Datawhale&kesci&伯禹教育-深度学习-第一次打卡1

模型的组成部分

数据， 模型， 损失函数， 优化函数

1.Read_data

import torch.utils.data as Data 
dataset = Data.TensorDataset(features, lobels)
batch_size = 128
Dataset = Dada.DataLoader(
dataset = dataset,
batch_size = batch_size,
shuffle =True,
num_worker = 8, )
for x, y in Dataset:
	print(x, '\n', y)
	break

2. define_model (线性模型)

class LinearNet（nn.module）:
	def __init__(self, n_feature):
	super(LinearNet, self).__init__()
	self.linear = nn.Linear(n_feature, 1)

	def forworad(self,x):
		y = self.linear(x)
		return y 
#调用
net = LinearNet(n_feature)
print(net)

3.define_lossfunction (MSEloss)

loss = nn.MSELoss()

4. define_optimizerfunction (sgd)

import torch.optim as optim
optimizer = optim.SGD(net.parameters(), lr = 0.03)
print(optimizer)

训练

num_epoches = 3 
for epoch in range (1, num_epoches+1):
	for x, y in dataloader:
		output = net(x)
		loss = loss(output, y.view(-1,1))
		optimizer.zero_grad()
		loss.backward()
		optimizer.step()
	print ('epoch %d, loss: %f'% (epoch, loss.item()))