def get_model(model_type, input_shape, learning_rate, weight_decay, optimizer,
momentum):
input_tensor = Input(shape=input_shape)
if model_type == 'resnet':
base_model = keras.applications.resnet50.ResNet50(
include_top=False,
weights='imagenet',
input_tensor=input_tensor,
input_shape=input_shape,
classes=None)
x = Flatten()(base_model.output)
predictions = Dense(NUM_CLASSES, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_type == 'vgg':
base_model = keras.applications.vgg19.VGG19(include_top=False,
weights=None,
input_tensor=input_tensor,
input_shape=input_shape,
classes=None)
x = Flatten()(base_model.output)
predictions = Dense(NUM_CLASSES, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
else:
model = get_custom_model(input_shape, learning_rate, weight_decay,
optimizer, momentum)
return model
def get_model(model_type):
print(f'====== Getting model architecture: {model_type} ======')
input_tensor = Input(shape=input_shape)
if model_type == 'resnet':
base_model = keras.applications.resnet50.ResNet50(
include_top=False,
weights='imagenet',
input_tensor=input_tensor,
input_shape=input_shape,
classes=None)
x = Flatten()(base_model.output)
predictions = Dense(NUM_CLASSES, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_type == 'vgg':
base_model = keras.applications.vgg19.VGG19(include_top=False,
weights=None,
input_tensor=input_tensor,
input_shape=input_shape,
classes=None)
x = Flatten()(base_model.output)
predictions = Dense(NUM_CLASSES, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
else:
model = get_custom_model(input_shape)
return model