What Is Keras? The Complete 2026 Guide to the World's Most Accessible Deep Learning Framework
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In 2015, a Google engineer named François Chollet got tired of how hard it was to build neural networks. The existing tools required dozens of lines of low-level code just to do something basic. So he built something different — a thin, elegant layer on top of all that complexity that let researchers and developers focus on ideas, not infrastructure. He called it Keras. A decade later, it powers everything from cancer-detection models at hospitals to recommendation engines at streaming companies. If you've ever wondered why Keras became the entry point for millions of people into deep learning, this guide has the full story.
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TL;DR
Keras is a high-level deep learning API written in Python, first released in March 2015 by François Chollet.
It simplifies building, training, and deploying neural networks by abstracting away low-level complexity.
Keras 3.0, released in November 2023, became fully backend-agnostic — it now runs on TensorFlow, JAX, and PyTorch.
It is included as the official high-level API of TensorFlow and is widely used in Google's own products.
Keras is known for its readable syntax, beginner-friendliness, and production readiness.
It is used in academia, industry, healthcare, finance, and autonomous systems worldwide.
What is Keras?
Keras is an open-source, high-level deep learning library for Python. It lets developers build, train, and deploy neural networks using simple, readable code. Created by François Chollet and first released in 2015, Keras works as a front-end interface over backends like TensorFlow, JAX, and PyTorch. It is designed for fast prototyping and production use.
Table of Contents
1. Background & History of Keras
Who Created Keras and Why
François Chollet is a French AI researcher employed at Google. In early 2015, he was working on a project called ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System), a research initiative funded by DARPA. He needed a way to quickly iterate on neural network architectures without writing thousands of lines of boilerplate code.
The dominant tools at the time — Theano and early TensorFlow — were powerful but verbose. You had to manually define every tensor operation, manage weight initialization, and wire gradient calculations by hand. This was a barrier for researchers whose primary expertise was not low-level systems programming.
Chollet wrote Keras as his personal solution. He published it publicly on GitHub on March 27, 2015 (Chollet, GitHub, 2015). The name "Keras" (κέρας) comes from ancient Greek literature. It appears in Homer's Odyssey and refers to a horn through which true dreams pass. Chollet chose it deliberately to evoke clarity and insight.
The Rise: 2015–2017
Keras spread rapidly through the research community. Its API was modeled on Torch7 (the predecessor to PyTorch), which Chollet admired for its clarity. Within months of its release, it became one of the most-starred machine learning repositories on GitHub.
In 2016, Keras added support for running on top of TensorFlow, in addition to its original Theano backend. This was a pivotal decision. TensorFlow had launched in November 2015 and was gaining enormous traction thanks to Google's backing. Keras on TensorFlow became a popular pairing almost immediately.
By 2017, Keras had over 100,000 users (Chollet, personal blog, 2017) and was being used at CERN, NASA, and numerous universities. It also became the deep learning framework of choice for Kaggle competitions — a benchmark of practical usability in the data science community.
Official Integration with TensorFlow: 2019
In September 2019, Google released TensorFlow 2.0. This version made Keras the official and default high-level API, integrating it as tf.keras. The message was clear: Keras was not just a compatible library; it was the recommended way to use TensorFlow.
This was a major endorsement. TensorFlow 2.0 adopted Keras's eager execution model and simplified the overall developer experience dramatically. The tf.keras module became the entry point for most TensorFlow users worldwide.
Keras 3.0: A New Chapter (November 2023)
The most significant shift in Keras's history came in November 2023, when Chollet and his team at Google released Keras 3.0. This version broke away from being TensorFlow-only. It introduced a fully backend-agnostic design, allowing the same Keras code to run on:
TensorFlow
JAX (Google's high-performance numerical computation library)
PyTorch
This was unprecedented. No other major deep learning framework had achieved true backend portability at this level of API completeness. Keras 3.0 was not a wrapper over each framework — it used each backend's native operations natively, meaning it could access JAX's JIT compilation or PyTorch's custom autograd without performance penalty. (Keras team, keras.io, 2023-11-28)
2. How Keras Works — Core Architecture
The Abstraction Layer Concept
Keras sits above numerical computation backends. Think of it as the steering wheel of a car — you interact with the wheel, not with the engine components underneath. The engine (TensorFlow, JAX, or PyTorch) does the heavy computation. Keras provides the interface.
This design has a formal name in software engineering: API abstraction layer. It hides implementation complexity without removing power. You can go deeper into the backend any time you want — Keras does not lock you out.
