""" import argparse import os import sys sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from cakechat.utils.env import init_keras init_keras() from cakechat.dialog_model.factory import get_trained_model from cakechat.utils.logger import get_tools_logger from cakechat.utils.w2v.model import get_w2v_model _logger = get_tools_logger(__file__) def parse_args(): argparser = argparse.ArgumentParser() argparser.add_argument( '-m', '--model', action='store', choices=['default', 'reverse', 'w2v', 'all'], help='Fetch models from s3 to disk', default='all') args = argparser.parse_args() return args
from cakechat.utils.env import init_theano_env init_theano_env() from cakechat.utils.text_processing import get_processed_corpus_path, get_index_to_token_path, \ get_index_to_condition_path, load_processed_dialogs_from_json, load_index_to_item, FileTextLinesIterator, \ get_flatten_dialogs, ProcessedLinesIterator, get_tokens_sequence from cakechat.utils.files_utils import is_non_empty_file from cakechat.utils.logger import get_tools_logger from cakechat.dialog_model.train import train_model from cakechat.dialog_model.model_utils import get_w2v_embedding_matrix, get_model_full_path from cakechat.dialog_model.model import get_nn_model from cakechat.config import BASE_CORPUS_NAME, TRAIN_CORPUS_NAME, CONTEXT_SENSITIVE_VAL_CORPUS_NAME, \ USE_PRETRAINED_W2V_EMBEDDINGS_LAYER _logger = get_tools_logger(__file__) def _look_for_saved_model(nn_model_path): if os.path.isfile(nn_model_path): _logger.info('Saved model is found: %s' % nn_model_path) else: _logger.info('Could not find previously saved model: %s\nWill train it from scratch' % nn_model_path) def _look_for_saved_files(files_paths): for f_path in files_paths: if not is_non_empty_file(f_path): raise Exception('\nCould not find the following file or it\'s empty: {0}'.format(f_path))