Global max pooling operation for spatial data.
layer_global_max_pooling_2d(object, data_format = NULL, keepdims = FALSE, ...)
What to compose the new
Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by
The return value depends on
Layer instance is returned.
Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from
layer_instance(object) is returned.
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
found in your Keras config file at
~/.keras/keras.json. If you never set
it, then it will be "channels_last".
A boolean, whether to keep the spatial dimensions or not. If
FALSE (default), the rank of the tensor is reduced for
spatial dimensions. If
TRUE, the spatial dimensions are
retained with length 1. The behavior is the same as for
standard layer arguments.
data_format='channels_last': 4D tensor with shape:
(batch_size, rows, cols, channels)
data_format='channels_first': 4D tensor with shape:
(batch_size, channels, rows, cols)
2D tensor with shape:
Other pooling layers: