code: def binary_crossentropy(predictions, targets):
      """Computes the binary cross-entropy between predictions and targets.
  
      .. math:: L = -t \\log(p) - (1 - t) \\log(1 - p)
  
      Parameters
      ----------
      predictions : Theano tensor
          Predictions in (0, 1), such as sigmoidal output of a neural network.
      targets : Theano tensor
          Targets in [0, 1], such as ground truth labels.
  
      Returns
      -------
      Theano tensor
          An expression for the element-wise binary cross-entropy.
  
      Notes
      -----
      This is the loss function of choice for binary classification problems
      and sigmoid output units.
      """
      predictions, targets = align_targets(predictions, targets)
      return theano.tensor.nnet.binary_crossentropy(
          predictions, targets
      )
  
file_path: Lasagne/lasagne/objectives.py
imports: import theano.tensor
  from .utils import as_theano_expression
label_desc: Use image node_id 2 for calling a global function with example usage: binary_crossentropy(predictions, targets) and returns: theano