About random batch sampling#2
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terryum wants to merge 1 commit intosjchoi86:masterfrom
terryum:patch-1
Open
About random batch sampling#2terryum wants to merge 1 commit intosjchoi86:masterfrom terryum:patch-1
terryum wants to merge 1 commit intosjchoi86:masterfrom
terryum:patch-1
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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.
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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.