123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189import { Activation, LayerType } from "../types.ts";import { BatchNormLayerConfig, Conv2DLayerConfig, ConvTranspose2DLayerConfig, DenseLayerConfig, DropoutLayerConfig, FlattenLayerConfig, Layer, Pool2DLayerConfig, PoolMode,} from "./layer.ts";
/** * Creates a dense layer (also known as a fully connected layer). * Dense layers feed all outputs from the previous layer to all its neurons, each neuron providing one output to the next layer. * See https://en.wikipedia.org/wiki/Feedforward_neural_network#Fully_connected_network */export function DenseLayer(config: DenseLayerConfig): Layer { return { type: LayerType.Dense, config };}
/** * Creates a dropout layer. Dropout is a regularization technique for reducing overfitting. * The technique temporarily drops units (artificial neurons) from the network, along with all of those units' incoming and outgoing connections. * See https://en.wikipedia.org/wiki/Dropout_(neural_networks) */export function Dropout1DLayer(config: DropoutLayerConfig): Layer { return { type: LayerType.Dropout1D, config };}
/** * Creates a dropout layer. Dropout is a regularization technique for reducing overfitting. * The technique temporarily drops units (artificial neurons) from the network, along with all of those units' incoming and outgoing connections. * See https://en.wikipedia.org/wiki/Dropout_(neural_networks) */export function Dropout2DLayer(config: DropoutLayerConfig): Layer { return { type: LayerType.Dropout2D, config };}
/** * Creates a convolutional layer. * Convolutional layers are used for feature extraction. * They are commonly used in image processing. * See https://en.wikipedia.org/wiki/Convolutional_neural_network */export function Conv2DLayer(config: Conv2DLayerConfig): Layer { return { type: LayerType.Conv2D, config };}
export function ConvTranspose2DLayer( config: ConvTranspose2DLayerConfig,): Layer { return { type: LayerType.ConvTranspose2D, config };}
/** * Creates a pooling layer. * Pooling layers are used for downsampling. * See https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer */export function Pool2DLayer(config: Pool2DLayerConfig): Layer { return { type: LayerType.Pool2D, config };}
/** * Creates a max pooling layer. * Pooling layers are used for downsampling. * See https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer */export function MaxPool2DLayer(config: Pool2DLayerConfig): Layer { config.mode = PoolMode.Max; return { type: LayerType.Pool2D, config };}
/** * Creates an average pooling layer. * Pooling layers are used for downsampling. * See https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer */export function AveragePool2DLayer(config: Pool2DLayerConfig): Layer { config.mode = PoolMode.Avg; return { type: LayerType.Pool2D, config };}
/** * Creates a softmax layer. Softmax layers are used for classification. * See https://en.wikipedia.org/wiki/Softmax_function */export function SoftmaxLayer(): Layer { return { type: LayerType.Softmax };}
/** * Creates a sigmoid layer. Sigmoid layers use the sigmoid activation function. * See https://en.wikipedia.org/wiki/Sigmoid_function */export function SigmoidLayer(): Layer { const config = { activation: Activation.Sigmoid }; return { type: LayerType.Activation, config };}
/** * Creates a leaky relu layer. * Leaky relu layers use the leaky relu activation function. */export function LeakyReluLayer(): Layer { const config = { activation: Activation.LeakyRelu }; return { type: LayerType.Activation, config };}
/** * Creates a tanh layer. * Tanh layers use the tanh activation function. */export function TanhLayer(): Layer { const config = { activation: Activation.Tanh }; return { type: LayerType.Activation, config };}
/** * Creates a relu layer. * Relu layers use the relu activation function. */export function ReluLayer(): Layer { const config = { activation: Activation.Relu }; return { type: LayerType.Activation, config };}
/** * Creates a relu6 layer. * Relu6 layers use the relu6 activation function. */export function Relu6Layer(): Layer { const config = { activation: Activation.Relu6 }; return { type: LayerType.Activation, config };}
/** * Creates an Elu layer. * Elu layers use the elu activation function. */export function EluLayer(): Layer { const config = { activation: Activation.Elu }; return { type: LayerType.Activation, config };}
/** * Creates a Selu layer. * Selu layers use the selu activation function. */export function SeluLayer(): Layer { const config = { activation: Activation.Selu }; return { type: LayerType.Activation, config };}
/** * Creates a Flatten layer. * Flatten layers flatten the input. * They are usually used to transition from convolutional layers to dense layers. */export function FlattenLayer(config: FlattenLayerConfig): Layer { return { type: LayerType.Flatten, config };}
/** * Creates a BatchNorm1D layer. * BatchNorm1D layers normalize the input. * They are usually used to improve the speed, performance, and stability of neural networks. * See https://en.wikipedia.org/wiki/Batch_normalization */export function BatchNorm1DLayer(config: BatchNormLayerConfig = {}): Layer { config.epsilon = config.epsilon || 0.001; config.momentum = config.momentum || 0.99; return { type: LayerType.BatchNorm1D, config };}
/** * Creates a BatchNorm2D layer. * BatchNorm2D layers normalize the input. * They are usually used to improve the speed, performance, and stability of neural networks. * See https://en.wikipedia.org/wiki/Batch_normalization */export function BatchNorm2DLayer(config: BatchNormLayerConfig = {}): Layer { config.epsilon = config.epsilon || 0.001; config.momentum = config.momentum || 0.99; return { type: LayerType.BatchNorm2D, config };}