Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

add cost_type constraint to weighted_cost interface #206

Merged
merged 1 commit into from
Oct 17, 2016
+5 −11
Merged

add cost_type constraint to weighted_cost interface #206

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion python/paddle/trainer/config_parser.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1715,7 +1715,6 @@ def init(cls, name, inputs, device=None, coeff=1.):
g_cost_map[cost_type] = cls

define_cost('MultiClassCrossEntropy', 'multi-class-cross-entropy')
define_cost('ClassificationErrorLayer', 'classification_error')
define_cost('RankingCost', 'rank-cost')
define_cost('AucValidation', 'auc-validation')
define_cost('PnpairValidation', 'pnpair-validation')
Expand Down
15 changes: 5 additions & 10 deletions python/paddle/trainer_config_helpers/layers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2799,7 +2799,7 @@ def __cost_input__(input, label, weight=None):


@wrap_name_default()
def regression_cost(input, label, weight=None, cost='square_error', name=None):
def regression_cost(input, label, weight=None, name=None):
"""
Regression Layer.

Expand All @@ -2814,21 +2814,18 @@ def regression_cost(input, label, weight=None, cost='square_error', name=None):
:param weight: The weight affects the cost, namely the scale of cost.
It is an optional argument.
:type weight: LayerOutput
:param cost: Cost method.
:type cost: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
ipts, parents = __cost_input__(input, label, weight)

Layer(inputs=ipts, type=cost, name=name)
Layer(inputs=ipts, type="square_error", name=name)
return LayerOutput(name, LayerType.COST, parents=parents)


@wrap_name_default("cost")
@layer_support()
def classification_cost(input, label, weight=None, name=None,
cost="multi-class-cross-entropy",
evaluator=classification_error_evaluator,
layer_attr=None):
"""
Expand All @@ -2843,8 +2840,6 @@ def classification_cost(input, label, weight=None, name=None,
:param weight: The weight affects the cost, namely the scale of cost.
It is an optional argument.
:type weight: LayerOutput
:param cost: cost method.
:type cost: basestring
:param evaluator: Evaluator method.
:param layer_attr: layer's extra attribute.
:type layer_attr: ExtraLayerAttribute
Expand All @@ -2857,7 +2852,7 @@ def classification_cost(input, label, weight=None, name=None,

ipts, parents = __cost_input__(input, label, weight)

Layer(name=name, type=cost, inputs=ipts,
Layer(name=name, type="multi-class-cross-entropy", inputs=ipts,
**ExtraLayerAttribute.to_kwargs(layer_attr))

def __add_evaluator__(e):
Expand Down Expand Up @@ -3819,8 +3814,8 @@ def multi_binary_label_cross_entropy(input, label, name=None, coeff=1.0):
if input.activation is None or \
not isinstance(input.activation, SigmoidActivation):
logger.log(logging.WARN,
"%s is not recommend for batch normalization's activation, "
"maybe the relu is better" % repr(input.activation))
"%s is not recommend for multi_binary_label_cross_entropy's activation, "
"maybe the sigmoid is better" % repr(input.activation))

Layer(name=name,
type=LayerType.MULTI_BIN_LABEL_CROSS_ENTROPY,
Expand Down