reminisce commented on a change in pull request #7067: variational dropout cell
URL: https://github.com/apache/incubator-mxnet/pull/7067#discussion_r140419392
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File path: python/mxnet/gluon/rnn/rnn_cell.py
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@@ -738,6 +736,164 @@ def unroll(self, length, inputs, begin_state=None,
layout='NTC', merge_outputs=N
return outputs, states
+class VariationalDropoutCell(ModifierCell):
+ """
+ Applies Variational Dropout on base cell.
+ (https://arxiv.org/pdf/1512.05287.pdf,
+ https://www.stat.berkeley.edu/~tsmoon/files/Conference/asru2015.pdf).
+
+ Variational dropout uses the same dropout mask across time-steps. It can
be applied to RNN
+ inputs, outputs, and states. The masks for them are not shared.
+
+ The dropout mask is initialized when stepping forward for the first time
and will remain
+ the same until .reset() is called. Thus, if using the cell and stepping
manually without calling
+ .unroll(), the .reset() should be called after each sequence.
+
+ Parameters
+ ----------
+ base_cell : RecurrentCell
+ The cell on which to perform variational dropout.
+ drop_inputs : float, default 0.
+ The dropout rate for inputs. Won't apply dropout if it equals 0.
+ drop_states : float, default 0.
+ The dropout rate for state inputs on the first state channel.
+ Won't apply dropout if it equals 0.
+ drop_outputs : float, default 0.
+ The dropout rate for outputs. Won't apply dropout if it equals 0.
+ """
+ def __init__(self, base_cell, drop_inputs=0., drop_states=0.,
drop_outputs=0.):
+ assert not isinstance(base_cell, BidirectionalCell), \
+ "BidirectionalCell doesn't support vardrop since it doesn't
support step. " \
+ "Please add VariationalDropoutCell to the cells underneath
instead."
+ assert not isinstance(base_cell, SequentialRNNCell) or not
base_cell._bidirectional, \
+ "Bidirectional SequentialRNNCell doesn't support vardrop. " \
+ "Please add VariationalDropoutCell to the cells underneath
instead."
+ super(VariationalDropoutCell, self).__init__(base_cell)
+ self.drop_inputs = drop_inputs
+ self.drop_states = drop_states
+ self.drop_outputs = drop_outputs
+ self.drop_inputs_mask = None
+ self.drop_states_mask = None
+ self.drop_outputs_mask = None
+
+ def _alias(self):
+ return 'vardrop'
+
+ def reset(self):
+ super(VariationalDropoutCell, self).reset()
+ self.drop_inputs_mask = None
+ self.drop_states_mask = None
+ self.drop_outputs_mask = None
+
+ def _initialize_input_masks(self, F, inputs, states):
+ if self.drop_states and self.drop_states_mask is None:
+ self.drop_states_mask = F.Dropout(F.ones_like(states[0]),
+ p=self.drop_states)
+
+ if self.drop_inputs and self.drop_inputs_mask is None:
+ self.drop_inputs_mask = F.Dropout(F.ones_like(inputs),
+ p=self.drop_inputs)
+
+ def _initialize_output_mask(self, F, output):
+ if self.drop_outputs and self.drop_outputs_mask is None:
+ self.drop_outputs_mask = F.Dropout(F.ones_like(output),
+ p=self.drop_outputs)
+
+
+ def hybrid_forward(self, F, inputs, states):
Review comment:
Since this is exposed to users, it would be very helpful to add
documentation here like you did for function unroll below.
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