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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 [i1, j1, ... iN, jN] for all N channels.

Read more here:

"Learning visual feature spaces for robotic manipulation with deep spatial autoencoders." Finn et al.,

features A Tensor of 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.
trainable If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
data_format A string. NHWC (default) and NCHW are supported.

feature_keypoints A Tensor with 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 [i1, j1, ... iN, jN].

ValueError If unexpected data_format specified.
ValueError If num_channels dimension is unspecified.