Core Building Blocks
Keras is organized around three fundamental concepts:
1. Layers A layer is a computation unit that transforms input data into output data. Keras provides dozens of pre-built layer types:
Dense — fully connected layer
Conv2D — 2D convolution for image processing
LSTM — Long Short-Term Memory for sequences
Dropout — regularization to prevent overfitting
BatchNormalization — normalizes activations for stable training
Embedding — converts discrete tokens into dense vectors
You can also write custom layers by subclassing keras.Layer.
2. Models A model is a container for layers. Keras offers three ways to define models:
Sequential API — stack layers one after another, best for simple linear architectures
Functional API — connect layers as a graph, enabling multi-input/multi-output models
Model subclassing — write fully custom forward passes in Python, maximum flexibility
3. The Compile → Fit → Evaluate Workflow Keras follows a three-step training pattern that has become the standard for deep learning education:
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=32)
model.evaluate(x_test, y_test)This three-line pattern handles the entire training loop, gradient computation, backpropagation, and metric tracking automatically. Researchers at Stanford's CS231n course adopted this pattern in their course materials, which contributed significantly to Keras's educational adoption.
Callbacks System
One of Keras's most practical features is its callbacks system — pluggable functions that execute at different stages of training. Built-in callbacks include:
ModelCheckpoint — saves the best model during training
EarlyStopping — halts training when a metric stops improving
ReduceLROnPlateau — reduces learning rate when progress stalls
TensorBoard — streams logs to TensorBoard for visualization
You can write custom callbacks for experiment tracking, alerting, or integrating with MLflow or Weights & Biases.
3. Keras 3.0 — The Multi-Backend Revolution
What Changed in Keras 3
Keras 3.0 is architecturally distinct from Keras 2. The key changes, as documented in the official release notes (keras.io, 2023-11-28):
Backend switching: Users set an environment variable or config file to choose their backend:
export KERAS_BACKEND="jax" # or "tensorflow" or "torch"Unified ops layer: Keras 3 introduced keras.ops, a namespace of mathematical operations that dispatches to the active backend's native ops. This means keras.ops.matmul() calls jax.numpy.matmul when JAX is the backend, or torch.matmul when PyTorch is active.
KerasTensor: A backend-agnostic symbolic tensor that works across all three backends.
New training loop: A redesigned fit() loop that can exploit JAX's jit (Just-In-Time compilation) for faster training, sometimes achieving 2–4x speedups on TPUs and GPUs compared to eager-mode TensorFlow training (Keras team benchmarks, keras.io, 2024).
Why Multi-Backend Matters
The deep learning infrastructure world is fractured. Many enterprise teams use PyTorch for research and TensorFlow for deployment. JAX is gaining rapid adoption at Google DeepMind and research labs for its speed. Before Keras 3, switching backend meant rewriting models almost entirely.
With Keras 3, a model written once can be:
Trained on JAX for maximum speed on TPU pods
Ported to PyTorch for integration with PyTorch-native deployment tooling
Exported via TensorFlow SavedModel for production serving via TensorFlow Serving
This portability is not purely theoretical. Google's own internal teams have used Keras 3 to migrate workloads between backends as infrastructure needs changed, according to statements from the Keras team at Google I/O 2024.
Keras NLP and Keras CV
Under the Keras 3 umbrella, two important sub-libraries have matured:
KerasNLP (now KerasHub): A library of pre-built NLP models, tokenizers, and fine-tuning utilities. As of early 2026, it includes implementations of BERT, RoBERTa, GPT-2, Llama, Gemma, Mistral, and others — all runnable across TensorFlow, JAX, and PyTorch backends.
KerasCV: Computer vision components including data augmentation pipelines, object detection models (YOLO, RetinaNet), and image segmentation architectures.
Both libraries follow Keras's design philosophy: readable APIs, documented components, and out-of-the-box training loops.
4. Key Features of Keras
User-Friendly API Design
Keras was designed to minimize the cognitive overhead of building neural networks. Every design decision prioritizes clarity over cleverness. The API follows Python conventions, uses consistent naming, and produces informative error messages — a stark contrast to earlier frameworks that would fail silently or produce cryptic C++ stack traces.
Modularity
Keras components are designed to be independent and interchangeable. You can mix a Keras Functional model with a custom training loop written in raw JAX. You can use Keras layers inside a PyTorch nn.Module. This composability is one reason experienced practitioners adopt Keras even after mastering lower-level frameworks.
