forked from mindspore-Ecosystem/mindspore
!8963 Updating notes of remaining examples in nn and ops' folders
From: @zhangz0911gm Reviewed-by: @liangchenghui,@zhunaipan Signed-off-by: @zhunaipan
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386f6e0d2b
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@ -553,8 +553,8 @@ class AdamOffload(Optimizer):
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>>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
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>>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
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>>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
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>>> {'params': no_conv_params, 'lr': 0.01},
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>>> {'order_params': net.trainable_params()}]
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... {'params': no_conv_params, 'lr': 0.01},
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... {'order_params': net.trainable_params()}]
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>>> optim = nn.AdamOffload(group_params, learning_rate=0.1, weight_decay=0.0)
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>>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
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>>> # The no_conv_params's parameters will use learning rate of 0.01 and defaule weight decay of 0.0.
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@ -265,7 +265,7 @@ class DistributedGradReducer(Cell):
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>>>
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>>> device_id = int(os.environ["DEVICE_ID"])
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>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True,
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>>> device_id=int(device_id))
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... device_id=int(device_id))
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>>> init()
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>>> context.reset_auto_parallel_context()
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>>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)
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@ -384,9 +384,11 @@ class MultitypeFuncGraph(MultitypeFuncGraph_):
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>>> @add.register("Tensor", "Tensor")
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... def add_tensor(x, y):
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... return tensor_add(x, y)
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>>> add(1, 2)
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>>> ourput = add(1, 2)
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>>> print(output)
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3
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>>> add(Tensor(1, mstype.float32), Tensor(2, mstype.float32))
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>>> output = add(Tensor(1, mstype.float32), Tensor(2, mstype.float32))
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>>> print(output)
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Tensor(shape=[], dtype=Float32, 3)
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"""
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@ -470,11 +472,13 @@ class HyperMap(HyperMap_):
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... return F.square(x)
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>>>
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>>> common_map = HyperMap()
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>>> common_map(square, nest_tensor_list)
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>>> output = common_map(square, nest_tensor_list)
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>>> print(output)
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((Tensor(shape=[], dtype=Float32, 1), Tensor(shape=[], dtype=Float32, 4)),
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(Tensor(shape=[], dtype=Float32, 9), Tensor(shape=[], dtype=Float32, 16))
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>>> square_map = HyperMap(square)
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>>> square_map(nest_tensor_list)
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>>> output = square_map(nest_tensor_list)
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>>> print(output)
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((Tensor(shape=[], dtype=Float32, 1), Tensor(shape=[], dtype=Float32, 4)),
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(Tensor(shape=[], dtype=Float32, 9), Tensor(shape=[], dtype=Float32, 16))
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"""
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@ -531,10 +535,12 @@ class Map(Map_):
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... return F.square(x)
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>>>
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>>> common_map = Map()
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>>> common_map(square, tensor_list)
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>>> output = common_map(square, tensor_list)
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>>> print(output)
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(Tensor(shape=[], dtype=Float32, 1), Tensor(shape=[], dtype=Float32, 4), Tensor(shape=[], dtype=Float32, 9))
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>>> square_map = Map(square)
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>>> square_map(tensor_list)
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>>> output = square_map(tensor_list)
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>>> print(output)
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(Tensor(shape=[], dtype=Float32, 1), Tensor(shape=[], dtype=Float32, 4), Tensor(shape=[], dtype=Float32, 9))
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"""
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@ -35,9 +35,9 @@ class Primitive(Primitive_):
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>>> # or work with prim_attr_register:
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>>> # init a Primitive class with attr1 and attr2
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>>> class Add(Primitive):
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>>> @prim_attr_register
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>>> def __init__(self, attr1, attr2):
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>>> # check attr1 and attr2 or do some initializations
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... @prim_attr_register
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... def __init__(self, attr1, attr2):
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... # check attr1 and attr2 or do some initializations
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>>> # init a Primitive obj with attr1=1 and attr2=2
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>>> add = Add(attr1=1, attr2=2)
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"""
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