Can we use decreasing step size to replace mini-batch in SGD?
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As far as I know, mini-batch can be used to reduce the variance of the gradient, but I am also considering if we can achieve the same result if we use the decreasing step size and only single sample in each iteration? Can we compare the convergence rate of them?
machine-learning optimization gradient-descent mini-batch-gradient-descent
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As far as I know, mini-batch can be used to reduce the variance of the gradient, but I am also considering if we can achieve the same result if we use the decreasing step size and only single sample in each iteration? Can we compare the convergence rate of them?
machine-learning optimization gradient-descent mini-batch-gradient-descent
asked yesterday
coolcatcoolcat
11
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add a comment |
$begingroup$
As far as I know, mini-batch can be used to reduce the variance of the gradient, but I am also considering if we can achieve the same result if we use the decreasing step size and only single sample in each iteration? Can we compare the convergence rate of them?
machine-learning optimization gradient-descent mini-batch-gradient-descent
asked yesterday
coolcatcoolcat
11
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coolcat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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As far as I know, mini-batch can be used to reduce the variance of the gradient, but I am also considering if we can achieve the same result if we use the decreasing step size and only single sample in each iteration? Can we compare the convergence rate of them?
machine-learning optimization gradient-descent mini-batch-gradient-descent
machine-learning optimization gradient-descent mini-batch-gradient-descent
asked yesterday
coolcatcoolcat
11
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asked yesterday
coolcatcoolcat
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asked yesterday
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asked yesterday
coolcatcoolcat
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asked yesterday
coolcatcoolcat
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Generally answer is "it's not known". Similarity of effects of increasing minibatches size and decreasing learning rate is mostly empirical, there is no known asymptotic formula for it. Also effect of small LR and big minibatch is not the same. For example batch normalization layer would act completely different on those two approaches. Probabilistic distribution of gradients produced by minibatches and single sample (or mb of significantly different size) would be also quite different
answered 22 hours ago
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1 Answer
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1 Answer
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$begingroup$
Generally answer is "it's not known". Similarity of effects of increasing minibatches size and decreasing learning rate is mostly empirical, there is no known asymptotic formula for it. Also effect of small LR and big minibatch is not the same. For example batch normalization layer would act completely different on those two approaches. Probabilistic distribution of gradients produced by minibatches and single sample (or mb of significantly different size) would be also quite different
answered 22 hours ago
mirror2imagemirror2image
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$begingroup$
Generally answer is "it's not known". Similarity of effects of increasing minibatches size and decreasing learning rate is mostly empirical, there is no known asymptotic formula for it. Also effect of small LR and big minibatch is not the same. For example batch normalization layer would act completely different on those two approaches. Probabilistic distribution of gradients produced by minibatches and single sample (or mb of significantly different size) would be also quite different
answered 22 hours ago
mirror2imagemirror2image
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$begingroup$
Generally answer is "it's not known". Similarity of effects of increasing minibatches size and decreasing learning rate is mostly empirical, there is no known asymptotic formula for it. Also effect of small LR and big minibatch is not the same. For example batch normalization layer would act completely different on those two approaches. Probabilistic distribution of gradients produced by minibatches and single sample (or mb of significantly different size) would be also quite different
answered 22 hours ago
mirror2imagemirror2image
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Generally answer is "it's not known". Similarity of effects of increasing minibatches size and decreasing learning rate is mostly empirical, there is no known asymptotic formula for it. Also effect of small LR and big minibatch is not the same. For example batch normalization layer would act completely different on those two approaches. Probabilistic distribution of gradients produced by minibatches and single sample (or mb of significantly different size) would be also quite different
answered 22 hours ago
mirror2imagemirror2image
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answered 22 hours ago
mirror2imagemirror2image
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answered 22 hours ago
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answered 22 hours ago
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