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.
|