!29013 modify cn api comment

Merge pull request !29013 from changzherui/code_docs_comment2

docs/api/api_python/mindspore.train/mindspore.train.callback.CheckpointConfig.rst

@@ -28,8 +28,8 @@
     >>> from mindspore import Model, nn
     >>> from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
     >>>
-    >>> class LeNet5(nn.Cell)：
-    ...     def __init__(self, num_class=10, num_channel=1)：
+    >>> class LeNet5(nn.Cell):
+    ...     def __init__(self):
     ...         super(LeNet5, self).__init__()
     ...         self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
     ...         self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
@@ -40,7 +40,7 @@
     ...         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
     ...         self.flatten = nn.Flatten()
     ...
-    ...     def construct(self, x)：
+    ...     def construct(self, x):
     ...         x = self.max_pool2d(self.relu(self.conv1(x)))
     ...         x = self.max_pool2d(self.relu(self.conv2(x)))
     ...         x = self.flatten(x)

docs/api/api_python/mindspore.train/mindspore.train.callback.LossMonitor.rst

@@ -15,6 +15,20 @@
 
     - **ValueError** - 当 `per_print_times` 不是整数或小于零。
 
+    **样例：**
+
+    >>> from mindspore import Model, nn
+    >>>
+    >>> net = LeNet5()
+    >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    >>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
+    >>> model = Model(net, loss_fn=loss, optimizer=optim)
+    >>> data_path = './MNIST_Data'
+    >>> dataset = create_dataset(data_path)
+    >>> time_monitor = TimeMonitor()
+    >>> model.train(10, dataset, callbacks=time_monitor)
+
+
     .. py:method:: step_end(run_context)
 
         step结束时打印训练loss。

docs/api/api_python/mindspore.train/mindspore.train.callback.ModelCheckpoint.rst

@@ -2,7 +2,7 @@
 
     checkpoint的回调函数。
 
-    在训练过程中调用该方法可以保存训练后的网络参数。
+    在训练过程中调用该方法可以保存网络参数。
 
     .. note::
         在分布式训练场景下，请为每个训练进程指定不同的目录来保存checkpoint文件。否则，可能会训练失败。
@@ -15,9 +15,47 @@
 
     **异常：**
 
-    - **ValueError** - 如果前缀无效。
+    - **ValueError** - 如果prefix参数不是str类型或包含'/'字符。
+    - **ValueError** - 如果directory参数不是str类型。
     - **TypeError** - config不是CheckpointConfig类型。
 
+    **样例：**
+
+    >>> from mindspore import Model, nn
+    >>> from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
+    >>>
+    >>> class LeNet5(nn.Cell):
+    ...     def __init__(self):
+    ...         super(LeNet5, self).__init__()
+    ...         self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
+    ...         self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
+    ...         self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
+    ...         self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
+    ...         self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
+    ...         self.relu = nn.ReLU()
+    ...         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+    ...         self.flatten = nn.Flatten()
+    ...
+    ...     def construct(self, x)：
+    ...         x = self.max_pool2d(self.relu(self.conv1(x)))
+    ...         x = self.max_pool2d(self.relu(self.conv2(x)))
+    ...         x = self.flatten(x)
+    ...         x = self.relu(self.fc1(x))
+    ...         x = self.relu(self.fc2(x))
+    ...         x = self.fc3(x)
+    ...         return x
+    >>>
+    >>> net = LeNet5()
+    >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    >>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
+    >>> model = Model(net, loss_fn=loss, optimizer=optim)
+    >>> data_path = './MNIST_Data'
+    >>> dataset = create_dataset(data_path)
+    >>> config = CheckpointConfig(saved_network=net)
+    >>> ckpoint_cb = ModelCheckpoint(prefix='LeNet5', directory='./checkpoint', config=config)
+    >>> model.train(10, dataset, callbacks=ckpoint_cb)
+
+
     .. py:method:: end(run_context)
 
