Fix typo

exam/step10/AnsAlexNet.py

 import sys
 import tensorflow as tf
 import os
-os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'
+os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
 import warnings
 warnings.filterwarnings('ignore')
 sys.path.append('step3')
@@ -85,5 +85,5 @@ with tf.Session() as sess:
         print('current accuracy: ', str(acc_v))
             # if acc_v \u003e 0.7 and acc_v \u003e max_acc:
             #     max_acc = acc_v
-            #     saver.save(sess, \"step10/Model/FinalNet\")
+            #     saver.save(sess, "step10/Model/FinalNet")
 #********** End **********#
\ No newline at end of file

exam/step10/AnsInception.py

@@ -80,5 +80,5 @@ with tf.Session() as sess:
 
             if acc_v \u003e 0.7 and acc_v \u003e max_acc:
                 max_acc = acc_v
-                saver.save(sess, \"step10/Model/FinalNet\")
+                saver.save(sess, "step10/Model/FinalNet")
 

exam/step10/AnsResNet.py

@@ -89,4 +89,4 @@ with tf.Session() as sess:
 
             if acc_v \u003e 0.7 and acc_v \u003e max_acc:
                 max_acc = acc_v
-                saver.save(sess, \"step10/Model/FinalNet\")
\ No newline at end of file
+                saver.save(sess, "step10/Model/FinalNet")
\ No newline at end of file

exam/step10/TrainSaveLoadForUsers.py

 import os
-os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'
+os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
 
 import warnings
 warnings.filterwarnings('ignore')

exam/step10/TraubSaveLoadTest.py

 import os
-os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'
+os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
 
 import warnings
 warnings.filterwarnings('ignore')
@@ -25,19 +25,19 @@ with tf.Session() as sess:
 
     # for op in graph.get_operations():
     #     print(op.name)
-    # mm = graph.get_tensor_by_name(\"batch_normalization/moving_mean:0\")
+    # mm = graph.get_tensor_by_name("batch_normalization/moving_mean:0")
     # print(sess.run(mm))
     # exit()
 
     G_Valid = batchGenerator(batchSize=8, basePath='data/processed/valid_224')
     X_v, Y_v = G_Valid.getBatch()
 
-    batchImgInput = graph.get_tensor_by_name(\"batchImgInput:0\")
-    labels = graph.get_tensor_by_name(\"Labels:0\")
-    keeProb = graph.get_tensor_by_name(\"dropout_keep_prob:0\")
+    batchImgInput = graph.get_tensor_by_name("batchImgInput:0")
+    labels = graph.get_tensor_by_name("Labels:0")
+    keeProb = graph.get_tensor_by_name("dropout_keep_prob:0")
     try:
-        BNTraining = graph.get_tensor_by_name(\"BNTraining:0\")
-        batchSize = graph.get_tensor_by_name(\"InputBatchSize:0\")
+        BNTraining = graph.get_tensor_by_name("BNTraining:0")
+        batchSize = graph.get_tensor_by_name("InputBatchSize:0")
     except:
         BNTraining, batchSize = None, None
 
@@ -52,7 +52,7 @@ with tf.Session() as sess:
         else:
             feed_dict = {batchImgInput: X_v, labels: Y_v, keeProb: 1.}
 
-        out = graph.get_tensor_by_name(\"model_outputs/BiasAdd:0\")
+        out = graph.get_tensor_by_name("model_outputs/BiasAdd:0")
         output_v = softmax(
             sess.run(out,
                      feed_dict=feed_dict))

exam/step3/generatorForUsers.py

@@ -11,7 +11,7 @@ class batchGenerator:
     def __init__(self, basePath='data/processed/train_224/', batchSize=256):
         '''
         数据集中有四类图片分别是'bus','family sedan','fire engine','racing car',
-        每个图片的文件名形式为\"XXX (id).jpg\"或\"XXX (id)_flipx.jpg\",例如\"bus (1).jpg\",\"bus (1)_flipx.jpg\"
+        每个图片的文件名形式为"XXX (id).jpg"或"XXX (id)_flipx.jpg",例如"bus (1).jpg","bus (1)_flipx.jpg"
 
         :param basePath:数据集路径
         :param batchSize: 每次获取的图片数量

exam/step4/AlexNetCompleted.py

 import tensorflow as tf
 import os
-# os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'
+# os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
 #----以下是答案部分 begin----#
 
 # 定义placeholder 开始
@@ -62,7 +62,7 @@ train = tf.train.AdamOptimizer().minimize(loss)
 
 with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
-    tf.train.export_meta_graph(filename=\"step4/modelInfo/AlexNet\",
+    tf.train.export_meta_graph(filename="step4/modelInfo/AlexNet",
                                graph=tf.get_default_graph())
 
 tf.reset_default_graph()
\ No newline at end of file

exam/step4/AlexNetForUsers.py

@@ -52,5 +52,5 @@ import tensorflow as tf
 #---否则影响测评---#
 with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
-    tf.train.export_meta_graph(filename=\"step4/userModelInfo/AlexNet\",
+    tf.train.export_meta_graph(filename="step4/userModelInfo/AlexNet",
                                graph=tf.get_default_graph())
\ No newline at end of file

exam/step4/AlexNetTest.py

 import os
-os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'
+os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
 
 import warnings
 warnings.filterwarnings('ignore')

exam/step6/VGGNetCompleted.py

@@ -54,8 +54,8 @@ train = tf.train.AdamOptimizer().minimize(loss)
 
 with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
-    # saver.save(sess, \"modelInfo/VGGNet\")
-    tf.train.export_meta_graph(filename=\"step6/modelInfo/VGGNet\",
+    # saver.save(sess, "modelInfo/VGGNet")
+    tf.train.export_meta_graph(filename="step6/modelInfo/VGGNet",
                                graph=tf.get_default_graph())
 tf.reset_default_graph()
 # if __name__ == '__main__':

exam/step6/VGGNetTest.py

 import os
-os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'
+os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
 import warnings
 warnings.filterwarnings('ignore')
 

exam/step7/InceptionTest.py

 import os
-os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'
+os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
 
 import warnings
 warnings.filterwarnings('ignore')

exam/step8/ResNetTest.py

 import os
-os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'
+os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
 
 import warnings
 warnings.filterwarnings('ignore')