Improve exception message when TensorShape.with_rank() fails

mrry · keveman · commit 6cc49690efd9 · 2016-01-20T17:20:00.000-08:00
Previously this would pass on the exception message from `TensorShape.merge_with()`, which is cryptic for users who don't (and shouldn't need to) understand how shape inference works. This would arise, for example, in the error message for `tf.matmul()` when passed a non-matrix, as noted here: http://stackoverflow.com/questions/34908033/tensorflow-exception-with-matmul Change: 112621185
diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py
@@ -611,7 +611,10 @@ def with_rank(self, rank):
     Raises:
       ValueError: If `self` does not represent a shape with the given `rank`.
     """
-    return self.merge_with(unknown_shape(ndims=rank))
+    try:
+      return self.merge_with(unknown_shape(ndims=rank))
+    except ValueError:
+      raise ValueError("Shape %s must have rank %d" % (self, rank))
 
   def with_rank_at_least(self, rank):
     """Returns a shape based on `self` with at least the given rank.
diff --git a/tensorflow/python/kernel_tests/matmul_op_test.py b/tensorflow/python/kernel_tests/matmul_op_test.py
@@ -148,6 +148,17 @@ def testMatMul_Inputs_Empty(self):
     self._testCpuMatmul(x, y)
     self._testGpuMatmul(x, y)
 
+  def testShapeErrors(self):
+    a = tf.placeholder(tf.float32, [32, 37])
+    b = tf.placeholder(tf.float32, [36, 2])
+    c = tf.placeholder(tf.float32, [37])
+    with self.assertRaisesRegexp(
+        ValueError,
+        r"Dimensions Dimension\(37\) and Dimension\(36\) are not compatible"):
+      tf.matmul(a, b)
+    with self.assertRaisesRegexp(ValueError, "must have rank 2"):
+      tf.matmul(a, c)
+
 
 # TODO(zhifengc): Figures out how to test matmul gradients on GPU.
 class MatMulGradientTest(tf.test.TestCase):
diff --git a/tensorflow/python/kernel_tests/xent_op_test.py b/tensorflow/python/kernel_tests/xent_op_test.py
@@ -70,7 +70,7 @@ def testRankTooLarge(self):
     np_labels = np.array(
         [[[0., 0., 0., 1.]], [[0., .5, .5, 0.]]]).astype(np.float32)
     self.assertRaisesRegexp(
-        ValueError, "must have the same rank",
+        ValueError, "must have rank 2",
         tf.nn.softmax_cross_entropy_with_logits, np_features, np_labels)
 
   def testNpXent(self):