         在训练结束后，会保存最后一个step的checkpoint。

docs/api/api_python/mindspore.train/mindspore.train.callback.TimeMonitor.rst

@@ -10,13 +10,27 @@
 
     - **ValueError** - `data_size` 不是正整数。
 
+    **样例：**
+
+    >>> from mindspore import Model, nn
+    >>>
+    >>> net = LeNet5()
+    >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    >>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
+    >>> model = Model(net, loss_fn=loss, optimizer=optim)
+    >>> data_path = './MNIST_Data'
+    >>> dataset = create_dataset(data_path)
+    >>> time_monitor = TimeMonitor()
+    >>> model.train(10, dataset, callbacks=time_monitor)
+
+
     .. py:method:: epoch_begin(run_context)
 
         在epoch开始时记录时间。
 
         **参数：**
 
-        - **run_context** (RunContext) - 包含模型的一些基本信息。
+        - **run_context** (RunContext) - 包含模型的相关信息。
 
     .. py:method:: epoch_end(run_context)
 
@@ -24,4 +38,4 @@
 
         **参数：**
 
-        - **run_context** (RunContext) - 包含模型的一些基本信息。
+        - **run_context** (RunContext) - 包含模型的相关信息。

mindspore/python/mindspore/train/callback/_checkpoint.py

@@ -355,7 +355,8 @@ class ModelCheckpoint(Callback):
         config (CheckpointConfig): Checkpoint strategy configuration. Default: None.
 
     Raises:
-        ValueError: If the prefix is invalid.
+        ValueError: If `prefix` is not str or contains the '/' character.
+        ValueError: If `directory` is not str.
         TypeError: If the config is not CheckpointConfig type.
     """
 

mindspore/python/mindspore/train/callback/_fl_manager.py

@@ -237,7 +237,7 @@ class FederatedLearningManager(Callback):
         adaptively adjusted here.
 
         Args:
-            run_context (RunContext): Context of the train running.
+            run_context (RunContext): Include some information of the model.
         """
         self._global_step += 1
         cb_params = run_context.original_args()

mindspore/python/mindspore/train/callback/_loss_monitor.py

@@ -35,6 +35,18 @@ class LossMonitor(Callback):
 
     Raises:
         ValueError: If per_print_times is not an integer or less than zero.
+
+    Examples:
+        >>> from mindspore import Model, nn
+        >>>
+        >>> net = LeNet5()
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+        >>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
+        >>> model = Model(net, loss_fn=loss, optimizer=optim)
+        >>> data_path = './MNIST_Data'
+        >>> dataset = create_dataset(data_path)
+        >>> loss_monitor = LossMonitor()
+        >>> model.train(10, dataset, callbacks=loss_monitor)
     """
 
     def __init__(self, per_print_times=1):
@@ -50,7 +62,7 @@ class LossMonitor(Callback):
         Print training loss at the end of step.
 
         Args:
-            run_context (RunContext): Context of the train running.
+            run_context (RunContext): Include some information of the model.
         """
         cb_params = run_context.original_args()
         loss = cb_params.net_outputs

mindspore/python/mindspore/train/callback/_lr_scheduler_callback.py

@@ -62,7 +62,7 @@ class LearningRateScheduler(Callback):
         Change the learning_rate at the end of step.
 
         Args:
-            run_context (RunContext): Context of the train running.
+            run_context (RunContext): Include some information of the model.
         """
         cb_params = run_context.original_args()
         arr_lr = cb_params.optimizer.learning_rate.asnumpy()

mindspore/python/mindspore/train/callback/_time_monitor.py

@@ -30,6 +30,18 @@ class TimeMonitor(Callback):
 
     Raises:
         ValueError: If data_size is not positive int.
+
+    Examples:
+        >>> from mindspore import Model, nn
+        >>>
+        >>> net = LeNet5()
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+        >>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
+        >>> model = Model(net, loss_fn=loss, optimizer=optim)
+        >>> data_path = './MNIST_Data'
+        >>> dataset = create_dataset(data_path)
+        >>> time_monitor = TimeMonitor()
+        >>> model.train(10, dataset, callbacks=time_monitor)
     """
 
     def __init__(self, data_size=None):