From e19134e0cc0b6f143bb31430765c3fad82d2f54f Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Wed, 22 Feb 2017 11:52:37 +0800 Subject: [PATCH] add cost function in v2.layer --- python/paddle/v2/layer.py | 53 +++++++++++++++++++++++++++++++++------ 1 file changed, 45 insertions(+), 8 deletions(-) diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index 507725ee4f..cd6dd5110a 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -77,7 +77,9 @@ import data_type __all__ = [ 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', - 'cross_entropy_cost' + 'cross_entropy_cost', 'cross_entropy_with_selfnorm_cost', 'regression_cost', + 'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost', + 'sum_cost', 'huber_cost' ] @@ -137,7 +139,8 @@ def __convert_to_v2__(method_name, name_prefix, parent_names): parent_layers = dict() other_kwargs = dict() for pname in parent_names: - parent_layers[pname] = kwargs[pname] + if kwargs.has_key(pname): + parent_layers[pname] = kwargs[pname] for key in kwargs.keys(): if key not in parent_names: @@ -189,27 +192,61 @@ class DataLayerV2(Layer): data = DataLayerV2 fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) max_id = __convert_to_v2__( - 'maxid_layer', name_prefix='maxid_layer', parent_names=['input']) + 'maxid_layer', name_prefix='maxid', parent_names=['input']) classification_cost = __convert_to_v2__( 'classification_cost', name_prefix='classification_cost', - parent_names=['input', 'label']) + parent_names=['input', 'label', 'weight']) +regression_cost = __convert_to_v2__( + 'regression_cost', + name_prefix='regression_cost', + parent_names=['input', 'label', 'weight']) cross_entropy_cost = __convert_to_v2__( 'cross_entropy', name_prefix='cross_entropy', parent_names=['input', 'label']) +cross_entropy_with_selfnorm_cost = __convert_to_v2__( + 'cross_entropy_with_selfnorm', + name_prefix='cross_entropy_with_selfnorm', + parent_names=['input', 'label']) +multi_binary_label_cross_entropy_cost = __convert_to_v2__( + 'multi_binary_label_cross_entropy', + name_prefix='multi_binary_label_cross_entropy', + parent_names=['input', 'label']) +rank_cost = __convert_to_v2__( + 'rank_cost', + name_prefix='rank_cost', + parent_names=['left', 'right', 'label', 'weight']) +lambda_cost = __convert_to_v2__( + 'lambda_cost', name_prefix='lambda_cost', parent_names=['input', 'score']) +sum_cost = __convert_to_v2__( + 'sum_cost', name_prefix='sum_cost', parent_names=['input']) +huber_cost = __convert_to_v2__( + 'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label']) if __name__ == '__main__': pixel = data(name='pixel', type=data_type.dense_vector(784)) label = data(name='label', type=data_type.integer_value(10)) + weight = data(name='weight', type=data_type.dense_vector(10)) + score = data(name='score', type=data_type.dense_vector(1)) + hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation()) inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation()) maxid = max_id(input=inference) cost1 = classification_cost(input=inference, label=label) - cost2 = cross_entropy_cost(input=inference, label=label) + cost2 = classification_cost(input=inference, label=label, weight=weight) + cost3 = cross_entropy_cost(input=inference, label=label) + cost4 = cross_entropy_with_selfnorm_cost(input=inference, label=label) + cost5 = regression_cost(input=inference, label=label) + cost6 = regression_cost(input=inference, label=label, weight=weight) + cost7 = multi_binary_label_cross_entropy_cost(input=inference, label=label) + cost8 = rank_cost(left=score, right=score, label=score) + cost9 = lambda_cost(input=inference, score=score) + cost10 = sum_cost(input=inference) + cost11 = huber_cost(input=score, label=label) - print parse_network(cost1) - print parse_network(cost2) print parse_network(cost1, cost2) - print parse_network(cost2) + print parse_network(cost3, cost4) + print parse_network(cost5, cost6) + print parse_network(cost7, cost8, cost9, cost10, cost11) print parse_network(inference, maxid) -- GitLab