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# wandb.keras.WandbCallback
[View source on GitHub](https://www.github.com/wandb/client/tree/v0.10.31.dev1/wandb/integration/keras/keras.py#L214-L888)
`WandbCallback` automatically integrates keras with wandb.
```python
WandbCallback(
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=[], data_type=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)
)
```
#### Example:
```python
model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
callbacks=[WandbCallback()]
)
```
`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` attribues. 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.
| Arguments | |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `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_model` | True - 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 (`model.save(filepath)`). |
| `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 `model.fit`. This is needed for calculating gradients - this is mandatory if `log_gradients` is `True`. |
| `validate_data` | (tuple) Same format `(X,y)` as passed to `model.fit`. 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. |
| `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. |
| `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`). |
| `output_type` | (string) type of the model output to help visualziation. can be one of: (`image`, `images`, `segmentation_mask`). |
| `log_evaluation` | (boolean) if True, save a Table containing validation data and the model's preditions 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. |
## Methods
### `set_model`
[View source](https://www.github.com/wandb/client/tree/v0.10.31.dev1/wandb/integration/keras/keras.py#L446-L455)
```python
set_model(
model
)
```
### `set_params`
[View source](https://www.github.com/wandb/client/tree/v0.10.31.dev1/wandb/integration/keras/keras.py#L443-L444)
```python
set_params(
params
)
```