Keras - All layer names should be unique

Question

I combine two VGG net in keras together to make classification task. When I run the program, it shows an error:

RuntimeError: The name "predictions" is used 2 times in the model. All layer names should be unique.

I was confused because I only use prediction layer once in my code:

from keras.layers import Dense
import keras
from keras.models import  Model
model1 = keras.applications.vgg16.VGG16(include_top=True, weights='imagenet',
                                input_tensor=None, input_shape=None,
                                pooling=None,
                                classes=1000)
model1.layers.pop()

model2 =  keras.applications.vgg16.VGG16(include_top=True, weights='imagenet',
                                input_tensor=None, input_shape=None,
                                pooling=None,
                                classes=1000)
model2.layers.pop()
for layer in model2.layers:
    layer.name = layer.name + str("two")
model1.summary()
model2.summary()
featureLayer1 = model1.output
featureLayer2 = model2.output
combineFeatureLayer = keras.layers.concatenate([featureLayer1, featureLayer2])
prediction = Dense(1, activation='sigmoid', name='main_output')(combineFeatureLayer)

model = Model(inputs=[model1.input, model2.input], outputs= prediction)
model.summary()

Thanks for @putonspectacles help, I follow his instruction and find some interesting part. If you use model2.layers.pop() and combine the last layer of two models using "model.layers.keras.layers.concatenate([model1.output, model2.output])", you will find that the last layer information is still showed using the model.summary(). But actually they do not exist in the structure. So instead, you can use model.layers.keras.layers.concatenate([model1.layers[-1].output, model2.layers[-1].output]). It looks tricky but it works.. I think it is a problem about synchronization of the log and structure.

Answer 1

First, based on the code you posted you have no layers with a name attribute 'predictions', so this error has nothing to do with your layer Dense layer prediction: i.e:

prediction = Dense(1, activation='sigmoid', 
             name='main_output')(combineFeatureLayer)

The VGG16 model has a Dense layer with name predictions. In particular this line:

x = Dense(classes, activation='softmax', name='predictions')(x)

And since you're using two of these models you have layers with duplicate names.

What you could do is rename the layer in the second model to something other than predictions, maybe predictions_1, like so:

model2 =  keras.applications.vgg16.VGG16(include_top=True, weights='imagenet',
                                input_tensor=None, input_shape=None,
                                pooling=None,
                                classes=1000)

# now change the name of the layer inplace.
model2.get_layer(name='predictions').name='predictions_1'

Answer 2

You can change the layer's name in keras, don't use 'tensorflow.python.keras'.

Here is my sample code:

from keras.layers import Dense, concatenate
from keras.applications import vgg16

num_classes = 10

model = vgg16.VGG16(include_top=False, weights='imagenet', input_tensor=None, input_shape=(64,64,3), pooling='avg')
inp = model.input
out = model.output

model2 = vgg16.VGG16(include_top=False,weights='imagenet', input_tensor=None, input_shape=(64,64,3), pooling='avg')

for layer in model2.layers:
    layer.name = layer.name + str("_2")

inp2 = model2.input
out2 = model2.output

merged = concatenate([out, out2])
merged = Dense(1024, activation='relu')(merged)
merged = Dense(num_classes, activation='softmax')(merged)

model_fusion = Model([inp, inp2], merged)
model_fusion.summary()

Answer 3

Example:

# Network for affine transform estimation
affine_transform_estimator = MobileNet(
                            input_tensor=None,
                            input_shape=(config.IMAGE_H // 2, config.IMAGE_W //2, config.N_CHANNELS),
                            alpha=1.0,
                            depth_multiplier=1,
                            include_top=False,
                            weights='imagenet'
                            )
affine_transform_estimator.name = 'affine_transform_estimator'
for layer in affine_transform_estimator.layers:
    layer.name = layer.name + str("_1")

# Network for landmarks regression
landmarks_regressor = MobileNet(
                        input_tensor=None,
                        input_shape=(config.IMAGE_H // 2, config.IMAGE_W // 2, config.N_CHANNELS),
                        alpha=1.0,
                        depth_multiplier=1,
                        include_top=False,
                        weights='imagenet'
                        )
landmarks_regressor.name = 'landmarks_regressor'
for layer in landmarks_regressor.layers:
    layer.name = layer.name + str("_2")

input_image = Input(shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS))
downsampled_image = MaxPooling2D(pool_size=(2,2))(input_image)
x1 = affine_transform_estimator(downsampled_image)
x2 = landmarks_regressor(downsampled_image)
x3 = add([x1,x2])

model = Model(inputs=input_image, outputs=x3)
optimizer = Adadelta()
model.compile(optimizer=optimizer, loss=mae_loss_masked)

Answer 4

you can use layer_.name instead of layer.name this worked for me