Format sequence_nest_rnn_multi_unequalength*.conf

paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf → paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py

@@ -16,12 +16,12 @@
 from paddle.trainer_config_helpers import *
 
 ######################## data source ################################
-define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/dummy.list',
     test_list=None,
     module='rnn_data_provider',
     obj='process_unequalength_subseq')
 
-
 settings(batch_size=2, learning_rate=0.01)
 ######################## network configure ################################
 dict_dim = 10
@@ -38,46 +38,46 @@ emb2 = embedding_layer(input=speaker2, size=word_dim)
 # This hierachical RNN is designed to be equivalent to the simple RNN in
 # sequence_rnn_multi_unequalength_inputs.conf
 
+
 def outer_step(x1, x2):
-    outer_mem1 = memory(name = "outer_rnn_state1", size = hidden_dim)
-    outer_mem2 = memory(name = "outer_rnn_state2", size = hidden_dim)
+    outer_mem1 = memory(name="outer_rnn_state1", size=hidden_dim)
+    outer_mem2 = memory(name="outer_rnn_state2", size=hidden_dim)
+
     def inner_step1(y):
-        inner_mem = memory(name = 'inner_rnn_state_' + y.name,
-                           size = hidden_dim,
-                           boot_layer = outer_mem1)
-        out = fc_layer(input = [y, inner_mem],
-                       size = hidden_dim,
-                       act = TanhActivation(),
-                       bias_attr = True,
-                       name = 'inner_rnn_state_' + y.name)
+        inner_mem = memory(
+            name='inner_rnn_state_' + y.name,
+            size=hidden_dim,
+            boot_layer=outer_mem1)
+        out = fc_layer(
+            input=[y, inner_mem],
+            size=hidden_dim,
+            act=TanhActivation(),
+            bias_attr=True,
+            name='inner_rnn_state_' + y.name)
         return out
 
     def inner_step2(y):
-        inner_mem = memory(name = 'inner_rnn_state_' + y.name,
-                           size = hidden_dim,
-                           boot_layer = outer_mem2)
-        out = fc_layer(input = [y, inner_mem],
-                       size = hidden_dim,
-                       act = TanhActivation(),
-                       bias_attr = True,
-                       name = 'inner_rnn_state_' + y.name)
+        inner_mem = memory(
+            name='inner_rnn_state_' + y.name,
+            size=hidden_dim,
+            boot_layer=outer_mem2)
+        out = fc_layer(
+            input=[y, inner_mem],
+            size=hidden_dim,
+            act=TanhActivation(),
+            bias_attr=True,
+            name='inner_rnn_state_' + y.name)
         return out
 
-    encoder1 = recurrent_group(
-        step = inner_step1,
-        name = 'inner1',
-        input = x1)
+    encoder1 = recurrent_group(step=inner_step1, name='inner1', input=x1)
 
-    encoder2 = recurrent_group(
-        step = inner_step2,
-        name = 'inner2',
-        input = x2)
+    encoder2 = recurrent_group(step=inner_step2, name='inner2', input=x2)
 
-    sentence_last_state1 = last_seq(input = encoder1, name = 'outer_rnn_state1')
-    sentence_last_state2_ = last_seq(input = encoder2, name = 'outer_rnn_state2')
+    sentence_last_state1 = last_seq(input=encoder1, name='outer_rnn_state1')
+    sentence_last_state2_ = last_seq(input=encoder2, name='outer_rnn_state2')
 
-    encoder1_expand = expand_layer(input = sentence_last_state1,
-                                   expand_as = encoder2)
+    encoder1_expand = expand_layer(
+        input=sentence_last_state1, expand_as=encoder2)
 
     return [encoder1_expand, encoder2]
 
@@ -88,19 +88,20 @@ encoder1_rep, encoder2_rep = recurrent_group(
     input=[SubsequenceInput(emb1), SubsequenceInput(emb2)],
     targetInlink=emb2)
 
-encoder1_last = last_seq(input = encoder1_rep)
-encoder1_expandlast = expand_layer(input = encoder1_last,
-                                   expand_as = encoder2_rep)
-context = mixed_layer(input = [identity_projection(encoder1_expandlast),
-                               identity_projection(encoder2_rep)],
-                      size = hidden_dim)
+encoder1_last = last_seq(input=encoder1_rep)
+encoder1_expandlast = expand_layer(input=encoder1_last, expand_as=encoder2_rep)
+context = mixed_layer(
+    input=[
+        identity_projection(encoder1_expandlast),
+        identity_projection(encoder2_rep)
+    ],
+    size=hidden_dim)
 
 rep = last_seq(input=context)
-prob = fc_layer(size=label_dim,
-                input=rep,
-                act=SoftmaxActivation(),
-                bias_attr=True)
-
-outputs(classification_cost(input=prob,
-                            label=data_layer(name="label", size=label_dim)))
+prob = fc_layer(
+    size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
 
