Module Expr Str ImportFrom alias ImportFrom alias ImportFrom alias alias ImportFrom alias ImportFrom alias alias alias ImportFrom alias ImportFrom alias ImportFrom alias alias alias ImportFrom alias ImportFrom alias alias Assign NameStore Call NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr keyword NameLoad Assign AttributeStore NameLoad attr Num Assign NameStore Num Assign NameStore Num Assign NameStore NameLoad Assign NameStore Call NameLoad Call NameLoad NameLoad NameLoad Assign NameStore Call NameLoad keyword AttributeLoad NameLoad attr Assign NameStore Call NameLoad keyword AttributeLoad NameLoad attr Assign NameStore Call NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad keyword AttributeLoad NameLoad attr Assign NameStore Call NameLoad keyword Num keyword Num Assign NameStore ListLoad Call NameLoad NameLoad NameLoad keyword Call NameLoad Call NameLoad Call NameLoad AttributeLoad NameLoad attr NameLoad keyword NameLoad keyword Call NameLoad Assign NameStore Call NameLoad keyword Call NameLoad keyword NameLoad Assign NameStore Call NameLoad keyword NameLoad Assign NameStore Call NameLoad keyword NameLoad keyword AttributeLoad NameLoad attr Assign NameStore Call NameLoad NameLoad keyword NameLoad AttributeLoad NameLoad attr Expr Call AttributeLoad NameLoad attr NameLoad keyword NameLoad keyword AttributeLoad NameLoad attr keyword NameLoad keyword NameLoad
N/A N/A 
Example that trains a network with one recurrent layer of tanh units.
The dataset uses Penn treebank data parsing on character-level.

Reference:
  Advances in optimizing recurrent networks `[Pascanu2012]`_
.. _[Pascanu2012]: http://arxiv.org/pdf/1212.0901.pdf
 neon.backends gen_backend neon.data.text Text neon.data.dataloaders load_ptb_train load_ptb_test neon.initializers Uniform neon.layers GeneralizedCost Affine Recurrent neon.models Model neon.optimizers RMSProp neon.transforms Tanh Softmax CrossEntropyMulti neon.callbacks.callbacks Callbacks neon.util.argparser NeonArgparser extract_valid_args N/A parser N/A NeonArgparser __doc__ N/A args N/A N/A parser parse_args gen_be False N/A N/A args batch_size 50 N/A time_steps 150 N/A hidden_size 500 N/A gradient_clip_value None N/A be N/A gen_backend N/A extract_valid_args args gen_backend N/A train_path N/A load_ptb_train path N/A args data_dir N/A valid_path N/A load_ptb_test path N/A args data_dir N/A train_set N/A Text time_steps train_path N/A valid_set N/A Text time_steps valid_path vocab N/A train_set vocab N/A init N/A Uniform low -0.08 high 0.08 N/A layers N/A N/A Recurrent hidden_size init activation N/A Tanh N/A Affine N/A len N/A train_set vocab init bias init activation N/A Softmax N/A cost N/A GeneralizedCost costfunc N/A CrossEntropyMulti usebits True N/A model N/A Model layers layers N/A optimizer N/A RMSProp gradient_clip_value gradient_clip_value stochastic_round N/A args rounding N/A callbacks N/A Callbacks model eval_set valid_set N/A args callback_args N/A N/A N/A model fit train_set optimizer optimizer num_epochs N/A args epochs cost cost callbacks callbacks