Deep learning has grown rapidly over the past decade, evolving from an area of academic research to a central force driving innovation across industries—from healthcare and autonomous driving to natural language processing and advanced recommendation systems. At the heart of this expansion lies a diverse ecosystem of frameworks, architectures, and modeling approaches. Among these, one particular architecture stands out for its simplicity, elegance, and accessibility: the Sequential Model.
Whether you’re a beginner exploring neural networks for the first time or an experienced engineer building quick prototypes, chances are you’ve worked with—or at least encountered—the Sequential Model. In frameworks like Keras, TensorFlow, PyTorch (via nn.Sequential), and several others, this model is positioned as the first recommended architecture for new learners. And there’s good reason for that.
This comprehensive post explores the popularity of the Sequential Model, diving into its historical roots, design philosophy, usability advantages, educational role, and continued relevance in modern deep learning practice. By the end, you’ll understand why the phrase “just keep adding layers in order” captures only a fraction of what makes this model so widely loved.
1. What Is the Sequential Model?
The Sequential Model is a linear stack of neural network layers. The defining characteristic is its strictly sequential flow of data:
Input → Layer 1 → Layer 2 → Layer 3 → … → Output
There are no branching paths, multiple inputs, or multiple outputs. Everything happens step-by-step.
In code (Keras example):
from keras.models import Sequential
from keras.layers import Dense
model = Sequential([
Dense(64, activation='relu'),
Dense(10, activation='softmax')
])
This straightforward construction embodies the core idea of the Sequential Model: add layers in the order you want them to execute.
2. Historical Context Why Simplicity Mattered
To appreciate the Sequential Model’s popularity, we must travel back to the early days of deep learning frameworks.
2.1. A Time Before High-Level APIs
Before Keras, TensorFlow 2.0, and PyTorch’s simplicity, neural network research required:
- Writing your own backpropagation code
- Manually managing computational graphs
- Handling low-level tensor operations
- Complex debugging without clear abstraction layers
These tasks were time-consuming and often intimidating for newcomers.
2.2. The Arrival of High-Level Frameworks
Keras, introduced in 2015, revolutionized the deep learning landscape by providing:
- Human-readable syntax
- Fast experimentation
- Modular, minimalist design
The Sequential Model was front and center in this revolution. By offering a simple API for beginners, it democratized deep learning. This model wasn’t just a tool—it became the entry point into neural networks for millions of developers.
3. The Philosophy Behind the Sequential Model
The Sequential Model is built on three core principles:
3.1. Clarity
There’s no ambiguity in how the model flows. Every layer feeds directly into the next.
3.2. Minimal Cognitive Load
Beginners don’t need to think about:
- Graphs
- Branches
- Skip connections
- Multiple inputs
- Concatenations
They simply stack layers.
3.3. Accessibility
The Sequential Model lowers the barrier to entry so dramatically that even someone with little coding or math experience can build and train a neural network in minutes.
4. Why the Sequential Model Is So Popular: The Deep Dive
Let’s explore the key reasons the Sequential Model remains a favorite across the deep learning ecosystem.
4.1. It’s Super Easy to Build
This is the most important and universally acknowledged reason.
4.1.1. Minimal Lines of Code
A complete model can be defined in as little as 3–4 lines, without any complex boilerplate.
4.1.2. Intuitive Like Building Blocks
Adding layers feels like stacking Lego pieces. You don’t need to define their connections manually.
4.1.3. Easy to Visualize
Because everything flows in a straight line, you can picture the architecture in your mind with no confusion.
4.2. Friendly for Absolute Beginners
For someone new to deep learning, the Sequential Model is a safe playground.
4.2.1. No Need to Understand Graphs
Complex computational graphs often confuse beginners. Sequential avoids that entirely.
4.2.2. It Teaches the Core Concepts First
Learners get to practice fundamental ideas:
- Layers
- Activations
- Loss functions
- Optimizers
- Forward and backward propagation
All without getting overwhelmed.
4.2.3. Perfect for First-Time Experiments
Want to try MNIST classification?
Sentiment analysis?
Simple regression?
Sequential gets you started faster than any other modeling technique.
4.3. Perfect for Prototyping
Even experts use it for quick prototypes.
4.3.1. Testing Hypotheses Quickly
You can sketch model ideas rapidly, adjust layer sizes, change activations, and train again.
4.3.2. Fast Debugging
If something goes wrong, the linear layout makes debugging trivial.
4.3.3. Great for Baseline Models
Sequential is often used to establish a baseline before attempting:
- Functional APIs
- Graph-based models
- Transformer architectures
- Multi-branch networks
Its simplicity helps evaluate whether more complexity is even necessary.
4.4. Works Well for Many Real-World Use Cases
Even though we often associate Sequential with beginners, it remains effective for many practical applications, especially when the task doesn’t require complex branching.
Suitable Tasks Include:
- Feedforward neural networks
- Multilayer perceptrons
- Text classification
- Simple convolutional networks
- Basic RNNs
- Time series forecasting
- Autoencoders (basic)
- Regression problems
Many real-world tasks can be solved using these architectures.
4.5. Highly Readable and Maintainable
In large codebases, readability is essential.
4.5.1. Clear and Predictable Execution
Anyone familiar with deep learning can understand a Sequential Model at a glance.
