Merge pull request #3162 from niboshi/bp-fix-doctest-errors

[Backport] Fix errors in docstrings
chainer · Aug 16, 2017 · cb7cc2b · cb7cc2b
2 parents feb3ad3 + f52aa66
commit cb7cc2b
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Showing 3 changed files with 15 additions and 8 deletions.
diff --git a/chainer/function.py b/chainer/function.py
@@ -23,9 +23,9 @@ def no_backprop_mode():
     In this example, ``y`` is created in this context. So you cannot call
     :func:`~chianer.Variable.backward`.
 
-    >>> x = chainer.Variable(numpy.array([1,], 'f'))
+    >>> x = chainer.Variable(np.array([1,], 'f'))
     >>> with chainer.no_backprop_mode():
-    ...    y = x + 1
+    ...   y = x + 1
 
     """
     return configuration.using_config('enable_backprop', False)
@@ -43,6 +43,7 @@ def force_backprop_mode():
     In this example, ``y`` has a computational graph and ``y.backward``
     computes gradients of variables in the graph.
 
+    >>> x = chainer.Variable(np.array([1,], 'f'))
     >>> with chainer.no_backprop_mode():
     ...   with chainer.force_backprop_mode():
     ...     y = x + 1

diff --git a/chainer/functions/activation/lstm.py b/chainer/functions/activation/lstm.py
@@ -223,8 +223,10 @@ def lstm(c_prev, x):
         >>> y = chainer.Variable(np.zeros((1, n_units), 'f'))
         >>> h = chainer.Variable(np.zeros((1, n_units), 'f'))
         >>> c = chainer.Variable(np.zeros((1, n_units), 'f'))
-        >>> model = chainer.Chain(w=L.Linear(n_units, 4 * n_units),
-        ...                       v=L.Linear(n_units, 4 * n_units),)
+        >>> model = chainer.Chain()
+        >>> with model.init_scope():
+        ...   model.w = L.Linear(n_units, 4 * n_units)
+        ...   model.v = L.Linear(n_units, 4 * n_units)
         >>> x = model.w(y) + model.v(h)
         >>> c, h = F.lstm(c, x)
 

diff --git a/chainer/functions/activation/slstm.py b/chainer/functions/activation/slstm.py
@@ -257,10 +257,14 @@ def slstm(c_prev1, c_prev2, x1, x2):
         >>> h2 = chainer.Variable(np.zeros((1, n_units), 'f'))
         >>> c1 = chainer.Variable(np.zeros((1, n_units), 'f'))
         >>> c2 = chainer.Variable(np.zeros((1, n_units), 'f'))
-        >>> model1 = chainer.Chain(w=L.Linear(n_units, 4 * n_units),
-        ...                        v=L.Linear(n_units, 4 * n_units))
-        >>> model2 = chainer.Chain(w=L.Linear(n_units, 4 * n_units),
-        ...                        v=L.Linear(n_units, 4 * n_units))
+        >>> model1 = chainer.Chain()
+        >>> with model1.init_scope():
+        ...   model1.w = L.Linear(n_units, 4 * n_units)
+        ...   model1.v = L.Linear(n_units, 4 * n_units)
+        >>> model2 = chainer.Chain()
+        >>> with model2.init_scope():
+        ...   model2.w = L.Linear(n_units, 4 * n_units)
+        ...   model2.v = L.Linear(n_units, 4 * n_units)
         >>> x1 = model1.w(c1) + model1.v(h1)
         >>> x2 = model2.w(c2) + model2.v(h2)
         >>> c, h = F.slstm(c1, c2, x1, x2)