my_callbacks.ModelCheckpointDetached Class Reference

Inheritance diagram for my_callbacks.ModelCheckpointDetached:

Public Member Functions
def	__init__ (self, filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1)
def	on_epoch_end (self, epoch, logs=None)

Public Attributes
	monitor
	verbose
	filepath
	save_best_only
	save_weights_only
	period
	epochs_since_last_save
	monitor_op
	best

Detailed Description

Save detached from multi-GPU encapsulation model
(very small) modification from https://github.com/fchollet/keras/blob/master/keras/callbacks.py#L331

`filepath` can contain named formatting options,
which will be filled the value of `epoch` and
keys in `logs` (passed in `on_epoch_end`).

For example: if `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`,
then the model checkpoints will be saved with the epoch number and
the validation loss in the filename.

# Arguments
    filepath: string, path to save the model file.
    monitor: quantity to monitor.
    verbose: verbosity mode, 0 or 1.
    save_best_only: if `save_best_only=True`,
        the latest best model according to
        the quantity monitored will not be overwritten.
    mode: one of {auto, min, max}.
        If `save_best_only=True`, the decision
        to overwrite the current save file is made
        based on either the maximization or the
        minimization of the monitored quantity. For `val_acc`,
        this should be `max`, for `val_loss` this should
        be `min`, etc. In `auto` mode, the direction is
        automatically inferred from the name of the monitored quantity.
    save_weights_only: if True, then only the model's weights will be
        saved (`model.save_weights(filepath)`), else the full model
        is saved (`model.save(filepath)`).
    period: Interval (number of epochs) between checkpoints.

Definition at line 92 of file my_callbacks.py.

Constructor & Destructor Documentation

def my_callbacks.ModelCheckpointDetached.__init__	(		self,
			filepath,
			monitor = 'val_loss',
			verbose = 0,
			save_best_only = False,
			save_weights_only = False,
			mode = 'auto',
			period = 1
	)

Definition at line 128 of file my_callbacks.py.

                 mode='auto', period=1):
        super(ModelCheckpointDetached, self).__init__()
        self.monitor = monitor
        self.verbose = verbose
        self.filepath = filepath
        self.save_best_only = save_best_only
        self.save_weights_only = save_weights_only
        self.period = period
        self.epochs_since_last_save = 0

        if mode not in ['auto', 'min', 'max']:
            warnings.warn('ModelCheckpoint mode %s is unknown, '
                          'fallback to auto mode.' % mode, RuntimeWarning)
            mode = 'auto'

        if mode == 'min':
            self.monitor_op = np.less
            self.best = np.Inf
        elif mode == 'max':
            self.monitor_op = np.greater
            self.best = -np.Inf
        else:
            if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
                self.monitor_op = np.greater
                self.best = -np.Inf
            else:
                self.monitor_op = np.less
                self.best = np.Inf

Member Function Documentation

def my_callbacks.ModelCheckpointDetached.on_epoch_end	(		self,
			epoch,
			logs = None
	)

Definition at line 157 of file my_callbacks.py.

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        self.epochs_since_last_save += 1
        if self.epochs_since_last_save >= self.period:
            self.epochs_since_last_save = 0
            filepath = self.filepath.format(epoch=epoch, **logs)
            if self.save_best_only:
                current = logs.get(self.monitor)
                if current is None:
                    warnings.warn('Can save best model only with %s available, '
                                  'skipping.' % self.monitor, RuntimeWarning)
                else:
                    if self.monitor_op(current, self.best):
                        if self.verbose > 0:
                            print('Epoch %05d: %s improved from %0.5f to %0.5f,'
                                  ' saving model to %s'
                                  % (epoch, self.monitor, self.best,
                                     current, filepath))
                        self.best = current
                        if self.save_weights_only:
                            detachmodel(self.model).save_weights(filepath, overwrite=True)
                        else:
                            detachmodel(self.model).save(filepath, overwrite=True)
                    else:
                        if self.verbose > 0:
                            print('Epoch %05d: %s did not improve' %
                                  (epoch, self.monitor))
            else:
                if self.verbose > 0:
                    print('Epoch %05d: saving model to %s' % (epoch, filepath))
                if self.save_weights_only:
                    detachmodel(self.model).save_weights(filepath, overwrite=True)
                else:
                    detachmodel(self.model).save(filepath, overwrite=True)

Member Data Documentation

my_callbacks.ModelCheckpointDetached.best

Definition at line 145 of file my_callbacks.py.

my_callbacks.ModelCheckpointDetached.epochs_since_last_save

Definition at line 136 of file my_callbacks.py.

my_callbacks.ModelCheckpointDetached.filepath

Definition at line 132 of file my_callbacks.py.

my_callbacks.ModelCheckpointDetached.monitor

Definition at line 130 of file my_callbacks.py.

my_callbacks.ModelCheckpointDetached.monitor_op

Definition at line 144 of file my_callbacks.py.

my_callbacks.ModelCheckpointDetached.period

Definition at line 135 of file my_callbacks.py.

my_callbacks.ModelCheckpointDetached.save_best_only

Definition at line 133 of file my_callbacks.py.

my_callbacks.ModelCheckpointDetached.save_weights_only

Definition at line 134 of file my_callbacks.py.

my_callbacks.ModelCheckpointDetached.verbose

Definition at line 131 of file my_callbacks.py.

---

The documentation for this class was generated from the following file:

• dunereco/dunereco/CVN/keras_scripts/modules/my_callbacks.py