tf.contrib.layers.spatial_softmax( features, temperature=None, name=None, variables_collections=None, trainable=True, data_format='NHWC' )
Computes the spatial softmax of a convolutional feature map.
First computes the softmax over the spatial extent of each channel of a convolutional feature map. Then computes the expected 2D position of the points of maximal activation for each channel, resulting in a set of feature keypoints [x1, y1, ... xN, yN] for all N channels.
Read more here: "Learning visual feature spaces for robotic manipulation with deep spatial autoencoders." Finn et al., http://arxiv.org/abs/1509.06113.
Tensorof size [batch_size, W, H, num_channels]; the convolutional feature map.
temperature: Softmax temperature (optional). If None, a learnable temperature is created.
name: A name for this operation (optional).
variables_collections: Collections for the temperature variable.
Truealso add variables to the graph collection
data_format: A string.
Tensorwith size [batch_size, num_channels * 2]; the expected 2D locations of each channel's feature keypoint (normalized to the range (-1,1)). The inner dimension is arranged as [x1, y1, ... xN, yN].
ValueError: If unexpected data_format specified.
ValueError: If num_channels dimension is unspecified.