class MXNet::Gluon::NN::Internal::Conv

Overview

Base class for convolution layers.

This layer creates a convolution kernel that is convolved with the input to produce a tensor of outputs.

Direct Known Subclasses

Defined in:

mxnet/gluon/nn/layers.cr

Constructors

Instance Method Summary

Instance methods inherited from class MXNet::Gluon::HybridBlock

export(filename, epoch = 0) export, forward(inputs : Array(T)) : Array(T) forall T forward, hybrid_forward(inputs : Array(T), params : Hash(String, T) = {} of String => T) : Array(T) forall T hybrid_forward, hybridize(active = true, flags = {} of String => String) hybridize, register_child(block, name = nil) register_child

Instance methods inherited from module MXNet::Gluon::CachedGraph

clear_cache clear_cache, infer_dtype(args) infer_dtype, infer_shape(args) infer_shape

Constructor methods inherited from module MXNet::Gluon::CachedGraph

new(**kwargs) new

Instance methods inherited from class MXNet::Gluon::Block

call(inputs : Array(T)) : Array(T) forall T call, children children, collect_params(selector = nil) collect_params, forward(inputs : Array(T)) : Array(T) forall T forward, get_attr(name : String) : Block | Parameter | Nil get_attr, hybridize(active = true) hybridize, init(init = nil, ctx = nil, force_reinit = false) init, load_parameters(fname, ctx = MXNet.cpu, allow_missing = false, ignore_extra = false) load_parameters, params : MXNet::Gluon::ParameterDict params, prefix : String prefix, register_child(block, name = nil) register_child, register_parameter(param, name = nil) register_parameter, save_parameters(fname) save_parameters, scope : MXNet::Gluon::BlockScope? scope, set_attr(name : String, value : Block | Parameter | Nil) set_attr, with_name_scope(&) with_name_scope

Constructor methods inherited from class MXNet::Gluon::Block

new(prefix = nil, params = nil) new

Constructor Detail

def self.new(*, channels : Int32, kernel_size : Array(Int32), strides : Array(Int32) | Int32, padding : Array(Int32) | Int32, dilation : Array(Int32) | Int32, layout : String, in_channels = 0, use_bias = true, activation = nil, **kwargs) #

Creates a new instance.

N is the number of dimensions of the convolution.

Parameters

  • channels (Int32) The dimensionality of the output space (the number of output channels in the convolution).
  • kernel_size (Array(Int32) of N integers) Specifies the dimensions of the convolution window.
  • strides (Int32 or Array(Int32) of N integers) Specifies the strides of the convolution.
  • padding (Int32 or Array(Int32) of N integers) If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.
  • dilation (Int32 or Array(Int32) of N integers) Specifies the dilation rate to use for dilated convolution.
  • layout (String) Dimension ordering of data and weight. Can be "NCW", "NWC", "NCHW", "NHWC", "NCDHW", "NDHWC", etc. "N", "C", "H", "W", "D" stands for batch, channel, height, width and depth dimensions respectively. Convolution is performed over "D", "H", and "W" dimensions.
  • in_channels (Int32, default = 0) The number of input channels to this layer. If not specified, initialization will be deferred to the first time #forward is called and in_channels will be inferred from the shape of the input data.
  • use_bias (Bool, default = true) Whether the layer uses a bias vector.
  • activation (String, optional) Activation function to use. If nothing is specified, no activation is applied (it acts like "linear" activation: a(x) = x).

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Instance Method Detail

def act #

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def act=(act : MXNet::Gluon::NN::Activation?) #

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def act? : MXNet::Gluon::NN::Activation? #

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def bias #

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def bias=(bias : MXNet::Gluon::Parameter?) #

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def bias? : MXNet::Gluon::Parameter? #

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def hybrid_forward(inputs : Array(T), params : Hash(String, T)) : Array(T) forall T #
Description copied from class MXNet::Gluon::HybridBlock

Override to construct symbolic graph for this HybridBlock.

Parameters


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def weight #

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def weight=(weight : MXNet::Gluon::Parameter?) #

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def weight? : MXNet::Gluon::Parameter? #

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