feature_column_ops_test.py 文件源码

def testHashedEmbeddingColumnSucceedsForDNN(self):
    wire_tensor = tf.SparseTensor(values=["omar", "stringer", "marlo", "omar"],
                                  indices=[[0, 0], [1, 0], [1, 1], [2, 0]],
                                  shape=[3, 2])

    features = {"wire": wire_tensor}
    # Big enough hash space so that hopefully there is no collision
    embedded_sparse = tf.contrib.layers.hashed_embedding_column("wire", 1000, 3)
    output = tf.contrib.layers.input_from_feature_columns(
        features, [embedded_sparse], weight_collections=["my_collection"])
    weights = tf.get_collection("my_collection")
    grad = tf.gradients(output, weights)
    with self.test_session():
      tf.initialize_all_variables().run()
      gradient_values = []
      # Collect the gradient from the different partitions (one in this test)
      for p in range(len(grad)):
        gradient_values.extend(grad[p].values.eval())
      gradient_values.sort()
      self.assertAllEqual(gradient_values, [0.5]*6 + [2]*3)