Uses of Interface
io.github.kirstenali.deepj.layers.Layer

Packages that use Layer

Package	Description
io.github.kirstenali.deepj.layers
io.github.kirstenali.deepj.layers.transformer
io.github.kirstenali.deepj.layers.transformer.attention
io.github.kirstenali.deepj.layers.transformer.blocks
io.github.kirstenali.deepj.layers.transformer.norm
io.github.kirstenali.deepj.nn	Neural networks composed from DeepJ tensors, layers, autograd and backend operations.
io.github.kirstenali.deepj.training
io.github.kirstenali.deepj.transformer

Uses of Layer in io.github.kirstenali.deepj.layers

Subinterfaces of Layer in io.github.kirstenali.deepj.layers

Modifier and Type	Interface	Description
interface	Projection

Classes in io.github.kirstenali.deepj.layers that implement Layer

Modifier and Type	Class	Description
final class	BiasFreeLinear
final class	Linear
final class	TiedEmbeddingProjection	Predicts tokens using the same parameter that supplies the input embeddings.

Uses of Layer in io.github.kirstenali.deepj.layers.transformer

Classes in io.github.kirstenali.deepj.layers.transformer that implement Layer

Modifier and Type	Class	Description
final class	SwiGLULayer

Uses of Layer in io.github.kirstenali.deepj.layers.transformer.attention

Classes in io.github.kirstenali.deepj.layers.transformer.attention that implement Layer

Modifier and Type	Class	Description
final class	GroupedQueryAttention
final class	MultiHeadLatentAttention
class	MultiHeadSelfAttention
final class	RoPEMultiHeadSelfAttention

Uses of Layer in io.github.kirstenali.deepj.layers.transformer.blocks

Classes in io.github.kirstenali.deepj.layers.transformer.blocks that implement Layer

Modifier and Type	Class	Description
final class	DeepJNovaTransformerBlock
final class	DeepJOrbitTransformerBlock
class	DeepJOriginTransformerBlock
final class	DeepJPrismTransformerBlock

Uses of Layer in io.github.kirstenali.deepj.layers.transformer.norm

Subinterfaces of Layer in io.github.kirstenali.deepj.layers.transformer.norm

Modifier and Type	Interface	Description
interface	NormLayer

Classes in io.github.kirstenali.deepj.layers.transformer.norm that implement Layer

Modifier and Type	Class	Description
final class	LayerNorm1D
final class	RMSNorm1D

Uses of Layer in io.github.kirstenali.deepj.nn

Classes in io.github.kirstenali.deepj.nn that implement Layer

Modifier and Type	Class	Description
final class	Activation	Adapts DeepJ activations to the existing Layer contract.
final class	Autoencoder	Composable encoder/decoder, sharing the same parameter and autograd system as every DeepJ layer.
final class	BatchNorm	Channel-axis (axis 1) normalization, using backend reductions and persistent FLOAT32 running statistics.
final class	Conv1D	Temporal cross-correlation for [batch, channels, length], sharing the 2D implementation.
final class	Conv2D	Cross-correlation of [batch, channels, height, width] inputs, using gather and matrix multiplication.
final class	Dropout	Inverted dropout.
final class	EncoderAttention	Bidirectional multi-head attention with optional padding masks and backend-resident softmax.
final class	Flatten	Flattens an inclusive axis range; defaults to preserving the first (batch) axis.
final class	FNN
final class	GlobalAveragePool	Averages every spatial axis of [batch, channels, ...], returning [batch, channels].
final class	GRU	Batch-first GRU layer; compose layers with Sequential for stacked recurrence.
final class	GRUCell	Reset-after GRU, with reset/update/candidate gate order and separate projection biases.
final class	LSTM	Batch-first LSTM layer; compose layers with Sequential for stacked recurrence.
final class	LSTMCell	LSTM with input/forget/candidate/output gate order and explicit cell memory.
class	Module	Additive layer base with recursive training mode and identity-deduplicated parameters.
final class	Pool1D	Max/average pooling of [batch, channels, length] inputs.
final class	Pool2D	Floor-mode max/average pooling of channel-first images.
class	Recurrent	Batch-first recurrent sequence execution, with optional lengths and caller-owned initial/final state.
class	RecurrentCell	Shared input/state validation and DeepJ linear projections for recurrent cells.
final class	Reshape	Reshapes the full tensor, with at most one inferred (-1) dimension.
final class	RNN	Batch-first RNN layer; compose layers with Sequential for stacked recurrence.
final class	RNNCell	Elman recurrent cell with tanh activation.
final class	Sequential	Ordered composition of existing or new DeepJ layers.
final class	TransformerEncoder	Stacked encoder for [batch, time, width] embeddings, with padding masks and masked mean pooling.
final class	TransformerEncoderLayer	Pre-normalized, bidirectional encoder block reusing DeepJ linear layers and LayerNorm.

Methods in io.github.kirstenali.deepj.nn that return types with arguments of type Layer

Modifier and Type	Method	Description
protected List<Layer>	Autoencoder.children()
protected List<Layer>	Conv1D.children()
protected List<Layer>	EncoderAttention.children()
protected List<Layer>	Module.children()
protected List<Layer>	Pool1D.children()
protected List<Layer>	Recurrent.children()
protected List<Layer>	RecurrentCell.children()
protected List<Layer>	Sequential.children()
protected List<Layer>	TransformerEncoder.children()
protected List<Layer>	TransformerEncoderLayer.children()
List<Layer>	Sequential.layers()

Methods in io.github.kirstenali.deepj.nn with parameters of type Layer

Modifier and Type	Method	Description
static void	NetworkWeights.load(Path path, Layer model)
static void	NetworkWeights.save(Path path, Layer model)
static Trainer	NdSupervisedTraining.trainer(Layer model, LossFunction loss, ParameterOptimizer optimizer, Tensor inputs, Tensor targets, long seed)

Constructors in io.github.kirstenali.deepj.nn with parameters of type Layer

Modifier	Constructor	Description
	Autoencoder(Layer encoder, Layer decoder)
	Sequential(Layer... layers)

Uses of Layer in io.github.kirstenali.deepj.training

Methods in io.github.kirstenali.deepj.training with parameters of type Layer

Modifier and Type	Method	Description
static Trainer	SupervisedTraining.trainer(Layer model, LossFunction lossFn, ParameterOptimizer opt, Tensor xAll, Tensor yAll, long seed)

Uses of Layer in io.github.kirstenali.deepj.transformer

Classes in io.github.kirstenali.deepj.transformer that implement Layer

Modifier and Type	Class	Description
final class	LoopedTransformer	Repeats shared grouped-query attention and SwiGLU blocks; each pass saves its own activations.
final record	TransformerStack

Methods in io.github.kirstenali.deepj.transformer that return types with arguments of type Layer

Modifier and Type	Method	Description
List<Layer>	TransformerStack.blocks()	Returns the value of the blocks record component.

Constructor parameters in io.github.kirstenali.deepj.transformer with type arguments of type Layer

Modifier	Constructor	Description
	TransformerStack(List<Layer> blocks)	Creates an instance of a TransformerStack record class.