Python sparse_column_with_integerized_feature Examples

Example #1

File: feature_column_test.py Project: dkumar95120/deep_learning

    def testSparseColumnDtypes(self):
        sc = fc.sparse_column_with_integerized_feature("sc", 10)
        self.assertDictEqual(
            {"sc": parsing_ops.VarLenFeature(dtype=dtypes.int64)}, sc.config)

        sc = fc.sparse_column_with_integerized_feature("sc",
                                                       10,
                                                       dtype=dtypes.int32)
        self.assertDictEqual(
            {"sc": parsing_ops.VarLenFeature(dtype=dtypes.int32)}, sc.config)

        with self.assertRaisesRegexp(ValueError, "dtype must be an integer"):
            fc.sparse_column_with_integerized_feature("sc",
                                                      10,
                                                      dtype=dtypes.float32)

Example #2

File: feature_column_test.py Project: Jackhuang945/tensorflow

  def testSparseColumnDtypes(self):
    sc = fc.sparse_column_with_integerized_feature("sc", 10)
    self.assertDictEqual(
        {
            "sc": parsing_ops.VarLenFeature(dtype=dtypes.int64)
        }, sc.config)

    sc = fc.sparse_column_with_integerized_feature("sc", 10, dtype=dtypes.int32)
    self.assertDictEqual(
        {
            "sc": parsing_ops.VarLenFeature(dtype=dtypes.int32)
        }, sc.config)

    with self.assertRaisesRegexp(ValueError, "dtype must be an integer"):
      fc.sparse_column_with_integerized_feature("sc", 10, dtype=dtypes.float32)

Example #3

File: feature_column_test.py Project: Jackhuang945/tensorflow

 def testSparseColumnSingleBucket(self):
   sc = fc.sparse_column_with_integerized_feature("sc", 1)
   self.assertDictEqual(
       {
           "sc": parsing_ops.VarLenFeature(dtype=dtypes.int64)
       }, sc.config)
   self.assertEqual(1, sc._wide_embedding_lookup_arguments(None).vocab_size)

Example #4

 def testSparseColumnSingleBucket(self):
   sc = fc.sparse_column_with_integerized_feature("sc", 1)
   self.assertDictEqual(
       {
           "sc": parsing_ops.VarLenFeature(dtype=dtypes.int64)
       }, sc.config)
   self.assertEqual(1, sc._wide_embedding_lookup_arguments(None).vocab_size)

Example #5

File: feature_column_test.py Project: AlbertXiebnu/tensorflow

 def testSparseColumnIntegerizedDeepCopy(self):
   """Tests deepcopy of sparse_column_with_integerized_feature."""
   column = fc.sparse_column_with_integerized_feature("a", 10)
   self.assertEqual("a", column.name)
   column_copy = copy.deepcopy(column)
   self.assertEqual("a", column_copy.name)
   self.assertEqual(10, column_copy.bucket_size)
   self.assertTrue(column_copy.is_integerized)

Example #6

 def testSparseColumnIntegerizedDeepCopy(self):
   """Tests deepcopy of sparse_column_with_integerized_feature."""
   column = fc.sparse_column_with_integerized_feature("a", 10)
   self.assertEqual("a", column.name)
   column_copy = copy.deepcopy(column)
   self.assertEqual("a", column_copy.name)
   self.assertEqual(10, column_copy.bucket_size)
   self.assertTrue(column_copy.is_integerized)

Example #7

File: feature_column_test.py Project: Jackhuang945/tensorflow

 def testBucketizedColumnRequiresRealValuedColumn(self):
   with self.assertRaisesRegexp(
       TypeError, "source_column must be an instance of _RealValuedColumn"):
     fc.bucketized_column("bbb", [0])
   with self.assertRaisesRegexp(
       TypeError, "source_column must be an instance of _RealValuedColumn"):
     fc.bucketized_column(
         fc.sparse_column_with_integerized_feature(
             column_name="bbb", bucket_size=10), [0])

Example #8

 def testBucketizedColumnRequiresRealValuedColumn(self):
   with self.assertRaisesRegexp(
       TypeError, "source_column must be an instance of _RealValuedColumn"):
     fc.bucketized_column("bbb", [0])
   with self.assertRaisesRegexp(
       TypeError, "source_column must be an instance of _RealValuedColumn"):
     fc.bucketized_column(
         fc.sparse_column_with_integerized_feature(
             column_name="bbb", bucket_size=10), [0])

Example #9

File: feature_column_test.py Project: AlbertXiebnu/tensorflow

 def testSparseColumnAcceptsDenseScalar(self):
   """Tests that `SparseColumn`s accept dense scalar inputs."""
   batch_size = 4
   dense_scalar_input = [1, 2, 3, 4]
   sparse_column = fc.sparse_column_with_integerized_feature("values", 10)
   features = {"values":
               constant_op.constant(dense_scalar_input, dtype=dtypes.int64)}
   sparse_column.insert_transformed_feature(features)
   sparse_output = features[sparse_column]
   expected_shape = [batch_size, 1]
   with self.test_session() as sess:
     sparse_result = sess.run(sparse_output)
   self.assertEquals(expected_shape, list(sparse_result.dense_shape))

Example #10

 def testSparseColumnAcceptsDenseScalar(self):
   """Tests that `SparseColumn`s accept dense scalar inputs."""
   batch_size = 4
   dense_scalar_input = [1, 2, 3, 4]
   sparse_column = fc.sparse_column_with_integerized_feature("values", 10)
   features = {"values":
               constant_op.constant(dense_scalar_input, dtype=dtypes.int64)}
   sparse_column.insert_transformed_feature(features)
   sparse_output = features[sparse_column]
   expected_shape = [batch_size, 1]
   with self.test_session() as sess:
     sparse_result = sess.run(sparse_output)
   self.assertEquals(expected_shape, list(sparse_result.dense_shape))