Weights are not getting updated while training logistic regression by using iris dataset - python-3.x

Question

Python code:
I have used the Python code as below. Here, machine is trained by using Logistic Regression algorithm and wine dataset. Here, problem is that weights are not getting updated. I don't understand where is the problem.
from sklearn import datasets
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
dataset = datasets.load_wine()
x = dataset.data
y = dataset.target
y = y.reshape(178,1)
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.15,shuffle=True)
print(x_train.shape)
class log_reg():
def __init__(self):
pass
def sigmoid(self,x):
return 1 / (1 + np.exp(-x))
def train(self,x,y,w1,w2,alpha,iterations):
cost_history = [0] * iterations
Y_train = np.zeros([y.shape[0],3])
for i in range(Y_train.shape[0]):
for j in range(Y_train.shape[1]):
if(y[i] == j):
Y_train[i,j] = 1
for iteration in range(iterations):
z1 = x.dot(w1)
a1 = self.sigmoid(z1)
z2 = a1.dot(w2)
a2 = self.sigmoid(z2)
sig_sum = np.sum(np.exp(a2),axis=1)
sig_sum = sig_sum.reshape(a2.shape[0],1)
op = np.exp(a2) / sig_sum
loss = (Y_train * np.log(op))
dl = (op-Y_train)
dz1 = ((dl*(self.sigmoid(z2))*(1-self.sigmoid(z2))).dot(w2.T))*(self.sigmoid(z1))*(1-self.sigmoid(z1))
dz2 = (dl * (self.sigmoid(z2))*(1-self.sigmoid(z2)))
dw1 = x.T.dot(dz1)
dw2 = a1.T.dot(dz2)
w1 += alpha * dw1
w2 += alpha * dw2
cost_history[iteration] = (np.sum(loss)/len(loss))
return w1,w2,cost_history
def predict(self,x,y,w1,w2):
z1 = x.dot(w1)
a1 = self.sigmoid(z1)
z2 = a1.dot(w2)
a2 = self.sigmoid(z2)
sig_sum = np.sum(np.exp(a2),axis=1)
sig_sum = sig_sum.reshape(a2.shape[0],1)
op = np.exp(a2) / sig_sum
y_preds = np.argmax(op,axis=1)
acc = self.accuracy(y_preds,y)
return y_preds,acc
def accuracy(self,y_preds,y):
y_preds = y_preds.reshape(len(y_preds),1)
correct = (y_preds == y)
accuracy = (np.sum(correct) / len(y)) * 100
return (accuracy)
if __name__ == "__main__":
network = log_reg()
w1 = np.random.randn(14,4) * 0.01
w2 = np.random.randn(4,3) * 0.01
X_train = np.ones([x_train.shape[0],x_train.shape[1]+1])
X_train[:,:-1] = x_train
X_test = np.ones([x_test.shape[0],x_test.shape[1]+1])
X_test[:,:-1] = x_test
new_w1,new_w2,cost = network.train(X_train,y_train,w1,w2,0.0045,10000)
y_preds,accuracy = network.predict(X_test,y_test,new_w1,new_w2)
print(y_preds,accuracy)
In the above code, parameters are mentioned as below
x--training set,
y--target(output),
w1--weights for first layer,
w2--weights for second layer,
I used logistic regression with 2 hidden layers.
I am trying to train dataset wine from sklearn.I don't know where the problem is, but weights are not updating. Any help would be appreciated.

Answer 1

Your weights are updating , but I think you cant see them changing because you are printing them after execution. Python has a object reference method for numpy arrays so when you passed w1 , its values change values too so new_w1 and w1 become the same .
Take this example
import numpy as np
x=np.array([1,2,3,4])
def change(x):
x+=3
return x
print(x)
change(x)
print(x)
if you see the output it comes out as
[1 2 3 4]
[4 5 6 7]
I recommend that you add a bias and fix your accuracy function as I get my accuracy as 1000.
My execution when i run the code
the w1 and w2 values are indeed changing .
the only thing i changed was the main code and enabled the original data set , please do the same and tell if your weights are still not updating
if __name__ == "__main__":
network = log_reg()
w1 = np.random.randn(13,4) * 0.01
w2 = np.random.randn(4,3) * 0.01
print(w1)
print(" ")
print(w2)
print(" ")
new_w1,new_w2,cost = network.train(x_train,y_train,w1,w2,0.0045,10000)
print(w1)
print(" ")
print(w2)
print(" ")
y_preds,accuracy = network.predict(x_test,y_test,new_w1,new_w2)
print(y_preds,accuracy)