Module: tfa.layers  |  TensorFlow Addons (original) (raw)

Module: tfa.layers

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Additional layers that conform to Keras API.

Classes

class AdaptiveAveragePooling1D: Average Pooling with adaptive kernel size.

class AdaptiveAveragePooling2D: Average Pooling with adaptive kernel size.

class AdaptiveAveragePooling3D: Average Pooling with adaptive kernel size.

class AdaptiveMaxPooling1D: Max Pooling with adaptive kernel size.

class AdaptiveMaxPooling2D: Max Pooling with adaptive kernel size.

class AdaptiveMaxPooling3D: Max Pooling with adaptive kernel size.

class CRF: Linear chain conditional random field (CRF).

class CorrelationCost: Correlation Cost Layer.

class ESN: Echo State Network layer.

class EmbeddingBag: EmbeddingBag Layer.

class FilterResponseNormalization: Filter response normalization layer.

class GELU: Gaussian Error Linear Unit.

class GroupNormalization: Group normalization layer.

class InstanceNormalization: Instance normalization layer.

class MaxUnpooling2D: Unpool the outputs of a maximum pooling operation.

class MaxUnpooling2DV2: Unpool the outputs of a maximum pooling operation.

class Maxout: Applies Maxout to the input.

class MultiHeadAttention: MultiHead Attention layer.

class NoisyDense: Noisy dense layer that injects random noise to the weights of dense layer.

class PoincareNormalize: Project into the Poincare ball with norm <= 1.0 - epsilon.

class PolynomialCrossing: Layer for Deep & Cross Network to learn explicit feature interactions.

class Snake: Snake layer to learn periodic functions with the trainable frequency scalar.

class Sparsemax: Sparsemax activation function.

class SpatialPyramidPooling2D: Performs Spatial Pyramid Pooling.

class SpectralNormalization: Performs spectral normalization on weights.

class StochasticDepth: Stochastic Depth layer.

class TLU: Thresholded Linear Unit.

class WeightNormalization: Performs weight normalization.