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

Before Change

                                   self.regularisation_coefficient_placeholder: self.regularisation_coefficient,
                                   }))
                + ", with unregularized 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: 0,
                                   })))

    def cross_validation_loss(self, params, costs):
        """

After Change

        // - train for train_epochs epochs
        // - if the new loss is greater than the threshold then we haven"t improved much, so stop
        // - else start from the top
        while True:
            threshold = 0.8 * self._loss(params, costs)[0]
            for i in range(self.train_epochs):
                // Split the data into random batches, and train on each batch
                indices = np.random.permutation(len(params))
                for j in range(math.ceil(len(params) / self.batch_size)):
                    batch_indices = indices[j * self.batch_size : (j + 1) * self.batch_size]
                    batch_input = [params[index] for index in batch_indices]
                    batch_output = [[costs[index]] for index in batch_indices]
                    self.tf_session.run(self.train_step,
                                        feed_dict={self.input_placeholder: batch_input,
                                                   self.output_placeholder: batch_output,
                                                   self.regularisation_coefficient_placeholder: self.regularisation_coefficient,
                                                   self.keep_prob_placeholder: self.keep_prob,
                                                   })
            (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