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!38078 updates op supported platform 

Merge pull request !38078 from 李林杰/code_docs_0714_update_op_supported_platfoorms_master
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i-robot 2022-07-15 01:31:07 +00:00 committed by Gitee
parent 101e69f1a7 bac9511822
commit 56e763be39
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GPG Key ID: 173E9B9CA92EEF8F
5 changed files with 7 additions and 7 deletions
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2
mindspore/python/mindspore/ops/function/array_func.py
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@ -2028,7 +2028,7 @@ def scatter_nd_min(input_x, indices, updates, use_locking=False):
is required when data type conversion of Parameter is not supported.
Supported Platforms:
``GPU``
``GPU`` ``CPU``
Examples:
>>> input_x = Parameter(Tensor(np.ones(8) * 10, mindspore.float32), name="x")

2
mindspore/python/mindspore/ops/operations/array_ops.py
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@ -5194,7 +5194,7 @@ class ScatterNdMin(_ScatterNdOp):
Refer to :func:`mindspore.ops.scatter_nd_min` for more details.
Supported Platforms:
``GPU``
``GPU`` ``CPU``
Examples:
>>> input_x = Parameter(Tensor(np.ones(8) * 10, mindspore.float32), name="x")

4
mindspore/python/mindspore/ops/operations/math_ops.py
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@ -5513,7 +5513,7 @@ class Real(Primitive):
TypeError: If the input is not a Tensor.
Supported Platforms:
``Ascend`` ``CPU`` ``GPU``
``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.asarray(np.complex(1.3+0.4j)), mindspore.complex64)
@ -5577,7 +5577,7 @@ class Imag(Primitive):
TypeError: If the input is not a Tensor.
Supported Platforms:
``Ascend`` ``CPU`` ``GPU``
``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.asarray(np.complex(1.3+0.4j)), mindspore.complex64)

2
mindspore/python/mindspore/ops/operations/nn_ops.py
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@ -4926,7 +4926,7 @@ class AdamWeightDecay(PrimitiveWithInfer):
- **v** (Tensor) - The same shape and data type as `v`.
Supported Platforms:
``GPU`` ``CPU``
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np

4
mindspore/python/mindspore/train/model.py
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@ -1019,7 +1019,7 @@ class Model:
>>> loss_scale_manager = ms.FixedLossScaleManager()
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim, metrics=None,
loss_scale_manager=loss_scale_manager)
... loss_scale_manager=loss_scale_manager)
>>> model.train(2, dataset)
"""
Validator.check_bool(dataset_sink_mode)
@ -1565,7 +1565,7 @@ class Model:
>>> loss_scale_manager = ms.FixedLossScaleManager()
>>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = ms.Model(net, loss_fn=loss, optimizer=optim, metrics=None,
loss_scale_manager=loss_scale_manager)
... loss_scale_manager=loss_scale_manager)
>>> layout_dict = model.infer_train_layout(dataset)
"""
self._infer_train_check(train_dataset, dataset_sink_mode, sink_size)