利用Pytorch中深度学习网络进行多分类预测（multi-class classification)
从下面的例子可以看出，在 Pytorch 中应用深度学习结构非常容易
执行多类分类任务。 在 iris 数据集的训练表现几乎是完美的。
import torch.nn as nn
import torch
#import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_iris
import numpy as np
torch.manual_seed(1121)
下载 iris数据集
iris = load_iris()
X = iris.data
Y = iris.target
X=pd.DataFrame(X)
Y=pd.DataFrame(Y)
display(X)
display(Y)
| 0 | 1 | 2 | 3 | |
|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
| ... | ... | ... | ... | ... |
| 145 | 6.7 | 3.0 | 5.2 | 2.3 |
| 146 | 6.3 | 2.5 | 5.0 | 1.9 |
| 147 | 6.5 | 3.0 | 5.2 | 2.0 |
| 148 | 6.2 | 3.4 | 5.4 | 2.3 |
| 149 | 5.9 | 3.0 | 5.1 | 1.8 |
150 rows × 4 columns
| 0 | |
|---|---|
| 0 | 0 |
| 1 | 0 |
| 2 | 0 |
| 3 | 0 |
| 4 | 0 |
| ... | ... |
| 145 | 2 |
| 146 | 2 |
| 147 | 2 |
| 148 | 2 |
| 149 | 2 |
150 rows × 1 columns
测试和验证数据分离
X=X.values
Y=Y.values
from sklearn.model_selection import train_test_split
x, x_val, y, y_val = train_test_split(X, Y, test_size=0.33, random_state=42)
x.shape, y.shape, x_val.shape, y_val.shape
((100, 4), (100, 1), (50, 4), (50, 1))
注意：标签y变量是一维数据，表示每个分类的编号，而不是编码向量 notice that: y needs to be one dimension indicating the class type, not encoded vectors
x_train = x.reshape(-1, x.shape[1]).astype('float32')
y_train = y.reshape(-1)
x_val = x_val.reshape(-1, x_val.shape[1]).astype('float32')
y_val = y_val.reshape(-1)
利用dataloader打包数据，方便batch训练
from torch.utils.data import Dataset, DataLoader
class Data(Dataset):
def __init__(self):
self.x=torch.from_numpy(x_train)
self.y=torch.from_numpy(y_train).long()
self.len=self.x.shape[0]
def __getitem__(self,index):
return self.x[index], self.y[index]
def __len__(self):
return self.len
data_set=Data()
# define batch sizes here
batch_size = 16
trainloader=DataLoader(dataset=data_set,batch_size=batch_size)
建立softmax 分类器
# D_in, dimension of input layer
# H , dimension of hidden layer
# D_out, dimension of output layer
class Net(nn.Module):
def __init__(self,D_in,H,D_out):
super(Net,self).__init__()
self.linear1=nn.Linear(D_in,H)
self.linear2=nn.Linear(H,D_out)
def forward(self,x):
x=torch.sigmoid(self.linear1(x))
x=self.linear2(x)
return x
input_dim=4 # how many features are in the dataset or how many input nodes in the input layer
hidden_dim = 20 # hidden layer size
output_dim=3 # number of classes
print(input_dim,hidden_dim,output_dim)
# Instantiate model
model=Net(input_dim,hidden_dim,output_dim)
print('W:',list(model.parameters())[0].size())
print('b',list(model.parameters())[1].size())
# loss function
criterion=nn.CrossEntropyLoss()
4 20 3
W: torch.Size([20, 4])
b torch.Size([20])
定义优化方法
learning_rate=0.05
optimizer=torch.optim.SGD(model.parameters(), lr=learning_rate)
n_epochs=1000
loss_list=[]
#n_epochs
for epoch in range(n_epochs):
if epoch %100==0:
print(epoch)
for x, y in trainloader:
#clear gradient
optimizer.zero_grad()
#make a prediction
z=model(x)
#print(z)
# calculate loss, da Cross Entropy benutzt wird muss ich in den loss Klassen vorhersagen,
# also Wahrscheinlichkeit pro Klasse. Das mach torch.max(y,1)[1])
loss=criterion(z,y)
# calculate gradients of parameters
loss.backward()
# update parameters
optimizer.step()
loss_list.append(loss.data)
#print('epoch {}, loss {}'.format(epoch, loss.item()))
0
100
200
300
400
500
600
700
800
900
利用验证数据检验训练效果
# predict the class and give probablity for each class label
x_val = torch.from_numpy(x_val)
z=model(x_val)
display(z)
tensor([[-3.1493e+00, 3.6905e+00, -5.0090e-01],
