本文整理匯總了Python中keras.applications.densenet.DenseNet169方法的典型用法代碼示例。如果您正苦於以下問題：Python densenet.DenseNet169方法的具體用法？Python densenet.DenseNet169怎麽用？Python densenet.DenseNet169使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras.applications.densenet的用法示例。
在下文中一共展示了densenet.DenseNet169方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: get_tst_neural_net
# 需要導入模塊: from keras.applications import densenet [as 別名]
# 或者: from keras.applications.densenet import DenseNet169 [as 別名]
def get_tst_neural_net(type):
model = None
custom_objects = dict()
if type == 'mobilenet_small':
from keras.applications.mobilenet import MobileNet
model = MobileNet((128, 128, 3), depth_multiplier=1, alpha=0.25, include_top=True, weights='imagenet')
elif type == 'mobilenet':
from keras.applications.mobilenet import MobileNet
model = MobileNet((224, 224, 3), depth_multiplier=1, alpha=1.0, include_top=True, weights='imagenet')
elif type == 'mobilenet_v2':
from keras.applications.mobilenetv2 import MobileNetV2
model = MobileNetV2((224, 224, 3), depth_multiplier=1, alpha=1.4, include_top=True, weights='imagenet')
elif type == 'resnet50':
from keras.applications.resnet50 import ResNet50
model = ResNet50(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
elif type == 'inception_v3':
from keras.applications.inception_v3 import InceptionV3
model = InceptionV3(input_shape=(299, 299, 3), include_top=True, weights='imagenet')
elif type == 'inception_resnet_v2':
from keras.applications.inception_resnet_v2 import InceptionResNetV2
model = InceptionResNetV2(input_shape=(299, 299, 3), include_top=True, weights='imagenet')
elif type == 'xception':
from keras.applications.xception import Xception
model = Xception(input_shape=(299, 299, 3), include_top=True, weights='imagenet')
elif type == 'densenet121':
from keras.applications.densenet import DenseNet121
model = DenseNet121(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
elif type == 'densenet169':
from keras.applications.densenet import DenseNet169
model = DenseNet169(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
elif type == 'densenet201':
from keras.applications.densenet import DenseNet201
model = DenseNet201(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
elif type == 'nasnetmobile':
from keras.applications.nasnet import NASNetMobile
model = NASNetMobile(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
elif type == 'nasnetlarge':
from keras.applications.nasnet import NASNetLarge
model = NASNetLarge(input_shape=(331, 331, 3), include_top=True, weights='imagenet')
elif type == 'vgg16':
from keras.applications.vgg16 import VGG16
model = VGG16(input_shape=(224, 224, 3), include_top=False, pooling='avg', weights='imagenet')
elif type == 'vgg19':
from keras.applications.vgg19 import VGG19
model = VGG19(input_shape=(224, 224, 3), include_top=False, pooling='avg', weights='imagenet')
elif type == 'multi_io':
model = get_custom_multi_io_model()
elif type == 'multi_model_layer_1':
model = get_custom_model_with_other_model_as_layer()
elif type == 'multi_model_layer_2':
model = get_small_model_with_other_model_as_layer()
elif type == 'Conv2DTranspose':
model = get_Conv2DTranspose_model()
elif type == 'RetinaNet':
model, custom_objects = get_RetinaNet_model()
elif type == 'conv3d_model':
model = get_simple_3d_model()
return model, custom_objects