PyTorch Beginner's Guide: Understanding Model Construction with Simple Examples
This PyTorch beginner's tutorial covers core knowledge points: PyTorch is Python-based with obvious advantages in dynamic computation graphs and simple installation (`pip install torch`). The core data structure is the Tensor, which supports GPU acceleration, and can be created, manipulated (addition, subtraction, multiplication, division, matrix multiplication), and converted to/from NumPy. Automatic differentiation (autograd) is implemented via `requires_grad=True` for gradient calculation, e.g., the derivative of \( y = x^2 + 3x \) at \( x = 2 \) is 7. A linear regression model inherits `nn.Module` for definition, with forward propagation implementing \( y = wx + b \). For data preparation, simulated data (\( y = 2x + 3 + \text{noise} \)) is generated, and batched loaded using `TensorDataset` and `DataLoader`. Training uses MSE loss and SGD optimizer, with gradient zeroing, backpropagation, and parameter updates in the loop. After 1000 epochs, results are validated and visualized, with learned parameters close to the true values. The core process covers tensor operations, automatic differentiation, model construction, data loading, and training optimization, enabling scalability to complex models.
Read MoreLearning PyTorch from Scratch: A Beginner's Guide from Tensors to Neural Networks
This article introduces the core content and basic applications of PyTorch. Renowned for its flexibility, intuitiveness, and Python-like syntax, PyTorch is suitable for deep learning beginners and supports GPU acceleration and automatic differentiation. The core content includes: 1. **Tensor**: The basic data structure, similar to a multi-dimensional array. It supports creation from data, all-zero/all-one, random numbers, conversion with NumPy, shape operations, arithmetic operations (element-wise/matrix), and device conversion (CPU/GPU). 2. **Automatic Differentiation**: Implemented through `autograd`. Tensors with `requires_grad=True` will track their computation history, and calling `backward()` automatically computes gradients. For example, for the function \( y = x^2 + 3x - 5 \), the gradient at \( x = 2 \) is 7.0. 3. **Neural Network Construction**: Based on the `torch.nn` module, it includes linear layers (`nn.Linear`), activation functions, loss functions (e.g., MSE), and optimizers (e.g., SGD). It supports custom model classes and composition with `nn.Sequential`. 4. **Practical Linear Regression**: Generates simulated data \( y = 2x + 3 + \text{noise} \), defines a linear model, MSE loss,
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