg, wrt_fprime_for_part,
self.mb_loss)
opts = {
part: iter(opt_obj)
for part, opt_obj in opts_obj.iteritems()
}
partioned_opt = True
opt_parts = set(opts.keys())
ps_parts = set(self.ps.partitions)
if opt_parts != ps_parts:
raise ValueError(
"optimizer config does not cover all ParameterSet partitions or vice versa: %s"
% repr(opt_parts ^ ps_parts))
else:
opt = iter(
optimizer_from_cfg(self.cfg, self.ps.data, self.mb_loss,
grad_func))
partioned_opt = False
# do training
self.ps.restore_constants()
last_pars = xp.copy(self.ps.data)
while not his.should_terminate:
# call optimizer(s)
if partioned_opt:
for part, opt in opts.iteritems():
# print "optimizing %s" % part
opt.next()
else:
opt.next()
# element change cap
if self.cfg.has('step_element_cap'):
d = self.ps.data - last_pars
if isinstance(self.cfg.step_element_cap, dict):
for par, lim in self.cfg.step_element_cap.iteritems():
start, stop = self.ps.extents_of_var(par)
dpar = d[start:stop] # dpar is a subview of d
# print "parameter diff for %s is %s (limit is %.4f)" % (par, repr(dpar), lim)
elems = xp.where(xp.abs(dpar) > lim)
dpar[elems] = xp.sign(dpar[elems]) * lim
elif isinstance(self.cfg.step_element_cap, (float, int)):
lim = float(self.cfg.step_element_cap)
elems = xp.where(xp.abs(d) > lim)
d[elems] = xp.sign(d[elems]) * lim
else:
raise TypeError(
"cfg.step_element_cap must either be a dict or a float"
)
self.ps.data[:] = last_pars + d
last_pars = xp.copy(self.ps.data)
# parameter printout
if print_parameters:
pars = gather(self.ps.data)
pars_var = self.ps.split(pars)
print "parameters at iteration %d:" % itr
for name, value in pars_var.iteritems():
print "%10s: %s" % (name, repr(list(value)))
# obtain gradient if required for debugging operations
if large_gradient_threshold > 0 or print_gradient_info or print_gradient:
gradient = gather(grad_func(self.ps.num_data))
else:
gradient = None
# check gradient for large elements
if large_gradient_threshold > 0:
lgv = self.ps.find_large_elements(
gradient, threshold=large_gradient_threshold)
if len(lgv) > 0:
print "parameters with large gradient: "
for (var, idx), value in lgv.itervalues():
print " %s[%d] = %.3f" % (
var, idx, value)
# gradient magnitude printout
if print_gradient_info:
gradient_magnitude = np.sqrt(np.sum(gradient**2))
print "|gradient| = %.3f" % gradient_magnitude
# gradient printout
if print_gradient:
gradient_var = self.ps.split(gradient)
print "gradient at iteration %d:" % itr
for name, value in gradient_var.iteritems():
print "%10s: %s" % (name, repr(list(value)))
# check gradient for NaNs and Infs
if check_gradient_finite or gradient is not None:
if not np.all(np.isfinite(gradient)):
his.should_terminate = True
his.termination_reason = 'inf_or_nan_gradient'
break
if self.next_minibatch():
# iteration finished
self.after_iteration(his, itr)
itr += 1
# log parameters
logger.log(itr, self.ps)
# calculate losses
if itr % loss_record_interval == 0:
self.record_loss(his, itr)
if step_element_cap_decrease_iteration is not None:
if 'step_element_cap_restore_iterations' in dir(
self.cfg):
restore_itrs = self.cfg.step_element_cap_restore_iterations
else:
restore_itrs = 100
if itr >= step_element_cap_decrease_iteration + restore_itrs:
self.cfg.step_element_cap = step_element_cap_orig
print "Restored step element cap to %g" % self.cfg.step_element_cap
step_element_cap_decrease_iteration = None
# save checkpoint if necessary
if checkpoint_handler is not None:
if checkpoint_handler.requested:
his.stop()
checkpoint_handler.save(
data=gather(self.ps.data),
his=his,
iter=itr,
logger=logger,
optimizer_step_rate=self.cfg.optimizer_step_rate)
if his.should_save_checkpoint:
checkpoint_handler.save(
data=gather(self.ps.data),
his=his,
iter=itr,
logger=logger,
optimizer_step_rate=self.cfg.optimizer_step_rate,
explicit=True)
his.checkpoint_saved()
# restore best parametes
self.ps.data[:] = his.best_pars
# check for retry conditions
restart = False
# temporarily reduce step element cap to move over regions with very large gradient
if (his.should_terminate
and his.termination_reason == 'nan_or_inf_loss'
and 'step_element_cap' in dir(self.cfg)
and 'step_element_cap_min' in dir(self.cfg)
and self.cfg.step_element_cap >=
self.cfg.step_element_cap_min):
self.cfg.step_element_cap /= 10.
