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To be honest, it's hard to understand the role of
if 0: else:
statements in the minibatch learning for-loop.I think using only
mnist.train.next_batch(batch_size)
can make decent results.I realized that if I use the Random batch sampling only, the performance decreases from 91-ish to 87-ish.
The reason is that random sampling doesn't exploit the whole set because of duplicated samples.
I changed this part by using
np.random.permutation(n_train)
to cover all data at each epoch.