TensorBoard: Tensorboard basic visualizations.
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

Description Usage Arguments Author(s) References See Also Examples

This callback writes a log for TensorBoard, which allows you to visualize dynamic graphs of your training and test metrics, as well as activation histograms for the different layers in your model.

1 2	TensorBoard(log_dir = "./logs", histogram_freq = 0, write_graph = TRUE, write_images = FALSE)

`log_dir`	the path of the directory where to save the log files to be parsed by Tensorboard.
`histogram_freq`	frequency (in epochs) at which to compute activation histograms for the layers of the model. If set to 0, histograms won't be computed.
`write_graph`	whether to visualize the graph in Tensorboard. The log file can become quite large when write_graph is set to True.
`write_images`	whether to write model weights to visualize as image in Tensorboard.

Taylor B. Arnold, taylor.arnold@acm.org

Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.

Other callbacks: CSVLogger, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau

if(keras_available()) {
  X_train <- matrix(rnorm(100 * 10), nrow = 100)
  Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3)

  mod <- Sequential()
  mod$add(Dense(units = 50, input_shape = dim(X_train)[2]))
  mod$add(Activation("relu"))
  mod$add(Dense(units = 3))
  mod$add(Activation("softmax"))
  keras_compile(mod,  loss = 'categorical_crossentropy', optimizer = RMSprop())

  callbacks <- list(CSVLogger(tempfile()),
                    EarlyStopping(),
                    ReduceLROnPlateau(),
                    TensorBoard(tempfile()))

  keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
            verbose = 0, callbacks = callbacks, validation_split = 0.2)
}