MXNet 定义新激活函数（Custom new activation function）

https://blog.csdn.net/weixin_34260991/article/details/87106463

这里使用比较简单的定义方式，只是在原有的激活函数调用中加入。

准备工作
下载MXNet源代码，确认可以顺利编译通过。推荐在Linux下进行此操作:

https://mxnet.incubator.apache.org/get_started/install.html

编写激活函数先前和先后传递
在src/operator/mshadow_op.h里面，加入新的激活函数向前传递和向后的函数：

/*!
* \brief RBF Unit
* \author Yuzhong Liu
*/
struct rbf {
template<typename DType>
MSHADOW_XINLINE static DType Map(DType x) {
return DType(expf(-x*x));
}
};

struct rbf_grad {
template<typename DType>
MSHADOW_XINLINE static DType Map(DType x, DType a) {
return DType(-2 * x * a);
}
};
添加调用方法
在src/operator/leaky_relu-inl.h里面，激活函数的调用方式：

namespace leakyrelu {
enum LeakyReLUOpInputs {kData, kGamma};
enum LeakyReLUOpOutputs {kOut, kMask};
# 定义新的激活函数名称
enum LeakyReLUOpType {kLeakyReLU, kPReLU, kRReLU, kELU, kRBF};
enum LeakyReLUOpResource {kRandom};
} // namespace leakyrelu

struct LeakyReLUParam : public dmlc::Parameter<LeakyReLUParam> {
// use int for enumeration
int act_type;
float slope;
float lower_bound;
float upper_bound;
DMLC_DECLARE_PARAMETER(LeakyReLUParam) {
DMLC_DECLARE_FIELD(act_type).set_default(leakyrelu::kLeakyReLU)
.add_enum("rrelu", leakyrelu::kRReLU)
.add_enum("leaky", leakyrelu::kLeakyReLU)
.add_enum("prelu", leakyrelu::kPReLU)
.add_enum("elu", leakyrelu::kELU)
# 添加激活函数枚举
.add_enum("rbf", leakyrelu::kRBF)
.describe("Activation function to be applied.");
DMLC_DECLARE_FIELD(slope).set_default(0.25f)
.describe("Init slope for the activation. (For leaky and elu only)");
DMLC_DECLARE_FIELD(lower_bound).set_default(0.125f)
.describe("Lower bound of random slope. (For rrelu only)");
DMLC_DECLARE_FIELD(upper_bound).set_default(0.334f)
.describe("Upper bound of random slope. (For rrelu only)");
}
};

template<typename xpu>
class LeakyReLUOp : public Operator {
public:
explicit LeakyReLUOp(LeakyReLUParam param) {
param_ = param;
}

virtual void Forward(const OpContext &ctx,
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &out_data,
const std::vector<TBlob> &aux_args) {
using namespace mshadow;
using namespace mshadow::expr;
size_t expected = param_.act_type == leakyrelu::kPReLU ? 2 : 1;
CHECK_EQ(in_data.size(), expected);
Stream<xpu> *s = ctx.get_stream<xpu>();
Tensor<xpu, 3> data;
Tensor<xpu, 3> out;
Tensor<xpu, 3> mask;
Tensor<xpu, 1> weight;
int n = in_data[leakyrelu::kData].shape_[0];
int k = in_data[leakyrelu::kData].shape_[1];
Shape<3> dshape = Shape3(n, k, in_data[leakyrelu::kData].Size()/n/k);
data = in_data[leakyrelu::kData].get_with_shape<xpu, 3, real_t>(dshape, s);
out = out_data[leakyrelu::kOut].get_with_shape<xpu, 3, real_t>(dshape, s);
if (param_.act_type == leakyrelu::kRReLU) {
mask = out_data[leakyrelu::kMask].get_with_shape<xpu, 3, real_t>(dshape, s);
}
switch (param_.act_type) {
case leakyrelu::kLeakyReLU: {
Assign(out, req[leakyrelu::kOut], F<mshadow_op::xelu>(data, param_.slope));
break;
}
case leakyrelu::kPReLU: {
weight = in_data[leakyrelu::kGamma].get<xpu, 1, real_t>(s);
Assign(out, req[leakyrelu::kOut],
F<mshadow_op::xelu>(data, broadcast<1>(weight, out.shape_)));
break;
}
case leakyrelu::kRReLU: {
if (ctx.is_train) {
Random<xpu>* prnd = ctx.requested[leakyrelu::kRandom].get_random<xpu, real_t>(s);
mask = prnd->uniform(mask.shape_);
mask = mask * (param_.upper_bound - param_.lower_bound) + param_.lower_bound;
Assign(out, req[leakyrelu::kOut], F<mshadow_op::xelu>(data, mask));
} else {
const float slope = (param_.lower_bound + param_.upper_bound) / 2.0f;
Assign(out, req[leakyrelu::kOut], F<mshadow_op::xelu>(data, slope));
}
break;
}
case leakyrelu::kELU: {
Assign(out, req[leakyrelu::kOut], F<mshadow_op::elu>(data, param_.slope));
break;
}
# RBF向前
case leakyrelu::kRBF: {
Assign(out, req[leakyrelu::kOut], F<mshadow_op::rbf>(data));
break;
}
default:
LOG(FATAL) << "Not implmented";
}
}

