Save Tensorflow Model To File

I create a tensorflow model which I would like to save to file so that I can predict against it later. In particular, I need to save the: input_placeholder (= tf.placeholder(tf

Solution 1:

The way I solved this was by pickleing Sklearn objects like binarizers, and using tensorflow's inbuilt save functions for the actual model:

Saving tensorflow model:
1) Build the model as you usually would
2) Save the session with tf.train.Saver(). For example:

oSaver = tf.train.Saver()

oSess = oSession
oSaver.save(oSess, sModelPath)  #filename ends with .ckpt

3) This saves all available variables etc in that session to their variable names.

Loading tensorflow model:
1) The entire flow needs to be re-initialized. In other words, variables, weights, bias, loss function etc need to be declared, and then initialized with tf.initialize_all_variables() being passed into oSession.run()
2) That session now needs to be passed to the loader. I abstracted the flow, so my loader looks like this:

dAlg = tf_training_algorithm()  #defines variables etc and initializes session

oSaver = tf.train.Saver()
oSaver.restore(dAlg['oSess'], sModelPath)

return {
    'oSess': dAlg['oSess'],
    #the other stuff I need from my algorithm, like my solution space etc
}

3) All objects you need for prediction need to be gotten out of your initialisation, which in my case sit in dAlg

PS: Pickle like this:

with open(sSavePathFilename, 'w') as fiModel:
    pickle.dump(dModel, fiModel)

with open(sFilename, 'r') as fiModel:
    dModel = pickle.load(fiModel)

Solution 2:

You should save your project into two separate parts, one is for tensorflow's objects, another is for other objects. I recommend you to use the following tools:

1. tf.saved_model: the procedures your want to saved and load tensorflow all in it.
2. dill: a more powerful pickle tool based on pickle, it can help you bypass most errors encountered by pickle