Fix test of shape_of_variables and raise a good error to explain that the…

theano/tensor/tests/test_utils.py

+import unittest
+
 import numpy
 
 import theano
@@ -50,23 +52,30 @@ def test_hash_from_dict():
     assert hash_from_dict({0: (0,)}) == hash_from_dict({0: [0]})
 
 
-def test_shape_of_variables_simple():
+class Tshape_of_variables(unittest.TestCase):
+    def test_simple(self):
         x = theano.tensor.matrix('x')
         y = x+x
-    fgraph = theano.FunctionGraph([x], [y])
-    assert shape_of_variables(fgraph, {x: (5, 5)}) == {x: (5, 5), y: (5, 5)}
+        fgraph = theano.FunctionGraph([x], [y], clone=False)
+        shapes = shape_of_variables(fgraph, {x: (5, 5)})
+        assert shapes == {x: (5, 5), y: (5, 5)}
 
         x = theano.tensor.matrix('x')
         y = theano.tensor.dot(x, x.T)
-    fgraph = theano.FunctionGraph([x], [y])
+        fgraph = theano.FunctionGraph([x], [y], clone=False)
         shapes = shape_of_variables(fgraph, {x: (5, 1)})
         assert shapes[x] == (5, 1)
         assert shapes[y] == (5, 5)
 
-
-def test_shape_of_variables_subtensor():
+    def test_subtensor(self):
         x = theano.tensor.matrix('x')
         subx = x[1:]
-    fgraph = theano.FunctionGraph([x], [subx])
+        fgraph = theano.FunctionGraph([x], [subx], clone=False)
         shapes = shape_of_variables(fgraph, {x: (10, 10)})
         assert shapes[subx] == (9, 10)
+
+    def test_err(self):
+        x = theano.tensor.matrix('x')
+        subx = x[1:]
+        fgraph = theano.FunctionGraph([x], [subx])
+        self.assertRaises(ValueError, shape_of_variables, fgraph, {x: (10, 10)})

theano/tensor/utils.py

 import numpy
 
 import theano
+from theano.compat.python2x import any
 from theano.gof.cc import hash_from_code
 
 
@@ -69,7 +70,7 @@ def shape_of_variables(fgraph, input_shapes):
     >>> import theano
     >>> x = theano.tensor.matrix('x')
     >>> y = x[512:]; y.name = 'y'
-    >>> fgraph = theano.FunctionGraph([x], [y])
+    >>> fgraph = theano.FunctionGraph([x], [y], clone=False)
     >>> shape_of_variables(fgraph, {x: (1024, 1024)})
     {y: (512, 1024), x: (1024, 1024)}
     """
@@ -85,6 +86,12 @@ def shape_of_variables(fgraph, input_shapes):
 
     compute_shapes = theano.function(input_dims, output_dims)
 
+    if any([i not in fgraph.inputs for i in input_shapes.keys()]):
+        raise ValueError(
+            "input_shapes keys aren't in the fgraph.inputs. FunctionGraph()"
+            " interface changed. Now by default, it clone the graph it receive."
+            " To have the old behavior, give him this new parameter `clone=False`.")
+
     numeric_input_dims  = [dim for inp in fgraph.inputs
                                for dim in input_shapes[inp]]
     numeric_output_dims = compute_shapes(*numeric_input_dims)