Differences
This shows you the differences between two versions of the page.
| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| data_mining:neural_network:neurons [2017/08/13 12:24] – [Softmax] phreazer | data_mining:neural_network:neurons [2017/08/19 17:43] (current) – [Rectified Linear Neurons] phreazer | ||
|---|---|---|---|
| Line 75: | Line 75: | ||
| $z=b+\sum_{i} x_{i} w_{i}$ | $z=b+\sum_{i} x_{i} w_{i}$ | ||
| - | $y = \begin{cases} z, & \text{if } z > 0 \\ 0, & \text{otherwhise}\end{cases}$ | + | $y = \begin{cases} z, & \text{if } z > 0 \\ 0, & \text{otherwhise}\end{cases} |
| Above 0, it is linear, at 0 it is 0 | Above 0, it is linear, at 0 it is 0 | ||
| + | |||
| + | Faster computation, | ||
| + | |||
| + | Leaky ReLU: | ||
| + | |||
| + | $y =\max(0.01 z,z)$ | ||
| Line 94: | Line 100: | ||
| Switch from Sigmoid to ReLU lead to performance improvement (Slope of Sigmoid gradually shrinks to zero). | Switch from Sigmoid to ReLU lead to performance improvement (Slope of Sigmoid gradually shrinks to zero). | ||
| - | ==== Softmax group ==== | + | |
| + | ===== tanh ===== | ||
| + | Works better than Sigmoid function. | ||
| + | |||
| + | $y = \frac{e^{z}-e^{-z}}{e^{z}+e^{-z}}$ | ||
| + | |||
| + | Centering of data to 0. | ||
| + | |||
| + | Exception: Output layer, since output should be in [0,1]. | ||
| + | ===== Softmax group ===== | ||
| + | |||
| + | Logistic function output is used for the classification between two target classes 0/1. Softmax function is generalized type of logistic function that can output a **multiclass** categorical **probability distribution**. | ||
| Derivates: | Derivates: | ||