fix hisotry and lambda callback

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

@@ -2,7 +2,7 @@
 
     将网络输出的相关信息记录到 `History` 对象中。
 
-    如果用户不自定义训练网络或评估网络，记录的内容将为损失值；如果用户自定义了训练网络/评估网络，如果定义的网络返回 `Tensor` 或 `numpy.ndarray`，则记录此返回值均值，如果返回 `tuple` 或 `list`，则记录第一个元素。
+    用户不自定义训练网络或评估网络情况下，记录的内容将为损失值；用户自定义了训练网络/评估网络的情况下，如果定义的网络返回 `Tensor` 或 `numpy.ndarray`，则记录此返回值均值，如果返回 `tuple` 或 `list`，则记录第一个元素。
 
     .. note::
         通常使用在 `mindspore.Model.train` 中。

mindspore/python/mindspore/nn/cell.py

@@ -1285,6 +1285,7 @@ class Cell(Cell_):
             ...         out = self.conv(x)
             ...         return out
             >>> names = []
+            >>> n = Net()
             >>> for m in n.cells_and_names():
             ...     if m[0]:
             ...         names.append(m[0])

mindspore/python/mindspore/train/callback/_history.py

@@ -31,6 +31,9 @@ class History(Callback):
         Normally used in `mindspore.Model.train`.
 
     Examples:
+        >>> import numpy as np
+        >>> import mindspore.dataset as ds
+        >>> from mindspore.train.callback import History
         >>> from mindspore import Model, nn
         >>> data = {"x": np.float32(np.random.rand(64, 10)), "y": np.random.randint(0, 5, (64,))}
         >>> train_dataset = ds.NumpySlicesDataset(data=data).batch(32)
@@ -42,7 +45,7 @@ class History(Callback):
         >>> model.train(2, train_dataset, callbacks=[history_cb])
         >>> print(history_cb.epoch)
         >>> print(history_cb.history)
-        [1, 2]
+        {'epoch': [1, 2]}
         {'net_output': [1.607877, 1.6033841]}
     """
     def __init__(self):
@@ -54,20 +57,20 @@ class History(Callback):
         Initialize the `epoch` property at the begin of training.
 
         Args:
-            run_context (RunContext): Context of the `mindspore.Model.train/eval`.
+            run_context (RunContext): Context of the `mindspore.Model.{train | eval}`.
         """
-        self.epoch = []
+        self.epoch = {"epoch": []}
 
     def epoch_end(self, run_context):
         """
         Records the first element of network outputs at the end of epoch.
 
         Args:
-            run_context (RunContext): Context of the `mindspore.Model.train/eval`.
+            run_context (RunContext): Context of the `mindspore.Model.{train | eval}`.
         """
         cb_params = run_context.original_args()
         epoch = cb_params.get("cur_epoch_num", 1)
-        self.epoch.append(epoch)
+        self.epoch.get("epoch").append(epoch)
         net_output = cb_params.net_outputs
         if isinstance(net_output, (tuple, list)):
             if isinstance(net_output[0], Tensor) and isinstance(net_output[0].asnumpy(), np.ndarray):

mindspore/python/mindspore/train/callback/_lambda_callback.py

@@ -33,7 +33,10 @@ class LambdaCallback(Callback):
         begin (Function): called at the beginning of model train/eval.
         end (Function): called at the end of model train/eval.
 
-    Example:
+    Examples:
+        >>> import numpy as np
+        >>> import mindspore.dataset as ds
+        >>> from mindspore.train.callback import History
         >>> from mindspore import Model, nn
         >>> data = {"x": np.float32(np.random.rand(64, 10)), "y": np.random.randint(0, 5, (64,))}
         >>> train_dataset = ds.NumpySlicesDataset(data=data).batch(32)

mindspore/python/mindspore/train/model.py

@@ -27,7 +27,7 @@ from .callback._checkpoint import _chg_ckpt_file_name_if_same_exist
 from ..common.tensor import Tensor
 from ..nn.metrics import get_metrics
 from .._checkparam import check_input_data, check_output_data, Validator
-from .callback import _InternalCallbackParam, RunContext, _CallbackManager, Callback, History
+from .callback import _InternalCallbackParam, RunContext, _CallbackManager, Callback
 from .. import context
 from ..parallel._utils import _get_parallel_mode, _get_device_num, _get_global_rank, \
     _get_parameter_broadcast, _device_number_check, _parameter_broadcast_check, _parallel_predict_check
@@ -940,10 +940,6 @@ class Model:
         if isinstance(self._eval_network, nn.GraphCell) and dataset_sink_mode:
             raise ValueError("Sink mode is currently not supported when evaluating with a GraphCell.")
 
-        if (isinstance(callbacks, Callback) and isinstance(callbacks, History)) or \
-                (isinstance(callbacks, list) and any(isinstance(cb, History) for cb in callbacks)):
-            logger.warning("History callback is recommended to be used in training process.")
-
         cb_params = _InternalCallbackParam()
         cb_params.eval_network = self._eval_network
         cb_params.valid_dataset = valid_dataset