diff --git a/tests/test_initialization.py b/tests/test_initialization.py
index 1fd8c285..b5fe0ffc 100644
--- a/tests/test_initialization.py
+++ b/tests/test_initialization.py
@@ -1,16 +1,17 @@
 import numpy
-from numpy.testing import assert_, assert_equal, assert_allclose, assert_raises
+import six
 import theano
+from numpy.testing import assert_equal, assert_allclose, assert_raises
 
-from blocks.initialization import Constant, IsotropicGaussian, Uniform
+from blocks.initialization import Constant, IsotropicGaussian, Sparse, Uniform
 
 
 def test_constant():
     def check_constant(const, shape, ground_truth):
         # rng unused, so pass None.
         init = Constant(const).generate(None, ground_truth.shape)
-        assert_(ground_truth.dtype == theano.config.floatX)
-        assert_(ground_truth.shape == init.shape)
+        assert ground_truth.dtype == theano.config.floatX
+        assert ground_truth.shape == init.shape
         assert_equal(ground_truth, init)
 
     # Test scalar init.
@@ -24,12 +25,12 @@ def check_constant(const, shape, ground_truth):
 
 
 def test_gaussian():
-    rng = numpy.random.RandomState([2014, 1, 20])
+    rng = numpy.random.RandomState(1)
 
     def check_gaussian(rng, mean, std, shape):
         weights = IsotropicGaussian(mean, std).generate(rng, shape)
-        assert_(weights.shape == shape)
-        assert_(weights.dtype == theano.config.floatX)
+        assert weights.shape == shape
+        assert weights.dtype == theano.config.floatX
         assert_allclose(weights.mean(), mean, atol=1e-2)
         assert_allclose(weights.std(), std, atol=1e-2)
     yield check_gaussian, rng, 0, 1, (500, 600)
@@ -37,13 +38,13 @@ def check_gaussian(rng, mean, std, shape):
 
 
 def test_uniform():
-    rng = numpy.random.RandomState([2014, 1, 20])
+    rng = numpy.random.RandomState(1)
 
     def check_uniform(rng, mean, width, std, shape):
         weights = Uniform(mean=mean, width=width,
                           std=std).generate(rng, shape)
-        assert_(weights.shape == shape)
-        assert_(weights.dtype == theano.config.floatX)
+        assert weights.shape == shape
+        assert weights.dtype == theano.config.floatX
         assert_allclose(weights.mean(), mean, atol=1e-2)
         if width is not None:
             std_ = width / numpy.sqrt(12)
@@ -55,3 +56,30 @@ def check_uniform(rng, mean, width, std, shape):
     yield check_uniform, rng, 5, None, 0.004, (700, 300)
 
     assert_raises(ValueError, Uniform, 0, 1, 1)
+
+
+def test_sparse():
+    rng = numpy.random.RandomState(1)
+
+    def check_sparse(rng, num_init, weights_init, sparse_init, shape, total):
+        weights = Sparse(num_init=num_init, weights_init=weights_init,
+                         sparse_init=sparse_init).generate(rng, shape)
+        assert weights.shape == shape
+        assert weights.dtype == theano.config.floatX
+        if sparse_init is None:
+            if isinstance(num_init, six.integer_types):
+                assert (numpy.count_nonzero(weights) <=
+                        weights.size - num_init * weights.shape[0])
+            else:
+                assert (numpy.count_nonzero(weights) <=
+                        weights.size - num_init * weights.shape[1])
+        if total is not None:
+            assert numpy.sum(weights) == total
+
+    yield check_sparse, rng, 5, Constant(1.), None, (10, 10), None
+    yield check_sparse, rng, 0.5, Constant(1.), None, (10, 10), None
+    yield check_sparse, rng, 0.5, Constant(1.), Constant(1.), (10, 10), None
+    yield check_sparse, rng, 3, Constant(1.), None, (10, 10), 30
+    yield check_sparse, rng, 3, Constant(0.), Constant(1.), (10, 10), 70
+    yield check_sparse, rng, 0.3, Constant(1.), None, (10, 10), 30
+    yield check_sparse, rng, 0.3, Constant(0.), Constant(1.), (10, 10), 70