Logistic regression is used to classify iris

Logistic regression is used to classify iris

• The code uses root mean square error

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch as t
from torch.autograd import Variable as var


file_path = "pyTorchTry/DNN/iris.csv"
df_iris = pd.read_csv(file_path, sep=",", header="infer")
np_iris = df_iris.values
np.random.shuffle(np_iris)

def normalize(temp) :
    temp = 2*(temp - np.mean(temp,axis = 0))/(np.max(temp,axis = 0)-np.min(temp,axis = 0))
    return(temp)

def convert2onehot(data) :
    # covert data to onehot representation
    return pd.get_dummies(data)

xs = normalize(np_iris[:,1:5]).astype(np.double)
ys = convert2onehot(np_iris[:,-1]).values


xs = var(t.Tensor(xs))
ys = var(t.Tensor(ys))

class softmax_model(t.nn.Module) :

    def __init__(self) :
        super(softmax_model,self).__init__()
        self.linear1 = t.nn.Linear(4.64)
        self.relu = t.nn.ReLU()
        self.linear2 = t.nn.Linear(64.16)
        self.linear3 = t.nn.Linear(16.3)
        self.softmax = t.nn.Softmax()

        self.criterion = t.nn.MSELoss()
        self.opt = t.optim.SGD(self.parameters(),lr=0.6)
    def forward(self, input) :
        y = self.linear1(input)
        y = self.relu(y)
        y = self.linear2(y)
        y = self.relu(y)
        y = self.linear3(y)
        y = self.softmax(y)
        return y

model = softmax_model()
for e in range(6001):
    y_pre = model(xs[:90,:])

    loss = model.criterion(y_pre,ys[:90,:])
    if(e%200= =0) :print(e,loss.data)
    
    # Zero gradients
    model.opt.zero_grad()
    # perform backward pass
    loss.backward()
    # update weights
    model.opt.step()

result = (np.argmax(model(xs[90:,:]).data.numpy(),axis=1) == np.argmax(ys[90:,:].data.numpy(),axis=1))
print(np.sum(result)/60)
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• The results of the log

Tensor 0 tensor (0.2260) 600 (0.0052) 1200 tensor tensor (0.0019) 1800 (0.0009) 2400 tensor tensor (0.0005) 3000 (0.0004) in 3600 Tensor (0.0003) 4200 tensor(0.0002) 4800 tensor(0.0002) 5400 tensor(0.0001) 6000 tensor(0.0001) 0.966666666667Copy the code

• Using cross entropy

class softmax_model(t.nn.Module) :

    def __init__(self) :
        super(softmax_model,self).__init__()
        self.linear1 = t.nn.Linear(4.64)
        self.relu = t.nn.ReLU()
        self.linear2 = t.nn.Linear(64.16)
        self.linear3 = t.nn.Linear(16.3)
        self.softmax = t.nn.functional.log_softmax

        self.criterion = t.nn.MSELoss()
        self.opt = t.optim.SGD(self.parameters(),lr=0.8)
    def forward(self, input) :
        y = self.linear1(input)
        y = self.relu(y)
        y = self.linear2(y)
        y = self.relu(y)
        y = self.linear3(y)
        y = self.softmax(y,dim=1)
        return y
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