rerenamed hypograph -> subgraph_grad

doc/library/tensor/basic.txt

@@ -1564,7 +1564,7 @@ Gradient / Differentiation
     :rtype: variable or list of variables (matching `wrt`)
     :returns: gradients of the cost with respect to each of the `wrt` terms
     
-.. function:: hypograd(wrt, end, start=None, cost=None, details=False)
+.. function:: subgraph_grad(wrt, end, start=None, cost=None, details=False)
 
     With respect to `wrt`, computes gradients of cost and/or from existing 
     `start` gradients, up to the `end` variables of a symbolic digraph. 
@@ -1578,13 +1578,13 @@ Gradient / Differentiation
     non-differentiable process could be approximated by user-defined 
     formula, which could be calculated using the gradients of a cost 
     with respect to samples (0s and 1s). These gradients are obtained 
-    by performing a hypograd from the `cost` or previously known gradients 
+    by performing a subgraph_grad from the `cost` or previously known gradients 
     (`start`) up to the outputs of the stochastic process (`end`). 
     A dictionary mapping gradients obtained from the user-defined 
     differentiation of the process, to variables, could then be fed into 
-    another hypograd as `start` with any other `cost` (e.g. weight decay).
+    another subgraph_grad as `start` with any other `cost` (e.g. weight decay).
     
-    In an MLP, we could use hypograd to iteratively backpropagate:
+    In an MLP, we could use subgraph_grad to iteratively backpropagate:
     >>> x, t = theano.tensor.fvector('x'), theano.tensor.fvector('t')
     >>> w1 = theano.shared(np.random.randn(3,4))
     >>> w2 = theano.shared(np.random.randn(4,2))
@@ -1601,7 +1601,7 @@ Gradient / Differentiation
     >>> next_grad = None
     >>> param_grads = []
     >>> for i in xrange(2):
-    >>>     param_grad, next_grad = theano.hypograd(
+    >>>     param_grad, next_grad = theano.subgraph_grad(
     >>>         wrt=params[i], end=grad_ends[i], 
     >>>         start=next_grad, cost=costs[i]
     >>>     )

theano/__init__.py

@@ -79,7 +79,7 @@ from theano.updates import Updates, OrderedUpdates
 #we don't import by default as we don't want to force having scipy installed.
 #import sparse
 
-from theano.gradient import Rop, Lop, grad, hypograd
+from theano.gradient import Rop, Lop, grad, subgraph_grad
 
 if config.device.startswith('gpu') or config.init_gpu_device.startswith('gpu'):
     import theano.sandbox.cuda

theano/gradient.py

@@ -544,7 +544,7 @@ def grad(cost, wrt, consider_constant=None,
         rval, = rval
     return rval
 
-def hypograd(wrt, end, start=None, cost=None, details=False):
+def subgraph_grad(wrt, end, start=None, cost=None, details=False):
     '''
     With respect to `wrt`, computes gradients of cost and/or from existing 
     `start` gradients, up to the `end` variables of a symbolic digraph. 
@@ -558,11 +558,11 @@ def hypograd(wrt, end, start=None, cost=None, details=False):
     non-differentiable process could be approximated by user-defined 
     formula, which could be calculated using the gradients of a cost 
     with respect to samples (0s and 1s). These gradients are obtained 
-    by performing a hypograd from the `cost` or previously known gradients 
+    by performing a subgraph_grad from the `cost` or previously known gradients 
     (`start`) up to the outputs of the stochastic process (`end`). 
     A dictionary mapping gradients obtained from the user-defined 
     differentiation of the process, to variables, could then be fed into 
-    another hypograd as `start` with any other `cost` (e.g. weight decay).
+    another subgraph_grad as `start` with any other `cost` (e.g. weight decay).
     
     :type wrt : List of Variables.
         Gradients are computed with respect to `wrt`.

theano/tests/test_gradient.py

@@ -555,10 +555,10 @@ def test_disconnected_cost_grad():
             return
         raise AssertionError("A disconnected gradient has been ignored.")
         
-def test_hypograd():
+def test_subgraph_grad():
 
     # Tests that the grad method with no known_grads
-    # matches what happens if you use successive hypograds
+    # matches what happens if you use successive subgraph_grads
 
     x = theano.tensor.fvector('x')
     t = theano.tensor.fvector('t')
@@ -588,7 +588,7 @@ def test_hypograd():
     next_grad = None
     param_grads = []
     for i in xrange(2):
-        param_grad, next_grad = theano.hypograd(
+        param_grad, next_grad = theano.subgraph_grad(
             wrt=params[i], end=grad_ends[i], 
             start=next_grad, cost=costs[i]
         )