Documentation ¶
Overview ¶
Package mist provides an implementation of the MIST (MIxed hiSTory) recurrent network as described in "Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies" by Di Pietro et al., 2018 (https://arxiv.org/pdf/1702.07805.pdf).
Index ¶
Constants ¶
This section is empty.
Variables ¶
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Functions ¶
This section is empty.
Types ¶
type Model ¶
type Model struct { nn.BaseModel Wx nn.Param `spago:"type:weights"` Wh nn.Param `spago:"type:weights"` B nn.Param `spago:"type:biases"` Wax nn.Param `spago:"type:weights"` Wah nn.Param `spago:"type:weights"` Ba nn.Param `spago:"type:biases"` Wrx nn.Param `spago:"type:weights"` Wrh nn.Param `spago:"type:weights"` Br nn.Param `spago:"type:biases"` NumOfDelays int States []*State `spago:"scope:processor"` }
Model contains the serializable parameters.
func (*Model) Forward ¶
Forward performs the forward step for each input node and returns the result.
func (*Model) LastState ¶
LastState returns the last state of the recurrent network. It returns nil if there are no states.
func (*Model) SetInitialState ¶
SetInitialState sets the initial state of the recurrent network. It panics if one or more states are already present.
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