权重初始化对于训练神经网络至关重要，好的初始化权重可以有效的避免梯度消失等问题的发生。
在pytorch的使用过程中有几种权重初始化的方法供大家参考。
注意：第一种方法不推荐。尽量使用后两种方法。
代码语言：javascript复制# not recommend
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)# recommend
def initialize_weights(m):
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0, 0.02)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.02)
m.bias.data.zero_()# recommend
def weights_init(m):
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight.data)
nn.init.xavier_normal_(m.bias.data)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight,1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight,1)
nn.init.constant_(m.bias, 0)编写好weights_init函数后，可以使用模型的apply方法对模型进行权重初始化。
net = Residual() # generate an instance network from the Net class net.apply(weights_init) # apply weight init
补充知识：Pytorch权值初始化及参数分组
1. 模型参数初始化
代码语言：javascript复制# ————————————————— 利用model.apply(weights_init)实现初始化
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif classname.find('BatchNorm') != -1:
m.weight.data.fill_(1)
m.bias.data.zero_()
elif classname.find('Linear') != -1:
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data = torch.ones(m.bias.data.size())
# ————————————————— 直接放在__init__构造函数中实现初始化
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
# —————————————————
self.weight = Parameter(torch.Tensor(out_features, in_features))
self.bias = Parameter(torch.FloatTensor(out_features))
nn.init.xavier_uniform_(self.weight)
nn.init.zero_(self.bias)
nn.init.constant_(m, initm)
# nn.init.kaiming_uniform_()
# self.weight.data.normal_(std=0.001)2. 模型参数分组weight_decay
代码语言：javascript复制def separate_bn_prelu_params(model, ignored_params=[]):
bn_prelu_params = []
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
ignored_params = list(map(id, m.parameters()))
bn_prelu_params = m.parameters()
if isinstance(m, nn.BatchNorm1d):
ignored_params = list(map(id, m.parameters()))
bn_prelu_params = m.parameters()
elif isinstance(m, nn.PReLU):
ignored_params = list(map(id, m.parameters()))
bn_prelu_params = m.parameters()
base_params = list(filter(lambda p: id(p) not in ignored_params, model.parameters()))
return base_params, bn_prelu_params, ignored_params
OPTIMIZER = optim.SGD([
{'params': base_params, 'weight_decay': WEIGHT_DECAY},
{'params': fc_head_param, 'weight_decay': WEIGHT_DECAY * 10},
{'params': bn_prelu_params, 'weight_decay': 0.0}
], lr=LR, momentum=MOMENTUM ) # , nesterov=TrueNote 1：PReLU(x) = max(0,x) a * min(0,x). Here a is a learnable parameter. When called without arguments, nn.PReLU() uses a single parameter a across all input channels. If called with nn.PReLU(nChannels), a separate a is used for each input channel. Note 2: weight decay should not be used when learning a for good performance. Note 3: The default number of a to learn is 1, the default initial value of a is 0.25.
3. 参数分组weight_decay–其他
第2节中的内容可以满足一般的参数分组需求，此部分可以满足更个性化的分组需求。参考：face_evoLVe_Pytorch-master
自定义schedule
代码语言：javascript复制def schedule_lr(optimizer):
for params in optimizer.param_groups:
params['lr'] /= 10.
print(optimizer)方法一：利用model.modules()和obj.__class__ （更普适）
代码语言：javascript复制# model.modules()和model.children()的区别：model.modules()会迭代地遍历模型的所有子层，而model.children()只会遍历模型下的一层
# 下面的关键词if 'model'，源于模型定义文件。如model_resnet.py中自定义的所有nn.Module子类，都会前缀'model_resnet'，所以可通过这种方式一次性筛选出自定义的模块
def separate_irse_bn_paras(model):
paras_only_bn = []
paras_no_bn = []
for layer in model.modules():
if 'model' in str(layer.__class__): # eg. a=[1,2] type(a): <class 'list' a.__class__: <class 'list'
continue
if 'container' in str(layer.__class__): # 去掉Sequential型的模块
continue
else:
if 'batchnorm' in str(layer.__class__):
paras_only_bn.extend([*layer.parameters()])
else:
paras_no_bn.extend([*layer.parameters()]) # extend()用于在列表末尾一次性追加另一个序列中的多个值（用新列表扩展原来的列表）
return paras_only_bn, paras_no_bn方法二：调用modules.parameters和named_parameters()
但是本质上，parameters()是根据named_parameters()获取，named_parameters()是根据modules()获取。使用此方法的前提是，须按下文1,2中的方式定义模型，或者利用Sequential OrderedDict定义模型。
代码语言：javascript复制def separate_resnet_bn_paras(model):
all_parameters = model.parameters()
paras_only_bn = []
for pname, p in model.named_parameters():
if pname.find('bn') = 0:
paras_only_bn.append(p)
paras_only_bn_id = list(map(id, paras_only_bn))
paras_no_bn = list(filter(lambda p: id(p) not in paras_only_bn_id, all_parameters))
return paras_only_bn, paras_no_bn两种方法的区别
参数分组的区别，其实对应了模型构造时的区别。举例：
1、构造ResNet的basic block，在__init__()函数中定义了
代码语言：javascript复制self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = BatchNorm2d(planes)
self.relu = ReLU(inplace = True)
…2、在forward()中定义
代码语言：javascript复制out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
…3、对ResNet取model.name_parameters()返回的pname形如：
代码语言：javascript复制‘layer1.0.conv1.weight'
‘layer1.0.bn1.weight'
‘layer1.0.bn1.bias'
# layer对应conv2_x, …, conv5_x； '0'对应各layer中的block索引，比如conv2_x有3个block，对应索引为layer1.0, …, layer1.2； 'conv1'就是__init__()中定义的self.conv14、若构造model时采用了Sequential()，则model.name_parameters()返回的pname形如：
‘body.3.res_layer.1.weight’，此处的1.weight实际对应了BN的weight，无法通过pname.find(‘bn’)找到该模块。
代码语言：javascript复制self.res_layer = Sequential(
Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
BatchNorm2d(depth),
ReLU(depth),
Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
BatchNorm2d(depth)
)5、针对4中的情况，两种解决办法：利用OrderedDict修饰Sequential，或利用方法一
代码语言：javascript复制downsample = Sequential( OrderedDict([
(‘conv_ds', conv1x1(self.inplanes, planes * block.expansion, stride)),
(‘bn_ds', BatchNorm2d(planes * block.expansion)),
]))
# 如此，相应模块的pname将会带有'conv_ds'，‘bn_ds'字样以上这篇pytorch 网络参数 weight bias 初始化详解就是小编分享给大家的全部内容了，希望能给大家一个参考。
