Package smile.deep.layer

Class Conv2dLayer

java.lang.Object

smile.deep.layer.Conv2dLayer

All Implemented Interfaces:

Function<Tensor,Tensor>, Layer

public class Conv2dLayer
extends Object
implements Layer

A convolutional layer.

Constructor Summary

Constructors

Constructor	Description
Conv2dLayer(int in, int out, int kernel, int stride, int padding, int dilation, int groups, boolean bias, String paddingMode)	Constructor.
Conv2dLayer(int in, int out, int kernel, int stride, String padding, int dilation, int groups, boolean bias, String paddingMode)	Constructor.

Method Summary

Modifier and Type	Method	Description
org.bytedeco.pytorch.Module	asTorch()	Returns the PyTorch Module object.
Tensor	forward(Tensor input)	Forward propagation (or forward pass) through the layer.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface java.util.function.Function

andThen, compose

Methods inherited from interface smile.deep.layer.Layer

apply, isTraining

Constructor Details

Conv2dLayer

public Conv2dLayer(int in,
 int out,
 int kernel,
 int stride,
 int padding,
 int dilation,
 int groups,
 boolean bias,
 String paddingMode)

Constructor.

Parameters:

in - the number of input channels.

out - the number of output channels/features.

kernel - the window/kernel size.

stride - controls the stride for the cross-correlation.

padding - controls the amount of padding applied on both sides.

dilation - controls the spacing between the kernel points.

groups - controls the connections between inputs and outputs. The in channels and out channels must both be divisible by groups.

bias - If true, adds a learnable bias to the output.

paddingMode - "zeros", "reflect", "replicate" or "circular".

Conv2dLayer

public Conv2dLayer(int in,
 int out,
 int kernel,
 int stride,
 String padding,
 int dilation,
 int groups,
 boolean bias,
 String paddingMode)

Constructor.

Parameters:

in - the number of input channels.

out - the number of output channels/features.

kernel - the window/kernel size.

stride - controls the stride for the cross-correlation.

padding - "valid" or "same". With "valid" padding, there's no "made-up" padding inputs. It drops the right-most columns (or bottom-most rows). "same" tries to pad evenly left and right, but if the amount of columns to be added is odd, it will add the extra column to the right. If stride is 1, the layer's outputs will have the same spatial dimensions as its inputs.

dilation - controls the spacing between the kernel points.

groups - controls the connections between inputs and outputs. The in channels and out channels must both be divisible by groups.

bias - If true, adds a learnable bias to the output.

paddingMode - "zeros", "reflect", "replicate" or "circular".

Method Details

asTorch

public org.bytedeco.pytorch.Module asTorch()

Description copied from interface: Layer

Returns the PyTorch Module object.

Specified by:

asTorch in interface Layer

Returns:

the PyTorch Module object.

forward

public Tensor forward(Tensor input)

Description copied from interface: Layer

Forward propagation (or forward pass) through the layer.

Specified by:

forward in interface Layer

Parameters:

input - the input tensor.

Returns:

the output tensor.