參考：官方

1. 均勻分佈

　　torch.nn.init.uniform_(tensor,a=0.0,b=1.0)

解釋：

　　Fills the input Tensor with values drawn from the uniform distribution $\mathcal{U}(a, b)$

引數：

• tensor– an n-dimensionaltorch.Tensor

• a– the lower bound of the uniform distribution

• b– the upper bound of the uniform distribution

例子：

w = torch.empty(2, 2)
print('before init w = \n',w)
torch.nn.init.uniform_(w, a=0.0, b=1.0)
print('after init w = \n',w)

結果：

before init w = 
 tensor([[1.4013e-45, 0.0000e+00],
        [0.0000e+00, 0.0000e+00]])
after init w = 
 tensor([[0.8658, 0.3711],
        [0.8950, 0.1419]])

2. 高斯分佈

torch.nn.init.normal_(tensor,mean=0.0,std=1.0)

解釋：

　　Fills the input Tensor with values drawn from the normal distribution $\mathcal{N}\left(\text { mean, } \operatorname{std}^{2}\right) $.

引數：

• tensor– an n-dimensionaltorch.Tensor

• mean– the mean of the normal distribution

• std– the standard deviation of the normal distribution

例子：

w = torch.empty(2, 2)
 
print('before init w = \n',w)
torch.nn.init.normal_(w,mean=10,std=0.01)
print('after init w = \n',w)

結果：

before init w = 
 tensor([[2.3877e-38, 1.0010e+01],
        [2.2421e-44, 0.0000e+00]])
after init w = 
 tensor([[10.0128, 10.0086],
        [10.0064,  9.9983]])

3. 初始化為常數

torch.nn.init.constant_(tensor,val)

解釋：

　　Fills the input Tensor with the value $val$.

引數：

• tensor– an n-dimensionaltorch.Tensor

• val– the value to fill the tensor with

例子：

w = torch.empty(2, 2)
print('before init w = \n',w)
torch.nn.init.constant_(w,18)
print('after init w = \n',w)

結果：

before init w = 
 tensor([[1.4013e-45, 0.0000e+00],
        [0.0000e+00, 0.0000e+00]])
after init w = 
 tensor([[18., 18.],
        [18., 18.]])

4.初始化為全 1

torch.nn.init.ones_(tensor)

解釋：

　　Fills the input Tensor with the scalar value 1.

引數：

• tensor– an n-dimensionaltorch.Tensor

例子：

w = torch.empty(2, 2)
print('before init w = \n',w)
torch.nn.init.ones_(w)
print('after init w = \n',w)

結果：

before init w = 
 tensor([[9.1477e-41, 0.0000e+00],
        [8.4078e-44, 0.0000e+00]])
after init w = 
 tensor([[1., 1.],
        [1., 1.]])

5.初始化為全 0

torch.nn.init.zeros_(tensor)

解釋：

　　Fills the input Tensor with the scalar value 0.

引數：

• tensor– an n-dimensionaltorch.Tensor

例子：

w = torch.empty(2, 2)
print('before init w = \n',w)
torch.nn.init.zeros_(w)
print('after init w = \n',w)

結果：

before init w = 
 tensor([[9.1477e-41, 0.0000e+00],
        [4.4842e-44, 0.0000e+00]])
after init w = 
 tensor([[0., 0.],
        [0., 0.]])

6.初始化為對角單位陣

torch.nn.init.eye_(tensor)

解釋：

　　Fills the 2-dimensional inputTensorwith the identity matrix. Preserves the identity of the inputs inLinearlayers, where as many inputs are preserved as possible.

引數：

• tensor– a 2-dimensionaltorch.Tensor

例子：

w = torch.empty(2, 2)
print('before init w = \n',w)
torch.nn.init.eye_(w)
print('after init w = \n',w)

結果：

before init w = 
 tensor([[1., 1.],
        [1., 1.]])
after init w = 
 tensor([[1., 0.],
        [0., 1.]])

