TensorBoard: Tensorboard basic visualizations.

In kerasR: R Interface to the Keras Deep Learning Library

Tensorboard basic visualizations.

Description

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.

Usage

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

Arguments

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.

Author(s)

Taylor B. Arnold, [e-mail]

References

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

See Also

Other callbacks: CSVLogger, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau

Examples

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)
}

kerasR documentation built on Aug. 17, 2022, 5:06 p.m.