ium_478815/biblioteka_DL

import torch
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()

# Model
class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(1,1) 

    def forward(self, x):
        y_predicted = torch.sigmoid(self.linear(x))
        return y_predicted

data = pd.read_csv('data.csv')
data.dropna()

training_data = data.sample(frac=0.9, random_state=25)
testing_data = data.drop(training_data.index)

print(f"No. of training examples: {training_data.shape[0]}")
print(f"No. of testing examples: {testing_data.shape[0]}")

training_data = training_data[['sqft_living', 'price']]

testing_data = testing_data[['sqft_living', 'price']]

training_data[['price']] = training_data[['price']] / 10000000
training_data[['sqft_living']] = training_data[['sqft_living']] / 10000

testing_data[['price']] = testing_data[['price']] / 10000000
testing_data[['sqft_living']] = testing_data[['sqft_living']] / 10000

# Tensory
X_training = training_data[['sqft_living']].to_numpy()
X_testing = testing_data[['sqft_living']].to_numpy()
y_training = training_data[['price']].to_numpy()
y_testing = testing_data[['price']].to_numpy()

import torch
torch.from_file
X_training = torch.from_numpy(X_training.astype(np.float32))
X_testing = torch.from_numpy(X_testing.astype(np.float32))
y_training = torch.from_numpy(y_training.astype(np.float32))
y_testing = torch.from_numpy(y_testing.astype(np.float32))


model = Model()
criterion = nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Trening
num_epochs = 1000
for epoch in range(num_epochs):
    y_predicted = model(X_training)
    loss = criterion(y_predicted,y_training)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

    if (epoch%100==0):
        print(f'epoch:{epoch+1},loss = {loss.item():.4f}')

with torch.no_grad():
    y_predicted = model(X_testing)
    y_predicted_cls = y_predicted.round()
    acc = y_predicted_cls.eq(y_testing).sum()/float(y_testing.shape[0])
    print(f'{acc:.4f}')
    result = open("output",'w+')
    result.write(f'{y_predicted}')

torch.save(model, "model.pkl")