Remove useless debugmode warning about optimization that change results

The error is due to rounding difference. The test that fail randomly is theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py:TestConv2DGPU.test_logical_shapes. Normally, we should set atol depending of the input shape, but the current structure don't allow this, so I use the output shape. So this could happen with other tests if the output shape is smaller.

Remove useless debugmode warning about optimization that change results
dfc7b0c8 · Frederic · e14e3a71 · dfc7b0c8
--- a/theano/sandbox/cuda/opt.py
+++ b/theano/sandbox/cuda/opt.py
@@ -1050,6 +1050,21 @@ def local_gpu_conv(node):
                return make_graph
        return ret

+    def values_eq_approx(a, b):
+        """This fct is needed to don't have DebugMode raise useless
+        error due to ronding error.
+
+        This happen as We reduce on the two last dimensions, so this
+        can raise the absolute error if the number of element we
+        reduce on is significant.
+
+        """
+        assert a.ndim == 4
+        atol = None
+        if a.shape[-1] * a.shape[-2] > 100:
+            atol = 3e-5
+        return tensor.TensorType.values_eq_approx(a, b, atol=atol)
+
    if node.op == gpu_from_host:
        #gpu_from_host(conv) -> gpu_conv(gpu_from_host)
        host_input = node.inputs[0]
@@ -1058,12 +1073,14 @@ def local_gpu_conv(node):
            if gpu_conv is None:
                return
            img, kern = host_input.owner.inputs
+            out = gpu_conv(gpu_from_host(img),
+                           gpu_from_host(kern))
+            out = tensor.patternbroadcast(out,
+                                          node.outputs[0].broadcastable)
+            out.values_eq_approx = values_eq_approx
            # in some case the ConvOp broadcast the last 2 dimensions
            # differently then the gpu ConvOp
-            return [tensor.patternbroadcast(
-                gpu_conv(gpu_from_host(img),
-                         gpu_from_host(kern)),
-                         node.outputs[0].broadcastable)]
+            return [out]

    if isinstance(node.op, conv.ConvOp):
        #conv(host_from_gpu) -> host_from_gpu(gpu_conv)
@@ -1074,12 +1091,15 @@ def local_gpu_conv(node):
            gpu_conv = GpuConvOp_from_ConvOp(node.op)
            if gpu_conv is None:
                return
+            out = gpu_conv(gpu_from_host(img),
+                           gpu_from_host(kern))
+            out = tensor.patternbroadcast(
+                host_from_gpu(out),
+                node.outputs[0].broadcastable)
+            out.values_eq_approx = values_eq_approx
            # in some case the ConvOp broadcast the last 2 dimensions
            # differently then the gpu ConvOp
-            return [tensor.patternbroadcast(
-                host_from_gpu(gpu_conv(gpu_from_host(img),
-                                       gpu_from_host(kern))),
-                node.outputs[0].broadcastable)]
+            return [out]

 import theano.tensor.signal.downsample as downsample