remove mx.rnn APIs

python/mxnet/__init__.py

@@ -84,7 +84,6 @@
 
 from . import test_utils
 
-from . import rnn
 from . import gluon
 
 from . import _deferred_compute

python/mxnet/initializer.py

@@ -713,57 +713,3 @@ def _init_weight(self, name, arr):
         # gate of the 4 LSTM gates, we modify the according values.
         num_hidden = int(arr.shape[0] / 4)
         arr[num_hidden:2*num_hidden] = self.forget_bias
-
-
-@register
-class FusedRNN(Initializer):
-    """Initialize parameters for fused rnn layers.
-
-    Parameters
-    ----------
-    init : Initializer
-        initializer applied to unpacked weights. Fall back to global
-        initializer if None.
-    num_hidden : int
-        should be the same with arguments passed to FusedRNNCell.
-    num_layers : int
-        should be the same with arguments passed to FusedRNNCell.
-    mode : str
-        should be the same with arguments passed to FusedRNNCell.
-    bidirectional : bool
-        should be the same with arguments passed to FusedRNNCell.
-    forget_bias : float
-        should be the same with arguments passed to FusedRNNCell.
-    """
-    def __init__(self, init, num_hidden, num_layers, mode, bidirectional=False, forget_bias=1.0):
-        if isinstance(init, string_types):
-            klass, kwargs = json.loads(init)
-            init = registry._REGISTRY[klass.lower()](**kwargs)
-        super(FusedRNN, self).__init__(init=init.dumps() if init is not None else None,
-                                       num_hidden=num_hidden, num_layers=num_layers, mode=mode,
-                                       bidirectional=bidirectional, forget_bias=forget_bias)
-        self._init = init
-        self._num_hidden = num_hidden
-        self._num_layers = num_layers
-        self._mode = mode
-        self._bidirectional = bidirectional
-        self._forget_bias = forget_bias
-
-    def _init_weight(self, desc, arr): # pylint: disable=arguments-differ
-        from .rnn import rnn_cell
-        cell = rnn_cell.FusedRNNCell(self._num_hidden, self._num_layers,
-                                     self._mode, self._bidirectional,
-                                     forget_bias=self._forget_bias, prefix='')
-        args = cell.unpack_weights({'parameters': arr})
-        for name in args:
-            arg_desc = InitDesc(name, global_init=desc.global_init)
-            # for lstm bias, we use a custom initializer
-            # which adds a bias to the forget gate
-            if self._mode == 'lstm' and name.endswith("_f_bias"):
-                args[name][:] = self._forget_bias
-            elif self._init is None:
-                desc.global_init(arg_desc, args[name])
-            else:
-                self._init(arg_desc, args[name])
-
-        arr[:] = cell.pack_weights(args)['parameters']