Merge pull request #3928 from lcy-seso/refine_softmax_op

Refine names and doc of some operators.

paddle/operators/identity_op.cc

@@ -18,17 +18,20 @@
 namespace paddle {
 namespace operators {
 
-// identity is a alias of scale op. This is also a example for creating a alias
-// operator.
+// The identity operator is an alias of the scale operator. This is also an
+// example for creating an alias for an existing operator.
 template <typename AttrType>
 class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   IdentityOpMaker(framework::OpProto *proto,
                   framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "input tensor of identity op");
-    AddOutput("Out", "output tensor of identity op");
-    AddComment("identity operator. Just a alias of scale op which scale = 1.0");
+    AddInput("X", "The input tensor of identity operator.");
+    AddOutput("Out", "The output tensor of identity operator.");
+    AddComment(R"DOC(
+The identity operator is an alias of the scale operator
+with the attribute scale fixed to 1.0.
+)DOC");
   }
 };
 

paddle/operators/scale_op.cc

@@ -44,11 +44,13 @@ class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
 
 The equation is: Out = scale*X
 )DOC");
-    AddAttr<AttrType>("scale", "scale of scale operator.").SetDefault(1.0);
+    AddAttr<AttrType>("scale", "The scaling factor of the scale operator.")
+        .SetDefault(1.0);
   }
 };
 
-// Scale Op's gradient is scale op, too.
+// The operator to calculate gradients of a scale operator is just the scale
+// operator itself.
 // Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out))
 template <typename AttrType>
 class ScaleGradOp : public NetOp {

paddle/operators/softmax_op.cc

@@ -51,7 +51,7 @@ the other dimensions in the K-dimensional vector input. Then the ratio of the
 exponential of the given dimension and the sum of exponential values of all
 the other dimensions is the output of the softmax operator.
 
-For each row `i` and each column `j` in X, we have:
+For each row `i` and each column `j` in input X, we have:
     Y[i, j] = exp(X[i, j]) / sum_j(exp(X[i, j]))
 
 )DOC");
@@ -64,14 +64,15 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE(ctx.InputVar("Y") != nullptr, "Input(Y) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should be not null.");
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")),
-                            "Input(Y@GRAD) should not be null");
-    PADDLE_ENFORCE(ctx.Input<Tensor>("Y")->dims() ==
-                       ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
-                   "the shape of Input(0) and Input(1) should be the same");
+                            "Input(Y@GRAD) should be not null.");
+    PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Y")->dims(),
+                      ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
+                      "Input(Y) and its gradients should have a same shape.");
+
     ctx.Output<Tensor>(framework::GradVarName("X"))
-        ->Resize(ctx.Input<Tensor>("Y")->dims());
+        ->Resize(ctx.Input<Tensor>("X")->dims());
   }
 };
 

paddle/operators/softmax_op.h

@@ -28,12 +28,12 @@ template <typename Place, typename T>
 class SoftmaxKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto input = context.Input<Tensor>("X");
-    auto output = context.Output<Tensor>("Y");
-    output->mutable_data<T>(context.GetPlace());
+    auto X = context.Input<Tensor>("X");
+    auto Y = context.Output<Tensor>("Y");
+    Y->mutable_data<T>(context.GetPlace());
 
-    auto logits = EigenMatrix<T>::From(*input);
-    auto softmax = EigenMatrix<T>::From(*output);
+    auto logits = EigenMatrix<T>::From(*X);
+    auto softmax = EigenMatrix<T>::From(*Y);
 
     const int kBatchDim = 0;
     const int kClassDim = 1;

python/paddle/v2/framework/op.py

@@ -4,8 +4,8 @@ import paddle.v2.framework.proto.framework_pb2 as framework_pb2
 
 def get_all_op_protos():
     """
-    Get all registered op proto from Paddle C++
-    :return: list of OpProto
+    Get all registered op proto from PaddlePaddle C++ end.
+    :return: A list of registered OpProto.
     """
     protostrs = core.get_all_op_protos()
     ret_values = []
@@ -21,8 +21,8 @@ def is_str(s):
 
 class OpDescCreationMethod(object):
     """
-    A Functor object to convert user input(use key word args) to OpDesc based on
-    OpProto.
+    Convert the user's input(only keyword arguments are supported) to OpDesc
+    based on the OpProto.
 
