Add ELU, PReLU, ThresholdedReLU layers#32
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src/nn/Modules/Activations.cpp
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| { | ||
| auto tmp = max(input, 0.0); | ||
| auto res = expandAs(m_parameters[0],tmp) * tmp; | ||
| //TODO: Determine if doing the max after the mul is preferable |
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That would be two different behaviors. max after multiply will have a different behavior when values of input and m_parameters are both negative. Can you double check what the exact behavior should be.
| { | ||
| auto mask = input > m_threshold; | ||
| return input * mask; | ||
| } |
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This should probably just be renamed to Threshold ?
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I believe Threshold may be misleading, since threshold usually denotes a function R -> {0,1} (e.g., return input > m_threshold;). ThresholdedReLU or ThresholdReLU seem a better fit to me.
| }; | ||
| return Variable(result, {lhs, rhs, mask}, grad_func); | ||
| } | ||
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This function should be indented by four spaces.
src/nn/Modules/Activations.cpp
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| auto res = max(input, m_alpha * (exp(input) - 1)); | ||
| return res; | ||
| auto mask = input >= 0.0; | ||
| return (mask * input) + (!mask * m_alpha * exp(input)); |
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Shouldn't this be (!mask * m_alpha * (exp(input) - 1)) ?
https://keras.io/layers/advanced-activations/#elu
fixed PReLU, ELU forward methods, mix name changes
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This is based off of the Keras documentation here The PReLU layer in Keras supports more features that we can discuss adding in the future. (e.g. weight initialization function, regularization).