[LAB] Week 2_Problem 1_Seohyun Lee

[seo-rii]

Problem

Week 2_Problem 1

Source Code

import torch
from random import random
from typing import Callable

##                         Problem 1                          ##
##                                                            ##
##            Arbitary x_train, y_train are given.            ##
##   Suppose that x and y have linear correlation, y=wx+b.    ##
##     In function training(), you should return [w, b].      ##
##          In function predict(), you should return          ##
##            list y_test corresponding to x_test.            ##
##                  Made by @jangyoujin0917                   ##
##                                                            ##

# NOTE : Feel free to use torch.optim and tensor.

def training(x_train: list[float], y_train: list[float]) -> list[float]:  # DO NOT MODIFY FUNCTION NAME
    # Data normalization code (prevents overflow when calculating MSE, prevents underfitting)
    # Note that you need to convert [w, b] to the original scale before returning value
    # w = w * (y_max - y_min)
    # b = b * (y_max - y_min) + y_min
    w = torch.tensor(0.0, requires_grad=True)
    b = torch.tensor(0.0, requires_grad=True)
    y_min = min(y_train)
    y_max = max(y_train)
    normalize = lambda y: (y - y_min) / (y_max - y_min)

    ### IMPLEMENT FROM HERE
    epoch = 10000
    lr = 0.01

    x = torch.tensor(x_train, requires_grad=True)
    y = torch.tensor([normalize(x) for x in y_train], requires_grad=True)

    opt = torch.optim.Adam([w, b], lr=lr)

    for _ in range(epoch):
        y_pred = w * x + b
        loss = torch.mean((y_pred - y) ** 2)
        loss.backward()
        opt.step()
        opt.zero_grad()

    w.data = w.data * (y_max - y_min)
    b.data = b.data * (y_max - y_min) + y_min
    return [w.item(), b.item()]

def predict(x_train: list[float], y_train: list[float], x_test: list[float]) -> list[
    float]:  # DO NOT MODIFY FUNCTION NAME
    w, b = training(x_train, y_train)
    return [w * x + b for x in x_test]

if __name__ == "__main__":
    x_train = [0.0, 1.0, 2.0, 3.0, 4.0]
    y_train = [2.0, 4.0, 6.0, 8.0, 10.0]  # Note that not all test cases give clear line.
    x_test = [5.0, 10.0, 8.0]

    w, b = training(x_train, y_train)
    y_test = predict(x_train, y_train, x_test)

    print(w, b)
    print(y_test)

Description

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This is an auto-generated grading output. Checking code of seo-rii {'seo-rii': 74.8}