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AI-MXNet-1.6
River stage one • 2 direct dependents • 2 total dependents
/ AI::MXNet::Gluon::RNN::RNN

NAME


            

              
              
AI::MXNet::Gluon::RNN::RNN

DESCRIPTION


            

              
              
Applies a multi-layer Elman RNN with `tanh` or `ReLU` non-linearity to an input sequence.
For each element in the input sequence, each layer computes the following
function:
.. math::
    h_t = \tanh(w_{ih} * x_t + b_{ih}  +  w_{hh} * h_{(t-1)} + b_{hh})
where :math:`h_t` is the hidden state at time `t`, and :math:`x_t` is the hidden
state of the previous layer at time `t` or :math:`input_t` for the first layer.
If nonlinearity='relu', then `ReLU` is used instead of `tanh`.
Parameters
----------
hidden_size: int
    The number of features in the hidden state h.
num_layers: int, default 1
    Number of recurrent layers.
activation: {'relu' or 'tanh'}, default 'tanh'
    The activation function to use.
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
    Initializer for the bias vector.
h2h_bias_initializer : str or Initializer
    Initializer for the bias vector.
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`.
Input shapes:
    The input shape depends on `layout`. For `layout='TNC'`, the
    input has shape `(sequence_length, batch_size, input_size)`
Output shape:
    The output shape depends on `layout`. For `layout='TNC'`, the
    output has shape `(sequence_length, batch_size, num_hidden)`.
    If `bidirectional` is True, output shape will instead be
    `(sequence_length, batch_size, 2*num_hidden)`
Recurrent state:
    The recurrent state is an NDArray with shape `(num_layers, batch_size, num_hidden)`.
    If `bidirectional` is True, the recurrent state shape will instead be
    `(2*num_layers, batch_size, num_hidden)`
    If input recurrent state is None, zeros are used as default begin states,
    and the output recurrent state is omitted.
Examples
--------
>>> layer = mx.gluon.rnn.RNN(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))
>>> output, hn = layer(input, h0)

NANE


            

              
              
AI::MXNet::Gluon::RNN::LSTM

DESCRIPTION


            

              
              
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:
.. math::
    \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}
where :math:`h_t` is the hidden state at time `t`, :math:`c_t` is the
cell state at time `t`, :math:`x_t` is the hidden state of the previous
layer at time `t` or :math:`input_t` for the first layer, and :math:`i_t`,
:math:`f_t`, :math:`g_t`, :math:`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.
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`.
Input shapes:
    The input shape depends on `layout`. For `layout='TNC'`, the
    input has shape `(sequence_length, batch_size, input_size)`
Output shape:
    The output shape depends on `layout`. For `layout='TNC'`, the
    output has shape `(sequence_length, batch_size, num_hidden)`.
    If `bidirectional` is True, output shape will instead be
    `(sequence_length, batch_size, 2*num_hidden)`
Recurrent state:
    The recurrent state is a list of two NDArrays. Both has shape
    `(num_layers, batch_size, num_hidden)`.
    If `bidirectional` is True, each recurrent state will instead have shape
    `(2*num_layers, batch_size, num_hidden)`.
    If input recurrent state is None, zeros are used as default begin states,
    and the output recurrent state is omitted.
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])

NANE


            

              
              
AI::MXNet::Gluon::RNN::GRU

DESCRIPTION


            

              
              
Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
For each element in the input sequence, each layer computes the following
function:
.. math::
    \begin{array}{ll}
    r_t = sigmoid(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
    i_t = sigmoid(W_{ii} x_t + b_{ii} + W_hi h_{(t-1)} + b_{hi}) \\
    n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\
    h_t = (1 - i_t) * n_t + i_t * h_{(t-1)} \\
    \end{array}
where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the hidden
state of the previous layer at time `t` or :math:`input_t` for the first layer,
and :math:`r_t`, :math:`i_t`, :math:`n_t` are the reset, input, and new 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
    Initializer for the bias vector.
h2h_bias_initializer : str or Initializer
    Initializer for the bias vector.
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`.
Input shapes:
    The input shape depends on `layout`. For `layout='TNC'`, the
    input has shape `(sequence_length, batch_size, input_size)`
Output shape:
    The output shape depends on `layout`. For `layout='TNC'`, the
    output has shape `(sequence_length, batch_size, num_hidden)`.
    If `bidirectional` is True, output shape will instead be
    `(sequence_length, batch_size, 2*num_hidden)`
Recurrent state:
    The recurrent state is an NDArray with shape `(num_layers, batch_size, num_hidden)`.
    If `bidirectional` is True, the recurrent state shape will instead be
    `(2*num_layers, batch_size, num_hidden)`
    If input recurrent state is None, zeros are used as default begin states,
    and the output recurrent state is omitted.
Examples
--------
>>> layer = mx.gluon.rnn.GRU(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))
>>> output, hn = layer(input, h0)