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为什么训练一次之后，显示placehold没有赋值 import os import numpy as np import tensorflow as tf from pre import get_file, get_batch, valuation_batch from nw import deep_CNN, losses, trainning, evaluation ratio = 0.2 #keep_prob = 0.5 num_epochs = 1 n_class = 5 image_h = 640 image_w = 480 target_height = 790 target_width = 630 batch_size = 8 capacity = 256 max_step = 15000 learning_rate = 0.001 train_dir = 'E:\\DeepLearn\\picture_mosaic' logs_train_dir = 'E:\\DeepLearn\\Classify_picture\\self\\' train, train_label, valuation, valuation_label = get_file(train_dir, ratio) train_batch, train_label_batch = get_batch(train, train_label, image_h, image_w, target_height, target_width, batch_size, capacity) val_image_batch, val_label_batch = valuation_batch(valuation, valuation_label, image_h, image_w, target_height, target_width, batch_size, capacity) is_training = tf.placeholder(tf.bool, shape=(), name='is_training') x = tf.cond(is_training, lambda: train_batch, lambda: val_image_batch) y = tf.cond(is_training, lambda: train_label_batch, lambda: val_label_batch) train_logits = deep_CNN(x, batch_size, n_class) #lish_train_logits = deep_CNN(val_image_batch, val_label_batch, n_class) train_loss = losses(train_logits, y) train_op = trainning(train_loss, learning_rate) train_acc = evaluation(train_logits, y) #可以将所有summary全部保存到磁盘，以便tensorboard显示。如果没有特殊要求，一般用这一句就可一显示训练时的各种信息了。 summary_op = tf.summary.merge_all() sess = tf.Session() # 指定一个文件用来保存图 train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph) saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) # 开启一个协调器 coord = tf.train.Coordinator() # 使用start_queue_runners 启动队列填充 threads = tf.train.start_queue_runners(sess=sess, coord=coord) try: for step in np.arange(max_step): if coord.should_stop(): #使用 coord.should_stop()来查询是否应该终止所有线程，当文件队列（queue）中的所有文件都已经读取出列的时候，会抛出一个 OutofRangeError 的异常，这时候就应该停止Sesson中的所有线程了; break _, tra_loss, tra_acc= sess.run([train_op, train_loss, train_acc], feed_dict={is_training: True}) if step % 100 == 0: print('Step %d, train loss = %.2f, train accuracy = %.2f%%' % (step, tra_loss, tra_acc * 100.0)) #print('The learning rate is %s' %(_[1])) val_loss, val_acc = sess.run([train_loss, train_acc], feed_dict={is_training: False}) print(val_loss, val_acc) checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt') saver.save(sess, checkpoint_path, global_step=step) summary_str = sess.run(summary_op) train_writer.add_summary(summary_str, step) ##调用train_writer的add_summary方法将训练过程以及训练步数保存 ''' if step % 2000 == 0 or (step + 1) == max_step: checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt') # os.path.join连接两个或更多的路径名组件 #saver.save(sess, checkpoint_path, global_step=step) summary.value.add(tag='evl_loss', simple_value=evl_loss) summary.value.add(tag='evl_acc', simple_value=evl_acc) saver.save(sess, checkpoint_path, global_step=step) ''' except tf.errors.OutOfRangeError: print('Done training -- epoch limit reached') finally: coord.request_stop() coord.join(threads) sess.close()
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