Documentation
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Index ¶
- Constants
- func Softmax(k tf32.Continuation, node int, a *tf32.V, options ...map[string]interface{}) bool
- func SphericalSoftmax(k tf32.Continuation, node int, a *tf32.V, options ...map[string]interface{}) bool
- type Entropy
- type Network
- func (n *Network) Analyzer(in []iris.Iris)
- func (n *Network) GetEntropy(inputs []iris.Iris) []Entropy
- func (n *Network) GetGradients(inputs []iris.Iris) [][]float32
- func (n *Network) GetVectors(inputs []iris.Iris) []iris.Iris
- func (n *Network) GetVectors2(inputs []iris.Iris) []iris.Iris
- func (n *Network) Iterate(data []float64) float32
Constants ¶
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const ( // B1 exponential decay of the rate for the first moment estimates B1 = 0.9 // B2 exponential decay rate for the second-moment estimates B2 = 0.999 // S is the scaling factor for the softmax S = 1.0 - 1e-300 // Eta is the learning rate Eta = .001 )
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const ( // StateM is the state for the mean StateM = iota // StateV is the state for the variance StateV // StateTotal is the total number of states StateTotal )
Variables ¶
This section is empty.
Functions ¶
func SphericalSoftmax ¶
func SphericalSoftmax(k tf32.Continuation, node int, a *tf32.V, options ...map[string]interface{}) bool
SphericalSoftmax is the spherical softmax function https://arxiv.org/abs/1511.05042
Types ¶
type Entropy ¶
type Entropy struct {
Entropy float32
Label string
Measures []float64
Index int
Order int
Optimized float32
}
Entropy is the self entropy of a point
type Network ¶
type Network struct {
Rnd *rand.Rand
Width int
Length int
Set tf32.Set
Others tf32.Set
Input *tf32.V
Point *tf32.V
L1 tf32.Meta
L2 tf32.Meta
Cost tf32.Meta
I int
Points plotter.XYs
}
Network is a clustering neural network
func (*Network) GetEntropy ¶
GetEntropy returns the entropy of the network
func (*Network) GetGradients ¶
GetGradients returns the gradients of the network
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