Max pooling operation for spatial data.

Max pooling operation for spatial data.

layer_max_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

Model or layer object
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_2d, 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_3d