# ============================================================
# MNIST Digit Classification using a Simple Neural Network (PyTorch)
# ============================================================

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

# ------------------------------------------------------------
# 1. Device Configuration (GPU if available)
# ------------------------------------------------------------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using device:", device)

# ------------------------------------------------------------
# 2. Load MNIST Dataset
# ------------------------------------------------------------
transform = transforms.Compose([
    transforms.ToTensor(),                # convert to tensor
    transforms.Normalize((0.5,), (0.5,))  # normalize
])

train_dataset = torchvision.datasets.MNIST(
    root='./data', train=True, transform=transform, download=True)

test_dataset = torchvision.datasets.MNIST(
    root='./data', train=False, transform=transform, download=True)

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# ------------------------------------------------------------
# 3. Define Simple Neural Network
# ------------------------------------------------------------
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(28*28, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = x.view(-1, 784)    # flatten image
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)        # no softmax (CrossEntropyLoss handles it)
        return x

model = SimpleNN().to(device)

# ------------------------------------------------------------
# 4. Loss and Optimizer
# ------------------------------------------------------------
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# ------------------------------------------------------------
# 5. Training Loop
# ------------------------------------------------------------
num_epochs = 5
train_loss_list = []
train_acc_list = []

for epoch in range(num_epochs):
    total_loss = 0
    correct = 0
    total = 0

    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)

        # forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

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

        total_loss += loss.item()

        # accuracy
        _, predicted = outputs.max(1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    avg_loss = total_loss / len(train_loader)
    accuracy = 100 * correct / total

    train_loss_list.append(avg_loss)
    train_acc_list.append(accuracy)

    print(f"Epoch [{epoch+1}/{num_epochs}]  Loss: {avg_loss:.4f}  Accuracy: {accuracy:.2f}%")

# ------------------------------------------------------------
# 6. Testing / Evaluation
# ------------------------------------------------------------
model.eval()
correct = 0
total = 0

with torch.no_grad():
    for images, labels in test_loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)

        _, predicted = outputs.max(1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

test_accuracy = 100 * correct / total
print("\nTest Accuracy:", test_accuracy)

# ------------------------------------------------------------
# 7. Plot Accuracy and Loss Graphs
# ------------------------------------------------------------
plt.figure(figsize=(8, 4))
plt.plot(train_acc_list, label="Training Accuracy")
plt.title("Training Accuracy")
plt.xlabel("Epoch")
plt.ylabel("Accuracy (%)")
plt.grid()
plt.legend()
plt.show()

plt.figure(figsize=(8, 4))
plt.plot(train_loss_list, label="Training Loss")
plt.title("Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.grid()
plt.legend()
plt.show()

# ------------------------------------------------------------
# 8. Show Predictions for First 5 Test Images
# ------------------------------------------------------------
data_iter = iter(test_loader)
images, labels = next(data_iter)
images, labels = images.to(device), labels.to(device)

outputs = model(images[:5])
_, preds = outputs.max(1)

print("\nPredicted:", preds.cpu().numpy())
print("Actual:   ", labels[:5].cpu().numpy())

# Plot the first 5 images
plt.figure(figsize=(10, 2))
for i in range(5):
    plt.subplot(1, 5, i + 1)
    plt.imshow(images[i].cpu().squeeze(), cmap="gray")
    plt.title(f"P:{preds[i].item()} / T:{labels[i].item()}")
    plt.axis("off")

plt.show()

