Keras is a high-level API for building and training deep learning models, developed by Google. It simplifies the process of creating neural networks by providing a user-friendly interface, while still being compatible with TensorFlow as its backend. Below are key features and usage examples:

🔑 Key Features of Keras API

  • User-Friendly: Simplifies complex operations with intuitive syntax
  • Modular: Build models using layers and functional APIs
  • Flexible: Supports both sequential and graph models
  • Integration: Seamlessly works with TensorFlow for advanced customization

🧪 Example Usage

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(32,)),
    Dense(64, activation='relu'),
    Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=5)

📚 Expand Reading

For deeper insights into TensorFlow and Keras integration, check our TensorFlow Tutorials section.

TensorFlow_Keras_API