Contour plot of sigmoid neuron for toy data

sigmoidneuron_surfaceplot.py

#toy data
X = [0.5,2.5]
Y = [0.2,0.9]

import numpy as np
import matplotlib.pyplot as plt

w_values = []
b_values = []
loss_values = []

def f(w,b,x):
    return 1. / (1. + np.exp(-(w*x)+b))
  
def error(w,b):
  err = 0.0
  for x,y in zip(X,Y):
    fx = f(w,b,x)
    err += 0.5*(fx-y)**2
  return(err)

def grad_w(w,b, x, y):
    y_pred = f(w,b,x)
    return (y_pred - y) * y_pred * (1 - y_pred) * x

def grad_b(w,b, x, y):
    y_pred = f(w,b,x)
    return (y_pred - y) * y_pred * (1 - y_pred)

def gradient_descent():
  w,b,eta = 0, -8, 1.0
  #values for illustration, we can choose randomly
  for i in range(1000):
    #iterating for 1000 epochs
      dw, db = 0, 0
      for x, y in zip(X, Y):
          dw += grad_w(w,b,x, y)
          db += grad_b(w,b,x, y)
      w -= eta*dw
      b -= eta*db
      w_values.append(w)
      b_values.append(b)
      loss_values.append(error(w,b))

#call the gradient descent function to find 
#the best values for w and b
gradient_descent()

#plotting the surface plot
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_surface(w_values, b_values,loss_values, cmap='seismic')
ax.set_xlabel('w')
ax.set_ylabel('b')
ax.set_zlabel('loss')
plt.show()