def __init__(self):
self.config = TCNNConfig()
self.model = TextCNN(self.config)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=self.session, save_path=save_path)
logging.info(metrics.classification_report(y_test_cls, y_pred_cls))
# 混淆矩阵
logging.info("Confusion Matrix...")
cm = metrics.confusion_matrix(y_test_cls, y_pred_cls)
logging.info(cm)
time_dif = get_time_dif(start_time)
logging.info("Time usage:", time_dif)
if __name__ == '__main__':
logging.basicConfig(format="%(asctime)s : %(levelname)s : %(message)s",
level=logging.INFO) # 显示INFO等级以上日志
if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']:
raise ValueError("""usage: python run_cnn.py [train / test]""")
logging.info('Configuring CNN model...')
config = TCNNConfig()
# if not os.path.exists(vocab_dir): # 如果不存在词汇表，重建
# build_voca b(train_dir, vocab_dir, config.vocab_size)
# categories, cat_to_id = read_category()
# words, word_to_id = read_vocab(vocab_dir)
# config.context_size = len(words)
model = TextCNN(config)
if sys.argv[1] == 'train':
train2(restore=False)
else:
test()
level=logging.INFO) # 显示INFO等级以上日志
if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']:
raise ValueError("""usage: python run_cnn.py [train / test]""")
logging.info('Configuring CNN model...')
config = TCNNConfig()
wv_model = get_wordvec_model()
# 随机初始化词向量
wv_model_vectors = np.random.randn(len(wv_model.wv.vectors),
config.embedding_dim)
np.save('random_embedding.npy', wv_model_vectors)
logging.info('Saved random embedding vectors.')
embedding_layer_w = np.row_stack(
(wv_model_vectors.astype('float32'),
np.zeros(shape=(config.embedding_dim)).astype('float32')))
# embedding_layer_w = np.row_stack((wv_model.wv.vectors, np.zeros(shape=(config.embedding_dim)).astype('float32')))
logging.info('embedding_layer_w shape: {}'.format(embedding_layer_w.shape))
# embedding_layer_w = np.load("cache/train2.model.wv.vectors.npy").astype('float32')
vocab_size = embedding_layer_w.shape[0]
logging.info('vocab_size:{}'.format(vocab_size))
model = TextCNN(config,
vocab_size=vocab_size,
embedding_layer_w=embedding_layer_w)
if sys.argv[1] == 'train':
train2(restore=False)
# generateDataMatrix()
else:
# generateDataMatrix()
test()