"""
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))
