1 files changed, 4 insertions, 202 deletions
diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py
index d1cf909004..fd8c53f6a9 100644
--- a/tensorflow/contrib/estimator/python/estimator/head_test.py
+++ b/tensorflow/contrib/estimator/python/estimator/head_test.py
@@ -316,14 +316,13 @@ class MultiLabelHead(test.TestCase):
       _initialize_variables(self, monitored_session.Scaffold())
       with self.assertRaisesRegexp(
           errors.InvalidArgumentError,
-          r'\[expected_labels_shape: \] \[2 2\] \[labels_shape: \] \[2 1\]'):
+          r'labels shape must be \[batch_size, 2\]\. Given: \] \[2 1\]'):
         actual_weighted_sum_loss.eval({
             labels_placeholder: np.array([[1], [1]], dtype=np.int64)
         })
       with self.assertRaisesRegexp(
           errors.InvalidArgumentError,
-          r'labels shape must be \[D0, D1, ... DN, 2\]\..*'
-          r'\[Received shape: \] \[2\]'):
+          r'labels shape must be \[batch_size, 2\]\. Given: \] \[2\]'):
         actual_weighted_sum_loss.eval({
             labels_placeholder: np.array([1, 1], dtype=np.int64)
         })
@@ -388,11 +387,9 @@ class MultiLabelHead(test.TestCase):
           logits=np.array([[-10., 10.], [-15., 10.]], dtype=np.float32),
           labels=None)
 
-  def _test_eval(
-      self, head, logits, labels, expected_loss, expected_metrics,
-      features=None):
+  def _test_eval(self, head, logits, labels, expected_loss, expected_metrics):
     spec = head.create_estimator_spec(
-        features=features or {},
+        features={'x': np.array(((42,),), dtype=np.int32)},
         mode=model_fn.ModeKeys.EVAL,
         logits=logits,
         labels=labels)
@@ -658,54 +655,6 @@ class MultiLabelHead(test.TestCase):
           labels=None,
           train_op_fn=_no_op_train_fn)
 
-  def test_train_invalid_indicator_labels(self):
-    head = head_lib.multi_label_head(n_classes=2)
-    logits = np.array([[-10., 10.], [-15., 10.]], dtype=np.float32)
-    # The value 2 is outside the allowed range.
-    labels = np.array([[2, 0], [1, 1]], dtype=np.int64)
-    def _train_op_fn(loss):
-      del loss
-      return control_flow_ops.no_op()
-
-    spec = head.create_estimator_spec(
-        features={},
-        mode=model_fn.ModeKeys.TRAIN,
-        logits=logits,
-        labels=labels,
-        train_op_fn=_train_op_fn)
-    with self.test_session() as sess:
-      _initialize_variables(self, spec.scaffold)
-      with self.assertRaisesRegexp(
-          errors.InvalidArgumentError,
-          r'labels must be an integer indicator Tensor with values in '
-          r'\[0, 1\]'):
-        sess.run(spec.loss)
-
-  def test_train_invalid_sparse_labels(self):
-    head = head_lib.multi_label_head(n_classes=2)
-    logits = np.array([[-10., 10.], [-15., 10.]], dtype=np.float32)
-    # The value 2 is outside the allowed range.
-    labels = sparse_tensor.SparseTensor(
-        values=[2, 0, 1],
-        indices=[[0, 0], [1, 0], [1, 1]],
-        dense_shape=[2, 2])
-    def _train_op_fn(loss):
-      del loss
-      return control_flow_ops.no_op()
-
-    spec = head.create_estimator_spec(
-        features={},
-        mode=model_fn.ModeKeys.TRAIN,
-        logits=logits,
-        labels=labels,
-        train_op_fn=_train_op_fn)
-    with self.test_session() as sess:
-      _initialize_variables(self, spec.scaffold)
-      with self.assertRaisesRegexp(
-          errors.InvalidArgumentError,
-          r'labels must be an integer SparseTensor with values in \[0, 2\)'):
-        sess.run(spec.loss)
-
   def _test_train(self, head, logits, labels, expected_loss):
     expected_train_result = 'my_train_op'
     def _train_op_fn(loss):
@@ -842,153 +791,6 @@ class MultiLabelHead(test.TestCase):
           metric_keys.MetricKeys.LOSS_MEAN: expected_loss / 3,
       }, summary_str, tol)
 
-  def test_multi_dim_weighted_train_create_loss(self):
-    """Logits and labels of shape [2, 2, 3], weights [2, 2]."""
