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:
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?
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.