Table Of Contents
Table Of Contents

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,

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.