本文整理汇总了Python中pylearn2.monitor.Monitor.add_channel方法的典型用法代码示例。如果您正苦于以下问题：Python Monitor.add_channel方法的具体用法？Python Monitor.add_channel怎么用？Python Monitor.add_channel使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pylearn2.monitor.Monitor的用法示例。
在下文中一共展示了Monitor.add_channel方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: channel_scaling_checker
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import add_channel [as 别名]
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))示例2: channel_scaling_checker
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import add_channel [as 别名]
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))