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 )
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 |
padding |
One of |
data_format |
A string, one of |
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. |
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()
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