from matplotlib import cm import matplotlib.pyplot as plt import platform import numpy as np from mlxtend.data import loadlocal_mnist from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split import pickle plt.rcParams.update({'font.size': 12}) plt.rcParams['figure.figsize'] = [8, 4] X, y = loadlocal_mnist(images_path='train-images-idx3-ubyte', labels_path='train-labels-idx1-ubyte') # Keeping only digits 0 and 1 index = y <2 X = X[index] y = y[index] # Normalizing the data X = X/X.max() # Splitting to training and testing groups. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False) def sigmoid(t): reasonable = (t>-100).astype(float) t = t*reasonable s = 1/(1+np.exp(-t)) s = s*reasonable return s Nsamples,Nfeatures = X_train.shape Nclasses = 2 a = np.random.randn(Nfeatures+1,Nclasses-1) Xtilde = np.concatenate((X_train,np.ones((Nsamples,1))),axis=1) gamma = 1e-1 for iter in range(1500): # YOUR CODE HERE a = a - gamma*gradient if(np.mod(iter,100)==0): print("Error = ",np.sum(error**2)/) fig,ax = plt.subplots(1,2) ax[0].plot(s[:,0:200].T) ax[0].plot(y_train[0:200]) ax[0].set_title('True and predicted labels') ax[1].plot(error) ax[1].set_title('Prediction Errors') plt.show() plt.imshow(np.reshape(a[:-1],(28,28))) plt.title("weights") Nsamples,Nfeatures = X_test.shape Xtilde = np.concatenate((X_test,np.ones((Nsamples,1))),axis=1).T s_test = sigmoid(a.T@Xtilde) error = (s_test - y_test).T print(np.sum(error**2)) plt.figure() plt.plot(s_test[:,400:700].T) plt.plot(y_test[400:700]) plt.title("True and predicted labels") plt.show() plt.figure() plt.plot(error) plt.title("Error") wrong_indices = np.where(np.abs(error[:,0])>0)[0] Xwrong = X_test[wrong_indices] fig, axs = plt.subplots(1, len(wrong_indices)) for i in range(len(wrong_indices)): axs[i].imshow(np.reshape(Xwrong[i],(28,28))) axs[i].set_title("Misclassified") This code has only one part missing where your code here is written. I need help with completion of that part. Can someone please help me with the same?

from matplotlib import cm
import matplotlib.pyplot as plt
import platform
import numpy as np
from mlxtend.data import loadlocal_mnist

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
import pickle

plt.rcParams.update({'font.size': 12})
plt.rcParams['figure.figsize'] = [8, 4]

X, y = loadlocal_mnist(images_path='train-images-idx3-ubyte', labels_path='train-labels-idx1-ubyte')

# Keeping only digits 0 and 1
index = y <2
X = X[index]
y = y[index]

# Normalizing the data
X = X/X.max()

# Splitting to training and testing groups.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)

def sigmoid(t):
    reasonable = (t>-100).astype(float)
    t = t*reasonable
    s = 1/(1+np.exp(-t))
    s = s*reasonable
    return s

Nsamples,Nfeatures = X_train.shape
Nclasses = 2
a = np.random.randn(Nfeatures+1,Nclasses-1)
Xtilde = np.concatenate((X_train,np.ones((Nsamples,1))),axis=1)
gamma = 1e-1
for iter in range(1500):
    
     # YOUR CODE HERE
    
    a = a - gamma*gradient 
    
    if(np.mod(iter,100)==0):
        print("Error = ",np.sum(error**2)/)
        fig,ax = plt.subplots(1,2)
        ax[0].plot(s[:,0:200].T)
        ax[0].plot(y_train[0:200])
        ax[0].set_title('True and predicted labels')
        ax[1].plot(error)
        ax[1].set_title('Prediction Errors')
        plt.show()
    
    
plt.imshow(np.reshape(a[:-1],(28,28)))
plt.title("weights")

Nsamples,Nfeatures = X_test.shape
Xtilde = np.concatenate((X_test,np.ones((Nsamples,1))),axis=1).T
s_test = sigmoid(a.T@Xtilde)

error = (s_test - y_test).T
print(np.sum(error**2))
plt.figure()
plt.plot(s_test[:,400:700].T)
plt.plot(y_test[400:700])
plt.title("True and predicted labels")
plt.show()

plt.figure()
plt.plot(error)
plt.title("Error")

wrong_indices = np.where(np.abs(error[:,0])>0)[0]
Xwrong = X_test[wrong_indices]
fig, axs = plt.subplots(1, len(wrong_indices))

for i in range(len(wrong_indices)):
    axs[i].imshow(np.reshape(Xwrong[i],(28,28)))
    axs[i].set_title("Misclassified")

This code has only one part missing where your code here is written. I need help with completion of that part. Can someone please help me with the same?