def test_mean_aggregator():
num_examples = 4
batch_size = 2
features = numpy.array([[0, 3], [2, 9], [2, 4], [5, 1]],
dtype=theano.config.floatX)
dataset = IndexableDataset(OrderedDict([('features', features)]))
data_stream = DataStream(dataset,
iteration_scheme=SequentialScheme(
num_examples, batch_size))
x = tensor.matrix('features')
y = (x**2).mean(axis=0)
y.name = 'y'
z = y.sum()
z.name = 'z'
y.tag.aggregation_scheme = Mean(y, 1.)
z.tag.aggregation_scheme = Mean(z, 1.)
assert_allclose(
DatasetEvaluator([y]).evaluate(data_stream)['y'],
numpy.array([8.25, 26.75], dtype=theano.config.floatX))
assert_allclose(
DatasetEvaluator([z]).evaluate(data_stream)['z'],
numpy.array([35], dtype=theano.config.floatX))
def _create_aggregators(self):
"""Create aggregators and collect updates."""
self.initialization_updates = []
self.accumulation_updates = []
self.readout_variables = OrderedDict()
for v in self.variables:
logger.debug('variable to evaluate: %s', v.name)
if not hasattr(v.tag, 'aggregation_scheme'):
if not self._computation_graph.has_inputs(v):
scheme = (TakeLast
if self.use_take_last else _DataIndependent)
logger.debug(
'Using %s aggregation scheme'
' for %s since it does not depend on'
' the data', scheme.__name__, v.name)
v.tag.aggregation_scheme = scheme(v)
else:
logger.debug(
'Using the default '
' (average over minibatches)'
' aggregation scheme for %s', v.name)
v.tag.aggregation_scheme = Mean(v, 1.0)
aggregator = v.tag.aggregation_scheme.get_aggregator()
self.initialization_updates.extend(
aggregator.initialization_updates)
self.accumulation_updates.extend(aggregator.accumulation_updates)
self.readout_variables[v.name] = aggregator.readout_variable