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mindspore
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zhangz0911gm 2020-11-24 19:14:39 +08:00
parent 71165dde9a
commit 7b1d1ca831
4 changed files with 18 additions and 12 deletions
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4
mindspore/nn/optim/adam.py
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@ -553,8 +553,8 @@ class AdamOffload(Optimizer):
>>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
>>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
>>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
>>> {'params': no_conv_params, 'lr': 0.01},
>>> {'order_params': net.trainable_params()}]
... {'params': no_conv_params, 'lr': 0.01},
... {'order_params': net.trainable_params()}]
>>> optim = nn.AdamOffload(group_params, learning_rate=0.1, weight_decay=0.0)
>>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
>>> # The no_conv_params's parameters will use learning rate of 0.01 and defaule weight decay of 0.0.

2
mindspore/nn/wrap/grad_reducer.py
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@ -265,7 +265,7 @@ class DistributedGradReducer(Cell):
>>>
>>> device_id = int(os.environ["DEVICE_ID"])
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True,
>>> device_id=int(device_id))
... device_id=int(device_id))
>>> init()
>>> context.reset_auto_parallel_context()
>>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)

18
mindspore/ops/composite/base.py
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@ -384,9 +384,11 @@ class MultitypeFuncGraph(MultitypeFuncGraph_):
>>> @add.register("Tensor", "Tensor")
... def add_tensor(x, y):
... return tensor_add(x, y)
>>> add(1, 2)
>>> ourput = add(1, 2)
>>> print(output)
3
>>> add(Tensor(1, mstype.float32), Tensor(2, mstype.float32))
>>> output = add(Tensor(1, mstype.float32), Tensor(2, mstype.float32))
>>> print(output)
Tensor(shape=[], dtype=Float32, 3)
"""
@ -470,11 +472,13 @@ class HyperMap(HyperMap_):
... return F.square(x)
>>>
>>> common_map = HyperMap()
>>> common_map(square, nest_tensor_list)
>>> output = common_map(square, nest_tensor_list)
>>> print(output)
((Tensor(shape=[], dtype=Float32, 1), Tensor(shape=[], dtype=Float32, 4)),
(Tensor(shape=[], dtype=Float32, 9), Tensor(shape=[], dtype=Float32, 16))
>>> square_map = HyperMap(square)
>>> square_map(nest_tensor_list)
>>> output = square_map(nest_tensor_list)
>>> print(output)
((Tensor(shape=[], dtype=Float32, 1), Tensor(shape=[], dtype=Float32, 4)),
(Tensor(shape=[], dtype=Float32, 9), Tensor(shape=[], dtype=Float32, 16))
"""
@ -531,10 +535,12 @@ class Map(Map_):
... return F.square(x)
>>>
>>> common_map = Map()
>>> common_map(square, tensor_list)
>>> output = common_map(square, tensor_list)
>>> print(output)
(Tensor(shape=[], dtype=Float32, 1), Tensor(shape=[], dtype=Float32, 4), Tensor(shape=[], dtype=Float32, 9))
>>> square_map = Map(square)
>>> square_map(tensor_list)
>>> output = square_map(tensor_list)
>>> print(output)
(Tensor(shape=[], dtype=Float32, 1), Tensor(shape=[], dtype=Float32, 4), Tensor(shape=[], dtype=Float32, 9))
"""

6
mindspore/ops/primitive.py
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@ -35,9 +35,9 @@ class Primitive(Primitive_):
>>> # or work with prim_attr_register:
>>> # init a Primitive class with attr1 and attr2
>>> class Add(Primitive):
>>> @prim_attr_register
>>> def __init__(self, attr1, attr2):
>>> # check attr1 and attr2 or do some initializations
... @prim_attr_register
... def __init__(self, attr1, attr2):
... # check attr1 and attr2 or do some initializations
>>> # init a Primitive obj with attr1=1 and attr2=2
>>> add = Add(attr1=1, attr2=2)
"""