step_element_cap_decrease_iteration = itr
print "Reduced step element cap to %g" % self.cfg.step_element_cap
his.should_terminate = False
restart = True
# advance learning rate schedule
if (his.should_terminate and his.termination_reason in [
'no_improvement', 'nan_or_inf_loss',
'user_learning_rate_decrease'
] and 'optimizer_step_rate_min' in dir(self.cfg)
and self.cfg.optimizer_step_rate / 10. >=
self.cfg.optimizer_step_rate_min):
self.cfg.optimizer_step_rate /= 10.
print "Decaying optimizer step rate to %g" % self.cfg.optimizer_step_rate
his.should_terminate = False
his.last_val_improvement = itr
restart = True
# training finished
self.after_training(his)
# save results and plot loss
if checkpoint_handler:
his.stop()
checkpoint_handler.save(
data=gather(self.ps.data),
his=his,
iter=itr,
logger=logger,
optimizer_step_rate=self.cfg.optimizer_step_rate,
explicit=True)
his.finish()
logger.plot()
return his
def load_parameters(self, filename):
"""Loads the parameters of the ParameterSet of the model from the given file."""
self.ps.data[:] = post(np.load(filename)['ps'])
def generic_training(self, cfg_dir, checkpoint=None, checkpoint_handler=None, loss_record_interval=10,
max_missed_val_improvements=200, iteration_gain=1.25, min_improvement=1e-7,
reset_termination_criteria=False, desired_loss=None, initialize=True,
large_gradient_threshold=0.0, print_gradient_info=False, print_gradient=False,
print_parameters=False, log_parameters=[], plot_logged_parameters=True,
print_logged_parameters=False, check_gradient_finite=False, log_modules=None):
"""
Generic training procedure.
:param cfg_dir: configuration directory
:param checkpoint: checkpoint
:param checkpoint_handler: checkpoint handler
:param loss_record_interval: number of iterations between calculating and recording losses
:param max_missed_val_improvements: maximum iterations without improvement of loss before training ist stopped
:param iteration_gain: If not set to 0, then training is performed up to iteration
(iteration_gain * iteration_of_last_improvement).
:param min_improvement: minimum loss change to count as improvement
:param reset_termination_criteria: resets the termination criteria after loading a checkpoint
:param desired_loss: if specified, training is terminated with this loss is reached
:param initialize: if True, the model parameters are initialized using the init_parameters method.
:param large_gradient_threshold: if specified, a check for large gradient elements that exceed the
given threshold is performed every iteration and they are printed.
:param print_gradient_info: if True, this function prints diagnostic gradient information
:param print_gradient: if True, this function prints the full gradient every minibatch
:param log_parameters: list of parameter names (from self.ps) that should be logged every iteration
to file params.out
:param plot_logged_parameters: if True, logged parameters are plotted to params.png
:param print_logged_parameters: if True, logged parameters are also printed to standard output
:param check_gradient_finite: if True, gradient is checked for infs and nans in every iteration
:param log_modules: list of python modules for which version or latest git commit should be logged
:return: ParameterHistory object of training
"""
max_iters = self.cfg.max_iters if self.cfg.has('max_iters') else None
# build gradient preprocessing chain
grad_func = self.mb_loss_grad
# gradient magnitude cap
if self.cfg.has('gradient_cap'):
grad_with_cap_orig_grad = grad_func
def grad_with_cap(pv):
g = grad_with_cap_orig_grad(pv)
g_mag = xp.sqrt(xp.sum(g**2))
if g_mag > self.cfg.gradient_cap:
print "gradient magnitude %f is being rescaled" % g_mag
g *= self.cfg.gradient_cap / g_mag
return g
grad_func = grad_with_cap
# gradient element cap
if self.cfg.has('gradient_element_cap'):
grad_with_element_cap_orig_grad = grad_func
def grad_with_element_cap(pv):
g = grad_with_element_cap_orig_grad(pv)
elems = xp.where(xp.abs(g) > self.cfg.gradient_element_cap)
