load_model.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import importlib
from tensorflow import flags

from data_process import get_data

# model relation
flags.DEFINE_string('classifier', 'bidirectional_lstm.BiLSTM', "path of the Class for the classifier")
flags.DEFINE_integer('nb_epoch', 50, "number of epoch")
flags.DEFINE_integer('embed_size', 300, "hidden size of embedding layer")
flags.DEFINE_integer('batch_size', 640, "batch size for train")
flags.DEFINE_string('optimizer', 'adam', "the optimizer for train")
flags.DEFINE_bool('use_pretrained', True, "if use pretrained vector for embedding layer")
flags.DEFINE_bool('trainable', True,
                  "if the embedding layer is trainable. this param is used only `use_pretrained` is true")
# data relation
flags.DEFINE_integer('max_len', 150, "regular sentence to a fixed length")
flags.DEFINE_string('kfold_model_path', '', "weights for keras model")

FLAGS = flags.FLAGS


def get_predict():
    pass


def main():
    data = get_data(max_len=FLAGS.max_len)

    cls_name = FLAGS.classifier
    module_name = ".".join(cls_name.split('.')[:-1])
    cls_name = cls_name.split('.')[-1]
    _module = importlib.import_module(module_name)
    cls = _module.__dict__.get(cls_name)

    model = cls(data=data, nb_epoch=FLAGS.nb_epoch, max_len=FLAGS.max_len, embed_size=FLAGS.embed_size,
                batch_size=FLAGS.batch_size, optimizer=FLAGS.optimizer,
                use_pretrained=FLAGS.use_pretrained, trainable=FLAGS.trainable,
                is_kfold=True, kfold=10, is_retrain=True)
    model.model_predict_with_weights(FLAGS.kfold_model_path)

if __name__ == '__main__':
    main()