提交 dc520da7 编写于 作者: C caoying03

update doc of softmax_op.

上级 843a8b1e
...@@ -24,7 +24,7 @@ class SoftmaxOp : public framework::OperatorWithKernel { ...@@ -24,7 +24,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
protected: protected:
void InferShape(const framework::InferShapeContext &ctx) const override { void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL, PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
"The input of softmax op must be matrix"); "The input of softmax op must be a matrix.");
ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims()); ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
} }
}; };
...@@ -34,9 +34,27 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -34,9 +34,27 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
SoftmaxOpMaker(framework::OpProto *proto, SoftmaxOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker) framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) { : OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "input of softmax"); AddInput("X",
AddOutput("Y", "output of softmax"); "The input tensor of softmax. "
AddComment("Softmax Op"); "2-D with shape [batch_size, input_feature_dimensions].");
AddOutput("Y", "The normalized values with the same shape as X.");
AddComment(R"DOC(
The input of softmax operator is a 2-D tensor with shape N x K (N is the
batch_size, K is the dimension of input feature). The output tensor has the
same shape as the input tensor.
For each row of the input tensor, the softmax operator squashes the
K-dimensional vector of arbitrary real values to a K-dimensional vector of real
values in the range [0, 1] that add up to 1. Specifically, it computes the
exponential of the given dimension and the sum of exponential values of all
the other dimensions in the K-dimensional vector input. Then the ratio of the
exponential of the given dimension and the sum of exponential values of all
the other dimensions is the output of the softmax operator.
For each row `i` and each column `j` in X, we have:
Y[i, j] = exp(X[i, j]) / sum_j(exp(X[i, j]))
)DOC");
} }
}; };
......
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