diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py index e1952ce747d1039282871119499d02bf7fd01344..f333c0af964bf5ecbc8f9b908c4e512a982eec33 100644 --- a/python/paddle/v2/layer.py +++ b/python/paddle/v2/layer.py @@ -281,8 +281,6 @@ if __name__ == '__main__': cost10 = sum_cost(input=inference) cost11 = huber_cost(input=score, label=label) - mem = memory(name="rnn_state", size=10) - # print parse_network(cost1) # print parse_network(cost2) # print parse_network(cost1, cost2) diff --git a/python/paddle/v2/tests/layer_test.py b/python/paddle/v2/tests/layer_test.py index 87e601a60abc574583a16a715eb11d659d50914b..6c4b458914701eba5d08670244d4e4a6204ed7f3 100644 --- a/python/paddle/v2/tests/layer_test.py +++ b/python/paddle/v2/tests/layer_test.py @@ -18,10 +18,45 @@ import paddle.trainer_config_helpers as conf_helps import paddle.v2.activation as activation import paddle.v2.data_type as data_type import paddle.v2.layer as layer +import paddle.v2.attr as attr from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as parse_network +class CostLyaerTest(unittest.TestCase): + def test_cost_layer(self): + pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) + label = layer.data(name='label', type=data_type.integer_value(10)) + weight = layer.data(name='weight', type=data_type.dense_vector(10)) + score = layer.data(name='score', type=data_type.dense_vector(1)) + hidden = layer.fc(input=pixel, + size=100, + act=activation.Sigmoid(), + param_attr=attr.Param(name='hidden')) + inference = layer.fc(input=hidden, size=10, act=activation.Softmax()) + + cost1 = layer.classification_cost(input=inference, label=label) + cost2 = layer.classification_cost( + input=inference, label=label, weight=weight) + cost3 = layer.cross_entropy_cost(input=inference, label=label) + cost4 = layer.cross_entropy_with_selfnorm_cost( + input=inference, label=label) + cost5 = layer.regression_cost(input=inference, label=label) + cost6 = layer.regression_cost( + input=inference, label=label, weight=weight) + cost7 = layer.multi_binary_label_cross_entropy_cost( + input=inference, label=label) + cost8 = layer.rank_cost(left=score, right=score, label=score) + cost9 = layer.lambda_cost(input=inference, score=score) + cost10 = layer.sum_cost(input=inference) + cost11 = layer.huber_cost(input=score, label=label) + + print layer.parse_network(cost1, cost2) + print layer.parse_network(cost3, cost4) + print layer.parse_network(cost5, cost6) + print layer.parse_network(cost7, cost8, cost9, cost10, cost11) + + class RNNTest(unittest.TestCase): def test_simple_rnn(self): dict_dim = 10