Keras vs TensorFlow: What's the Difference

Quick answer: Understand the relationship between Keras and TensorFlow, when to use each, and how they compare to PyTorch for building deep learning models.

The Short Answer

Keras is not a competitor to TensorFlow. Keras is a high-level API that is now built directly into TensorFlow, providing a simpler, more readable way to build neural networks on top of TensorFlow's underlying computation engine.

A Bit of History

Keras began as an independent, framework-agnostic library that could run on top of several backends, including TensorFlow and Theano. Since TensorFlow 2.0, Keras has been tightly integrated as TensorFlow's official high-level API, which is why most current usage is simply "TensorFlow with Keras" rather than a separate choice between the two.

Building a Model with Keras (Inside TensorFlow)

from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=(10,)),
    layers.Dense(32, activation='relu'),
    layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10)

This is genuinely TensorFlow code, using Keras's simpler syntax for defining the layers rather than TensorFlow's lower-level operations directly.

What TensorFlow Provides Beyond Keras

TensorFlow offers lower-level control for custom operations, strong production deployment tooling through TensorFlow Serving and TensorFlow Lite, and distributed training support, which Keras's simpler interface abstracts away for most standard use cases.

Keras/TensorFlow vs PyTorch

The practical gap between the two ecosystems has narrowed considerably in recent years, and either is a reasonable choice for most projects today.

When You Would Skip Keras and Use TensorFlow Directly

For highly custom training loops, novel layer types, or specialised research architectures, working with TensorFlow's lower-level API directly gives more control than Keras's higher-level abstractions provide.

Common Interview Questions

Is Keras a separate library from TensorFlow?

Not anymore in practice. Since TensorFlow 2.0, Keras is TensorFlow's official built-in high-level API, so writing Keras code today is writing TensorFlow code.

Why would someone use TensorFlow's lower-level API instead of Keras?

For highly custom training loops or novel architectures that Keras's higher-level, more opinionated interface does not easily accommodate.