pass # 初始化模型 net = MyNet() # 定义一个初始化函数 def init_weights(m): if type(m) == nn.Linear or type(m) == nn.Conv2d: init.normal_(m.weight) if m.bias is not None: init.zeros_(m.bias) # 使用net.apply来应用初始化函数 net.appl
init_weights() nn.MaxPool2d() 各种网络形式 空洞卷积 深度可分离卷积 1.观察数据集PASCAL-VOC 1.1 处理标签,p模式读取 1.3 用cv2方式读取 2.1 数据集读取 3.1 FCN模型 ResNet模型结构 FCN模型结构 识别,网络训练不充分识别困难,欠拟合。 卷积层,单通道图像输入,filter 卷积滑动,生成下层feature map. filter数...
- an output layer with 1 neuron (o1) Each neuron has the same weights and bias: - w = [0, 1] - b = 0 ''' def__init__(self): weights = np.array([0,1]) bias =0 # 这里是来自前一节的神经元类 self.h1 = Neuron(weights, bias) self...
import numpy as npdef sigmoid(z):"""Sigmoid函数"""return 1 / (1 + np.exp(-z))def cost_function(X, y, weights):"""逻辑回归的代价函数(负对数似然函数)"""m = len(y)h = sigmoid(X @ weights)cost = (-y.T @ np.log(h) - (1-y).T @ np.log(1-h)) / mreturn costdef g...
如果我们使用同一个权重数组weights, 但是要多次得到随机结果, 多次的调用weighted_choice方法,totals变量还是有必要的, 提前计算好它, 每次获取随机数的消耗会变得小很多 代码语言:javascript 代码运行次数:0 运行 AI代码解释 classWeightedRandomGenerator(object):def__init__(self,weights):self.totals=[]running_to...
defpredict(self,X):# Insert constant onesforbias weightsX=np.insert(X,0,1,axis=1)y_pred=X.dot(self.w)returny_pred 岭回归的核心就是l2正则化项: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 classl2_regularization():""" Regularization for Ridge Regression """def__init__(self,alpha...
class加权:def__init__(self):self.数据=[]self.权重=[]self.加权策略='线性加权'def设置数据(self,data):self.数据=datadef设置权重(self,weights):self.权重=weightsdef设置加权策略(self,strategy):self.加权策略=strategydef计算加权(self):加权结果=[]ifself.加权策略=='线性加权':foriinrange(len(self...
self.init_weights() def init_weights(self): self.conv1.weight.data.normal_(0, 0.01) self.conv2.weight.data.normal_(0, 0.01) if self.downsample is not None: self.downsample.weight.data.normal_(0, 0.01) def forward(self, x):
init_params: {‘kmeans’, ‘random’}, defaults to ‘kmeans’.初始化参数实现方式,默认用kmeans实现,也可以选择随机产生 weights_init:各组成模型的先验权重,可以自己设,默认按照7产生 means_init:初始化均值,同8 precisions_init:初始化精确度(模型个数,特征个数),默认按照7实现 ...
self.init_weights()definit_weights(self): self.conv1.weight.data.normal_(0,0.01) self.conv2.weight.data.normal_(0,0.01)ifself.downsampleisnotNone: self.downsample.weight.data.normal_(0,0.01)defforward(self, x): out = self.net(x) ...