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GridSearchCV的verbose参数
grid_search.py
813行
838行
555行
if self.verbose > 0:
if isinstance(parameter_iterable, Sized):
n_candidates = len(parameter_iterable)
print("Fitting {0} folds for each of {1} candidates, totalling"
" {2} fits".format(len(cv), n_candidates,
n_candidates * len(cv)))
570行
cross_validation.py
1585行
def _fit_and_score(estimator, X, y, scorer, train, test, verbose,
parameters, fit_params, return_train_score=False,
return_parameters=False, error_score='raise'):
1612行
verbose : integer
The verbosity level.
1650行，一轮CV开始时的输出日志
if verbose > 1:
if parameters is None:
msg = ''
else:
msg = '%s' % (', '.join('%s=%s' % (k, v)
for k, v in parameters.items()))
print("[CV] %s %s" % (msg, (64 - len(msg)) * '.'))
1700行，结束时的日志
if verbose > 2:
msg += ", score=%f" % test_score
if verbose > 1:
end_msg = "%s -%s" % (msg, logger.short_format_time(scoring_time))
print("[CV] %s %s" % ((64 - len(end_msg)) * '.', end_msg))
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KerasClassifier的verbose参数
scikit-learn.py
175行class KerasClassifier(BaseWrapper):
209行return super(KerasClassifier, self).fit(x, y, **kwargs)
17行BaseWrapper
151行history = self.model.fit(x, y, **fit_args)
model是Sequentail()
sequential.py
24行class Sequential(Model):
父类Model
training.py
31行class Model(Network):
828行fit
def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
**kwargs)
看注释871行
verbose: Integer. 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.
1030行
training_arrays.py
19行
125行
callbacks.on_train_begin()
144行callbacks.on_epoch_begin(epoch)
151行callbacks.on_batch_begin(step_index, batch_logs)
callbacks.py
296行
def on_epoch_begin(self, epoch, logs=None):
if self.verbose:
print('Epoch %d/%d' % (epoch + 1, self.epochs))