Global average pooling operation for spatial data.
layer_global_average_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 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.
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".
A boolean, whether to keep the spatial dimensions or not. If
keepdims is FALSE (default), the rank of the tensor is reduced for
spatial dimensions. If keepdims is TRUE, the spatial dimensions are
retained with length 1. The behavior is the same as for tf.reduce_mean or
np.mean.
standard layer arguments.
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)
2D tensor with shape: (batch_size, channels)
Other pooling layers:
layer_average_pooling_1d(),
layer_average_pooling_2d(),
layer_average_pooling_3d(),
layer_global_average_pooling_1d(),
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(),
layer_max_pooling_3d()