add python wrap for sequence_first/last_step

python/paddle/v2/fluid/layers/nn.py

@@ -13,7 +13,7 @@ __all__ = [
     'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
     'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
     'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
-    'lstm_unit', 'reduce_sum'
+    'lstm_unit', 'reduce_sum', 'sequence_first_step', 'sequence_last_step'
 ]
 
 
@@ -583,6 +583,14 @@ def sequence_pool(input, pool_type, **kwargs):
     return pool_out
 
 
+def sequence_first_step(input, **kwargs):
+    return sequence_pool(input=input, pool_type="first")
+
+
+def sequence_last_step(input, **kwargs):
+    return sequence_pool(input=input, pool_type="last")
+
+
 def pool2d(input,
            pool_size,
            pool_type,

python/paddle/v2/fluid/tests/book/test_machine_translation.py

@@ -33,7 +33,7 @@ def encoder_decoder():
 
     fc1 = fluid.layers.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
     lstm_hidden0, lstm_0 = layers.dynamic_lstm(input=fc1, size=hidden_dim * 4)
-    encoder_out = layers.sequence_pool(input=lstm_hidden0, pool_type="last")
+    encoder_out = layers.sequence_last_step(input=lstm_hidden0)
 
     # decoder
     trg_language_word = layers.data(

python/paddle/v2/fluid/tests/test_dyn_rnn.py

@@ -63,8 +63,7 @@ class TestDynRNN(unittest.TestCase):
 
             all_timesteps = fluid.layers.array_to_lod_tensor(
                 x=out, table=rank_table)
-            last = fluid.layers.sequence_pool(
-                input=all_timesteps, pool_type='last')
+            last = fluid.layers.sequence_last_step(input=all_timesteps)
             logits = fluid.layers.fc(input=last, size=1, act=None)
             loss = fluid.layers.sigmoid_cross_entropy_with_logits(
                 x=logits, label=label)
@@ -101,7 +100,7 @@ class TestDynRNN(unittest.TestCase):
                 rnn.update_memory(mem, out_)
                 rnn.output(out_)
 
-            last = fluid.layers.sequence_pool(input=rnn(), pool_type='last')
+            last = fluid.layers.sequence_last_step(input=rnn())
             logits = fluid.layers.fc(input=last, size=1, act=None)
             label = fluid.layers.data(name='label', shape=[1], dtype='float32')
             loss = fluid.layers.sigmoid_cross_entropy_with_logits(