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code/python/backward.py

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+# 导入文件
+import os
+import numpy as np
+import tensorflow as tf
+import batchdealing
+import forward
+
+# 变量声明
+N_CLASSES = 9  # 9类 分别是：'one','two','three','four','five','six','seven','eight','nine'
+IMG_W = 64  # resize图像，太大的话训练时间久
+IMG_H = 64
+BATCH_SIZE = 20  # 一次喂入多少
+CAPACITY = 200  # 容量
+MAX_STEP = 200  # 一般大于10K
+learning_rate = 0.0001  # 一般小于0.0001
+
+# 获取批次batch  /Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/
+train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'  # 训练样本的读入路径
+# logs_train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'  # logs存储路径
+logs_test_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/test'        # logs存储路径
+
+# train, train_label = batchdealing.get_files(train_dir)
+train, train_label, val, val_label = batchdealing.get_files(train_dir, 0.3)
+# 训练数据及标签
+train_batch, train_label_batch = batchdealing.get_batch(train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
+# 测试数据及标签
+val_batch, val_label_batch = batchdealing.get_batch(val, val_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
+
+# 训练操作定义
+train_logits = forward.inference(train_batch, BATCH_SIZE, N_CLASSES)
+train_loss = forward.losses(train_logits, train_label_batch)
+train_op = forward.trainning(train_loss, learning_rate)
+train_acc = forward.evaluation(train_logits, train_label_batch)
+
+# 测试操作定义
+test_logits = forward.inference(val_batch, BATCH_SIZE, N_CLASSES)
+test_loss = forward.losses(test_logits, val_label_batch)
+test_acc = forward.evaluation(test_logits, val_label_batch)
+
+# 这个是log汇总记录
+summary_op = tf.compat.v1.summary.merge_all()
+
+# 产生一个会话
+sess = tf.compat.v1.Session()
+
+# 产生一个writer来写log文件
+train_writer = tf.compat.v1.summary.FileWriter(logs_test_dir, sess.graph)
+
+# val_writer = tf.summary.FileWriter(logs_test_dir, sess.graph)
+# 产生一个saver来存储训练好的模型
+saver = tf.compat.v1.train.Saver()
+
+# 所有节点初始化
+sess.run(tf.compat.v1.global_variables_initializer())
+
+# 队列监控
+coord = tf.train.Coordinator()
+threads = tf.compat.v1.train.start_queue_runners(sess=sess, coord=coord)
+
+# 进行batch的训练
+try:
+    # 执行MAX_STEP步的训练，一步一个batch
+    for step in np.arange(MAX_STEP):
+        if coord.should_stop():
+            break
+        # 启动以下操作节点
+        _, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])
+
+        # 每隔50步打印一次当前的loss以及acc，同时记录log，写入writer
+        if step % 10 == 0:
+            print('Step %d, train loss = %.2f, train accuracy = %.2f%%' % (step, tra_loss, tra_acc * 100.0))
+            summary_str = sess.run(summary_op)
+            train_writer.add_summary(summary_str, step)
+        # 每隔100步，保存一次训练好的模型
+        if (step + 1) == MAX_STEP:
+            checkpoint_path = os.path.join(logs_test_dir, 'model.ckpt')
+            saver.save(sess, checkpoint_path, global_step=step)
+
+except tf.errors.OutOfRangeError:
+    print('Done training -- epoch limit reached')
+
+finally:
+    coord.request_stop()
+