However, in other cases, evaluating the sum-gradient may require expensive evaluations of the gradients from all summvà functions. When the training phối is enormous và no simple formulas exist, evaluating the sums of gradients becomes very expensive, because evaluating the gradient requires evaluating all the summ& functions" gradients. To economize on the computational cost at every iteration, stochastic gradient descent samples a submix of summvà functions at every step. This is very effective in the case of large-scale machine learning problems.
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Above information is describing chạy thử data? Is this same as batch_kích cỡ in keras (Number of samples per gradient update)?
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edited Sep 7 "17 at 14:15
pasbi
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asked May 22 "15 at 9:15
user2991243user2991243
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The batch size defines the number of samples that will be propagated through the network.
For instance, let"s say you have sầu 1050 training samples and you want to lớn set up a batch_kích thước equal to 100. The algorithm takes the first 100 samples (from 1st khổng lồ 100th) from the training datamix và trains the network. Next, it takes the second 100 samples (from 101st khổng lồ 200th) & trains the network again. We can keep doing this procedure until we have propagated all samples through of the network. Problem might happen with the last set of samples. In our example, we"ve used 1050 which is not divisible by 100 without remainder. The simplest solution is just khổng lồ get the final 50 samples và train the network.
Advantages of using a batch kích thước
It requires less memory. Since you train the network using fewer samples, the overall training procedure requires less memory. That"s especially important if you are not able to lớn fit the whole dataphối in your machine"s memory.
Typically networks train faster with mini-batches. That"s because we update the weights after each propagation. In our example we"ve propagated 11 batches (10 of them had 100 samples & 1 had 50 samples) & after each of them we"ve sầu updated our network"s parameters. If we used all samples during propagation we would make only 1 update for the network"s parameter.
Disadvantages of using a batch kích cỡ The smaller the batch the less accurate the estimate of the gradient will be. In the figure below, you can see that the direction of the mini-batch gradient (green color) fluctuates much more in comparison khổng lồ the direction of the full batch gradient (blue color).
Stochastic is just a mini-batch with batch_kích cỡ equal to 1. In that case, the gradient changes its direction even more often than a mini-batch gradient.
