cnn import libs

product-titles-classifiy-text-cnn.py

import numpy as np
import pandas as pd
from gensim.models import KeyedVectors
from keras.layers import Flatten
from keras.layers import MaxPooling1D
from keras.models import Model
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils import to_categorical
from nltk.corpus import stopwords

MAX_NB_WORDS = 200000
MAX_SEQUENCE_LENGTH = 30
EMBEDDING_DIM = 300

EMBEDDING_FILE = "../lib/GoogleNews-vectors-negative300.bin"
category_index = {"clothing":0, "camera":1, "home-appliances":2}
category_reverse_index = dict((y,x) for (x,y) in category_index.items())
STOPWORDS = set(stopwords.words("english"))