Introduction to PyTorch Neural Networks: Fully Connected Layers and Backpropagation Principles
This paper introduces the basics of PyTorch neural networks, with a core focus on fully connected layers and backpropagation. A fully connected layer enables full connectivity between neurons of the previous layer and the current layer, producing an output calculated as the product of a weight matrix and the input, plus a bias vector. Forward propagation is the forward computation process of data from the input layer through fully connected layers and activation functions to the output layer, for example, in a two - layer network: input → fully connected → ReLU → fully connected → output. Backpropagation is the core of neural network learning, adjusting parameters through gradient descent. Based on the chain rule, it reversely calculates the gradient of the loss with respect to each parameter starting from the output layer. PyTorch's autograd automatically records the computation graph and completes gradient calculation. The process includes forward propagation, loss calculation, backpropagation (loss.backward()), and parameter update (using an optimizer like SGD). Key concepts: Fully connected layers implement feature combination, forward propagation performs forward computation, backpropagation minimizes loss through gradient descent, and automatic differentiation simplifies gradient calculation. Understanding these principles is conducive to model debugging and optimization.
Read MoreQuick Start with PyTorch: Tensor Dimension Transformation and Common Operations
This article introduces the core knowledge of PyTorch tensors, including basics, dimension transformations, common operations, and exercise suggestions. Tensors are the basic structure for storing data in PyTorch, similar to NumPy arrays, and support GPU acceleration and automatic differentiation. They can be created using `torch.tensor()` from lists/numbers, `torch.from_numpy()` from NumPy arrays, or built-in functions to generate tensors of all zeros, ones, or random values. Dimension transformation is a key operation: `reshape()` flexibly adjusts the shape (keeping the total number of elements unchanged), `squeeze()` removes singleton dimensions, `unsqueeze()` adds singleton dimensions, and `transpose()`/`permute()` swap dimensions. Common operations include basic arithmetic operations, matrix multiplication with `matmul()`, broadcasting (automatic dimension expansion for operations), and aggregation operations such as `sum()`, `mean()`, and `max()`. The article suggests consolidating tensor operations through exercises, such as dimension adjustment, broadcasting mechanisms, and dimension swapping, to master the "shape language" and lay a foundation for subsequent model construction.
Read MorePyTorch Basics Tutorial: Practical Data Loading with Dataset and DataLoader
Data loading is a crucial step in machine learning training, and PyTorch's `Dataset` and `DataLoader` are core tools for efficient data management. As an abstract base class for data storage, `Dataset` requires inheriting to implement `__getitem__` (to read a single sample) and `__len__` (to get the total number of samples). Alternatively, `TensorDataset` can be directly used to wrap tensor data. `DataLoader`, on the other hand, handles batch processing and supports parameters such as `batch_size` (batch size), `shuffle` (shuffling order), and `num_workers` (multithreaded loading) to optimize training efficiency. In practice, taking MNIST as an example, image data can be loaded via `torchvision`, and combined with `Dataset` and `DataLoader` to achieve efficient iteration. It should be noted that under Windows, `num_workers` is defaulted to 0 to avoid memory issues. During training, `shuffle=True` should be used to shuffle the data, while `shuffle=False` is set for the validation/test sets to ensure reproducibility. Key steps: 1. Define a `Dataset` to store data; 2. Create a `DataLoader` with specified parameters; 3. Iterate over the `DataLoader` to input data into the model for training. These two components are the cornerstones of data processing. Once mastered, they can be flexibly applied to various data loading requirements.
Read MorePlaying with PyTorch from Scratch: Data Visualization and Model Evaluation Techniques
This article introduces core skills of data visualization and model evaluation in PyTorch to facilitate efficient model debugging. For data visualization, Matplotlib can observe data distributions (e.g., histograms of MNIST samples and labels), and TensorBoard can monitor training processes (e.g., scalar changes, model structures). In model evaluation, classification tasks should focus on accuracy and confusion matrices (e.g., MNIST classification example), while regression tasks use MSE and MAE. In practice, using visualization to identify issues (e.g., confusion between "8" and "9") enables iterative model optimization. Advanced applications include GAN visualization and real-time metric calculation. Mastering these skills allows quick problem localization and data understanding, laying a foundation for developing complex models.
Read MorePyTorch 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 MoreBeginner-Friendly: Basics of PyTorch Loss Functions and Training Loops
This article introduces the roles and implementation of loss functions and training loops in machine learning. Loss functions measure the gap between model predictions and true labels, while training loops adjust parameters to minimize loss for model learning. Common loss functions include: Mean Squared Error (MSE) for regression tasks (e.g., housing price prediction), accessible via `nn.MSELoss()` in PyTorch, and Cross-Entropy Loss for classification tasks (e.g., cat-dog recognition), accessible via `nn.CrossEntropyLoss()`. The core four steps of a training loop are: forward propagation (model prediction) → loss calculation → backpropagation (gradient computation) → parameter update (optimizer adjustment). It is critical to zero out gradients before backpropagation. Using linear regression as an example, the article generates simulated data, defines a linear model, trains it with MSE loss and the Adam optimizer, and iteratively optimizes parameters. Key considerations include: gradient zeroing, switching between training/inference modes, optimizer selection (e.g., Adam), and batch training with DataLoader. Mastering these concepts enables models to learn patterns from data, laying the foundation for complex models.
