model_name = args.model if model_name != "cnn" and model_name != "lstm": print("model name invalid! Please use -m cnn or -m lstm in command") sys.exit() start_time = time.time() seq_len = args.seq_len model_path = args.save_path output_path = args.output_path print("Preparing test data...") test = helper.loadFile(args.test_path) map_dir = "token_label_id_mapping" X_test, y_test, feat2id, label2id, id2label = helper.getTest( test, map_dir, seq_len) num_chars = len(feat2id) num_classes = len(id2label) with tf.Session() as sess: initializer = tf.random_normal_initializer(stddev=0.1) with tf.variable_scope("model", reuse=None, initializer=initializer): model = DNN_Model(num_classes, num_chars, seq_len, 15, model_name) print("loading model parameter...") saver = tf.train.Saver() saver.restore(sess, model_path) print("Testing...")
parser.add_argument("test_path", help="the path of test file") parser.add_argument("-c", "--char_emb", help="the char embedding file", default=None) parser.add_argument("-g", "--gpu", help="the id of gpu, the default is 0", default=0, type=int) args = parser.parse_args() model_path = args.model_path test_path = args.test_path output_path = None gpu_config = "/gpu:"+str(args.gpu) emb_path = args.char_emb num_steps = 200 # it must consist with the train start_time = time.time() print "preparing test data" X_test, X_left_test, X_right_test, X_pos_test, X_lpos_test, X_rpos_test, X_rel_test, X_dis_test = helper.getTest(test_path=test_path, seq_max_len=num_steps) test_data = {} test_data['char'] = X_test test_data['left'] = X_left_test test_data['right'] = X_right_test test_data['pos'] = X_pos_test test_data['lpos'] = X_lpos_test test_data['rpos'] = X_rpos_test test_data['rel'] = X_rel_test test_data['dis'] = X_dis_test char2id, id2char = helper.loadMap("char2id") pos2id, id2pos = helper.loadMap("pos2id") label2id, id2label = helper.loadMap("label2id")
functions.get(arguments[1], newNode) elif len(arguments) == 3: if arguments[0] == "join": if newNode.checkInRing(): print( "The node is already in a ring, so can't add it to a new ring\n" ) else: try: temp = int(arguments[2]) except ValueError: print("Please enter an integer") functions.join(newNode, arguments[1], arguments[2]) elif arguments[0] == "test": try: temp = int(arguments[2]) except ValueError: print("Please enter an integer") helper.getTest(arguments[1], arguments[2]) elif arguments[0] == "put": if newNode.checkInRing() == False: print("Sorry this node is not in the ring") else: functions.put(str(arguments[1]), str(arguments[2]), newNode) else: print("Invalid Command") command = ''
default=0, type=int) args = parser.parse_args() model_path = args.model_path test_path = args.test_path output_path = args.output_path # gpu_config = "/gpu:"+str(args.gpu) gpu_config = "/gpu:0" emb_path = args.char_emb num_steps = 200 # it must consist with the train start_time = time.time() print("preparing test data") X_test, X_test_str = helper.getTest(test_path=test_path, seq_max_len=num_steps) char2id, id2char = helper.loadMap("char2id") label2id, id2label = helper.loadMap("label2id") num_chars = len(id2char.keys()) num_classes = len(id2label.keys()) if emb_path != None: embedding_matrix = helper.getEmbedding(emb_path) else: embedding_matrix = None print("building model") config = tf.ConfigProto(allow_soft_placement=True) with tf.Session(config=config) as sess: with tf.device(gpu_config): initializer = tf.random_uniform_initializer(-0.1, 0.1) with tf.variable_scope("model", reuse=None, initializer=initializer):
parser.add_argument("-c", "--char_emb", help="the char embedding file", default=None) parser.add_argument("-g", "--gpu", help="the id of gpu, the default is 0", default=0, type=int) args = parser.parse_args() model_path = args.model_path test_path = args.test_path output_path = args.output_path # gpu_config = "/gpu:"+str(args.gpu) gpu_config = "/cpu:0" emb_path = args.char_emb num_steps = 200 # it must consist with the train start_time = time.time() print "preparing test data" X_test, X_test_str = helper.getTest(test_path=test_path, seq_max_len=num_steps) char2id, id2char = helper.loadMap("char2id") label2id, id2label = helper.loadMap("label2id") num_chars = len(id2char.keys()) num_classes = len(id2label.keys()) if emb_path != None: embedding_matrix = helper.getEmbedding(emb_path) else: embedding_matrix = None print "building model" config = tf.ConfigProto(allow_soft_placement=True) with tf.Session(config=config) as sess: with tf.device(gpu_config): initializer = tf.random_uniform_initializer(-0.1, 0.1) with tf.variable_scope("model", reuse=None, initializer=initializer):