7 .Xavier 均勻分佈

torch.nn.init.xavier_uniform_(tensor,gain=1.0)

解釋：

　　Fills the inputTensorwith values according to the method described inUnderstanding the difficulty of training deep feedforward neural networks- Glorot, X. & Bengio, Y. (2010), using a uniform distribution. The resulting tensor will have values sampled from $U(−a,a) $ where

　　　　$a=\operatorname{gain} \times \sqrt{\frac{6}{\text { fan_in }+\text { fan_out }}}$

引數：

• tensor– an n-dimensionaltorch.Tensor
• gain– an optional scaling factor

例子：

w = torch.empty(2, 2)
print('before init w = \n',w)
torch.nn.init.xavier_uniform_(w,gain=nn.init.calculate_gain('relu'))
print('after init w = \n',w)

結果：

before init w = 
 tensor([[1.4013e-45, 0.0000e+00],
        [0.0000e+00, 0.0000e+00]])
after init w = 
 tensor([[ 0.6120, -0.9743],
        [-1.5010,  0.5827]])

例子：

gain=nn.init.calculate_gain('relu')
gain

結果：

1.4142135623730951

例子：

gain=nn.init.calculate_gain('sigmoid')
gain

結果：

1

8 .Xavier 高斯分佈

torch.nn.init.xavier_normal_(tensor,gain=1.0)

解釋：

　　Fills the input Tensor with values according to the method described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. \& Bengio, Y. (2010), using a normal distribution. The resulting tensor will have values sampled from $\mathcal{N}\left(0, \mathrm{std}^{2}\right)$ where

　　　　$\operatorname{std}=\operatorname{gain} \times \sqrt{\frac{2}{\text { fan_in }+\text { fan_out }}}$

引數：

• tensor– an n-dimensionaltorch.Tensor

• gain– an optional scaling factor

例子：

w = torch.empty(2, 2)
print('before init w = \n',w)
torch.nn.init.xavier_normal_(w,gain=nn.init.calculate_gain('relu'))
print('after init w = \n',w)

結果：

before init w = 
 tensor([[0., 0.],
        [0., 0.]])
after init w = 
 tensor([[ 0.9703,  1.0088],
        [ 1.1271, -0.0602]])

9.He均勻分佈

torch.nn.init.kaiming_uniform_(tensor,a=0,mode='fan_in',nonlinearity='leaky_relu')

解釋：

　　Fills the input Tensor with values according to the method described in Delving deep into rectifiers: Surpassing humanlevel performance on ImageNet classification - He, K. et al. (2015), using a uniform distribution. The resulting tensor will have values sampled from $\mathcal{U}(- bound, bound)$ where

　　　　$\text { bound }=\text { gain } \times \sqrt{\frac{3}{\text { fan_mode }}}$

引數：

• tensor– an n-dimensionaltorch.Tensor

• a– the negative slope of the rectifier used after this layer (only used with'leaky_relu')

• mode– either'fan_in'(default) or'fan_out'. Choosing'fan_in'preserves the magnitude of the variance of the weights in the forward pass. Choosing'fan_out'preserves the magnitudes in the backwards pass.

• nonlinearity– the non-linear function (nn.functionalname), recommended to use only with'relu'or'leaky_relu'(default).

例子：

w = torch.empty(2, 2)
print('before init w = \n',w)
torch.nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu')
print('after init w = \n',w)

結果：

before init w = 
 tensor([[-3.6893e+19,  1.3658e+00],
        [ 2.2421e-44,  0.0000e+00]])
after init w = 
 tensor([[-0.8456,  1.3498],
        [-0.8480, -1.1506]])

10.He高斯分佈

torch.nn.init.kaiming_normal_(tensor,a=0,mode='fan_in',nonlinearity='leaky_relu')

解釋：

　　Fills the input Tensor with values according to the method described in Delving deep into rectifiers: Surpassing humanlevel performance on ImageNet classification - He, K. et al. (2015), using a normal distribution. The resulting tensor will have values sampled from $\mathcal{N}\left(0, \mathrm{std}^{2}\right)$ where

　　　　$\operatorname{std}=\frac{\text { gain }}{\sqrt{\text { fan_mode }}}$

引數：

• tensor– an n-dimensionaltorch.Tensor

• a– the negative slope of the rectifier used after this layer (only used with'leaky_relu')

• mode– either'fan_in'(default) or'fan_out'. Choosing'fan_in'preserves the magnitude of the variance of the weights in the forward pass. Choosing'fan_out'preserves the magnitudes in the backwards pass.

• nonlinearity– the non-linear function (nn.functionalname), recommended to use only with'relu'or'leaky_relu'(default).

例子：

w = torch.empty(2, 2)
print('before init w = \n',w)
torch.nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')
print('after init w = \n',w)

結果：

before init w = 
 tensor([[-0.8456,  1.3498],
        [-0.8480, -1.1506]])
after init w = 
 tensor([[-1.0357, -1.1732],
        [ 0.1517,  0.4935]])

因上求緣，果上努力~~~~ 作者：希望每天漲粉，轉載請註明原文連結：https://www.cnblogs.com/BlairGrowing/p/15428616.html