Init struct variables correctly and print

theano/gpuarray/dnn_fwd.c

 #section init_code_struct
 
-if (PARAMS->choose_algo) {
-  reuse_algo = 0;
-  prev_algo = PARAMS->conv_algo;
-  if (!PARAMS->choose_once) {
-    memset(prev_img_dims, 0, sizeof(prev_img_dims));
-    memset(prev_kern_dims, 0, sizeof(prev_kern_dims));
-  }
-}
+reuse_algo = 0;
+prev_algo = PARAMS->conv_algo;
+memset(prev_img_dims, 0, sizeof(prev_img_dims));
+memset(prev_kern_dims, 0, sizeof(prev_kern_dims));
 
 #section support_code_struct
 
@@ -146,9 +142,12 @@ APPLY_SPECIFIC(conv_fwd)(PyGpuArrayObject *input, PyGpuArrayObject *kerns,
         if (count == 0) {
             PyErr_SetString(PyExc_RuntimeError, "No best-timed conv fwd algorithm found");
             return 1;
-        } else {
-            fprintf(stderr, "(%d best-timed conv fwd algorithms)\n", count);
-        }
+        } else if (choice.status != CUDNN_STATUS_SUCCESS) {
+            PyErr_Format(PyExc_RuntimeError,
+                         "error getting best-timed FWD algo: %s",
+                         cudnnGetErrorString(choice.status));
+            return 1;
+        } // Else, count is necessarly 1 for current implementation.
         #endif
 
       } else {

theano/gpuarray/dnn_gi.c

 #section init_code_struct
 
-if (PARAMS->choose_algo) {
-  reuse_algo = 0;
-  prev_algo = PARAMS->conv_algo;
-  if (!PARAMS->choose_once) {
-      memset(prev_kern_dims, 0, sizeof(prev_kern_dims));
-      memset(prev_top_dims, 0, sizeof(prev_top_dims));
-  }
-}
+reuse_algo = 0;
+prev_algo = PARAMS->conv_algo;
+memset(prev_kern_dims, 0, sizeof(prev_kern_dims));
+memset(prev_top_dims, 0, sizeof(prev_top_dims));
 
 #section support_code_struct
 
@@ -186,9 +182,12 @@ APPLY_SPECIFIC(conv_gi)(PyGpuArrayObject *kerns, PyGpuArrayObject *output,
       if (count == 0) {
           PyErr_SetString(PyExc_RuntimeError, "No best-timed conv gradinput algorithm found");
           return 1;
-      } else {
-          fprintf(stderr, "(%d best-timed conv gradinput algorithms)\n", count);
-      }
+      } else if (choice.status != CUDNN_STATUS_SUCCESS) {
+          PyErr_Format(PyExc_RuntimeError,
+                       "error getting best-timed gradinput algo: %s",
+                       cudnnGetErrorString(choice.status));
+          return 1;
+      } // Else, count is necessarly 1 for current implementation.
       #endif
 
     } else {

theano/gpuarray/dnn_gw.c

 #section init_code_struct
 
-if (PARAMS->choose_algo) {
-  reuse_algo = 0;
-  prev_algo = PARAMS->conv_algo;
-  if (!PARAMS->choose_once) {
-      memset(prev_img_dims, 0, sizeof(prev_img_dims));
-      memset(prev_top_dims, 0, sizeof(prev_top_dims));
-  }
-}
+reuse_algo = 0;
+prev_algo = PARAMS->conv_algo;
+memset(prev_img_dims, 0, sizeof(prev_img_dims));
+memset(prev_top_dims, 0, sizeof(prev_top_dims));
 
 #section support_code_struct
 
@@ -188,9 +184,12 @@ APPLY_SPECIFIC(conv_gw)(PyGpuArrayObject *input, PyGpuArrayObject *output,
         if (count == 0) {
             PyErr_SetString(PyExc_RuntimeError, "No best-timed conv gradweight algorithm found");
             return 1;
-        } else {
-            fprintf(stderr, "(%d best-timed conv gradweight algorithms)\n", count);
-        }
+        } else if (choice.status != CUDNN_STATUS_SUCCESS) {
+            PyErr_Format(PyExc_RuntimeError,
+                         "error getting best-timed gradweight algo: %s",
+                         cudnnGetErrorString(choice.status));
+            return 1;
+        } // Else, count is necessarly 1 for current implementation.
         #endif
 
       } else {