Temporary solution. Keras model that uses a custom data adapter inside fit.
Trains the model for a fixed number of epochs (iterations on a dataset).
x- Input data.
y- Target data.
batch_size- Number of samples per gradient update.
epochs- Number of epochs to train the model.
verbose- Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
callbacks- List of
validation_split- Fraction of the training data to be used as validation data.
validation_data- Data on which to evaluate the loss and any model metrics at the end of each epoch.
shuffle- whether to shuffle the training data before each epoch
class_weight- Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only).
sample_weight- Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only).
initial_epoch- Epoch at which to start training
steps_per_epoch- Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch.
validation_steps- Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch.
validation_batch_size- Number of samples per validation batch.
validation_freq- specifies how many training epochs to run before a new validation run is performed
max_queue_size- Maximum size for the generator queue.
workers- Maximum number of processes to spin up when using process-based threading.
True, use process-based threading.
History object. Its
History.history attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
RuntimeError- 1. If the model was never compiled or, 2. If
model.fitis wrapped in
ValueError- In case of mismatch between the provided input data and what the model expects.
Handles iterating over epoch-level