In [6]:

    

"""Example of DNNClassifier for Iris plant dataset, with run config."""

from sklearn import cross_validation
from sklearn import datasets
from sklearn import metrics
import tensorflow as tf

print(tf.__version__)


    

    




1.0.0

In [7]:

    

# load dataset
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
    iris.data, iris.target, test_size=0.2, random_state=42)

run_config = tf.contrib.learn.estimators.RunConfig(
    num_cores=3, gpu_memory_fraction=0.6)


feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(x_train)
classifier = tf.contrib.learn.DNNClassifier(
    feature_columns=feature_columns,
    hidden_units = [10,20,10],
    n_classes=3,
    config=run_config)

# fit and predict
classifier.fit(x_train, y_train, steps=200)
predictions = list(classifier.predict(x_test, as_iterable=True))
score = metrics.accuracy_score(y_test, predictions)
print('Accuracy:{0:f}'.format(score))


    

    




---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-7-b1c3ad51876e> in <module>()
      4     iris.data, iris.target, test_size=0.2, random_state=42)
      5 
----> 6 run_config = tf.contrib.learn.estimators.RunConfig(
      7     num_cores=3, gpu_memory_fraction=0.6)
      8 

AttributeError: module 'tensorflow.contrib.learn' has no attribute 'estimators'

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