在神經網路模型中之前，要對資料進行一系列的預處理，如果是型別變數，可使用one-hot編碼；對於數值型別，可進行標準化，讓其屬性值在0左右波動
import torch
import numpy as np
import pandas as pd
from torch.autograd import variable
#輸入特徵，最後一列為分類
feature = [[200,6975,1],
[800,56797,0],
[400,45875,1],
[200,59245,0],
[300,469372,1],
[500,32467,1],
[700,183481,1]
]feature = pd.dataframe(feature)
#標準化
for each in feature.columns[:-1]:
mean, std = feature[each].mean(), feature[each].std()
feature.loc[:, each] = (feature[each] - mean)/std
#手動編寫
input_size = 2
hidden_size = 28
outpu_size = 1
batch_size = 4
w1 = variable(torch.randn([input_size,hidden_size]),requires_grad = true)
b1 = variable(torch.randn(hidden_size),requires_grad = true)
w2 = variable(torch.randn([hidden_size,outpu_size]),requires_grad = true)
def neu(x):
hidden = x.mm(w1)+b1.expand(x.shape[0],hidden_size)
hidden = torch.sigmoid(hidden)
output = hidden.mm(w2)
return output
def cost(x,y):
error = torch.mean((x-y)**2)
return error
def zero_grad():
if w1.grad is not none and b1.grad is not none and w2.grad is not none:
w1.grad.data.zero_()
w2.grad.data.zero_()
b1.grad.data.zero_()
def optimizer(lr):
w1.data.add_(-lr*w1.grad.data)
w2.data.add_(-lr*w2.grad.data)
b1.data.add_(-lr*b1.grad.data)
for start in range(0,len(feature.index),batch_size):
end = start+batch_size if start+batch_sizex = variable(torch.floattensor(feature.iloc[start:end,:-1].values))
y = variable(torch.floattensor(feature.iloc[start:end,2].values))
pre = neu(x)
batch_loss =
loss = cost(pre,y)
zero_grad()
loss.backward()
optimizer(0.0001)
print(batch_loss)
new_x = torch.floattensor([[0.324,-0.34431]])
new_y = neu(new_x)
print(new_y)
input_size = 2
hidden_size = 28
output_size = 1
batch_size = 4
neu = torch.nn.sequential(
torch.nn.linear(input_size, hidden_size),
torch.nn.sigmoid(),
torch.nn.linear(hidden_size, output_size),
)cost = torch.nn.mseloss()
optimizer = torch.optim.sgd(neu.parameters(), lr = 0.01)
losses =
for i in range(1000):
batch_loss =
for start in range(0, len(feature.index), batch_size):
end = start + batch_size if start + batch_size < len(feature.index) else len(feature.index)
x = variable(torch.floattensor(feature.iloc[start:end,:-1].values))
y = variable(torch.floattensor(feature.iloc[start:end,2].values))
pre = neu(x)
loss = cost(pre, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 每隔100步輸出一下損失值（loss）
if i % 100==0:
print(i, np.mean(batch_loss))
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