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))