added version numbers to the Conv3D ops

theano/tensor/basic.py

@@ -4414,6 +4414,7 @@ class numeric_grad:
 
             x[i] += eps
             f_eps = f(*apt)
+
             gx[i] = numpy.asarray((f_eps - f_x)/eps)
 
         if packed_pt:
@@ -4594,6 +4595,7 @@ def verify_grad(fun, pt, n_tests=2, rng=None, eps=None, abs_tol=None, rel_tol=No
     for test_num in xrange(n_tests):
         num_grad = numeric_grad(cost_fn, [p.copy() for p in pt], eps)
 
+
         analytic_grad = grad_fn(*[p.copy() for p in pt])
 
         if not isinstance(analytic_grad, (list, tuple)):
@@ -4621,6 +4623,7 @@ class GradientError(Exception):
         self.abs_tol = abs_tol
         self.rel_tol = rel_tol
 
+
     def __str__(self):
         return """GradientError: numeric gradient and analytic gradient exceed tolerance:
         At position %i of argument %i,

theano/tensor/nnet/Conv3D.py

@@ -49,6 +49,10 @@ class Conv3D(theano.Op):
     def __str__(self):
         return "Conv3D"
 
+    def c_code_cache_version(self):
+        return (1,)
+
+
     def make_node(self, V, W, b, d):
         """
             :param V: Visible unit, input(batch,row,column,time,in channel)

theano/tensor/nnet/ConvGrad3D.py

@@ -3,6 +3,10 @@ from theano.tensor import basic as T
 from theano.misc import strutil
 import numpy as N
 
+
+#TODO: speed up by reordering loops. Should pass through the videos once, incrementing all weight gradients, rather
+# than visiting each weight gradient element once and passing through whole video
+
 class ConvGrad3D(theano.Op):
     """ Gradient of Conv3D with respect to W """
     def __eq__(self,other):
@@ -11,6 +15,9 @@ class ConvGrad3D(theano.Op):
     def __hash__(self):
         return hash(type(self))
 
+    def c_code_cache_version(self):
+        return (1,)
+
     def make_node(self, V, d, WShape, dCdH):
         V_ = T.as_tensor_variable(V)
         d_ = T.as_tensor_variable(d)

theano/tensor/nnet/ConvTransp3D.py

@@ -11,6 +11,9 @@ class ConvTransp3D(theano.Op):
     def __hash__(self):
 	    return hash(type(self))
 
+    def c_code_cache_version(self):
+        return (1,)
+
     def make_node(self, W, b, d, H, RShape = None):
         """
         :param W: Weights, filter