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activation.go
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activation.go
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package layer
import (
"fmt"
"github.com/aunum/log"
"github.com/pkg/errors"
g "gorgonia.org/gorgonia"
t "gorgonia.org/tensor"
)
// ActivationFn is an activation function.
type ActivationFn interface {
// Fwd is a forward pass through x.
Fwd(x *g.Node) (*g.Node, error)
// Clone the activation.
Clone() ActivationFn
}
// SigmoidActivation is a sigmoid activation layer.
type SigmoidActivation struct{}
// Sigmoid activation function.
var Sigmoid = &SigmoidActivation{}
// NewSigmoid returns a new sigmoid activation layer.
func NewSigmoid() *SigmoidActivation {
return &SigmoidActivation{}
}
// Fwd is a forward pass through the layer.
func (s *SigmoidActivation) Fwd(x *g.Node) (*g.Node, error) {
return g.Sigmoid(x)
}
// Learnables returns all learnable nodes within this layer.
func (s *SigmoidActivation) Learnables() (n g.Nodes) {
return n
}
// Compile the layer.
func (s *SigmoidActivation) Compile(x *g.Node, opts ...CompileOpt) {}
// Clone the activation.
func (s *SigmoidActivation) Clone() ActivationFn {
return NewSigmoid()
}
// TanhActivation is a tanh activation layer.
type TanhActivation struct{}
// Tanh activation.
var Tanh = &TanhActivation{}
// NewTanh returns a new tanh activation layer.
func NewTanh() *TanhActivation {
return &TanhActivation{}
}
// Fwd is a forward pass through the layer.
func (t *TanhActivation) Fwd(x *g.Node) (*g.Node, error) {
return g.Tanh(x)
}
// Learnables returns all learnable nodes within this layer.
func (t *TanhActivation) Learnables() (n g.Nodes) {
return n
}
// Compile the layer.
func (t *TanhActivation) Compile(x *g.Node, opts ...CompileOpt) {}
// Clone the activation.
func (t *TanhActivation) Clone() ActivationFn {
return NewTanh()
}
// ReLUActivation is a relu activation layer.
type ReLUActivation struct{}
// ReLU activation.
var ReLU = &ReLUActivation{}
// NewReLU returns a new relu activation layer.
func NewReLU() *ReLUActivation {
return &ReLUActivation{}
}
// Fwd is a forward pass through the layer.
func (r *ReLUActivation) Fwd(x *g.Node) (*g.Node, error) {
return g.Rectify(x)
}
// Learnables returns all learnable nodes within this layer.
func (r *ReLUActivation) Learnables() (n g.Nodes) {
return n
}
// Compile the layer.
func (r *ReLUActivation) Compile(x *g.Node, opts ...CompileOpt) {}
// Clone the activation.
func (r *ReLUActivation) Clone() ActivationFn {
return NewReLU()
}
// LeakyReLUActivation is a leaky relu activation layer.
type LeakyReLUActivation struct {
alpha float64
}
// LeakyReLU is default leaky relu activation.
var LeakyReLU = &LeakyReLUActivation{0.01}
// NewLeakyReLU returns a new leaky relu activation layer.
func NewLeakyReLU(alpha float64) *LeakyReLUActivation {
return &LeakyReLUActivation{alpha: alpha}
}
// Fwd is a forward pass through the layer.
func (r *LeakyReLUActivation) Fwd(x *g.Node) (*g.Node, error) {
return g.LeakyRelu(x, r.alpha)
}
// Learnables returns all learnable nodes within this layer.
func (r *LeakyReLUActivation) Learnables() (n g.Nodes) {
return n
}
// Compile the layer.
func (r *LeakyReLUActivation) Compile(x *g.Node, opts ...CompileOpt) {}
// Clone the activation.
func (r *LeakyReLUActivation) Clone() ActivationFn {
return NewLeakyReLU(r.alpha)
}
// SoftmaxActivation is a softmax activation layer.
type SoftmaxActivation struct {
axis []int
}
// Softmax is the default softmax activation.
var Softmax = &SoftmaxActivation{}
// NewSoftmax returns a new leaky softmax activation layer.
func NewSoftmax(axis ...int) *SoftmaxActivation {
// if len(axis) == 0 {
// axis = append(axis, 0)
// }
return &SoftmaxActivation{axis: axis}
}
// Fwd is a forward pass through the layer.
func (s *SoftmaxActivation) Fwd(x *g.Node) (*g.Node, error) {
// fmt.Printf("running softmax with x shape: %v dims: %v \n", x.Shape(), x.Dims())
return softMax(x, s.axis...)
}
// Learnables returns all learnable nodes within this layer.
func (s *SoftmaxActivation) Learnables() (n g.Nodes) {
return n
}
// Compile the layer.
func (s *SoftmaxActivation) Compile(x *g.Node, opts ...CompileOpt) {}
// Clone the activation.
func (s *SoftmaxActivation) Clone() ActivationFn {
return NewSoftmax(s.axis...)
}
// LinearActivation is a linear (identity) activation layer.
type LinearActivation struct{}
// Linear activation.
var Linear = &LinearActivation{}
// NewLinear is a linear activation layer.
func NewLinear() *LinearActivation {
return &LinearActivation{}
}
// Fwd is a forward pass through the layer.
func (l *LinearActivation) Fwd(x *g.Node) (*g.Node, error) {
return x, nil
}
// Learnables returns all learnable nodes within this layer.
func (l *LinearActivation) Learnables() (n g.Nodes) {
return n
}
// Compile the layer.
func (l *LinearActivation) Compile(x *g.Node, opts ...CompileOpt) {}
// Clone the activation.
func (l *LinearActivation) Clone() ActivationFn {
return NewLinear()
}
// softMax performs softmax on the input. Specifically this is used:
// e^(a[i]) / sum((e^(a[i])))
// For a more numerically stable SoftMax, use StableSoftMax.
//
// This is ripped from Gorgonia core and tweaked as there was a bug in it https://github.com/gorgonia/gorgonia/issues/373
// which is currently being worked on.
func softMax(a *g.Node, axes ...int) (retVal *g.Node, err error) {
aShape := a.Shape()
if aShape[0] == 1 {
aShape = aShape[1:]
a, err = g.Reshape(a, aShape)
log.Debugf("a reshaped to %v", a.Shape())
}
axis := aShape.Dims() - 1 // default: last dim
if a.IsColVec() || (a.IsVector() && !a.IsRowVec()) {
axis = 0
}
if len(axes) > 0 {
if axes[0] >= axis+1 || axes[0] < 0 {
return nil, fmt.Errorf("Cannot perform SoftMax on axis %d. Input has shape %v", axes[0], a.Shape())
}
axis = axes[0]
}
var exp, sum *g.Node
if exp, err = g.Exp(a); err != nil {
return nil, err
}
if sum, err = g.Sum(exp, axis); err != nil {
return nil, err
}
if sum.IsScalar() {
return g.HadamardDiv(exp, sum)
}
// reshape if necessary
ss := sum.Shape()
diff := exp.Shape().Dims() - ss.Dims()
// TODO: multirank softmax
if diff > 0 {
newShape := t.Shape(t.BorrowInts(ss.Dims() + diff))
copy(newShape, ss)
copy(newShape[axis+1:], newShape[axis:])
newShape[axis] = 1
if sum, err = g.Reshape(sum, newShape); err != nil {
return nil, errors.Wrap(err, "Failed to reshape")
}
}
retVal, err = g.BroadcastHadamardDiv(exp, sum, nil, []byte{byte(axis)})
if err != nil {
return
}
return
}