-    head = head_lib.multi_label_head(n_classes=3, weight_column='weights')
-
-    logits = np.array([[[-10., 10., -10.], [10., -10., 10.]],
-                       [[-12., 12., -12.], [12., -12., 12.]]], dtype=np.float32)
-    labels = np.array([[[1, 0, 0], [1, 0, 0]],
-                       [[0, 1, 1], [0, 1, 1]]], dtype=np.int64)
-    weights = np.array([[1., 1.5], [2., 2.5]], dtype=np.float32)
-    # loss = [[10 + 10 + 0, 0 + 0 + 10], [0 + 0 + 12, 12 + 12 + 0]] / 3
-    #      = [[20/3, 10/3], [4, 8]]
-    # weighted_sum_loss = 1*20/3 + 1.5*10/3 + 2*4 + 2.5*8 = 39.6667
-    expected_weighted_sum_loss = 39.6667
-    expected_example_weight_sum = np.sum(weights)
-    actual_weighted_sum_loss, actual_example_weight_sum, _ = head.create_loss(
-        features={'weights': weights},
-        mode=model_fn.ModeKeys.TRAIN,
-        logits=logits,
-        labels=labels)
-    atol = 1.e-3
-    with self.test_session():
-      _initialize_variables(self, monitored_session.Scaffold())
-      self.assertAllClose(
-          expected_weighted_sum_loss, actual_weighted_sum_loss.eval(),
-          atol=atol)
-      self.assertAllClose(
-          expected_example_weight_sum, actual_example_weight_sum.eval(),
-          atol=atol)
-
-  def test_multi_dim_weighted_train(self):
-    """Logits and labels of shape [2, 2, 3], weights [2, 2]."""
-    head = head_lib.multi_label_head(n_classes=3, weight_column='weights')
-
-    logits = np.array([[[-10., 10., -10.], [10., -10., 10.]],
-                       [[-12., 12., -12.], [12., -12., 12.]]], dtype=np.float32)
-    labels = np.array([[[1, 0, 0], [1, 0, 0]],
-                       [[0, 1, 1], [0, 1, 1]]], dtype=np.int64)
-    weights = np.array([[1., 1.5], [2., 2.5]], dtype=np.float32)
-    # loss = [[10 + 10 + 0, 0 + 0 + 10], [0 + 0 + 12, 12 + 12 + 0]] / 3
-    #      = [[20/3, 10/3], [4, 8]]
-    # weighted_sum_loss = 1*20/3 + 1.5*10/3 + 2*4 + 2.5*8 = 39.6667
-    expected_loss = 39.6667
-    expected_train_result = 'my_train_op'
-    def _train_op_fn(loss):
-      return string_ops.string_join(
-          [constant_op.constant(expected_train_result),
-           string_ops.as_string(loss, precision=3)])
-
-    spec = head.create_estimator_spec(
-        features={'weights': weights},
-        mode=model_fn.ModeKeys.TRAIN,
-        logits=logits,
-        labels=labels,
-        train_op_fn=_train_op_fn)
-
-    atol = 1.e-3
-    with self.test_session() as sess:
-      _initialize_variables(self, monitored_session.Scaffold())
-      loss, train_result = sess.run((spec.loss, spec.train_op))
-      self.assertAllClose(expected_loss, loss, atol=atol)
-      self.assertEqual(
-          six.b('{0:s}{1:.3f}'.format(expected_train_result, expected_loss)),
-          train_result)
-
-  def test_multi_dim_weights_wrong_inner_dim(self):
-    """Logits and labels of shape [2, 2, 3], weights [2, 1]."""