Production Readiness
Keras models can be exported for deployment through multiple pathways:
TensorFlow SavedModel format — deployable via TensorFlow Serving, Google Cloud Vertex AI, or AWS SageMaker
ONNX — via conversion tools, enabling deployment on edge devices and non-Python runtimes
TensorFlow Lite / TensorFlow.js — for mobile and browser deployments
Keras export to JAX/PyTorch — native model portability
Built-in Preprocessing
Keras includes keras.layers for data preprocessing that runs inside the model graph, not as a separate pipeline. This means preprocessing happens on the GPU/TPU during training and is bundled inside the saved model for inference. This eliminates a common class of training/serving skew bugs — where data gets preprocessed differently during training versus production.
Preprocessing layers include:
Normalization
TextVectorization
Rescaling
RandomFlip, RandomRotation (for data augmentation)
KerasTuner
KerasTuner is Keras's official hyperparameter optimization library. It supports:
Random search
Bayesian optimization
Hyperband algorithm
It integrates natively with model.fit() and requires minimal code changes to turn a fixed model into an auto-tuned one.
5. Keras vs TensorFlow vs PyTorch — Comparison
This comparison reflects the state of these frameworks as of early 2026.
Feature | Keras 3 | TensorFlow (raw) | PyTorch |
Abstraction level | High | Low to mid | Low to mid |
Learning curve | Low | Steep | Moderate |
Multi-backend | Yes (TF, JAX, PyTorch) | No (TF only) | No (PT only) |
Eager execution | Yes | Yes (TF2+) | Yes |
Production deployment | Strong (via TF, ONNX) | Very strong | Strong (TorchServe) |
Research flexibility | High (Keras 3) | Moderate | Very high |
TPU support | Yes (via JAX or TF) | Yes | Limited |
Community size | Very large | Very large | Largest |
Pre-built models | KerasHub, KerasCV | TF Hub | torchvision, HuggingFace |
Primary users | Beginners to experts | Production engineers | Researchers |
Sources: keras.io documentation (2025); tensorflow.org (2025); pytorch.org (2025)
A Note on HuggingFace
HuggingFace Transformers is the dominant library for NLP in 2026 and supports both PyTorch and TensorFlow backends. KerasHub (formerly KerasNLP) provides an alternative Keras-native route to transformer models, but HuggingFace's ecosystem — with its Model Hub and Datasets library — remains larger by user count and model availability.
6. How to Get Started with Keras (Step-by-Step)
Prerequisites
Python 3.9 or higher (Python 3.12 is the stable recommendation as of early 2026)
pip or conda package manager
Basic Python knowledge
Step 1: Install Keras
pip install kerasKeras 3 is now a standalone package. You install your preferred backend separately:
pip install tensorflow # for TF backend
pip install torch # for PyTorch backend
pip install jax jaxlib # for JAX backendStep 2: Set Your Backend
export KERAS_BACKEND="tensorflow"Or create a ~/.keras/keras.json config file:
{
"backend": "jax",
"floatx": "float32",
"epsilon": 1e-07,
"image_data_format": "channels_last"
}Step 3: Build a Simple Model
Here is a fully working, documented Keras model for image classification on MNIST (a 70,000-image handwritten digit dataset — real, public-domain data from Yann LeCun, 1998):
import keras
from keras import layers
# Load and preprocess real data
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0
# Define model using Sequential API
model = keras.Sequential([
layers.Input(shape=(28, 28)),
layers.Flatten(),
layers.Dense(128, activation="relu"),
layers.Dropout(0.2),
layers.Dense(10, activation="softmax")
])
# Compile
model.compile(
optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"]
)
# Train
model.fit(x_train, y_train, epochs=5, batch_size=64, validation_split=0.1)
# Evaluate
model.evaluate(x_test, y_test)A properly trained model on MNIST typically achieves 98%+ accuracy on the test set. This is a well-established benchmark.
Step 4: Save and Load
model.save("my_model.keras")
loaded_model = keras.saving.load_model("my_model.keras")The .keras format is the recommended format in Keras 3. It stores the full model architecture, weights, and optimizer state in a single file.
Step 5: Deploy
For production serving, export to TensorFlow SavedModel:
model.export("saved_model_dir", format="tf_savedmodel")Then serve with TensorFlow Serving, Google Cloud AI Platform, or any ONNX-compatible runtime after conversion.
7. Real-World Case Studies
Case Study 1: CERN — Particle Physics Classification
The European Organization for Nuclear Research (CERN) in Geneva, Switzerland uses deep learning to classify particle collision events from the Large Hadron Collider (LHC). In a documented project published by CERN researchers, Keras was used to build dense neural networks for distinguishing signal events (new physics) from background noise in high-energy collision data.
