mxnet.gluon.contrib.rnn.VariationalDropoutCell¶

class mxnet.gluon.contrib.rnn.VariationalDropoutCell(base_cell, drop_inputs=0.0, drop_states=0.0, drop_outputs=0.0)[source]¶

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.

__init__(base_cell, drop_inputs=0.0, drop_states=0.0, drop_outputs=0.0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(base_cell[, drop_inputs, …])	Initialize self.
`apply`(fn)	Applies `fn` recursively to every child block as well as self.
`begin_state`([func])	Initial state for this cell.
`cast`(dtype)	Cast this Block to use another data type.
`collect_params`([select])	Returns a `ParameterDict` containing this `Block` and all of its children’s Parameters(default), also can returns the select `ParameterDict` which match some given regular expressions.
`export`(path[, epoch])	Export HybridBlock to json format that can be loaded by SymbolBlock.imports, mxnet.mod.Module or the C++ interface.
`forward`(inputs, states)	Unrolls the recurrent cell for one time step.
`hybrid_forward`(F, inputs, states)	Overrides to construct symbolic graph for this Block.
`hybridize`([active])	Activates or deactivates `HybridBlock` s recursively.
`infer_shape`(*args)	Infers shape of Parameters from inputs.
`infer_type`(*args)	Infers data type of Parameters from inputs.
`initialize`([init, ctx, verbose, force_reinit])	Initializes `Parameter` s of this `Block` and its children.
`load_parameters`(filename[, ctx, …])	Load parameters from file previously saved by save_parameters.
`load_params`(filename[, ctx, allow_missing, …])	[Deprecated] Please use load_parameters.
`name_scope`()	Returns a name space object managing a child `Block` and parameter names.
`register_child`(block[, name])	Registers block as a child of self.
`register_forward_hook`(hook)	Registers a forward hook on the block.
`register_forward_pre_hook`(hook)	Registers a forward pre-hook on the block.
`reset`()	Reset before re-using the cell for another graph.
`save_parameters`(filename)	Save parameters to file.
`save_params`(filename)	[Deprecated] Please use save_parameters.
`state_info`([batch_size])	shape and layout information of states
`summary`(*inputs)	Print the summary of the model’s output and parameters.
`unroll`(length, inputs[, begin_state, …])	Unrolls an RNN cell across time steps.

Attributes

`name`	Name of this `Block`, without ‘_’ in the end.
`params`	Returns this `Block`’s parameter dictionary (does not include its children’s parameters).
`prefix`	Prefix of this `Block`.