layer_average_pooling_2d: Average pooling operation for spatial data.

In keras: R Interface to 'Keras'

Average pooling operation for spatial data.

Description

Average pooling operation for spatial data.

Usage

layer_average_pooling_2d(
  object,
  pool_size = c(2L, 2L),
  strides = NULL,
  padding = "valid",
  data_format = NULL,
  batch_size = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

object	What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: missing or NULL, the Layer instance is returned. a Sequential model, the model with an additional layer is returned. a Tensor, the output tensor from layer_instance(object) is returned.
pool_size	integer or list of 2 integers, factors by which to downscale (vertical, horizontal). (2, 2) will halve the input in both spatial dimension. If only one integer is specified, the same window length will be used for both dimensions.
strides	Integer, list of 2 integers, or NULL. Strides values. If NULL, it will default to pool_size.
padding	One of "valid" or "same" (case-insensitive).
data_format	A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape ⁠(batch, height, width, channels)⁠ while channels_first corresponds to inputs with shape ⁠(batch, channels, height, width)⁠. It defaults to the image_data_format value found in your Keras config file at ⁠~/.keras/keras.json⁠. If you never set it, then it will be "channels_last".
batch_size	Fixed batch size for layer
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.

Input shape

• If data_format='channels_last': 4D tensor with shape: ⁠(batch_size, rows, cols, channels)⁠

• If data_format='channels_first': 4D tensor with shape: ⁠(batch_size, channels, rows, cols)⁠

Output shape

• If data_format='channels_last': 4D tensor with shape: ⁠(batch_size, pooled_rows, pooled_cols, channels)⁠

• If data_format='channels_first': 4D tensor with shape: ⁠(batch_size, channels, pooled_rows, pooled_cols)⁠

See Also

Other pooling layers: layer_average_pooling_1d(), layer_average_pooling_3d(), layer_global_average_pooling_1d(), layer_global_average_pooling_2d(), layer_global_average_pooling_3d(), layer_global_max_pooling_1d(), layer_global_max_pooling_2d(), layer_global_max_pooling_3d(), layer_max_pooling_1d(), layer_max_pooling_2d(), layer_max_pooling_3d()

keras documentation built on May 29, 2024, 3:20 a.m.