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!23686 code_docs_model 

Merge pull request !23686 from wanyiming/model_docs
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i-robot 2021-09-18 07:03:12 +00:00 committed by Gitee
parent 0846831308 1c916fef8d
commit f6bab08bde
1 changed files with 9 additions and 8 deletions
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mindspore/train/model.py
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@ -58,18 +58,18 @@ class _StepSync(Callback):
class Model:
"""
High-Level API for Training or Testing.
High-Level API for training or inference.
`Model` groups layers into an object with training and inference features.
Args:
network (Cell): A training or testing network.
loss_fn (Cell): Objective function, if loss_fn is None, the
network should contain the logic of loss and grads calculation, and the logic
of parallel if needed. Default: None.
network should contain the logic of loss and grads calculation,
and parallel if needed. Default: None.
optimizer (Cell): Optimizer for updating the weights. Default: None.
metrics (Union[dict, set]): A Dictionary or a set of metrics to be evaluated by the model during
training and testing. eg: {'accuracy', 'recall'}. Default: None.
training and inference. eg: {'accuracy', 'recall'}. Default: None.
eval_network (Cell): Network for evaluation. If not defined, `network` and `loss_fn` would be wrapped as
`eval_network` . Default: None.
eval_indexes (list): When defining the `eval_network`, if `eval_indexes` is None, all outputs of the
@ -845,7 +845,8 @@ class Model:
Default: True.
Returns:
Dict, which returns the loss value and metrics values for the model in the test mode.
Dict, the key is the metric name defined by users and the value is the metrics value for
the model in the test mode.
Examples:
>>> from mindspore import Model, nn
@ -905,8 +906,8 @@ class Model:
This is a pre-compile function. The arguments should be the same with model.predict() function.
Args:
predict_data (Tensor): The predict data, can be bool, int, float, str, None, tensor,
or tuple, list and dict that store these types.
predict_data (Optional[Tensor, list[Tensor], tuple[Tensor]]): The predict data, can be a single tensor,
a list of tensor, or a tuple of tensor.
Returns:
Tensor, array(s) of predictions.
@ -972,7 +973,7 @@ class Model:
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned. The data and label would be passed to the network and loss
function respectively.
dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
dataset_sink_mode (bool): Determines whether to pass the data through dataset channel.
Configure pynative mode or CPU, the training process will be performed with
dataset not sink. Default: True.
sink_size (int): Control the amount of data in each sink.