estimator_0.py

# Feature columns describe how to use the input.
my_feature_columns = []
for key in iris_data.train_x.keys():
    my_feature_columns.append(tf.feature_column.numeric_column(key=key))

# Build 2 hidden layer DNN with 10, 10 units respectively.
classifier = tf.estimator.DNNClassifier(
    feature_columns=my_feature_columns,
    # Two hidden layers of 10 nodes each.
    hidden_units=[10, 10],
    # The model must choose between 3 classes.
    n_classes=3,
    # The directory which model to be saved
    model_dir='./tmp'
)

# Train the Model.
classifier.train(input_fn=iris_data.train_input_fn, steps=1000)

# Evaluate the model.
eval_result = classifier.evaluate(input_fn=iris_data.eval_input_fn)

print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))