tf.contrib.layers.spatial_softmax

Computes the spatial softmax of a convolutional feature map.

tf.contrib.layers.spatial_softmax(
    features, temperature=None, name=None, variables_collections=None,
    trainable=True, data_format='NHWC'
)

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., http://arxiv.org/abs/1509.06113

Args
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.

Returns
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].

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