Category: Facts

While Keras started as a standalone library with support for multiple backends (TensorFlow, Theano, CNTK), it is now tightly integrated with TensorFlow. This integration enables the full suite of TensorFlow’s power (like eager execution, distributed training, and TensorFlow Extended), while keeping Keras’s user-friendly API. When using Keras with TensorFlow, developers can access TensorFlow-specific features and optimizations.

Keras is a user-friendly, high-level API built to simplify the process of defining, training, and evaluating deep learning models. It abstracts many low-level operations, allowing developers to focus on high-level model design. With Keras, models can be written in a more readable and modular way, often in just a few lines of code.

• Sequential API: The most common way of defining a model. It allows for a linear stack of layers to be built sequentially. It’s ideal for simple models.pythonCopy codefrom keras.models import Sequential from keras.layers import Dense model = Sequential() model.add(Dense(units=64, activation='relu', input_dim=100)) model.add(Dense(units=10, activation='softmax'))
• Functional API: This allows for more complex models with shared layers or models with multiple inputs and outputs.pythonCopy codefrom keras.layers import Input, Dense from keras.models import Model inputs = Input(shape=(100,)) x = Dense(64, activation='relu')(inputs) outputs = Dense(10, activation='softmax')(x) model = Model(inputs=inputs, outputs=outputs)