virtual void Backward(const OpContext & ctx,
const std::vector<TBlob> &out_grad,
const std::vector<TBlob> &in_data,
const std::vector<TBlob> &out_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &in_grad,
const std::vector<TBlob> &aux_args) {
using namespace mshadow;
using namespace mshadow::expr;
size_t expected = param_.act_type == leakyrelu::kPReLU ? 2 : 1;
CHECK_EQ(out_grad.size(), 1U);
CHECK_EQ(req.size(), expected);
CHECK_EQ(in_data.size(), expected);
Stream<xpu> *s = ctx.get_stream<xpu>();
Tensor<xpu, 3> output;
Tensor<xpu, 3> data;
Tensor<xpu, 3> gdata;
Tensor<xpu, 3> grad;
Tensor<xpu, 3> mask;
Tensor<xpu, 1> weight;
Tensor<xpu, 1> grad_weight;
int n = out_grad[leakyrelu::kOut].shape_[0];
int k = out_grad[leakyrelu::kOut].shape_[1];
Shape<3> dshape = Shape3(n, k, out_grad[leakyrelu::kOut].Size()/n/k);
grad = out_grad[leakyrelu::kOut].get_with_shape<xpu, 3, real_t>(dshape, s);
gdata = in_grad[leakyrelu::kData].get_with_shape<xpu, 3, real_t>(dshape, s);
output = out_data[leakyrelu::kOut].get_with_shape<xpu, 3, real_t>(dshape, s);
if (param_.act_type == leakyrelu::kRReLU) {
mask = out_data[leakyrelu::kMask].get_with_shape<xpu, 3, real_t>(dshape, s);
}
if (param_.act_type == leakyrelu::kPReLU) {
data = in_data[leakyrelu::kData].get_with_shape<xpu, 3, real_t>(dshape, s);
}
switch (param_.act_type) {
case leakyrelu::kLeakyReLU: {
Assign(gdata, req[leakyrelu::kData], F<mshadow_op::xelu_grad>(output, param_.slope) * grad);
break;
}
case leakyrelu::kPReLU: {
weight = in_data[leakyrelu::kGamma].get<xpu, 1, real_t>(s);
grad_weight = in_grad[leakyrelu::kGamma].get<xpu, 1, real_t>(s);
grad_weight = sumall_except_dim<1>(F<prelu_grad>(data) * grad);
gdata = F<mshadow_op::xelu_grad>(data, broadcast<1>(weight, data.shape_)) * grad;
break;
}
case leakyrelu::kRReLU: {
Assign(gdata, req[leakyrelu::kData], F<mshadow_op::xelu_grad>(output, mask) * grad);
break;
}
case leakyrelu::kELU: {
Assign(gdata, req[leakyrelu::kData], F<mshadow_op::elu_grad>(output, param_.slope) * grad);
break;
}
# RBF向前
case leakyrelu::kRBF: {
data = in_data[leakyrelu::kData].get_with_shape<xpu, 3, real_t>(dshape, s);
Assign(gdata, req[leakyrelu::kData], F<mshadow_op::rbf_grad>(data, output) * grad);
break;
}
default:
LOG(FATAL) << "Not implmented";
}
}

private:
LeakyReLUParam param_;
}; // class LeakyReLUOp
从重新编译，并测试
import mxnet as mx
from mxnet import autograd
a = mx.nd.random_uniform(-1, 1, shape=[3, 3]) +0.5
a.attach_grad()

with autograd.record():
b = mx.nd.LeakyReLU(data=a, act_type='rbf')

print a, b
参考资料
https://mxnet.incubator.apache.org/how_to/new_op.html
http://blog.csdn.net/qq_20965753/article/details/66975622?utm_source=debugrun&utm_medium=referral
---------------------
作者：weixin_34260991
来源：CSDN
原文：https://blog.csdn.net/weixin_34260991/article/details/87106463
版权声明：本文为博主原创文章，转载请附上博文链接！