Merge pull request #155 from sony/hotfix/20180606-mean-subtraction

change a variable name of mean_subtraction, "rmean" to "mean"
sony · Jun 7, 2018 · 97669f9 · 97669f9
2 parents 08c3c0c + 06670eb
commit 97669f9
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diff --git a/python/src/nnabla/functions.py b/python/src/nnabla/functions.py
@@ -223,7 +223,7 @@ def batch_normalization(x, beta, gamma, mean, variance, axes=[1], decay_rate=0.9
                                     n_outputs=n_outputs)
 
 
-def mean_subtraction(x, rmean, t, base_axis=1, update_running_mean=True):
+def mean_subtraction(x, mean, t, base_axis=1, update_running_mean=True):
     r"""
     It subtracts the mean of the elements of the input array,
     and normalizes it to :math:`0`. Preprocessing arrays with this function has the effect of improving accuracy
@@ -244,7 +244,7 @@ def mean_subtraction(x, rmean, t, base_axis=1, update_running_mean=True):
 
     Args:
         x(~nnabla.Variable): N-D array of input.
-        rmean(~nnabla.Variable): N-D array of running mean (modified during forward execution).
+        mean(~nnabla.Variable): N-D array of running mean (modified during forward execution).
         t(~nnabla.Variable): Scalar of num of iteration of running mean (modified during forward execution).
         base_axis(int): Base axis of Mean Subtraction operation. Dimensions up to base_axis is treated as sample dimension.
             [default=``1``]
@@ -259,7 +259,7 @@ def mean_subtraction(x, rmean, t, base_axis=1, update_running_mean=True):
 
     """
     from .function_bases import mean_subtraction as mean_subtraction_base
-    return mean_subtraction_base(x, rmean, t,
+    return mean_subtraction_base(x, mean, t,
                                  base_axis=base_axis,
                                  update_running_mean=update_running_mean)
 

diff --git a/python/src/nnabla/parametric_functions.py b/python/src/nnabla/parametric_functions.py
@@ -1297,11 +1297,11 @@ def mean_subtraction(inp, base_axis=1, update_running_mean=True, fix_parameters=
     """
     assert len(inp.shape) >= base_axis
     shape = inp.shape[base_axis:]
-    rmean = get_parameter_or_create(
-        "rmean", shape, ConstantInitializer(0), False)
+    mean = get_parameter_or_create(
+        "mean", shape, ConstantInitializer(0), False)
     t = get_parameter_or_create(
         "t", (1, ), ConstantInitializer(0), False)
-    return F.mean_subtraction(inp, rmean, t, base_axis=base_axis, update_running_mean=update_running_mean)
+    return F.mean_subtraction(inp, mean, t, base_axis=base_axis, update_running_mean=update_running_mean)
 
 
 @parametric_function_api("embed")