"test gaussian random in python side"

paddle/framework/CMakeLists.txt

@@ -43,4 +43,5 @@ cc_library(paddle_pybind SHARED
 	add_op
 	mean_op
 	cross_entropy_op
+  gaussian_random_op
 	recurrent_op)

paddle/framework/pybind.cc

@@ -41,6 +41,7 @@ USE_OP(sigmoid);
 USE_OP(softmax);
 USE_OP(rowwise_add);
 USE_OP_WITHOUT_KERNEL(recurrent_op);
+USE_OP(gaussian_random);
 namespace paddle {
 namespace framework {
 template <typename ClassType>

python/paddle/v2/framework/tests/CMakeLists.txt

@@ -21,3 +21,5 @@ py_test(test_rowwise_add_op SRCS test_rowwise_add_op.py)
 
 py_test(test_default_scope_funcs SRCS test_default_scope_funcs.py)
 py_test(test_op_creation_methods SRCS test_op_creation_methods.py)
+
+py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py)

python/paddle/v2/framework/tests/test_gaussian_random_op.py

@@ -0,0 +1,33 @@
+import unittest
+import paddle.v2.framework.core as core
+import paddle.v2.framework.op as Operator
+import numpy
+
+
+class GaussianRandomTest(unittest.TestCase):
+    def test_cpu(self):
+        self.test_gaussian_random(place=core.CPUPlace())
+
+    def test_gpu(self):
+        self.test_gaussian_random(place=core.GPUPlace(0))
+
+    def test_gaussian_random(self, place):
+        scope = core.Scope()
+        scope.new_var("Out").get_tensor()
+        op = Operator(
+            "gaussian_random",
+            Out="Out",
+            dims=[1000, 784],
+            mean=.0,
+            std=1.,
+            seed=0)
+        op.infer_shape(scope)
+        context = core.DeviceContext.create(place)
+        op.run(scope, context)
+        tensor = numpy.array(scope.find_var("Out").get_tensor())
+        self.assertAlmostEqual(numpy.mean(tensor), .0, places=3)
+        self.assertAlmostEqual(numpy.std(tensor), 1., places=3)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_random_op.py

@@ -1,29 +0,0 @@
-import unittest
-import paddle.v2.framework.create_op_creation_methods as creation
-import paddle.v2.framework.core as core
-from op_test_util import OpTestMeta
-import numpy
-
-
-class TestRandomOp(unittest.TestCase):
-    def test_random(self):
-        scope = core.Scope(None)
-        # Out = scope.create_var("Out")
-        op = creation.op_creations.gaussian_random(
-            shape=[1000, 1000], mean=5.0, std=1.0, Out="Out")
-        for out in op.outputs():
-            if scope.get_var(out) is None:
-                scope.create_var(out).get_tensor()
-
-        tensor = scope.get_var("Out").get_tensor()
-        op.infer_shape(scope)
-        self.assertEqual([1000, 1000], tensor.shape())
-        ctx = core.DeviceContext.cpu_context()
-        op.run(scope, ctx)
-        tensor_array = numpy.array(tensor)
-        self.assertAlmostEqual(numpy.mean(tensor_array), 5.0, places=3)
-        self.assertAlmostEqual(numpy.std(tensor_array), 1.0, places=3)
-
-
-if __name__ == '__main__':
-    unittest.main()