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@ -17,8 +17,6 @@ limitations under the License. */
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namespace paddle {
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namespace operators {
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using framework::LoDTensor;
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class CrossEntropyOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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@ -35,23 +33,21 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
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PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank must be 2.");
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PADDLE_ENFORCE_EQ(label->dims().size(), 2,
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"Input(Label)'s rank must be 2.");
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// TODO(xinghai-sun): remove this check after swtiching to bool
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PADDLE_ENFORCE(ctx.Attr<int>("soft_label") == 0 ||
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ctx.Attr<int>("soft_label") == 1);
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PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
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"The 1st dimension of Input(X) and Input(Label) must "
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"be equal.");
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if (ctx.Attr<int>("soft_label") == 1) {
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if (ctx.Attr<bool>("soft_label")) {
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PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
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"If Attr(soft_label) == 1, The 2nd dimension of "
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"If Attr(soft_label) == true, The 2nd dimension of "
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"Input(X) and Input(Label) must be equal.");
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} else {
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PADDLE_ENFORCE_EQ(label->dims()[1], 1,
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"If Attr(soft_label) == 0, The 2nd dimension of "
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"If Attr(soft_label) == false, The 2nd dimension of "
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"Input(Label) must be 1.");
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}
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ctx.Output<LoDTensor>("Y")->Resize({x->dims()[0], 1});
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ctx.Output<Tensor>("Y")->Resize({x->dims()[0], 1});
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ctx.ShareLoD("X", /*->*/ "Y");
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}
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};
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@ -74,9 +70,6 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
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PADDLE_ENFORCE_EQ(dy->dims().size(), 2, "Input(Y@Grad)'s rank must be 2.");
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PADDLE_ENFORCE_EQ(label->dims().size(), 2,
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"Input(Label)'s rank must be 2.");
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// TODO(xinghai-sun): remove this check after swtiching to bool
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PADDLE_ENFORCE(ctx.Attr<int>("soft_label") == 0 ||
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ctx.Attr<int>("soft_label") == 1);
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PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
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"The 1st dimension of Input(X) and Input(Label) must "
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"be equal.");
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@ -85,17 +78,17 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
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"be equal.");
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PADDLE_ENFORCE_EQ(dy->dims()[1], 1,
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"The 2nd dimension of Input(Y@Grad) must be 1.");
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if (ctx.Attr<int>("soft_label") == 1) {
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if (ctx.Attr<bool>("soft_label")) {
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PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
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"If Attr(soft_label) == 1, The 2nd dimension of "
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"If Attr(soft_label) == true, The 2nd dimension of "
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"Input(X) and Input(Label) must be equal.");
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} else {
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PADDLE_ENFORCE_EQ(label->dims()[1], 1,
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"If Attr(soft_label) == 0, The 2nd dimension of "
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"If Attr(soft_label) == false, The 2nd dimension of "
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"Input(Label) must be 1.");
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}
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auto dx = ctx.Output<LoDTensor>(framework::GradVarName("X"));
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auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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dx->Resize(x->dims());
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}
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};
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@ -108,7 +101,8 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
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AddInput("X", "The first input of CrossEntropyOp");
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AddInput("Label", "The second input of CrossEntropyOp");
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AddOutput("Y", "The output of CrossEntropyOp");
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AddAttr<int>("soft_label", "Is soft label. Default zero.").SetDefault(0);
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AddAttr<bool>("soft_label", "Is soft label. Default zero.")
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.SetDefault(false);
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AddComment(R"DOC(
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CrossEntropy Operator.
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@ -116,12 +110,12 @@ CrossEntropy Operator.
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It supports both standard cross-entropy and soft-label cross-entropy loss
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computation.
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1) One-hot cross-entropy:
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soft_label = 0, Label[i, 0] indicates the class index for sample i:
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soft_label = False, Label[i, 0] indicates the class index for sample i:
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Y[i] = -log(X[i, Label[i]])
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2) Soft-label cross-entropy:
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soft_label = 1, Label[i, j] indicates the soft label of class j
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soft_label = True, Label[i, j] indicates the soft label of class j
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for sample i:
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Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
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@ -133,6 +127,9 @@ computation.
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As a special case of 2), when each row of Input(Label) has only one
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non-zero element (equals 1), soft-label cross-entropy degenerates to a
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one-hot cross-entropy with one-hot label representation.
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Both the input `X` and `Label` can carry the LoD (Level of Details) information,
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or not. But the output only shares the LoD with input `X`.
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)DOC");
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}
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};
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fengjiayi
7 years ago