What does DNN mean in a TensorFlow Estimator.DNNClassifier?

Question

I'm guessing that DNN in the sense used in TensorFlow means "deep neural network". But I find this deeply confusing since the notion of a "deep" neural network seems to be in wide use elsewhere to mean a network with typically several convolutional and/or associated layers (ReLU, pooling, dropout, etc).

In contrast, the first instance many people will encounter this term (in the tfEstimator Quickstart example code) we find:

# Build 3 layer DNN with 10, 20, 10 units respectively.
  classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
                                          hidden_units=[10, 20, 10],
                                          n_classes=3,
                                          model_dir="/tmp/iris_model")

This sounds suspiciously shallow, and even more suspiciously like an old-style multilayer perceptron (MLP) network. However, there is no mention of DNN as an alternative term on that close-to-definitive source. So is a DNN in the TensorFlow tf.estimator context actually an MLP? Documentation on the hidden_units parameter suggests this is the case:

• hidden_units: Iterable of number hidden units per layer. All layers are fully connected. Ex. [64, 32] means first layer has 64 nodes and second one has 32.

That has MLP written all over it. Is this understanding correct? Is DNN therefore a misnomer, and if so should DNNClassifier ideally be deprecated in favour of MLPClassifier? Or does DNN stand for something other than deep neural network?

Answer 1

Give me your definition of "deep" neural network and you get your answer.

But yes, it is simply a MLP and a proper naming would be MLPclassifier indeed. But this does not sounds as cool as the current name.