fix comments

mindspore/python/mindspore/_checkparam.py

@@ -149,7 +149,7 @@ def check_number(arg_value, value, rel, arg_type=int, arg_name=None, prim_name=N
     Check argument integer.
 
     Usage:
-    - number = check_number(number, 0, Rel.GE, "number", None) # number >= 0
+    - number = check_number(number, 0, Rel.GE, "number", None)
     """
     rel_fn = Rel.get_fns(rel)
     prim_name = f' in `{prim_name}`' if prim_name else ''

mindspore/python/mindspore/common/api.py

@@ -792,8 +792,8 @@ class _CellGraphExecutor:
 def ms_memory_recycle():
     """
     Recycle memory used by MindSpore.
-    When train multi Neural network models in one process, memory used by mindspore is very large,
-    this is because mindspore cached runtime memory for every model.
+    When train multi Neural network models in one process, memory used by MindSpore is very large,
+    this is because MindSpore cached runtime memory for every model.
     To recycle these cached memory, users can call this function after training of one model.
     """
     if ms_compile_cache:

mindspore/python/mindspore/common/seed.py

@@ -61,7 +61,7 @@ def set_seed(seed):
         >>> import mindspore.ops as ops
         >>> from mindspore import Tensor, set_seed, Parameter
         >>> from mindspore.common.initializer import initializer
-        >>>
+        >>> import mindspore as ms
         >>> # Note: (1) Please make sure the code is running in PYNATIVE MODE;
         >>> # (2) Because Composite-level ops need parameters to be Tensors, for below examples,
         >>> # when using ops.uniform operator, minval and maxval are initialised as:
@@ -129,7 +129,7 @@ def set_seed(seed):
         >>> # condition 5.
         >>> c1 = ops.uniform((1, 4), minval, maxval, seed=2) # C1
         >>> c2 = ops.uniform((1, 4), minval, maxval, seed=2) # C2
-        >>> # Rerun the program will get the same results:
+        >>> # Rerun the program will get the different results:
         >>> c1 = ops.uniform((1, 4), minval, maxval, seed=2) # C1
         >>> c2 = ops.uniform((1, 4), minval, maxval, seed=2) # C2
         >>>

mindspore/python/mindspore/nn/metrics/accuracy.py

@@ -34,6 +34,9 @@ class Accuracy(EvaluationBase):
           'multilabel'. 'classification' means the dataset label is single. 'multilabel' means the dataset has multiple
           labels. Default: 'classification'.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> import mindspore

mindspore/python/mindspore/nn/metrics/error.py

@@ -29,6 +29,9 @@ class MAE(Metric):
 
     where :math:`n` is batch size.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> import mindspore
@@ -102,6 +105,9 @@ class MSE(Metric):
 
     where :math:`n` is batch size.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> import mindspore

mindspore/python/mindspore/nn/metrics/fbeta.py

@@ -30,7 +30,10 @@ class Fbeta(Metric):
                 {(1+\beta^2) \cdot true\_positive +\beta^2 \cdot false\_negative + false\_positive}
 
     Args:
-        beta (Union[float, int]): Beta coefficient in the F measure.
+        beta (Union[float, int]): Beta coefficient in the F measure. `beta` should be greater than 0.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> import numpy as np

mindspore/python/mindspore/nn/metrics/loss.py

@@ -24,6 +24,9 @@ class Loss(Metric):
     .. math::
         loss = \frac{\sum_{k=1}^{n}loss_k}{n}
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> import mindspore

mindspore/python/mindspore/nn/metrics/mean_surface_distance.py

@@ -58,9 +58,7 @@ class MeanSurfaceDistance(Metric):
 
     Examples:
         >>> import numpy as np
-
         >>> from mindspore import nn, Tensor
-        >>>
         >>> x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]))
         >>> y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]))
         >>> metric = nn.MeanSurfaceDistance(symmetric=False, distance_metric="euclidean")

mindspore/python/mindspore/nn/metrics/metric.py

@@ -28,6 +28,7 @@ def rearrange_inputs(func):
     This decorator is currently applied on the `update` of :class:`mindspore.nn.Metric`.
 
