權重初始化對于訓練神經網絡至關重要，好的初始化權重可以有效的避免梯度消失等問題的發生。

在pytorch的使用過程中有幾種權重初始化的方法供大家參考。

注意：第一種方法不推薦。盡量使用后兩種方法。

# 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)

編寫好weights_init函數后，可以使用模型的apply方法對模型進行權重初始化。

net = Residual() # generate an instance network from the Net class

net.apply(weights_init) # apply weight init

補充知識：Pytorch權值初始化及參數分組

1. 模型參數初始化

# ————————————————— 利用model.apply(weights_init)實現初始化

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__構造函數中實現初始化

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. 模型參數分組weight_decay

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=True

Note 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. 參數分組weight_decay–其他

第2節中的內容可以滿足一般的參數分組需求，此部分可以滿足更個性化的分組需求。參考：face_evoLVe_Pytorch-master

自定義schedule

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__ (更普適)

# model.modules()和model.children()的區別：model.modules()會迭代地遍歷模型的所有子層，而model.children()只會遍歷模型下的一層

# 下面的關鍵詞if 'model'，源于模型定義文件。如model_resnet.py中自定義的所有nn.Module子類，都會前綴'model_resnet'，所以可通過這種方式一次性篩選出自定義的模塊

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): a.__class__:

continue

if 'container' in str(layer.__class__): # 去掉Sequential型的模塊

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個序列中的多個值(用新列表擴展原來的列表)

return paras_only_bn, paras_no_bn

方法二：調用modules.parameters和named_parameters()

但是本質上，parameters()是根據named_parameters()獲取，named_parameters()是根據modules()獲取。使用此方法的前提是，須按下文1,2中的方式定義模型，或者利用Sequential+OrderedDict定義模型。

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

兩種方法的區別

參數分組的區別，其實對應了模型構造時的區別。舉例：

1、構造ResNet的basic block，在__init__()函數中定義了

self.conv1 = conv3x3(inplanes, planes, stride)

self.bn1 = BatchNorm2d(planes)

self.relu = ReLU(inplace = True)

…

2、在forward()中定義

out = self.conv1(x)

out = self.bn1(out)

out = self.relu(out)

…

3、對ResNet取model.name_parameters()返回的pname形如：

‘layer1.0.conv1.weight'

‘layer1.0.bn1.weight'

‘layer1.0.bn1.bias'

# layer對應conv2_x, …, conv5_x； '0'對應各layer中的block索引，比如conv2_x有3個block，對應索引為layer1.0, …, layer1.2； 'conv1'就是__init__()中定義的self.conv1

4、若構造model時采用了Sequential()，則model.name_parameters()返回的pname形如：

‘body.3.res_layer.1.weight'，此處的1.weight實際對應了BN的weight，無法通過pname.find(‘bn')找到該模塊。

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、針對4中的情況，兩種解決辦法：利用OrderedDict修飾Sequential，或利用方法一

downsample = Sequential( OrderedDict([

(‘conv_ds', conv1x1(self.inplanes, planes * block.expansion, stride)),

(‘bn_ds', BatchNorm2d(planes * block.expansion)),

]))

# 如此，相應模塊的pname將會帶有'conv_ds'，‘bn_ds'字樣

以上這篇pytorch 網絡參數 weight bias 初始化詳解就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持腳本之家。

總結

以上是生活随笔為你收集整理的初始化模型参数 python_pytorch 网络参数 weight bias 初始化详解_python_脚本之家的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。