Table Of Contents
Table Of Contents

LSTMPCell

class mxnet.gluon.contrib.rnn.LSTMPCell(hidden_size, projection_size, i2h_weight_initializer=None, h2h_weight_initializer=None, h2r_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', input_size=0, prefix=None, params=None)[source]

Long-Short Term Memory Projected (LSTMP) network cell. (https://arxiv.org/abs/1402.1128) Each call computes the following function: .. math:

\begin{array}{ll}
i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{ri} r_{(t-1)} + b_{ri}) \\
f_t = sigmoid(W_{if} x_t + b_{if} + W_{rf} r_{(t-1)} + b_{rf}) \\
g_t = \tanh(W_{ig} x_t + b_{ig} + W_{rc} r_{(t-1)} + b_{rg}}) \\
o_t = sigmoid(W_{io} x_t + b_{io} + W_{ro} r_{(t-1)} + b_{ro}) \\
c_t = f_t * c_{(t-1)} + i_t * g_t \\
h_t = o_t * \tanh(c_t) \\
r_t = W_{hr} h_t
\end{array}

where \(r_t\) is the projected recurrent activation at time t, math:h_t is the hidden state at time t, \(c_t\) is the cell state at time t, \(x_t\) is the input at time t, and \(i_t\), \(f_t\), \(g_t\), \(o_t\) are the input, forget, cell, and out gates, respectively. :param hidden_size: Number of units in cell state symbol. :type hidden_size: int :param projection_size: Number of units in output symbol. :type projection_size: int :param i2h_weight_initializer: Initializer for the input weights matrix, used for the linear

transformation of the inputs.
Parameters:
  • h2h_weight_initializer (str or Initializer) – Initializer for the recurrent weights matrix, used for the linear transformation of the hidden state.
  • h2r_weight_initializer (str or Initializer) – Initializer for the projection weights matrix, used for the linear transformation of the recurrent state.
  • i2h_bias_initializer (str or Initializer, default 'lstmbias') – Initializer for the bias vector. By default, bias for the forget gate is initialized to 1 while all other biases are initialized to zero.
  • h2h_bias_initializer (str or Initializer) – Initializer for the bias vector.
  • prefix (str, default ‘lstmp_’) – Prefix for name of Block`s (and name of weight if params is `None).
  • params (Parameter or None) – Container for weight sharing between cells. Created if None.
  • Inputs
    • data: input tensor with shape (batch_size, input_size).
    • states: a list of two initial recurrent state tensors, with shape (batch_size, projection_size) and (batch_size, hidden_size) respectively.
  • Outputs
    • out: output tensor with shape (batch_size, num_hidden).
    • next_states: a list of two output recurrent state tensors. Each has the same shape as states.
__init__(hidden_size, projection_size, i2h_weight_initializer=None, h2h_weight_initializer=None, h2r_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', input_size=0, prefix=None, params=None)[source]

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

Methods

__init__(hidden_size, projection_size[, …]) 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.