def __init__(self):
"""The path represents the config file path"""
# Initialize class variables and import configuration manager and data manager with respective items
print("Importing configurations from files")
self.confman = ConfigManager.ConfigurationManager()
print("Importing datas from files")
self.dataman = DataManager.DataManager()
print("Importing embeddings")
self.embeddings = self.dataman.restore_embeddings("Constant") # Importing Embeddings and doc_embeddings
print("Importing word dictionnaries")
self.dict,self.rev_dict = self.dataman.restore_dictionaries() # Importing dictionary and rev_dictionary
self.logistic_learning_rate = self.confman.logistic_learning_rate
# Initialize model variables
print("Initializing model variables")
self.weights = tf.Variable(tf.random_normal([self.confman.doc_embedding_size, self.confman.num_class]),dtype=tf.float32)
# Initialize model inputs
self.class_target = tf.placeholder(tf.int32,[None, self.confman.num_class])
self.tweet_vectors = tf.placeholder(tf.float32,[None, self.confman.doc_embedding_size])
# Initialize tensorflow session
print("Creating tf session")
self.sess = tf.Session()
pass
#%% cell 0
import ConfigManager
import numpy as np
import tensorflow as tf
import pickle
from DatabaseManager import *
from multiprocessing import Pool
import os
#importing configurations to the application
print("Importing configurations")
confman = ConfigManager.ConfigurationManager()
#%% cell 1
# restoring dictionnaries
print("Importing word dictionnary")
with open(confman.dictionary_path, "rb") as f:
word_dictionary = pickle.load(f)
word_dictionary_rev = dict(
zip(word_dictionary.values(), word_dictionary.keys()))
print("Restoring word and doc embeddings")
word_embeddings = tf.Variable(
tf.random_uniform([confman.vocabulary_size, confman.embedding_size], -1.0,
1.0))
saver = tf.train.Saver({"embeddings": word_embeddings})
sess = tf.Session()
saver.restore(sess, confman.checkpoint_path)
#%% cell 2
extracted_tweet_folder = confman.extracted_tweets
