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Classes
class Activation: Applies an activation function to an output.
class ActivityRegularization: Layer that applies an update to the cost function based input activity.
class AdaptiveAveragePooling1D: Adaptive average pooling operation for 1D temporal or spatial data.
class AdaptiveAveragePooling2D: Adaptive average pooling operation for 2D spatial data.
class AdaptiveAveragePooling3D: Adaptive average pooling operation for 3D volumetric data.
class AdaptiveMaxPooling1D: Adaptive max pooling operation for 1D temporal or spatial data.
class AdaptiveMaxPooling2D: Adaptive max pooling operation for 2D spatial data.
class AdaptiveMaxPooling3D: Adaptive max pooling operation for 3D volumetric data.
class Add: Performs elementwise addition operation.
class AdditiveAttention: Additive attention layer, a.k.a. Bahdanau-style attention.
class AlphaDropout: DEPRECATED.
class Attention: Dot-product attention layer, a.k.a. Luong-style attention.
class AugMix: Performs the AugMix data augmentation technique.
class AutoContrast: Performs the auto-contrast operation on an image.
class Average: Averages a list of inputs element-wise..
class AveragePooling1D: Average pooling for temporal data.
class AveragePooling2D: Average pooling operation for 2D spatial data.
class AveragePooling3D: Average pooling operation for 3D data (spatial or spatio-temporal).
class AvgPool1D: Average pooling for temporal data.
class AvgPool2D: Average pooling operation for 2D spatial data.
class AvgPool3D: Average pooling operation for 3D data (spatial or spatio-temporal).
class BatchNormalization: Layer that normalizes its inputs.
class Bidirectional: Bidirectional wrapper for RNNs.
class CategoryEncoding: A preprocessing layer which encodes integer features.
class CenterCrop: A preprocessing layer which crops images.
class Concatenate: Concatenates a list of inputs.
class ContrastLimitedAdaptiveHistogramEqualization: Contrast Limited Adaptive Histogram Equalization (CLAHE) layer.
class Conv1D: 1D convolution layer (e.g. temporal convolution).
class Conv1DTranspose: 1D transposed convolution layer.
class Conv2D: 2D convolution layer.
class Conv2DTranspose: 2D transposed convolution layer.
class Conv3D: 3D convolution layer.
class Conv3DTranspose: 3D transposed convolution layer.
class ConvLSTM1D: 1D Convolutional LSTM.
class ConvLSTM2D: 2D Convolutional LSTM.
class ConvLSTM3D: 3D Convolutional LSTM.
class Convolution1D: 1D convolution layer (e.g. temporal convolution).
class Convolution1DTranspose: 1D transposed convolution layer.
class Convolution2D: 2D convolution layer.
class Convolution2DTranspose: 2D transposed convolution layer.
class Convolution3D: 3D convolution layer.
class Convolution3DTranspose: 3D transposed convolution layer.
class Cropping1D: Cropping layer for 1D input (e.g. temporal sequence).
class Cropping2D: Cropping layer for 2D input (e.g. picture).
class Cropping3D: Cropping layer for 3D data (e.g. spatial or spatio-temporal).
class CutMix: CutMix data augmentation technique.
class Dense: Just your regular densely-connected NN layer.
class DepthwiseConv1D: 1D depthwise convolution layer.
class DepthwiseConv2D: 2D depthwise convolution layer.
class Discretization: A preprocessing layer which buckets continuous features by ranges.
class Dot: Computes element-wise dot product of two tensors.
class Dropout: Applies dropout to the input.
class ELU: Applies an Exponential Linear Unit function to an output.
class EinsumDense: A layer that uses einsum as the backing computation.
class Embedding: Turns nonnegative integers (indexes) into dense vectors of fixed size.
class Equalization: Preprocessing layer for histogram equalization on image channels.
class Flatten: Flattens the input. Does not affect the batch size.
class FlaxLayer: Keras Layer that wraps a Flax module.
class GRU: Gated Recurrent Unit - Cho et al. 2014.
class GRUCell: Cell class for the GRU layer.
