Model consistency

[lanyiyun]

Hi,

I do not know if you have tested on model consistency, that is, feeding the same data and observe if the prediction is the same. I noticed between line 136 to line 141 in src/core/train.py, the samples used in one batch is different between the Unlabeled and the Orthonorm (as well as the Labeled). The Unlabeled uses sequential data from batch_start to batch_end, but Orthonorm uses random sampling. Is there a reason for this?

for input_type, input_placeholder in inputs.items():
if input_type == 'Unlabeled':
feed_dict[input_placeholder] = x[batch_start:batch_end]
elif input_type == 'Orthonorm':
batch_ids = np.random.choice(len(x), size=min(len(x), batch_sizes[input_type]), replace=False)
feed_dict[input_placeholder] = x[batch_ids]