PyTorch之存取

要点

当我们训练好了一个学习模型后，当然是要保存这个模型，以便下次需要这个模型的时候直接提取应用，这一节中我们就用神经网络举例实现保存提取。

保存

import torch

from torch.autograd import Variable

import matplotlib.pyplot as plt

# torch.manual_seed(1)    # reproducible

# fake data

x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)

y = x.pow(2) + 0.2*torch.rand(x.size())  # noisy y data (tensor), shape=(100, 1)

x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False)

def save():

    # save net1

    net1 = torch.nn.Sequential(

        torch.nn.Linear(1, 10),

        torch.nn.ReLU(),

        torch.nn.Linear(10, 1)

    )

    optimizer = torch.optim.SGD(net1.parameters(), lr=0.5)

    loss_func = torch.nn.MSELoss()

    for t in range(100):

        prediction = net1(x)

        loss = loss_func(prediction, y)

        optimizer.zero_grad()

        loss.backward()

        optimizer.step()

    # plot result

    plt.figure(1, figsize=(10, 3))

    plt.subplot(131) 

    plt.title('Net1')

    plt.scatter(x.data.numpy(), y.data.numpy())

    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)

    # 2 ways to save the net

    torch.save(net1, 'net.pkl')  # save entire net

    torch.save(net1.state_dict(), 'net_params.pkl')  # save only the parameters