Table Of Contents
Table Of Contents

Conv1DGRUCell

class mxnet.gluon.contrib.rnn.Conv1DGRUCell(input_shape, hidden_channels, i2h_kernel, h2h_kernel, i2h_pad=(0, ), i2h_dilate=(1, ), h2h_dilate=(1, ), i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', conv_layout='NCW', activation='tanh', prefix=None, params=None)[source]

1D Convolutional Gated Rectified Unit (GRU) network cell.

\[\begin{split}\begin{array}{ll} r_t = \sigma(W_r \ast x_t + R_r \ast h_{t-1} + b_r) \\ z_t = \sigma(W_z \ast x_t + R_z \ast h_{t-1} + b_z) \\ n_t = tanh(W_i \ast x_t + b_i + r_t \circ (R_n \ast h_{t-1} + b_n)) \\ h^\prime_t = (1 - z_t) \circ n_t + z_t \circ h \\ \end{array}\end{split}\]
Parameters:
  • input_shape (tuple of int) – Input tensor shape at each time step for each sample, excluding dimension of the batch size and sequence length. Must be consistent with conv_layout. For example, for layout ‘NCW’ the shape should be (C, W).
  • hidden_channels (int) – Number of output channels.
  • i2h_kernel (int or tuple of int) – Input convolution kernel sizes.
  • h2h_kernel (int or tuple of int) – Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.
  • i2h_pad (int or tuple of int, default (0,)) – Pad for input convolution.
  • i2h_dilate (int or tuple of int, default (1,)) – Input convolution dilate.
  • h2h_dilate (int or tuple of int, default (1,)) – Recurrent convolution dilate.
  • i2h_weight_initializer (str or Initializer) – Initializer for the input weights matrix, used for the input convolutions.
  • h2h_weight_initializer (str or Initializer) – Initializer for the recurrent weights matrix, used for the input convolutions.
  • i2h_bias_initializer (str or Initializer, default zeros) – Initializer for the input convolution bias vectors.
  • h2h_bias_initializer (str or Initializer, default zeros) – Initializer for the recurrent convolution bias vectors.
  • conv_layout (str, default 'NCW') – Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.
  • activation (str or Block, default 'tanh') – Type of activation function used in n_t. If argument type is string, it’s equivalent to nn.Activation(act_type=str). See Activation() for available choices. Alternatively, other activation blocks such as nn.LeakyReLU can be used.
  • prefix (str, default ‘conv_gru_’) – Prefix for name of layers (and name of weight if params is None).
  • params (RNNParams, default None) – Container for weight sharing between cells. Created if None.
__init__(input_shape, hidden_channels, i2h_kernel, h2h_kernel, i2h_pad=(0, ), i2h_dilate=(1, ), h2h_dilate=(1, ), i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', conv_layout='NCW', activation='tanh', prefix=None, params=None)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(input_shape, hidden_channels, …) Initialize self.
apply(fn) Applies fn recursively to every child block as well as self.
begin_state([batch_size, 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.