def make_model(self):
# create the base pre-trained model
if self.model_architecture == 'vgg16':
from keras.applications.vgg16 import VGG16
self.base_model = VGG16(weights='imagenet', include_top=False)
elif self.model_architecture == 'resnet':
from keras.applications.resnet50 import ResNet50
self.base_model = ResNet50(weights='imagenet', include_top=False)
elif self.model_architecture == 'inception':
from keras.applications.inception_v3 import InceptionV3
self.base_model = InceptionV3(weights='imagenet', include_top=False)
else:
print 'Model architecture parameter unknown. Options are: vgg16, resnet, and inception'
rospy.signal_shutdown("Model architecture unknown.")
# now we add a new dense layer to the end of the network inplace of the old layers
x = self.base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
# add the outplut layer
predictions = Dense(len(self.categories.keys()), activation='softmax')(x)
# create new model composed of pre-trained network and new final layers
# if you want to change the input size, you can do this with the input parameter below
self.model = Model(input=self.base_model.input, output=predictions)
# now we go through and freeze all of the layers that were pretrained
for layer in self.base_model.layers:
layer.trainable = False
if self.verbose:
print 'compiling model ... '
start_time = time.time()
# in finetuning, these parameters can matter a lot, it is wise to observe
# how well your model is learning for this to work well
self.model.compile(optimizer=self.optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
if self.verbose:
end_time = time.time()
self.print_time(start_time,end_time,'compiling model')
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