网络保存与加载

1.保存

torch.manual_seed(1)    # reproducible# 假数据
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)def save():# 建网络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()# 训练for t in range(100):prediction = net1(x)loss = loss_func(prediction, y)optimizer.zero_grad()loss.backward()optimizer.step()torch.save(net1, 'net.pkl')  # 保存整个网络
torch.save(net.state_dict(), 'net_params.pkl') # 只保存网络中的参数(速度快，占内存少)

2.加载网络

def restore_net():# restore entire net1 to net2net2 = torch.load('net.pkl')prediction = net2(x)# 只提取网络参数
def restore_params():# 新建 net3net3 = torch.nn.Sequential(torch.nn.Linear(1, 10),torch.nn.ReLU(),torch.nn.Linear(10, 1))# 将保存的参数复制到 net3net3.load_state_dict(torch.load('net_params.pkl'))prediction = net3(x)# 保存 net1 (1. 整个网络, 2. 只有参数)
save()
# 提取整个网络
restore_net()
# 提取网络参数, 复制到新网络
restore_params()

3.批训练

DataLoader是torch给你用来包装你的数据的工具。所以要将自己的(numpy array或其他)数据形式转换成Tensor, 然后再放进这个包装器中。使用DataLoader的好处就是帮你有效地迭代数据。

import torch
import torch.utils.data as Data
torch.manual_seed(1)    # reproducibleBATCH_SIZE = 5      # 批训练的数据个数x = torch.linspace(1, 10, 10)       # x data (torch tensor)
y = torch.linspace(10, 1, 10)       # y data (torch tensor)# 先转换成 torch 能识别的 Dataset
torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)# 把 dataset 放入 DataLoader
loader = Data.DataLoader(dataset=torch_dataset,      # torch TensorDataset formatbatch_size=BATCH_SIZE,      # mini batch sizeshuffle=True,               # 要不要打乱数据 (打乱比较好)num_workers=2,              # 多线程来读数据
)for epoch in range(3):   # 训练所有!整套!数据 3 次for step, (batch_x, batch_y) in enumerate(loader):  # 每一步 loader 释放一小批数据用来学习# 假设这里就是你训练的地方...# 打出来一些数据print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',batch_x.numpy(), '| batch y: ', batch_y.numpy())"""
Epoch:  0 | Step:  0 | batch x:  [ 6.  7.  2.  3.  1.] | batch y:  [  5.   4.   9.   8.  10.]
Epoch:  0 | Step:  1 | batch x:  [  9.  10.   4.   8.   5.] | batch y:  [ 2.  1.  7.  3.  6.]
Epoch:  1 | Step:  0 | batch x:  [  3.   4.   2.   9.  10.] | batch y:  [ 8.  7.  9.  2.  1.]
Epoch:  1 | Step:  1 | batch x:  [ 1.  7.  8.  5.  6.] | batch y:  [ 10.   4.   3.   6.   5.]
Epoch:  2 | Step:  0 | batch x:  [ 3.  9.  2.  6.  7.] | batch y:  [ 8.  2.  9.  5.  4.]
Epoch:  2 | Step:  1 | batch x:  [ 10.   4.   8.   1.   5.] | batch y:  [  1.   7.   3.  10.   6.]
"""

当数据最后不足batch时，就会返回这个epoch中剩下的数据。