optim.Adam(params, lr, betas) # 函数形式: optim.Adam(net_Adam.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0) # 参数 net_Adam.parameters():用于迭代优化的参数或者定义参数组的dict,一般是网络模型参数 lr:学习率(默认 1e-3) betas(float, float):用于计算梯度的平...
The new methods adaptively reuse stale Adam gradients, conserving communication while maintaining similar convergence rates to the Adam optimizer. Table 4 shows some of the tasks that commonly use the AdaGrad optimizer algorithm. A notable drawback of AdaGrad is the decreasing LR over time because ...
optimizer = optim.Adam(net.parameters(), lr = LR) decoder_optimizer = optim.Adam(net.decoder.parameters(), lr=LR) criterion = nn.CrossEntropyLoss() 1. 2. 3. 4. 5. 第一个optimaizer为整个net的parameters,第二个decoder_optimizer的参数只传入了net.decoder.parameters(),所以 decoder_optimizer....
net = LeNet() loss_function = nn.CrossEntropyLoss() # Adam()的第一个参数是model的所有可训练参数, 即 net.parameters() # lr: learning rate optimizer = Adahessian(net.parameters()) # training model in 5 epochs for epoch in range(10): # loop over the dataset multiple times running_loss...
optimizer = AdamWeightDecay( learning_rate=lr_schedule, weight_decay_rate=0.01, beta_1=beta_1, beta_2=0.999, epsilon=1e-6, exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']) elif optimizer_type == 'lamb': logging.info('using Lamb optimizer') optimizer = ...
Biodiesel manufacturing from renewable feedstocks has received a lot of attention as a viable alternative to fossil fuels. The Box-Behnken design, analysis of variance (ANOVA), and the Grey Wolf Optimizer (GWO) algorithm were used in this work to optimise biodiesel produc...
Additionally, the ADAM algorithm is capable of optimizing the 2497 parameters of the model. Comparing the TFDeepNN model with the benchmarks, i.e., RF, SVM, and LR, it indicates superior performance, thereby confirming TFDeepNN as a promising new tool that can be used for forest fire ...
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Surrogate modelingParticle swarm optimizationIn ultrasonic testing field, the design and optimization of point-focusing shear vertical waves electromagnetic acoustic transducer (PFSV-EMAT) with high energy conversion efficiency have been a challenging task. In this work, a novel multi-parameter optimization...
Competitive swarm optimizerEVOLUTIONARY ALGORITHMSOFFSPRING GENERATIONREDUCTIONSTRATEGYLarge-scale multiobjective optimization problems (LSMOPs), whose exponentially expanded search space and conflicting objectives have brought great challenges to traditional multiobjective evolutionary algorithms (MOEAs), frequently ...