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Merged
herrtao merged 5 commits into from
Nov 28, 2018
Merged

thanks #6

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81e0874
Create README.md
zhaoyingjun Nov 25, 2018
7afe3dc
更新keep_prop 成keep_prob
Nov 26, 2018
4ce7f8e
Merge branch 'master' of https://github.com/zhaoyingjun/TensorFlow-Co…
Nov 26, 2018
197e69e
更新learningrate自动更新的Bug
Nov 26, 2018
be99238
更新lessonSIx的learningrate自动更新问题
Nov 26, 2018
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4 changes: 4 additions & 0 deletions README.md
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Original file line number Diff line number Diff line change
@@ -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
2 changes: 1 addition & 1 deletion lessonOne/imgClassifierWeb/execute.py
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Expand Up @@ -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")
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6 changes: 3 additions & 3 deletions lessonSix/vgg16/execute.py
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Expand Up @@ -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")
Expand All @@ -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)
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