提交 fb49bc2c 编写于 作者: C caoying03

rename mse_cost into square_error_cost.

上级 c1feb27f
......@@ -53,7 +53,7 @@ __all__ = [
'cos_sim',
'hsigmoid',
'conv_projection',
'mse_cost',
'square_error_cost',
'regression_cost',
'classification_cost',
'LayerOutput',
......@@ -4238,13 +4238,18 @@ def __cost_input__(input, label, weight=None):
@wrap_name_default()
@layer_support()
def mse_cost(input, label, weight=None, name=None, coeff=1.0, layer_attr=None):
def square_error_cost(input,
label,
weight=None,
name=None,
coeff=1.0,
layer_attr=None):
"""
mean squared error cost:
sum of square error cost:
.. math::
\\frac{1}{N}\sum_{i=1}^N(t_i-y_i)^2
cost = \\sum_{i=1}^N(t_i-y_i)^2
:param name: layer name.
:type name: basestring
......@@ -4273,7 +4278,7 @@ def mse_cost(input, label, weight=None, name=None, coeff=1.0, layer_attr=None):
return LayerOutput(name, LayerType.COST, parents=parents, size=1)
regression_cost = mse_cost
regression_cost = square_error_cost
@wrap_name_default("cost")
......
......@@ -45,7 +45,7 @@ layers {
coeff: 1.0
}
layers {
name: "__mse_cost_0__"
name: "__square_error_cost_0__"
type: "square_error"
size: 1
active_type: ""
......@@ -130,7 +130,7 @@ input_layer_names: "label"
input_layer_names: "weight"
input_layer_names: "multi_class_label"
output_layer_names: "__cost_0__"
output_layer_names: "__mse_cost_0__"
output_layer_names: "__square_error_cost_0__"
output_layer_names: "__nce_layer_0__"
evaluators {
name: "classification_error_evaluator"
......@@ -146,7 +146,7 @@ sub_models {
layer_names: "weight"
layer_names: "__fc_layer_0__"
layer_names: "__cost_0__"
layer_names: "__mse_cost_0__"
layer_names: "__square_error_cost_0__"
layer_names: "multi_class_label"
layer_names: "__nce_layer_0__"
input_layer_names: "input"
......@@ -154,7 +154,7 @@ sub_models {
input_layer_names: "weight"
input_layer_names: "multi_class_label"
output_layer_names: "__cost_0__"
output_layer_names: "__mse_cost_0__"
output_layer_names: "__square_error_cost_0__"
output_layer_names: "__nce_layer_0__"
evaluator_names: "classification_error_evaluator"
is_recurrent_layer_group: false
......
......@@ -10,7 +10,7 @@ fc = fc_layer(input=data, size=10, act=SoftmaxActivation())
outputs(
classification_cost(
input=fc, label=lbl, weight=wt),
mse_cost(
square_error_cost(
input=fc, label=lbl, weight=wt),
nce_layer(
input=fc,
......
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