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data_mining:neural_network:tuning [2018/05/20 15:10] – [Algo] phreazer | data_mining:neural_network:tuning [2018/05/20 15:21] (current) – [Batch norm at test time] phreazer | ||
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* For minibatch t: | * For minibatch t: | ||
* Compute forward prop for $X^{\{t\}}$ | * Compute forward prop for $X^{\{t\}}$ | ||
- | * In each hidden layer use BN to replace $Z^l$ with $\tilde{Z}^l$ | + | |
* Use backprop to compute $dW^{l}, d\beta^{l}, d\gamma^{l}$ | * Use backprop to compute $dW^{l}, d\beta^{l}, d\gamma^{l}$ | ||
* Update parameters ... | * Update parameters ... | ||
+ | ==== Why does it work ==== | ||
+ | |||
+ | Covariance shift (shifting input distribution) | ||
+ | |||
+ | * Batch norm reduces amount in which hidden units shifts around, become more stable (input to later layers) | ||
+ | * Slight regularization effect: Adds some noise, because it's normed on the mini batch | ||
+ | |||
+ | ==== Batch norm at test time ==== | ||
+ | |||
+ | Here no mini-batch, but one sample at a time | ||
+ | |||
+ | Estimate $\sigma^2, \mu$ using exponentially weighted average across mini-batches |