Gradient Descent algoritmi weight "w" ning optimal qiymatini topishda qo'llaniladigan algoritmdir. Sodda qilib aytganda, bu algoritm bizlarga avtomatik tarzda optimal yechimni topib beradi. Ushbu algortim nafaqat w ning qiymatini topishda balki keyinchalik cost function(kelasi darslarimizda o'rganamiz) ni hisoblashda ham qo'llaniladi.
# Training Data(O'rgatishdagi ma'lumotlar)
x_soat = [1.0, 2.0, 3.0]
y_baho = [2.0, 4.0, 6.0]
w = 1.0 #w uchun dastalbki taxminiy qiymat
# (Modelimiz)To'g'ri hisoblash uchun funksiya
def forward(x):
return x * w
# Xatolik (Loss) ning funkisyasi
def loss(x, y):
y_pred = forward(x)
return (y_pred - y) * (y_pred - y)
# Gradient uchun funksiya
def gradient(x, y): # d_loss/d_w
return 2 * x * (x * w - y)
# Training dan avval
print("Bashorat (training dan avval)", "4 soat o'qilganda:", forward(4))
# Training zanjiri (loop)
learning_rate =0.01
for epoch in range(10):
for x_hb_qiym, y_hb_qiym in zip(x_soat, y_baho):
# Hosilani hisoblash
# w ning qiymatini yangilash
# xatolikni hisoblab progressni chop qilish
grad = gradient(x_hb_qiym, y_hb_qiym)
w = w - learning_rate * grad
print("\tgrad: ", x_hb_qiym, y_hb_qiym, round(grad, 2))
l = loss(x_hb_qiym, y_hb_qiym)
print("progress:", epoch, "w=", round(w, 2), "loss=", round(l, 2))
# Traningdan so'ng
print("Bashorat (training dan keyin)", "4 saot o'qilganda: ", forward(4))
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