feature_column_ops_test.py 文件源码

def testIntegerizedColumn(self):
    product = tf.contrib.layers.sparse_column_with_integerized_feature(
        "product", bucket_size=5)
    with tf.Graph().as_default():
      features = {"product": tf.SparseTensor(values=[0, 4, 2],
                                             indices=[[0, 0], [1, 0], [2, 0]],
                                             shape=[3, 1])}
      output, column_to_variable, _ = (
          tf.contrib.layers.weighted_sum_from_feature_columns(features,
                                                              [product],
                                                              num_outputs=1))
      with self.test_session() as sess:
        tf.initialize_all_variables().run()
        tf.initialize_all_tables().run()
        product_weights = column_to_variable[product][0]
        sess.run(product_weights.assign([[0.1], [0.2], [0.3], [0.4], [0.5]]))
        self.assertAllClose(output.eval(), [[0.1], [0.5], [0.3]])