class GaussianDropout: Apply multiplicative 1-centered Gaussian noise.
class GaussianNoise: Apply additive zero-centered Gaussian noise.
class GlobalAveragePooling1D: Global average pooling operation for temporal data.
class GlobalAveragePooling2D: Global average pooling operation for 2D data.
class GlobalAveragePooling3D: Global average pooling operation for 3D data.
class GlobalAvgPool1D: Global average pooling operation for temporal data.
class GlobalAvgPool2D: Global average pooling operation for 2D data.
class GlobalAvgPool3D: Global average pooling operation for 3D data.
class GlobalMaxPool1D: Global max pooling operation for temporal data.
class GlobalMaxPool2D: Global max pooling operation for 2D data.
class GlobalMaxPool3D: Global max pooling operation for 3D data.
class GlobalMaxPooling1D: Global max pooling operation for temporal data.
class GlobalMaxPooling2D: Global max pooling operation for 2D data.
class GlobalMaxPooling3D: Global max pooling operation for 3D data.
class GroupNormalization: Group normalization layer.
class GroupQueryAttention: Grouped Query Attention layer.
class HashedCrossing: A preprocessing layer which crosses features using the "hashing trick".
class Hashing: A preprocessing layer which hashes and bins categorical features.
class Identity: Identity layer.
class InputLayer: This is the class from which all layers inherit.
class InputSpec: Specifies the rank, dtype and shape of every input to a layer.
class IntegerLookup: A preprocessing layer that maps integers to (possibly encoded) indices.
class JaxLayer: Keras Layer that wraps a JAX model.
class LSTM: Long Short-Term Memory layer - Hochreiter 1997.
class LSTMCell: Cell class for the LSTM layer.
class Lambda: Wraps arbitrary expressions as a Layer object.
class Layer: This is the class from which all layers inherit.
class LayerNormalization: Layer normalization layer (Ba et al., 2016).
class LeakyReLU: Leaky version of a Rectified Linear Unit activation layer.
class Masking: Masks a sequence by using a mask value to skip timesteps.
class MaxNumBoundingBoxes: Ensure the maximum number of bounding boxes.
class MaxPool1D: Max pooling operation for 1D temporal data.
class MaxPool2D: Max pooling operation for 2D spatial data.
class MaxPool3D: Max pooling operation for 3D data (spatial or spatio-temporal).
class MaxPooling1D: Max pooling operation for 1D temporal data.
class MaxPooling2D: Max pooling operation for 2D spatial data.
class MaxPooling3D: Max pooling operation for 3D data (spatial or spatio-temporal).
class Maximum: Computes element-wise maximum on a list of inputs.
class MelSpectrogram: A preprocessing layer to convert raw audio signals to Mel spectrograms.
class Minimum: Computes elementwise minimum on a list of inputs.
class MixUp: MixUp implements the MixUp data augmentation technique.
class MultiHeadAttention: MultiHeadAttention layer.
class Multiply: Performs elementwise multiplication.
class Normalization: A preprocessing layer that normalizes continuous features.
class PReLU: Parametric Rectified Linear Unit activation layer.
class Permute: Permutes the dimensions of the input according to a given pattern.
class Pipeline: Applies a series of layers to an input.
class RMSNormalization: Root Mean Square (RMS) Normalization layer.
class RNN: Base class for recurrent layers.
class RandAugment: RandAugment performs the Rand Augment operation on input images.
class RandomBrightness: A preprocessing layer which randomly adjusts brightness during training.
class RandomColorDegeneration: Randomly performs the color degeneration operation on given images.
class RandomColorJitter: RandomColorJitter class randomly apply brightness, contrast, saturation and hue image processing operation sequentially and randomly on the input.
class RandomContrast: A preprocessing layer which randomly adjusts contrast during training.
class RandomCrop: A preprocessing layer which randomly crops images during training.
class RandomElasticTransform: A preprocessing layer that applies random elastic transformations.
class RandomErasing: Random Erasing data augmentation technique.
class RandomFlip: A preprocessing layer which randomly flips images during training.
