torch.nn.init
参考 torch.nn.init - 云+社区 - 腾讯云
torch.nn.init.calculate_gain(nonlinearity, param=None)[source]
Return the recommended gain value for the given nonlinearity function. The values are as follows:
| nonlinearity |
gain |
|---|---|
| Linear / Identity |
1 |
| Conv{1,2,3}D |
1 |
| Sigmoid |
1 |
| Tanh |
|
| ReLU |
|
| Leaky Relu |
 |
Parameters
-
nonlinearity – the non-linear function (nn.functional name)
-
param – optional parameter for the non-linear function
Examples
>>> gain = nn.init.calculate_gain('leaky_relu', 0.2) # leaky_relu with negative_slope=0.2torch.nn.init.uniform_(tensor, a=0.0, b=1.0)[source]
Fills the input Tensor with values drawn from the uniform distribution U(a,b)\mathcal{U}(a, b)U(a,b) .
Parameters
-
tensor – an n-dimensional torch.Tensor
-
a – the lower bound of the uniform distribution
-
b – the upper bound of the uniform distribution
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.uniform_(w)torch.nn.init.normal_(tensor, mean=0.0, std=1.0)[source]
Fills the input Tensor with values drawn from the normal distribution 
Parameters
-
tensor – an n-dimensional torch.Tensor
-
mean – the mean of the normal distribution
-
std – the standard deviation of the normal distribution
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.normal_(w)torch.nn.init.constant_(tensor, val)[source]
Fills the input Tensor with the value val\text{val}val .
Parameters
-
tensor – an n-dimensional torch.Tensor
-
val – the value to fill the tensor with
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.constant_(w, 0.3)torch.nn.init.ones_(tensor)[source]
Fills the input Tensor with the scalar value 1.
Parameters
tensor – an n-dimensional torch.Tensor
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.ones_(w)torch.nn.init.zeros_(tensor)[source]
Fills the input Tensor with the scalar value 0.
Parameters
tensor – an n-dimensional torch.Tensor
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.zeros_(w)torch.nn.init.eye_(tensor)[source]
Fills the 2-dimensional input Tensor with the identity matrix. Preserves the identity of the inputs in Linear layers, where as many inputs are preserved as possible.
Parameters
tensor – a 2-dimensional torch.Tensor
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.eye_(w)torch.nn.init.dirac_(tensor)[source]
Fills the {3, 4, 5}-dimensional input Tensor with the Dirac delta function. Preserves the identity of the inputs in Convolutional layers, where as many input channels are preserved as possible.
Parameters
tensor – a {3, 4, 5}-dimensional torch.Tensor
Examples
>>> w = torch.empty(3, 16, 5, 5)
>>> nn.init.dirac_(w)torch.nn.init.xavier_uniform_(tensor, gain=1.0)[source]
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 uniform distribution. The resulting tensor will have values sampled from 
where
Also known as Glorot initialization.
Parameters
-
tensor – an n-dimensional torch.Tensor
-
gain – an optional scaling factor
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))torch.nn.init.xavier_normal_(tensor, gain=1.0)[source]
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 
where
Also known as Glorot initialization.
Parameters
-
tensor – an n-dimensional torch.Tensor
-
gain – an optional scaling factor
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.xavier_normal_(w)
torch.nn.init.kaiming_uniform_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')[source]
Fills the input Tensor with values according to the method described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a uniform distribution. The resulting tensor will have values sampled from 
where
Also known as He initialization.
Parameters
-
tensor – an n-dimensional torch.Tensor
-
a – the negative slope of the rectifier used after this layer (0 for ReLU by default)
-
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.functional name), recommended to use only with
'relu'or'leaky_relu'(default).
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu')torch.nn.init.kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')[source]
Fills the input Tensor with values according to the method described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a normal distribution. The resulting tensor will have values sampled from where
Also known as He initialization.
Parameters
-
tensor – an n-dimensional torch.Tensor
-
a – the negative slope of the rectifier used after this layer (0 for ReLU by default)
-
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.functional name), recommended to use only with
'relu'or'leaky_relu'(default).
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')torch.nn.init.orthogonal_(tensor, gain=1)[source]
Fills the input Tensor with a (semi) orthogonal matrix, as described in Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe, A. et al. (2013). The input tensor must have at least 2 dimensions, and for tensors with more than 2 dimensions the trailing dimensions are flattened.
Parameters
-
tensor – an n-dimensional torch.Tensor, where n≥2n \geq 2n≥2
-
gain – optional scaling factor
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.orthogonal_(w)torch.nn.init.sparse_(tensor, sparsity, std=0.01)[source]
Fills the 2D input Tensor as a sparse matrix, where the non-zero elements will be drawn from the normal distribution 
, as described in Deep learning via Hessian-free optimization - Martens, J. (2010).
Parameters
-
tensor – an n-dimensional torch.Tensor
-
sparsity – The fraction of elements in each column to be set to zero
-
std – the standard deviation of the normal distribution used to generate the non-zero values
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.sparse_(w, sparsity=0.1)