Documentation
¶
Overview ¶
Package layer provides the layers for sequential models.
Index ¶
- Variables
- func AsBatch() func(Layer)
- func AsType(dtype t.Dtype) func(Layer)
- func WithLayerOpts(opts ...CompileOpt) func(*Chain)
- func WithSharedChainLearnables(shared *Chain) func(*Chain)
- func WithSharedLearnables(shared Layer) func(Layer)
- type ActivationFn
- type Chain
- type ChainOpt
- type CompileOpt
- type Config
- type Conv2D
- type Dropout
- type FC
- type Flatten
- type Layer
- type LeakyReLUActivation
- type LinearActivation
- type MaxPooling2D
- type ReLUActivation
- type Reshape
- type SigmoidActivation
- type SoftmaxActivation
- type TanhActivation
Constants ¶
This section is empty.
Variables ¶
var LeakyReLU = &LeakyReLUActivation{0.01}
LeakyReLU is default leaky relu activation.
var Linear = &LinearActivation{}
Linear activation.
var ReLU = &ReLUActivation{}
ReLU activation.
var Sigmoid = &SigmoidActivation{}
Sigmoid activation function.
var Softmax = &SoftmaxActivation{}
Softmax is the default softmax activation.
var Tanh = &TanhActivation{}
Tanh activation.
Functions ¶
func WithLayerOpts ¶
func WithLayerOpts(opts ...CompileOpt) func(*Chain)
WithLayerOpts adds the given layer opts to all layers.
func WithSharedChainLearnables ¶
WithSharedChainLearnables shares the learnables from another chain.
func WithSharedLearnables ¶
WithSharedLearnables shares the learnables from another layer.
Types ¶
type ActivationFn ¶
type ActivationFn interface {
// Fwd is a forward pass through x.
Fwd(x *g.Node) (*g.Node, error)
// Clone the activation.
Clone() ActivationFn
}
ActivationFn is an activation function.
type Chain ¶
type Chain struct {
// Layers are the layers to chain together.
Layers []Config
// contains filtered or unexported fields
}
Chain of layers.
func (*Chain) Learnables ¶
Learnables are all of the learnable parameters in the chain.
type Config ¶
type Config interface {
// Compile the layer.
Compile(graph *g.ExprGraph, opts ...CompileOpt) Layer
// ApplyDefaults to the config.
ApplyDefaults() Config
// Validate the config.
Validate() error
// Clone the layer config.
Clone() Config
}
Config is the config for a layer.
type Conv2D ¶
type Conv2D struct {
// Input channels.
// required
Input int
// Output channels.
// required
Output int
// Height of the filter.
// required
Height int
// Width of the filter.
// required
Width int
// Name of the layer.
Name string
// Activation function for the layer.
// Defaults to ReLU
Activation ActivationFn
// Pad
// Defaults to (1, 1)
Pad []int
// Stride
// Defaults to (1, 1)
Stride []int
// Dilation
// Defaults to (1, 1)
Dilation []int
// Init function fot the weights.
// Defaults to GlorotN(1)
Init g.InitWFn
}
Conv2D is a 2D convolution.
type Dropout ¶
type Dropout struct {
// Probability of dropping out.
// Defaults to 0.6
Probability float64
}
Dropout implements layer dropout.
func (Dropout) ApplyDefaults ¶
ApplyDefaults applys defaults to the layers.
type FC ¶
type FC struct {
// Input is the number of units in input.
// required
Input int
// Output is the number of units in the output.
// required
Output int
// Name of the layer.
Name string
// Activation is the activation function.
// Defaults to ReLU
Activation ActivationFn
// Init is the init function.
// Defaults to GlorotN(1)
Init g.InitWFn
// NoBias indicates to not use a bias with the layer
// Defaults to true.
NoBias bool
// BiasInit is the init function for the bias.
// Defaults to GlorotN(1)
BiasInit g.InitWFn
}
FC is a fully connected layer of neurons.
type Flatten ¶
type Flatten struct{}
Flatten reshapes the incoming tensor to be flat, preserving the batch.
func (Flatten) ApplyDefaults ¶
ApplyDefaults to the flatten layer.
type Layer ¶
type Layer interface {
// Fwd is a forward pass through the layer.
Fwd(x *g.Node) (*g.Node, error)
// Learnables returns all learnable nodes within this layer.
Learnables() g.Nodes
// Clone the layer.
Clone() Layer
// Graph returns the graph for this layer.
