def channel_scaling_checker(num_examples, mode, num_batches, batch_size):
num_features = 2
monitor = Monitor(DummyModel(num_features))
dataset = DummyDataset(num_examples, num_features)
monitor.add_dataset(dataset=dataset, mode=mode,
num_batches=num_batches, batch_size=batch_size)
vis_batch = T.matrix()
mean = vis_batch.mean()
data_specs = (monitor.model.get_input_space(),
monitor.model.get_input_source())
monitor.add_channel(name='mean', ipt=vis_batch, val=mean, dataset=dataset,
data_specs=data_specs)
monitor()
assert 'mean' in monitor.channels
mean = monitor.channels['mean']
assert len(mean.val_record) == 1
actual = mean.val_record[0]
X = dataset.get_design_matrix()
if batch_size is not None and num_batches is not None:
total = min(num_examples, num_batches * batch_size)
else:
total = num_examples
expected = X[:total].mean()
if not np.allclose(expected, actual):
raise AssertionError("Expected monitor to contain %f but it has "
"%f" % (expected, actual))
def channel_scaling_checker(num_examples, mode, num_batches, batch_size):
num_features = 2
monitor = Monitor(DummyModel(num_features))
dataset = DummyDataset(num_examples, num_features)
monitor.add_dataset(dataset=dataset, mode=mode,
num_batches=num_batches, batch_size=batch_size)
vis_batch = T.matrix()
mean = vis_batch.mean()
data_specs = (monitor.model.get_input_space(),
monitor.model.get_input_source())
monitor.add_channel(name='mean', ipt=vis_batch, val=mean, dataset=dataset,
data_specs=data_specs)
monitor()
assert 'mean' in monitor.channels
mean = monitor.channels['mean']
assert len(mean.val_record) == 1
actual = mean.val_record[0]
X = dataset.get_design_matrix()
if batch_size is not None and num_batches is not None:
total = min(num_examples, num_batches * batch_size)
else:
total = num_examples
expected = X[:total].mean()
if not np.allclose(expected, actual):
raise AssertionError("Expected monitor to contain %f but it has "
"%f" % (expected, actual))
def channel_scaling_checker(num_examples, mode, num_batches, batch_size):
num_features = 2
monitor = Monitor(DummyModel(num_features))
dataset = DummyDataset(num_examples, num_features)
try:
monitor.add_dataset(dataset=dataset, mode=mode,
num_batches=num_batches, batch_size=batch_size)
except NotImplementedError:
# make sure this was due to the unimplemented batch_size case
if num_batches is None:
assert num_examples % batch_size != 0
else:
assert num_examples % num_batches != 0
raise SkipTest()
vis_batch = T.matrix()
mean = vis_batch.mean()
monitor.add_channel(name='mean', ipt=vis_batch, val=mean, dataset=dataset)
monitor()
assert 'mean' in monitor.channels
mean = monitor.channels['mean']
assert len(mean.val_record) == 1
actual = mean.val_record[0]
X = dataset.get_design_matrix()
if batch_size is not None and num_batches is not None:
total = min(num_examples, num_batches * batch_size)
else:
total = num_examples
expected = X[:total].mean()
if not np.allclose(expected, actual):
raise AssertionError("Expected monitor to contain %f but it has "
"%f" % (expected, actual))
