Learning 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: 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,
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