Introduction

In Keras, a Sequential model means a linear stack of layers where each layer has exactly one input and one output, and the data flows through them in order. It is the simplest model-building API in Keras and is ideal when your network really is just one layer after another with no branching or shared paths.

What "Sequential" Actually Means

The word sequential is literal. Keras takes the output of layer one and feeds it into layer two, then layer three, and so on. There is no graph branching, no skip connection, and no multiple-input or multiple-output wiring.

That makes Sequential models easy to read and easy to build.

A Minimal Example

1from keras import Sequential
2from keras.layers import Dense
3
4model = Sequential([
5    Dense(16, activation="relu", input_shape=(4,)),
6    Dense(8, activation="relu"),
7    Dense(1, activation="sigmoid"),
8])
9
10model.summary()

This defines a simple feedforward network with one hidden stack and a single output.

Why Beginners Like It

The Sequential API is popular because it removes most of the boilerplate involved in model construction. You can focus on the order of layers without thinking about tensor wiring.

That makes it a good fit for:

• basic multilayer perceptrons
• straightforward convolutional stacks
• simple recurrent stacks

If the model really is a straight chain, Sequential is often the cleanest API.

Training a Sequential Model

Once built, you train it the same way as other Keras models.

1import numpy as np
2from keras import Sequential
3from keras.layers import Dense
4
5x = np.array([
6    [0.0, 0.0],
7    [0.0, 1.0],
8    [1.0, 0.0],
9    [1.0, 1.0],
10], dtype="float32")
11
12y = np.array([0, 1, 1, 0], dtype="float32")
13
14model = Sequential([
15    Dense(8, activation="relu", input_shape=(2,)),
16    Dense(1, activation="sigmoid"),
17])
18
19model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
20model.fit(x, y, epochs=10, verbose=0)

The compile and fit workflow looks familiar because Sequential models are still just Keras models.

When Sequential Is Not Enough

Sequential breaks down when the network is not a straight line. Examples include:

• multiple inputs
• multiple outputs
• residual or skip connections
• shared layers
• architectures where one layer feeds two later paths

In those cases, use the Keras Functional API or model subclassing instead.

Sequential vs Functional API

The Sequential API is about convenience. The Functional API is about expressiveness. A Sequential model is actually a special case of a more general computation graph.

So when someone says, "use Sequential," what they really mean is, "your model is simple enough to be described as a plain ordered stack."

A Practical Rule of Thumb

If you can describe the network as "layer A, then B, then C" with no exceptions, Sequential is probably fine. The moment you need to say "and also" or "this output goes over here too," you are probably in Functional API territory.

That rule is more useful than memorizing abstract definitions.

Common Pitfalls

• Using Sequential for a model that really needs branching makes the code awkward or impossible.
• Confusing the simplicity of the API with a limitation on model quality is a mistake. Simple architectures can still be powerful.
• Forgetting the input shape on the first layer can make the model summary less clear until the model is built.
• Treating Sequential as the default for every network can slow you down once architectures get more complex.
• Assuming recurrent or convolutional models cannot be Sequential is incorrect if the data flow is still linear.

Summary

• A Sequential model in Keras is a linear stack of layers.
• It works best when each layer feeds directly into the next one.
• It is simple, readable, and great for straightforward model architectures.
• Use the Functional API when you need branching, shared layers, or multiple inputs and outputs.
• Sequential is about model shape, not about model difficulty or importance.