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
)Model or layer object
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.
Integer, list of 2 integers, or NULL. Strides values. If NULL,
it will default to pool_size.
One of "valid" or "same" (case-insensitive).
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".
Fixed batch size for layer
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
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)
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)
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()