diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index f93ab6190f7..d0884de2a6e 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -780,12 +780,6 @@ class _EagerTensorBase(Tensor):
       return maybe_arr.__index__()
     return int(maybe_arr)  # Must be a NumPy scalar.
 
-  def __array__(self, dtype=None):
-    # This is only called if the buffer interface conversion failed.
-    # Remove once numpy/numpy#13507 is merged and released or py_function
-    # creates EagerTensors with a non-nullptr context.
-    return np.asarray(self.numpy(), dtype=dtype)
-
   def __format__(self, format_spec):
     return self._numpy().__format__(format_spec)
 
diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py
index d6d103815f8..e9bda38652a 100644
--- a/tensorflow/python/framework/tensor_util.py
+++ b/tensorflow/python/framework/tensor_util.py
@@ -369,6 +369,21 @@ def _AssertCompatible(values, dtype):
                       (dtype.name, repr(mismatch), type(mismatch).__name__))
 
 
+def _is_array_like(obj):  # pylint: disable=invalid-name
+  """Check if a given object is array-like."""
+  # TODO(slebedev): an object could also implement C-level array interface.
+  if (callable(getattr(obj, "__array__", None)) or
+      isinstance(getattr(obj, "__array_interface__", None), dict)):
+    return True
+
+  try:
+    memoryview(obj)
+  except TypeError:
+    return False
+  else:
+    return not isinstance(obj, bytes)
+
+
 # pylint: disable=invalid-name
 @tf_export("make_tensor_proto")
 def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False,
@@ -441,21 +456,15 @@ def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False,
           dtypes.qint32
       ])
 
+  if _is_array_like(values):
+    values = np.asarray(values)
+
   # We first convert value to a numpy array or scalar.
   if isinstance(values, (np.ndarray, np.generic)):
-    if dtype:
+    if dtype and dtype.is_numpy_compatible:
       nparray = values.astype(dtype.as_numpy_dtype)
     else:
       nparray = values
-  elif callable(getattr(values, "__array__", None)) or isinstance(
-      getattr(values, "__array_interface__", None), dict):
-    # If a class has the __array__ method, or __array_interface__ dict, then it
-    # is possible to convert to numpy array.
-    nparray = np.asarray(values, dtype=dtype)
-
-    # This is the preferred way to create an array from the object, so replace
-    # the `values` with the array so that _FlattenToStrings is not run.
-    values = nparray
   else:
     if values is None:
       raise ValueError("None values not supported.")