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本篇内容介绍了“PyTorch怎么设置随机种子”的有关知识,在实际案例的操作过程中,不少人都会遇到这样的困境,接下来就让小编带领大家学习一下如何处理这些情况吧!希望大家仔细阅读,能够学有所成!
import torch import torch.nn as nn import matplotlib.pyplot as plt from tools import set_seed from torch.utils.tensorboard import SummaryWriter set_seed(1) # 设置随机种子 n_hidden = 200 max_iter = 2000 disp_interval = 200 lr_init = 0.01 def gen_data(num_data=10, x_range=(-1, 1)): w = 1.5 train_x = torch.linspace(*x_range, num_data).unsqueeze_(1) train_y = w*train_x + torch.normal(0, 0.5, size=train_x.size()) test_x = torch.linspace(*x_range, num_data).unsqueeze_(1) test_y = w*test_x + torch.normal(0, 0.3, size=test_x.size()) return train_x, train_y, test_x, test_y train_x, train_y, test_x, test_y = gen_data(num_data=10, x_range=(-1, 1)) class MLP(nn.Module): def __init__(self, neural_num): super(MLP, self).__init__() self.linears = nn.Sequential( nn.Linear(1, neural_num), nn.ReLU(inplace=True), nn.Linear(neural_num, neural_num), nn.ReLU(inplace=True), nn.Linear(neural_num, neural_num), nn.ReLU(inplace=True), nn.Linear(neural_num, 1), ) def forward(self, x): return self.linears(x) net_n = MLP(neural_num=n_hidden) net_weight_decay = MLP(neural_num=n_hidden) optim_n = torch.optim.SGD(net_n.parameters(), lr=lr_init, momentum=0.9) optim_wdecay = torch.optim.SGD(net_weight_decay.parameters(), lr=lr_init, momentum=0.9, weight_decay=1e-2) loss_fun = torch.nn.MSELoss() #均方损失 writer = SummaryWriter(comment='test', filename_suffix='test') for epoch in range(max_iter): pred_normal, pred_wdecay = net_n(train_x), net_weight_decay(train_x) loss_n, loss_wdecay = loss_fun(pred_normal, train_y), loss_fun(pred_wdecay, train_y) optim_n.zero_grad() optim_wdecay.zero_grad() loss_n.backward() loss_wdecay.backward() optim_n.step() #参数更新 optim_wdecay.step() if (epoch + 1) % disp_interval == 0: for name, layer in net_n.named_parameters(): ## writer.add_histogram(name + '_grad_normal', layer.grad, epoch) writer.add_histogram(name + '_data_normal', layer, epoch) for name, layer in net_weight_decay.named_parameters(): writer.add_histogram(name + '_grad_weight_decay', layer.grad, epoch) writer.add_histogram(name + '_data_weight_decay', layer, epoch) test_pred_normal, test_pred_wdecay = net_n(test_x), net_weight_decay(test_x) plt.scatter(train_x.data.numpy(), train_y.data.numpy(), c='blue', s=50, alpha=0.3, label='trainc') plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='red', s=50, alpha=0.3, label='test') plt.plot(test_x.data.numpy(), test_pred_normal.data.numpy(), 'r-', lw=3, label='no weight decay') plt.plot(test_x.data.numpy(), test_pred_wdecay.data.numpy(), 'b--', lw=3, label='weight decay') plt.text(-0.25, -1.5, 'no weight decay loss={:.6f}'.format(loss_n.item()), fontdict={'size': 15, 'color': 'red'}) plt.text(-0.25, -2, 'weight decay loss={:.6f}'.format(loss_wdecay.item()), fontdict={'size': 15, 'color': 'red'}) plt.ylim(-2.5, 2.5) plt.legend() plt.title('Epoch: {}'.format(epoch + 1)) plt.show() plt.close()
1. weight decay在pytorch的SGD中实现代码是哪一行?它对应的数学公式为?
2. PyTorch中,Dropout在训练的时候权值尺度会进行什么操作?
optim_wdecay = torch.optim.SGD(net_weight_decay.parameters(), lr=lr_init, momentum=0.9, weight_decay=1e-2) optim_wdecay.step()
Dropout随机失活,隐藏单元以一定概率被丢弃,以1-p的概率除以1-p做拉伸,即输出单元的计算不依赖于丢弃的隐藏层单元
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