-    head = head_lib.multi_label_head(n_classes=3, weight_column='weights')
-
-    logits = np.array([[[-10., 10., -10.], [10., -10., 10.]],
-                       [[-12., 12., -12.], [12., -12., 12.]]], dtype=np.float32)
-    labels = np.array([[[1, 0, 0], [1, 0, 0]],
-                       [[0, 1, 1], [0, 1, 1]]], dtype=np.int64)
-    weights = np.array([[1.], [2.]], dtype=np.float32)
-    def _train_op_fn(loss):
-      del loss
-      return control_flow_ops.no_op()
-
-    spec = head.create_estimator_spec(
-        features={'weights': weights},
-        mode=model_fn.ModeKeys.TRAIN,
-        logits=logits,
-        labels=labels,
-        train_op_fn=_train_op_fn)
-    with self.test_session():
-      _initialize_variables(self, monitored_session.Scaffold())
-      with self.assertRaisesRegexp(
-          errors.InvalidArgumentError,
-          r'\[logits_shape: \] \[2 2 3\] \[weights_shape: \] \[2 1\]'):
-        spec.loss.eval()
-
-  def test_multi_dim_weights_wrong_outer_dim(self):
-    """Logits and labels of shape [2, 2, 3], weights [2, 2, 3]."""
-    head = head_lib.multi_label_head(n_classes=3, weight_column='weights')
-
-    logits = np.array([[[-10., 10., -10.], [10., -10., 10.]],
-                       [[-12., 12., -12.], [12., -12., 12.]]], dtype=np.float32)
-    labels = np.array([[[1, 0, 0], [1, 0, 0]],
-                       [[0, 1, 1], [0, 1, 1]]], dtype=np.int64)
-    weights = np.array([[[1., 1., 1.], [1.5, 1.5, 1.5]],
-                        [[2., 2., 2.], [2.5, 2.5, 2.5]]], dtype=np.float32)
-    weights_placeholder = array_ops.placeholder(dtype=dtypes.float32)
-    def _train_op_fn(loss):
-      del loss
-      return control_flow_ops.no_op()
-
-    spec = head.create_estimator_spec(
-        features={'weights': weights_placeholder},
-        mode=model_fn.ModeKeys.TRAIN,
-        logits=logits,
-        labels=labels,
-        train_op_fn=_train_op_fn)
-    with self.test_session():
-      _initialize_variables(self, monitored_session.Scaffold())
-      with self.assertRaisesRegexp(
-          errors.InvalidArgumentError,
-          r'\[logits_shape: \] \[2 2 3\] \[weights_shape: \] \[2 2 3\]'):
-        spec.loss.eval({weights_placeholder: weights})
-
-  def test_multi_dim_weighted_eval(self):
-    """Logits and labels of shape [2, 2, 3], weights [2, 2]."""
-    head = head_lib.multi_label_head(n_classes=3, weight_column='weights')
-
-    logits = np.array([[[-10., 10., -10.], [10., -10., 10.]],
-                       [[-12., 12., -12.], [12., -12., 12.]]], dtype=np.float32)
-    labels = np.array([[[1, 0, 0], [1, 0, 0]],
-                       [[0, 1, 1], [0, 1, 1]]], dtype=np.int64)
-    weights = np.array([[1., 1.5], [2., 2.5]], dtype=np.float32)
-    # loss = [[10 + 10 + 0, 0 + 0 + 10], [0 + 0 + 12, 12 + 12 + 0]] / 3
-    #      = [[20/3, 10/3], [4, 8]]
-    # weighted_sum_loss = 1*20/3 + 1.5*10/3 + 2*4 + 2.5*8 = 39.6667
-    expected_loss = 39.6667
-    keys = metric_keys.MetricKeys
-    expected_metrics = {
-        keys.LOSS_MEAN: expected_loss / np.sum(weights),
-        # auc and auc_pr cannot be reliably calculated for only 4 samples, but
-        # this assert tests that the algorithm remains consistent.
-        keys.AUC: 0.4977,
-        keys.AUC_PR: 0.6645,
-    }
-    self._test_eval(
-        head=head,
-        features={'weights': weights},
-        logits=logits,
-        labels=labels,
-        expected_loss=expected_loss,
-        expected_metrics=expected_metrics)
-
 
 if __name__ == '__main__':
   test.main()