import os import sys import time import logging import numpy as np sys.path.append('../') logging.getLogger('tensorflow').disabled = True import tensorflow as tf from utils import checkmate as cm from utils import data_helpers as dh from utils import param_parser as parser from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score args = parser.parameter_parser() MODEL = dh.get_model_name() logger = dh.logger_fn("tflog", "logs/Test-{0}.log".format(time.asctime())) CPT_DIR = 'runs/' + MODEL + '/checkpoints/' BEST_CPT_DIR = 'runs/' + MODEL + '/bestcheckpoints/' SAVE_DIR = 'output/' + MODEL def create_input_data(data: dict): return zip(data['f_pad_seqs'], data['b_pad_seqs'], data['onehot_labels']) def test_cnn(): """Test CNN model.""" # Print parameters used for the model
# Load word2vec model print("Loading data...") word2idx, embedding_matrix = dh.load_word2vec_matrix(args.word2vec_file) # Load sentences, labels, and training parameters print("Data processing...") train_data = TextData(args, args.train_file, word2idx, embedding_matrix) test_data = TextData(args, args.test_file, word2idx, embedding_matrix) train_loader = torch.utils.data.DataLoader(train_data, args.batch_size, shuffle=True, num_workers=1) test_loader = torch.utils.data.DataLoader(test_data, args.batch_size, shuffle=False, num_workers=1) model = CNN(args).to(device) #print(model) for epoch in range(1, args.epochs + 1): train(args, model, train_loader, device, epoch) test(model, device, test_loader) #torch.save(model.state_dict(), "../data/TextCNN.pt") if __name__ == '__main__': args = parser.parameter_parser() # add parser by using argparse module train_CNN(args) x = print("Press any key to continue...")