parser.add_argument("--checkpoint_dir", type=str, default="saved_model", help="Checkpoint dir for saved model.") parser.add_argument("--batch_size", type=int, default=24, help="Batch size.") parser = argparse.ArgumentParser() add_arguments(parser) args = parser.parse_args() print("Loading dictionary...") word_dict, reversed_dict, document_max_len = build_dict(args.test_tsv, is_train=False) print("Building test dataset...") test_x, test_y = build_dataset(args.test_tsv, word_dict, document_max_len) checkpoint_file = tf.train.latest_checkpoint(args.checkpoint_dir) # File for saving the predicted values nameHandle = open('y_pred.txt', 'w') graph = tf.Graph() with graph.as_default(): with tf.Session() as sess: saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file)) saver.restore(sess, checkpoint_file)
if SIGHT == True: #new adoption event, init "source" field as empty list adopt = dict() adopt["source"] = list() #check all potential source repos user is subscribed to #if most recent lib commit to repo is after user subscribed, #log as an adoption event for r in user_repos[user]: #if repo has library and most recent commit of lib was after #user subscribed, adoption! if lib in repo_imports[r] and repo_imports[r][lib][ "time"] > repo_users[r][user]: #save the adoption! add repo commit to source list adopt["source"].append( data.build_dict(repo_imports[r][lib]["user"], r, repo_imports[r][lib]["time"])) #if adoption has valid sources, set up "target" and save if len(adopt["source"]) != 0: #set up target data adopt["target"] = data.build_dict(user, repo, time) #save adoption event to list adoption_events[lib].append(adopt) adoption_count = adoption_count + 1 #adoption are not, update the data structures to reflect commit user_quiver[user].add(lib) #user has used lib, add to quiver #update most recent commit of this lib in repo repo_imports[repo][lib]["user"] = user repo_imports[repo][lib]["time"] = time #add user to repo user list, tracking time user first "joined"
import tensorflow as tf import pickle from model import Model from data_utils import build_dict, build_dataset, batch_iter from train import hyper_params_path, word2index_path, seq2seq_model_dir with open(hyper_params_path, "rb") as f: args = pickle.load(f) print("Loading dictionary...") word_dict, reversed_dict, article_list, _ = build_dict( word2index_path=word2index_path) print("Loading validation dataset...") valid_x = build_dataset(word_dict, article_list, args.article_max_len) with tf.Session() as sess: print("Loading saved model...") model = Model(word_dict, args, train=False) saver = tf.train.Saver(tf.global_variables()) ckpt = tf.train.get_checkpoint_state(seq2seq_model_dir) saver.restore(sess, ckpt.model_checkpoint_path) batches = batch_iter(valid_x, [0] * len(valid_x), args.batch_size, 1) print("Writing summaries to 'result.txt'...") for batch_x, _ in batches: batch_x_len = [len([y for y in x if y != 0]) for x in batch_x] valid_feed_dict = { model.batch_size: len(batch_x), model.X: batch_x,
parser.add_argument("--checkpoint_dir", type=str, default="saved_model", help="Checkpoint directory.") parser = argparse.ArgumentParser() add_arguments(parser) args = parser.parse_args() num_class = 3 if not os.path.exists(args.checkpoint_dir): os.mkdir(args.checkpoint_dir) print("Building dictionary...") word_dict, reversed_dict, document_max_len = build_dict(args.train_tsv) print("Building dataset...") x, y = build_dataset(args.train_tsv, word_dict, document_max_len) # Split to train and validation data train_x, valid_x, train_y, valid_y = train_test_split(x, y, test_size=0.10, random_state=42) #train_x, train_y = build_dataset(args.train_tsv, word_dict, document_max_len) #print("Building validation dictionary...") # #valid_tsv = 'data/lstm_single/africell_calls/dev_data.tsv' #word_dict_valid, reversed_dict, document_max_len_valid = build_dict(valid_tsv) #print("Building validation dataset...")
os.makedirs(os.path.dirname(hyper_params_path)) with open(hyper_params_path, "wb") as f: pickle.dump(args, f) if not os.path.exists(seq2seq_model_dir): os.mkdir(seq2seq_model_dir) else: if args.with_model: pre_model_checkpoint = open(seq2seq_model_dir + 'checkpoint', 'r') pre_model_checkpoint = "".join([ seq2seq_model_dir, pre_model_checkpoint.read().splitlines()[0].split('"')[1] ]) print("Building dictionary...") word_dict, reversed_dict, article_list, headline_list = build_dict( train=True, word2index_path=word2index_path) print("Loading training dataset...") train_x, train_y = build_dataset(word_dict, article_list, args.article_max_len, headline_list=headline_list, headline_max_len=args.headline_max_len, train=True) with tf.Session() as sess: model = Model(word_dict, args) sess.run(tf.global_variables_initializer()) saver = tf.train.Saver(tf.global_variables()) if 'pre_model_checkpoint' in globals(): print("Continuing training from pre-trained model:", pre_model_checkpoint, "......")