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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！
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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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