xidulu commented on a change in pull request #17302: [numpy]add op
random.logistic
URL: https://github.com/apache/incubator-mxnet/pull/17302#discussion_r366410192
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File path: tests/python/unittest/test_numpy_op.py
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@@ -3325,15 +3363,15 @@ def hybrid_forward(self, F, loc, scale):
for hybridize in [False, True]:
for dtype in dtypes:
for ((shape1, shape2), out_shape) in zip(param_shape,
output_shapes):
- test_normal_grad = TestNormalGrad(out_shape)
+ test_logistic_grad = TestLogisticGrad(out_shape)
if hybridize:
- test_normal_grad.hybridize()
+ test_logistic_grad.hybridize()
loc = np.zeros(shape1)
loc.attach_grad()
scale = np.ones(shape2)
scale.attach_grad()
with mx.autograd.record():
- samples = test_normal_grad(loc, scale)
+ samples = test_logistic_grad(loc, scale)
samples.backward()
assert loc.grad.shape == shape1
assert scale.grad.shape == shape2
Review comment:
Should include cases testing correctness of the gradients.
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