[docs] dynamic rnn params

Author: zsdonghao

tensorlayer/layers.py

@@ -3803,12 +3803,11 @@ class DynamicRNNLayer(Layer):
         The number of hidden units in the layer.
     initializer : initializer
         The initializer for initializing the parameters.
-    sequence_length : a tensor, array or None
-        The sequence length of each row of input data, see ``Advanced Ops for Dynamic RNN``.
-            - If None, it uses ``retrieve_seq_length_op`` to compute the sequence_length, i.e. when the features of padding (on right hand side) are all zeros.
-            - If using word embedding, you may need to compute the sequence_length from the ID array (the integer features before word embedding) by using ``retrieve_seq_length_op2`` or ``retrieve_seq_length_op``.
-            - You can also input an numpy array.
-            - More details about TensorFlow dynamic_rnn in `Wild-ML Blog <http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/>`_.
+    sequence_length : a tensor, array or None. The sequence length of each row of input data, see ``Advanced Ops for Dynamic RNN``.
+        - If None, it uses ``retrieve_seq_length_op`` to compute the sequence_length, i.e. when the features of padding (on right hand side) are all zeros.
+        - If using word embedding, you may need to compute the sequence_length from the ID array (the integer features before word embedding) by using ``retrieve_seq_length_op2`` or ``retrieve_seq_length_op``.
+        - You can also input an numpy array.
+        - More details about TensorFlow dynamic_rnn in `Wild-ML Blog <http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/>`_.
     initial_state : None or RNN State
         If None, initial_state is zero_state.
     dropout : `tuple` of `float`: (input_keep_prob, output_keep_prob).