parser.add_argument('--graph_folder', default='../lm_graph', dest='graphs') args = parser.parse_args() # Fit the model if args.mode == 'train': # Read the initial word vectors train_data = np.load(open('lm_train_data.npy','r')) train_labels = np.load(open('lm_train_labels.npy','r')) lm = LanguageModel(args.lr, args.num_steps, args.vocab_len, args.minibatch_size) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) lm.fit(sess, train_data, train_labels, num_epochs=args.num_epochs, folder=args.folder, graph_folder=args.graphs) else: tweets = dill.load(open("tweets", "rb")) w2i = dill.load(open("w2i","rb")) i2w = dill.load(open("i2w","rb")) word_vector = dill.load(open("word_vecs","rb")) start_wd = ["president", "@netanyahu", "democrats", "gop", "congress", "white", "my", "the", "#makeamericagreatagain" ,"republicans", "wall", "@realdonaldtrump", "crooked"] input_list = [np.array([[word_vector[w2i[item]]]]) for item in start_wd] model = LanguageModel(args.lr, args.num_steps, args.vocab_len, args.minibatch_size) saver = tf.train.Saver() folder = args.folder with tf.Session() as sess: saver.restore(sess, os.path.join(folder, 'model.ckpt'))
import pickle from lm import LanguageModel train_filename = "train_sequence.pkl" model_filename = "model.pkl" dataset = pickle.load(open(train_filename, "rb")) lm = LanguageModel(lidstone_param=3e-4) lm.fit(dataset) pickle.dump(lm, open(model_filename, "wb"))