diff --git a/.travis.yml b/.travis.yml
index 8a84a9ea..ae3801c2 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -15,13 +15,13 @@ before_install:
   - conda update --yes conda
 
 install:
-  - conda create --yes -q -n pyenv mkl python=2.7 numpy=1.10 scipy=0.16.0 pip nose yaml pyflakes pillow pyparsing=1.5
+  - conda create --yes -q -n pyenv mkl python=2.7 numpy=1.10 scipy=0.16.1 pip nose yaml pyflakes pillow pyparsing=1.5
   - source activate pyenv
   - pip install git+git://github.com/Theano/Theano.git
 
 env:
   - PART="test.py:test_logistic_sgd test.py:test_logistic_cg test.py:test_mlp test.py:test_convolutional_mlp test.py:test_dA"
-  - PART="test.py:test_SdA"
+  - PART="test.py:test_SdA test.py:test_lstm"
   - PART="test.py:test_dbn"
   - PART="test.py:test_rbm test.py:test_rnnrbm"
   - PART="-e test.py"
diff --git a/code/hmc/test_hmc.py b/code/hmc/test_hmc.py
index 0a70190a..f6c3b522 100644
--- a/code/hmc/test_hmc.py
+++ b/code/hmc/test_hmc.py
@@ -1,5 +1,4 @@
 import numpy
-from scipy import linalg
 import theano
 
 from hmc import HMC_sampler
@@ -15,7 +14,7 @@ def sampler_on_nd_gaussian(sampler_cls, burnin, n_samples, dim=10):
     cov = numpy.array(rng.rand(dim, dim), dtype=theano.config.floatX)
     cov = (cov + cov.T) / 2.
     cov[numpy.arange(dim), numpy.arange(dim)] = 1.0
-    cov_inv = linalg.inv(cov)
+    cov_inv = numpy.linalg.inv(cov)
 
     # Define energy function for a multi-variate Gaussian
     def gaussian_energy(x):
diff --git a/code/test.py b/code/test.py
index 94e03b8d..41749231 100644
--- a/code/test.py
+++ b/code/test.py
@@ -87,9 +87,9 @@ def speed():
     # 7.1-2 (python 2.7.2, mkl unknow). BLAS with only 1 thread.
 
     expected_times_64 = numpy.asarray([9.8, 22.0, 76.1, 73.7, 116.4,
-                                       346.9, 355.0, 558.1, 130.4, 23.2, 106])
-    expected_times_32 = numpy.asarray([8.1, 17.9, 42.5, 66.5, 71,
-                                       191.2, 199.0, 432.8, 119.5, 36.9, 78.0])
+                                       346.9, 355.0, 510.9, 130.4, 23.2, 106])
+    expected_times_32 = numpy.asarray([6.4, 17.9, 42.5, 66.5, 71,
+                                       191.2, 199.0, 400.4, 119.5, 36.9, 67.2])
 
     # Number with just 1 decimal are new value that are faster with
     # the Theano version 0.5rc2 Other number are older. They are not
@@ -110,7 +110,7 @@ def speed():
 
     expected_times_gpu = numpy.asarray([3.0, 7.55523491, 18.99226785,
                                         5.8, 20.0,
-                                        11.2, 17.2, 257.7, 118.8, 34.2, 8.7])
+                                        11.2, 17.2, 244.3, 118.8, 34.2, 8.7])
     expected_times_64 = [s for idx, s in enumerate(expected_times_64)
                          if to_exec[idx]]
     expected_times_32 = [s for idx, s in enumerate(expected_times_32)