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