def model_confirm(self, choosed_model):
if choosed_model == 'VGG16':
model = MODEL(self.config).VGG16()
elif choosed_model == 'VGG19':
model = MODEL(self.config).VGG19()
elif choosed_model == 'AlexNet':
model = MODEL(self.config).AlexNet()
elif choosed_model == 'LeNet':
model = MODEL(self.config).LeNet()
elif choosed_model == 'ZF_Net':
model = MODEL(self.config).ZF_Net()
elif choosed_model == 'ResNet18':
model = ResnetBuilder().build_resnet18(self.config)
elif choosed_model == 'ResNet34':
model = ResnetBuilder().build_resnet34(self.config)
elif choosed_model == 'ResNet101':
model = ResnetBuilder().build_resnet101(self.config)
elif choosed_model == 'ResNet152':
model = ResnetBuilder().build_resnet152(self.config)
elif choosed_model == 'mnist_net':
model = MODEL(self.config).mnist_net()
elif choosed_model == 'TSL16':
model = MODEL(self.config).TSL16()
elif choosed_model == 'ResNet50':
model = keras.applications.ResNet50(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,
self.normal_size,
self.channles),
pooling='max',
classes=self.classes)
elif choosed_model == 'InceptionV3':
model = keras.applications.InceptionV3(
include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size, self.normal_size,
self.channles),
pooling='max',
classes=self.classes)
elif choosed_model == 'Xception':
model = keras.applications.Xception(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,
self.normal_size,
self.channles),
pooling='max',
classes=self.classes)
elif choosed_model == 'MobileNet':
model = keras.applications.MobileNet(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,
self.normal_size,
self.channles),
pooling='max',
classes=self.classes)
# elif choosed_model == 'DenseNet':
# depth = 40
# nb_dense_block = 3
# growth_rate = 12
# nb_filter = 12
# bottleneck = False
# reduction = 0.0
# dropout_rate = 0.0
#
# img_dim = (self.channles, self.normal_size) if K.image_dim_ordering() == "th" else (
# self.normal_size, self.normal_size, self.channles)
#
# model = densenet.DenseNet(img_dim, classes=self.classes, depth=depth, nb_dense_block=nb_dense_block,
# growth_rate=growth_rate, nb_filter=nb_filter, dropout_rate=dropout_rate,
# bottleneck=bottleneck, reduction=reduction, weights=None)
return model
def build_model(self):
if self.model_name == 'VGG16':
model = keras.applications.VGG16(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,self.normal_size,self.channles),
pooling='max',
classes=self.classes)
elif self.model_name == 'VGG19':
model = keras.applications.VGG19(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,self.normal_size,self.channles),
pooling='max',
classes=self.classes)
elif self.model_name == 'ResNet50':
model = keras.applications.ResNet50(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,self.normal_size,self.channles),
pooling='max',
classes=self.classes)
elif self.model_name == 'InceptionV3':
model = keras.applications.InceptionV3(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,self.normal_size,self.channles),
pooling='max',
classes=self.classes)
elif self.model_name == 'Xception':
model = keras.applications.Xception(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,self.normal_size,self.channles),
pooling='max',
classes=self.classes)
elif self.model_name == 'MobileNet':
model = keras.applications.MobileNet(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,self.normal_size,self.channles),
pooling='max',
classes=self.classes)
elif self.model_name == 'DenseNet':
depth = 40
nb_dense_block = 3
growth_rate = 12
nb_filter = 12
bottleneck = False
reduction = 0.0
dropout_rate = 0.0
img_dim = (self.channles, self.normal_size) if K.image_dim_ordering() == "th" else (
self.normal_size, self.normal_size, self.channles)
model = densenet.DenseNet(img_dim, classes=self.classes, depth=depth, nb_dense_block=nb_dense_block,
growth_rate=growth_rate, nb_filter=nb_filter, dropout_rate=dropout_rate,
bottleneck=bottleneck, reduction=reduction, weights=None)
elif self.model_name == 'AlexNet':
model = MODEL(self.config).AlexNet()
elif self.model_name == 'LeNet':
model = MODEL(self.config).LeNet()
elif self.model_name == 'ZF_Net':
model = MODEL(self.config).ZF_Net()
elif self.model_name == 'ResNet18':
model = ResnetBuilder().build_resnet18(self.config)
elif self.model_name == 'ResNet34':
model = ResnetBuilder().build_resnet34(self.config)
elif self.model_name == 'ResNet101':
model = ResnetBuilder().build_resnet101(self.config)
elif self.model_name == 'ResNet152':
model = ResnetBuilder().build_resnet152(self.config)
if self.default_optimizers:
adam = keras.optimizers.Adam(lr=self.lr, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0)
model.compile(loss="categorical_crossentropy", optimizer=adam, metrics=["accuracy"]) # compile之后才会更新权重和模型
else:
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
