# Gradient Descent¶

## 1. Gradient¶

In [57]:
import numpy as np
import matplotlib.pylab as plt

In [58]:
def numerical_diff(f, x):
h = 1e-4 # 0.0001
return (f(x+h) - f(x-h)) / (2*h)

def function_1(x):
return 0.01*x**2 + 0.1*x

def tangent_line(f, x):
d = numerical_diff(f, x)
return lambda t: d * (t - x) + f(x)

x = np.arange(0.0, 20.0, 0.1)
y = function_1(x)
plt.xlabel("x")
plt.ylabel("f(x)")

tf = tangent_line(function_1, 5)
y2 = tf(x)

plt.plot(x, y)
plt.plot(x, y2)
plt.show()


## 2. Implement Gradient Descent in Python¶

In [103]:
f = lambda x: (x + 5)**2

from IPython.display import Image
Image(url= "https://cdn-images-1.medium.com/max/800/1*5-56UEwcZHgzqIAtlnsLog.png", width=300, height=300)

Out[103]:
• Step 1 : Initialize parameters
In [108]:
f = lambda x: (x + 5)**2

cur_x = 3 # The algorithm starts at x=3

learning_rate = 0.1 # Learning rate

precision = 0.000001 #This tells us when to stop the algorithm

previous_step_size = 1 #

max_iters = 10000 # maximum number of iterations

iters = 0 #iteration counter

df = lambda x: 2 * (x + 5) #Gradient of our function

• Step 2 : Run a loop to perform gradient descent
• Stop loop when difference between x values from 2 consecutive iterations is less than 0.000001 or when number of iterations exceeds 10,000
In [109]:
print("Iteration: {0:2d} - X is {1:8.5f}".format(0, cur_x))

while previous_step_size > precision and iters < max_iters:
prev_x = cur_x #Store current x value in prev_x

cur_x = cur_x - learning_rate * df(prev_x) #Grad descent

previous_step_size = abs(cur_x - prev_x) #Change in x

iters = iters + 1 #iteration count

print("Iteration: {0:2d} - X value is {1:8.5f}".format(iters, cur_x)) #Print iterations

print("The local minimum occurs at {0:8.5f}".format(cur_x))

Iteration:  0 - X is  3.00000
Iteration:  1 - X value is  1.40000
Iteration:  2 - X value is  0.12000
Iteration:  3 - X value is -0.90400
Iteration:  4 - X value is -1.72320
Iteration:  5 - X value is -2.37856
Iteration:  6 - X value is -2.90285
Iteration:  7 - X value is -3.32228
Iteration:  8 - X value is -3.65782
Iteration:  9 - X value is -3.92626
Iteration: 10 - X value is -4.14101
Iteration: 11 - X value is -4.31281
Iteration: 12 - X value is -4.45024
Iteration: 13 - X value is -4.56020
Iteration: 14 - X value is -4.64816
Iteration: 15 - X value is -4.71853
Iteration: 16 - X value is -4.77482
Iteration: 17 - X value is -4.81986
Iteration: 18 - X value is -4.85588
Iteration: 19 - X value is -4.88471
Iteration: 20 - X value is -4.90777
Iteration: 21 - X value is -4.92621
Iteration: 22 - X value is -4.94097
Iteration: 23 - X value is -4.95278
Iteration: 24 - X value is -4.96222
Iteration: 25 - X value is -4.96978
Iteration: 26 - X value is -4.97582
Iteration: 27 - X value is -4.98066
Iteration: 28 - X value is -4.98453
Iteration: 29 - X value is -4.98762
Iteration: 30 - X value is -4.99010
Iteration: 31 - X value is -4.99208
Iteration: 32 - X value is -4.99366
Iteration: 33 - X value is -4.99493
Iteration: 34 - X value is -4.99594
Iteration: 35 - X value is -4.99675
Iteration: 36 - X value is -4.99740
Iteration: 37 - X value is -4.99792
Iteration: 38 - X value is -4.99834
Iteration: 39 - X value is -4.99867
Iteration: 40 - X value is -4.99894
Iteration: 41 - X value is -4.99915
Iteration: 42 - X value is -4.99932
Iteration: 43 - X value is -4.99946
Iteration: 44 - X value is -4.99956
Iteration: 45 - X value is -4.99965
Iteration: 46 - X value is -4.99972
Iteration: 47 - X value is -4.99978
Iteration: 48 - X value is -4.99982
Iteration: 49 - X value is -4.99986
Iteration: 50 - X value is -4.99989
Iteration: 51 - X value is -4.99991
Iteration: 52 - X value is -4.99993
Iteration: 53 - X value is -4.99994
Iteration: 54 - X value is -4.99995
Iteration: 55 - X value is -4.99996
Iteration: 56 - X value is -4.99997
Iteration: 57 - X value is -4.99998
Iteration: 58 - X value is -4.99998
Iteration: 59 - X value is -4.99998
Iteration: 60 - X value is -4.99999
Iteration: 61 - X value is -4.99999
Iteration: 62 - X value is -4.99999
Iteration: 63 - X value is -4.99999
Iteration: 64 - X value is -4.99999
Iteration: 65 - X value is -5.00000
Iteration: 66 - X value is -5.00000
The local minimum occurs at -5.00000


## 3. Implement 2D Gradient Descent in Python¶

In [110]:
f = lambda x, y: x ** 2 + y ** 2 + 10

cur_x = 3 # The algorithm starts at x=3
cur_y = -3 # The algorithm starts at y=-3

learning_rate = 0.1 # Learning rate

precision = 0.000001 #This tells us when to stop the algorithm

previous_step_size = 1 #

max_iters = 10000 # maximum number of iterations

iters = 0 #iteration counter

df_x = lambda x, y: 2 * x #Gradient of our function for x
df_y = lambda x, y: 2 * y #Gradient of our function for y

