Port fully connected operator, the FCOp c++ implementation and python unittest.

paddle/operators/CMakeLists.txt

@@ -47,12 +47,15 @@ endfunction()
 add_subdirectory(math)
 
 list(REMOVE_ITEM GENERAL_OPS
+     fc_op
      net_op
      minus_op
      mul_op
      recurrent_op
      scale_op)
 
+op_library(fc_op SRCS fc_op.cc
+           DEPS mul_op rowwise_add_op scale_op softmax_op sigmoid_op)
 op_library(net_op SRCS net_op.cc)
 op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
 op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)

paddle/operators/fc_op.cc

+/* 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. */
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/net_op.h"
+
+namespace paddle {
+namespace operators {
+
+class FCOp : public NetOp {
+ public:
+  FCOp(const std::string &type, const framework::VariableNameMap &inputs,
+       const framework::VariableNameMap &outputs,
+       const framework::AttributeMap &attrs)
+      : NetOp(type, inputs, outputs, attrs) {
+    AppendOp(framework::OpRegistry::CreateOp(
+        "mul", {{"X", {Input("X")}}, {"Y", {Input("W")}}},
+        {{"Out", {Output("mul_out")}}}, {}));
+    auto b = Input("b");
+    if (b != framework::kEmptyVarName) {
+      AppendOp(framework::OpRegistry::CreateOp(
+          "rowwise_add", {{"X", {Output("mul_out")}}, {"b", {Input("b")}}},
+          {{"Out", {Output("mul_out")}}}, {}));
+    }
+
+    auto activation = GetAttr<std::string>("activation");
+    AppendOp(framework::OpRegistry::CreateOp(
+        activation, {{"X", {Output("mul_out")}}}, {{"Y", {Output("Y")}}}, {}));
+    CompleteAddOp(false);
+  }
+};
+
+class FCOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  FCOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The 2D input matrix of FC operator.");
+    AddInput("W", "The 2D weight matrix of FC operator.");
+    AddInput("b", "The 1D bias vector of FC operator");
+
+    AddOutput("Y", "The activated output matrix of FC operator");
+    AddOutput("mul_out", "The non-actived output of FC operator, X * W + b")
+        .AsIntermediate();
+    AddAttr<std::string>("activation", "The activation type of FC operator.")
+        .SetDefault("identity")
+        .InEnum({"identity", "sigmoid", "softmax"});
+
+    AddComment(R"DOC(
+Fully Connected Operator, known as Fully Connected Layer or Inner Product Layer
+in Convolutional Neural Networks. Neurons in a fully connected layer have
+full connections to all activations in the previous layer.
+It computes an inner product of a set of
+learned weights with a matrix multiplication followed by a bias offset
+(optionally).
+
+Equation:
+  Y = Act(sum_n{X_i * W_i} + b)
+
+where X_i is a 2D matrix of size (M x K), usually M is the minibatch size and
+K is the number of features. W_i is also a 2D matrix of size (K x N),
+where N means the number of neurons in the fully connected layer.
+b is a 1D vector of size N. Thus, the output Y is a 2D matrix of size (M x N).
+Activation type can be set to `identity` (default), `sigmoid` or `softmax`.
+
+  The config api is `paddle.v2.layer.fc`.
+)DOC");
+  }
+};
+
+class FCGradOp : public NetOp {
+ public:
+  FCGradOp(const std::string &type, const framework::VariableNameMap &inputs,
+           const framework::VariableNameMap &outputs,
+           const framework::AttributeMap &attrs)
+      : NetOp(type, inputs, outputs, attrs) {
+    auto y_grad = Input(framework::GradVarName("Y"));
+    auto mul_out_grad = Input(framework::GradVarName("mul_out"));
+    auto x_grad = Output(framework::GradVarName("X"));
+    auto w_grad = Output(framework::GradVarName("W"));
+    auto b_grad = Output(framework::GradVarName("b"));
+
+    CompleteAddOp(false);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+USE_OP(mul);
+USE_OP(rowwise_add);
+USE_NO_KERNEL_OP(identity);
+USE_OP(sigmoid);
+USE_OP(softmax);
+
+namespace ops = paddle::operators;
+REGISTER_OP(fc, ops::FCOp, ops::FCOpMaker, fc_grad, ops::FCGradOp);

paddle/operators/scale_op.cc

@@ -89,6 +89,7 @@ class IdentityOp : public NetOp {
     AppendOp(framework::OpRegistry::CreateOp(
         "scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
         {{"scale", static_cast<AttrType>(1)}}));
+    CompleteAddOp(false);
   }
 };
 

paddle/pybind/CMakeLists.txt

 if(WITH_PYTHON)
-cc_library(paddle_pybind SHARED
+  cc_library(paddle_pybind SHARED
     SRCS pybind.cc
     DEPS pybind python backward
     ${GLOB_OP_LIB})

paddle/pybind/pybind.cc

@@ -45,6 +45,7 @@ USE_OP(uniform_random);
 USE_OP(lookup_table);
 USE_OP(scale);
 USE_NO_KERNEL_OP(identity);
+USE_NO_KERNEL_OP(fc);
 USE_OP(minus);
 USE_CPU_ONLY_OP(gather);
 USE_CPU_ONLY_OP(scatter);

python/paddle/v2/framework/tests/CMakeLists.txt

@@ -16,6 +16,7 @@ py_test(test_cross_entropy_op SRCS test_cross_entropy_op.py)
 py_test(test_gather_op SRCS test_gather_op.py)
 py_test(test_scatter_op SRCS test_scatter_op.py)
 py_test(test_fill_zeros_like_op SRCS test_fill_zeros_like_op.py)
+py_test(test_fc_op SRCS test_fc_op.py)
 
 py_test(gradient_checker SRCS gradient_checker.py)
 

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

+import unittest
+import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
+
+
+class TestFCOp(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "fc"
+        self.inputs = {
+            "X": np.random.random((32, 784)).astype("float32"),
+            "W": np.random.random((784, 1000)).astype("float32"),
+            "b": np.random.random(1000).astype("float32")
+        }
+        self.attrs = {"activation": "sigmoid"}
+        mul_out = np.dot(self.inputs["X"], self.inputs["W"])
+        add_out = np.add(mul_out, self.inputs["b"])
+        sigmoid_out = 1 / (1 + np.exp(-add_out))
+        self.outputs = {"mul_out": add_out, "Y": sigmoid_out}
+
+
+class TestFCGradOp(GradientChecker):
+    def test_normal(self):
+        print "nothing"
+
+
+if __name__ == '__main__':
+    unittest.main()