diff --git a/pymc3/variational/svgd.py b/pymc3/variational/svgd.py
index 8d4663bd29..bca03e4864 100644
--- a/pymc3/variational/svgd.py
+++ b/pymc3/variational/svgd.py
@@ -5,6 +5,7 @@
 
 import numpy as np
 import theano
+from theano.ifelse import ifelse
 import theano.tensor as tt
 from tqdm import tqdm
 from .updates import adagrad
@@ -12,6 +13,7 @@
 import pymc3 as pm
 from pymc3.model import modelcontext
 
+
 def rbf_kernel(X):
     # TODO. rbf may not be a good choice for high dimension data
     XY = tt.dot(X, X.transpose())
@@ -22,11 +24,11 @@ def rbf_kernel(X):
     V = H.flatten()
 
     # median distance
-    h = tt.switch(tt.eq((V.shape[0] % 2), 0),
-                  # if even vector
-                  tt.mean(tt.sort(V)[ ((V.shape[0] // 2) - 1) : ((V.shape[0] // 2) + 1) ]),
-                  # if odd vector
-                  tt.sort(V)[V.shape[0] // 2])
+    h = ifelse(tt.eq((V.shape[0] % 2), 0),
+               # if even vector
+               tt.mean(tt.sort(V)[ ((V.shape[0] // 2) - 1) : ((V.shape[0] // 2) + 1) ]),
+               # if odd vector
+               tt.sort(V)[V.shape[0] // 2])
 
     h = tt.sqrt(0.5 * h / tt.log(X.shape[0].astype('float32') + 1.0))
 
@@ -35,7 +37,7 @@ def rbf_kernel(X):
     sumkxy = tt.sum(Kxy, axis=1).dimshuffle(0, 'x')
     dxkxy = tt.add(dxkxy, tt.mul(X, sumkxy)) / (h ** 2)
 
-    return (Kxy, dxkxy)
+    return Kxy, dxkxy
 
 
 def _make_vectorized_logp_grad(vars, model, X):
@@ -70,7 +72,7 @@ def svgd(vars=None, n=5000, n_particles=100, jitter=.01,
          random_seed=None, model=None):
 
     if random_seed is not None:
-        seed(random_seed)
+        np.random.seed(random_seed)
 
     model = modelcontext(model)
     if vars is None:
@@ -102,18 +104,28 @@ def svgd(vars=None, n=5000, n_particles=100, jitter=.01,
     else:
         progress = np.arange(n)
 
-    for ii in progress:
-        svgd_step(ii)
+    try:
+        for ii in progress:
+            svgd_step(ii)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        if hasattr(progress, 'close'):
+            progress.close()
 
     theta_val = theta.get_value()
 
     # Build trace
-    strace = pm.backends.NDArray()
-    strace.setup(theta_val.shape[0], 1)
 
-    for p in theta_val:
-        strace.record(model.bijection.rmap(p))
-    strace.close()
+    strace = pm.backends.NDArray()
+    try:
+        strace.setup(theta_val.shape[0], 1)
+        for p in theta_val:
+            strace.record(model.bijection.rmap(p))
+    except KeyboardInterrupt:
+        pass
+    finally:
+        strace.close()
 
     trace = pm.backends.base.MultiTrace([strace])