Can someone explain the differance between tf.contirb.DNNClassifier (learn) and tf.estimator.DNNClassifier.tf.contirb.DNNClassifier (learn) works but gives warningsWARNING:tensorflow:From C:\Anaconda3\lib.. scalar_summary ...is deprecated and will be removed after 2016-11-30.Please switch to tf.summary.scalar. ..but I can load the layers names and values with get_variable_names and get_variable_value.tf.estimator works fine but how do I get the layers name and values???See code below with a switch (if True/False) for the two versionsInstalation in Windows 103.6.1 |Anaconda 4.4.0 (64-bit)| (default, May 11 2017, 13:25:24) [MSC v.1900 64 bit (AMD64)]Tensorflow is installed in the root with:pip install --upgrade tensorflow --ignore-installed (This is the only combination that works for me)pip list givestensorflow (1.3.0)tensorflow-tensorboard (0.1.4)#Example of DNNClassifier for Iris plant dataset.from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport numpy as npfrom sklearn import datasetsfrom sklearn import metricsfrom sklearn import model_selectionimport osimport tensorflow as tffrom tensorflow.contrib import learnos.environ['TF_CPP_MIN_LOG_LEVEL']='3'  # get rid oc tf_jenkins WARNINGdef main():    # Load dataset.    iris = datasets.load_iris()    x_train, x_test, y_train, y_test = model_selection.train_test_split(        iris.data, iris.target, test_size=0.2, random_state=42)    # Define the training inputs and train    get_train_input_fn = tf.estimator.inputs.numpy_input_fn(        x={'x':x_train}, y=y_train, num_epochs=None, shuffle=True)    #Predict     get_test_input_fn = tf.estimator.inputs.numpy_input_fn(        x={'x':x_test}, y=y_test, num_epochs=1, shuffle=True)    # Build 3 layer DNN with 10, 20, 10 units respectively.        feature_columns = [tf.feature_column.numeric_column('x', shape=np.array(x_train).shape[1:])]        #Select code     if True: # tf.estimator...        classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,             hidden_units=[10, 20, 10], n_classes=3)        #Train        classifier.train(input_fn=get_train_input_fn, steps=200)        scores = classifier.evaluate(input_fn=get_test_input_fn)        print('Accuracy (tf.estimator): {0:f}'.format(scores['accuracy']))        # get_variable_names, get_variable_value. How ?????    else: # learn (tf.contrib)        classifier = learn.DNNClassifier(feature_columns=feature_columns,            hidden_units=[10, 20, 10], n_classes=3)        #Train            classifier.fit(input_fn=get_train_input_fn, steps=200)         scores = classifier.evaluate(input_fn=get_test_input_fn,steps=1)        print(\nTest Accuracy (learn): {0:f}\n.format(scores[accuracy]))        # Get data        names=classifier.get_variable_names()        data={}        for name in names:            data[name]=classifier.get_variable_value(name)if __name__ == __main__:    main()
Tensorflow: tf.contrib.DNNClassifier(..) or tf.estimator.DNNClassifier(..)
tensorflow