TypedFlow.Layers.Core

Contents

• Dense
• Dropout
• Embedding
• Convolutional

Description

Synopsis

• data DenseP t a b = DenseP {
  • denseWeights :: Tensor '[a, b] (Flt t)
  • denseBiases :: Tensor '[b] (Flt t)
  }
• dense :: forall m n batchSize t. DenseP t n m -> Tensor '[n, batchSize] (Flt t) -> Tensor '[m, batchSize] (Flt t)
• (#) :: forall m n batchSize t. DenseP t n m -> Tensor '[n, batchSize] (Flt t) -> Tensor '[m, batchSize] (Flt t)
• data DropProb = DropProb Float
• mkDropout :: forall s t. KnownShape s => KnownBits t => DropProb -> Gen (Tensor s (Typ Float t) -> Tensor s (Typ Float t))
• mkDropouts :: KnownBits t => KnownLen shapes => All KnownShape shapes => DropProb -> Gen (HTV (Typ Float t) shapes -> HTV (Typ Float t) shapes)
• newtype EmbeddingP numObjects embeddingSize t = EmbeddingP (Tensor '[numObjects, embeddingSize] (Typ Float t))
• embedding :: forall embeddingSize numObjects batchSize t. EmbeddingP numObjects embeddingSize t -> Tensor '[batchSize] Int32 -> Tensor '[embeddingSize, batchSize] (Typ Float t)
• data ConvP t outChannels inChannels filterSpatialShape = ConvP (T ('[outChannels, inChannels] ++ filterSpatialShape) (Typ Float t)) (T '[outChannels] (Typ Float t))
• conv :: forall outChannels filterSpatialShape inChannels s t. ((1 + Length filterSpatialShape) ~ Length s, Length filterSpatialShape <= 3, KnownLen filterSpatialShape) => ConvP t outChannels inChannels filterSpatialShape -> T ('[inChannels] ++ s) (Typ Float t) -> T ('[outChannels] ++ s) (Typ Float t)
• maxPool2D :: forall stridex stridey batch height width channels t. (KnownNat stridex, KnownNat stridey) => T '[channels, width * stridex, height * stridex, batch] (Flt t) -> T '[channels, width, height, batch] (Flt t)

Dense

Dropout

Embedding

Convolutional