Lasagne vs Keras

Side-by-side — pricing, strengths, tradeoffs, and fit.

Lasagne

Lightweight library for building and training neural networks in Theano.

Keras

High-level neural networks API for TensorFlow and other backends.

At a glance

Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

Lightweight library for building and training neural networks in Theano.

One-liner

High-level neural networks API for TensorFlow and other backends.

Maturity

Declining

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Researchers and developers who need a lightweight, flexible library to build and train neural networks using Theano.
Projects that require high performance and flexibility in deep learning model development without the overhead of larger frameworks.

✓ Best for

Data scientists who need a high-level API to quickly prototype deep learning models without worrying about low-level details.
Teams working on image and text classification tasks that require rapid experimentation with different neural network architectures.

Not ideal for

✕ Not for

Teams looking for a comprehensive end-to-end solution with extensive pre-built models and integrations.
Developers who prefer higher-level abstractions or user interfaces for building neural networks.

✕ Not for

Projects requiring real-time inference where performance is critical, as Keras might introduce additional overhead compared to lower-level frameworks.
Developers who prefer a more low-level control over the training process and need fine-grained tuning of their models.

Strengths

↑ Strengths

Lightweight and easy to use interface for building neural networks.
Built on top of Theano, providing high performance and flexibility.
Supports a wide range of layer types and activation functions.
Integrates seamlessly with other Python libraries for data processing.

↑ Strengths

User-friendly API for rapid prototyping and experimentation.
Modular and extensible architecture supporting custom layers, loss functions, and metrics.
Supports multiple backends including TensorFlow, CNTK, and Theano.
Built-in support for convolutional neural networks (CNN), recurrent neural networks (RNN), and more.

Weaknesses

↓ Weaknesses

Limited active development and support
Dependence on Theano, which is no longer actively maintained
Steep learning curve for new users unfamiliar with Theano
Documentation lacks comprehensive examples and tutorials

↓ Weaknesses

Limited support for advanced neural network architectures
Performance overhead due to abstraction layer
Dependence on backend library stability and support
Limited customization options for advanced users
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