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tf.contrib.layers.spatial_softmax

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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 [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.