- Keras and TensorFlow are the state of the art in deep learning tools and with the keras package you can now access both with a fluent R interface.
- To prepare this data for training we one-hot encode the vectors into binary class matrices using the Keras function:
The core data structure of Keras is a model, a way to organize layers.
- We begin by creating a sequential model and then adding layers using the pipe ( ) operator:
The argument to the first layer specifies the shape of the input data (a length 784 numeric vector representing a grayscale image).
- Use the function to print the details of the model:
Next, compile the model with appropriate loss function, optimizer, and metrics:
Use the function to train the model for 30 epochs using batches of 128 images:
The object returned by includes loss and accuracy metrics which we can plot:
Evaluate the model’s performance on the test data:
Keras provides a vocabulary for building deep learning models that is simple, elegant, and intuitive.
- After you’ve become familiar with the basics, these articles are a good next step:
Keras provides a productive, highly flexible framework for developing deep learning models.
We are excited to announce that the keras package is now available on CRAN. The package provides an R interface to Keras, a high-level neural networks API developed with a focus on enabling fast experimentation. Keras has the following key features:
Continue reading “Keras for R”
- In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.
- The core data structure of Keras is a model, a way to organize layers.
- A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).
- You can now iterate on your training data in batches:
Alternatively, you can feed batches to your model manually:
Evaluate your performance in one line:
Or generate predictions on new data:
Building a question answering system, an image classification model, a Neural Turing Machine, or any other model is just as fast.
- For a more in-depth tutorial about Keras, you can check out:
In the examples folder of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Continue reading “Keras: Deep Learning library for Theano and TensorFlow”
- The core data structure of Keras is a model , a way to organize layers.
- By default, Keras will use Theano as its tensor manipulation library.
- The main type of model is the Sequential model, a linear stack of layers.
- To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
- Getting started: 30 seconds to Keras
Read the full article, click here.
@gcosma1: “Keras:Deep Learning library for Theano & TensorFlow Tutorial #DataScience #MachineLearning”
Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Keras: Deep Learning library for Theano and TensorFlow