"random op test"

paddle/operators/random_op.h

@@ -6,6 +6,7 @@
 
 namespace paddle {
 namespace operators {
+
 template <typename T, typename DeviceContext>
 bool Gaussian(DeviceContext& ctx,
               framework::Tensor* output,

paddle/pybind/pybind.cc

@@ -36,6 +36,7 @@ USE_OP(mul);
 USE_OP(sigmoid);
 USE_OP(softmax);
 USE_OP(rowwise_add);
+USE_OP(random_op);
 
 template <typename ClassType>
 void ExposeOperator(ClassType& m) {

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

@@ -16,13 +16,13 @@ class TestNet(unittest.TestCase):
         net.complete_add_op(True)
 
         expected = '''
-Op(plain_net), inputs:(@EMPTY@, X, Y, w), outputs:(@TEMP@fc@0, Out, fc.out).
+        Op(plain_net), inputs:(@EMPTY@, X, Y, w), outputs:(@TEMP@fc@0, Out, fc.out).
         Op(add_two), inputs:(X, Y), outputs:(Out).
         Op(plain_net), inputs:(@EMPTY@, X, w), outputs:(@TEMP@fc@0, fc.out).
         Op(fc), inputs:(X, w, @EMPTY@), outputs:(fc.out, @TEMP@fc@0).
         Op(mul), inputs:(X, w), outputs:(@TEMP@fc@0).
         Op(sigmoid), inputs:(@TEMP@fc@0), outputs:(fc.out).
-'''
+        '''
         self.assertEqual(expected, "\n" + str(net))
 
 

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

+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.random(
+            shape=[1000, 1000], mean=5.0, std=1.0, seed=1701, Out="Out")
+        for out in op.outputs():
+            if scope.get_var(out) is None:
+                scope.create_var(out).get_tensor()
+
+        tensor = scope.get_var("Y").get_tensor()
+        op.infer_shape(scope)
+        self.assertEqual([1000, 1000], tensor.shape())
+        ctx = core.DeviceContext.cpu_context()
+        op.run(scope, ctx)
+        self.assertAlmostEqual(numpy.std(tensor), 1.0)
+        self.assertAlmostEqual(numpy.mean(tensor), 5.0)
+
+
+if __name__ == '__main__':
+    unittest.main()