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Deboning simple linear regression output in TensorFlow

A simple linear regression example in TensorFlow(TM) is given at (https://www.tensorflow.org/tutorials/keras/basic_regression). By default, it produces none of the outputs of linear regression, other than MAE or MSE, on request through a print statement.

Testing set Mean Abs Error: $2788.86

Time spent searching the documentation turned up no references to “intercept”, “coefficient”, “F-statistic”, “t-value,” or “p-value.”

I am only beginning to dip my toes into TensorFlow(TM). I suspect that it exists not so much as a suite of modeling tools so much as a platform to express them in n-dimensional space very efficiently. Like the output of the example itself, some users seem concerned only with the spread between the model prediction and results when testing (https://web.stanford.edu/class/cs20si/2017/lectures/notes_03.pdf).

There must be a way to use stats packages in Python to specify a model, pass it to TF, have it processed, and retrieve the full diagnostics. Otherwise, it seems too much like trust me, would I … .