g[elems] = xp.sign(g[elems]) * self.cfg.gradient_element_cap
return g
grad_func = grad_with_element_cap
# gradient of constants set to zero
grad_without_const_orig_grad = grad_func
def grad_without_const(pv):
g = grad_without_const_orig_grad(pv)
return self.ps.nullify_gradient_of_constants(g)
grad_func = grad_without_const
# initialize or restore checkpoint, if available
if not checkpoint:
itr = 0
if initialize:
self.init_parameters()
his = ParameterHistory(cfg=self.cfg, state_dir=cfg_dir, max_iters=max_iters,
max_missed_val_improvements=max_missed_val_improvements,
min_improvement=min_improvement,
desired_loss=desired_loss, iteration_gain=iteration_gain)
logger = ParameterLogger(out_dir=cfg_dir, parameters=log_parameters,
plot=plot_logged_parameters, print_stdout=print_logged_parameters)
git_log(modules=log_modules, log_dir=cfg_dir)
# Record initial loss and parameters
self.record_loss(his, itr)
logger.log(itr, self.ps)
else:
itr = checkpoint['iter']
self.ps.data[:] = post(checkpoint['data'])
if 'optimizer_step_rate' in checkpoint:
self.cfg.optimizer_step_rate = checkpoint['optimizer_step_rate']
his = checkpoint['his']
his.state_dir = cfg_dir
his.max_missed_val_improvements = max_missed_val_improvements
his.desired_loss = desired_loss
his.iteration_gain = iteration_gain
# start and endtimes in his should have the same length, this is
# not the case in explicit auto-save checkpoints, therefore set the
# end to the saved
if len(his.start_time) != len(his.end_time):
his.end_time.append(checkpoint['save_time'])
his.start()
logger = checkpoint['logger']
git_log(modules=log_modules, log_dir=cfg_dir, check=True)
# reset termination criteria if requested
second_chance_file = join(cfg_dir, "2nd_chance")
if exists(second_chance_file):
print "Resetting termination criteria because %s is present" % second_chance_file
reset_termination_criteria = True
unlink(second_chance_file)
if self.cfg.continue_training:
print "Resetting termination criteria because --continue flag was specified"
reset_termination_criteria = True
if reset_termination_criteria:
his.reset_best()
if 'step_element_cap' in dir(self.cfg):
step_element_cap_orig = self.cfg.step_element_cap
step_element_cap_decrease_iteration = None
restart = True
while restart and (max_iters is None or max_iters > 0):
# create optimizer
if isinstance(self.cfg.optimizer, dict):
def wrt_fprime_for_part(partition):
wrt_for_part = self.ps.num_partition(partition)
def fprime_for_part(pv_part):
# we assume that the optimizer updates the ParameterSet inplace and
# evaluates the gradient at the current values of the parameters
start, stop = self.ps.extents_of_partition(partition)
return grad_func(self.ps.num_data)[start : stop]
return wrt_for_part, fprime_for_part
opts_obj = optimizers_from_cfg(self.cfg, wrt_fprime_for_part, self.mb_loss)
opts = {part: iter(opt_obj) for part, opt_obj in opts_obj.iteritems()}
partioned_opt = True
opt_parts = set(opts.keys())
ps_parts = set(self.ps.partitions)
if opt_parts != ps_parts:
raise ValueError("optimizer config does not cover all ParameterSet partitions or vice versa: %s" %
repr(opt_parts ^ ps_parts))
else:
opt = iter(optimizer_from_cfg(self.cfg, self.ps.data, self.mb_loss, grad_func))
partioned_opt = False
# do training
self.ps.restore_constants()
last_pars = xp.copy(self.ps.data)
while not his.should_terminate:
# call optimizer(s)
if partioned_opt:
for part, opt in opts.iteritems():
# print "optimizing %s" % part
opt.next()
else:
opt.next()
# element change cap
if self.cfg.has('step_element_cap'):
d = self.ps.data - last_pars
if isinstance(self.cfg.step_element_cap, dict):
for par, lim in self.cfg.step_element_cap.iteritems():
start, stop = self.ps.extents_of_var(par)
dpar = d[start:stop] # dpar is a subview of d
# print "parameter diff for %s is %s (limit is %.4f)" % (par, repr(dpar), lim)
elems = xp.where(xp.abs(dpar) > lim)
dpar[elems] = xp.sign(dpar[elems]) * lim
elif isinstance(self.cfg.step_element_cap, (float, int)):
lim = float(self.cfg.step_element_cap)
elems = xp.where(xp.abs(d) > lim)
