Keras layers API.
Modules
experimental
module: Public API for tf.keras.layers.experimental namespace.
Classes
class AbstractRNNCell
: Abstract object representing an RNN cell.
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
: Layer that adds a list of inputs.
class AdditiveAttention
: Additive attention layer, a.k.a. Bahdanau-style attention.
class AlphaDropout
: Applies Alpha Dropout to the input.
class Attention
: Dot-product attention layer, a.k.a. Luong-style attention.
class Average
: Layer that averages a list of inputs element-wise.
class AveragePooling1D
: Average pooling for temporal data.
class AveragePooling2D
: Average pooling operation for 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 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
: Layer that concatenates a list of inputs.
class Conv1D
: 1D convolution layer (e.g. temporal convolution).
class Conv1DTranspose
: Transposed convolution layer (sometimes called Deconvolution).
class Conv2D
: 2D convolution layer (e.g. spatial convolution over images).
class Conv2DTranspose
: Transposed convolution layer (sometimes called Deconvolution).
class Conv3D
: 3D convolution layer (e.g. spatial convolution over volumes).
class Conv3DTranspose
: Transposed convolution layer (sometimes called Deconvolution).
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
: Transposed convolution layer (sometimes called Deconvolution).
class Convolution2D
: 2D convolution layer (e.g. spatial convolution over images).
class Convolution2DTranspose
: Transposed convolution layer (sometimes called Deconvolution).
class Convolution3D
: 3D convolution layer (e.g. spatial convolution over volumes).
class Convolution3DTranspose
: Transposed convolution layer (sometimes called Deconvolution).
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 DenseFeatures
: A layer that produces a dense Tensor
based on given feature_columns
.
class DepthwiseConv1D
: Depthwise 1D convolution.
class DepthwiseConv2D
: Depthwise 2D convolution.
class Discretization
: A preprocessing layer which buckets continuous features by ranges.
class Dot
: Layer that computes a dot product between samples in two tensors.
class Dropout
: Applies Dropout to the input.
class ELU
: Exponential Linear Unit.
class EinsumDense
: A layer that uses tf.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 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 spatial 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 spatial data.
class GlobalAvgPool3D
: Global Average pooling operation for 3D data.
class GlobalMaxPool1D
: Global max pooling operation for 1D temporal data.
class GlobalMaxPool2D
: Global max pooling operation for spatial data.
class GlobalMaxPool3D
: Global Max pooling operation for 3D data.
class GlobalMaxPooling1D
: Global max pooling operation for 1D temporal data.
class GlobalMaxPooling2D
: Global max pooling operation for spatial data.
class GlobalMaxPooling3D
: Global Max pooling operation for 3D data.
class GroupNormalization
: Group normalization 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
: Layer to be used as an entry point into a Network (a graph of layers).
class InputSpec
: Specifies the rank, dtype and shape of every input to a layer.
class IntegerLookup
: A preprocessing layer which maps integer features to contiguous ranges.
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.
class LocallyConnected1D
: Locally-connected layer for 1D inputs.
class LocallyConnected2D
: Locally-connected layer for 2D inputs.
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
: Layer that computes the maximum (element-wise) a list of inputs.
class Minimum
: Layer that computes the minimum (element-wise) a list of inputs.
class MultiHeadAttention
: MultiHeadAttention layer.
class Multiply
: Layer that multiplies (element-wise) a list of inputs.
class Normalization
: A preprocessing layer which normalizes continuous features.
class PReLU
: Parametric Rectified Linear Unit.
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
: A preprocessing layer which randomly varies image height during training.
class RandomRotation
: A preprocessing layer which randomly rotates images during training.
class RandomTranslation
: A preprocessing layer which randomly translates images during training.
class RandomWidth
: A preprocessing layer which randomly varies image width during training.
class RandomZoom
: A preprocessing layer which randomly zooms images during training.
class ReLU
: Rectified Linear Unit activation function.
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
: Depthwise separable 1D convolution.
class SeparableConv2D
: Depthwise separable 2D convolution.
class SeparableConvolution1D
: Depthwise separable 1D convolution.
class SeparableConvolution2D
: Depthwise separable 2D convolution.
class SimpleRNN
: Fully-connected RNN where the output is to be fed back to input.
class SimpleRNNCell
: Cell class for SimpleRNN.
class Softmax
: Softmax activation function.
class SpatialDropout1D
: Spatial 1D version of Dropout.
class SpatialDropout2D
: Spatial 2D version of Dropout.
class SpatialDropout3D
: Spatial 3D version of Dropout.
class StackedRNNCells
: Wrapper allowing a stack of RNN cells to behave as a single cell.
class StringLookup
: A preprocessing layer which maps string features to integer indices.
class Subtract
: Layer that subtracts two inputs.
class TextVectorization
: A preprocessing layer which maps text features to integer sequences.
class ThresholdedReLU
: Thresholded Rectified Linear Unit.
class TimeDistributed
: This wrapper allows to apply a layer to every temporal slice of an input.
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(...)
: Input()
is used to instantiate a Keras tensor.
add(...)
: Functional interface to the tf.keras.layers.Add
layer.
average(...)
: Functional interface to the tf.keras.layers.Average
layer.
concatenate(...)
: Functional interface to the Concatenate
layer.
deserialize(...)
: Instantiates a layer from a config dictionary.
dot(...)
: Functional interface to the Dot
layer.
maximum(...)
: Functional interface to compute maximum (element-wise) list of inputs
.
minimum(...)
: Functional interface to the Minimum
layer.
multiply(...)
: Functional interface to the Multiply
layer.
serialize(...)
: Serializes a Layer
object into a JSON-compatible representation.
subtract(...)
: Functional interface to the Subtract
layer.