PEP8

theano/tests/test_breakpoint.py

@@ -14,7 +14,7 @@ class TestPdbBreakpoint:
         self.output = T.dot((self.input1 - self.input2),
                             (self.input1 - self.input2).transpose())
 
-        # Declare the conditional breakpoint 
+        # Declare the conditional breakpoint
         self.breakpointOp = PdbBreakpoint("Sum of output too high")
         self.condition = T.gt(self.output.sum(), 100)
         (self.monitored_input1,
@@ -24,43 +24,48 @@ class TestPdbBreakpoint:
                                                     self.input2, self.output)
 
     def test_infer_shape(self):
-        
+
         input1_value = numpy.arange(9).reshape(3,3).astype("float32")
         input2_value = 10.0
-    
+
         fct = theano.function([self.input1, self.input2],
                               [self.monitored_input1.shape,
                                self.monitored_input2.shape,
                                self.monitored_output.shape])
-                               
+
         shapes = fct(input1_value, input2_value)
-        
+
         assert tuple(shapes[0]) == input1_value.shape
         assert tuple(shapes[1]) == tuple()
         assert tuple(shapes[2]) == (input1_value.shape[0],
                                     input1_value.shape[0])
 
     def test_grad(self):
-        
+
         input1_value = numpy.arange(9).reshape(3,3).astype("float32")
         input2_value = 10.0
 
         grads = [T.grad(self.monitored_input1.sum(), self.input1),
                  T.grad(self.monitored_input2.sum(), self.input2)]
-        fct = theano.function([self.input1, self.input2], grads)
-        
-        gradients = fct(input1_value, input2_value)
-        
+
+        # Add self.monitored_input1 as an output to the Theano function to
+        # prevent Theano from optimizing the PdbBreakpoint op out of the
+        # function graph
+        fct = theano.function([self.input1, self.input2],
+                              grads + [self.monitored_input1])
+
+        gradients = fct(input1_value, input2_value)[:-1]
+
         expected_gradients = [numpy.ones((3, 3), dtype="float32"),
                               numpy.array(1., dtype="float32")]
-        
+
         for i in range(len(gradients)):
-            numpy.testing.assert_allclose(gradients[i], expected_gradients[i])        
+            numpy.testing.assert_allclose(gradients[i], expected_gradients[i])
 
     def test_connection_pattern(self):
-        
+
         node = self.monitored_output.owner
         connection_pattern = self.breakpointOp.connection_pattern(node)
         expected_pattern = [[0,0,0],[1,0,0],[0,1,0],[0,0,1]]
-        
+
         assert connection_pattern == expected_pattern