I scoured the internet and I can see that this question has been asked by many users, but I'm still unable to understand how to do this.
I have a simple network (as given in the CNN tutorial <http://deeplearning.net/tutorial/lenet.html>), except modified to work on 100*100 images, and a smaller dataset. This is basically 1. LeNetConvPoolLayer 2. LeNetConvPoolLayer 3. HiddenLayer 4. LogisticRegression (outputs a 0 or 1). *1. (Logistically) How do I save the parameters? Do I save them for each of the layers or do I simply save the shared variable "params"*? params = layer3.params + layer2.params + layer1.params + layer0.params *2. Once saved, do I have to reconstruct this network in another script using these params? If so, how do I use these params? I can't find any line which assigns the params anywhere.* I would like to have the predicted class and the confidence of the prediction, to be able to perform verification for face recognition. *3. If I want to predict the class for every image, do I simply change the batch size to 1? Where can I actually print this prediction? There is a function call* test_losses = [ test_model(i) for i in range(n_test_batches) ] *which is defined before as * # create a function to compute the mistakes that are made by the model test_model = theano.function( [index], layer3.errors(y), givens={ x: test_set_x[index * batch_size: (index + 1) * batch_size], y: test_set_y[index * batch_size: (index + 1) * batch_size] } ) *I can't understand where the class is actually being predicted/which variable it is stored in. * If anyone could help me out with even one of these questions, I'd be *extremely* grateful! Please let me know if I could provide any other info. -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
