def __init__(self,
variables,
mini_batch_size,
state_updates,
dataset,
updates=None):
theano_variables = []
monitored_quantities = []
for variable in variables:
if isinstance(variable, MonitoredQuantity):
monitored_quantities.append(variable)
else:
theano_variables.append(variable)
self.theano_variables = theano_variables
self.monitored_quantities = monitored_quantities
variable_names = [v.name for v in variables]
if len(set(variable_names)) < len(variables):
raise ValueError("variables should have different names")
self.theano_buffer = AggregationBuffer(theano_variables)
self.monitored_quantities_buffer = MonitoredQuantityBuffer(
monitored_quantities)
self.dataset = dataset
self.updates = updates
self.mini_batch_size = mini_batch_size
self._compile(state_updates)
def __init__(self, variables, **kwargs):
kwargs.setdefault("before_training", True)
super(TrainingDataMonitoring, self).__init__(**kwargs)
self.add_condition(['after_batch'], arguments=('just_aggregate', ))
self._non_variables = []
self._variables = []
for variable_or_not in variables:
if isinstance(variable_or_not, theano.Variable):
self._variables.append(variable_or_not)
elif isinstance(variable_or_not, MonitoredQuantity):
self._non_variables.append(variable_or_not)
else:
raise ValueError("can not monitor {}".format(variable_or_not))
self._non_variables = MonitoredQuantityBuffer(self._non_variables)
self._required_for_non_variables = AggregationBuffer(
[take_last(v) for v in self._non_variables.requires])
self._variables = AggregationBuffer(self._variables,
use_take_last=True)
self._last_time_called = -1
