Source code for torch.nn.init

import math
import random

import torch
from torch.autograd import Variable


[docs]def uniform(tensor, a=0, b=1): """Fills the input Tensor or Variable with values drawn from a uniform U(a,b) Args: tensor: a n-dimension torch.Tensor a: the lower bound of the uniform distribution b: the upper bound of the uniform distribution Examples: >>> w = torch.Tensor(3, 5) >>> nn.init.uniform(w) """ if isinstance(tensor, Variable): uniform(tensor.data, a=a, b=b) return tensor return tensor.uniform_(a, b)
[docs]def normal(tensor, mean=0, std=1): """Fills the input Tensor or Variable with values drawn from a normal distribution with the given mean and std Args: tensor: a n-dimension torch.Tensor mean: the mean of the normal distribution std: the standard deviation of the normal distribution Examples: >>> w = torch.Tensor(3, 5) >>> nn.init.normal(w) """ if isinstance(tensor, Variable): normal(tensor.data, mean=mean, std=std) return tensor return tensor.normal_(mean, std)
[docs]def constant(tensor, val): """Fills the input Tensor or Variable with the value `val` Args: tensor: a n-dimension torch.Tensor val: the value to fill the tensor with Examples: >>> w = torch.Tensor(3, 5) >>> nn.init.constant(w) """ if isinstance(tensor, Variable): constant(tensor.data, val) return tensor return tensor.fill_(val)
def _calculate_fan_in_and_fan_out(tensor): if tensor.ndimension() < 2: raise ValueError("fan in and fan out can not be computed for tensor of size ", tensor.size()) if tensor.ndimension() == 2: # Linear fan_in = tensor.size(1) fan_out = tensor.size(0) else: num_input_fmaps = tensor.size(1) num_output_fmaps = tensor.size(0) receptive_field_size = 1 if tensor.dim() > 2: receptive_field_size = tensor[0][0].numel() fan_in = num_input_fmaps * receptive_field_size fan_out = num_output_fmaps * receptive_field_size return fan_in, fan_out
[docs]def xavier_uniform(tensor, gain=1): """Fills the input Tensor or Variable with values according to the method described in "Understanding the difficulty of training deep feedforward neural networks" - Glorot, X. and Bengio, Y., using a uniform distribution. The resulting tensor will have values sampled from U(-a, a) where a = gain * sqrt(2/(fan_in + fan_out)) * sqrt(3) Args: tensor: a n-dimension torch.Tensor gain: an optional scaling factor to be applied Examples: >>> w = torch.Tensor(3, 5) >>> nn.init.xavier_uniform(w, gain=math.sqrt(2.0)) """ if isinstance(tensor, Variable): xavier_uniform(tensor.data, gain=gain) return tensor fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) std = gain * math.sqrt(2.0 / (fan_in + fan_out)) a = math.sqrt(3.0) * std return tensor.uniform_(-a, a)
[docs]def xavier_normal(tensor, gain=1): """Fills the input Tensor or Variable with values according to the method described in "Understanding the difficulty of training deep feedforward neural networks" - Glorot, X. and Bengio, Y., using a normal distribution. The resulting tensor will have values sampled from normal distribution with mean=0 and std = gain * sqrt(2/(fan_in + fan_out)) Args: tensor: a n-dimension torch.Tensor gain: an optional scaling factor to be applied Examples: >>> w = torch.Tensor(3, 5) >>> nn.init.xavier_normal(w) """ if isinstance(tensor, Variable): xavier_normal(tensor.data, gain=gain) return tensor fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) std = gain * math.sqrt(2.0 / (fan_in + fan_out)) return tensor.normal_(0, std)
def _calculate_correct_fan(tensor, mode): mode = mode.lower() valid_modes = ['fan_in', 'fan_out'] if mode not in valid_modes: raise ValueError("mode {} not supported, please use one of {}".format(mode, valid_modes)) fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) if mode == 'fan_in': return fan_in else: return fan_out
