evaluation.py

# Define evaluation function
def evaluation_function(attributes, classes, batch_size):
    attributes=dict(attributes)
    if classes is None:
        inputs = attributes
    else:
        inputs = (attributes, classes)
    dataset = tf.data.Dataset.from_tensor_slices(inputs)
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)
    return dataset.make_one_shot_iterator().get_next()

# Evaluate the model.
eval_result = classifier.evaluate(
    input_fn=lambda:evaluation_function(test_x, test_y, 100))