Understanding autoencoder loss function I've never understood how to calculate an autoencoder loss function because the prediction has many dimensions, and I always thought that a loss function had to output a single number / scalar estimate for a given record. However, on GitHub I recently came across a repository that has implemented an example autoencoder in TensorFlow, and the squared error implementation is a lot simpler than I thought: 

cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))


The TensorFlow documentation on reduce_mean says, among other things:


  If axis has no entries, all dimensions are reduced, and a tensor with
  a single element is returned.


Can I conclude from all of this that the squared error of an autoencoder prediction is just the average across all of the record's dimensions?
Yes, you are correct in thinking that the squared error of an autoencoder prediction for a single example is the average of the squared error of the prediction for all dimensions. Similarly, the squared error for a whole batch of examples will be the average of the error of each example in the batch.