| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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. |
| */ |
| |
| /*! |
| * Copyright (c) 2017 by Contributors |
| * \file log_softmax.cc |
| * \brief CPU Implementation of log_softmax |
| */ |
| #include "./softmax-inl.h" |
| #include "../tensor/elemwise_unary_op.h" |
| #include "../tensor/elemwise_binary_op.h" |
| #include "../operator_common.h" |
| |
| namespace mxnet { |
| namespace op { |
| |
| NNVM_REGISTER_OP(log_softmax) |
| .add_alias("_npx_log_softmax") |
| .describe(R"code(Computes the log softmax of the input. |
| This is equivalent to computing softmax followed by log. |
| |
| Examples:: |
| |
| >>> x = mx.nd.array([1, 2, .1]) |
| >>> mx.nd.log_softmax(x).asnumpy() |
| array([-1.41702998, -0.41702995, -2.31702995], dtype=float32) |
| |
| >>> x = mx.nd.array( [[1, 2, .1],[.1, 2, 1]] ) |
| >>> mx.nd.log_softmax(x, axis=0).asnumpy() |
| array([[-0.34115392, -0.69314718, -1.24115396], |
| [-1.24115396, -0.69314718, -0.34115392]], dtype=float32) |
| |
| |
| )code") |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| .set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>) |
| .set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_log_softmax"}) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType) |
| .set_num_inputs(1) |
| .set_num_outputs(1) |
| .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", |
| [](const NodeAttrs& attrs){ |
| return std::vector<std::pair<int, int> >{{0, 0}}; |
| }) |
| .add_argument("data", "NDArray-or-Symbol", "The input array.") |
| .add_arguments(SoftmaxParam::__FIELDS__()); |
| |
| NNVM_REGISTER_OP(_backward_log_softmax) |
| .set_num_inputs(SoftmaxGradOpNumInputs) |
| .set_num_outputs(1) |
| .set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames) |
| .set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption) |
| .add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments") |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| .set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, mshadow_op::left, |
| mxnet_op::log_softmax_bwd>); |
| |
| } // namespace op |
| } // namespace mxnet |
