weights acts as a coefficient for the loss. If a scalar is provided, then
the loss is simply scaled by the given value. If weights is a tensor of size
[batch_size], then the total loss for each sample of the batch is rescaled
by the corresponding element in the weights vector. If the shape of
weights matches the shape of predictions, then the loss of each
measurable element of predictions is scaled by the corresponding value of
The predicted outputs.
The ground truth output tensor, same dimensions as 'predictions'.
Coefficients for the loss a scalar, a tensor of shape
[batch_size] or a tensor whose shape matches predictions.
A small increment to add to avoid taking a log of zero.
The scope for the operations performed in computing the loss.
A scalar Tensor representing the loss value.
If the shape of predictions doesn't match that of labels or
if the shape of weights is invalid.