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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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| ==== Algo ==== | ==== Algo ==== | ||
| - | * 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 $\tilde{Z}^l$ | + | * 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 | ||