标签:class ble sgd mat code step lin stat auto
1 import torch 2 import matplotlib.pyplot as plt 3 4 # torch.manual_seed(1) # reproducible 5 6 # fake data 7 x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1) 8 y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1) 9 10 # The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors 11 # x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False) 12 13 14 def save(): 15 # save net1 16 net1 = torch.nn.Sequential( 17 torch.nn.Linear(1, 10), 18 torch.nn.ReLU(), 19 torch.nn.Linear(10, 1) 20 ) 21 optimizer = torch.optim.SGD(net1.parameters(), lr=0.5) 22 loss_func = torch.nn.MSELoss() 23 24 for t in range(100): 25 prediction = net1(x) 26 loss = loss_func(prediction, y) 27 optimizer.zero_grad() 28 loss.backward() 29 optimizer.step() 30 31 # plot result 32 plt.figure(1, figsize=(10, 3)) 33 plt.subplot(131) 34 plt.title(‘Net1‘) 35 plt.scatter(x.data.numpy(), y.data.numpy()) 36 plt.plot(x.data.numpy(), prediction.data.numpy(), ‘r-‘, lw=5) 37 38 # 2 ways to save the net 39 torch.save(net1, ‘net.pkl‘) # save entire net 40 torch.save(net1.state_dict(), ‘net_params.pkl‘) # save only the parameters 41 42 43 def restore_net(): 44 # restore entire net1 to net2 45 net2 = torch.load(‘net.pkl‘) 46 prediction = net2(x) 47 48 # plot result 49 plt.subplot(132) 50 plt.title(‘Net2‘) 51 plt.scatter(x.data.numpy(), y.data.numpy()) 52 plt.plot(x.data.numpy(), prediction.data.numpy(), ‘r-‘, lw=5) 53 54 55 def restore_params(): 56 # restore only the parameters in net1 to net3 57 net3 = torch.nn.Sequential( 58 torch.nn.Linear(1, 10), 59 torch.nn.ReLU(), 60 torch.nn.Linear(10, 1) 61 ) 62 63 # copy net1‘s parameters into net3 64 net3.load_state_dict(torch.load(‘net_params.pkl‘)) 65 prediction = net3(x) 66 67 # plot result 68 plt.subplot(133) 69 plt.title(‘Net3‘) 70 plt.scatter(x.data.numpy(), y.data.numpy()) 71 plt.plot(x.data.numpy(), prediction.data.numpy(), ‘r-‘, lw=5) 72 plt.show() 73 74 # save net1 75 save() 76 77 # restore entire net (may slow) 78 restore_net() 79 80 # restore only the net parameters 81 restore_params()
标签:class ble sgd mat code step lin stat auto
原文地址:https://www.cnblogs.com/dhName/p/11742959.html