src/operator/leaky_relu.cc

/*!
 * Copyright (c) 2015 by Contributors
 * \file leaky_relu.cc
 * \brief
 * \author Bing Xu
*/

#include "./leaky_relu-inl.h"

#include <nnvm/op_attr_types.h>
namespace mxnet {
namespace op {
template<>
Operator *CreateOp<cpu>(LeakyReLUParam param) {
  return new LeakyReLUOp<cpu>(param);
}

Operator *LeakyReLUProp::CreateOperator(Context ctx) const {
  DO_BIND_DISPATCH(CreateOp, param_);
}

DMLC_REGISTER_PARAMETER(LeakyReLUParam);

MXNET_REGISTER_OP_PROPERTY(LeakyReLU, LeakyReLUProp)
.describe(R"code(Leaky ReLu activation

The following types are supported:

- *elu*: ``y = x > 0 ? x : slop * (exp(x)-1)``
- *leaky*: ``y = x > 0 ? x : slope * x``
- *prelu*: same as *leaky* but the ``slope`` is learnable.
- *rrelu*: same as *leaky* but the ``slope`` is uniformly randomly chosen from
  *[lower_bound, upper_bound)* for training, while fixed to be
  *(lower_bound+upper_bound)/2* for inference.

)code" ADD_FILELINE)
.add_argument("data", "ndarray-or-symbol", "Input data to activation function.")
.add_arguments(LeakyReLUParam::__FIELDS__());

NNVM_REGISTER_OP(LeakyReLU)
.set_attr<nnvm::FSetInputVarAttrOnCompose>("FSetInputVarAttrOnCompose",
    [](const nnvm::NodeAttrs& attrs, nnvm::NodePtr var, const int index) {
      if (index == 1 && var->attrs.dict.find("__init__") == var->attrs.dict.end()) {
        var->attrs.dict["__init__"] = "[\"Constant\", {\"value\": 0.25}]";
      }
    });

}  // namespace op
}  // namespace mxnet