class RandomGaussianBlur: Applies random Gaussian blur to images for data augmentation.
class RandomGrayscale: Preprocessing layer for random conversion of RGB images to grayscale.
class RandomHeight: DEPRECATED.
class RandomHue: Randomly adjusts the hue on given images.
class RandomInvert: Preprocessing layer for random inversion of image colors.
class RandomPerspective: A preprocessing layer that applies random perspective transformations.
class RandomPosterization: Reduces the number of bits for each color channel.
class RandomRotation: A preprocessing layer which randomly rotates images during training.
class RandomSaturation: Randomly adjusts the saturation on given images.
class RandomSharpness: Randomly performs the sharpness operation on given images.
class RandomShear: A preprocessing layer that randomly applies shear transformations to images.
class RandomTranslation: A preprocessing layer which randomly translates images during training.
class RandomWidth: DEPRECATED.
class RandomZoom: A preprocessing layer which randomly zooms images during training.
class ReLU: Rectified Linear Unit activation function layer.
class RepeatVector: Repeats the input n times.
class Rescaling: A preprocessing layer which rescales input values to a new range.
class Reshape: Layer that reshapes inputs into the given shape.
class Resizing: A preprocessing layer which resizes images.
class ReversibleEmbedding: An embedding layer which can project backwards to the input dim.
class STFTSpectrogram: Layer to compute the Short-Time Fourier Transform (STFT) on a 1D signal.
class SeparableConv1D: 1D separable convolution layer.
class SeparableConv2D: 2D separable convolution layer.
class SeparableConvolution1D: 1D separable convolution layer.
class SeparableConvolution2D: 2D separable convolution layer.
class SimpleRNN: Fully-connected RNN where the output is to be fed back as the new input.
class SimpleRNNCell: Cell class for SimpleRNN.
class Softmax: Softmax activation layer.
class Solarization: Applies (max_value - pixel + min_value) for each pixel in the image.
class SpatialDropout1D: Spatial 1D version of Dropout.
class SpatialDropout2D: Spatial 2D version of Dropout.
class SpatialDropout3D: Spatial 3D version of Dropout.
class SpectralNormalization: Performs spectral normalization on the weights of a target layer.
class StackedRNNCells: Wrapper allowing a stack of RNN cells to behave as a single cell.
class StringLookup: A preprocessing layer that maps strings to (possibly encoded) indices.
class Subtract: Performs elementwise subtraction.
class TFSMLayer: Reload a Keras model/layer that was saved via SavedModel / ExportArchive.
class TextVectorization: A preprocessing layer which maps text features to integer sequences.
class ThresholdedReLU: DEPRECATED.
class TimeDistributed: This wrapper allows to apply a layer to every temporal slice of an input.
class TorchModuleWrapper: Torch module wrapper layer.
class UnitNormalization: Unit normalization layer.
class UpSampling1D: Upsampling layer for 1D inputs.
class UpSampling2D: Upsampling layer for 2D inputs.
class UpSampling3D: Upsampling layer for 3D inputs.
class Wrapper: Abstract wrapper base class.
class ZeroPadding1D: Zero-padding layer for 1D input (e.g. temporal sequence).
class ZeroPadding2D: Zero-padding layer for 2D input (e.g. picture).
class ZeroPadding3D: Zero-padding layer for 3D data (spatial or spatio-temporal).
Functions
Input(...): Used to instantiate a Keras tensor.
add(...): Functional interface to the keras.layers.Add layer.
average(...): Functional interface to the keras.layers.Average layer.
concatenate(...): Functional interface to the Concatenate layer.
deserialize(...): Returns a Keras layer object via its configuration.
dot(...): Functional interface to the Dot layer.
maximum(...): Functional interface to the keras.layers.Maximum layer.
minimum(...): Functional interface to the keras.layers.Minimum layer.
multiply(...): Functional interface to the keras.layers.Multiply layer.
serialize(...): Returns the layer configuration as a Python dict.
subtract(...): Functional interface to the keras.layers.Subtract layer.