d[elems] = xp.sign(d[elems]) * lim
else:
raise TypeError("cfg.step_element_cap must either be a dict or a float")
self.ps.data[:] = last_pars + d
last_pars = xp.copy(self.ps.data)
# parameter printout
if print_parameters:
pars = gather(self.ps.data)
pars_var = self.ps.split(pars)
print "parameters at iteration %d:" % itr
for name, value in pars_var.iteritems():
print "%10s: %s" % (name, repr(list(value)))
# obtain gradient if required for debugging operations
if large_gradient_threshold > 0 or print_gradient_info or print_gradient:
gradient = gather(grad_func(self.ps.num_data))
else:
gradient = None
# check gradient for large elements
if large_gradient_threshold > 0:
lgv = self.ps.find_large_elements(gradient, threshold=large_gradient_threshold)
if len(lgv) > 0:
print "parameters with large gradient: "
for (var, idx), value in lgv.itervalues():
print " %s[%d] = %.3f" % (var, idx, value)
# gradient magnitude printout
if print_gradient_info:
gradient_magnitude = np.sqrt(np.sum(gradient ** 2))
print "|gradient| = %.3f" % gradient_magnitude
# gradient printout
if print_gradient:
gradient_var = self.ps.split(gradient)
print "gradient at iteration %d:" % itr
for name, value in gradient_var.iteritems():
print "%10s: %s" % (name, repr(list(value)))
# check gradient for NaNs and Infs
if check_gradient_finite or gradient is not None:
if not np.all(np.isfinite(gradient)):
his.should_terminate = True
his.termination_reason = 'inf_or_nan_gradient'
break
if self.next_minibatch():
# iteration finished
self.after_iteration(his, itr)
itr += 1
# log parameters
logger.log(itr, self.ps)
# calculate losses
if itr % loss_record_interval == 0:
self.record_loss(his, itr)
if step_element_cap_decrease_iteration is not None:
if 'step_element_cap_restore_iterations' in dir(self.cfg):
restore_itrs = self.cfg.step_element_cap_restore_iterations
else:
restore_itrs = 100
if itr >= step_element_cap_decrease_iteration + restore_itrs:
self.cfg.step_element_cap = step_element_cap_orig
print "Restored step element cap to %g" % self.cfg.step_element_cap
step_element_cap_decrease_iteration = None
# save checkpoint if necessary
if checkpoint_handler is not None:
if checkpoint_handler.requested:
his.stop()
checkpoint_handler.save(data=gather(self.ps.data), his=his, iter=itr, logger=logger,
optimizer_step_rate=self.cfg.optimizer_step_rate)
if his.should_save_checkpoint:
checkpoint_handler.save(data=gather(self.ps.data), his=his, iter=itr, logger=logger,
optimizer_step_rate=self.cfg.optimizer_step_rate,
explicit=True)
his.checkpoint_saved()
# restore best parametes
self.ps.data[:] = his.best_pars
# check for retry conditions
restart = False
# temporarily reduce step element cap to move over regions with very large gradient
if (his.should_terminate and his.termination_reason == 'nan_or_inf_loss' and
'step_element_cap' in dir(self.cfg) and 'step_element_cap_min' in dir(self.cfg) and
self.cfg.step_element_cap >= self.cfg.step_element_cap_min):
self.cfg.step_element_cap /= 10.
step_element_cap_decrease_iteration = itr
print "Reduced step element cap to %g" % self.cfg.step_element_cap
his.should_terminate = False
restart = True
# advance learning rate schedule
if (his.should_terminate and
his.termination_reason in ['no_improvement', 'nan_or_inf_loss', 'user_learning_rate_decrease'] and
'optimizer_step_rate_min' in dir(self.cfg) and
self.cfg.optimizer_step_rate / 10. >= self.cfg.optimizer_step_rate_min):
self.cfg.optimizer_step_rate /= 10.
print "Decaying optimizer step rate to %g" % self.cfg.optimizer_step_rate
his.should_terminate = False
his.last_val_improvement = itr
restart = True
# training finished
self.after_training(his)
# save results and plot loss
if checkpoint_handler:
his.stop()
checkpoint_handler.save(data=gather(self.ps.data), his=his, iter=itr, logger=logger,
optimizer_step_rate=self.cfg.optimizer_step_rate,
explicit=True)
his.finish()
logger.plot()
return his
def __setstate__(self, state):
state['best_pars'] = post(state['best_pars'])
self.__dict__ = state