def test_build_raw_supervised_input_receiver_fn_overlapping_keys(self): features = {"feature_1": constant_op.constant(["hello"]), "feature_2": constant_op.constant([42])} labels = {"feature_1": constant_op.constant([5]), "bar": constant_op.constant([6])} with self.assertRaises(ValueError): export.build_raw_supervised_input_receiver_fn(features, labels)
def test_build_raw_supervised_input_receiver_fn_raw_tensors(self): features = { "feature_1": constant_op.constant(["hello"]), "feature_2": constant_op.constant([42]) } labels = { "foo": constant_op.constant([5]), "bar": constant_op.constant([6]) } input_receiver_fn1 = export.build_raw_supervised_input_receiver_fn( features["feature_1"], labels) input_receiver_fn2 = export.build_raw_supervised_input_receiver_fn( features["feature_1"], labels["foo"]) with ops.Graph().as_default(): input_receiver = input_receiver_fn1() self.assertIsInstance(input_receiver.features, ops.Tensor) self.assertEqual(set(["foo", "bar"]), set(input_receiver.labels.keys())) self.assertEqual(set(["input", "foo", "bar"]), set(input_receiver.receiver_tensors.keys())) input_receiver = input_receiver_fn2() self.assertIsInstance(input_receiver.features, ops.Tensor) self.assertIsInstance(input_receiver.labels, ops.Tensor) self.assertEqual(set(["input", "label"]), set(input_receiver.receiver_tensors.keys()))
def test_build_raw_supervised_input_receiver_fn(self): features = { "feature_1": constant_op.constant(["hello"]), "feature_2": constant_op.constant([42]) } labels = { "foo": constant_op.constant([5]), "bar": constant_op.constant([6]) } input_receiver_fn = export.build_raw_supervised_input_receiver_fn( features, labels) with ops.Graph().as_default(): input_receiver = input_receiver_fn() self.assertEqual(set(["feature_1", "feature_2"]), set(input_receiver.features.keys())) self.assertEqual(set(["foo", "bar"]), set(input_receiver.labels.keys())) self.assertEqual(set(["feature_1", "feature_2", "foo", "bar"]), set(input_receiver.receiver_tensors.keys())) self.assertEqual( dtypes.string, input_receiver.receiver_tensors["feature_1"].dtype) self.assertEqual( dtypes.int32, input_receiver.receiver_tensors["feature_2"].dtype)
def dummy_supervised_receiver_fn(): feature_spec = { 'x': array_ops.placeholder( dtype=dtypes.int64, shape=(2, 1), name='feature_x'), } label_spec = array_ops.placeholder( dtype=dtypes.float32, shape=[2, 1], name='truth') return export.build_raw_supervised_input_receiver_fn( feature_spec, label_spec)
def test_build_raw_supervised_input_receiver_fn_batch_size(self): features = {"feature_1": constant_op.constant(["hello"]), "feature_2": constant_op.constant([42])} labels = {"foo": constant_op.constant([5]), "bar": constant_op.constant([6])} input_receiver_fn = export.build_raw_supervised_input_receiver_fn( features, labels, default_batch_size=10) with ops.Graph().as_default(): input_receiver = input_receiver_fn() self.assertEqual([10], input_receiver.receiver_tensors["feature_1"].shape) self.assertEqual([10], input_receiver.features["feature_1"].shape)
def dummy_supervised_receiver_fn(): feature_spec = { 'x': array_ops.placeholder(dtype=dtypes.int64, shape=(2, 1), name='feature_x'), } label_spec = array_ops.placeholder(dtype=dtypes.float32, shape=[2, 1], name='truth') return export.build_raw_supervised_input_receiver_fn( feature_spec, label_spec)
def test_build_raw_supervised_input_receiver_fn_raw_tensors(self): features = {"feature_1": constant_op.constant(["hello"]), "feature_2": constant_op.constant([42])} labels = {"foo": constant_op.constant([5]), "bar": constant_op.constant([6])} input_receiver_fn1 = export.build_raw_supervised_input_receiver_fn( features["feature_1"], labels) input_receiver_fn2 = export.build_raw_supervised_input_receiver_fn( features["feature_1"], labels["foo"]) with ops.Graph().as_default(): input_receiver = input_receiver_fn1() self.assertIsInstance(input_receiver.features, ops.Tensor) self.assertEqual(set(["foo", "bar"]), set(input_receiver.labels.keys())) self.assertEqual(set(["input", "foo", "bar"]), set(input_receiver.receiver_tensors.keys())) input_receiver = input_receiver_fn2() self.assertIsInstance(input_receiver.features, ops.Tensor) self.assertIsInstance(input_receiver.labels, ops.Tensor) self.assertEqual(set(["input", "label"]), set(input_receiver.receiver_tensors.keys()))
def test_build_raw_supervised_input_receiver_fn(self): features = {"feature_1": constant_op.constant(["hello"]), "feature_2": constant_op.constant([42])} labels = {"foo": constant_op.constant([5]), "bar": constant_op.constant([6])} input_receiver_fn = export.build_raw_supervised_input_receiver_fn( features, labels) with ops.Graph().as_default(): input_receiver = input_receiver_fn() self.assertEqual(set(["feature_1", "feature_2"]), set(input_receiver.features.keys())) self.assertEqual(set(["foo", "bar"]), set(input_receiver.labels.keys())) self.assertEqual(set(["feature_1", "feature_2", "foo", "bar"]), set(input_receiver.receiver_tensors.keys())) self.assertEqual( dtypes.string, input_receiver.receiver_tensors["feature_1"].dtype) self.assertEqual( dtypes.int32, input_receiver.receiver_tensors["feature_2"].dtype)