Liu Yiqun
8 years ago
parent
843a8b1e5d
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/framework/op_registry.h"
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#include "paddle/operators/net_op.h"
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namespace paddle {
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namespace operators {
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class FCOp : public NetOp {
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public:
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FCOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: NetOp(type, inputs, outputs, attrs) {
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AppendOp(framework::OpRegistry::CreateOp(
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"mul", {{"X", {Input("X")}}, {"Y", {Input("W")}}},
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{{"Out", {Output("mul_out")}}}, {}));
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auto b = Input("b");
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if (b != framework::kEmptyVarName) {
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AppendOp(framework::OpRegistry::CreateOp(
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"rowwise_add", {{"X", {Output("mul_out")}}, {"b", {Input("b")}}},
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{{"Out", {Output("mul_out")}}}, {}));
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}
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auto activation = GetAttr<std::string>("activation");
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AppendOp(framework::OpRegistry::CreateOp(
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activation, {{"X", {Output("mul_out")}}}, {{"Y", {Output("Y")}}}, {}));
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CompleteAddOp(false);
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}
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};
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class FCOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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FCOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The 2D input matrix of FC operator.");
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AddInput("W", "The 2D weight matrix of FC operator.");
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AddInput("b", "The 1D bias vector of FC operator");
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AddOutput("Y", "The activated output matrix of FC operator");
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AddOutput("mul_out", "The non-actived output of FC operator, X * W + b")
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.AsIntermediate();
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AddAttr<std::string>("activation", "The activation type of FC operator.")
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.SetDefault("identity")
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.InEnum({"identity", "sigmoid", "softmax"});
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AddComment(R"DOC(
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Fully Connected Operator, known as Fully Connected Layer or Inner Product Layer
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in Convolutional Neural Networks. Neurons in a fully connected layer have
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full connections to all activations in the previous layer.
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It computes an inner product of a set of
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learned weights with a matrix multiplication followed by a bias offset
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(optionally).
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Equation:
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Y = Act(sum_n{X_i * W_i} + b)
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where X_i is a 2D matrix of size (M x K), usually M is the minibatch size and
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K is the number of features. W_i is also a 2D matrix of size (K x N),
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where N means the number of neurons in the fully connected layer.
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b is a 1D vector of size N. Thus, the output Y is a 2D matrix of size (M x N).
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Activation type can be set to `identity` (default), `sigmoid` or `softmax`.
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The config api is `paddle.v2.layer.fc`.
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)DOC");
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}
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};
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class FCGradOp : public NetOp {
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public:
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FCGradOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: NetOp(type, inputs, outputs, attrs) {
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auto y_grad = Input(framework::GradVarName("Y"));
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auto mul_out_grad = Input(framework::GradVarName("mul_out"));
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auto x_grad = Output(framework::GradVarName("X"));
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auto w_grad = Output(framework::GradVarName("W"));
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auto b_grad = Output(framework::GradVarName("b"));
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CompleteAddOp(false);
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}
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};
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} // namespace operators
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} // namespace paddle
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USE_OP(mul);
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USE_OP(rowwise_add);
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USE_NO_KERNEL_OP(identity);
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USE_OP(sigmoid);
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USE_OP(softmax);
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namespace ops = paddle::operators;
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REGISTER_OP(fc, ops::FCOp, ops::FCOpMaker, fc_grad, ops::FCGradOp);
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@ -0,0 +1,30 @@
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import unittest
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import numpy as np
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from gradient_checker import GradientChecker, create_op
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from op_test_util import OpTestMeta
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class TestFCOp(unittest.TestCase):
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__metaclass__ = OpTestMeta
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def setUp(self):
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self.type = "fc"
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self.inputs = {
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"X": np.random.random((32, 784)).astype("float32"),
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"W": np.random.random((784, 1000)).astype("float32"),
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"b": np.random.random(1000).astype("float32")
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}
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self.attrs = {"activation": "sigmoid"}
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mul_out = np.dot(self.inputs["X"], self.inputs["W"])
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add_out = np.add(mul_out, self.inputs["b"])
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sigmoid_out = 1 / (1 + np.exp(-add_out))
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self.outputs = {"mul_out": add_out, "Y": sigmoid_out}
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class TestFCGradOp(GradientChecker):
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def test_normal(self):
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print "nothing"
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if __name__ == '__main__':
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unittest.main()
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