神經網路學習 PyTorch學習03 搭建模型

torch.nn

(1)用於搭建網路結構的序列容器：torch.nn.sequential 

models =torch.nn.sequential(

torch.nn.linear(input_data, hidden_layer),
torch.nn.relu(),
torch.nn.linear(hidden_layer, output_data)
)from collections import ordereddict #
使用有序字典 使模組有自定義的名次
models2 =torch.nn.sequential(ordereddict([
("line1
",torch.nn.linear(input_data, hidden_layer)),
("relu1
",torch.nn.relu()),
("line2
",torch.nn.linear(hidden_layer, output_data))])
)

(2)線性層：torch.nn.linear

(3)啟用函式：torch.nn.relu

(4)損失函式：torch.nn.mseloss(均方誤差函式)，troch.nn.l1loss(平均絕對誤差函式)，torch.nn.crossentropyloss(交叉熵)

import

torch
from torch.autograd import
variable
batch_n = 100hidden_layer = 100input_data = 1000output_data = 10x = variable(torch.randn(batch_n, input_data), requires_grad=false) #
x封裝為節點，設定為不自動求導
y = variable(torch.randn(batch_n, output_data), requires_grad=false)
models =torch.nn.sequential(
torch.nn.linear(input_data, hidden_layer),
torch.nn.relu(),
torch.nn.linear(hidden_layer, output_data))#
from collections import ordereddict # 使用有序字典 使模組有自定義的名次
#models2 = torch.nn.sequential(ordereddict([
#("line1",torch.nn.linear(input_data, hidden_layer)),
#("relu1",torch.nn.relu()),
#("line2",torch.nn.linear(hidden_layer, output_data))])#)
epoch_n = 10000learning_rate = 0.0001loss_fn =torch.nn.mseloss()
for epoch in
range(epoch_n):
y_pred =models(x)
loss =loss_fn(y_pred,y)
if epoch%1000 ==0:
print("
epoch:{},loss:
".format(epoch,loss.data[0]))
models.zero_grad() 
#梯度歸零
loss.backward()
for param in models.parameters(): #
遍歷節點引數更新
param.data -= param.grad.data*learning_rate

torch.optim包

引數自動優化類：sgd，adagrad，rmsprop，adam

import

torch
from torch.autograd import
variable
batch_n = 100hidden_layer = 100input_data = 1000output_data =10x = variable(torch.randn(batch_n, input_data), requires_grad=false)
y = variable(torch.randn(batch_n, output_data), requires_grad=false)
models =torch.nn.sequential(
torch.nn.linear(input_data,hidden_layer),
torch.nn.relu(),
torch.nn.linear(hidden_layer,output_data)
)epoch_n = 20learning_rate = 0.0001loss_fn =torch.nn.mseloss()
optimzer = torch.optim.adam(models.parameters(), lr=learning_rate) #
torch.optim.adam對梯度更新使用到的學習率進行自適應調節
for epoch in
range(epoch_n):
y_pred =models(x)
loss =loss_fn(y_pred,y)
print("
eproch:{},loss:
".format(epoch,loss.data[0]))
optimzer.zero_grad() 
#引數梯度歸零
loss.backward()
optimzer.step() 
#節點引數更新

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