Through the scikit-learn library, the tf.contrib.learn function is used to generate three layers of neural network model, and the data is trained directly, and the data can be predicted.

boston
boston\boston.py
boston\models
boston\models\dnnregressor
boston\models\dnnregressor\checkpoint
boston\models\dnnregressor\eval
boston\models\dnnregressor\eval\events.out.tfevents.1505876573.ADMIN-PC
boston\models\dnnregressor\eval\events.out.tfevents.1505876614.ADMIN-PC
boston\models\dnnregressor\events.out.tfevents.1505876555.ADMIN-PC
boston\models\dnnregressor\events.out.tfevents.1505876596.ADMIN-PC
boston\models\dnnregressor\graph.pbtxt
boston\models\dnnregressor\model.ckpt-1.data-00000-of-00001
boston\models\dnnregressor\model.ckpt-1.index
boston\models\dnnregressor\model.ckpt-1.meta
boston\models\dnnregressor\model.ckpt-10000.data-00000-of-00001
boston\models\dnnregressor\model.ckpt-10000.index
boston\models\dnnregressor\model.ckpt-10000.meta
boston\models\dnnregressor\model.ckpt-5000.data-00000-of-00001
boston\models\dnnregressor\model.ckpt-5000.index
boston\models\dnnregressor\model.ckpt-5000.meta
boston\models\dnnregressor\model.ckpt-5001.data-00000-of-00001
boston\models\dnnregressor\model.ckpt-5001.index
boston\models\dnnregressor\model.ckpt-5001.meta

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