layer_max_pooling_2d
Max pooling operation for spatial data.
Description
Max pooling operation for spatial data.
Usage
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
| 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 | 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. |
Section
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()