diff --git a/README.md b/README.md
new file mode 100644
index 0000000..bd4e9ca
--- /dev/null
+++ b/README.md
@@ -0,0 +1,4 @@
+# TensorFlow-Coding
+
+本代码库为我在小象学院开的一个课程《深度学习之Tensorflow高级编程的代码》,需要了解代码的编写思路和相应的知识可以在小象学院上找相应视频。小象课程链接：
+http://www.chinahadoop.cn/course/1227
diff --git a/lessonOne/imgClassifierWeb/execute.py b/lessonOne/imgClassifierWeb/execute.py
index 5688c34..807c50d 100644
--- a/lessonOne/imgClassifierWeb/execute.py
+++ b/lessonOne/imgClassifierWeb/execute.py
@@ -131,7 +131,7 @@ def train():
             # 达到一个训练模型保存点后，将模型保存下来，并打印出这个保存点的平均准确率
             if current_step % gConfig['steps_per_checkpoint'] == 0:
                 #如果超过三次预测正确率没有升高则改变学习率
-                if len(previous_correct) > 2 and accuracy == min(previous_correct[-3:]):
+                if len(previous_correct) > 2 and accuracy < min(previous_correct[-3:]):
                     sess.run(model.learning_rate_decay_op)
                 previous_correct.append(accuracy)
                 checkpoint_path = os.path.join(gConfig['working_directory'], "cnn.ckpt")
diff --git a/lessonSix/vgg16/execute.py b/lessonSix/vgg16/execute.py
index 63849a4..fba45ff 100644
--- a/lessonSix/vgg16/execute.py
+++ b/lessonSix/vgg16/execute.py
@@ -132,7 +132,7 @@ def train():
             # 达到一个训练模型保存点后，将模型保存下来，并打印出这个保存点的平均准确率
             if current_step % gConfig['steps_per_checkpoint'] == 0:
                 #如果超过三次预测正确率没有升高则改变学习率
-                if len(previous_correct) > 2 and accuracy == min(previous_correct[-3:]):
+                if len(previous_correct) > 2 and accuracy < min(previous_correct[-3:]):
                     sess.run(model.learning_rate_decay_op)
                 previous_correct.append(accuracy)
                 checkpoint_path = os.path.join(gConfig['working_directory'], "cnn.ckpt")
@@ -147,11 +147,11 @@ def train():
                 softmax_predictions = tf.argmax(softmax_propabilities, axis=1)
                 data_tensor = graph.get_tensor_by_name(name="data_tensor:0")
                 label_tensor = graph.get_tensor_by_name(name="label_tensor:0")
-                keep_prop = graph.get_tensor_by_name(name="keep_prop:0")
+                keep_prob = graph.get_tensor_by_name(name="keep_prob:0")
 
                 feed_dict_testing = {data_tensor: shuffled_datas_test,
                     label_tensor: shuffled_labels_test,
-                     keep_prop: 1.0}
+                     keep_prob: 1.0}
 
                 softmax_propabilities_, softmax_predictions_ = sess.run([softmax_propabilities, softmax_predictions],
                                                       feed_dict=feed_dict_testing)