multiml.task.keras.KerasBaseTask

class multiml.task.keras.KerasBaseTask(run_eagerly=None, callbacks=['EarlyStopping', 'ModelCheckpoint'], save_tensorboard=False, **kwargs)

Base task for Keras model.

Examples

>>> # your keras model
>>> class MyKerasModel(Model):
>>>     def __init__(self, units=1):
>>>         super(MyKerasModel, self).__init__()
>>>
>>>         self.dense = Dense(units)
>>>         self.relu = ReLU()
>>>
>>>     def call(self, x):
>>>         return self.relu(self.dense(x))
>>>
>>> # create task instance
>>> task = KerasBaseTask(storegate=storegate,
>>>                      model=MyKerasModel,
>>>                      input_var_names=('x0', 'x1'),
>>>                      output_var_names='outputs-keras',
>>>                      true_var_names='labels',
>>>                      optimizer='adam',
>>>                      optimizer_args=dict(lr=0.1),
>>>                      loss='binary_crossentropy')
>>> task.set_hps({'num_epochs': 5})
>>> task.execute()
>>> task.finalize()

__init__(run_eagerly=None, callbacks=['EarlyStopping', 'ModelCheckpoint'], save_tensorboard=False, **kwargs)

Parameters:

run_eagerly (bool) – Run on eager execution mode (not graph mode).
callbacks (list(str or keras.Callback)) – callback for keras model training. Predefined callbacks (EarlyStopping, ModelCheckpoint, and TensorBoard) can be selected by str. Other user-defined callbacks should be given as keras.Callback object.
save_tensorboard (bool) – use tensorboard callback in training.
**kwargs – Arbitrary keyword arguments.

Methods

`__init__`([run_eagerly, callbacks, ...])	param run_eagerly: Run on eager execution mode (not graph mode).
`build_model`()	Build model.
`compile`()	Compile model, optimizer and loss.
`compile_loss`()	Compile keras model.
`compile_model`()	Compile keras model.
`compile_optimizer`()	Compile optimizer.
`compile_var_names`()	Compile var_names.
`do_test`()	Perform test phase or not.
`do_train`()	Perform train phase or not.
`do_valid`()	Perform valid phase or not.
`dump_model`([extra_args])	Dump current keras model.
`execute`()	Execute a task.
`finalize`()	Finalize base task.
`fit`([train_data, valid_data])	Training model.
`fit_predict`([fit_args, predict_args])	Fit and predict model.
`get_input_true_data`(phase)	Get input and true data.
`get_input_var_shapes`([phase])	Get shape of input_var_names.
`get_inputs`()	Returns keras Input from input_var_names.
`get_metadata`(metadata_key)	Returns metadata.
`get_pred_index`()	Returns prediction index passed to loss calculation.
`get_unique_id`()	Returns unique identifier of task.
`load_metadata`()	Load metadata.
`load_model`()	Load pre-trained keras model weights.
`predict`([data, phase])	Evaluate model prediction.
`predict_update`([data, phase])	Predict and update data in StoreGate.
`set_hps`(params)	Set hyperparameters to this task.
`show_info`()	Print information.
`update`(data[, phase])	Update data in storegate.

Attributes

`input_saver_key`	Return input_saver_key.
`input_var_names`	Returns input_var_names.
`job_id`	Return job_id of task.
`ml`	Returns ML data class.
`name`	Return name of task.
`output_saver_key`	Return output_saver_key.
`output_var_names`	Returns output_var_names.
`phases`	Returns ML phases.
`pool_id`	Return pool_id of task.
`pred_var_names`	Returns pred_var_names.
`save_var_names`	Returns save_var_names.
`saver`	Return saver of task.
`storegate`	Return storegate of task.
`subtask_id`	Return subtask_id of task.
`task_id`	Return task_id of task.
`trial_id`	Return trial_id of task.
`true_var_names`	Returns true_var_names.

__init__(run_eagerly=None, callbacks=['EarlyStopping', 'ModelCheckpoint'], save_tensorboard=False, **kwargs)

Parameters:

run_eagerly (bool) – Run on eager execution mode (not graph mode).
callbacks (list(str or keras.Callback)) – callback for keras model training. Predefined callbacks (EarlyStopping, ModelCheckpoint, and TensorBoard) can be selected by str. Other user-defined callbacks should be given as keras.Callback object.
save_tensorboard (bool) – use tensorboard callback in training.
**kwargs – Arbitrary keyword arguments.