From 4de3b5a28498ee83f34a684e60492b7a631fa0fd Mon Sep 17 00:00:00 2001
From: Kornelia Girejko <korgir@st.amu.edu.pl>
Date: Sun, 24 Apr 2022 22:10:16 +0200
Subject: [PATCH] pytorch

---
 biblioteka_DL | 76 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 biblioteka_DL

diff --git a/biblioteka_DL b/biblioteka_DL
new file mode 100644
index 0000000..d3cdfc2
--- /dev/null
+++ b/biblioteka_DL
@@ -0,0 +1,76 @@
+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}')
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