Contrived tf.estimator.DNNClassifier

sample.py

import tensorflow as tf
import numpy as np
import sys
from tensorflow.python import debug as tf_debug
hooks = [tf_debug.LocalCLIDebugHook()]
tf.logging.set_verbosity(tf.logging.INFO)
trainX = np.array([[1,1,0,1],[0,0,1,0],[1,0,1,1],[0,0,1,1]])
labelX = np.array([[1],[0],[1],[0]])
num_classes = 2
feature_names = ['f1','f2','f3','f4']
feature_columns = [tf.feature_column.numeric_column(k) for k in feature_names]
classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns, 
                                            n_classes=num_classes, #setting number of classes here
                                            hidden_units=[10])

def input_fn():
    def gen1(a,b):
        print(a.shape, b.shape)
        features = tf.split(a,4)
        return dict(zip(feature_names, features)),b
    dataset = (tf.data.Dataset.from_tensor_slices((trainX, labelX)).map(gen1))
    dataset = dataset.repeat(8)
    dataset = dataset.batch(32)
    iterator = dataset.make_one_shot_iterator()
    data, labels = iterator.get_next()
    return data, labels


def input_fn_pred(in_arr):
    def gen1(a):
        features = tf.split(a,4)
        return dict(zip(feature_names, features))
    dataset = tf.data.Dataset.from_tensor_slices((in_arr)).map(gen1)
    iterator = dataset.make_one_shot_iterator()
    data = iterator.get_next()
    return data, None



# check values
# next_batch = input_fn()
# with tf.Session() as sess:
#     first_batch = sess.run(next_batch)
# print(first_batch)
# sys.exit(0)

classifier.train(input_fn=lambda: input_fn())

evaluate_result = classifier.evaluate(input_fn=lambda: input_fn())
print("Evaluation results")
for key in evaluate_result:
    print("   {}, was: {}".format(key, evaluate_result[key]))

test_set = np.array([[1,1,1,1],[0,0,0,0],[0,0,1,1],[1,1,0,0]])
predict_results = classifier.predict(
    input_fn=lambda: input_fn_pred(test_set))
for prediction in predict_results:
   print prediction["class_ids"][0]