In [111]:
print("Iteration: {0:2d} - X is {1:8.5f}, Y is {2:8.5f},".format(0, cur_x, cur_y))

while previous_step_size > precision and iters < max_iters:
prev_x = cur_x #Store current x value in prev_x
prev_y = cur_y #Store current x value in prev_x

cur_x = cur_x - learning_rate * df_x(prev_x, prev_y) #Grad descent for x
cur_y = cur_y - learning_rate * df_y(prev_x, prev_y) #Grad descent for x

previous_step_size = abs(cur_x - prev_x) + abs(cur_y - prev_y) #Change in x

iters = iters + 1 #iteration count

print("Iteration: {0:2d} - X is {1:8.5f}, Y is {2:8.5f},".format(iters, cur_x, cur_y)) #Print iterations

print("The local minimum occurs at {0:8.5f}, {1:8.5f}".format(cur_x, cur_y))

Iteration:  0 - X is  3.00000, Y is -3.00000,
Iteration:  1 - X is  2.40000, Y is -2.40000,
Iteration:  2 - X is  1.92000, Y is -1.92000,
Iteration:  3 - X is  1.53600, Y is -1.53600,
Iteration:  4 - X is  1.22880, Y is -1.22880,
Iteration:  5 - X is  0.98304, Y is -0.98304,
Iteration:  6 - X is  0.78643, Y is -0.78643,
Iteration:  7 - X is  0.62915, Y is -0.62915,
Iteration:  8 - X is  0.50332, Y is -0.50332,
Iteration:  9 - X is  0.40265, Y is -0.40265,
Iteration: 10 - X is  0.32212, Y is -0.32212,
Iteration: 11 - X is  0.25770, Y is -0.25770,
Iteration: 12 - X is  0.20616, Y is -0.20616,
Iteration: 13 - X is  0.16493, Y is -0.16493,
Iteration: 14 - X is  0.13194, Y is -0.13194,
Iteration: 15 - X is  0.10555, Y is -0.10555,
Iteration: 16 - X is  0.08444, Y is -0.08444,
Iteration: 17 - X is  0.06755, Y is -0.06755,
Iteration: 18 - X is  0.05404, Y is -0.05404,
Iteration: 19 - X is  0.04323, Y is -0.04323,
Iteration: 20 - X is  0.03459, Y is -0.03459,
Iteration: 21 - X is  0.02767, Y is -0.02767,
Iteration: 22 - X is  0.02214, Y is -0.02214,
Iteration: 23 - X is  0.01771, Y is -0.01771,
Iteration: 24 - X is  0.01417, Y is -0.01417,
Iteration: 25 - X is  0.01133, Y is -0.01133,
Iteration: 26 - X is  0.00907, Y is -0.00907,
Iteration: 27 - X is  0.00725, Y is -0.00725,
Iteration: 28 - X is  0.00580, Y is -0.00580,
Iteration: 29 - X is  0.00464, Y is -0.00464,
Iteration: 30 - X is  0.00371, Y is -0.00371,
Iteration: 31 - X is  0.00297, Y is -0.00297,
Iteration: 32 - X is  0.00238, Y is -0.00238,
Iteration: 33 - X is  0.00190, Y is -0.00190,
Iteration: 34 - X is  0.00152, Y is -0.00152,
Iteration: 35 - X is  0.00122, Y is -0.00122,
Iteration: 36 - X is  0.00097, Y is -0.00097,
Iteration: 37 - X is  0.00078, Y is -0.00078,
Iteration: 38 - X is  0.00062, Y is -0.00062,
Iteration: 39 - X is  0.00050, Y is -0.00050,
Iteration: 40 - X is  0.00040, Y is -0.00040,
Iteration: 41 - X is  0.00032, Y is -0.00032,
Iteration: 42 - X is  0.00026, Y is -0.00026,
Iteration: 43 - X is  0.00020, Y is -0.00020,
Iteration: 44 - X is  0.00016, Y is -0.00016,
Iteration: 45 - X is  0.00013, Y is -0.00013,
Iteration: 46 - X is  0.00010, Y is -0.00010,
Iteration: 47 - X is  0.00008, Y is -0.00008,
Iteration: 48 - X is  0.00007, Y is -0.00007,
Iteration: 49 - X is  0.00005, Y is -0.00005,
Iteration: 50 - X is  0.00004, Y is -0.00004,
Iteration: 51 - X is  0.00003, Y is -0.00003,
Iteration: 52 - X is  0.00003, Y is -0.00003,
Iteration: 53 - X is  0.00002, Y is -0.00002,
Iteration: 54 - X is  0.00002, Y is -0.00002,
Iteration: 55 - X is  0.00001, Y is -0.00001,
Iteration: 56 - X is  0.00001, Y is -0.00001,
Iteration: 57 - X is  0.00001, Y is -0.00001,
Iteration: 58 - X is  0.00001, Y is -0.00001,
Iteration: 59 - X is  0.00001, Y is -0.00001,
Iteration: 60 - X is  0.00000, Y is -0.00000,
Iteration: 61 - X is  0.00000, Y is -0.00000,
Iteration: 62 - X is  0.00000, Y is -0.00000,
Iteration: 63 - X is  0.00000, Y is -0.00000,
Iteration: 64 - X is  0.00000, Y is -0.00000,
The local minimum occurs at  0.00000, -0.00000

In [ ]: