@@ -4,8 +4,8 @@ import iris_data

 (train_x, train_y), (test_x, test_y) = iris_data.load_data()

-print np.shape(train_x)

-print np.shape(train_y)

+print (np.shape(train_x))

+print (np.shape(train_y))

 feature_columns = []

 for key in train_x.keys():

@@ -41,7 +41,7 @@ def test_input_fn(x, y, batch_size):

     return dataset

 result = classifier.evaluate(input_fn = lambda:iris_data.eval_input_fn(test_x, test_y, batch_size))

-print result

+print (result)

 predict_x = {

     'SepalLength': [5.1, 5.9, 6.9],

@@ -53,4 +53,4 @@ predict_x = {

 predictions = classifier.predict(input_fn=lambda:test_input_fn(predict_x, y=None, batch_size=batch_size))

 for p in predictions:

-    print p

+    print (p)