anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199028119
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File path: tests/python/unittest/test_operator.py
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@@ -3009,6 +3009,49 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
grad_nodes={'data': req, 'gamma': req, 'beta':
req},
numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
+@with_seed()
+def test_norm():
+ def l1norm(input_data, axis=0, keepdims=True):
+ return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
+ def l2norm(input_data, axis=0, keepdims=True):
+ return np.linalg.norm(input_data, axis=axis, keepdims=keepdims)
+
+ ctx = default_context()
+ data = mx.symbol.Variable('data')
+ in_data_dim = random_sample([4,5,6], 1)[0]
+ in_shape = rand_shape_nd(in_data_dim)
+ for order in [1, 2]:
+ for dtype in [np.float16, np.float32, np.float64]:
+ in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
+ in_data[abs(in_data) < 1e-2] = 1e-2
Review comment:
sure, i will use 1e-3. that is the epsilon i am using in check_gradient
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