[docs]def kaiming_uniform(tensor, a=0, mode='fan_in'): """Fills the input Tensor or Variable with values according to the method described in "Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification" - He, K. et al using a uniform distribution. The resulting tensor will have values sampled from U(-bound, bound) where bound = sqrt(2/((1 + a^2) * fan_in)) * sqrt(3) Args: tensor: a n-dimension torch.Tensor a: the coefficient of the 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. Examples: >>> w = torch.Tensor(3, 5) >>> nn.init.kaiming_uniform(w, mode='fan_in') """ if isinstance(tensor, Variable): kaiming_uniform(tensor.data, a=a, mode=mode) return tensor fan = _calculate_correct_fan(tensor, mode) std = math.sqrt(2.0 / ((1 + a ** 2) * fan)) bound = math.sqrt(3.0) * std return tensor.uniform_(-bound, bound)
[docs]def kaiming_normal(tensor, a=0, mode='fan_in'): """Fills the input Tensor or Variable with values according to the method described in "Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification" - He, K. et al using a normal distribution. The resulting tensor will have values sampled from normal distribution with mean=0 and std = sqrt( 2/((1 + a^2) * fan_in)) Args: tensor: a n-dimension torch.Tensor a: the coefficient of the 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. Examples: >>> w = torch.Tensor(3, 5) >>> nn.init.kaiming_normal(w, mode='fan_out') """ if isinstance(tensor, Variable): kaiming_normal(tensor.data, a=a, mode=mode) return tensor fan = _calculate_correct_fan(tensor, mode) std = math.sqrt(2.0 / ((1 + a ** 2) * fan)) return tensor.normal_(0, std)
[docs]def orthogonal(tensor, gain=1): """Fills the input Tensor or Variable with a (semi) orthogonal matrix. The input tensor must have at least 2 dimensions, and for tensors with more than 2 dimensions the trailing dimensions are flattened. viewed as 2D representation with rows equal to the first dimension and columns equal to the product of as a sparse matrix, where the non-zero elements will be drawn from a normal distribution with mean=0 and std=`std`. Reference: "Exact solutions to the nonlinear dynamics of learning in deep linear neural networks"-Saxe, A. et al. Args: tensor: a n-dimension torch.Tensor, where n >= 2 gain: optional gain to be applied Examples: >>> w = torch.Tensor(3, 5) >>> nn.init.orthogonal(w) """ if isinstance(tensor, Variable): orthogonal(tensor.data, gain=gain) return tensor if tensor.ndimension() < 2: raise ValueError("Only tensors with 2 or more dimensions are supported.") rows = tensor.size(0) cols = tensor[0].numel() flattened = torch.Tensor(rows, cols).normal_(0, 1) u, s, v = torch.svd(flattened, some=True) if u.is_same_size(flattened): tensor.view_as(u).copy_(u) else: tensor.view_as(v.t()).copy_(v.t()) tensor.mul_(gain) return tensor
[docs]def sparse(tensor, sparsity, std=0.01): """Fills the 2D input Tensor or Variable as a sparse matrix, where the non-zero elements will be drawn from a normal distribution with mean=0 and std=`std`. Args: tensor: a n-dimension 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.Tensor(3, 5) >>> nn.init.sparse(w, sparsity=0.1) """ if isinstance(tensor, Variable): sparse(tensor.data, sparsity, std=std) return tensor if tensor.ndimension() != 2: raise ValueError("Sparse initialization only supported for 2D inputs") tensor.normal_(0, std) rows, cols = tensor.size(0), tensor.size(1) num_zeros = int(math.ceil(cols * sparsity)) for col_idx in range(tensor.size(1)): row_indices = list(range(rows)) random.shuffle(row_indices) zero_indices = row_indices[:num_zeros] for row_idx in zero_indices: tensor[row_idx, col_idx] = 0 return tensor