add cost test

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)

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