eric-haibin-lin commented on a change in pull request #11229: [MXNET-379] L1
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r195276646
##########
File path: tests/python/unittest/test_operator.py
##########
@@ -2879,6 +2879,32 @@ 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_l1_norm():
+ 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 dtype in [np.float16, np.float32, np.float64]:
+ in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
+ for i in range(in_data_dim):
+ for keep_dims in [True, False]:
+ norm_sym = mx.symbol.norm(data=data, ord=1, axis=i,
keepdims=keep_dims)
+ npy_out = np.sum(abs(in_data), axis=i, keepdims=keep_dims)
+ check_symbolic_forward(norm_sym, [in_data], [npy_out],
+ rtol=1e-2 if dtype is np.float16 else
1e-5,
+ atol=1e-5, ctx=ctx)
+ # check gradient
+ #check_numeric_gradient(norm_sym, [in_data], numeric_eps=1e-3,
rtol=1e-2, atol=1e-3)
+ if i < in_data_dim-1:
+ norm_sym = mx.symbol.norm(data=data, ord=1, axis=(i, i+1),
keepdims=keep_dims)
+ npy_out = np.sum(abs(in_data), axis=(i, i+1),
keepdims=keep_dims)
+ check_symbolic_forward(norm_sym, [in_data], [npy_out],
+ rtol=1e-2 if dtype is np.float16 else
1e-5,
+ atol=1e-5, ctx=ctx)
+ # check gradient
+ #check_numeric_gradient(norm_sym, [in_data],
numeric_eps=1e-3, rtol=1e-2, atol=1e-3)
Review comment:
why comment this out?
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