标签：ber ack dem bsp sum 数据 rop ring drop
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader
?
train_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True)
?
valid_ds = TensorDataset(x_valid, y_valid)
valid_dl = DataLoader(valid_ds, batch_size=bs * 2)
def get_data(train_ds, valid_ds, bs):
return (
DataLoader(train_ds, batch_size=bs, shuffle=True),
DataLoader(valid_ds, batch_size=bs * 2),
)
import numpy as np
?
def fit(steps, model, loss_func, opt, train_dl, valid_dl):
for step in range(steps):
model.train()
for xb, yb in train_dl:
loss_batch(model, loss_func, xb, yb, opt)
?
model.eval()
with torch.no_grad():
losses, nums = zip(
*[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl]
)
val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)
print(‘当前step:‘+str(step), ‘验证集损失：‘+str(val_loss))
from torch import optim
def get_model():
model = Mnist_NN()
return model, optim.SGD(model.parameters(), lr=0.001)
def loss_batch(model, loss_func, xb, yb, opt=None):
loss = loss_func(model(xb), yb)
?
if opt is not None:
loss.backward()
opt.step()
opt.zero_grad()
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return loss.item(), len(xb)
train_dl, valid_dl = get_data(train_ds, valid_ds, bs)
model, opt = get_model()
fit(25, model, loss_func, opt, train_dl, valid_dl)
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标签：ber ack dem bsp sum 数据 rop ring drop
原文地址：https://www.cnblogs.com/BetterThanEver_Victor/p/13280586.html
