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train.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+import numpy as np
+
+# Wczytanie danych
+data = pd.read_csv("Sales.csv")
+
+# Przygotowanie danych
+data["Profit_Category"] = pd.cut(data["Profit"], bins=[-np.inf, 500, 1000, np.inf], labels=[0, 1, 2])
+bike = data.loc[:, ['Customer_Age', 'Customer_Gender', 'Country','State', 'Product_Category', 'Sub_Category', 'Profit_Category']]  
+bikes = pd.get_dummies(bike, columns=['Country', 'State', 'Product_Category', 'Sub_Category', 'Customer_Gender'])
+X = bikes.drop('Profit_Category', axis=1).values
+y = bikes['Profit_Category'].values
+X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)
+scaler = StandardScaler()
+X = scaler.fit_transform(X)
+#### Tworzenie tensorów
+X_train = X_train.astype(np.float32)
+X_test = X_test.astype(np.float32)
+y_train = y_train.astype(np.float32)
+y_test = y_test.astype(np.float32)
+X_train=torch.FloatTensor(X_train)
+X_test=torch.FloatTensor(X_test)
+y_train=torch.LongTensor(y_train)
+y_test=torch.LongTensor(y_test)
+
+#### Model
+
+class ANN_Model(nn.Module):
+    def __init__(self,input_features=82,hidden1=20,hidden2=20,out_features=3):
+        super().__init__()
+        self.f_connected1=nn.Linear(input_features,hidden1)
+        self.f_connected2=nn.Linear(hidden1,hidden2)
+        self.out=nn.Linear(hidden2,out_features)
+    def forward(self, x):
+        x=F.relu(self.f_connected1(x))
+        x=F.relu(self.f_connected2(x))
+        x=self.out(x)
+        return x
+
+
+torch.manual_seed(20)
+model=ANN_Model()
+model.parameters
+
+def calculate_accuracy(model, X, y):
+    with torch.no_grad():
+        outputs = model(X)
+        _, predicted = torch.max(outputs.data, 1)
+        total = y.size(0)
+        correct = (predicted == y).sum().item()
+        accuracy = correct / total * 100
+    return accuracy
+
+loss_function = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+
+epochs = 100
+final_losses = []
+accuracy_list = []
+
+for i in range(epochs):
+    i = i + 1
+    y_pred = model(X_train)
+    loss = loss_function(y_pred, y_train)
+    final_losses.append(loss)
+    
+    train_accuracy = calculate_accuracy(model, X_train, y_train)
+    test_accuracy = calculate_accuracy(model, X_test, y_test)
+    print(f"Epoch: {i}, Loss: {loss.item()}, Train Accuracy: {train_accuracy}%, Test Accuracy: {test_accuracy}%")
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+torch.save(model,"classificationn_model.pt")