diff --git a/sknn/backend/lasagne/mlp.py b/sknn/backend/lasagne/mlp.py
index 4b73f6f..f7473d5 100644
--- a/sknn/backend/lasagne/mlp.py
+++ b/sknn/backend/lasagne/mlp.py
@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-from __future__ import (absolute_import, unicode_literals, print_function)
+from __future__ import (absolute_import, division, unicode_literals, print_function)
 
 __all__ = ['MultiLayerPerceptronBackend']
 
@@ -261,7 +261,7 @@ def cast(array, indices):
         if shuffle:
             numpy.random.shuffle(indices)
 
-        for start_idx in range(0, total_size - batch_size + 1, batch_size):
+        for start_idx in range(0, total_size, batch_size):
             excerpt = indices[start_idx:start_idx + batch_size]
             Xb, yb, wb = cast(X, excerpt), cast(y, excerpt), cast(w, excerpt)
             yield Xb, yb, wb
diff --git a/sknn/tests/test_training.py b/sknn/tests/test_training.py
index 8adf7f3..d995b6e 100644
--- a/sknn/tests/test_training.py
+++ b/sknn/tests/test_training.py
@@ -53,6 +53,37 @@ def terminate(**_):
         assert_equals(self.counter, 1)
 
 
+class TestBatchSize(unittest.TestCase):
+
+    def setUp(self):
+        self.batch_count = 0
+        self.nn = MLP(
+                    layers=[L("Rectifier")],
+                    learning_rate=0.001, n_iter=1,
+                    callback={'on_batch_start': self.on_batch_start})
+
+    def on_batch_start(self, **args):
+        self.batch_count += 1
+
+    def test_BatchSizeLargerThanInput(self):
+        self.nn.batch_size = 32
+        a_in, a_out = numpy.zeros((8,16)), numpy.ones((8,4))
+        self.nn._fit(a_in, a_out)
+        assert_equals(1, self.batch_count)
+
+    def test_BatchSizeSmallerThanInput(self):
+        self.nn.batch_size = 4
+        a_in, a_out = numpy.ones((8,16)), numpy.zeros((8,4))
+        self.nn._fit(a_in, a_out)
+        assert_equals(2, self.batch_count)
+
+    def test_BatchSizeNonMultiple(self):
+        self.nn.batch_size = 4
+        a_in, a_out = numpy.zeros((9,16)), numpy.ones((9,4))
+        self.nn._fit(a_in, a_out)
+        assert_equals(3, self.batch_count)
+
+
 class TestCustomLogging(unittest.TestCase):
 
     def setUp(self):