The work was published in the Journal of Instrumentation and is publicly cited in CERN's open-data initiatives. Keras was chosen for its readable model definitions that allowed physicists — not software engineers — to modify architectures directly. The team reported significant reduction in time-to-experiment compared to their previous workflow using raw TensorFlow.
Source: CERN Open Data Portal (opendata.cern.ch); multiple Keras-based ML publications indexed on arXiv under hep-ex (high-energy physics, experiment).
Case Study 2: NASA Frontier Development Lab — Satellite Imagery Analysis
NASA's Frontier Development Lab (FDL) is a public-private research accelerator that runs annual AI sprint programs. In the 2019 FDL program (publicly documented on the FDL website, frontierdevelopmentlab.org), multiple teams used Keras to build convolutional neural networks for analyzing satellite imagery. Applications included wildfire spread prediction and heliophysics (solar event detection).
The FDL program specifically chose Keras for rapid prototyping — teams have only 8 weeks to go from concept to working prototype. Keras's Sequential and Functional APIs allowed researchers without deep ML engineering backgrounds to iterate quickly on model architectures.
The wildfire prediction project produced a documented model that estimated fire spread probability from satellite-derived vegetation and topography data. The work was presented at NeurIPS workshops.
Source: NASA Frontier Development Lab, frontierdevelopmentlab.org, program reports 2019–2022 (publicly available).
Case Study 3: Waymo — Autonomous Vehicle Perception Research
Waymo, Alphabet's autonomous driving subsidiary, is publicly documented as a heavy user of TensorFlow and Keras in its ML infrastructure. In technical blog posts published on the Waymo Research site (waymo.com/research) and in papers published at CVPR and ICCV, Waymo researchers describe using Keras-based architectures for sensor fusion (combining LIDAR and camera data) and 3D object detection.
Waymo's open dataset — the Waymo Open Dataset, released in 2019 and regularly updated — includes baseline models written in Keras/TensorFlow for the research community. As of 2024, the dataset had been used in over 300 academic publications (Waymo, waymo.com, 2024).
The company's use of Keras for prototyping and TensorFlow serving for production is cited as a standard pattern in the ML engineering community, often referenced in MLOps literature.
Source: Waymo Research blog (waymo.com/research); Waymo Open Dataset documentation; CVPR/ICCV papers citing Waymo methodology.
8. Industry & Regional Variations
Academia vs Industry
In academic research, PyTorch has been the dominant framework since approximately 2019, particularly for natural language processing and computer vision research. A study of papers at NeurIPS, ICML, and ICLR consistently shows PyTorch adoption exceeding 70% of submitted research code (Hooker, Papers With Code, 2021 — trend has continued).
Keras, however, maintains strong penetration in applied research environments — where speed of prototyping matters more than low-level control — and in production engineering teams that prioritize deployment stability over research flexibility.
Enterprise Adoption
Google's internal teams use Keras as the default interface for TensorFlow-based work. This extends to products including Google Search, Google Translate, and Google Photos, all of which use TensorFlow-based models in production (Google AI Blog, various posts).
Outside Google, companies in healthcare AI (diagnostics, medical imaging) commonly use Keras because its clean API is accessible to domain experts — radiologists and clinicians collaborating with data scientists — who need to modify models without deep ML engineering backgrounds.
Geographic Variation
India has the largest absolute number of Keras learners, driven by the country's enormous population of software developers and the popularity of online courses on Coursera and Udemy that use Keras as the primary teaching framework. Brazil and Southeast Asia show similar patterns.
In Europe, Keras is common in academic institutions and public research labs (CERN, Fraunhofer Institute, Max Planck Society) where reproducibility and readable code are priorities.
In the United States, usage splits across PyTorch (research-heavy companies like Meta, OpenAI) and Keras/TensorFlow (Google, legacy enterprise ML teams).
9. Pros and Cons of Keras
Pros
Beginner-friendly: The Sequential API lets a first-time practitioner build and train a working neural network in under 20 lines of code. No framework comes closer to this accessibility threshold.
Multi-backend (Keras 3): Running the same model on TensorFlow, JAX, or PyTorch without code changes is uniquely powerful for teams that operate across multiple infrastructure environments.
Production-grade: Keras integrates directly with Google Cloud Vertex AI, TensorFlow Serving, TFX (TensorFlow Extended) pipelines, and ONNX conversion. The path from prototype to production is well-paved.
Strong documentation: keras.io is consistently rated as one of the best-documented ML libraries. François Chollet wrote the book Deep Learning with Python (Manning, 2017; 2nd edition 2021), which uses Keras throughout and serves as the de facto Keras textbook.