     Examples:
+        >>> from mindspore.nn import rearrange_inputs
         >>> class RearrangeInputsExample:
         ...     def __init__(self):
         ...         self._indexes = None

mindspore/python/mindspore/nn/metrics/perplexity.py

@@ -28,8 +28,8 @@ class Perplexity(Metric):
         PP(W)=P(w_{1}w_{2}...w_{N})^{-\frac{1}{N}}=\sqrt[N]{\frac{1}{P(w_{1}w_{2}...w_{N})}}
 
     Args:
-        ignore_label (int): Index of an invalid label to be ignored when counting. If set to `None`, it will include all
-                            entries. Default: None.
+        ignore_label (Union[int, None]): Index of an invalid label to be ignored when counting. If set to `None`,
+        it will include all entries. Default: None.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -40,7 +40,6 @@ class Perplexity(Metric):
     Examples:
         >>> import numpy as np
         >>> from mindspore import nn, Tensor
-        >>>
         >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
         >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.Perplexity(ignore_label=None)

mindspore/python/mindspore/nn/metrics/precision.py

@@ -39,6 +39,9 @@ class Precision(EvaluationBase):
     Args:
         eval_type (str): 'classification' or 'multilabel' are supported. Default: 'classification'.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> from mindspore import nn, Tensor

mindspore/python/mindspore/nn/metrics/recall.py

@@ -40,6 +40,9 @@ class Recall(EvaluationBase):
         eval_type (str): 'classification' or 'multilabel' are supported. Default: 'classification'.
             Default: 'classification'.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> from mindspore import nn, Tensor

mindspore/python/mindspore/nn/metrics/topk.py

@@ -32,6 +32,9 @@ class TopKCategoricalAccuracy(Metric):
         TypeError: If `k` is not int.
         ValueError: If `k` is less than 1.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> from mindspore import nn, Tensor
@@ -108,6 +111,9 @@ class Top1CategoricalAccuracy(TopKCategoricalAccuracy):
     Calculates the top-1 categorical accuracy. This class is a specialized class for TopKCategoricalAccuracy.
     Refer to :class:`TopKCategoricalAccuracy` for more details.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> from mindspore import nn, Tensor
@@ -131,6 +137,9 @@ class Top5CategoricalAccuracy(TopKCategoricalAccuracy):
     Calculates the top-5 categorical accuracy. This class is a specialized class for TopKCategoricalAccuracy.
     Refer to :class:`TopKCategoricalAccuracy` for more details.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> from mindspore import nn, Tensor

mindspore/python/mindspore/nn/optim/adam.py

@@ -296,7 +296,7 @@ class Adam(Optimizer):
         ``Ascend`` ``GPU``  ``CPU``
 
     Examples:
-        >>>from mindspore import nn, Model
+        >>> from mindspore import nn, Model
         >>>
         >>> net = Net()
         >>> #1) All parameters use the same learning rate and weight decay
@@ -476,7 +476,7 @@ class AdamWeightDecay(Optimizer):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
-        >>>from mindspore import nn, Model
+        >>> from mindspore import nn, Model
         >>>
         >>> net = Net()
         >>> #1) All parameters use the same learning rate and weight decay

mindspore/python/mindspore/nn/wrap/cell_wrapper.py

@@ -692,12 +692,20 @@ class ParameterUpdate(Cell):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import numpy as np
+        >>> import mindspore
+        >>> from mindspore import nn, Tensor
         >>> network = nn.Dense(3, 4)
         >>> param = network.parameters_dict()['weight']
         >>> update = nn.ParameterUpdate(param)
         >>> update.phase = "update_param"
         >>> weight = Tensor(np.arange(12).reshape((4, 3)), mindspore.float32)
         >>> output = update(weight)
+        >>> print(output)
+        [[ 0.  1.  2.]
+         [ 3.  4.  5.]
+         [ 6.  7.  8.]
+         [ 9. 10. 11.]]
     """
 
     def __init__(self, param):

mindspore/python/mindspore/train/model.py

@@ -996,6 +996,7 @@ class Model:
             Tensor, array(s) of predictions.
 
         Examples:
+            >>> import numpy as np
             >>> import mindspore as ms
             >>> from mindspore import Model, Tensor
             >>>

mindspore/python/mindspore/train/serialization.py

@@ -222,7 +222,7 @@ def save_checkpoint(save_obj, ckpt_file_name, integrated_save=True,
         integrated_save (bool): Whether to integrated save in automatic model parallel scene. Default: True
         async_save (bool): Whether to open an independent thread to save the checkpoint file. Default: False
         append_dict (dict): Additional information that needs to be saved.  The key of dict must be str,
-            the value of dict must be one of int float and bool. Default: None
+            the value of dict must be one of int, float or bool. Default: None
         enc_key (Union[None, bytes]): Byte type key used for encryption. If the value is None, the encryption
                                       is not required. Default: None.
         enc_mode (str): This parameter is valid only when enc_key is not set to None. Specifies the encryption
@@ -734,7 +734,7 @@ def _fill_param_into_net(net, parameter_list):
 
 def export(net, *inputs, file_name, file_format='AIR', **kwargs):
     """
-    Export the mindspore network into an offline model in the specified format.
+    Export the MindSpore network into an offline model in the specified format.
 
     Note:
         1. When exporting AIR, ONNX format, the size of a single tensor can not exceed 2GB.