Loss Layers¶
- class MultinomialLogisticLossLayer¶
The multinomial logistic loss is defined as \(\ell = -w_g\log(x_g)\), where \(x_1,\ldots,x_C\) are probabilities for each of the \(C\) classes conditioned on the input data, \(g\) is the corresponding ground-truth category, and \(w_g\) is the weight for the \(g\)-th class (default 1, see bellow).
The conditional probability blob should be of the shape \((W,H,C,N)\), and the ground-truth blob should be of the shape \((W,H,1,N)\). Typically there is only one label for each instance, so \(W=H=1\). The ground-truth should be a zero-based index in the range of \(0,\ldots,C-1\).
- bottoms¶
Should be a vector containing two symbols. The first one specifies the name for the conditional probability input blob, and the second one specifies the name for the ground-truth input blob.
- weights¶
This could be used to specify weights for different classes. The following values are allowed
- Empty array (default). This means each category should be equally weighted.
- A 3D tensor of the shape (width, height, channels). Here the (w,h,c) entry indicates the weights for category c at location (w,h).
- A 1D vector of length channels. When both width and height are 1, this is equivalent to the case above. Otherwise, the weight vector across channels is repeated at every location (w,h).
- normalize¶
Indicating how weights should be normalized if given. The following values are allowed
- :local (default): Normalize the weights locally at each location (w,h), across the channels.
- :global: Normalize the weights globally.
- :no: Do not normalize the weights.
The weights normalization are done in a way that you get the same objective function when specifying equal weights for each class as when you do not specify any weights. In other words, the total sum of the weights are scaled to be equal to weights ⨉ height ⨉ channels. If you specify :no, it is your responsibility to properly normalize the weights.
- class SoftmaxLossLayer¶
This is essentially a combination of MultinomialLogisticLossLayer and SoftmaxLayer. The given predictions \(x_1,\ldots,x_C\) for the \(C\) classes are transformed with a softmax function
\[\sigma(x_1,\ldots,x_C) = (\sigma_1,\ldots,\sigma_C) = \left(\frac{e^{x_1}}{\sum_j e^{x_j}},\ldots,\frac{e^{x_C}}{\sum_je^{x_j}}\right)\]which essentially turn the predictions into non-negative values with exponential function and then re-normalize to make them look like probabilties. Then the transformed values are used to compute the multinomial logsitic loss as
\[\ell = -w_g \log(\sigma_g)\]Here \(g\) is the ground-truth label, and \(w_g\) is the weight for the \(g\)-th category. See the document of MultinomialLogisticLossLayer for more details on what the weights mean and how to specify them.
The shapes of inputs is the same as MultinomialLogisticLossLayer: the multi-class predictions are assumed to be along the channel dimension.
The reason we provide a combined softmax loss layer instead using one softmax layer and one multinomial logistic layer is that the combined layer produces the back-propagation error in a more numerically robust way.
\[\frac{\partial \ell}{\partial x_i} = w_g\left(\frac{e^{x_i}}{\sum_j e^{x_j}} - \delta_{ig}\right) = w_g\left(\sigma_i - \delta_{ig}\right)\]Here \(\delta_{ig}\) is 1 if \(i=g\), and 0 otherwise.
- bottoms¶
Should be a vector containing two symbols. The first one specifies the name for the conditional probability input blob, and the second one specifies the name for the ground-truth input blob.
- weights¶
- normalize¶
Properties for the underlying MultinomialLogisticLossLayer. See document there for details.