आपने TensorFlow और Keras सीख लिया — अब बारी है PyTorch की, जो research और flexibility के लिए Deep Learning community में सबसे लोकप्रिय framework है।
PyTorch एक open-source deep learning framework है जिसे Facebook AI Research Lab (FAIR) ने 2016 में विकसित किया।
यह framework खासकर researchers और advanced developers के बीच पसंदीदा है, क्योंकि यह:
- Dynamic computation graph प्रदान करता है (runtime पर बदलता है)
- Pythonic और NumPy जैसा syntax देता है
- GPU acceleration आसानी से करता है
🔑 Features:
| Feature | Description |
|---|---|
| 🧮 Dynamic Graphs | Real-time control (ज़्यादा flexibility) |
| 📊 Tensor Library | NumPy जैसे operations with GPU support |
| 🧠 Autograd | Automatic gradient calculation |
| 🔧 Modular API | Neural nets = Modules |
| 🖥️ GPU Ready | CUDA support |
🔶 2. PyTorch Installation
pip install torch torchvision
🔷 3. Tensors in PyTorch
Tensors are multi-dimensional arrays (similar to NumPy arrays) but with GPU support.
import torch
x = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
print(x.shape) # torch.Size([2, 2])
print(x + x) # Tensor addition
print(x @ x.T) # Matrix multiplication
✅ Use GPU:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
x = x.to(device)
🔷 4. Autograd – Automatic Differentiation
x = torch.tensor(2.0, requires_grad=True)
y = x**3 + 2*x
y.backward()
print(x.grad) # dy/dx = 3x^2 + 2 = 3*2^2 + 2 = 14
🔷 5. Building a Simple Neural Network
🔨 Step 1: Import Libraries
🔨 Step 1: Import Libraries
import torch
import torch.nn as nn
import torch.optim as optim
🔨 Input and Output for XOR
X = torch.tensor([[0.,0.],[0.,1.],[1.,0.],[1.,1.]])
y = torch.tensor([[0.],[1.],[1.],[0.]])
🔨 Step 2: Define Model
class MyNet(nn.Module):
def __init__(self):
super(MyNet, self).__init__()
self.fc1 = nn.Linear(2, 4)
self.fc2 = nn.Linear(4, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
return torch.sigmoid(self.fc2(x))
🔨 Step 3: Instantiate Model
model = MyNet()
🔨 Step 4: Define Loss and Optimizer
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
🔨 Step 5: Train the Model
for epoch in range(100):
y_pred = model(X)
loss = criterion(y_pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(f"Epoch {epoch}, Loss: {loss.item()}")
🔧 Important PyTorch Modules
| Module | Description |
|---|---|
torch.Tensor | Main data structure |
torch.nn | For building neural nets |
torch.nn.functional | Activation functions, loss functions |
torch.optim | Optimizers like Adam, SGD |
torch.utils.data | Dataset and DataLoader tools |
torchvision | Image datasets and transformations |
🔷 6. Example: XOR with MLP
X = torch.tensor([[0.,0.],[0.,1.],[1.,0.],[1.,1.]])
y = torch.tensor([[0.],[1.],[1.],[0.]])
model = nn.Sequential(
nn.Linear(2, 4),
nn.ReLU(),
nn.Linear(4, 1),
nn.Sigmoid()
)
loss_fn = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
for epoch in range(2000):
y_pred = model(X)
loss = loss_fn(y_pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % 200 == 0:
print(f"Epoch {epoch}, Loss: {loss.item()}")
📝 Practice Questions:
- PyTorch और TensorFlow में मुख्य अंतर क्या है?
- PyTorch में tensor कैसे बनाते हैं?
- Autograd का उपयोग gradient के लिए कैसे करते हैं?
- एक simple model class कैसे बनाते हैं?
- Sequential API और custom model class में क्या फर्क है?
🧠 Summary Table
| Concept | Explanation |
|---|---|
| Tensor | PyTorch का data container (NumPy + GPU) |
| Autograd | Automatic differentiation |
nn.Module | Neural network architecture का base class |
| Optimizer | Parameters को update करता है |
| Loss Function | Model की गलती measure करता है |
