Max pooling operation for 3D data (spatial or spatio-temporal).
layer_max_pooling_3d(object, pool_size = c(2L, 2L, 2L), strides = NULL,
padding = "valid", data_format = NULL, batch_size = NULL,
name = NULL, trainable = NULL, weights = NULL)Model or layer object
list of 3 integers, factors by which to downscale (dim1, dim2, dim3). (2, 2, 2) will halve the size of the 3D input in each dimension.
list of 3 integers, or NULL. Strides values.
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, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds
to inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). 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': 5D tensor with shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)
If data_format='channels_first': 5D tensor with shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)
If data_format='channels_last': 5D tensor with shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)
If data_format='channels_first': 5D tensor with shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)
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_2d