kerasR: keras_fit – R documentation

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keras_fit

Fit a keras model

Description

Learn the weight and bias values for am model given training data. Model must be compiled first. The model is modified in place.

Usage

keras_fit(model, x, y, batch_size = 32, epochs = 10, verbose = 1,
  callbacks = NULL, validation_split = 0, validation_data = NULL,
  shuffle = TRUE, class_weight = NULL, sample_weight = NULL,
  initial_epoch = 0)

Arguments

`model`	a keras model object created with Sequential
`x`	input data as a numeric matrix
`y`	labels; either a numeric matrix or numeric vector
`batch_size`	integer. Number of samples per gradient update.
`epochs`	integer, the number of epochs to train the model.
`verbose`	0 for no logging to stdout, 1 for progress bar logging, 2 for one log line per epoch.
`callbacks`	list of 'keras.callbacks.Callback“ instances. List of callbacks to apply during training.
`validation_split`	float (`0 < x < 1`). Fraction of the data to use as held-out validation data.
`validation_data`	`list(x_val, y_val)` or `list(x_val, y_val, val_sample_weights)` to be used as held-out validation data. Will override `validation_split`.
`shuffle`	boolean or string (for `batch`). Whether to shuffle the samples at each epoch. `batch` is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks.
`class_weight`	dictionary mapping classes to a weight value, used for scaling the loss function (during training only).
`sample_weight`	Numpy array of weights for the training samples
`initial_epoch`	epoch at which to start training

Author(s)

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

References

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

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(Dropout(rate = 0.5))
  mod$add(Activation("relu"))
  mod$add(Dense(units = 3))
  mod$add(ActivityRegularization(l1 = 1))
  mod$add(Activation("softmax"))
  keras_compile(mod,  loss = 'categorical_crossentropy', optimizer = RMSprop())

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

kerasR

R Interface to the Keras Deep Learning Library

v0.6.1

LGPL-2

Authors

Taylor Arnold [aut, cre]

Initial release