Posit AI Blog: Keras for R

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Posit AI Blog: Keras for R


We are excited to announce that the keras package deal is now out there on CRAN. The package deal gives an R interface to Keras, a high-level neural networks API developed with a concentrate on enabling quick experimentation. Keras has the next key options:

  • Allows the identical code to run on CPU or on GPU, seamlessly.

  • User-friendly API which makes it straightforward to rapidly prototype deep studying fashions.

  • Built-in help for convolutional networks (for laptop imaginative and prescient), recurrent networks (for sequence processing), and any mixture of each.

  • Supports arbitrary community architectures: multi-input or multi-output fashions, layer sharing, mannequin sharing, and so forth. This signifies that Keras is acceptable for constructing basically any deep studying mannequin, from a reminiscence community to a neural Turing machine.

  • Is able to operating on prime of a number of back-ends together with TensorFlow, CNTK, or Theano.

If you’re already accustomed to Keras and need to soar proper in, try https://tensorflow.rstudio.com/keras which has all the things you could get began together with over 20 full examples to be taught from.

To be taught a bit extra about Keras and why we’re so excited to announce the Keras interface for R, learn on!

Keras and Deep Learning

Interest in deep studying has been accelerating quickly over the previous few years, and several other deep studying frameworks have emerged over the identical timeframe. Of all of the out there frameworks, Keras has stood out for its productiveness, flexibility and user-friendly API. At the identical time, TensorFlow has emerged as a next-generation machine studying platform that’s each extraordinarily versatile and well-suited to manufacturing deployment.

Not surprisingly, Keras and TensorFlow have of late been pulling away from different deep studying frameworks:

The excellent news about Keras and TensorFlow is that you just don’t want to decide on between them! The default backend for Keras is TensorFlow and Keras could be integrated seamlessly with TensorFlow workflows. There can be a pure-TensorFlow implementation of Keras with deeper integration on the roadmap for later this 12 months.

Keras and TensorFlow are the cutting-edge in deep studying instruments and with the keras package deal now you can entry each with a fluent R interface.

Getting Started

Installation

To start, set up the keras R package deal from CRAN as follows:

The Keras R interface makes use of the TensorFlow backend engine by default. To set up each the core Keras library in addition to the TensorFlow backend use the install_keras() perform:

This will give you default CPU-based installations of Keras and TensorFlow. If you need a extra personalized set up, e.g. if you wish to reap the benefits of NVIDIA GPUs, see the documentation for install_keras().

MNIST Example

We can be taught the fundamentals of Keras by strolling via a easy instance: recognizing handwritten digits from the MNIST dataset. MNIST consists of 28 x 28 grayscale pictures of handwritten digits like these:

The dataset additionally consists of labels for every picture, telling us which digit it’s. For instance, the labels for the above pictures are 5, 0, 4, and 1.

Preparing the Data

The MNIST dataset is included with Keras and could be accessed utilizing the dataset_mnist() perform. Here we load the dataset then create variables for our check and coaching information:

library(keras)
mnist <- dataset_mnist()
x_train <- mnist$prepare$x
y_train <- mnist$prepare$y
x_test <- mnist$check$x
y_test <- mnist$check$y

The x information is a 3D array (pictures,width,top) of grayscale values. To put together the information for coaching we convert the 3D arrays into matrices by reshaping width and top right into a single dimension (28×28 pictures are flattened into size 784 vectors). Then, we convert the grayscale values from integers ranging between 0 to 255 into floating level values ranging between 0 and 1:

# reshape
dim(x_train) <- c(nrow(x_train), 784)
dim(x_test) <- c(nrow(x_test), 784)
# rescale
x_train <- x_train / 255
x_test <- x_test / 255

The y information is an integer vector with values starting from 0 to 9. To put together this information for coaching we one-hot encode the vectors into binary class matrices utilizing the Keras to_categorical() perform:

y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)

Defining the Model

The core information construction of Keras is a mannequin, a technique to set up layers. The easiest sort of mannequin is the sequential mannequin, a linear stack of layers.

We start by making a sequential mannequin after which including layers utilizing the pipe (%>%) operator:

mannequin <- keras_model_sequential() 
mannequin %>% 
  layer_dense(items = 256, activation = "relu", input_shape = c(784)) %>% 
  layer_dropout(fee = 0.4) %>% 
  layer_dense(items = 128, activation = "relu") %>%
  layer_dropout(fee = 0.3) %>%
  layer_dense(items = 10, activation = "softmax")

The input_shape argument to the primary layer specifies the form of the enter information (a size 784 numeric vector representing a grayscale picture). The remaining layer outputs a size 10 numeric vector (chances for every digit) utilizing a softmax activation perform.

