def evaluate(sess, x_, y_, wv_model=None): '''评估数据的准确率和损失''' data_len = len(x_) batch_eval = batch_iter(x_, y_, wv_model, config.batch_size) total_loss = 0.0 total_acc = 0.0 for x_batch, y_batch in batch_eval: batch_len = len(x_batch) feed_dict = feed_data(x_batch, y_batch, 1.0) loss, acc = sess.run([model.loss, model.acc], feed_dict=feed_dict) total_loss += loss * batch_len total_acc += acc * batch_len return total_loss / data_len, total_acc / data_len
def train2(restore=False): logging.info('Configuring TensorBoard and Saver...') # 配置tensor board tensorboard_dir = 'text_cnn/tmp' if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) tf.summary.scalar('loss', model.loss) tf.summary.scalar('accuracy', model.acc) merged_summary = tf.summary.merge_all() writer = tf.summary.FileWriter(tensorboard_dir) #配置Saver saver = tf.train.Saver() if not os.path.exists(save_dir): os.makedirs(save_dir) logging.info('Loading training and validation data...') # 载入训练集与验证集 start_time = time.time() train_set_data = process_file(train_set_file, config.num_classes, config.seq_length) x_train, y_train = train_set_data['train_set'] x_val, y_val = train_set_data['validate_set'] del train_set_data logging.info('Time usage: {}'.format(get_time_dif(start_time, ))) # 创建session conf = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)) session = tf.Session(config=conf) if restore and os.path.exists(save_dir + "checkpoint"): logging.info("Restoring Variables from Checkpoint for cnn model.") saver.restore(session, tf.train.latest_checkpoint(save_dir)) else: logging.info('first training cnn model, Initializing Variables') session.run(tf.global_variables_initializer()) writer.add_graph(session.graph) logging.info('Training and evaluating...') start_time = time.time() best_acc_val = 0.0 flag = False # wv_model = get_wordvec_model() for epoch in range(1, config.num_epochs + 1): logging.info('Epoch: {}'.format(epoch)) batch_train = batch_iter(x_train, y_train, wv_model, config.batch_size) total_batch = 0 for x_batch, y_batch in batch_train: feed_dict = feed_data(x_batch, y_batch, config.dropout_keep_prob) if total_batch % config.print_per_batch == 0: loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict) time_dif = get_time_dif(start_time) logging.info( 'epoch:{4: >3}, Iter: {0:>6}, Train Loss: {1:>6.8}, Train Acc: {2:>7.8%}, Time: {3}' .format(total_batch, loss_train, acc_train, time_dif, epoch)) # 运行优化 session.run(model.optim, feed_dict=feed_dict) total_batch += 1 feed_dict[model.keep_prob] = 1.0 loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict) loss_val, acc_val = evaluate(session, x_val, y_val, wv_model=wv_model) if acc_val > best_acc_val: best_acc_val = acc_val last_improved = total_batch saver.save(sess=session, save_path=save_path) improved_str = '*' else: improved_str = '' time_dif = get_time_dif(start_time) msg = 'epoch:{0: >3}, Iter: {1:>6}, Train Loss: {2:>6.8}, Train Acc: {3:>7.8%},' \ 'Val Loss: {4:>6.8}, Val Acc: {5:>7.8%}, Time: {6} {7}' logging.info( msg.format(epoch, total_batch, loss_train, acc_train, loss_val, acc_val, time_dif, improved_str)) if epoch % config.save_per_epoch == 0: s = session.run(merged_summary, feed_dict=feed_dict) writer.add_summary(s, total_batch) # epoch_name = os.path.join(save_dir, "epoch_{0}".format(epoch)) # saver.save(sess=session, save_path=epoch_name) session.close()
def train(restore=False): logging.info('Configuring TensorBoard and Saver...') # 配置tensor board tensorboard_dir = 'textcnn/tensorboard' if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) tf.summary.scalar('loss', model.loss) tf.summary.scalar('accuracy', model.acc) merged_summary = tf.summary.merge_all() writer = tf.summary.FileWriter(tensorboard_dir) #配置Saver saver = tf.train.Saver() if not os.path.exists(save_dir): os.makedirs(save_dir) logging.info('Loading training and validation data...') # 载入训练集与验证集 start_time = time.time() train_set_data = process_file(train_set_file, config.num_classes, config.seq_length) x_train, y_train = train_set_data['train_set'] x_val, y_val = train_set_data['validate_set'] logging.info('Time usage: {}'.format(get_time_dif(start_time,))) random_vector_generate(train_set_file) # 创建session conf = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)) session = tf.Session(config=conf) if restore and os.path.exists(save_dir+"checkpoint"): logging.info("Restoring Variables from Checkpoint for cnn model.") saver.restore(session, tf.train.latest_checkpoint(save_dir)) else: logging.info('first training cnn model, Initializing Variables') session.run(tf.global_variables_initializer()) writer.add_graph(session.graph) logging.info('Training and evaluating...') start_time = time.time() total_batch = 0 best_acc_val = 0.0 last_improved = 0 require_improvement = 10000 flag = False for epoch in range(config.num_epochs): logging.info('Epoch: {}'.format(epoch + 1,)) batch_train = batch_iter(x_train, y_train, config.batch_size) for x_batch, y_batch in batch_train: feed_dict = feed_data(x_batch, y_batch, config.dropout_keep_prob) if total_batch % config.save_per_batch == 0: # 训练结果写入tensorboard轮数 s = session.run(merged_summary, feed_dict=feed_dict) writer.add_summary(s, total_batch) if total_batch % config.print_per_batch == 0: # 输出训练集和验证集性能轮数 feed_dict[model.keep_prob] = 1.0 loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict) time_dif = get_time_dif(start_time) msg = 'Iter: {0:>6}, Train Loss: {1:>6.4}, Train Acc: {2:>8.4%}, ' \ 'Time: {3:>8.4}' logging.info(msg.format(total_batch, loss_train, acc_train, time_dif)) # 运行优化 session.run(model.optim, feed_dict=feed_dict) total_batch += 1 # if total_batch - last_improved > require_improvement: # # 验证集正确率长期不提升,提前结束训练 # logging.info('No optimization for a long time, auto-stopping...') # flag = True # break # 输出训练集和验证集性能轮数 feed_dict[model.keep_prob] = 1.0 loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict) loss_val, acc_val = evaluate(session, x_val, y_val) if acc_val > best_acc_val: best_acc_val = acc_val last_improved = total_batch saver.save(sess=session, save_path=save_path) improved_str = '*' else: improved_str = '' time_dif = get_time_dif(start_time) msg = 'Iter: {0:>6}, Train Loss: {1:>6.4}, Train Acc: {2:>7.4%},' \ 'Val Loss: {3:>6.4}, Val Acc: {4:>7.4%}, Time: {5} {6}' logging.info(msg.format(total_batch, loss_train, acc_train, loss_val, acc_val, time_dif, improved_str)) if flag: break