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WandbCallback automatically integrates keras with wandb.

monitor="val_loss", verbose=0, mode="auto", save_weights_only=(False),
log_weights=(False), log_gradients=(False), save_model=(True),
training_data=None, validation_data=None, labels=None, predictions=36,
generator=None, input_type=None, output_type=None, log_evaluation=(False),
validation_steps=None, class_colors=None, log_batch_frequency=None,
log_best_prefix="best_", save_graph=(True), validation_indexes=None,
validation_row_processor=None, prediction_row_processor=None,
infer_missing_processors=(True), log_evaluation_frequency=0,
compute_flops=(False), **kwargs

validation_data=(X_test, y_test),

WandbCallback will automatically log history data from any metrics collected by keras: loss and anything passed into keras_model.compile().

WandbCallback will set summary metrics for the run associated with the "best" training step, where "best" is defined by the monitor and mode attributes. This defaults to the epoch with the minimum val_loss. WandbCallback will by default save the model associated with the best epoch.

WandbCallback can optionally log gradient and parameter histograms.

WandbCallback can optionally save training and validation data for wandb to visualize.

monitor(str) name of metric to monitor. Defaults to val_loss.
mode(str) one of {auto, min, max}. min - save model when monitor is minimized max - save model when monitor is maximized auto - try to guess when to save the model (default).
save_modelTrue - save a model when monitor beats all previous epochs False - don't save models
save_graph(boolean) if True save model graph to wandb (default to True).
save_weights_only(boolean) if True, then only the model's weights will be saved (model.save_weights(filepath)), else the full model is saved (
log_weights(boolean) if True save histograms of the model's layer's weights.
log_gradients(boolean) if True log histograms of the training gradients
training_data(tuple) Same format (X,y) as passed to This is needed for calculating gradients - this is mandatory if log_gradients is True.
validation_data(tuple) Same format (X,y) as passed to A set of data for wandb to visualize. If this is set, every epoch, wandb will make a small number of predictions and save the results for later visualization. In case you are working with image data, please also set input_type and output_type in order to log correctly.
generator(generator) a generator that returns validation data for wandb to visualize. This generator should return tuples (X,y). Either validate_data or generator should be set for wandb to visualize specific data examples. In case you are working with image data, please also set input_type and output_type in order to log correctly.
validation_steps(int) if validation_data is a generator, how many steps to run the generator for the full validation set.
labels(list) If you are visualizing your data with wandb this list of labels will convert numeric output to understandable string if you are building a multiclass classifier. If you are making a binary classifier you can pass in a list of two labels ["label for false", "label for true"]. If validate_data and generator are both false, this won't do anything.
predictions(int) the number of predictions to make for visualization each epoch, max is 100.
input_type(string) type of the model input to help visualization. can be one of: (image, images, segmentation_mask, auto).
output_type(string) type of the model output to help visualization. can be one of: (image, images, segmentation_mask, label).
log_evaluation(boolean) if True, save a Table containing validation data and the model's predictions at each epoch. See validation_indexes, validation_row_processor, and output_row_processor for additional details.
class_colors([float, float, float]) if the input or output is a segmentation mask, an array containing an rgb tuple (range 0-1) for each class.
log_batch_frequency(integer) if None, callback will log every epoch. If set to integer, callback will log training metrics every log_batch_frequency batches.
log_best_prefix(string) if None, no extra summary metrics will be saved. If set to a string, the monitored metric and epoch will be prepended with this value and stored as summary metrics.
validation_indexes([wandb.data_types._TableLinkMixin]) an ordered list of index keys to associate with each validation example. If log_evaluation is True and validation_indexes is provided, then a Table of validation data will not be created and instead each prediction will be associated with the row represented by the TableLinkMixin. The most common way to obtain such keys are is use Table.get_index() which will return a list of row keys.
validation_row_processor(Callable) a function to apply to the validation data, commonly used to visualize the data. The function will receive an ndx (int) and a row (dict). If your model has a single input, then row["input"] will be the input data for the row. Else, it will be keyed based on the name of the input slot. If your fit function takes a single target, then row["target"] will be the target data for the row. Else, it will be keyed based on the name of the output slots. For example, if your input data is a single ndarray, but you wish to visualize the data as an Image, then you can provide lambda ndx, row: {"img": wandb.Image(row["input"])} as the processor. Ignored if log_evaluation is False or validation_indexes are present.
output_row_processor(Callable) same as validation_row_processor, but applied to the model's output. row["output"] will contain the results of the model output.
infer_missing_processors(bool) Determines if validation_row_processor and output_row_processor should be inferred if missing. Defaults to True. If labels are provided, we will attempt to infer classification-type processors where appropriate.
log_evaluation_frequency(int) Determines the frequency which evaluation results will be logged. Default 0 (only at the end of training). Set to 1 to log every epoch, 2 to log every other epoch, and so on. Has no effect when log_evaluation is False.
compute_flops(bool) Compute the FLOPs of your Keras Sequential or Functional model in GigaFLOPs unit.



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get_flops() -> float

Calculate FLOPS [GFLOPs] for a tf.keras.Model or tf.keras.Sequential model in inference mode.

It uses tf.compat.v1.profiler under the hood.


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