Preprocessing in-graph: Running data preprocessing inside the model graph prevents training-serving skew — a real and costly production problem.
Active development: Keras 3 is actively maintained by a full-time team at Google. Release cadence is regular, with changelogs published on keras.io.
Cons
Abstraction hides details: For researchers who need fine-grained control over gradient computation, loss landscape analysis, or custom CUDA kernels, Keras's abstractions can feel like obstacles. Pure PyTorch or JAX offer more direct access.
PyTorch ecosystem gap: The HuggingFace ecosystem — arguably the most important ML ecosystem in 2026 for LLM work — is primarily PyTorch-native. While KerasHub provides Keras-native transformer models, the catalog and community are smaller.
Multi-backend complexity: While backend switching is powerful, debugging model behavior differences across backends requires understanding each backend's quirks. A model that trains identically on TensorFlow and JAX in theory may produce numerical differences in practice due to floating-point precision handling differences.
Legacy tf.keras confusion: Pre-Keras-3 code written as tf.keras does not directly import into standalone Keras 3. Teams migrating older TensorFlow 2.x codebases need to update imports and sometimes refactor custom layers.
10. Myths vs Facts
Myth | Fact |
"Keras is just for beginners." | Keras 3 supports full model subclassing, custom training loops, and low-level op access. Google uses it in production systems at massive scale. |
"Keras is slow compared to PyTorch." | Keras 3 with JAX backend and JIT compilation achieves competitive or superior throughput on TPUs. Benchmarks are workload-dependent. |
"Keras is the same as TensorFlow." | Keras is a separate library with its own codebase, installed as keras. tf.keras is the TensorFlow-bundled version, which diverges from standalone Keras 3. |
"Keras is being abandoned." | Keras 3.0 (November 2023) was the largest architectural change in Keras's history. It is under active development with a full-time team at Google. |
"You can't use Keras with PyTorch models." | Since Keras 3, you can use Keras layers inside PyTorch nn.Module and use PyTorch modules inside Keras models. |
"Keras doesn't support transformers." | KerasHub includes pre-built transformer models including Gemma, BERT, and Mistral, with fine-tuning utilities. |
11. Common Pitfalls & How to Avoid Them
Pitfall 1: Mixing tf.keras and standalone Keras 3
Problem: Code written with from tensorflow import keras uses tf.keras, which is TensorFlow's bundled older Keras version. It does not import from the standalone keras package (Keras 3).
Fix: Use import keras and install the standalone keras package. Update all imports. Check the migration guide at keras.io/guides/migrating_to_keras_3.
Pitfall 2: Training/Serving Skew from External Preprocessing
Problem: Normalizing images with NumPy before training but forgetting to normalize at inference time. This is one of the most common production ML bugs.
Fix: Use Keras preprocessing layers (layers.Rescaling, layers.Normalization) inside the model graph, not in an external pipeline. The preprocessing becomes part of the saved model.
Pitfall 3: Not Using validation_data or validation_split
Problem: Training without monitoring validation loss leads to undetected overfitting. A model that reaches 99% training accuracy but 70% validation accuracy is useless.
Fix: Always pass validation_split=0.15 or a dedicated validation_data argument to model.fit(). Monitor both training and validation curves.
Pitfall 4: Ignoring Callback Outputs
Problem: ModelCheckpoint saves the best model, but if you don't reload it after training, you use the last epoch's weights, which may be worse.
Fix: After training with ModelCheckpoint, reload with model = keras.saving.load_model("checkpoint_path.keras") before evaluation and deployment.
Pitfall 5: Wrong Loss Function for the Task
Problem: Using categorical_crossentropy when labels are integers (not one-hot encoded) causes shape errors or silent incorrect training.
Fix: Use sparse_categorical_crossentropy when labels are integers. Use categorical_crossentropy only when labels are one-hot encoded vectors. Check Keras documentation for your specific task type.
Pitfall 6: Not Setting batch_size for Large Datasets
Problem: model.fit(x_train, y_train) with default batch size 32 on a dataset with 1 million samples takes significantly longer than necessary and may underutilize GPU memory.
Fix: Benchmark your GPU memory with nvidia-smi. Set batch size to the largest power of 2 that fits in memory without OOM error.
