コード例 #1
0
    def run_evaluate(self, inputs, targets, epoch):
        """Runs evaluation on validation inputs and targets.
        Optionally: - writes summary to Tensorboard.
        """
        if self.summary_dir is not None:
            eval_losses = []
            for inps, trgts in summarizer_model_utils.minibatches(
                    inputs, targets, self.batch_size):
                fd, sl, s2 = self.get_feed_dict(inps, trgts)
                eval_loss = self.sess.run([self.eval_loss], feed_dict=fd)
                eval_losses.append(eval_loss)

            avg_eval_loss = self.sess.run(tf.reduce_mean(eval_losses))

            print('Eval_loss: {}\n'.format(avg_eval_loss))
            eval_summ = self.sess.run([self.eval_summary], feed_dict=fd)
            self.eval_writer.add_summary(eval_summ, epoch)

        else:
            eval_losses = []
            for inps, trgts in summarizer_model_utils.minibatches(
                    inputs, targets, self.batch_size):
                fd, sl, s2 = self.get_feed_dict(inps, trgts)
                eval_loss = self.sess.run([self.eval_loss], feed_dict=fd)
                eval_losses.append(eval_loss)

            avg_eval_loss = self.sess.run(tf.reduce_mean(eval_losses))

            print('Eval_loss: {}\n'.format(avg_eval_loss))
コード例 #2
0
    def run_epoch(self, inputs, targets, epoch):
        """Runs a single epoch.
           Returns the average loss value on the epoch."""
        batch_size = self.batch_size
        nbatches = (len(inputs) + batch_size - 1) // batch_size
        losses = []

        for i, (inps, trgts) in enumerate(
                summarizer_model_utils.minibatches(inputs, targets,
                                                   batch_size)):
            if inps is not None and trgts is not None:
                fd, sl, s2 = self.get_feed_dict(inps, trgts=trgts)

                if i % 10 == 0 and self.summary_dir is not None:
                    _, train_loss, training_summ = self.sess.run([
                        self.train_op, self.train_loss, self.training_summary
                    ],
                                                                 feed_dict=fd)
                    self.training_writer.add_summary(training_summ,
                                                     epoch * nbatches + i)

                else:
                    _, train_loss = self.sess.run(
                        [self.train_op, self.train_loss], feed_dict=fd)

                if i % 2 == 0 or i == (nbatches - 1):
                    print('Iteration: {} of {}\ttrain_loss: {:.4f}'.format(
                        i, nbatches - 1, train_loss))
                losses.append(train_loss)

            else:
                print('Minibatch empty.')
                continue

        avg_loss = self.sess.run(tf.reduce_mean(losses))
        print('Average Score for this Epoch: {}'.format(avg_loss))

        return avg_loss