为了更好地调整学习率,提高模型的训练效果,一种常用的方法是使用学习率调度器(learning ratescheduler)。本文将介绍如何使用一种常见的学习率调度器——余弦学习率调度器(Cosine Learning Rate Scheduler)。 1.什么是余弦学习率调度器? 余弦学习率调度器是一种根据余弦函数变化调整学习率的方法。该调度器首先将学习率通过
use cosine learning rate scheduler -回复use cosine learning rate scheduler -回复 什么是余弦学习率调度器(Cosine Learning Rate Scheduler)? 余弦学习率调度器是一种用于优化算法中的学习率调整方法。它根据余弦函数的周期性特征,动态地调整学习率,使得模型在训练过程中能够更好地收敛。 学习率是指在训练神经网络...
def configure_optimizers(self): opt=torch.optim.AdamW(params=self.parameters(),lr=self.lr ) scheduler=CosineAnnealingLR(opt,T_max=10, eta_min=1e-6, last_epoch=-1) # Define the lr_scheduler_step method to update the learning rate of the optimizer def lr_scheduler_step(self, epoch): ...
A learning rate scheduler for Pytorch. This implements 2 modes: Geometrically increasing cycle restart intervals, as demonstrated by:[Loshchilov & Hutter 2017]: SGDR: Stochastic Gradient Descent with Warm Restarts Fixed cycle restart intervals, as seen in:[Athiwaratkun et al 2019]: There Are Ma...
We employed the AdamW optimizer with a cosine learning rate scheduler, and the number of epochs was set to 100. The hyperparameters are set as follows: batch size = 6, base learning rate = 0.00001, weight decay = 0.0001, beta1 = 0.9, and beta2 = 0.999. 4.2.2. Evaluation metrics We...
lr_scheduler_typecosine lora_rank8 lora_targetq_proj, v_proj additional_targetembed_tokens, lm_head, norm learning_rate2×10−4 num train epochs1 gradient accumulation steps2 max grad norm1 lora dropout0.05 warmup steps0 fp16TRUE
- Gemma-2-27B-Chinese-Chat是基于google/gemma-2-27b-it的指导调优语言模型,适用于中英文用户,具有多种能力。 - 提供了Gemma-2-27B-Chinese-Chat的GGUF文件和官方ollama模型的链接。 - 模型基于google/gemma-2-27b-it,模型大小为27.2B,上下文长度为8K。 - 使用LLaMA-Factory进行训练,训练细节包括3个epochs、...
An interactive electronic program guide is disclosed for use with a television delivery system. The guide is generated by a set top terminal that receives a television signal from an operations center and extracts from the signal individual programs for display on a television screen associated with...
The cosine annealing strategy is utilized as a learning scheduler, and the initial learning rate is set to 0.001. The parameter of the batch size of all networks is set to 32. In addition, all aforementioned networks are implemented in the study on a work station equipped with, i.e., an...
dataset_root:data/cityscapes# 验证数据集存放的目录transforms:-type:Normalize# 对图像进行标准化mode:val# 验证模式optimizer:# 使用何种优化器type:sgd# 随机梯度下降momentum:0.9weight_decay:4.0e-5lr_scheduler:# 学习率的相关设置type:PolynomialDecay# 一种学习率类型。共支持12种策略learning_rate:0.01power:...