Merge pull request #2720 from yingzha/ccw

theano/tensor/opt.py

@@ -24,6 +24,7 @@ from theano.gof import opt, InconsistencyError, TopoOptimizer, graph
 from theano.gof import Variable, Constant
 from theano.compat.python2x import maxsize
 from theano.gof.utils import MethodNotDefined
+from theano.gradient import DisconnectedType
 from theano.configparser import config
 from theano.tensor.elemwise import Elemwise, DimShuffle
 from theano.tensor.subtensor import (get_idx_list, get_canonical_form_slice,
@@ -1492,11 +1493,22 @@ class Assert(T.Op):
     """
     Implements assertion in a computational graph.
 
+    Returns the first parameter if the condition is true, otherwise, triggers
+    AssertionError.
+
+    Example:
+      T = theano.tensor
+      x = T.vector('x')
+      assert_op = T.opt.Assert()
+      func = theano.function([x], assert_op(x, x.size<2))
+
     Notes:
-    This Op can be removed from the graph because of optimizations, and can
-    hide some possible optimizations to the optimizer.
-    Also, the output of the Op must be returned by the function computing the
-    graph, otherwise it will not be used.
+    This Op is a debugging feature. It can be removed from the graph
+    because of optimizations, and can hide some possible optimizations to
+    the optimizer. Specifically, removing happens if it can be determined
+    that condition will always be true. Also, the output of the Op must be
+    used in the function computing the graph, but it doesn't have to be
+    returned.
     """
     view_map = {0: [0]}
 
@@ -1533,7 +1545,7 @@ class Assert(T.Op):
         return hash(type(self)) ^ hash(self.msg)
 
     def grad(self, input, output_gradients):
-        return output_gradients
+        return output_gradients + [DisconnectedType()()] * (len(input) - 1)
 
     def c_code(self, node, name, inames, onames, sub):
         value = inames[0]

theano/tensor/tests/test_assert_op_gradient.py

+import numpy
+
+import theano
+import theano.tensor as T
+from theano.tensor.opt import Assert
+
+
+def test_assert_op_gradient():
+    x = T.vector('x')
+    assert_op = Assert()
+    cost = T.sum(assert_op(x, x.size < 2))
+    grad = T.grad(cost, x)
+    func = theano.function([x], grad)
+
+    x_val = numpy.ones(shape=(1,))
+    assert func(x_val) == 1