Read MoreIntroduction to PyTorch Optimizers: Practical Implementation of Optimization Algorithms like SGD and Adam
### Optimizers: The "Navigation System" for Deep Learning Optimizers are core tools in deep learning for updating model parameters and minimizing loss functions, similar to a navigation system when climbing a mountain, guiding the model from "high-loss" peaks to "low-loss" valleys. Their core task is to adjust parameters to improve the model's performance on training data. Different optimizers are designed for distinct scenarios: The basic SGD (Stochastic Gradient Descent) is simple but converges slowly and requires manual hyperparameter tuning; SGD+Momentum incorporates "inertia" to accelerate convergence; Adam combines momentum and adaptive learning rates, performing exceptionally well with default parameters and being the first choice for most tasks; AdamW adds weight decay (L2 regularization) to Adam, effectively preventing overfitting. PyTorch's `torch.optim` module provides various optimizers: SGD is suitable for simple models, SGD+Momentum accelerates models with fluctuations (e.g., RNNs), Adam adapts to most tasks (e.g., CNNs, Transformers), and AdamW is ideal for small datasets or complex models. In practical tasks, comparing linear regression (e.g., `y=2x+3`), Adam converges faster with smoother loss and parameters closer to the true values, while SGD is prone to oscillations. Beginners are advised to prioritize Adam, and if parameter control is required... (Note: The original text cuts off here, so the translation concludes at the available content.)
Read MoreLearning PyTorch from Scratch: A Basic Explanation of Activation Functions and Convolutional Layers
### Overview of Activation Functions and Convolutional Layers **Activation Functions**: Neural networks require non-linear transformations to fit complex relationships, and activation functions introduce this non-linearity. Common functions include: - **ReLU**: `y = max(0, x)`, simple computation, solves the vanishing gradient problem, and is the most widely used (PyTorch: `nn.ReLU()`). - **Sigmoid**: `y = 1/(1+exp(-x))`, outputs in (0,1) for binary classification but suffers from vanishing gradients (PyTorch: `nn.Sigmoid()`). - **Tanh**: `y=(exp(x)-exp(-x))/(exp(x)+exp(-x))`, outputs in (-1,1) with a mean of 0, easier to train but still prone to vanishing gradients (PyTorch: `nn.Tanh()`). **Convolutional Layers**: A core component of CNNs, convolutional layers extract local features via convolution kernels. Key concepts include: input (e.g., RGB images with shape `(batch, in_channels, H, W)`), convolution kernel (small matrix), stride (number of pixels the kernel slides), and padding (edge zero-padding to control output size). Implemented in PyTorch via `nn.Conv2d`, critical parameters include `in_channels` (input
Read MoreBeginner's Guide to PyTorch: A Practical Tutorial on Data Loading and Preprocessing
Data loading and preprocessing are crucial foundations for training deep learning models, and PyTorch efficiently implements this through tools like `Dataset`, `DataLoader`, and `transforms`. As a data container, `Dataset` defines how samples are retrieved—for example, built-in datasets such as MNIST in `torchvision.datasets` can be used directly, while custom datasets require implementing `__getitem__` and `__len__`. `DataLoader` handles batch loading, with core parameters including `batch_size`, `shuffle` (set to `True` during training), and `num_workers` (for multi-threaded acceleration). Data preprocessing is achieved via `transforms`, such as `ToTensor` for converting to tensors, `Normalize` for normalization, and data augmentation techniques like `RandomCrop` (used only for the training set). `Compose` allows combining multiple transformations. For practical implementation using MNIST as an example, the full workflow involves defining preprocessing steps, loading the dataset, and creating a `DataLoader`. Key considerations include normalization parameters, applying data augmentation only to the training set, and setting `num_workers=0` under Windows to avoid multi-thread errors. Mastering these skills enables efficient data handling and lays the groundwork for model training.
Read MoreMastering PyTorch Basics: A Detailed Explanation of Tensor Operations and Automatic Differentiation
This article introduces the basics of Tensors in PyTorch. Tensors are the fundamental units for storing and manipulating data, similar to NumPy arrays but with GPU acceleration support, making them a core structure of neural networks. Creation methods include converting from lists/NumPy arrays (`torch.tensor()`/`as_tensor()`) and using constructors like `zeros()`/`ones()`/`rand()`. Key attributes include shape (`.shape`/`.size()`), data type (`.dtype`), and device (`.device`), which can be converted via `.to()`. Major operations cover arithmetic (addition, subtraction, multiplication, division, matrix multiplication), indexing/slicing, reshaping (`reshape()`/`squeeze()`/`unsqueeze()`), and concatenation/splitting (`cat()`/`stack()`/`split()`). Autograd is central: `requires_grad=True` enables gradient tracking, `backward()` computes gradients, and `grad` retrieves them. Important considerations include handling gradients of non-leaf nodes, gradient accumulation, and `detach()` for tensor separation. Mastering tensor operations and autograd is foundational for neural network learning.
Read MoreBeginner's Guide to PyTorch: Build Your First Neural Network Model Step by Step
This article is an introductory PyTorch tutorial that explains core operations by building a fully connected neural network (MLP) model based on the MNIST dataset. First, install PyTorch (CPU/GPU version), load the MNIST dataset using torchvision, convert it to tensors with ToTensor, normalize with Normalize, and then use DataLoader for batch processing (batch_size=64). The model is defined as an MLP with an input layer of 784 (flattened 28×28 images), a hidden layer of 128 (ReLU activation), and an output layer of 10 (Softmax), implemented by inheriting nn.Module for forward propagation. CrossEntropyLoss is chosen as the loss function, and SGD with lr=0.01 is used as the optimizer. The model is trained for 5 epochs, with forward propagation, loss calculation, backpropagation, and parameter updates executed cyclically, printing the loss every 100 batches. During testing, the model is set to eval mode, gradient computation is disabled, and the accuracy on the test set is calculated. The tutorial also suggests extension directions, such as adjusting the network structure, replacing optimizers, or changing datasets.
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,
Read More