README.md

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np

# Load and preprocess the Iris dataset
iris = load_iris()
X = iris['data']  # Features
y = iris['target']  # Labels (Classes)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Convert data to PyTorch tensors
X_train = torch.tensor(X_train, dtype=torch.float32)
X_test = torch.tensor(X_test, dtype=torch.float32)
y_train = torch.tensor(y_train, dtype=torch.long)
y_test = torch.tensor(y_test, dtype=torch.long)

# Define the Neural Network Model
class IrisNet(nn.Module):
    def __init__(self):
        super(IrisNet, self).__init__()
        self.fc1 = nn.Linear(4, 16)  # Input layer -> Hidden layer 1
        self.fc2 = nn.Linear(16, 8)  # Hidden layer 1 -> Hidden layer 2
        self.fc3 = nn.Linear(8, 3)   # Hidden layer 2 -> Output layer (3 classes)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)  # Output layer (no softmax because CrossEntropyLoss expects raw logits)
        return x

# Initialize the model, loss function, and optimizer
model = IrisNet()
criterion = nn.CrossEntropyLoss()  # For multi-class classification
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train the model
num_epochs = 100
for epoch in range(num_epochs):
    model.train()

    # Forward pass
    outputs = model(X_train)
    loss = criterion(outputs, y_train)

    # Backward pass and optimization
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch + 1) % 10 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# Test the model
model.eval()  # Set model to evaluation mode
with torch.no_grad():
    test_outputs = model(X_test)
    _, predicted = torch.max(test_outputs, 1)
    accuracy = (predicted == y_test).sum().item() / y_test.size(0)
    print(f'Accuracy on test set: {accuracy * 100:.2f}%')
Epoch [10/100], Loss: 1.1041
Epoch [20/100], Loss: 1.0783
Epoch [30/100], Loss: 1.0541
Epoch [40/100], Loss: 1.0287
Epoch [50/100], Loss: 0.9986
Epoch [60/100], Loss: 0.9607
Epoch [70/100], Loss: 0.9155
Epoch [80/100], Loss: 0.8622
Epoch [90/100], Loss: 0.8021
Epoch [100/100], Loss: 0.7376
Accuracy on test set: 70.00%
data = pd.read_csv('your_data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

X = torch.tensor(X, dtype=torch.float32)
y = torch.tensor(y, dtype=torch.long)

train_dataset = TensorDataset(X, y)
test_dataset = TensorDataset(X, y)

train_loader = DataLoader(train_dataset, batch_size=batch_size)
test_loader = DataLoader(test_dataset, batch_size=batch_size)
class Model3(nn.Module):
    def __init__(self):
        super(Model3, self).__init__()
        self.layer1 = nn.Linear(256, 128)
        self.layer2 = nn.Linear(128, 10)
        self.sigmoid = nn.Sigmoid()
        self.softmax = nn.Softmax()

    def forward(self, x):
        x = self.layer1(x)
        x = self.sigmoid(x)
        x = self.layer2(x)
        x = self.softmax(x)
        return x
def train_model(model, dataset, epochs=15, batch_size=32):
    model.train()

    for epoch in range(epochs):
        for inputs, targets in train_loader:
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            loss.backward()
            optimizer.step()
        print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}")

train_model(model3, dataset3)
def evaluate_model(model, dataset):
    model.eval()
    correct, total = 0, 0

    with torch.no_grad():
        for inputs, targets in test_loader:
            outputs = model(inputs)
            _, predicted = torch.max(outputs, 1)
            total += targets.size(0)
            correct += (predicted == targets).sum().item()
    accuracy = correct / total
    print(f"Accuracy: {accuracy * 100:.2f}%")

evaluate_model(model3, dataset3)