layer_max_pooling_3d
Max pooling operation for 3D data (spatial or spatio-temporal).
Description
Max pooling operation for 3D data (spatial or spatio-temporal).
Usage
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
) Arguments
| Arguments | Description |
|---|---|
| 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 | 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. |
| strides | list of 3 integers, or NULL. Strides values. |
| 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, 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”. |
| 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. |
Section
Input shape
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)
Output shape
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)
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_2d()