Conv3d
class Conv3d<T : DType, V>(val inChannels: Int, val outChannels: Int, val kernelSize: Triple<Int, Int, Int>, val stride: Triple<Int, Int, Int> = Triple(1, 1, 1), val padding: Triple<Int, Int, Int> = Triple(0, 0, 0), val dilation: Triple<Int, Int, Int> = Triple(1, 1, 1), val groups: Int = 1, val bias: Boolean = true, val name: String = "Conv3d", initWeights: Tensor<T, V>, initBias: Tensor<T, V>? = null, val trainable: Boolean = true) : Module<T, V> , ModuleParameters<T, V> (source)
3D Convolutional layer that applies a convolution operation over 3D input.
This layer is commonly used for volumetric data like video or medical imaging.
Parameters
inChannels
Number of input channels
outChannels
Number of output channels/filters
kernelSize
Size of the convolving kernel (depth, height, width)
stride
Stride of the convolution (default: 1, 1, 1)
padding
Padding added to all sides of the input (default: 0, 0, 0)
dilation
Spacing between kernel elements (default: 1, 1, 1)
groups
Number of blocked connections from input channels to output channels (default: 1)
bias
Whether to add a learnable bias to the output (default: true)
name
Name of the module
initWeights
Initial weights tensor
initBias
Initial bias tensor (if bias is true)
Constructors
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constructor(inChannels: Int, outChannels: Int, kernelSize: Triple<Int, Int, Int>, stride: Triple<Int, Int, Int> = Triple(1, 1, 1), padding: Triple<Int, Int, Int> = Triple(0, 0, 0), dilation: Triple<Int, Int, Int> = Triple(1, 1, 1), groups: Int = 1, bias: Boolean = true, name: String = "Conv3d", initWeights: Tensor<T, V>, initBias: Tensor<T, V>? = null, trainable: Boolean = true)