'--training_filename',
type=str,
help='Result of run_data_processing.py. '
'Something like: /home/premy/BreachCompilationAnalysis/edit-distances/1.csv',
required=True)
# parser.add_argument('--encoding_output_folder', type=str, help='Will be used for training')
arg_p = parser.parse_args()
print('Building vocabulary...')
build_vocabulary(arg_p.training_filename)
print('Vectorization...')
DATA_LOADER = LazyDataLoader(arg_p.training_filename)
_, _, training_records_count = DATA_LOADER.statistics()
# TOKEN_INDICES = get_token_indices()
chars, c_table = get_chars_and_ctable()
inputs = []
targets = []
print('Generating data...')
for i in tqdm(range(training_records_count),
desc='Generating inputs and targets'):
x_, y_ = DATA_LOADER.next()
# Pad the data with spaces such that it is always MAXLEN.
inputs.append(x_)
# -*- coding: utf-8 -*-
import os
import numpy as np
from keras import layers
from keras.models import Sequential
from six.moves import range
from constants import INVERT
from data_gen import LazyDataLoader
from utils import colors
from utils import get_chars_and_ctable
ls -alrt
DATA_LOADER = LazyDataLoader()
INPUT_MAX_LEN, OUTPUT_MAX_LEN, TRAINING_SIZE = DATA_LOADER.statistics()
chars, ctable = get_chars_and_ctable()
if not os.path.exists('x_y.npz'):
raise Exception('Please run the vectorization script before.')
print('Loading data from prefetch...')
data = np.load('x_y.npz')
x_train = data['x_train']
x_val = data['x_val']
y_train = data['y_train']
y_val = data['y_val']