Configures callbacks for use in various training loops. Arguments: callbacks: List of Callbacks. model: Model being trained. do_validation: Whether or not validation loop will be run. batch_size: Number of samples per batch. epochs: Number of epoch to train. st
(callbacks,
model,
do_validation=False,
batch_size=None,
epochs=None,
steps_per_epoch=None,
samples=None,
verbose=1,
count_mode='steps',
mode=ModeKeys.TRAIN)
| 58 | |
| 59 | |
| 60 | def configure_callbacks(callbacks, |
| 61 | model, |
| 62 | do_validation=False, |
| 63 | batch_size=None, |
| 64 | epochs=None, |
| 65 | steps_per_epoch=None, |
| 66 | samples=None, |
| 67 | verbose=1, |
| 68 | count_mode='steps', |
| 69 | mode=ModeKeys.TRAIN): |
| 70 | """Configures callbacks for use in various training loops. |
| 71 | |
| 72 | Arguments: |
| 73 | callbacks: List of Callbacks. |
| 74 | model: Model being trained. |
| 75 | do_validation: Whether or not validation loop will be run. |
| 76 | batch_size: Number of samples per batch. |
| 77 | epochs: Number of epoch to train. |
| 78 | steps_per_epoch: Number of batches to run per training epoch. |
| 79 | samples: Number of training samples. |
| 80 | verbose: int, 0 or 1. Keras logging verbosity to pass to ProgbarLogger. |
| 81 | count_mode: One of 'steps' or 'samples'. Per-batch or per-sample count. |
| 82 | mode: String. One of ModeKeys.TRAIN, ModeKeys.TEST, or ModeKeys.PREDICT. |
| 83 | Which loop mode to configure callbacks for. |
| 84 | |
| 85 | Returns: |
| 86 | Instance of CallbackList used to control all Callbacks. |
| 87 | """ |
| 88 | # Check if callbacks have already been configured. |
| 89 | if isinstance(callbacks, CallbackList): |
| 90 | return callbacks |
| 91 | |
| 92 | if not callbacks: |
| 93 | callbacks = [] |
| 94 | |
| 95 | # Add additional callbacks during training. |
| 96 | if mode == ModeKeys.TRAIN: |
| 97 | model.history = History() |
| 98 | callbacks = [BaseLogger()] + (callbacks or []) + [model.history] |
| 99 | if verbose: |
| 100 | callbacks.append(ProgbarLogger(count_mode)) |
| 101 | callback_list = CallbackList(callbacks) |
| 102 | |
| 103 | # Set callback model |
| 104 | callback_model = model._get_callback_model() # pylint: disable=protected-access |
| 105 | callback_list.set_model(callback_model) |
| 106 | |
| 107 | set_callback_parameters( |
| 108 | callback_list, |
| 109 | model, |
| 110 | do_validation=do_validation, |
| 111 | batch_size=batch_size, |
| 112 | epochs=epochs, |
| 113 | steps_per_epoch=steps_per_epoch, |
| 114 | samples=samples, |
| 115 | verbose=verbose, |
| 116 | mode=mode) |
| 117 |
nothing calls this directly
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