其他DRL库缺少评测智能体性能的模块,这让我不满意。因此,ElegantRL里面有一个评测器 Evaluator 用来画出 learning curve,然后自动保存保存表现好的Agent。不用担心这会拖慢训练速度,因为我们开启了一个独立于训练进程的子进程利用于运行评测器。评测器还会画出其他指标,帮助我们调整超参数,或者修改自定义的环境或者算法。 4.
ax.set_title('learning rate curve') ax.scatter([iforiinrange(len(lr_list))], lr_list) plt.show()if__name__=='__main__': test_scheduler() 首先测试了学习率变化,可以看出每10个epoch为余弦函数的半个周期变化, 若将scheduler的step内参数设置为1,则学习率固定为此时的第1轮数值。 总的来说...
以下是不同语言的代码示例,首先是 Python: importtorchimportmatplotlib.pyplotasplt# 模拟数据x=torch.linspace(-3.14,3.14,100)y=torch.sin(x)plt.plot(x.numpy(),y.numpy())plt.title("Sin Curve")plt.xlabel("X")plt.ylabel("sin(X)")plt.grid()plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9...
y.append(scheduler.get_lr()[0]) # 画出lr的变化 plt.plot(x, y)plt.xlabel("epoch")plt.ylabel("lr")plt.title("learning rate's curve changes as epoch goes on!")plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 自适应学习率调整 torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer...
writer.add_scalars('Learning Curve',{'Train Loss':train_loss,'Val Loss':val_loss},epoch) writer.add_scalar('Learning Rate',optimizer.param_groups[0]["lr"],epoch) if epoch%5==0: for name, param in model.named_parameters():
# For plotting learning curve 画曲线 from torch.utils.tensorboard import SummaryWriter 随机数种子函数: def same_seed(seed): '''Fixes random number generator seeds for reproducibility.''' torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False ...
min_lr:0.000100000000# initial learning ratemax_lr:0.010000000000# maximum learning ratemomentum:0.9370000000# SGD momentum/Adam beta1weight_decay:0.000500# optimizer weight decaywarmup_epochs:3.00000# warmup epochsbox:7.500000000000000# box loss gaincls:0.500000000000000# cls loss gaindfl:1.500000000000000#...
x 1, learning rate is 1 x 1 (scalar), inputs are 1000 x 1, and deltas_scaled are 1000 x 1# We must transpose the inputs so that we get an allowed operation.weights = weights - learning_rate * np.dot(x....
title("curve") plt.plot(xs.asnumpy(),ys.asnumpy()) plt.plot(xs.asnumpy(),ys_pre.asnumpy()) plt.show() 输出结果 代码语言:javascript 代码运行次数:0 运行 AI代码解释 Sequential( (0): Dense(None -> 16, Activation(relu)) (1): Dense(None -> 1, linear) ) epoch 100, loss: ...
Reading/Writing Data 读写数据import pandas as pdimport osimport csv# For Progress Bar 进度条from tqdm import tqdm# Pytorchimport torchimport torch.nn as nnfrom torch.utils.data import Dataset, DataLoader, random_split# For plotting learning curve 画曲线from torch.utils.tensorboard import Summary...