Warning: This project is deprecated. Swift for TensorFlow was an experiment in the
next-generation platform for machine learning, incorporating the latest research across
machine learning, compilers, differentiable programming, systems design, and beyond. It was
archived in February 2021.
MaxPool3D
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A max pooling layer for spatial or spatio-temporal data.
-
-
The size of the sliding reduction window for pooling.
Declaration
@noDerivative
public let poolSize: (Int, Int, Int, Int, Int)
-
The strides of the sliding window for each dimension of a 5-D input.
Strides in non-spatial dimensions must be 1
.
Declaration
@noDerivative
public let strides: (Int, Int, Int, Int, Int)
-
The padding algorithm for pooling.
Declaration
@noDerivative
public let padding: Padding
-
Creates a max pooling layer.
Declaration
public init(
poolSize: (Int, Int, Int, Int, Int),
strides: (Int, Int, Int, Int, Int),
padding: Padding
)
-
Returns the output obtained from applying the layer to the given input.
Declaration
@differentiable
public func forward(_ input: Tensor<Scalar>) -> Tensor<Scalar>
-
Creates a max pooling layer.
Declaration
public init(poolSize: (Int, Int, Int), strides: (Int, Int, Int), padding: Padding = .valid)
Parameters
poolSize
|
Vertical and horizontal factors by which to downscale.
|
strides
|
|
padding
|
|
-
Creates a max pooling layer with the specified pooling window size and stride. All pooling
sizes and strides are the same.
Declaration
public init(poolSize: Int, stride: Int, padding: Padding = .valid)
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Last updated 2021-09-28 UTC.
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