anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r194531662
##########
File path: tests/python/unittest/test_operator.py
##########
@@ -2879,6 +2879,36 @@ 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_norml1():
+ def npy_l1norm(data, axis):
+ np.sum(abs(np_arr), axis=i, keepdims=True)
+ ctx = default_context()
+ data = mx.symbol.Variable('data')
+
+ for dtype in [np.float16, np.float32, np.float64]:
+ dtype = np.float32
+ in_data = np.random.uniform(-1, 1, (4,4,4,4)).astype(dtype)
+ for i in range(4):
+ for keep_dims in [True, False]:
+ norm_sym = mx.symbol.norm(data=data, ord=1, axis=i,
keepdims=keep_dims)
+ exe = norm_sym.simple_bind(ctx, data=(4,4,4,4))
+ exe.arg_dict['data'][:] = in_data
+ npy_out = np.sum(abs(in_data), axis=i, keepdims=keep_dims)
+ out = exe.forward()[0]
+ assert_almost_equal(out, npy_out, rtol=1e-2, atol=1e-5)
+ # check gradient
+ check_numeric_gradient(out, [in_data], numeric_eps=1e-3,
rtol=1e-2, atol=1e-3)
Review comment:
what should the values be for fp64 and fp32?
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