     :param op_proto: The OpProto object.
     :type op_proto: op_proto_pb2.OpProto
@@ -30,17 +30,18 @@ class OpDescCreationMethod(object):
 
     def __init__(self, op_proto):
         if not isinstance(op_proto, framework_pb2.OpProto):
-            raise TypeError("Argument should be OpProto")
+            raise TypeError(
+                "Type of op_proto should be OpProto in PaddlePaddle.")
         self.__op_proto__ = op_proto
 
     def __call__(self, *args, **kwargs):
         """
-        Convert user input to OpDesc. Only key-word args are supported. 
-        :return: OpDesc based on user input
+        Convert user's input to OpDesc. Only keyword arguments are supported.
+        :return: The OpDesc based on user input.
         :rtype: op_desc_pb2.OpDesc
         """
         if len(args) != 0:
-            raise ValueError("Only keyword arguments is supported by Paddle")
+            raise ValueError("Only keyword arguments are supported.")
         op_desc = framework_pb2.OpDesc()
 
         for input_parameter in self.__op_proto__.inputs:
@@ -49,8 +50,9 @@ class OpDescCreationMethod(object):
                 input_arguments = [input_arguments]
 
             if not input_parameter.duplicable and len(input_arguments) > 1:
-                raise ValueError("Input %s only accepts one input, but give %d"
-                                 % (input_parameter.name, len(input_arguments)))
+                raise ValueError(
+                    "Input %s expects only one input, but %d are given." %
+                    (input_parameter.name, len(input_arguments)))
 
             ipt = op_desc.inputs.add()
             ipt.parameter = input_parameter.name
@@ -63,7 +65,7 @@ class OpDescCreationMethod(object):
 
             if not output_parameter.duplicable and len(output_arguments) > 1:
                 raise ValueError(
-                    "Output %s only accepts one output, but give %d" %
+                    "Output %s expects only one output, but %d are given." %
                     (output_parameter.name, len(output_arguments)))
 
             out = op_desc.outputs.add()
@@ -100,15 +102,17 @@ class OpDescCreationMethod(object):
                         pair.first = p[0]
                         pair.second = p[1]
                 else:
-                    raise NotImplementedError("Not support attribute type " +
-                                              str(attr.type))
+                    raise NotImplementedError(
+                        "A not supported attribute type: %s." % (
+                            str(attr.type)))
 
         return op_desc
 
     @staticmethod
     def any_is_true(generator):
         """
-        Reduce a bool array to one. If any of them is True, then return True.
+        Reduce a boolean array to a single boolean parameter. If any element in
+        the array is True, this function will return True, otherwise False.
         """
         for flag in generator:
             if flag:
@@ -127,7 +131,7 @@ class OpInfo(object):
 
 def create_op_creation_method(op_proto):
     """
-    Generate op creation method for an OpProto
+    Generate op creation method for an OpProto.
     """
     method = OpDescCreationMethod(op_proto)
 
@@ -146,20 +150,23 @@ def create_op_creation_method(op_proto):
 class OperatorFactory(object):
     def __init__(self):
         self.op_methods = dict()
+
         for op_proto in get_all_op_protos():
             method = create_op_creation_method(op_proto)
             self.op_methods[method.name] = method
 
     def __call__(self, *args, **kwargs):
-        if 'type' in kwargs:
+        if "type" in kwargs:
             if len(args) != 0:
-                raise ValueError("All Paddle argument should be key-word "
-                                 "argument except type")
-            t = kwargs.pop('type')
+                raise ValueError(
+                    "Except the argument \"type\","
+                    "all of the other arguments should be keyword arguments.")
+            t = kwargs.pop("type")
         else:
             if len(args) != 1:
-                raise ValueError("All Paddle argument should be key-word "
-                                 "argument except type")
+                raise ValueError(
+                    "Except the argument \"type\","
+                    "all of the other arguments should be keyword arguments.")
             t = args[0]
 
         return self.get_op_info(t).method(**kwargs)
@@ -169,7 +176,7 @@ class OperatorFactory(object):
 
     def get_op_info(self, t):
         if t not in self.op_methods:
-            raise ValueError("operator %s is not registered", t)
+            raise ValueError("The operator: %s is not registered." % t)
         return self.op_methods.get(t)
 
     def get_op_input_names(self, type):
@@ -184,7 +191,7 @@ class OperatorFactory(object):
 
 class __RecurrentOp__(object):
     __proto__ = None
-    type = 'recurrent'
+    type = "recurrent"
 
     def __init__(self):
         # cache recurrent_op's proto
@@ -194,8 +201,8 @@ class __RecurrentOp__(object):
                     self.__proto__ = op_proto
 
     def __call__(self, *args, **kwargs):
-        if self.type not in args and 'type' not in kwargs:
-            kwargs['type'] = self.type
+        if self.type not in args and "type" not in kwargs:
+            kwargs["type"] = self.type
         # create proto
         create_method = OpDescCreationMethod(self.__proto__)
         proto = create_method(*args, **kwargs)
@@ -203,5 +210,5 @@ class __RecurrentOp__(object):
         return core.RecurrentOp.create(proto.SerializeToString())
 
 
-Operator = OperatorFactory()  # Default global factory
+Operator = OperatorFactory()  # The default global factory
 RecurrentOp = __RecurrentOp__()

