Description Usage Arguments Details Value See Also
this function is used to fit or train a DLmodel
by using generator
functions for training and validation data.
1 2 3 4 5 |
.model |
( |
generator |
(function) The generator function for training data, built by |
steps_per_epoch |
(integer) Number of steps per training epoch, Default: NULL |
train_config |
(list) The training configuration, the output of |
epochs |
(numeric) Maximum number of epochs to train, Default: 10 |
starting_epoch |
(numeric) strating epoch, useful when we want to resume a previous fit, Default: 1 |
validation_data |
(function or matrix) Data for validation. It can be a generator function, built by |
validation_steps |
(integer) Number of steps of validation per epoch, Default: NULL |
validation_config |
(list) The testing configuration, the output of |
keep_best |
(logical) Should the training always store the best model up-to-date?, Default: TRUE |
verbose |
(logical) Provide additional information on training, Default: TRUE |
metrics_viewer |
(logical) Visualize training loss interactively while fitting?, Default: FALSE |
reset_optimizer |
(logical) Reset optimizer state after each subepoch?, Default: FALSE |
... |
extra arguments passed to other functions. |
generator
, steps_per_epoch
, validation_data
and validation_steps
are completely and automatically determined if one uses the train_config
and validation_config
parameters, both being the outputs of create_generator_from_config
.
Additionally, we can pass this function two arguments: path
and prefix
, the best model will be stored in the corresponding path with the given prefix (usually something indicative or with a timestamp).
The trained DLmodel
.
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