stu1130 commented on a change in pull request #14582: Fix randomized relu
URL: https://github.com/apache/incubator-mxnet/pull/14582#discussion_r324439400
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File path: tests/python/unittest/test_operator.py
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@@ -862,6 +863,35 @@ def fselu_grad(grad, x, y):
check_symbolic_backward(y, [xa], [np.ones(shape)], [ga], rtol=rtol,
atol=atol, dtype=dtype)
+@with_seed()
+def test_rrelu():
+ def rrelu(x, lower_bound):
+ neg_indices = x < 0
+ out = x.copy()
+ out[neg_indices] = lower_bound * out[neg_indices]
+ return out
+ def rrelu_grad(grad, x, lower_bound):
+ neg_indices = x < 0
+ out = np.ones(x.shape)
+ out[neg_indices] = lower_bound
+ return out * grad
+ for ndim in range(1, 4):
+ shape = rand_shape_nd(ndim)
+ x = mx.symbol.Variable("x")
+ eps = 1e-4
+ lower_bound = 1
+ upper_bound = 2
+ for dtype in [np.float16, np.float32, np.float64]:
+ xa = np.random.uniform(low=-1.0,high=1.0,size=shape).astype(dtype)
+ xa[abs(xa) < eps] = 1.0
+ y = mx.symbol.LeakyReLU(data=x, act_type="rrelu",
+ lower_bound=lower_bound, upper_bound=upper_bound)
+ ya = rrelu(xa, lower_bound=lower_bound)
+ ga = rrelu_grad(np.ones(shape), xa, lower_bound=lower_bound)
+ check_symbolic_forward(y, [xa], [ya], rtol=0, atol=1, dtype=dtype)
Review comment:
the slope was picked randomly, check_numberic_gradient might not be able to
apply here
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