use test_layer instead of layer_test

python/paddle/v2/tests/test_layer.py

@@ -51,12 +51,57 @@ class CostLayerTest(unittest.TestCase):
         cost10 = layer.sum_cost(input=inference)
         cost11 = layer.huber_cost(input=score, label=label)
 
-        print dir(layer)
-        layer.parse_network(cost1, cost2)
-        print dir(layer)
-        #print layer.parse_network(cost3, cost4)
-        #print layer.parse_network(cost5, cost6)
-        #print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
+        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
+        word_dim = 8
+        hidden_dim = 8
+
+        def test_old_rnn():
+            def step(y):
+                mem = conf_helps.memory(name="rnn_state", size=hidden_dim)
+                out = conf_helps.fc_layer(
+                    input=[y, mem],
+                    size=hidden_dim,
+                    act=activation.Tanh(),
+                    bias_attr=True,
+                    name="rnn_state")
+                return out
+
+            def test():
+                data1 = conf_helps.data_layer(name="word", size=dict_dim)
+                embd = conf_helps.embedding_layer(input=data1, size=word_dim)
+                conf_helps.recurrent_group(name="rnn", step=step, input=embd)
+
+            return str(parse_network(test))
+
+        def test_new_rnn():
+            def new_step(y):
+                mem = layer.memory(name="rnn_state", size=hidden_dim)
+                out = layer.fc(input=[mem],
+                               step_input=y,
+                               size=hidden_dim,
+                               act=activation.Tanh(),
+                               bias_attr=True,
+                               name="rnn_state")
+                return out.to_proto(dict())
+
+            data1 = layer.data(
+                name="word", type=data_type.integer_value(dict_dim))
+            embd = layer.embedding(input=data1, size=word_dim)
+            rnn_layer = layer.recurrent_group(
+                name="rnn", step=new_step, input=embd)
+            return str(layer.parse_network(rnn_layer))
+
+        diff = difflib.unified_diff(test_old_rnn().splitlines(1),
+                                    test_new_rnn().splitlines(1))
+        print ''.join(diff)
 
 
 if __name__ == '__main__':