[GitHub] piiswrong closed pull request #8956: Fix __repr__ for gluon.Parameter

[GitBox <gi...@apache.org>]

piiswrong closed pull request #8956: Fix __repr__ for gluon.Parameter
URL: https://github.com/apache/incubator-mxnet/pull/8956
 
 
   

This is a PR merged from a forked repository.
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diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 1c42e03958..9d8542ec2d 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -152,3 +152,4 @@ List of Contributors
 * [Andre Tamm](https://github.com/andretamm)
 * [Marco de Abreu](https://github.com/marcoabreu)
  - Marco is the creator of the current MXNet CI.
+* [Julian Salazar](https://github.com/JulianSlzr)
diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index 537d6365a4..fa38285911 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -117,7 +117,7 @@ def __init__(self, name, grad_req='write', shape=None, dtype=mx_real_t,
 
     def __repr__(self):
         s = 'Parameter {name} (shape={shape}, dtype={dtype})'
-        return s.format(**self.__dict__)
+        return s.format(name=self.name, shape=self.shape, dtype=self.dtype)
 
     @property
     def grad_req(self):
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index c619056c11..f2d001a702 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -70,6 +70,23 @@ def forward(self, x):
     net3.load_params('net1.params', mx.cpu())
 
 
+def test_parameter_str():
+    class Net(gluon.Block):
+        def __init__(self, **kwargs):
+            super(Net, self).__init__(**kwargs)
+            with self.name_scope():
+                self.dense0 = nn.Dense(10, in_units=5, use_bias=False)
+
+    net = Net(prefix='net1_')
+    lines = str(net.collect_params()).splitlines()
+
+    assert lines[0] == 'net1_ ('
+    assert 'net1_dense0_weight' in lines[1]
+    assert '(10, 5)' in lines[1]
+    assert 'numpy.float32' in lines[1]
+    assert lines[2] == ')'
+
+
 def test_basic():
     model = nn.Sequential()
     model.add(nn.Dense(128, activation='tanh', in_units=10, flatten=False))


 

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