12. Keras Ecosystem & Tools
Tool | What It Does | Where to Get It |
KerasHub | Pre-built NLP and multimodal models (BERT, Gemma, Llama, Mistral) | |
KerasCV | Computer vision layers, augmentation, object detection | |
KerasTuner | Hyperparameter optimization | |
TensorBoard | Training visualization and debugging | |
Weights & Biases | Experiment tracking, integrates with Keras callbacks | |
MLflow | Experiment tracking and model registry | |
ONNX | Export Keras models to cross-platform format | |
TensorFlow Serving | Production REST/gRPC serving for Keras/TF models | |
Vertex AI | Managed ML platform on Google Cloud, native Keras support |
13. Future Outlook for Keras
Keras in the LLM Era
Large Language Models (LLMs) have reshaped the ML landscape since 2022. Keras's response has been KerasHub — a growing library of pre-built, fine-tunable LLM components. As of early 2026, KerasHub includes Gemma 2 (Google's open-weight model), Mistral, and Llama architecture implementations.
The challenge is ecosystem gravity. HuggingFace Transformers has a near-monopoly on the LLM fine-tuning workflow, and its community of models, datasets, and tutorials is vast. Keras offers an alternative, but adoption requires compelling differentiation — which multi-backend portability and TPU efficiency provide for Google-infrastructure users.
JAX as the Performance Backend
JAX has become the preferred backend for high-performance ML training at Google DeepMind and at TPU-heavy research shops. Keras 3 with JAX backend can compile entire training loops to XLA (Accelerated Linear Algebra), achieving significantly better hardware utilization than eager-mode execution.
As JAX matures and its ecosystem expands, Keras's role as JAX's high-level API is likely to grow. This positions Keras well in the research-to-production pipeline for teams that use Google Cloud TPUs.
Keras and Edge AI
There is growing momentum in deploying neural network models on edge devices — smartphones, embedded systems, and IoT hardware. Keras models exported to TensorFlow Lite (via TFLite converter) or to MediaPipe are used in production edge deployments.
Google's MediaPipe framework, which powers on-device ML in Android applications, is compatible with Keras-trained models. As edge AI grows — driven by privacy regulations that require on-device processing and latency requirements that rule out cloud calls — Keras's TFLite export pathway becomes more strategically important.
Maintained and Funded
Unlike community-only projects that risk abandonment, Keras is maintained by a dedicated team at Google. Its development is tied to TensorFlow's ongoing investment, JAX's growth, and Google's AI product roadmap. This makes it one of the most reliably maintained ML libraries available.
14. FAQ
Q1: What is Keras used for?
Keras is used to build, train, and deploy deep learning models. Applications include image classification, object detection, natural language processing, time series forecasting, recommendation systems, and generative modeling. It is used in healthcare, automotive, finance, research, and consumer technology.
Q2: Is Keras the same as TensorFlow?
No. Keras is a separate high-level API library. TensorFlow is a low-level numerical computation framework. tf.keras is TensorFlow's bundled version of Keras (an older version). Standalone Keras 3 is a separate package that supports TensorFlow, JAX, and PyTorch as backends.
Q3: Is Keras free?
Yes. Keras is fully open source, released under the Apache 2.0 license. It is free to use, modify, and distribute, including for commercial purposes.
Q4: What is the difference between Keras and PyTorch?
PyTorch offers fine-grained control over tensor operations and is preferred in research. Keras provides a higher-level API optimized for readability and rapid development. Since Keras 3, Keras can use PyTorch as its backend, meaning you can combine both.
Q5: Can Keras be used for production deployments?
Yes. Keras models can be exported to TensorFlow SavedModel, ONNX, TensorFlow Lite, and other formats suitable for production. Major companies including Google use Keras-trained models in production systems.
Q6: What programming language is Keras written in?
Keras is written in Python. Its backend operations execute in optimized C++/CUDA code inside TensorFlow, JAX, or PyTorch, but the user-facing API is entirely Python.
Q7: What is KerasHub?
KerasHub (formerly KerasNLP) is the official Keras library for pre-built, fine-tunable NLP and multimodal models. It includes transformer architectures like BERT, Gemma, Mistral, and Llama, with integrated tokenizers and fine-tuning workflows that work across all Keras backends.
Q8: How long does it take to learn Keras?
A developer with Python experience can build a working image classifier in Keras in one day. Mastery of advanced Keras — custom training loops, model subclassing, distributed training — takes several weeks of deliberate practice. François Chollet's book Deep Learning with Python (2nd edition, 2021) is the most recommended resource.
Q9: Does Keras support GPUs?
Yes. Keras automatically uses GPU acceleration when a compatible GPU is available and the backend is properly configured. On NVIDIA GPUs, this requires CUDA and cuDNN. On Google TPUs, the JAX or TensorFlow backend is used.
Q10: What is the difference between the Sequential and Functional API in Keras?