Graph() *g.ExprGraph
}
Layer in a network.
type LeakyReLUActivation ¶
type LeakyReLUActivation struct {
// contains filtered or unexported fields
}
LeakyReLUActivation is a leaky relu activation layer.
func NewLeakyReLU ¶
func NewLeakyReLU(alpha float64) *LeakyReLUActivation
NewLeakyReLU returns a new leaky relu activation layer.
func (*LeakyReLUActivation) Clone ¶
func (r *LeakyReLUActivation) Clone() ActivationFn
Clone the activation.
func (*LeakyReLUActivation) Compile ¶
func (r *LeakyReLUActivation) Compile(x *g.Node, opts ...CompileOpt)
Compile the layer.
func (*LeakyReLUActivation) Learnables ¶
func (r *LeakyReLUActivation) Learnables() (n g.Nodes)
Learnables returns all learnable nodes within this layer.
type LinearActivation ¶
type LinearActivation struct{}
LinearActivation is a linear (identity) activation layer.
func (*LinearActivation) Clone ¶
func (l *LinearActivation) Clone() ActivationFn
Clone the activation.
func (*LinearActivation) Compile ¶
func (l *LinearActivation) Compile(x *g.Node, opts ...CompileOpt)
Compile the layer.
func (*LinearActivation) Learnables ¶
func (l *LinearActivation) Learnables() (n g.Nodes)
Learnables returns all learnable nodes within this layer.
type MaxPooling2D ¶
type MaxPooling2D struct {
// Shape of the kernel.
// Defaults to (2, 2)
Kernel t.Shape
// Pad
// Defaults to (0, 0)
Pad []int
// Stride
// Defaults to (2, 2)
Stride []int
// Name
Name string
}
MaxPooling2D implements the max pooling 2d function.
func (MaxPooling2D) ApplyDefaults ¶
func (m MaxPooling2D) ApplyDefaults() Config
ApplyDefaults applys defaults to the layers.
func (MaxPooling2D) Compile ¶
func (m MaxPooling2D) Compile(graph *g.ExprGraph, opts ...CompileOpt) Layer
Compile the config as a layer.
type ReLUActivation ¶
type ReLUActivation struct{}
ReLUActivation is a relu activation layer.
func (*ReLUActivation) Compile ¶
func (r *ReLUActivation) Compile(x *g.Node, opts ...CompileOpt)
Compile the layer.
func (*ReLUActivation) Learnables ¶
func (r *ReLUActivation) Learnables() (n g.Nodes)
Learnables returns all learnable nodes within this layer.
type Reshape ¶
Reshape the incoming tensor.
func (Reshape) ApplyDefaults ¶
ApplyDefaults to the flatten layer.
type SigmoidActivation ¶
type SigmoidActivation struct{}
SigmoidActivation is a sigmoid activation layer.
func NewSigmoid ¶
func NewSigmoid() *SigmoidActivation
NewSigmoid returns a new sigmoid activation layer.
func (*SigmoidActivation) Clone ¶
func (s *SigmoidActivation) Clone() ActivationFn
Clone the activation.
func (*SigmoidActivation) Compile ¶
func (s *SigmoidActivation) Compile(x *g.Node, opts ...CompileOpt)
Compile the layer.
func (*SigmoidActivation) Learnables ¶
func (s *SigmoidActivation) Learnables() (n g.Nodes)
Learnables returns all learnable nodes within this layer.
type SoftmaxActivation ¶
type SoftmaxActivation struct {
// contains filtered or unexported fields
}
SoftmaxActivation is a softmax activation layer.
func NewSoftmax ¶
func NewSoftmax(axis ...int) *SoftmaxActivation
NewSoftmax returns a new leaky softmax activation layer.
func (*SoftmaxActivation) Clone ¶
func (s *SoftmaxActivation) Clone() ActivationFn
Clone the activation.
func (*SoftmaxActivation) Compile ¶
func (s *SoftmaxActivation) Compile(x *g.Node, opts ...CompileOpt)
Compile the layer.
func (*SoftmaxActivation) Learnables ¶
func (s *SoftmaxActivation) Learnables() (n g.Nodes)
Learnables returns all learnable nodes within this layer.
type TanhActivation ¶
type TanhActivation struct{}
TanhActivation is a tanh activation layer.
func (*TanhActivation) Compile ¶
func (t *TanhActivation) Compile(x *g.Node, opts ...CompileOpt)
Compile the layer.
func (*TanhActivation) Learnables ¶
func (t *TanhActivation) Learnables() (n g.Nodes)
Learnables returns all learnable nodes within this layer.
Source Files