README
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This lib is the engine of the LSTM
Documentation
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Overview ¶
Package lstm is a basic implementation of this kind of recural network.
WARNING: This is only a skeleton of a new API for a LSTM implementation with Gorgonia. Nothing is implemented at the present time and the API may change. There should be a working version with a less complete API working on the master branch of the project
Index ¶
- type Model
- func (m Model) MarshalBinary() ([]byte, error)
- func (m *Model) Predict(ctx context.Context, dataSet datasetter.ReadWriter) error
- func (m *Model) Train(ctx context.Context, dset datasetter.FullTrainer, solver G.Solver, ...) (<-chan TrainingInfos, <-chan error)
- func (m *Model) UnmarshalBinary(data []byte) error
- type TrainingInfos
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type Model ¶
type Model struct {
// contains filtered or unexported fields
}
Model holds the structure of the LSTM model
func NewModel ¶
NewModel creates a new model from a string. the strings represent a forward pass as well as the definitions of the weights expressed in unicode. The hidden vector must be called `h` and the memory cell `c` The input vector and output vector must be called `xₜ` and `yₜ` ex:
definitions := `
iₜ=(Wᵢ·xₜ+Uᵢ·hₜ₋₁+Bᵢ)
fₜ=σ(Wf·xₜ+Uf·hₜ₋₁+Bf)
oₜ=σ(Wₒ·xₜ+Uₒ·hₜ₋₁+Bₒ)
ĉₜ=tanh(Wc·xₜ+Uc·hₜ₋₁+Bc)
cₜ=fₜ*cₜ₋₁+iₜ*ĉₜ
hₜ=oₜ*tanh(cₜ)
y=Wy·hₜ+By
xₜ∈R⁶⁵
fₜ∈R¹⁰⁰
iₜ∈R¹⁰⁰
oₜ∈R¹⁰⁰
hₜ∈R¹⁰⁰
cₜ∈R¹⁰⁰
Wᵢ∈R¹⁰⁰x⁶⁵
Uᵢ∈R¹⁰⁰x¹⁰⁰
Bᵢ∈R¹⁰⁰
Wₒ∈R¹⁰⁰x⁶⁵
Uₒ∈R¹⁰⁰x¹⁰⁰
Bₒ∈R¹⁰⁰
Wf∈R¹⁰⁰x⁶⁵
Uf∈R¹⁰⁰x¹⁰⁰
Bf∈R¹⁰⁰
Wc∈R¹⁰⁰x⁶⁵
Uc∈R¹⁰⁰x¹⁰⁰
Bc∈R¹⁰⁰
`
The subscript 'ₜ' will be replaces at runtime by a number corresponding to the step
func (Model) MarshalBinary ¶
MarshalBinary for backup. This function saves the equations, the content of the weights matrices and the biais but not the graph structure
func (*Model) Predict ¶
func (m *Model) Predict(ctx context.Context, dataSet datasetter.ReadWriter) error
Predict ...
func (*Model) Train ¶
func (m *Model) Train(ctx context.Context, dset datasetter.FullTrainer, solver G.Solver, pauseChan <-chan struct{}) (<-chan TrainingInfos, <-chan error)
Train the model
func (*Model) UnmarshalBinary ¶
UnmarshalBinary for restore
type TrainingInfos ¶
TrainingInfos returns info about the current training process
Source Files
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Directories
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| Path | Synopsis |
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this reads the stdin until EOF and output a list of all characters used
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this reads the stdin until EOF and output a list of all characters used |
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src
Expr is a simple expression evaluator that serves as a working example of how to use Go's yacc implementation.
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Expr is a simple expression evaluator that serves as a working example of how to use Go's yacc implementation. |