ChaiBapchya closed pull request #12775: randn operator for symbol and NDarray
API
URL: https://github.com/apache/incubator-mxnet/pull/12775
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diff --git a/python/mxnet/ndarray/random.py b/python/mxnet/ndarray/random.py
index 1e941f79aa1..2d550f52664 100644
--- a/python/mxnet/ndarray/random.py
+++ b/python/mxnet/ndarray/random.py
@@ -152,7 +152,7 @@ def normal(loc=0, scale=1, shape=_Null, dtype=_Null,
ctx=None, out=None, **kwarg
[loc, scale], shape, dtype, ctx, out, kwargs)
-def randn(*shape, **kwargs):
+def randn(loc=0, scale=1, shape=_Null, dtype=_Null, ctx=None, out=None,
**kwargs):
"""Draw random samples from a normal (Gaussian) distribution.
Samples are distributed according to a normal distribution parametrized
@@ -193,11 +193,6 @@ def randn(*shape, **kwargs):
[5.357444 5.7793283 3.9896927]]
<NDArray 2x3 @cpu(0)>
"""
- loc = kwargs.pop('loc', 0)
- scale = kwargs.pop('scale', 1)
- dtype = kwargs.pop('dtype', _Null)
- ctx = kwargs.pop('ctx', None)
- out = kwargs.pop('out', None)
assert isinstance(loc, (int, float))
assert isinstance(scale, (int, float))
return _random_helper(_internal._random_normal, _internal._sample_normal,
diff --git a/python/mxnet/symbol/random.py b/python/mxnet/symbol/random.py
index e9abe9c4a18..32e2f6a7cef 100644
--- a/python/mxnet/symbol/random.py
+++ b/python/mxnet/symbol/random.py
@@ -96,6 +96,33 @@ def normal(loc=0, scale=1, shape=_Null, dtype=_Null,
**kwargs):
[loc, scale], shape, dtype, kwargs)
+def randn(loc=0, scale=1, shape=_Null, dtype=_Null, **kwargs):
+ """Draw random samples from a normal (Gaussian) distribution.
+
+ Samples are distributed according to a normal distribution parametrized
+ by *loc* (mean) and *scale* (standard deviation).
+
+
+ Parameters
+ ----------
+ loc : float or NDArray
+ Mean (centre) of the distribution.
+ scale : float or NDArray
+ Standard deviation (spread or width) of the distribution.
+ shape : int or tuple of ints
+ The number of samples to draw. If shape is, e.g., `(m, n)` and `loc`
and
+ `scale` are scalars, output shape will be `(m, n)`. If `loc` and
`scale`
+ are NDArrays with shape, e.g., `(x, y)`, then output will have shape
+ `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)`
pair.
+ dtype : {'float16','float32', 'float64'}
+ Data type of output samples. Default is 'float32'
+ """
+ assert isinstance(loc, (int, float))
+ assert isinstance(scale, (int, float))
+ return _random_helper(_internal._random_normal, _internal._sample_normal,
+ [loc, scale], shape, dtype, kwargs)
+
+
def poisson(lam=1, shape=_Null, dtype=_Null, **kwargs):
"""Draw random samples from a Poisson distribution.
diff --git a/tests/python/unittest/test_loss.py
b/tests/python/unittest/test_loss.py
index 18d1ebf8fb1..b34132fa195 100644
--- a/tests/python/unittest/test_loss.py
+++ b/tests/python/unittest/test_loss.py
@@ -351,9 +351,9 @@ def test_triplet_loss():
@with_seed()
def test_cosine_loss():
#Generating samples
- input1 = mx.nd.random.randn(3, 2)
- input2 = mx.nd.random.randn(3, 2)
- label = mx.nd.sign(mx.nd.random.randn(input1.shape[0]))
+ input1 = mx.nd.random.randn(shape=(3, 2))
+ input2 = mx.nd.random.randn(shape=(3, 2))
+ label = mx.nd.sign(mx.nd.random.randn(shape=(input1.shape[0])))
#Calculating loss from cosine embedding loss function in Gluon
Loss = gluon.loss.CosineEmbeddingLoss()
loss = Loss(input1, input2, label)
diff --git a/tests/python/unittest/test_random.py
b/tests/python/unittest/test_random.py
index 6a59d8627ba..809df12f1d2 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -57,6 +57,7 @@ def check_with_device(device, dtype):
},
{
'name': 'randn',
+ 'symbol':mx.sym.random.randn,
'ndop': mx.nd.random.randn,
'params': { 'loc': 10.0, 'scale': 0.5 },
'checks': [
@@ -203,9 +204,6 @@ def check_with_device(device, dtype):
params = symbdic['params'].copy()
params.update(shape=shape, dtype=dtype, ctx=device)
args = ()
- if name == 'randn':
- params.pop('shape') # randn does not accept shape param
- args = shape
if name.endswith('_like'):
params['data'] = mx.nd.ones(params.pop('shape'),
dtype=params.pop('dtype'),
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