How does the Softmax layer of Caffe calculates the probability values?

Does anyone know what computations take place inside the Caffe softmax layer?

I am using a pre-trained network with a softmax layer at the end.

In testing phase, for a simple forward of an image, the output of the second-last layer ("InnerProduct") is the following: -0.20095, 0.39989, 0.22510, -0.36796, -0.21991, 0.43291, -0.22714, -0.22229, -0.08174, 0.01931, -0.05791, 0.21699, 0.00437, -0.02350, 0.02924, -0.28733, 0.19157, -0.04191, -0.07360, 0.30252

The last layer's ("Softmax") output is the following values: 0.00000, 0.44520, 0.01115, 0.00000, 0.00000, 0.89348, 0.00000, 0.00000, 0.00002, 0.00015, 0.00003, 0.00940, 0.00011, 0.00006, 0.00018, 0.00000, 0.00550, 0.00004, 0.00002, 0.05710

If i apply a Softmax (using an external tool, like matlab) on the inner product layer's output i get the following values: 0.0398, 0.0726, 0.0610, 0.0337, 0.0391, 0.0751, 0.0388, 0.0390, 0.0449, 0.0496, 0.0460, 0.0605, 0.0489, 0.0476, 0.0501, 0.0365, 0.0590, 0.0467, 0.0452, 0.0659

The latter makes sense to me, since the probabilities add up to 1.0 (notice that the sum of Caffe's Softmax layer values is > 1.0).

Apparently, the softmax layer in Caffe is not a straight-forward Softmax operation.

(I do not think that it makes any difference, but i will just mention that i am using the pre-trained flickr style network, see description here).

EDIT:

Here is the definition of the two last layers in the proto txt. Notice that the type of the last layer is "Softmax".

layer {
  name: "fc8_flickr"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8_flickr"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 20
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "fc8_flickr"
  top: "prob"
}