y of exponentialy moving weighted average. In batch norm, the mean and variance at test time are calculated using th us mini batches at training time using exponentiality moving weighted average. The means across four minibatches at 2, and 0.29 respectively. The variances across the same four minibatches are 0.14, 0.12, 0.1, and 0.37 respectively. Comp st time with and without bias correction. What conclusions can you draw based on the obtained results? Momentum

MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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State the necessity of exponentiaily moving weighted average. In batch norm, the mean and variance at test time are calculated using the means and
variances of various mini batches at training time using exponentiality moving weighted average. The means across four minibatches at training time
are 0.78, 0.92, 0.82, and 0.29 respectively. The variances across the same four minibatches are 0.14, 0.12, 0.1, and 0.37 respectively. Compute the mean
and variance at test time with and without bias correction. What conclusions can you draw based on the obtained results? Momentum hyperparameter
Fi
is 0.9.
Transcribed Image Text:State the necessity of exponentiaily moving weighted average. In batch norm, the mean and variance at test time are calculated using the means and variances of various mini batches at training time using exponentiality moving weighted average. The means across four minibatches at training time are 0.78, 0.92, 0.82, and 0.29 respectively. The variances across the same four minibatches are 0.14, 0.12, 0.1, and 0.37 respectively. Compute the mean and variance at test time with and without bias correction. What conclusions can you draw based on the obtained results? Momentum hyperparameter Fi is 0.9.
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