LSTM¶

class LSTM(hidden_size, num_layers=1, layout='TNC', dropout=0, bidirectional=False, input_size=0, i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', projection_size=None, h2r_weight_initializer=None, state_clip_min=None, state_clip_max=None, state_clip_nan=False, dtype='float32', **kwargs)¶

Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.

For each element in the input sequence, each layer computes the following function:

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

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

The number of features in the hidden state h.

num_layers: int, default 1

Number of recurrent layers.

layout : str, default ‘TNC’

The format of input and output tensors. T, N and C stand for sequence length, batch size, and feature dimensions respectively.

dropout: float, default 0

If non-zero, introduces a dropout layer on the outputs of each RNN layer except the last layer.

bidirectional: bool, default False

If True, becomes a bidirectional RNN.

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 ‘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.

projection_size: int, default None

The number of features after projection.

h2r_weight_initializer : str or Initializer, default None

Initializer for the projected recurrent weights matrix, used for the linear transformation of the recurrent state to the projected space.

state_clip_min : float or None, default None

Minimum clip value of LSTM states. This option must be used together with state_clip_max. If None, clipping is not applied.

state_clip_max : float or None, default None

Maximum clip value of LSTM states. This option must be used together with state_clip_min. If None, clipping is not applied.

state_clip_nan : boolean, default False

Whether to stop NaN from propagating in state by clipping it to min/max. If the clipping range is not specified, this option is ignored.

dtype : str, default ‘float32’

Type to initialize the parameters and default states to

input_size: int, default 0

The number of expected features in the input x. If not specified, it will be inferred from input.

prefix : str or None

Prefix of this Block.

params : ParameterDict or None

Shared Parameters for this Block.

Inputs:

data: input tensor with shape (sequence_length, batch_size, input_size) when layout is “TNC”. For other layouts, dimensions are permuted accordingly using transpose() operator which adds performance overhead. Consider creating batches in TNC layout during data batching step.
states: a list of two initial recurrent state tensors. Each has shape (num_layers, batch_size, num_hidden). If bidirectional is True, shape will instead be (2*num_layers, batch_size, num_hidden). If states is None, zeros will be used as default begin states.

Outputs:

out: output tensor with shape (sequence_length, batch_size, num_hidden) when layout is “TNC”. If bidirectional is True, output shape will instead be (sequence_length, batch_size, 2*num_hidden)
out_states: a list of two output recurrent state tensors with the same shape as in states. If states is None out_states will not be returned.

Examples

>>> layer = mx.gluon.rnn.LSTM(100, 3)
>>> layer.initialize()
>>> input = mx.nd.random.uniform(shape=(5, 3, 10))
>>> # by default zeros are used as begin state
>>> output = layer(input)
>>> # manually specify begin state.
>>> h0 = mx.nd.random.uniform(shape=(3, 3, 100))
>>> c0 = mx.nd.random.uniform(shape=(3, 3, 100))
>>> output, hn = layer(input, [h0, c0])