def get_model():
model = resnet_50()
if config.model == "resnet18":
model = resnet_18()
if config.model == "resnet34":
model = resnet_34()
if config.model == "resnet101":
model = resnet_101()
if config.model == "resnet152":
model = resnet_152()
model.build(input_shape=(None, config.image_height, config.image_width,
config.channels))
model.summary()
return model
def get_model(cf):
# TODO: build your model
model = resnet_50()
model.build(input_shape=(None, configs.INPUT_SHAPE[0],
configs.INPUT_SHAPE[1], 3))
model.summary()
return model
# create model
model = get_model()
# define loss and optimizer
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adadelta()
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
name='train_accuracy')
valid_loss = tf.keras.metrics.Mean(name='valid_loss')
valid_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
name='valid_accuracy')
def get_model():
if model_index == 0:
return mobilenet_v1.MobileNetV1()
elif model_index == 1:
return mobilenet_v2.MobileNetV2()
elif model_index == 2:
return mobilenet_v3_large.MobileNetV3Large()
elif model_index == 3:
return mobilenet_v3_small.MobileNetV3Small()
elif model_index == 4:
return efficientnet.efficient_net_b0()
elif model_index == 5:
return efficientnet.efficient_net_b1()
elif model_index == 6:
return efficientnet.efficient_net_b2()
elif model_index == 7:
return efficientnet.efficient_net_b3()
elif model_index == 8:
return efficientnet.efficient_net_b4()
elif model_index == 9:
return efficientnet.efficient_net_b5()
elif model_index == 10:
return efficientnet.efficient_net_b6()
elif model_index == 11:
return efficientnet.efficient_net_b7()
elif model_index == 12:
return resnext.ResNeXt50()
elif model_index == 13:
return resnext.ResNeXt101()
elif model_index == 14:
return inception_v4.InceptionV4()
elif model_index == 15:
return inception_resnet_v1.InceptionResNetV1()
elif model_index == 16:
return inception_resnet_v2.InceptionResNetV2()
elif model_index == 17:
return se_resnet.se_resnet_50()
elif model_index == 18:
return se_resnet.se_resnet_101()
elif model_index == 19:
return se_resnet.se_resnet_152()
elif model_index == 20:
return squeezenet.SqueezeNet()
elif model_index == 21:
return densenet.densenet_121()
elif model_index == 22:
return densenet.densenet_169()
elif model_index == 23:
return densenet.densenet_201()
elif model_index == 24:
return densenet.densenet_264()
elif model_index == 25:
return shufflenet_v2.shufflenet_0_5x()
elif model_index == 26:
return shufflenet_v2.shufflenet_1_0x()
elif model_index == 27:
return shufflenet_v2.shufflenet_1_5x()
elif model_index == 28:
return shufflenet_v2.shufflenet_2_0x()
elif model_index == 29:
return resnet.resnet_18()
elif model_index == 30:
return resnet.resnet_34()
elif model_index == 31:
return resnet.resnet_50()
elif model_index == 32:
return resnet.resnet_101()
elif model_index == 33:
return resnet.resnet_152()
elif model_index == 34:
return se_resnext.SEResNeXt50()
elif model_index == 35:
return se_resnext.SEResNeXt101()
else:
raise ValueError("The model_index does not exist.")