optimizer = optim.SGD(model.parameters(),lr = 0.01, momentum = 0.9)optimizer = optim.Adam([var1,var2]
. # 有两个`param_group`即,len(optim.param_groups)==2 optim.SGD([ {'params': model.base.parameters...{'params': model.classifier.parameters(), 'lr': 1e-3} ], lr=1e-2, momentum=0.9) #一个参数组 optim.SGD
# 有两个`param_group`即,len(optim.param_groups)==2 optim.SGD([ {'params': model.base.parameters()},...{'params': model.classifier.parameters(), 'lr': 1e-3} ], lr=1e-2, momentum=0.9) #一个参数组 optim.SGD
= Net() net_RAdam = Net() nets = [net_SGD, net_Momentum, net_Adam, net_RAdam] opt_SGD = optim.SGD...(net_SGD.parameters(), lr=LR) opt_Momentum = optim.SGD(net_Momentum.parameters(), lr=LR, momentum
torch.optim.Adam(参数,学习率) 注意: 参数可以使用model.parameters()来获取,获取模型中所有requires_grad=True的参数 optimizer = optim.SGD...nn.CrossEntropyLoss(),常用于分类问题 model = Lr() # 实例化模型 criterion = nn.MSELoss() # 实例化损失函数 optimizer = optim.SGD...self.linear(x) return out # 实例化模型,loss,和优化器 model = Lr() criterion = nn.MSELoss() optimizer = optim.SGD...cpu") x,y = x.to(device),y.to(device) model = Lr().to(device) criterion = nn.MSELoss() optimizer = optim.SGD
#导入torch.potim模块 criterion = nn.CrossEntropyLoss() #同样是用到了神经网络工具箱 nn 中的交叉熵损失函数 optimizer = optim.SGD
通常我们有optimizer = optim.SGD(model.parameters(), lr = 0.01, momentum = 0.9)scheduler = lr_scheduler.StepLR
return x def run(): torch.manual_seed(1024) model = Model() model.train() optimizer = optim.SGD...return x def run(): torch.manual_seed(1024) model = Model() model.train() optimizer = optim.SGD
cuda:0'当中的0为想要使用显卡的编号 # 这里的0表示使用的是第一张显卡 net = MLP().to(device) # 使用.to函数将神经网络模块搬到MLP上进行运算 optimizer = optim.SGD
训练过程首先要建立一个优化器,引入相关工具包 import torch.optim as optim import torch.nn as nn learning_rate = 1e-3 optimizer = optim.SGD...x@w2.t() + b2 x = F.relu(x) x = x@w3.t() + b3 x = F.relu(x) return x optimizer = optim.SGD
首先构建优化器对象: optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) optimizer = optim.Adam([var1
100, input_dim) y_client2 = torch.randn(100, output_dim) # 本地训练(简化示例,实际中可能更复杂) optimizer_client1 = optim.SGD...(model_client1.parameters(), lr=0.01) optimizer_client2 = optim.SGD(model_client2.parameters(), lr=0.01
torch.optim as optim import torch.nn as nn lr = 1e-3 # learning_rate # 优化器优化的目标是三个全连接层的变量 optimizer = optim.SGD...x@w2.t() + b2 x = F.relu(x) x = x@w3.t() + b3 x = F.relu(x) return x optimizer = optim.SGD
定义优化算法 pytorch在torch.optim模块中提供了很多优化算法 #本节使用小批量随机梯度下降算法(SGD) import torch.optim as optim optimizer = optim.SGD...lr,设置学习率为0.03;net.parameters()导入模型的参数 print(optimizer) #输出优化算法的各项参数 扩展内容: #为不同网络设置不同学习率 optimizer = optim.SGD...net.linear.bias,val=0) #初始化偏差为0 #定义损失函数(均方差损失函数) loss = nn.MSELoss() #定义优化算法(同样的小批量随机梯度下降算法) optimizer = optim.SGD
criterion = nn.CrossEntropyLoss() # Observe that all parameters are being optimized optimizer_ft = optim.SGD...Observe that only parameters of final layer are being optimized as# opoosed to before.optimizer_conv = optim.SGD...criterion = nn.CrossEntropyLoss() # Observe that all parameters are being optimized optimizer_ft = optim.SGD
torch.randint(0, 2, (100, 2)).float() # 100个样本,每个样本有2个类别# 创建模型、优化器和损失函数model = SimpleNN()optimizer = optim.SGD
nn.MaxPool2d 对二维信号进行最大值池化 nn.ReLU 最常用的激活函数 nn.CrossEntropyLoss 损失函数,瘵nn.LogSoftmax()与nn.NLLLoss()结合,进行交叉熵计算 optim.SGD...kernel_size:池化核尺寸 stride:步长 padding :填充个数 dilation:池化核间隔大小 ceil_mode:尺寸向上取整 return_indices:记录池化像素索引 optim.SGD
后向过程optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.001)optimizer
简单使用: import torch import torch.optim as optim # 创建一个模型和优化器 model = torch.nn.Linear(3, 1) optimizer = optim.SGD...import torch.nn as nn import torch.optim as optim # 创建一个模型和优化器 model = nn.Linear(3, 1) optimizer = optim.SGD...import torch.optim as optim # 创建模型和优化器 model = SimpleModel(input_size=10, output_size=5) optimizer = optim.SGD...init__(optimizer) def get_lr(self): # 自定义学习率调度逻辑 pass # 使用自定义学习率调度器 optimizer = optim.SGD...# 使用 DistributedDataParallel 包装模型 model = DistributedDataParallel(model) # 定义优化器和损失函数 optimizer = optim.SGD
num_classes = 10# 创建模型model = YourModel()# 定义交叉熵损失函数criterion = nn.CrossEntropyLoss()# 定义优化器optimizer = optim.SGD...个类别num_classes = 10# 创建模型model = YourModel()# 定义负对数似然损失函数criterion = nn.NLLLoss()# 定义优化器optimizer = optim.SGD...model.fc.in_featuresmodel.fc = nn.Linear(num_ftrs, 10)# 定义损失函数和优化器criterion = nn.CrossEntropyLoss()optimizer = optim.SGD
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