load multiple models in Tensorflow

Question

I have written the following convolutional neural network (CNN) class in Tensorflow [I have tried to omit some lines of code for clarity.]

class CNN:
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Answer 1

Yes there is. Use separate graphs.

g1 = tf.Graph()
g2 = tf.Graph()

with g1.as_default():
    cnn1 = CNN(..., restore_file='snapshot-model1-10000',..........) 
with g2.as_default():
    cnn2 = CNN(..., restore_file='snapshot-model2-10000',..........)

EDIT:

If you want them into same graph. You'll have to rename some variables. One idea is have each CNN in separate scope and let saver handle variables in that scope e.g.:

saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES), scope='model1')

and in cnn wrap all your construction in scope:

with tf.variable_scope('model1'):
    ...

EDIT2:

Other idea is renaming variables which saver manages (since I assume you want to use your saved checkpoints without retraining everything. Saving allows different variable names in graph and in checkpoint, have a look at documentation for initialization.