message: infogain loss: fix bottom blobs description
diff: namespace caffe {
  /**
  - * @brief A generalization of MultinomialLogisticLossLayer that takes an
  + * @brief A generalization of SoftmaxWithLossLayer that takes an
  * "information gain" (infogain) matrix specifying the "value" of all label
  * pairs.
  *
  - * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
  + * Equivalent to the SoftmaxWithLossLayer if the infogain matrix is the
  * identity.
  *
  * @param bottom input Blob vector (length 2-3)
  * -# @f$ (N \times C \times H \times W) @f$
  - * the predictions @f$ \hat{p} @f$, a Blob with values in
  - * @f$ [0, 1] @f$ indicating the predicted probability of each of the
  - * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$
  - * should sum to 1 as in a probability distribution: @f$
  - * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
  + * the predictions @f$ x @f$, a Blob with values in
  + * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
  + * the @f$ K = CHW @f$ classes. This layer maps these scores to a
  + * probability distribution over classes using the softmax function
  + * @f$ \hat{p}_{nk} = \exp(x_{nk}) /
  + * \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
  * -# @f$ (N \times 1 \times 1 \times 1) @f$
  * the labels @f$ l @f$, an integer-valued Blob with values
  * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
  @@ -35,7 +36,7 @@ namespace caffe {
  * (\b optional) the infogain matrix @f$ H @f$. This must be provided as
  * the third bottom blob input if not provided as the infogain_mat in the
  * InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
  - * MultinomialLogisticLossLayer.
  + * SoftmaxWithLossLayer.
  * @param top output Blob vector (length 1)
  * -# @f$ (1 \times 1 \times 1 \times 1) @f$
  * the computed infogain multinomial logistic loss: @f$ E =
  @@ -98,8 +99,8 @@ class InfogainLossLayer : public LossLayer<Dtype> {
  * infogain matrix, if provided as bottom[2])
  * @param bottom input Blob vector (length 2-3)
  * -# @f$ (N \times C \times H \times W) @f$
  - * the predictions @f$ \hat{p} @f$; Backward computes diff
  - * @f$ \frac{\partial E}{\partial \hat{p}} @f$
  + * the predictions @f$ x @f$; Backward computes diff
  + * @f$ \frac{\partial E}{\partial x} @f$
  * -# @f$ (N \times 1 \times 1 \times 1) @f$
  * the labels -- ignored as we can't compute their error gradients
  * -# @f$ (1 \times 1 \times K \times K) @f$
  