Fix numpy 1.13 incompatibilities (#10501)

* Fix numpy 1.13 incompatibilities

* Skip tests with numpy 1.13.0

tensorflow/python/kernel_tests/sparse_ops_test.py

@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+import unittest
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -587,6 +588,7 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
     self._compare(sp_t, reduction_axes, ndims, False)
     self._compare(sp_t, reduction_axes, ndims, True)
 
+  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testSimpleAndRandomInputs(self):
     sp_t = sparse_tensor.SparseTensor(self.ind, self.vals, self.dense_shape)
 
@@ -619,6 +621,7 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase):
       with self.assertRaisesOpError("Invalid reduction dimension 2"):
         sparse_ops.sparse_reduce_sum(sp_t, 2).eval()
 
+  @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug")
   def testGradient(self):
     np.random.seed(8161)
     test_dims = [(11, 1, 5, 7, 1), (2, 2)]

tensorflow/python/ops/check_ops.py

@@ -726,7 +726,7 @@ def _assert_ranks_condition(
 
   # Attempt to statically defined rank.
   ranks_static = tuple([tensor_util.constant_value(rank) for rank in ranks])
-  if None not in ranks_static:
+  if not any(r is None for r in ranks_static):
     for rank_static in ranks_static:
       if rank_static.ndim != 0:
         raise ValueError('Rank must be a scalar.')

tensorflow/python/ops/embedding_ops.py

@@ -97,7 +97,7 @@ def embedding_lookup(params, ids, partition_strategy="mod", name=None,
   Raises:
     ValueError: If `params` is empty.
   """
-  if params in (None, (), []):
+  if params is None or params in ((), []):
     raise ValueError("Need at least one param")
   if isinstance(params, variables.PartitionedVariable):
     params = list(params)  # Iterate to get the underlying Variables.