//IMG DATASET
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, random_split
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
# -----------------------------------------------------------
# 1. Device Configuration
# -----------------------------------------------------------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using device:", device)
# -----------------------------------------------------------
# 2. Dataset Path
# -----------------------------------------------------------
dataset_dir = r"C:\Users\ADMIN\Downloads\archive\natural_images"
# -----------------------------------------------------------
# 3. Transformations (preprocessing + augmentation)
# -----------------------------------------------------------
transform = transforms.Compose([
transforms.Resize((128,128)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])
])
# Load the full dataset
full_dataset = datasets.ImageFolder(root=dataset_dir, transform=transform)
# Split dataset into training and validation (80%-20%)
train_size = int(0.8 * len(full_dataset))
val_size = len(full_dataset)-train_size
train_dataset, val_dataset = random_split(full_dataset, [train_size, val_size])
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
num_classes = len(full_dataset.classes)
print("Classes:", full_dataset.classes)
# -----------------------------------------------------------
# 4. Define CNN Model
# -----------------------------------------------------------
class SimpleCNN(nn.Module):
def __init__(self, num_classes):
super(SimpleCNN, self).__init__()
self.conv_layers = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2,2),
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2,2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2,2)
)
self.fc_layers = nn.Sequential(
nn.Flatten(),
nn.Linear(128*16*16, 256),
nn.ReLU(),
nn.Linear(256, num_classes)
)
def forward(self, x):
x = self.conv_layers(x)
x = self.fc_layers(x)
return x
model = SimpleCNN(num_classes=num_classes).to(device)
# -----------------------------------------------------------
# 5. Loss and Optimizer
# -----------------------------------------------------------
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# -----------------------------------------------------------
# 6. Training Loop
# -----------------------------------------------------------
num_epochs = 10
train_loss_list, train_acc_list = [], []
for epoch in range(num_epochs):
model.train()
total_loss, correct, total = 0, 0, 0
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
_, predicted = outputs.max(1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
avg_loss = total_loss / len(train_loader)
accuracy = 100 * correct / total
train_loss_list.append(avg_loss)
train_acc_list.append(accuracy)
print(f"Epoch [{epoch+1}/{num_epochs}]  Loss: {avg_loss:.4f} Accuracy: {accuracy
# -----------------------------------------------------------
# 7. Validation / Evaluation
# -----------------------------------------------------------
model.eval()
correct, total = 0, 0
with torch.no_grad():
for images, labels in val_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = outputs.max(1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
val_accuracy = 100 * correct / total
print("\nValidation Accuracy:", val_accuracy)
# -----------------------------------------------------------
# 8. Plot Training Accuracy and Loss
# -----------------------------------------------------------
plt.figure(figsize=(8,4))
plt.plot(train_acc_list, label="Training Accuracy")
plt.title("Training Accuracy")
plt.xlabel("Epoch")
plt.ylabel("Accuracy (%)")
plt.grid()
plt.legend()
plt.show()
plt.figure(figsize=(8,4))
plt.plot(train_loss_list, label="Training Loss")
plt.title("Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.grid()
plt.legend()
plt.show()
# -----------------------------------------------------------
# 9. Show Predictions for First 5 Validation Images
# -----------------------------------------------------------
data_iter = iter(val_loader)
images, labels = next(data_iter)
images, labels = images.to(device), labels.to(device)
outputs = model(images[:5])
_, preds = outputs.max(1)
print("\nPredicted:", [full_dataset.classes[i] for i in preds.cpu().numpy()])
print("Actual:   ", [full_dataset.classes[i] for i in labels[:5].cpu().numpy()])
plt.figure(figsize=(10,2))
for i in range(5):
plt.subplot(1,5,i+1)
img = images[i].cpu().permute(1,2,0).numpy()
img = img * 0.229 + 0.485 # unnormalize
plt.imshow(img)
plt.title(f"P:{full_dataset.classes[preds[i]]}\nT:{full_dataset.classes[labels
plt.axis("off")
plt.show()

//IRIS DATASET
import torch, torch.nn as nn, torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset, random_split
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt

device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("Dataset: IRIS — built-in, no external files needed")

# ----- DATA -----
X, y = load_iris(return_X_y=True)
X = torch.tensor(X, dtype=torch.float32)
y = torch.tensor(y, dtype=torch.long)
X = (X - X.mean(0)) / X.std(0)

train_ds, test_ds = random_split(TensorDataset(X,y), [120,30])
train_loader = DataLoader(train_ds, 16, shuffle=True)
test_loader  = DataLoader(test_ds, 16)

# ----- MODEL -----
model = nn.Sequential(
    nn.Linear(4, 32), nn.ReLU(),
    nn.Linear(32, 3)
).to(device)

loss_fn = nn.CrossEntropyLoss()
opt = optim.Adam(model.parameters(), lr=0.01)

# ----- TRAIN -----
loss_hist, acc_hist = [], []
for _ in range(50):
    correct = total = running_loss = 0
    for x,y in train_loader:
        x,y = x.to(device), y.to(device)
        out = model(x)
        loss = loss_fn(out,y)

        opt.zero_grad(); loss.backward(); opt.step()
        running_loss += loss.item()
        correct += (out.argmax(1)==y).sum().item()
        total += y.size(0)

    loss_hist.append(running_loss/len(train_loader))
    acc_hist.append(100*correct/total)

print(f"Train Accuracy: {acc_hist[-1]:.2f}%")

# ----- TEST -----
correct = 0
with torch.no_grad():
    for x,y in test_loader:
        x,y = x.to(device), y.to(device)
        correct += (model(x).argmax(1)==y).sum().item()

print(f"Test Accuracy: {100*correct/len(test_ds):.2f}%")

# ----- PLOT -----
plt.plot(loss_hist); plt.title("Loss"); plt.show()
plt.plot(acc_hist); plt.title("Accuracy"); plt.show()