We’re excited to announce that the keras package is now obtainable on CRAN. The bundle supplies an R interface to Keras, a high-level neural networks API developed with a give attention to enabling quick experimentation. Keras has the next key options:

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

  • Person-friendly API which makes it simple to shortly prototype deep studying fashions.

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

  • Helps arbitrary community architectures: multi-input or multi-output fashions, layer sharing, mannequin sharing, and so forth. Which means that Keras is acceptable for constructing primarily 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.

In case you are already aware of Keras and wish to leap proper in, take a look at https://tensorflow.rstudio.com/keras which has the whole lot you might want to 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 Studying

Curiosity in deep studying has been accelerating quickly over the previous few years, and a number of other deep studying frameworks have emerged over the identical time-frame. Of all of the obtainable frameworks, Keras has stood out for its productiveness, flexibility and user-friendly API. On 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 may be integrated seamlessly with TensorFlow workflows. There’s additionally a pure-TensorFlow implementation of Keras with deeper integration on the roadmap for later this yr.

Keras and TensorFlow are the state-of-the-art in deep studying instruments and with the keras bundle now you can entry each with a fluent R interface.

Getting Began

Set up

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

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

This can offer you default CPU-based installations of Keras and TensorFlow. If you would like a extra personalized set up, e.g. if you wish to benefit from NVIDIA GPUs, see the documentation for install_keras().

MNIST Instance

We are able to be taught the fundamentals of Keras by strolling by means of a easy instance: recognizing handwritten digits from the MNIST dataset. MNIST consists of 28 x 28 grayscale photographs 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 photographs are 5, 0, 4, and 1.

Getting ready the Information

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

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 3-D array (photographs,width,peak) of grayscale values. To organize the information for coaching we convert the 3-D arrays into matrices by reshaping width and peak right into a single dimension (28×28 photographs 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 organize 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 Mannequin

The core information construction of Keras is a mannequin, a solution to manage layers. The only kind of mannequin is the sequential model, 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(models = 256, activation = "relu", input_shape = c(784)) %>% 
  layer_dropout(price = 0.4) %>% 
  layer_dense(models = 128, activation = "relu") %>%
  layer_dropout(price = 0.3) %>%
  layer_dense(models = 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 ultimate layer outputs a size 10 numeric vector (possibilities for every digit) utilizing a softmax activation function.

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

Layer (kind)                        Output Form                    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        
Complete params: 235,146
Trainable params: 235,146
Non-trainable params: 0

Subsequent, compile the mannequin with applicable loss perform, optimizer, and metrics:

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

Coaching and Analysis

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

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:

Consider the mannequin’s efficiency on the check information:

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

[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 supplies a vocabulary for constructing deep studying fashions that’s easy, elegant, and intuitive. Constructing a query answering system, a picture classification mannequin, a neural Turing machine, or every other mannequin is simply as simple.

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


Over 20 full examples can be found (particular due to [@dfalbel](https://github.com/dfalbel) for his work on these!). The examples cowl picture classification, textual content technology 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 photographs dataset.
conv_lstm Demonstrates using a convolutional LSTM community.
deep_dream Deep Goals in Keras.
imdb_bidirectional_lstm Trains a Bidirectional LSTM on the IMDB sentiment classification job.
imdb_cnn Demonstrates using 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 tips on how to write customized layers for Keras
mnist_cnn Trains a easy convnet on the MNIST dataset.
mnist_irnn Copy of the IRNN experiment with pixel-by-pixel sequential MNIST in “A Easy Solution to Initialize Recurrent Networks of Rectified Linear Models” 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 Switch studying toy instance.
neural_style_transfer Neural model switch (producing a picture with the identical “content material” as a base picture, however with the “model” of a unique image).
reuters_mlp Trains and evaluates a easy MLP on the Reuters newswire subject classification job.
stateful_lstm Demonstrates tips on how to use stateful RNNs to mannequin lengthy sequences effectively.
variational_autoencoder Demonstrates tips on how to construct a variational autoencoder.
variational_autoencoder_deconv Demonstrates tips on how to construct a variational autoencoder with Keras utilizing deconvolution layers.

Studying Extra

After you’ve turn into aware of the fundamentals, these articles are an excellent 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 useful API is the way in which to go for outlining advanced fashions, reminiscent of multi-output fashions, directed acyclic graphs, or fashions with shared layers.

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

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

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

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

Leave a Reply

Your email address will not be published. Required fields are marked *