[ 8.6639e+00, 2.5913e+00, -1.1538e+01],
[-7.7513e+00, 4.1365e-01, 7.2997e+00],
[-3.1786e+00, 3.3443e+00, -1.6125e-01],
[-3.0974e+00, 3.8265e+00, -6.9203e-01],
[ 8.4413e+00, 2.6384e+00, -1.1355e+01],
[-1.4718e-02, 4.3311e+00, -4.3462e+00],
[-5.4910e+00, 1.5831e+00, 3.8781e+00],
[-4.7908e+00, 2.2980e+00, 2.5141e+00],
[-1.2132e+00, 4.3453e+00, -3.1134e+00],
[-5.0060e+00, 1.9947e+00, 2.9958e+00],
[ 8.1200e+00, 2.7792e+00, -1.1151e+01],
[ 8.8828e+00, 2.5214e+00, -1.1683e+01],
[ 8.0960e+00, 2.7942e+00, -1.1141e+01],
[ 8.8283e+00, 2.5107e+00, -1.1654e+01],
[-2.5941e+00, 3.7417e+00, -1.1520e+00],
[-7.0112e+00, 6.8188e-01, 6.2887e+00],
[-1.7457e+00, 4.1789e+00, -2.4035e+00],
[-3.3078e+00, 3.3204e+00, 6.4886e-03],
[-7.0187e+00, 6.5605e-01, 6.3214e+00],
[ 7.9682e+00, 2.8094e+00, -1.1046e+01],
[-4.8998e+00, 2.0493e+00, 2.8430e+00],
[ 8.2123e+00, 2.6980e+00, -1.1198e+01],
[-6.9197e+00, 7.3409e-01, 6.1537e+00],
[-5.4359e+00, 2.1912e+00, 3.2544e+00],
[-6.1038e+00, 1.1452e+00, 4.9261e+00],
[-6.9835e+00, 8.1716e-01, 6.1667e+00],
[-6.8937e+00, 7.5489e-01, 6.1001e+00],
[ 8.0317e+00, 2.7680e+00, -1.1061e+01],
[ 7.8392e+00, 2.8607e+00, -1.0952e+01],
[ 9.1619e+00, 2.3973e+00, -1.1890e+01],
[ 9.2721e+00, 2.3691e+00, -1.1973e+01],
[-1.2764e+00, 4.5603e+00, -3.2536e+00],
[ 8.2595e+00, 2.7126e+00, -1.1257e+01],
[ 8.4267e+00, 2.6317e+00, -1.1359e+01],
[-6.1753e+00, 1.1836e+00, 4.9843e+00],
[-1.8449e+00, 4.1870e+00, -2.3373e+00],
[ 8.5922e+00, 2.6133e+00, -1.1488e+01],
[ 8.7905e+00, 2.5362e+00, -1.1630e+01],
[ 9.1236e+00, 2.4451e+00, -1.1888e+01],
[-6.4186e+00, 9.5598e-01, 5.4381e+00],
[-1.9872e+00, 3.8494e+00, -1.8866e+00],
[-2.3989e+00, 4.0929e+00, -1.6734e+00],
[ 9.1132e+00, 2.4113e+00, -1.1844e+01],
[ 8.8116e+00, 2.5441e+00, -1.1646e+01],
[-1.2458e+00, 4.3741e+00, -3.0913e+00],
[-4.9320e+00, 2.3103e+00, 2.6452e+00],
[-5.8854e+00, 1.5035e+00, 4.3829e+00],
[-1.6208e+00, 4.4212e+00, -2.7724e+00],
[-6.7249e+00, 8.5383e-01, 5.8288e+00]], grad_fn=)
# choose the predicted class to be the one with maximum probablity
yhat=torch.max(z.data,1)
display(yhat)
torch.return_types.max(
values=tensor([3.6905, 8.6639, 7.2997, 3.3443, 3.8265, 8.4413, 4.3311, 3.8781, 2.5141,
4.3453, 2.9958, 8.1200, 8.8828, 8.0960, 8.8283, 3.7417, 6.2887, 4.1789,
3.3204, 6.3214, 7.9682, 2.8430, 8.2123, 6.1537, 3.2544, 4.9261, 6.1667,
6.1001, 8.0317, 7.8392, 9.1619, 9.2721, 4.5603, 8.2595, 8.4267, 4.9843,
4.1870, 8.5922, 8.7905, 9.1236, 5.4381, 3.8494, 4.0929, 9.1132, 8.8116,
4.3741, 2.6452, 4.3829, 4.4212, 5.8288]),
indices=tensor([1, 0, 2, 1, 1, 0, 1, 2, 2, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2, 0, 2,
2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0, 0, 1, 2, 2,
1, 2]))
# get the indicies
y_pred=yhat.indices.detach().numpy()
display(y_pred)
array([1, 0, 2, 1, 1, 0, 1, 2, 2, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,
0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0,
0, 1, 2, 2, 1, 2], dtype=int64)
display(y_val)
array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,
0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0,
0, 1, 2, 2, 1, 2])
多分类混淆矩阵核验
from sklearn.metrics import multilabel_confusion_matrix
display(multilabel_confusion_matrix(y_val, y_pred))
array([[[31, 0],
[ 0, 19]],
[[35, 0],
[ 1, 14]],
[[33, 1],
[ 0, 16]]], dtype=int64)
更多关注：
https://www.datasciencebyexample.com/2021/12/26/2021-12-26-1/
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