# Copyright 2024 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""basic"""
from __future__ import absolute_import
import math
import mindspore.common.dtype as mstype
from mindspore import _checkparam as Validator
from mindspore._extends import cell_attr_register
from mindspore.common.initializer import initializer, HeUniform, Uniform
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
from mindspore.nn.cell import Cell
from mindspore.ops import operations as P
__all__ = ['Linear']
[文档]class Linear(Cell):
r"""
The linear connected layer.
Applies linear connected layer for the input. This layer implements the operation as:
.. math::
\text{outputs} = X * kernel + bias
where :math:`X` is the input tensors, :math:`\text{kernel}` is a weight matrix with the same
data type as the :math:`X` created by the layer, and :math:`\text{bias}` is a bias vector
with the same data type as the :math:`X` created by the layer (only if has_bias is True).
Args:
in_features (int): The number of features in the input space.
out_features (int): The number of features in the output space.
bias (bool): Specifies whether the layer uses a bias vector :math:`\text{bias}`. Default: ``True``.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
is same as `x`. The values of str refer to the function `initializer`. Default: ``None`` ,
weight will be initialized using HeUniform.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
same as `x`. The values of str refer to the function `initializer`. Default: ``None`` ,
bias will be initialized using Uniform.
dtype (:class:`mindspore.dtype`): Data type of Parameter. Default: ``None`` .
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(*, in\_features)`. The `in_features` in `Args` should be equal
to :math:`in\_features` in `Inputs`.
Outputs:
Tensor of shape :math:`(*, out\_features)`.
Raises:
TypeError: If `in_features` or `out_features` is not an int.
TypeError: If `bias` is not a bool.
ValueError: If length of shape of `weight_init` is not equal to 2 or shape[0] of `weight_init`
is not equal to `out_features` or shape[1] of `weight_init` is not equal to `in_features`.
ValueError: If length of shape of `bias_init` is not equal to 1
or shape[0] of `bias_init` is not equal to `out_features`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore import nn
>>> import numpy as np
>>> x = Tensor(np.array([[180, 234, 154], [244, 48, 247]]), mindspore.float32)
>>> net = nn.extend.Linear(3, 4)
>>> output = net(x)
>>> print(output.shape)
(2, 4)
"""
@cell_attr_register(attrs=['has_bias'])
def __init__(self,
in_features,
out_features,
bias=True,
weight_init=None,
bias_init=None,
dtype=None):
"""Initialize Linear."""
super(Linear, self).__init__()
self.in_features = Validator.check_positive_int(
in_features, "in_features", self.cls_name)
self.out_features = Validator.check_positive_int(
out_features, "out_features", self.cls_name)
self.has_bias = Validator.check_bool(
bias, "has_bias", self.cls_name)
self.dense = P.Dense()
if dtype is None:
dtype = mstype.float32
if isinstance(weight_init, Tensor):
if weight_init.ndim != 2 or weight_init.shape[0] != out_features or \
weight_init.shape[1] != in_features:
raise ValueError(f"For '{self.cls_name}', weight init shape error. The ndim of 'weight_init' must "
f"be equal to 2, and the first dim must be equal to 'out_features', and the "
f"second dim must be equal to 'in_features'. But got 'weight_init': {weight_init}, "
f"'out_features': {out_features}, 'in_features': {in_features}.")
if weight_init is None:
weight_init = HeUniform(math.sqrt(5))
self.weight = Parameter(initializer(
weight_init, [out_features, in_features], dtype=dtype), name="weight")
self.bias = None
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.ndim != 1 or bias_init.shape[0] != out_features:
raise ValueError(f"For '{self.cls_name}', bias init shape error. The ndim of 'bias_init' must "
f"be equal to 1, and the first dim must be equal to 'out_features'. But got "
f"'bias_init': {bias_init}, 'out_features': {out_features}.")
if bias_init is None:
bound = 1 / math.sqrt(in_features)
bias_init = Uniform(scale=bound)
self.bias = Parameter(initializer(
bias_init, [out_features], dtype=dtype), name="bias")
def construct(self, x):
x = self.dense(x, self.weight, self.bias)
return x
def extend_repr(self):
s = f'input_features={self.in_features}, output_features={self.out_features}'
if self.has_bias:
s += f', has_bias={self.has_bias}'
return s