本文整理汇总了Python中sklearn.svm.SVC.fit_generator方法的典型用法代码示例。如果您正苦于以下问题：Python SVC.fit_generator方法的具体用法？Python SVC.fit_generator怎么用？Python SVC.fit_generator使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.svm.SVC的用法示例。
在下文中一共展示了SVC.fit_generator方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from sklearn.svm import SVC [as 别名]
# 或者: from sklearn.svm.SVC import fit_generator [as 别名]
def main(hypes_file, data_dir, override):
"""Orchestrate."""
with open(hypes_file, 'r') as f:
hypes = json.load(f)
model_file_path = os.path.abspath('%s.pkl' % hypes['model']['name'])
color_changes = {0: (0, 0, 0, 0),
1: (0, 255, 0, 127),
'default': (0, 0, 0, 0)}
if not os.path.isfile(model_file_path) or override:
if not os.path.isfile(model_file_path):
logging.info("Did not find '%s'. Start training...",
model_file_path)
else:
logging.info("Override '%s'. Start training...",
model_file_path)
# Get data
# x_files, y_files = inputs(hypes, None, 'train', data_dir)
x_files, y_files = get_file_list(hypes, 'train')
x_files, y_files = sklearn.utils.shuffle(x_files,
y_files,
random_state=0)
x_train, y_train = get_traindata_single_file(hypes,
x_files[0],
y_files[0])
nb_features = x_train[0].shape[0]
logging.info("Input gets %i features", nb_features)
# Make model
from sklearn.svm import LinearSVC, SVC
from sklearn.tree import DecisionTreeClassifier
model = SVC(probability=False, # cache_size=200,
kernel="linear", C=2.8, gamma=.0073)
model = LinearSVC(C=2.8)
model = DecisionTreeClassifier()
print("Start fitting. This may take a while")
generator = generate_training_data(hypes, x_files, y_files)
t0 = time.time()
if False:
sep = hypes['solver']['samples_per_epoch']
model.fit_generator(generator,
samples_per_epoch=sep,
nb_epoch=hypes['solver']['epochs'],
verbose=1,
# callbacks=[callb],
validation_data=(x_train, y_train))
else:
logging.info("Fit with .fit")
x_train, y_train = inputs(hypes, None, 'train', data_dir)
print(len(y_train))
model.fit(x_train, y_train)
t1 = time.time()
print("Training Time: %0.4f" % (t1 - t0))
# save as YAML
joblib.dump(model, model_file_path)
# Evaluate
data = get_file_list(hypes, 'test')
logging.info("Start segmentation")
analyze.evaluate(hypes,
data,
data_dir,
model,
elements=[0, 1],
load_label_seg=load_label_seg,
color_changes=color_changes,
get_segmentation=get_segmentation)
else:
model = joblib.load(model_file_path)
data = get_file_list(hypes, 'test')
analyze.evaluate(hypes,
data,
data_dir,
model,
elements=[0, 1],
load_label_seg=load_label_seg,
color_changes=color_changes,
get_segmentation=get_segmentation)