Uses of Class
org.deeplearning4j.nn.conf.layers.NoParamLayer

Packages that use NoParamLayer

Package	Description
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.convolutional
org.deeplearning4j.nn.conf.layers.util

Uses of NoParamLayer in org.deeplearning4j.nn.conf.layers

Subclasses of NoParamLayer in org.deeplearning4j.nn.conf.layers

Modifier and Type	Class and Description
class	ActivationLayer Activation layer is a simple layer that applies the specified activation function to the input activations
class	BaseUpsamplingLayer Upsampling base layer
class	GlobalPoolingLayer Global pooling layer - used to do pooling over time for RNNs, and 2d pooling for CNNs. Supports the following PoolingTypes: SUM, AVG, MAX, PNORM Global pooling layer can also handle mask arrays when dealing with variable length inputs.
class	Pooling1D 1D Pooling (subsampling) layer.
class	Pooling2D 2D Pooling (subsampling) layer.
class	SpaceToBatchLayer Space to batch utility layer configuration for convolutional input types.
class	SpaceToDepthLayer Space to channels utility layer configuration for convolutional input types.
class	Subsampling1DLayer 1D (temporal) subsampling layer - also known as pooling layer. Expects input of shape [minibatch, nIn, sequenceLength].
class	Subsampling3DLayer 3D subsampling / pooling layer for convolutional neural networks
class	SubsamplingLayer Subsampling layer also referred to as pooling in convolution neural nets Supports the following pooling types: MAX, AVG, SUM, PNORM
class	Upsampling1D Upsampling 1D layer Repeats each step size times along the temporal/sequence axis (dimension 2) For input shape [minibatch, channels, sequenceLength] output has shape [minibatch, channels, size * sequenceLength] Example:
class	Upsampling2D Upsampling 2D layer Repeats each value (or rather, set of depth values) in the height and width dimensions by size[0] and size[1] times respectively. If input has shape [minibatch, channels, height, width] then output has shape [minibatch, channels, height*size[0], width*size[1]] Example:
class	Upsampling3D Upsampling 3D layer Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and size[2] If input has shape [minibatch, channels, depth, height, width] then output has shape [minibatch, channels, size[0] * depth, size[1] * height, size[2] * width]
class	ZeroPadding1DLayer Zero padding 1D layer for convolutional neural networks.
class	ZeroPadding3DLayer Zero padding 3D layer for convolutional neural networks.
class	ZeroPaddingLayer Zero padding layer for convolutional neural networks (2D CNNs).

Uses of NoParamLayer in org.deeplearning4j.nn.conf.layers.convolutional

Subclasses of NoParamLayer in org.deeplearning4j.nn.conf.layers.convolutional

Modifier and Type	Class and Description
class	Cropping1D Cropping layer for convolutional (1d) neural networks.
class	Cropping2D Cropping layer for convolutional (2d) neural networks.
class	Cropping3D Cropping layer for convolutional (3d) neural networks.

Uses of NoParamLayer in org.deeplearning4j.nn.conf.layers.util

Subclasses of NoParamLayer in org.deeplearning4j.nn.conf.layers.util

Modifier and Type	Class and Description
class	MaskLayer MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through this layer.