[LAB] Week 2_Problem 1_이찬솔

[Nucobi]

Problem

Week 2_Problem 1

Source Code

import torch
from random import random
from typing import Callable, List

##                         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
    y_min = min(y_train)
    y_max = max(y_train)
    normalize = lambda y: (y - y_min) / (y_max - y_min)

    ### IMPLEMENT FROM HERE
    def returnTensor(train: List[float]):
        return torch.FloatTensor([[n] for n in train])

    y_train = [normalize(_) for _ in y_train]

    x_tensor = returnTensor(x_train)
    y_tensor = returnTensor(y_train)

    w = torch.zeros(1, requires_grad=True)
    b = torch.zeros(1, requires_grad=True)

    optimizer = torch.optim.Adam([w, b], lr=0.00055)

    epoch = 5000
    for _ in range(epoch):
        h = x_tensor * w + b
        loss = torch.mean((h - y_tensor) ** 2)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # if (_%100==0): print(f"Epoch: {_}/{epoch} - w:{w.item()},b:{b.item()} , loss: {loss}")

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

def predict(x_train: List[float], y_train: List[float], x_test: List[float]) -> List[float]:  # DO NOT MODIFY FUNCTION NAME
    ### IMPLEMENT FROM HERE
    W,B = training(x_train,y_train)
    linearRegression = lambda x:(W*x+B)

    return [linearRegression(x_) 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

a

Output (Optional)

1.9999985694885254 2.0000028610511436 [11.99999570849377, 21.999988555936397, 17.999991416959347]

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

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

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

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This is an auto-generated grading output. Checking code of Nucobi {'Nucobi': 71.9}