Python keras.applications.densenet.DenseNet169() Examples

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Example #1
Source File: test_bench.py    From Keras-inference-time-optimizer with MIT License 4 votes vote down vote up 
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