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: Normalize and scale inputs or activations.

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<