keras (version 2.0.5)

fit: Train a Keras model

Description

Trains the model for a fixed number of epochs (iterations on a dataset).

Usage

fit(object, 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

object

Model to train.

x

Vector, matrix, or array of training data (or list if the model has multiple inputs). If all inputs in the model are named, you can also pass a list mapping input names to data.

y

Vector, matrix, or array of target data (or list if the model has multiple outputs). If all outputs in the model are named, you can also pass a list mapping output names to data.

batch_size

Number of samples per gradient update.

epochs

Number of times to iterate over the training data arrays.

verbose

Verbosity mode (0 = silent, 1 = verbose, 2 = one log line per epoch).

callbacks

List of callbacks to be called during training.

validation_split

Float between 0 and 1: fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch.

validation_data

Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. This could be a list (x_val, y_val) or a list (x_val, y_val, val_sample_weights).

shuffle

TRUE to shuffle the training data before each epoch.

class_weight

Optional named list mapping indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class.

sample_weight

Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode="temporal" in compile().

initial_epoch

epoch at which to start training (useful for resuming a previous training run).

...

Unused

See Also

Other model functions: compile, evaluate_generator, evaluate, fit_generator, get_config, get_layer, keras_model_sequential, keras_model, pop_layer, predict.keras.engine.training.Model, predict_generator, predict_on_batch, predict_proba, summary.keras.engine.training.Model, train_on_batch