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

@@ -38,9 +38,9 @@ def feed_data(name, data):
     assert isinstance(data, numpy.ndarray)
     tensor = scope.find_var(name).get_tensor()
     tensor.set_dims(data.shape)
-    if data.dtype == numpy.dtype('int32'):
+    if data.dtype == numpy.dtype("int32"):
         tensor.alloc_int(place)
-    elif data.dtype == numpy.dtype('float32'):
+    elif data.dtype == numpy.dtype("float32"):
         tensor.alloc_float(place)
     else:
         raise ValueError("data type not supported")
@@ -74,22 +74,25 @@ def init_param(net, param_name, dims):
 # fc_layer
 def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
     """
-    Add a fc layer to net
+    The fully connected layer.
 
-    :param input: input variable name.
+    :param input: The name of input variable.
     :type input: str
-    :param size: fully connected layer size.
-    :param act: activation name
-    :param param: parameter attribute, used for initialize parameters.
-    :param bias: bias attribute. False will not have a bias.
-    :param name: the name of fc layer. If not set, model will generate a
-    readable name
-    :return: output variable name.
+    :param size: The size of fully connected layer.
+    :param act: The name of activation.
+    :param param: The attribute of learnable parameter which can be used to
+                  modify initialization mean and std of the parameter.
+    :param bias: The attribute of bias. If set False, this layer does not have
+                 a bias.
+    :param name: The name of this layer. If it is not set explictly, a name
+                 will be generated automatically.
+    :return: The name of the output variable.
     """
+
     if name is None:
-        name = 'fc_%d' % uniq_id()
+        name = "fc_%d" % uniq_id()
     if not isinstance(name, str):
-        raise ValueError("name should be string")
+        raise ValueError("The name of a layer should be a string.")
 
     input_dims = scope.find_var(input).get_tensor().get_dims()
 
@@ -123,7 +126,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
 
 
 def cross_entropy_layer(net, input, label):
-    cost_name = 'cross_entropy_%d' % uniq_id()
+    cost_name = "cross_entropy_%d" % uniq_id()
     cross_entropy_op = Operator(
         "onehot_cross_entropy", X=input, label=label, Y=cost_name)
     net.append_op(cross_entropy_op)
@@ -177,8 +180,8 @@ def error_rate(predict, label):
     return error_num / float(len(label))
 
 
-images = data_layer(name='pixel', dims=[BATCH_SIZE, 784])
-labels = data_layer(name='label', dims=[BATCH_SIZE])
+images = data_layer(name="pixel", dims=[BATCH_SIZE, 784])
+labels = data_layer(name="label", dims=[BATCH_SIZE])
 fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid")
 fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid")
 predict = fc_layer(net=forward_net, input=fc2, size=10, act="softmax")

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

@@ -7,11 +7,11 @@ from gradient_checker import get_numeric_gradient
 
 class GetNumericGradientTest(unittest.TestCase):
     def test_add_op(self):
-        add_op = Operator('add', X="X", Y="Y", Out="Z")
+        add_op = Operator("add", X="X", Y="Y", Out="Z")
         x = numpy.random.random((10, 1)).astype("float32")
         y = numpy.random.random((10, 1)).astype("float32")
 
-        arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X')
+        arr = get_numeric_gradient(add_op, {"X": x, "Y": y}, "Z", "X")
         self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
 
     def test_softmax_op(self):
@@ -35,9 +35,9 @@ class GetNumericGradientTest(unittest.TestCase):
         dY = numpy.ones(Y.shape)
         dX = label_softmax_grad(Y, dY)
 
-        arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X')
+        arr = get_numeric_gradient(softmax_op, {"X": X}, "Y", "X")
         numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()

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

@@ -18,18 +18,22 @@ class TestSoftmaxOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "softmax"
-        self.inputs = {'X': np.random.random((32, 100)).astype("float32")}
+        self.inputs = {"X": np.random.random((10, 10)).astype("float32")}
         self.outputs = {
-            'Y': np.apply_along_axis(stable_softmax, 1, self.inputs['X'])
+            "Y": np.apply_along_axis(stable_softmax, 1, self.inputs["X"])
         }
 
 
-class SoftmaxGradOpTest(GradientChecker):
-    def test_softmax(self):
-        op = create_op("softmax")
-        inputs = {"X": np.random.uniform(0.1, 1, [10, 10]).astype("float32")}
-        self.check_grad(op, inputs, set("X"), "Y")
+class TestSoftmaxGradOp(GradientChecker):
+    def setUp(self):
+        self.op = create_op("softmax")
+        self.inputs = {
+            "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32")
+        }
+
+    def test_softmax_grad(self):
+        self.check_grad(self.op, self.inputs, ["X"], "Y")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()