6e5cf676b113af8d70e34224f23bc8eace618856,mloop/nnlearner.py,SingleNeuralNet,fit,#SingleNeuralNet#Any#Any#,120

Before Change

                                               })

        self.log.debug("Fit neural network with total training cost "
                + str(self.tf_session.run(
                        self.loss_func,
                        feed_dict={self.input_placeholder: params,
                                   self.output_placeholder: [[c] for c in costs],
                                   self.regularisation_coefficient_placeholder: self.regularisation_coefficient,
                                   }))
                + ", with unregularized cost "
                + str(self.tf_session.run(
                        self.loss_func,
                        feed_dict={self.input_placeholder: params,

After Change

            (l, ul) = self._loss(params, costs)
            self.log.debug("Fit neural network with total training cost " + str(l)
                    + ", with unregularized cost " + str(ul))
            if l > threshold:
                break
            self.log.debug("Cost decreased by a lot, train again")

    def cross_validation_loss(self, params, costs):
        """
        Returns the loss of the network on a cross validation set.

Instances