The document discusses Keras, a high-level neural network API written in Python that can integrate with TensorFlow, Theano, and CNTK. Keras allows for fast prototyping of neural networks with convolutional and recurrent layers and supports common activation functions and loss functions. It can be used to easily turn models into products that run on devices, browsers, and platforms like iOS, Android, Google Cloud, and Raspberry Pi. Keras uses a simple pipeline of defining a network, compiling it, fitting it to data, evaluating it, and making predictions.

2. The API
for Human Beings
Haritha Thilakarathne

6. François Chollet

9. What is
• High Level neural network API
• Written in Python
• Integration with TensorFlow, Theano & CNTK.
• (MXNet backend for Keras on the way!)

10. Keras API spec
TensorFlow
TensorFlow
workflow
Theano CNTK

11. Why
• Fast prototyping
• Supports CNN, RNN & combination of both
• Modularity
• Easy extensibility
• Simple to get started, simple to keep going
• Deep enough to build serious models.
• Well-written document.
• Runs seamlessly on CPU and GPU.

12. Easy to turn Models into Products
• On iOS, via Apple’s CoreML
• On Android, via the TensorFlow Android runtime.
• In the browser, via GPU-accelerated JavaScript runtimes such
as Keras.js and WebDNN.
• On Google Cloud, via TensorFlow-Serving.
• In a Python webapp backend (such as a Flask app).
• On the JVM, via DL4J model import provided by SkyMind.
• On Raspberry Pi.

13. General Design Idea of
1. Prepare your inputs and output tensors
2. Create first layer to handle input tensor
3. Create output layer to handle targets
4. Build virtually any model you like in between

14. Keras Pipeline
Define Network
Compile Network
Fit Network
Evaluate Network
Make Predictions

15. Layers & Layers in
• Regular dense, MLP type
• Recurrent layers, LSTM, GRU, etc.

16. • Convolution 1D
• Convolution 2D

17. Activations in
• More or less all your favorite activations are available:
• Sigmoid, tanh, ReLu, softplus, hard sigmoid, linear
• Advanced activations implemented as a layer.
• Advanced activations: LeakyReLu, PReLu, ELU, Parametric
Softplus, Thresholded linear and Thresholded Relu

18. Objectives & Optimizers in
• Objective Functions:
• Error loss: rmse, mse, mae, mape, msle
• Hinge loss: squared hinge, hinge
• Class loss: binary crossentropy, categorical crossentropy
• Optimization:
• Provides SGD, Adagrad, Adadelta, Rmsprop and Adam
• All optimizers can be customized via parameters

22. http://haritha.me
@naadiya007