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.
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
: Normalize and scale inputs or activations. (Ioffe and Szegedy, 2014).
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 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 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 CuDNNGRU
: Fast GRU implementation backed by cuDNN.
class CuDNNLSTM
: Fast LSTM implementation backed by cuDNN.
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 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 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 ndim, 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
: Base layer class.
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 temporal data.
class MaxPool2D
: Max pooling operation for spatial data.
class MaxPool3D
: Max pooling operation for 3D data (spatial or spatio-temporal).
class MaxPooling1D
: Max pooling operation for temporal data.
class MaxPooling2D
: Max pooling operation for 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 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
: Reshapes an output to a certain 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 Add
layer.
average(...)
: Functional interface to the 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 the Maximum
layer that computes
minimum(...)
: Functional interface to the Minimum
layer.
multiply(...)
: Functional interface to the Multiply
layer.
subtract(...)
: Functional interface to the Subtract
layer.