\end{cases}
This is used for measuring the error of a reconstruction in for
   example an auto-encoder. Note that the targets t[i] should be
   numbers between 0 and 1.
It's possible to trade off recall and precision by adding weights
   to positive examples. In the case of multi-label classification the
   loss can be described as:
  \ell_c(x, y) = L_c = \{l_{1,c},\dots,l_{N,c}\}^\top, \quad
  l_{n,c} = - w_{n,c} \left[ p_c y_{n,c} \cdot \log
  \sigma(x_{n,c}) + (1 - y_{n,c}) \cdot \log (1 - \sigma(x_{n,c}))
  \right],

where c is the class number (c > 1 for multi-label binary
   classification, c = 1 for single-label binary classification), n is
   the number of the sample in the batch and p_c is the weight of the
   positive answer for the class c.
p_c > 1 increases the recall, p_c < 1 increases the precision.
For example, if a dataset contains 100 positive and 300 negative
   examples of a single class, then pos_weight for the class should