AveragePooling2D

  • namespace: Rindow\NeuralNetworks\Layer
  • classname: AveragePooling2D

2D pooling layer. This layer reduces the size of two-dimensional input data by using the average value in the window as a representative value. The number of channels is preserved. It can be used for 2D image data of more than one channel. Input data can be a 4D array of batch size, height, width and number of channels.

AveragePooling2D implements the operation:

  • output := averagepooling(input)

Methods

constructor

$builer->AveragePooling2D(
    int|array $pool_size=2,
    int|array $strides=null,
    string $padding='valid',
    string $data_format='channels_last',
    int|array $dilation_rate=1,
    array $input_shape=null,
    string $name=null,
)

You can create a AveragePooling2D layer instances with the Laqyer Builder.

Options

  • pool_size: Positive integer or array of integer. For a single integer, use a pooling size that has the same height and width. Specify the window size when pooling. default is 2.
  • strides: Positive integer or array of integer. For a single integer, use a kernel size that has the same height and width. Specify the stride length of the pooling. default is same as pool_size.
  • padding: Either “valid” or “same”. If it is “valid”, there is no padding. Performs pooling with the valid range of input. In the case of “same”, the size of the input is expanded so that the output becomes the same size as the input, and that part is padded with zeros. default is “valid”.
  • data_format: Either “channels_last” or “channels_first”. Specify which of the input shapes is the channel.
  • input_shape: Specify the first layer the shape of the input data. In input_shape, the batch dimension is not included.

Input shape

[Batch size, input data height, input data width, number of channels] when data_format is “channels_last”, [Batch size, number of input channels, input data height, input data width] when “channels_first”. Four-dimensional NDArray.

Output shape

[Batch size, input data height, input data width, number of filters] regardless of input shape and data_format. Four-dimensional NDArray.

Examples

$model->add($nn->layers()->AveragePooling2D(pool_size:2));