# Notes

## Notes - notes.io

import numpy as np
import random
x_data = ([1, 2, 3, 4, 5]).reshape(5, 1)
t_data = ([2, 3, 4, 5, 6]).reshape(5, 1)
W = (1, 1)
b = (1)
print("W= ", W, "W.shape= ", W.shape, ", b= ", b, ", = ", )
def loss_func(x, t):
y = (x, W) + b
return (( (t-y)**2 ))/(len(x))
def numerical_derivative(f, x) :
delta_x = 1e-4
while not :
idx =
tmp_val = x[idx]
x[idx] = float(tmp_val) + delta_x
fx1 = f(x) # f(x+delta_x)
x[idx] = tmp_val - delta_x
fx2 = f(x) # f(x-delta_x)
grad[idx] = (fx1 - fx2) / (2*delta_x)
x[idx] = tmp_val
()

def error_val(x, t):
y = (x, W) + b
return (( (t - y) **2 ) ) / (len(x))
def predict(x):
y = (x, W) + b
return y
learning_rate = 1e-2
f = lambda x : loss_func (x_data, t_data)
print("Initial error value = ", error_val(x_data, t_data), "Initial W = ", W, "b = ", b)
for step in range(8001):
W -= learning_rate * numerical_derivative (f, W)
b -= learning_rate * numerical_derivative (f, b)
if (step % 400 == 0):
print("step = ", step, "error value = ", error_val(x_data, t_data), "W= ", W, "b= ", b)
print(predict(43))

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