a334cd6f04461ca8d57f35f0ebde68f5520fd689,mloop/neuralnet.py,SingleNeuralNet,load,#SingleNeuralNet#Any#Any#,173

Before Change

        saver_path = str(archive["saver_path"])
        filename = os.path.basename(saver_path)
        searching = True
        try:
            // Check if the neural net archive exists at the location specified
            // in the passed archive dictionary.
            self.saver.restore(self.tf_session, os.path.join(".", saver_path))
            searching = False
        except ValueError:
            // Check if a neural net archive with the same name exists in
            // any other given paths.
            if extra_search_dirs:
                j = 0
                j_max = len(extra_search_dirs)
                while searching and j < j_max:
                    search_dir = extra_search_dirs[j]
                    full_path = os.path.join(search_dir, filename)
                    try:
                        self.saver.restore(self.tf_session, full_path)
                        searching = False
                    except ValueError:
                        j += 1
                    
        // Throw an error if the file wasn"t found.
        if searching:
            message = "Could not find neural net archive {filename}.".format(filename=filename)
            self.log.error(message)
            raise ValueError(message)

    def save(self):
        """
        Exports the net to an archive dictionary.
        """

After Change

        """
        // Set default value of extra_search_dirs if necessary.
        if extra_search_dirs is None:
            extra_search_dirs = []
        
        // Get the saved filename and construct a list of directories to in which
        // to look for it.
        self.log.info("Loading neural network")
        saver_path = str(archive["saver_path"])
        saved_dirname, filename = os.path.split(saver_path)
        saved_dirname = os.path.join(".", saved_dirname)
        search_dirs = [saved_dirname] + extra_search_dirs
        
        // Check each directory for the file.
        for dirname in search_dirs:
            try:
                full_path = os.path.join(dirname, filename)
                self.saver.restore(self.tf_session, full_path)
                return
            except ValueError:
                pass
        
        // If the method hasn"t returned by now then it"s run out of places to
        // look.
        message = "Could not find neural net archive {filename}.".format(filename=filename)
        self.log.error(message)
        raise ValueError(message)

Instances