[examples] correct failed examples

mindspore/nn/layer/basic.py

@@ -129,7 +129,7 @@ class Dropout(Cell):
         >>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32)
         >>> net = nn.Dropout(keep_prob=0.8)
         >>> net.set_train()
-        Dropout<keep_prob=0.8, dtype=Float32>
+        Dropout<keep_prob=0.8>
         >>> output = net(x)
         >>> print(output.shape)
         (2, 2, 3)

mindspore/nn/layer/container.py

@@ -42,9 +42,9 @@ class _CellListBase():
     The sequential cell may be iterated using the construct method using for-in statement.
     But there are some scenarios that the construct method built-in does not fit.
     For convenience, we provide an interface that indicates the sequential
-    cell may be interpretated as list of cells, so it can be accessed using
+    cell may be interpreted as list of cells, so it can be accessed using
     iterator or subscript when a sequential cell instantiate is accessed
-    by iterator or subscript , it will be interpretated as a list of cells.
+    by iterator or subscript , it will be interpreted as a list of cells.
     """
     def __init__(self):
         self.__cell_as_list__ = True
@@ -151,7 +151,7 @@ class SequentialCell(Cell):
         """Appends a given cell to the end of the list.
 
         Examples:
-            >>> conv = nn.Conv2d(3, 2, 3, pad_mode='valid')
+            >>> conv = nn.Conv2d(3, 2, 3, pad_mode='valid', weight_init="ones")
             >>> bn = nn.BatchNorm2d(2)
             >>> relu = nn.ReLU()
             >>> seq = nn.SequentialCell([conv, bn])
@@ -159,10 +159,10 @@ class SequentialCell(Cell):
             >>> x = Tensor(np.ones([1, 3, 4, 4]), dtype=mindspore.float32)
             >>> output = seq(x)
             >>> print(output)
-            [[[[0.08789019 0.08789019]
-               [0.08789019 0.08789019]]
-              [[0.07690391 0.07690391]
-               [0.07690391 0.07690391]]]]
+            [[[[26.999863 26.999863]
+               [26.999863 26.999863]]
+              [[26.999863 26.999863]
+               [26.999863 26.999863]]]]
         """
         if _valid_cell(cell):
             self._cells[str(len(self))] = cell

mindspore/nn/loss/loss.py

@@ -316,6 +316,7 @@ class SampledSoftmaxLoss(_Loss):
         ``GPU``
 
     Examples:
+        >>> mindspore.set_seed(1)
         >>> loss = nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, num_true=1)
         >>> weights = Tensor(np.random.randint(0, 9, [7, 10]), mindspore.float32)
         >>> biases = Tensor(np.random.randint(0, 9, [7]), mindspore.float32)
@@ -323,7 +324,7 @@ class SampledSoftmaxLoss(_Loss):
         >>> inputs = Tensor(np.random.randint(0, 9, [3, 10]), mindspore.float32)
         >>> output = loss(weights, biases, labels, inputs)
         >>> print(output)
-        [ 4.0181947 46.050743   7.0009117]
+        [4.6051701e+01 1.4000047e+01 6.1989022e-06]
     """
 
     def __init__(self, num_sampled, num_classes, num_true=1,

mindspore/ops/composite/clip_ops.py

@@ -163,10 +163,11 @@ def clip_by_global_norm(x, clip_norm=1.0, use_norm=None):
         >>> input_x = (Tensor(x1), Tensor(x2))
         >>> out = clip_by_global_norm(input_x, 1.0)
         >>> print(out)
-        ([[ 2.98142403e-01,  4.47213590e-01],
-         [ 1.49071202e-01,  2.98142403e-01]],
+        (Tensor(shape=[2, 2], dtype=Float32, value=
+        [[ 2.98142403e-01,  4.47213590e-01],
+         [ 1.49071202e-01,  2.98142403e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
         [[ 1.49071202e-01,  5.96284807e-01],
-         [ 4.47213590e-01,  1.49071202e-01]])
+         [ 4.47213590e-01,  1.49071202e-01]]))
     """
 
     clip_norm = _check_value(clip_norm)

mindspore/ops/composite/math_ops.py

@@ -144,7 +144,7 @@ def _axes_int_check(x1_shape, x2_shape, axes):
 def _validate_axes(x1_shape, x2_shape, axes):
     """
     Checks for axes having the correct length according to input, for any value in axis
-    being out of range with given shape and also checking for compatiable axes values
+    being out of range with given shape and also checking for compatible axes values
     with given inputs.
     """
     shapes = [x1_shape, x2_shape]
@@ -246,7 +246,7 @@ def tensor_dot(x1, x2, axes):
     x2_type = F.dtype(x2)
     axes = _check_axes(axes)
     _typecheck_input(x1_type, x2_type)
-    # input compability check & axes format update
+    # input compatibility check & axes format update
     axes = _axes_int_check(x1_shape, x2_shape, axes)
     _validate_axes(x1_shape, x2_shape, axes)
     x1_reshape_fwd, x1_transpose_fwd, x1_ret = _calc_new_shape(x1_shape, axes, 0)

mindspore/ops/operations/array_ops.py

@@ -1943,7 +1943,7 @@ class UnsortedSegmentMin(PrimitiveWithCheck):
         Tensor, set the number of `num_segments` as `N`, the shape is :math:`(N, x_2, ..., x_R)`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> input_x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32))