Module: tf.keras.layers 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 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 ConvLSTM2D: 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 DepthwiseConv2D: Depthwise separable 2D convolution. 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 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 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 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 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 ReLU: Rectified Linear Unit activation function. class RepeatVector: Repeats the input n times. class Reshape: Layer that reshapes inputs into the given shape. 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 Subtract: Layer that subtracts two inputs. class ThresholdedReLU: Thresholded Rectified Linear Unit. class TimeDistributed: This wrapper allows to apply a layer to every temporal slice of an input. 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(...) subtract(...): Functional interface to the Subtract layer.