[LAB] Week 3_Problem 1_남태웅

[YePpLe]

Problem

Week 3_Problem 1

Source Code

import torch
import torch.nn.functional as F
from random import random 
from typing import Callable

##                         Problem 1                          ##
##                                                            ##
##            Arbitary x_train, y_train are given.            ##
##          In function predict(), you should return          ##
##            list y_test corresponding to x_test.            ##
##               y_train only contains 0 and 1.               ##
##             Therefore, use logstic regression.             ##
##                  Made by @jangyoujin0917                   ##
##                                                            ##

# NOTE : 1. Feel free to use torch.optim and tensor.
#        2. In this problem, we will only grade "predict" function.
#           Function "training" is only for modularization.

def training(x_train : list[list[float]], y_train : list[float]) -> tuple[list[float], float]: # DO NOT MODIFY FUNCTION NAME
    x_train = torch.tensor(x_train)
    y_train = torch.tensor(y_train)

    w = torch.zeros(len(x_train[0]), requires_grad=True)
    b = torch.zeros(1, requires_grad=True)

    optimizer = torch.optim.SGD([w, b], lr=1e-3)

    for _ in range(10**4):
        loss = F.binary_cross_entropy(torch.sigmoid(x_train.matmul(w) + b), y_train)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    return (w, b)

def predict(x_train : list[list[float]], y_train : list[float], x_test : list[list[float]]) -> list[float]: # DO NOT MODIFY FUNCTION NAME
    w, b = training(x_train, y_train)
    x_test = torch.tensor(x_test)
    return torch.sigmoid(x_test.matmul(w) + b)

if __name__ == "__main__":
    # This is very simple case. Passing this testcase do not mean that the code is perfect.
    # Please consider for the practial problems when score is not high.
    x_train = [[0., 1.], [1., 0.], [2., 5.], [3., 1.], [4., 2.]]
    y_train = [0., 0., 1., 0., 1.]
    x_test = [[7., 2.], [1.5, 1.], [2.5, 0.5]]

    y_test = predict(x_train, y_train, x_test)

    print(y_test.tolist())

Description

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[github-actions[bot]]

This is an auto-generated grading output. Checking code of YePpLe {'YePpLe': 74.0}