Calculating gradients for partial graph

Added backward.calc_gradient to backpropagate gradient from given targets to inputs.

paddle/framework/grad_op_desc_maker.h

@@ -87,7 +87,11 @@ class GradOpDescMakerBase {
     auto onames = this->Output(name);
     ret_val.reserve(onames.size());
     std::transform(onames.begin(), onames.end(), std::back_inserter(ret_val),
-                   GradVarName);
+                   [this](const std::string& fwd_var_name) -> std::string {
+                     auto g_name = GradVarName(fwd_var_name);
+                     (*this->grad_to_var_)[g_name] = fwd_var_name;
+                     return g_name;
+                   });
     return ret_val;
   }
 

paddle/framework/op_desc.h

@@ -129,7 +129,7 @@ class OpDesc {
   }
 
   proto::OpDesc desc_;
-  // input arg name => output variable names
+  // input arg name => input variable names
   VariableNameMap inputs_;
   // output arg name => output variable names
   VariableNameMap outputs_;

python/paddle/v2/fluid/layers/ops.py

@@ -1,7 +1,17 @@
 from ..registry import register_layer
 
 __activations__ = [
-    'abs', 'tanh', 'sigmoid', 'relu', 'sqrt', 'ceil', 'floor', 'log', 'round'
+    'abs',
+    'ceil',
+    'exp',
+    'floor',
+    'log',
+    'relu',
+    'round',
+    'sigmoid',
+    'sqrt',
+    'square',
+    'tanh',
 ]
 
 __all__ = [

python/paddle/v2/fluid/layers/tensor.py

@@ -1,7 +1,8 @@
 from ..layer_helper import LayerHelper
+from ..param_attr import ParamAttr
 
 __all__ = [
-    'create_tensor', 'cast', 'concat', 'sums', 'assign',
+    'create_tensor', 'create_parameter', 'cast', 'concat', 'sums', 'assign',
     'fill_constant_batch_size_like', 'fill_constant', 'ones', 'zeros'
 ]
 
@@ -11,6 +12,33 @@ def create_tensor(dtype, name=None):
     return helper.create_variable(name=helper.name, dtype=dtype)
 
 
+def create_parameter(shape,
+                     dtype,
+                     attr=None,
+                     is_bias=False,
+                     default_initializer=None):
+    """
+    Create a parameter
+    Args:
+        shape(list[int]): shape of the parameter
+        dtype(string): element type of the parameter
+        attr(ParamAttr): attributes of the parameter
+        is_bias(bool): This can affect which default initializer is chosen
+                       when default_initializer is None. If is_bias,
+                       initializer.Constant(0.0) will be used. Otherwise,
+                       Xavier() will be used.
+        default_initializer(Initializer): initializer for the parameter
+
+    Returns:
+        Parameter: the created parameter
+    """
+    helper = LayerHelper("create_parameter")
+    if attr is None:
+        attr = ParamAttr()
+    return helper.create_parameter(attr, shape, dtype, is_bias,
+                                   default_initializer)
+
+
 def cast(x, dtype):
     """
     This function takes in the input with input_dtype
@@ -180,7 +208,8 @@ def fill_constant_batch_size_like(input,
     Examples:
         .. code-block:: python
 
-          data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
+          data = fluid.layers.fill_constant_batch_size_like(
+              input=like, shape=[1], value=0, dtype='int64')
     """
     helper = LayerHelper("fill_constant_batch_size_like", **locals())
     out = helper.create_tmp_variable(dtype=dtype)