+outputs(
+    classification_cost(
+        input=prob, label=data_layer(
+            name="label", size=label_dim)))

paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.conf → paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.py

@@ -16,12 +16,12 @@
 from paddle.trainer_config_helpers import *
 
 ######################## data source ################################
-define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/dummy.list',
     test_list=None,
     module='rnn_data_provider',
     obj='process_unequalength_seq')
 
-
 settings(batch_size=2, learning_rate=0.01)
 ######################## network configure ################################
 dict_dim = 10
@@ -38,38 +38,40 @@ emb2 = embedding_layer(input=speaker2, size=word_dim)
 # This hierachical RNN is designed to be equivalent to the RNN in
 # sequence_nest_rnn_multi_unequalength_inputs.conf
 
+
 def step(x1, x2):
     def calrnn(y):
-		mem = memory(name = 'rnn_state_' + y.name, size = hidden_dim)
-		out = fc_layer(input = [y, mem],
-					   size = hidden_dim,
-					   act = TanhActivation(),
-					   bias_attr = True,
-					   name = 'rnn_state_' + y.name)
+        mem = memory(name='rnn_state_' + y.name, size=hidden_dim)
+        out = fc_layer(
+            input=[y, mem],
+            size=hidden_dim,
+            act=TanhActivation(),
+            bias_attr=True,
+            name='rnn_state_' + y.name)
         return out
 
     encoder1 = calrnn(x1)
     encoder2 = calrnn(x2)
     return [encoder1, encoder2]
 
+
 encoder1_rep, encoder2_rep = recurrent_group(
-    name="stepout",
-    step=step,
-    input=[emb1, emb2])
+    name="stepout", step=step, input=[emb1, emb2])
 
-encoder1_last = last_seq(input = encoder1_rep)
-encoder1_expandlast = expand_layer(input = encoder1_last,
-                                   expand_as = encoder2_rep)
-context = mixed_layer(input = [identity_projection(encoder1_expandlast),
-                               identity_projection(encoder2_rep)],
-                      size = hidden_dim)
+encoder1_last = last_seq(input=encoder1_rep)
+encoder1_expandlast = expand_layer(input=encoder1_last, expand_as=encoder2_rep)
+context = mixed_layer(
+    input=[
+        identity_projection(encoder1_expandlast),
+        identity_projection(encoder2_rep)
+    ],
+    size=hidden_dim)
 
 rep = last_seq(input=context)
-prob = fc_layer(size=label_dim,
-                input=rep,
-                act=SoftmaxActivation(),
-                bias_attr=True)
-
-outputs(classification_cost(input=prob,
-                            label=data_layer(name="label", size=label_dim)))
+prob = fc_layer(
+    size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
 
+outputs(
+    classification_cost(
+        input=prob, label=data_layer(
+            name="label", size=label_dim)))

paddle/gserver/tests/test_RecurrentGradientMachine.cpp

@@ -13,12 +13,12 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <gtest/gtest.h>
-#include <paddle/utils/Util.h>
-#include <paddle/utils/Version.h>
-#include <paddle/utils/PythonUtil.h>
+#include <paddle/gserver/gradientmachines/GradientMachine.h>
 #include <paddle/trainer/Trainer.h>
 #include <paddle/trainer/TrainerInternal.h>
-#include <paddle/gserver/gradientmachines/GradientMachine.h>
+#include <paddle/utils/PythonUtil.h>
+#include <paddle/utils/Util.h>
+#include <paddle/utils/Version.h>
 
 P_DECLARE_int32(seed);
 
@@ -45,10 +45,9 @@ public:
     auto p = const_cast<TrainerForTest*>(this);
     auto& params = p->getGradientMachine()->getParameters();
     return std::accumulate(
-        params.begin(),
-        params.end(),
-        0UL,
-        [](size_t a, const ParameterPtr& p) { return a + p->getSize(); });
+        params.begin(), params.end(), 0UL, [](size_t a, const ParameterPtr& p) {
+          return a + p->getSize();
+        });
   }
 };
 
@@ -148,8 +147,8 @@ TEST(RecurrentGradientMachine, rnn_multi_input) {
 
 TEST(RecurrentGradientMachine, rnn_multi_unequalength_input) {
   for (bool useGpu : {false, true}) {
-    test("gserver/tests/sequence_rnn_multi_unequalength_inputs.conf",
-         "gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf",
+    test("gserver/tests/sequence_rnn_multi_unequalength_inputs.py",
+         "gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py",
          1e-6,
          useGpu);
   }