Module: tf.keras.layers  |  TensorFlow v2.16.1 (original) (raw)

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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 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 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 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 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 positive integers (indexes) into dense vectors of fixed size.

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 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 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 RNN: Base class for recurrent layers.

class RandomBrightness: A preprocessing layer which randomly adjusts brightness during training.

class RandomContrast: A preprocessing layer which randomly adjusts contrast during training.

class RandomCrop: A preprocessing layer which randomly crops images during training.

class RandomFlip: A preprocessing layer which randomly flips images during training.

class RandomHeight: DEPRECATED.

class RandomRotation: A preprocessing layer which randomly rotates images during training.

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 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 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.