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

@@ -10,8 +10,24 @@ def get_numeric_gradient(op,
                          input_to_check,
                          delta=1e-5,
                          local_scope=None):
+    """
+    Get Numeric Gradient for an operator's input.
+    
+    :param op: C++ operator instance, could be an network 
+    :param input_values: The input variables. Should be an dictionary, key is 
+    variable name. Value is numpy array.
+    :param output_name: The final output variable name. 
+    :param input_to_check: The input variable need to get gradient.
+    :param delta: The perturbation value for numeric gradient method. The 
+    smaller delta is, the more accurate result will get. But if that delta is
+     too small, it could occur numerical stability problem.
+    :param local_scope: The local scope used for get_numeric_gradient.
+    :return: The gradient array in numpy format.
+    """
     if local_scope is None:
         local_scope = core.Scope()
+
+    # Create all input variable in local_scope
     for var_name in input_values:
         var = local_scope.new_var(var_name)
         tensor = var.get_tensor()
@@ -19,14 +35,18 @@ def get_numeric_gradient(op,
         tensor.alloc_float()
         tensor.set(input_values[var_name])
 
+    # Create all output variable in local_scope
     for output in op.outputs():
+        if local_scope.find_var(output) is None:
             local_scope.new_var(output).get_tensor()
 
     op.infer_shape(local_scope)
 
+    # allocate output memory
     for output in op.outputs():
         local_scope.find_var(output).get_tensor().alloc_float()
 
+    # TODO(yuyang18): Only CPU is support now.
     cpu_ctx = core.DeviceContext.cpu_context()
 
     def get_output():