This article reviews eight mainstream neural network architectures—FNN, CNN, RNN, LSTM, GRU, GAN, GNN, and Transformer—to solve a common beginner challenge: how to match a task to the right model. Keywords: deep learning, neural networks, PyTorch.
The Technical Specification Snapshot
| Parameter | Description |
|---|---|
| Topic | Overview of mainstream neural network architectures in deep learning |
| Language | Python |
| Framework/Core Dependencies | PyTorch, torch-geometric |
| Task Types | Classification, regression, generation, sequence modeling, graph learning |
| License | Originally घोषित as CC 4.0 BY-SA |
| Stars | Not provided in the original content |
The Core of Deep Learning Is Not a Single Model but a Method for Choosing Architectures Based on Data Structure
Neural networks are the foundational implementation form of deep learning. The key difference is not simply how many layers a model has, but whether the model applies the right inductive bias to the data structure.
In engineering practice, the first principle of model selection is not to chase the newest architecture. Instead, start by identifying whether your input data is tabular, image-based, sequential, graph-structured, or part of a generative task.
The Quick Task-to-Model Mapping Works Like This
| Model | Core Mechanism | Best-Suited Data |
|---|---|---|
| FNN/MLP | Fully connected mapping | Tabular data, structured features |
| CNN | Convolution and local receptive fields | Images, video frames |
| RNN | Recurrent state propagation | Short sequences, basic time series |
| LSTM/GRU | Gated memory | Long sequences, speech, text |
| Autoencoder | Encoder-decoder compression | Dimensionality reduction, denoising, anomaly detection |
| GAN | Generator-discriminator adversarial game | Image generation, data augmentation |
| Transformer | Self-attention | NLP, long sequences, multimodal tasks |
| GNN | Neighbor message passing | Social graphs, molecular graphs, recommendation graphs |
The Capability Boundaries of Different Neural Networks Define Their Application Scenarios
FNN is the most basic universal approximator. It works well for low- to mid-dimensional structured features, but it does not naturally model spatial or temporal relationships.
CNN extracts spatial features through local connectivity and parameter sharing, making it highly efficient for image tasks. RNN models sequential relationships through time-step recurrence, but it struggles with long-range dependency learning.
import torch
import torch.nn as nn
class FNNModel(nn.Module):
def __init__(self, in_dim=8):
super().__init__()
self.net = nn.Sequential(
nn.Linear(in_dim, 16), # Map tabular features to the hidden layer
nn.ReLU(),
nn.Linear(16, 8), # Further compress the feature representation
nn.ReLU(),
nn.Linear(8, 1), # Output the binary classification probability
nn.Sigmoid()
)
def forward(self, x):
return self.net(x)This code shows a minimal FNN implementation for binary classification on tabular data.
CNN Is Better Suited to Data with Local Spatial Structure
Because convolution kernels share parameters across positions, CNNs use far fewer parameters than fully connected networks of similar scale. They are naturally suited for extracting edges, textures, and shapes from images.
import torch
import torch.nn as nn
import torch.nn.functional as F
class CNNModel(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5) # Extract shallow local features
self.pool = nn.MaxPool2d(2, 2) # Downsample to reduce computation
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10) # Output predictions for 10 classes
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # Flatten convolution features before the fully connected layers
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
return self.fc3(x)This code demonstrates the forward path of a typical convolutional classifier.
Sequence Modeling Naturally Evolved from RNN to LSTM and GRU and Then to Transformer
RNN can process variable-length sequences, but it suffers from vanishing gradients and poor parallel efficiency. As a result, it is better suited to short-dependency tasks. LSTM and GRU mitigate long-term dependency problems through gating mechanisms.
Transformer completely removes recurrence and uses self-attention to model relationships between any positions directly, making it the dominant architecture in NLP and large language models.
import torch
import torch.nn as nn
class LSTMModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True) # Process sequential input
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, _ = self.lstm(x) # Get the output for each time step
out = out[:, -1, :] # Use the last time step as the global representation
return self.fc(out)This code shows that the core of LSTM is hidden-state propagation across time.
import torch
import torch.nn as nn
class TransformerClassifier(nn.Module):
def __init__(self, hidden_dim=128, num_layers=2, num_classes=2):
super().__init__()
encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=8)
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
self.fc = nn.Linear(hidden_dim, num_classes)
def forward(self, src):
x = self.encoder(src) # Encode the full sequence with self-attention
x = x.mean(dim=0) # Pool into a sentence-level representation
return self.fc(x)This code presents a minimal skeleton of a Transformer classifier.
Generative Models and Graph Models Solve Two Different Problems: Creating Data and Understanding Relationships
Autoencoders focus on compression and reconstruction, which makes them useful for unsupervised feature learning. GANs focus on generating realistic samples, but training is unstable and mode collapse is a common issue.
GNNs are designed specifically for graph-structured data. They aggregate neighbor information through message passing and are a key method for knowledge graphs, recommender systems, and molecular modeling.
import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
class GNN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super().__init__()
self.conv1 = GCNConv(in_channels, hidden_channels)
self.conv2 = GCNConv(hidden_channels, out_channels)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index) # Aggregate first-order neighbor information
x = F.relu(x)
x = self.conv2(x, edge_index) # Output node-level predictions
return F.log_softmax(x, dim=1)This code shows how a graph convolutional network propagates node information based on edge_index.
Model Selection Should Be Driven by Data Modality, Sample Size, and Compute Budget Together
For tabular tasks, start with FNN or tree-based models. For images, prioritize CNN. For long text and large-scale sequences, prioritize Transformer. For small-sample time series, LSTM or GRU is often a good first choice. For graph-structured tasks, consider GNN directly.
If your goal is high-fidelity image generation, GANs still have value. If your goal is representation learning, denoising, or anomaly detection, autoencoders are the safer choice. No model is absolutely better than another; only task fit matters.
AI Visual Insight: This image is the brand mark for the platform’s AI reading assistant. It mainly serves as an entry-point indicator and does not display any specific technical architecture details.
The Common Engineering Pattern Is Actually Highly Consistent Across Models
Although these models differ in structure, they all follow the same pattern in PyTorch: inherit from nn.Module, define layers in __init__, and describe the forward path in forward.
The training loop is also largely the same: run the forward pass, evaluate the loss function, clear gradients, backpropagate, and update parameters. The real differences mainly lie in input tensor dimensions, feature organization, and loss design.
FAQ
Q1: Do text tasks always require Transformer?
A: Not necessarily. If the dataset is small, the sequences are short, and compute is limited, LSTM or GRU is often easier to train. If you need long-range dependency modeling and better parallel efficiency, Transformer is the better choice.
Q2: Has Transformer already replaced CNN?
A: No. CNN remains highly efficient for small- to medium-scale vision tasks, edge deployment, and scenarios with strong local priors. Vision Transformer depends more heavily on data scale and training resources.
Q3: What is the biggest engineering bottleneck in GNN?
A: It is usually not the network layer itself, but graph data construction. Node definitions, edge quality, sampling strategy, and large-graph training methods often affect final performance more than the model architecture does.
AI Readability Summary: This article systematically reviews eight core neural network architectures in deep learning, covering structural principles, suitable data types, strengths, weaknesses, and PyTorch implementation patterns. It helps developers quickly select the right model and get started with practical engineering work.