Keras is an open-source neural network library written in Python. It is designed to enable fast experimentation with deep learning models. Keras is user-friendly and supports both TensorFlow and Theano backends.
Features
- User-friendly API: Keras provides a high-level API that makes it easy to build and experiment with deep learning models.
- Extensive model zoo: Keras comes with a wide range of pre-trained models and layers.
- Integration with TensorFlow and Theano: Keras can be used with both TensorFlow and Theano backends, providing flexibility and compatibility.
- Scalable: Keras can be used to build models of any size, from small projects to large-scale applications.
Getting Started
To get started with Keras, you can install it using pip:
pip install keras
After installation, you can import Keras and start building your models.
Example
Here's a simple example of a neural network using Keras:
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(10, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
Resources
For more information on Keras, you can visit the official Keras website.