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)

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