diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 1e2e3611..d8f1e758 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -235,6 +235,8 @@ class GP(Model):
                     self.link_parameter(self.X, index=index)
             else:
                 self.X = ObsAr(X)
+
+            self.num_data, self.input_dim = self.X.shape
         self.update_model(True)
 
     def set_X(self,X):
@@ -587,9 +589,9 @@ class GP(Model):
         :param size: the number of a posteriori samples.
         :type size: int.
         :returns: set of simulations
-        :rtype: np.ndarray (Nnew x D x samples) 
+        :rtype: np.ndarray (Nnew x D x samples)
         """
-        predict_kwargs["full_cov"] = True  # Always use the full covariance for posterior samples. 
+        predict_kwargs["full_cov"] = True  # Always use the full covariance for posterior samples.
         m, v = self._raw_predict(X,  **predict_kwargs)
         if self.normalizer is not None:
             m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
diff --git a/GPy/testing/gp_tests.py b/GPy/testing/gp_tests.py
index 97e3718d..1f44304d 100644
--- a/GPy/testing/gp_tests.py
+++ b/GPy/testing/gp_tests.py
@@ -28,11 +28,14 @@ class Test(unittest.TestCase):
         Xnew = NormalPosterior(m.X.mean[:10].copy(), m.X.variance[:10].copy())
         m.set_XY(Xnew, m.Y[:10].copy())
         assert(m.checkgrad())
+
+        assert(m.num_data == m.X.shape[0])
+        assert(m.input_dim == m.X.shape[1])
+
         m.set_XY(X, self.Y)
         mu2, var2 = m.predict(m.X)
         np.testing.assert_allclose(mu, mu2)
         np.testing.assert_allclose(var, var2)
-        
 
     def test_setxy_gplvm(self):
         k = GPy.kern.RBF(1)
@@ -42,6 +45,10 @@ class Test(unittest.TestCase):
         Xnew = X[:10].copy()
         m.set_XY(Xnew, m.Y[:10].copy())
         assert(m.checkgrad())
+
+        assert(m.num_data == m.X.shape[0])
+        assert(m.input_dim == m.X.shape[1])
+
         m.set_XY(X, self.Y)
         mu2, var2 = m.predict(m.X)
         np.testing.assert_allclose(mu, mu2)
@@ -54,6 +61,10 @@ class Test(unittest.TestCase):
         X = m.X.copy()
         m.set_XY(m.X[:10], m.Y[:10])
         assert(m.checkgrad())
+
+        assert(m.num_data == m.X.shape[0])
+        assert(m.input_dim == m.X.shape[1])
+
         m.set_XY(X, self.Y)
         mu2, var2 = m.predict(m.X)
         np.testing.assert_allclose(mu, mu2)