# tf.contrib.layers.spatial_softmax

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.

#### 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 [x1, y1, ... xN, yN].

#### Raises:

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