Trains the model for a fixed number of epochs (iterations on a dataset).
# S3 method for keras.engine.training.Model
fit(
object,
x = NULL,
y = NULL,
batch_size = NULL,
epochs = 10,
verbose = getOption("keras.fit_verbose", default = 1),
callbacks = NULL,
view_metrics = getOption("keras.view_metrics", default = "auto"),
validation_split = 0,
validation_data = NULL,
shuffle = TRUE,
class_weight = NULL,
sample_weight = NULL,
initial_epoch = 0,
steps_per_epoch = NULL,
validation_steps = NULL,
...
)Model to train.
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. x can be NULL (default) if feeding
from framework-native tensors (e.g. TensorFlow data tensors).
Vector, matrix, or array of target (label) 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. y can be NULL (default) if feeding
from framework-native tensors (e.g. TensorFlow data tensors).
Integer or NULL. Number of samples per gradient update.
If unspecified, batch_size will default to 32.
Number of epochs to train the model.
Note that in conjunction with initial_epoch,
epochs is to be understood as "final epoch". The model is
not trained for a number of iterations given by epochs, but
merely until the epoch of index epochs is reached.
Verbosity mode (0 = silent, 1 = progress bar, 2 = one line per epoch).
List of callbacks to be called during training.
View realtime plot of training metrics (by epoch). The
default ("auto") will display the plot when running within RStudio,
metrics were specified during model compile(), epochs > 1 and
verbose > 0. Use the global keras.view_metrics option to establish a
different default.
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. The validation data
is selected from the last samples in the x and y data provided,
before shuffling.
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). validation_data will override validation_split.
shuffle: Logical (whether to shuffle the training data
before each epoch) or string (for "batch"). "batch" is a special option
for dealing with the limitations of HDF5 data; it shuffles in batch-sized
chunks. Has no effect when steps_per_epoch is not NULL.
Optional named list mapping indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
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().
Integer, Epoch at which to start training (useful for resuming a previous training run).
Total number of steps (batches of samples) before
declaring one epoch finished and starting the next epoch. When training
with input tensors such as TensorFlow data tensors, the default NULL is
equal to the number of samples in your dataset divided by the batch
size, or 1 if that cannot be determined.
Only relevant if steps_per_epoch is specified.
Total number of steps (batches of samples) to validate before stopping.
Unused
A history object that contains all information collected
during training.
Other model functions:
compile.keras.engine.training.Model(),
evaluate.keras.engine.training.Model(),
evaluate_generator(),
fit_generator(),
get_config(),
get_layer(),
keras_model_sequential(),
keras_model(),
multi_gpu_model(),
pop_layer(),
predict.keras.engine.training.Model(),
predict_generator(),
predict_on_batch(),
predict_proba(),
summary.keras.engine.training.Model(),
train_on_batch()