Like Convolution(), AveragePooling() processes items arranged on an N-dimensional grid, such as an image. Typically, each item is a vector. For each item, average-pooling computes the element-wise mean over a window (“receptive field”) of items surrounding the item’s position on the grid.
AveragePooling(filter_shape, strides = 1, pad = FALSE, name = "")
| filter_shape | int or list of int - shape (spatial extent) of the receptive field, not including the input feature-map depth. E.g. (3,3) for a 2D convolution. receptive field, not including the input feature-map depth. E.g. (3,3) for a 2D convolution. |
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| strides | (int or tuple of ints, defaults to 1) – stride of the operation. Use a list of ints to specify a per-axis value. (int or tuple of ints, defaults to 1) – stride (increment when sliding over the input). Use a tuple to specify a per-axis value. |
| pad | (bool or list of bools) – if False, then the operation will be shifted over the “valid” area of input, that is, no value outside the area is used. If pad=True on the other hand, the operation will be applied to all input positions, and positions outside the valid region will be considered containing zero. Use a list to specify a per-axis value. (bool or tuple of bools, defaults to False) – if False, then the |
| name | string (optional) the name of the Function instance in the network |
A function that accepts one argument and applies the average-pooling operation to it
The size (spatial extent) of the receptive field is given by filter <- shape. E.g. for 2D pooling, filter <- shape should be a tuple of two integers, such as (5,5).
(f <- AveragePooling(c(3, 3), strides = 2))#> AveragePooling(x: Sequence[tensor]) -> Sequence[tensor]#> Input('Input102', [#], [4])