def __init__(self):
self.config = TCNNConfig()
self.categories, self.cat_to_id = read_category()
self.words, self.word_to_id = read_vocab(vocab_dir)
self.config.vocab_size = len(self.words)
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)
exit()
# if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']:
# raise ValueError("""usage: python run_rnn.py [train / test]""")
d = 'data/cnews/'
train_dir = d + 'sen_class.train'
test_dir = d + 'sen_class.test'
val_dir = d + 'sen_class.val'
vocab_dir = d + 'sen_class.vocab'
print('Configuring RNN model...')
config = TRNNConfig()
labels = build_vocab(train_dir, test_dir, val_dir, vocab_dir,
config.vocab_size)
config.num_classes = len(labels)
categories, cat_to_id = read_category(labels)
words, word_to_id = read_vocab(vocab_dir)
config.vocab_size = len(words)
model = TextRNN(config)
print('labels: {}, vocabulary size: {}'.format(config.num_classes,
len(words)))
with open(map_path, "wb") as f:
pickle.dump(
[word_to_id, cat_to_id, config.seq_length, config.num_classes], f)
train()
exit()
if sys.argv[1] == 'train':
train()
elif sys.argv[1] == 'test':
}
y_pred_cls[start_id:end_id] = session.run(model.y_pred_cls,
feed_dict=feed_dict)
# 评估
print("Precision, Recall and F1-Score...")
print(
metrics.classification_report(y_test_cls,
y_pred_cls,
target_names=categories))
# 混淆矩阵
print("Confusion Matrix...")
cm = metrics.confusion_matrix(y_test_cls, y_pred_cls)
print(cm)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
if __name__ == '__main__':
print('Configuring CNN model...')
config = TCNNConfig()
build_vocab(train_dir, vocab_dir)
categories, cat_to_id = read_category()
words, word_to_id = read_vocab(vocab_dir)
config.vocab_size = len(words)
model = TextCNN(config)
test()