epoch_end Logs Default to Steps Instead of Epochs
🐛 Bug
Logs generated within validation_epoch_end have their iteration set to the number of steps instead of number of epochs.
To Reproduce
Steps to reproduce the behavior:

Create LightningModile with the below functions.
Train the module for 1 or more epochs
Run Tensorboard
Note the iteration for logs generated in validation_epoch_end

Code Sample
    def training_step(self, train_batch, batch_idx):
        x, y = train_batch
        y_hat = self.forward(x)
        loss = self.cross_entropy_loss(y_hat, y)
        accuracy = self.accuracy(y_hat, y)
        logs = {'batch/train_loss': loss}
        return {'loss': loss, 'log': logs}

    def validation_step(self, val_batch, batch_idx):
        x, y = val_batch
        y_hat = self.forward(x)
        loss = self.cross_entropy_loss(y_hat, y)
        accuracy = self.accuracy(y_hat, y)
        return {'batch/val_loss': loss, 'batch/accuracy': accuracy}

    def validation_epoch_end(self, outputs):
        avg_loss = torch.stack([x['batch/val_loss'] for x in outputs]).mean()
        avg_accuracy = torch.stack([x['batch/accuracy'] for x in outputs]).mean()
        tensorboard_logs = {'epoch/val_loss': avg_loss, 'epoch/accuracy' : avg_accuracy}
        return {'avg_val_loss': avg_loss, 'accuracy' : avg_accuracy, 'log': tensorboard_logs}

Version
v0.7.1
Screenshot

Expected behavior
Logs generated in *_epoch_end functions use the epoch as their iteration on the y axis

The documentation seems unclear on the matter. The Loggers documentation is empty. The LightningModule class doesn't describe logging in detail and the Introduction Guide has a bug in the example self.logger.summary.scalar('loss', loss) -> AttributeError: 'TensorBoardLogger' object has no attribute 'summary'.
Is there a workaround for this issue?