Keras layers API.
Aliases:
- Module
tf.compat.v1.keras.layers
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: Base class of Batch normalization layer (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 denseTensorbased on givenfeature_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 aLayerobject. -
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 theAddlayer. -
average(...): Functional interface to theAveragelayer. -
concatenate(...): Functional interface to theConcatenatelayer. -
deserialize(...): Instantiates a layer from a config dictionary. -
dot(...): Functional interface to theDotlayer. -
maximum(...): Functional interface to theMaximumlayer that computes -
minimum(...): Functional interface to theMinimumlayer. -
multiply(...): Functional interface to theMultiplylayer. serialize(...)-
subtract(...): Functional interface to theSubtractlayer.
