HomeĐời SốngEpoch là gì

Epoch là gì

06:08, 29/03/2021
I"m using Pyhẹp Keras package for neural network. This is the links. Is batch_size equals to number of chạy thử samples? From Wikipedia we have sầu this information:

However, in other cases, evaluating the sum-gradient may require expensive sầu evaluations of the gradients from all summvà functions. When the training phối is enormous & no simple formulas exist, evaluating the sums of gradients becomes very expensive sầu, 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 summ& functions at every step. This is very effective sầu in the case of large-scale machine learning problems.

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Above sầu information is describing thử nghiệm data? Is this same as batch_kích cỡ in keras (Number of samples per gradient update)?

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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 1050 training samples and you want to set up a batch_form size equal to 100. The algorithm takes the first 100 samples (from 1st lớn 100th) from the training datamix & trains the network. Next, it takes the second 100 samples (from 101st khổng lồ 200th) and 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 mix of samples. In our example, we"ve sầu used 1050 which is not divisible by 100 without remainder. The simplest solution is just to lớn get the final 50 samples & 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 fit the whole dataset in your machine"s memory.

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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 and 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 size 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 to the direction of the full batch gradient (xanh color).


Stochastic is just a mini-batch with batch_kích thước equal khổng lồ 1. In that case, the gradient changes its direction even more often than a mini-batch gradient.