CODEWITHSUNDEEP
pythontensorflow
Elaine Chen
Elaine Chen

Reputation: 197

tensorflow: save model and load model

Currently trying to make this repo works.

I'm trying to save the trained model in the local machine so can be applied later. I read in tensorflow's doc, seems pretty intuitive to save the model, by calling tf.save_model.save(object). But I'm not sure how to apply.

Original code is here: model.py Following is my changes:

import tensorflow as tf

class ICON(tf.Module): # make it a tensorflow modul

    def __init__(self, config, embeddingMatrix, session=None):

    def _build_inputs(self):

    def _build_vars(self):

    def _convolution(self, input_to_conv):

    def _inference(self):

    def batch_fit(self, queries, ownHistory, otherHistory, labels):

        feed_dict = {self._input_queries: queries, self._own_histories: ownHistory, self._other_histories: otherHistory,
                     self._labels: labels}
        loss, _ = self._sess.run([self.loss_op, self.train_op], feed_dict=feed_dict)
        return loss

    def predict(self, queries, ownHistory, otherHistory, ):

        feed_dict = {self._input_queries: queries, self._own_histories: ownHistory, self._other_histories: otherHistory}
        return self._sess.run(self.predict_op, feed_dict=feed_dict)

    def save(self): # attempt to save the model
        tf.saved_model.save(
            self, './output/model')

The code above produces ValueError as following: ValueError: Tensor("ICON/CNN/embedding_matrix:0", shape=(16832, 300), dtype=float32_ref) must be from the same graph as Tensor("saver_filename:0", shape=(), dtype=string).

Upvotes: 1

Views: 1434

Answers (1)

emilyfy
emilyfy

Reputation: 313

I believe you can use the tf.train.Saver class for this

def save(self): # attempt to save the model
    saver = tf.train.Saver()
    saver.save(self._sess, './output/model')

You can then restore the model this way

saver = tf.train.import_meta_graph('./output/model.meta')
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('./output'))

You might also find this tutorial helpful in understanding this more.

Edit: if you want to use SavedModel

def save(self):
    inputs = {'input_queries': self._input_queries, 'own_histories': self._own_histories, 'other_histories': self._other_histories}
    outputs = {'output': self.predict_op}
    tf.saved_model.simple_save(self._sess, './output/model', inputs, outputs)

You can then use tf.contrib.predictor.from_saved_model to load and serve using the SavedModel

from tensorflow.contrib.predictor import from_saved_model
predictor = from_saved_model('./output/model')
predictions = predictor({'input_queries': input_queries, 'own_histories': own_histories, 'other_histories': other_histories})

Upvotes: 1

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