add softmax_with_cost_op.

paddle/operators/softmax_with_cost_op.cc

@@ -0,0 +1,82 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+:A
+limitations under the License. */
+
+#include "paddle/operators/softmax_op.h"
+
+namespace paddle {
+namespace operators {
+
+class SoftmaxWithLossOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    auto logits = ctx.Input<Tensor>("logits");
+    PADDLE_ENFORCE(logits->dims().size() == 2UL,
+                   "The input of softmax_with_loss_op should be a 2-d tensor.");
+    PADDLE_ENFORCE(ctx.Input<Tensor>("lables")->dims().size() == 1UL,
+                   "The label should be a 1-d tensor.");
+    ctx.Output<Tensor>("loss")->Resize({logits->dims()[0]});
+  }
+};
+
+class SoftmaxWithLossOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SoftmaxWithLossOpMaker(framework::OpProto *proto,
+                         framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("logits",
+             "The unscaled log probabilities which is a 2-D tensor<float> with"
+             "shape [N x K]. N is the batch_size, and K is the class number.");
+    AddInput("label", "The ground truth. A 1-D tensor<int> with shape N.");
+    AddOutput("loss", "A 1-D tensor<float> with shape N.");
+    AddComment(R"DOC(
+Cross entropy loss with softmax are used as the output layer extensively. This
+operator computes the softmax normalized values for each row of the input
+tensor, after which cross-entropy loss is then computed. This provides a more
+numerically stable gradient.
+
+Because this operators performs a softmax on logits internally, it expects
+unscaled logits. Please do not call this op with the output of softmax operator,
+which will produce incorrect results.
+
+This operators expects mutually exclusive hard labels, each sample in a batch
+is in exactly one class with probabilities 1. Each sample in the batch with one
+and only one label.
+)DOC");
+  }
+};
+
+class SoftmaxWithLossOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {}
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP(softmax, ops::SoftmaxWithLossOp, ops::SoftmaxWithLossOpMaker,
+            softmax_grad, ops::SoftmaxWithLossOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    softmax, ops::SoftmaxWithLossKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    softmax_grad,
+    ops::SoftmaxWithLossGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/softmax_with_cost_op.h

@@ -0,0 +1,40 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+template <typename Place, typename T>
+class SoftmaxWithLossKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {}
+};
+
+template <typename Place, typename T>
+class SoftmaxWithLossGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {}
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/pybind/pybind.cc

@@ -37,6 +37,7 @@ USE_OP(mul);
 USE_OP(mean);
 USE_OP(sigmoid);
 USE_OP(softmax);
+USE_OP(softmax_with_loss);
 USE_OP(rowwise_add);
 USE_OP(fill_zeros_like);
 USE_NO_KERNEL_OP(recurrent);

python/paddle/v2/framework/tests/test_softmax_with_cost_op.py

@@ -0,0 +1,22 @@
+import unittest
+
+import numpy as np
+
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
+
+
+class TestSoftmaxWithLossOp(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        pass
+
+
+class SoftmaxWithLossGradOpTest(GradientChecker):
+    def test_softmax(self):
+        pass
+
+
+if __name__ == '__main__':
+    unittest.main()