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!9058 Fix MultiFieldEmbedding Doc Error 

From: @huangxinjing
Reviewed-by: @stsuteng,@zh_qh
Signed-off-by: @stsuteng
This commit is contained in:
mindspore-ci-bot 2020-11-26 19:25:01 +08:00 committed by Gitee
parent d6024f8e96 5ae360cc02
commit 5ae37ce350
1 changed files with 10 additions and 8 deletions
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18
mindspore/nn/layer/embedding.py
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@ -273,7 +273,7 @@ class EmbeddingLookup(Cell):
class MultiFieldEmbeddingLookup(EmbeddingLookup):
r"""
Returns a slice of input tensor based on the specified indices based on the filed ids. This operation
Returns a slice of input tensor based on the specified indices based on the field ids. This operation
supports looking up embeddings within multi hot and one hot fields simultaneously.
Note:
@ -284,7 +284,7 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
specified 'axis = 0' to lookup table.
The vectors with the same field_ids will be combined by the `operator`, such as `SUM`, `MAX` and
`MEAN`. Ensure the input_values of the padded id is zero, so that they can be ignored. The final
output will be zeros if the summed of absolute weight of the field is zero. This class only
output will be zeros if the sum of absolute weight of the field is zero. This class only
supports ['table_row_slice', 'batch_slice' and 'table_column_slice']
Args:
@ -300,29 +300,31 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
max_norm (Union[float, None]): A maximum clipping value. The data type must be float16, float32
or None. Default: None
sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.
operator (string): The pooling method for the features in one field. Support `SUM`, `MEAN` and 'MAX'
operator (string): The pooling method for the features in one field. Support 'SUM, 'MEAN' and 'MAX'
Inputs:
- **input_indices** (Tensor) - The shape of tensor is :math:`(batch_size, seq_length)`.
Specifies the indices of elements of the original Tensor. Values can be out of range of embedding_table,
and the exceeding part will be filled with 0 in the output. Input_indices must be a 2d tensor in
Specifies the indices of elements of the original Tensor. Input_indices must be a 2d tensor in
this interface. Type is Int16, Int32, Int64.
- **input_values** (Tensor) - The shape of tensor is :math:`(batch_size, seq_length)`.
Specifies the weights of elements of the input_indices. The lookout vector will multiply with
the input_values. Type is Float32.
- **field_ids** (Tensor) - The shape of tensor is :math:`(batch_size, seq_length)`.
Specifics the field id of elements of the input_indices. Type is Type is Int16, Int32, Int64.
Specifies the field id of elements of the input_indices. Type is Type is Int16, Int32.
Outputs:
Tensor, the shape of tensor is :math:`(batch_size, field_size, embedding_size)`. Type is Float32.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> input_indices = Tensor([[2, 4, 6, 0, 0], [1, 3, 5, 0, 0]], mindspore.int32)
>>> input_values = Tensor([[1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], mindspore.float32)
>>> field_ids = Tensor([[0, 1, 1, 0, 0], [0, 0, 1, 0, 0]], mindspore.int32)
>>> net = nn.MultiFieldEmbeddingLookup(10, 2, field_size=2, operator='SUM')
>>> out = net(input_indices, input_values, field_ids)
>>> print(result)
>>> print(out)
[[[-0.00478983 -0.00772568]
[-0.00968955 -0.00064902]]
[[-0.01251151 -0.01251151]
@ -335,7 +337,7 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
slice_mode='batch_slice', feature_num_list=None, max_norm=None, sparse=True, operator='SUM'):
super(MultiFieldEmbeddingLookup, self).__init__(vocab_size, embedding_size, param_init, target,
slice_mode, feature_num_list, max_norm, sparse)
self.field_size = field_size
self.field_size = validator.check_value_type('field_size', field_size, [int], self.cls_name)
self.operator = operator
self.mul = P.Mul()