Use the abstract() perform to print the main points of the mannequin:

Model
________________________________________________________________________________
Layer (sort)                        Output Shape                    Param #     
================================================================================
dense_1 (Dense)                     (None, 256)                     200960      
________________________________________________________________________________
dropout_1 (Dropout)                 (None, 256)                     0           
________________________________________________________________________________
dense_2 (Dense)                     (None, 128)                     32896       
________________________________________________________________________________
dropout_2 (Dropout)                 (None, 128)                     0           
________________________________________________________________________________
dense_3 (Dense)                     (None, 10)                      1290        
================================================================================
Total params: 235,146
Trainable params: 235,146
Non-trainable params: 0
________________________________________________________________________________

Next, compile the mannequin with acceptable loss perform, optimizer, and metrics:

mannequin %>% compile(
  loss = "categorical_crossentropy",
  optimizer = optimizer_rmsprop(),
  metrics = c("accuracy")
)

Training and Evaluation

Use the match() perform to coach the mannequin for 30 epochs utilizing batches of 128 pictures:

historical past <- mannequin %>% match(
  x_train, y_train, 
  epochs = 30, batch_size = 128, 
  validation_split = 0.2
)

The historical past object returned by match() consists of loss and accuracy metrics which we will plot:

Evaluate the mannequin’s efficiency on the check information:

mannequin %>% consider(x_test, y_test,verbose = 0)
$loss
[1] 0.1149

$acc
[1] 0.9807

Generate predictions on new information:

mannequin %>% predict_classes(x_test)
  [1] 7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4 9 6 6 5 4 0 7 4 0 1 3 1 3 4 7 2 7 1 2
 [40] 1 1 7 4 2 3 5 1 2 4 4 6 3 5 5 6 0 4 1 9 5 7 8 9 3 7 4 6 4 3 0 7 0 2 9 1 7 3 2
 [79] 9 7 7 6 2 7 8 4 7 3 6 1 3 6 9 3 1 4 1 7 6 9
 [ reached getOption("max.print") -- omitted 9900 entries ]

Keras gives a vocabulary for constructing deep studying fashions that’s easy, elegant, and intuitive. Building a query answering system, a picture classification mannequin, a neural Turing machine, or another mannequin is simply as easy.

The Guide to the Sequential Model article describes the fundamentals of Keras sequential fashions in additional depth.

Examples

Over 20 full examples can be found (particular because of [@dfalbel](https://github.com/dfalbel) for his work on these!). The examples cowl picture classification, textual content era with stacked LSTMs, question-answering with reminiscence networks, switch studying, variational encoding, and extra.

addition_rnn Implementation of sequence to sequence studying for performing addition of two numbers (as strings).
babi_memnn Trains a reminiscence community on the bAbI dataset for studying comprehension.
babi_rnn Trains a two-branch recurrent community on the bAbI dataset for studying comprehension.
cifar10_cnn Trains a easy deep CNN on the CIFAR10 small pictures dataset.
conv_lstm Demonstrates the usage of a convolutional LSTM community.
deep_dream Deep Dreams in Keras.
imdb_bidirectional_lstm Trains a Bidirectional LSTM on the IMDB sentiment classification job.
imdb_cnn Demonstrates the usage of Convolution1D for textual content classification.
imdb_cnn_lstm Trains a convolutional stack adopted by a recurrent stack community on the IMDB sentiment classification job.
imdb_fasttext Trains a FastText mannequin on the IMDB sentiment classification job.
imdb_lstm Trains a LSTM on the IMDB sentiment classification job.
lstm_text_generation Generates textual content from Nietzsche’s writings.
mnist_acgan Implementation of AC-GAN (Auxiliary Classifier GAN ) on the MNIST dataset
mnist_antirectifier Demonstrates write customized layers for Keras
mnist_cnn Trains a easy convnet on the MNIST dataset.
mnist_irnn Reproduction of the IRNN experiment with pixel-by-pixel sequential MNIST in “A Simple Way to Initialize Recurrent Networks of Rectified Linear Units” by Le et al.
mnist_mlp Trains a easy deep multi-layer perceptron on the MNIST dataset.
mnist_hierarchical_rnn Trains a Hierarchical RNN (HRNN) to categorise MNIST digits.
mnist_transfer_cnn Transfer studying toy instance.
neural_style_transfer Neural type switch (producing a picture with the identical “content” as a base picture, however with the “style” of a unique image).
reuters_mlp Trains and evaluates a easy MLP on the Reuters newswire subject classification job.
stateful_lstm Demonstrates use stateful RNNs to mannequin lengthy sequences effectively.
variational_autoencoder Demonstrates construct a variational autoencoder.
variational_autoencoder_deconv Demonstrates construct a variational autoencoder with Keras utilizing deconvolution layers.

Learning More

After you’ve develop into accustomed to the fundamentals, these articles are subsequent step:

  • Guide to the Sequential Model. The sequential mannequin is a linear stack of layers and is the API most customers ought to begin with.

  • Guide to the Functional API. The Keras practical API is the best way to go for outlining complicated fashions, equivalent to multi-output fashions, directed acyclic graphs, or fashions with shared layers.

  • Training Visualization. There are all kinds of instruments out there for visualizing coaching. These embrace plotting of coaching metrics, actual time show of metrics inside the RStudio IDE, and integration with the TensorBoard visualization device included with TensorFlow.

  • Using Pre-Trained Models. Keras consists of a variety of deep studying fashions (Xception, VGG16, VGG19, ResNet50, InceptionVV3, and MobileNet) which can be made out there alongside pre-trained weights. These fashions can be utilized for prediction, function extraction, and fine-tuning.

  • Frequently Asked Questions. Covers many further subjects together with streaming coaching information, saving fashions, coaching on GPUs, and extra.

Keras gives a productive, extremely versatile framework for growing deep studying fashions. We can’t wait to see what the R group will do with these instruments!

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