4.5.2. Easy to Share
The simplicity makes it safe for:
- Student projects
- Tutorials
- Research prototypes
- Collaborative engineering environments
4.5.3. Low Risk of Architectural Mistakes
There’s practically no chance of accidentally feeding the wrong tensor into the wrong part of the model.
4.6. A Perfect Teaching Tool
This is perhaps one of the biggest reasons for its popularity.
4.6.1. Used in Tutorials Worldwide
Most deep learning courses begin with:
- “Let’s build a model using Sequential.”
- “Start with Sequential before understanding more complex APIs.”
4.6.2. Helps Build Intuition
It teaches essential neural network concepts without unnecessary abstraction.
4.6.3. Encourages Experimentation
Because it’s so easy, students feel comfortable trying ideas:
- Adding more layers
- Changing activation functions
- Experimenting with optimizers
- Adjusting learning rates
This nurtures creativity.
4.7. The Default Option in Keras/TensorFlow
Sequential is popular partly because it’s the first model most learners encounter.
4.7.1. Keras Introduces It Before Anything Else
Its simplicity aligns perfectly with Keras’s mission:
Make deep learning accessible to humans.
4.7.2. Documentation Emphasizes It
Most introductory tutorials use Sequential.
4.7.3. Rapid Ecosystem Adoption
Because Keras became so widely used, the Sequential Model’s popularity skyrocketed as well.
4.8. Portability Across Frameworks
The concept of a sequential architecture exists in:
- TensorFlow
- Keras
- PyTorch (
nn.Sequential) - MXNet
- PaddlePaddle
- Chainer
- CNTK
- Deep Java Library
- ONNX
This universal design philosophy reinforces its dominance.
4.9. Ideal for Simple Model Deployment
Sequential Models are easier to:
- Convert to TensorFlow Lite
- Export via ONNX
- Serve using TensorFlow Serving
- Run on mobile or embedded devices
Their straightforward structure reduces conversion errors.
4.10. Fewer Errors and Shape Mismatches
More advanced APIs can cause:
- Tensor mismatches
- Shape errors
- Unexpected branches
- Incorrect merges
The Sequential Model avoids all of this thanks to its linear structure.
5. The Sequential Model vs. Functional API
To appreciate the Sequential Model, it helps to contrast it with the Functional API in frameworks like Keras.
| Feature | Sequential Model | Functional API |
|---|---|---|
| Simplicity | Very high | Moderate |
| Supports Multiple Inputs? | No | Yes |
| Supports Complex Graphs? | No | Yes |
| Ideal For? | Beginners, simple tasks, prototypes | Advanced architectures |
| Cognitive Load | Low | Medium–High |
The existence of the Functional API actually increases appreciation for the Sequential Model, because it highlights how pleasant simplicity can be when starting out.
6. Limitations of the Sequential Model
To be fair, the Sequential Model isn’t perfect. It has limitations that encourage users to eventually explore more advanced architectures.
6.1. No Support for Complex Architectures
You cannot use Sequential for:
- Skip connections
- Residual networks
- U-Nets
- Attention mechanisms
- Transformers
- Inception modules
- Multi-output networks
6.2. Limited Flexibility
Anything that requires non-linear data flow (branching, merging) is impossible.
6.3. Not Ideal for Edge-Case Experimentation
Researchers often need custom layers or non-standard architectures.
Even with these limitations, the Sequential Model remains a beloved starting point.
7. Why Simplicity Is a Superpower
Simplicity does not mean lack of capability. In fact, simplicity can be an advantage.
Here’s why:
7.1. Encourages Adoption
New learners feel empowered, not overwhelmed.
7.2. Boosts Productivity
Experienced users enjoy extremely fast prototyping.
7.3. Reduces Errors
Fewer components → fewer mistakes.
7.4. Improves Educational Outcomes
Teaching is easier and more effective.
7.5. Provides Conceptual Clarity
Learners understand neural networks before exploring more complex graphs.
8. Examples of Real Models Built With Sequential
8.1. Basic CNN for Image Classification
Many early convolutional networks can be built in sequential form:
Conv → Pool → Conv → Pool → Dense → Output
8.2. MLP for Tabular Data
Most regression or classification tasks on tabular data use simple feedforward networks.
8.3. RNN for Time Series
Simple LSTM or GRU stacks can be built sequentially.
8.4. Autoencoders
A straightforward encoder → decoder pipeline fits Sequential perfectly.
9. The Psychological Factor: Smooth Learning Curve
While technical reasons matter, human psychology also contributes to the popularity of the Sequential Model.
9.1. Instant Gratification
You can build something real within minutes.
9.2. Confidence Building
Beginners feel empowered when things “just work.”
9.3. Lower Frustration
The absence of complexity reduces early frustration.
9.4. Encourages Exploration
Because it’s easy, people try more ideas, learn faster, and enjoy the process.
10. The Sequential Model’s Lasting Legacy
As deep learning frameworks evolve, one thing remains constant: the Sequential Model continues to stand strong as the foundation of deep learning education and rapid prototyping.
10.1. It Has Introduced Millions to Deep Learning
Generations of engineers first learned neural networks through Sequential.
10.2. It Set the Standard for Clean Deep Learning APIs
Other frameworks imitated its simplicity.
10.3. It Continues to Be the First Step for Beginners
Books, tutorials, bootcamps, and courses universally start with Sequential.
Leave a Reply