The Sequential API stacks layers linearly — one input, one output, each layer feeds into the next. The Functional API allows branching, merging, and multi-input/multi-output designs. The Functional API is more flexible; the Sequential API is simpler for straightforward models.
Q11: Can Keras handle very large datasets?
Yes. Keras integrates with tf.data (TensorFlow's data pipeline API) for efficient, streaming data loading. You can train on datasets far larger than RAM by feeding data in batches directly from disk or cloud storage.
Q12: Is Keras suitable for large language models (LLMs)?
Keras 3 and KerasHub support LLM fine-tuning via pre-built model implementations. However, training LLMs from scratch requires distributed training infrastructure (multi-GPU/TPU) and frameworks like JAX's pmap/pjit or TensorFlow's MirroredStrategy, all of which Keras supports. For most users, fine-tuning pre-trained models via KerasHub is the practical route.
Q13: What is the Keras .keras file format?
Introduced in Keras 3, the .keras format is the recommended way to save and load Keras models. It stores architecture, weights, optimizer state, and metadata in a single file. It replaced the older HDF5 (.h5) format, which is still supported for backward compatibility.
Q14: Who maintains Keras?
Keras is maintained by a team at Google led by François Chollet, who created it. Development is publicly visible on GitHub (github.com/keras-team/keras). The project accepts community contributions and tracks issues openly.
Q15: Can I use pre-trained models in Keras?
Yes. KerasHub provides pre-trained models for NLP and vision tasks. Keras also integrates with TensorFlow Hub (tfhub.dev), where hundreds of pre-trained models can be loaded with a single line of code.
Q16: Is Keras good for reinforcement learning?
Keras can be used to build the neural network components of RL systems (policy networks, value functions). However, Keras does not include a reinforcement learning framework itself. Libraries like Stable Baselines3 (PyTorch-based) and TF-Agents (TensorFlow-based) are more complete RL solutions that can use Keras models internally.
Q17: Does Keras support distributed training?
Yes. With the TensorFlow backend, Keras supports tf.distribute.MirroredStrategy for multi-GPU training and tf.distribute.TPUStrategy for TPU pods. With the JAX backend, jax.pmap enables data-parallel distributed training.
Q18: What datasets are built into Keras?
keras.datasets includes several standard benchmark datasets: MNIST, CIFAR-10, CIFAR-100, IMDB movie reviews, Reuters news classification, Boston Housing, and Fashion-MNIST. All are real, public datasets used widely in ML benchmarking.
15. Key Takeaways
Keras is an open-source, high-level deep learning API for Python, created by François Chollet and first released March 27, 2015.
Keras 3.0 (November 2023) is backend-agnostic, supporting TensorFlow, JAX, and PyTorch — a unique capability in the framework landscape.
The Sequential → Functional → Subclassing spectrum provides options for every level of architectural complexity.
Keras is simultaneously beginner-friendly and production-ready, used in research labs (CERN, NASA) and in products serving billions of users (Google).
The .keras file format and in-graph preprocessing layers are key features for reliable production deployments.
KerasHub, KerasCV, and KerasTuner extend Keras into specialized domains with maintained, high-quality components.
The primary gap remains the HuggingFace ecosystem, which is larger and more PyTorch-native for LLM workflows.
Keras's JAX backend positions it well for high-performance training on Google Cloud TPUs.
Keras is Apache 2.0 licensed, fully free, and actively maintained by a team at Google.
Learning Keras is one of the fastest paths into applied deep learning for developers with Python experience.
16. Actionable Next Steps
Install Keras 3 — run pip install keras tensorflow in your environment to get started with the TensorFlow backend today.
Run the MNIST example — execute the code in Section 6 of this article. Verify it reaches >98% test accuracy to confirm your environment is correctly configured.
Read keras.io/guides — the official Keras guides cover every major API. Prioritize: Introduction to Keras, The Functional API, Training and Evaluation, and Saving Models.
Work through Deep Learning with Python (2nd edition) — François Chollet's book (Manning, 2021) covers Keras with full practical examples. It is the most comprehensive Keras learning resource available.
Explore KerasHub — load a pre-trained BERT or Gemma model and fine-tune it on a text classification dataset. This will ground your understanding of transfer learning, the dominant ML paradigm in 2026.
Set up experiment tracking — integrate Keras with Weights & Biases or MLflow using a custom callback. This habit separates hobbyists from professionals.
Try the JAX backend — install jax jaxlib and switch your backend to JAX. Run the same training script and compare throughput. This demonstrates Keras 3's portability concretely.
Join the Keras community — the Keras GitHub Discussions (github.com/keras-team/keras/discussions) and the r/MachineLearning subreddit are active communities for questions and peer learning.
