Keras rename model and layers

Question

1) I try to rename a model and the layers in Keras with TF backend, since I am using multiple models in one script. Class Model seem to have the property model.name, but whe

Answer 1

Your first problem about the model name is not reproducible on my machine. I can set it like this. many a times these errors are caused by software versions.

model=Sequential()
model.add(Dense(2,input_shape=(....)))
model.name="NAME"

As far as naming the layers, you can do it in Sequential model like this

model=Sequential()
model.add(Dense(2,input_shape=(...),name="NAME"))

Answer 2

Detailed answer is here How to rename Pre-Trained model ? ValueError 'Trained Model' is not a valid scope name

We can use model.name = "Model_Name" when are developing model and making it ready to train. We can also give name to layers. Ex:

model = Sequential()
model.name = "My_Model" #Naming model
model.add(Dense(2,input_shape=(...),name="Name") #Naming layer

Answer 3

For changing names of model.layers with tf.keras you can use the following lines:

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

I needed this in a two-input model case and ran into the "AttributeError: can't set attribute", too. The thing is that there is an underlying hidden attribute _name, which causes the conflict.

Answer 4

for 1), I think you may build another model with right name and same structure with the exist one. then set weights from layers of the exist model to layers of the new model.

Answer 5

Just to cover all the options, regarding the title of the question, if you are using the Keras functional API you can define the model and the layers name by:

inputs = Input(shape=(value, value))

output_layer = Dense(2, activation = 'softmax', name = 'training_output')(dropout_new_training_layer)

model = Model(inputs= inputs, outputs=output_layer, name="my_model")

Answer 6

The Answer from user239457 only works with Standard keras.

If you want to use the Tensorflow Keras, you can do it like this:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential(name='Name')
model.add(Dense(2,input_shape=(5, 1)))