return model
def model_confirm(self, choosed_model):
if choosed_model == 'VGG16':
model = MODEL(self.config).VGG16()
elif choosed_model == 'VGG19':
model = MODEL(self.config).VGG19()
elif choosed_model == 'AlexNet':
model = MODEL(self.config).AlexNet()
elif choosed_model == 'LeNet':
model = MODEL(self.config).LeNet()
elif choosed_model == 'ZF_Net':
model = MODEL(self.config).ZF_Net()
elif choosed_model == 'ResNet18':
model = ResnetBuilder().build_resnet18(self.config)
elif choosed_model == 'ResNet34':
model = ResnetBuilder().build_resnet34(self.config)
elif choosed_model == 'ResNet101':
model = ResnetBuilder().build_resnet101(self.config)
elif choosed_model == 'ResNet152':
model = ResnetBuilder().build_resnet152(self.config)
elif choosed_model == 'mnist_net':
model = MODEL(self.config).mnist_net()
elif choosed_model == 'TSL16':
model = MODEL(self.config).TSL16()
elif choosed_model == 'ResNet50':
model = keras.applications.ResNet50(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,
self.normal_size,
self.channles),
pooling='max',
classes=self.classNumber)
elif choosed_model == 'InceptionV3':
model = keras.applications.InceptionV3(
include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size, self.normal_size,
self.channles),
pooling='max',
classes=self.classNumber)
elif choosed_model == 'Xception':
model = keras.applications.Xception(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,
self.normal_size,
self.channles),
pooling='max',
classes=self.classNumber)
elif choosed_model == 'MobileNet':
model = keras.applications.MobileNet(include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size,
self.normal_size,
self.channles),
pooling='max',
classes=self.classNumber)
elif choosed_model == 'InceptionResNetV2':
model = keras.applications.InceptionResNetV2(
include_top=True,
weights=None,
input_tensor=None,
input_shape=(self.normal_size, self.normal_size,
self.channles),
pooling='max',
classes=self.classNumber)
elif choosed_model == 'SEResNetXt':
model = SEResNetXt(self.config).model
elif choosed_model == 'DenseNet':
depth = 40
nb_dense_block = 3
growth_rate = 12
nb_filter = 12
bottleneck = False
reduction = 0.0
dropout_rate = 0.0
img_dim = (self.channles, self.normal_size
) if K.image_data_format == 'channels_last' else (
self.normal_size, self.normal_size, self.channles)
model = densenet.DenseNet(img_dim,
classNumber=self.classNumber,
depth=depth,
nb_dense_block=nb_dense_block,
growth_rate=growth_rate,
nb_filter=nb_filter,
dropout_rate=dropout_rate,
bottleneck=bottleneck,
reduction=reduction,
weights=None)
elif choosed_model == 'SENet':
model = sm.Unet('senet154',
input_shape=(self.normal_size, self.normal_size,
self.channles),
classes=4,
activation='softmax',
encoder_weights=None)
#model.summary()
elif choosed_model == 'EfficientNetB5':
model = EfficientNetB5(input_shape=(self.normal_size,
self.normal_size,
self.channles),
classes=4,
weights=None)
elif choosed_model == 'EfficientNetB4':
model = EfficientNetB4(input_shape=(self.normal_size,
self.normal_size,
self.channles),
classes=4,
weights=None)
elif choosed_model == 'EfficientNetB3':
model = EfficientNetB3(input_shape=(self.normal_size,
self.normal_size,
self.channles),
classes=4,
weights=None)
elif choosed_model == 'EfficientNetB2':
model = EfficientNetB2(input_shape=(self.normal_size,
self.normal_size,
self.channles),
classes=4,
weights=None)
elif choosed_model == 'EfficientNetB1':
model = EfficientNetB1(input_shape=(self.normal_size,
self.normal_size,
self.channles),
classes=4,
weights=None)
elif choosed_model == 'EfficientNetB0':
model = EfficientNetB0(input_shape=(self.normal_size,
self.normal_size,
self.channles),
classes=4,
weights=None)
elif choosed_model == 'MobileNetV3_Large':
model = MobileNetV3_Large(shape=(self.normal_size,
self.normal_size, self.channles),
n_class=4).build()
elif choosed_model == 'MobileNetV3_Small':
model = MobileNetV3_Small(shape=(self.normal_size,
self.normal_size, self.channles),
n_class=4).build()
elif choosed_model == 'NASNetLarge':
model = NASNetLarge(input_shape=(self.normal_size,
self.normal_size, self.channles),
weights=None,
use_auxiliary_branch=False,
classes=4)
elif choosed_model == 'NASNetMobile':
model = NASNetMobile(input_shape=(self.normal_size,
self.normal_size, self.channles),
weights=None,
use_auxiliary_branch=False,
classes=4)
elif choosed_model == 'NASNetMiddle':
model = NASNetMiddle(input_shape=(self.normal_size,
self.normal_size, self.channles),
weights=None,
use_auxiliary_branch=False,
classes=4)
elif choosed_model == 'ShuffleNet':
model = ShuffleNet(input_shape=(self.normal_size, self.normal_size,
self.channles),
classes=4)
elif choosed_model == 'ShuffleNetV2':
model = ShuffleNetV2(input_shape=(self.normal_size,
self.normal_size, self.channles),
classes=4)
return model