mxnet.gluon.rnn.LSTMCell¶

class mxnet.gluon.rnn.LSTMCell(hidden_size, i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', input_size=0, prefix=None, params=None, activation='tanh', recurrent_activation='sigmoid')[source]¶

Long-Short Term Memory (LSTM) network cell.

Each call computes the following function:

\[\begin{split}\begin{array}{ll} i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\ f_t = sigmoid(W_{if} x_t + b_{if} + W_{hf} h_{(t-1)} + b_{hf}) \\ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hc} h_{(t-1)} + b_{hg}) \\ o_t = sigmoid(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\ c_t = f_t * c_{(t-1)} + i_t * g_t \\ h_t = o_t * \tanh(c_t) \end{array}\end{split}\]

where \(h_t\) is the hidden state at time t, \(c_t\) is the cell state at time t, \(x_t\) is the hidden state of the previous layer at time t or \(input_t\) for the first layer, and \(i_t\), \(f_t\), \(g_t\), \(o_t\) are the input, forget, cell, and out gates, respectively.

Parameters

hidden_size (int) – Number of units in output symbol.
i2h_weight_initializer (str or Initializer) – Initializer for the input weights matrix, used for the linear transformation of the inputs.
h2h_weight_initializer (str or Initializer) – Initializer for the recurrent weights matrix, used for the linear transformation of the recurrent state.
i2h_bias_initializer (str or Initializer, default 'zeros') – Initializer for the bias vector.
h2h_bias_initializer (str or Initializer, default 'zeros') – Initializer for the bias vector.
prefix (str, default 'lstm_') – Prefix for name of Block`s (and name of weight if params is `None).
params (Parameter or None, default None) – Container for weight sharing between cells. Created if None.
activation (str, default 'tanh') – Activation type to use. See nd/symbol Activation for supported types.
recurrent_activation (str, default 'sigmoid') – Activation type to use for the recurrent step. See nd/symbol Activation for supported types.
Inputs –
- data: input tensor with shape (batch_size, input_size).
- states: a list of two initial recurrent state tensors. Each has shape (batch_size, num_hidden).
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, i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', input_size=0, prefix=None, params=None, activation='tanh', recurrent_activation='sigmoid')[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(hidden_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`.