def run_test():
import problem_unittests as t
t.test_create_lookup_tables(create_lookup_tables)
t.test_get_batches(get_batches)
t.test_tokenize(token_lookup)
t.test_get_inputs(get_inputs)
t.test_get_init_cell(get_init_cell)
t.test_get_embed(get_embed)
t.test_build_rnn(build_rnn)
t.test_build_nn(build_nn)
t.test_get_tensors(get_tensors)
t.test_pick_word(pick_word)
"""
tokens = dict()
tokens['.'] = '<PERIOD>'
tokens[','] = '<COMMA>'
tokens['"'] = '<QUOTATION_MARK>'
tokens[';'] = '<SEMICOLON>'
tokens['!'] = '<EXCLAMATION_MARK>'
tokens['?'] = '<QUESTION_MARK>'
tokens['('] = '<LEFT_PAREN>'
tokens[')'] = '<RIGHT_PAREN>'
tokens['?'] = '<QUESTION_MARK>'
tokens['-'] = '<HYPHEN>'
tokens['\n'] = '<NEW_LINE>'
#tokens[':'] = '<COLON>'
return tokens
tests.test_tokenize(token_lookup)
#################################################
## Pre-process all the data and save it
#################################################
# pre-process training data
helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)
#################################################
## Check Point
#################################################
int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
#################################################
## Build the Neural Network
def test_create_punctuation_map(self):
test_tokenize(token_lookup=create_punctuation_map)