17. Glossary
API (Application Programming Interface): A defined set of functions and conventions through which software components communicate. Keras's API defines how you build and train models.
Backend: The underlying computation library that performs tensor operations. In Keras 3, the backend can be TensorFlow, JAX, or PyTorch.
Backpropagation: The algorithm that computes how much each weight in a neural network contributed to the error, enabling weight updates during training.
Batch normalization: A technique that normalizes activations within a mini-batch during training, improving stability and speed.
Callback: A function triggered at specific points during training (e.g., end of epoch) to perform actions like saving checkpoints or reducing learning rate.
CNN (Convolutional Neural Network): A neural network architecture that uses convolution operations, primarily used for image and video processing.
Dropout: A regularization technique that randomly sets a fraction of layer outputs to zero during training to prevent overfitting.
Eager execution: Executing operations immediately as they are called, as opposed to building a computation graph first. Keras uses eager execution by default.
Epoch: One complete pass through the entire training dataset during model training.
Fine-tuning: Taking a pre-trained model and continuing training on a new, task-specific dataset.
GPU (Graphics Processing Unit): Hardware that executes many parallel computations simultaneously, dramatically accelerating matrix operations central to deep learning.
Gradient descent: An optimization algorithm that iteratively adjusts model weights in the direction that reduces loss.
JAX: A Python library from Google for high-performance numerical computing, supporting automatic differentiation and XLA compilation.
JIT (Just-In-Time) compilation: Compiling code at runtime (rather than ahead of time), enabling optimization for specific hardware. JAX uses XLA for JIT compilation.
Layer: A computational unit in a neural network that transforms inputs to outputs. Layers are the building blocks of Keras models.
Loss function: A function that measures how far a model's predictions are from the true labels. Training minimizes this value.
LSTM (Long Short-Term Memory): A recurrent neural network architecture designed to handle long-range dependencies in sequential data.
Optimizer: An algorithm that updates model weights based on computed gradients. Common optimizers: Adam, SGD, RMSprop.
Overfitting: When a model learns the training data too precisely and fails to generalize to new, unseen data.
Tensor: A multidimensional array of numbers. Tensors are the fundamental data structure in deep learning frameworks.
TPU (Tensor Processing Unit): A custom ASIC designed by Google specifically for neural network computations, offering high throughput for matrix operations.
Transfer learning: Using a model pre-trained on a large dataset as the starting point for training on a different, typically smaller, dataset.
XLA (Accelerated Linear Algebra): A domain-specific compiler for linear algebra computations, used by TensorFlow and JAX for hardware optimization.
18. Sources & References
Chollet, F. (2015-03-27). Keras: Deep Learning for Python. GitHub. https://github.com/keras-team/keras
Keras Team. (2023-11-28). Keras 3.0 Release Notes. keras.io. https://keras.io/keras_3/
Chollet, F. (2021). Deep Learning with Python, 2nd Edition. Manning Publications. ISBN: 9781617296864.
TensorFlow Team. (2019-09-30). TensorFlow 2.0 is now available. TensorFlow Blog. https://blog.tensorflow.org/2019/09/tensorflow-20-is-now-available.html
Keras Team. (2024). Keras 3 backend performance benchmarks. keras.io. https://keras.io/getting_started/
NASA Frontier Development Lab. (2019–2022). FDL Program Reports. frontierdevelopmentlab.org. https://frontierdevelopmentlab.org
Waymo. (2024). Waymo Open Dataset. waymo.com. https://waymo.com/open
CERN Open Data Portal. (n.d.). Machine Learning at CERN. opendata.cern.ch. https://opendata.cern.ch
Hooker, S.; Papers With Code. (2021). Framework usage trends in ML research. paperswithcode.com. https://paperswithcode.com/trends
LeCun, Y., Cortes, C., Burges, C.J.C. (1998). The MNIST Database of Handwritten Digits. yann.lecun.com. http://yann.lecun.com/exdb/mnist/
Keras Team. (2025). KerasHub documentation. keras.io. https://keras.io/keras_hub/
TensorFlow Team. (2025). TensorFlow Serving documentation. tensorflow.org. https://www.tensorflow.org/tfx/guide/serving
Google Cloud. (2025). Vertex AI documentation. cloud.google.com. https://cloud.google.com/vertex-ai/docs
JAX Team. (2024). JAX documentation. jax.readthedocs.io. https://jax.readthedocs.io
ONNX. (2025). ONNX: Open Neural Network Exchange. onnx.ai. https://onnx.ai
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