TinghuiWang / pyActLearn

            tf.train.Saver().restore(session, os.path.join(summaries_dir, 'best.ckpt'))

        logger.debug('Total Epoch: %d, current batch %d', injector.num_epochs, injector.cur_batch)

    def predict_proba(self, x, session=None, batch_size=500):

        """Predict probability (Softmax)

        """

        if session is None:

            if self.sess is None:

                session = tf.Session()

                self.sess = session

            else:

                session = self.sess

        injector = BatchSequenceInjector(batch_size=batch_size, data_x=x, seq_len=self.num_steps)

        injector.reset()

        result = None

        while injector.num_epochs == 0:

            batch_x = injector.next_batch()

            batch_y = session.run(self.y,

                                  feed_dict={self.x: batch_x,

                                             self.init_state: np.zeros((batch_x.shape[0], 2 * self.num_units))})

            if result is None:

                result = batch_y

            else:

                result = np.concatenate((result, batch_y), axis=0)

        return result

    def predict(self, x, session=None, batch_size=500):

        if session is None:

                result = np.concatenate((result, batch_y), axis=0)

        return result

    def predict(self, x, session=None, batch_size=500):

        if session is None:

            if self.sess is None:

                session = tf.Session()

                self.sess = session

            else:

                session = self.sess

        injector = BatchSequenceInjector(batch_size=batch_size, data_x=x, seq_len=self.num_steps)

        injector.reset()

        result = None

        while injector.num_epochs == 0:

            batch_x = injector.next_batch()

            batch_y = session.run(self.y_class,

                                  feed_dict={self.x: batch_x,

                                             self.init_state: np.zeros((batch_x.shape[0], 2 * self.num_units))})

            if result is None:

                result = batch_y

            else:

                result = np.concatenate((result, batch_y), axis=0)

        return result

    def predict_accuracy(self, x, y, session=None):

        """Get Accuracy given feature array and corresponding labels