Python 恢复 TensorFlow 模型
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Restoring TensorFlow model
提问by Tomas
I'm trying to restore TensorFlow model. I followed this example: http://nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow/
我正在尝试恢复 TensorFlow 模型。我按照这个例子：http: //nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow/
At the end of the code in the example I added these lines:
在示例代码的末尾，我添加了以下几行：
saver = tf.train.Saver()
save_path = saver.save(sess, "model.ckpt")
print("Model saved in file: %s" % save_path)
Two files were created: checkpoint and model.ckpt.
创建了两个文件：checkpoint 和model.ckpt。
In a new python file (tomas_bees_predict.py), I have this code:
在一个新的python文件（tomas_bees_predict.py）中，我有这个代码：
import tensorflow as tf
saver = tf.train.Saver()
with tf.Session() as sess:
# Restore variables from disk.
saver.restore(sess, "model.ckpt")
print("Model restored.")
However when I execute the code, I get this error:
但是，当我执行代码时，出现此错误：
Traceback (most recent call last):
File "tomas_bees_predict.py", line 3, in <module>
saver = tf.train.Saver()
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 705, in __init__
raise ValueError("No variables to save")
ValueError: No variables to save
ValueError：没有要保存的变量
Is there a way to read mode.ckpt file and see what variables are saved? Or maybe someone can help with saving the model and restoring it based on the example described above?
有没有办法读取mode.ckpt文件并查看保存了哪些变量？或者也许有人可以帮助保存模型并根据上述示例进行恢复？
EDIT 1:
编辑 1：
I think I tried running the same code in order to recreate model structure and I was getting the error. I think it could be related to the fact that code described here isn't using named variables: http://nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow/
我想我尝试运行相同的代码以重新创建模型结构，但出现错误。我认为这可能与此处描述的代码未使用命名变量有关：http: //nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow/
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
So I did this experiment. I wrote two versions of the code (with and without named variables) to save the model and the code to restore the model.
所以我做了这个实验。我写了两个版本的代码（有和没有命名变量）来保存模型和恢复模型的代码。
tensor_save_named_vars.py:
tensor_save_named_vars.py：
import tensorflow as tf
# Create some variables.
v1 = tf.Variable(1, name="v1")
v2 = tf.Variable(2, name="v2")
# Add an op to initialize the variables.
init_op = tf.initialize_all_variables()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, initialize the variables, do some work, save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
print "v1 = ", v1.eval()
print "v2 = ", v2.eval()
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print "Model saved in file: ", save_path
tensor_save_not_named_vars.py:
tensor_save_not_named_vars.py：
import tensorflow as tf
# Create some variables.
v1 = tf.Variable(1)
v2 = tf.Variable(2)
# Add an op to initialize the variables.
init_op = tf.initialize_all_variables()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, initialize the variables, do some work, save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
print "v1 = ", v1.eval()
print "v2 = ", v2.eval()
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print "Model saved in file: ", save_path
tensor_restore.py:
张量_restore.py：
import tensorflow as tf
# Create some variables.
v1 = tf.Variable(0, name="v1")
v2 = tf.Variable(0, name="v2")
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
# Restore variables from disk.
saver.restore(sess, "/tmp/model.ckpt")
print "Model restored."
print "v1 = ", v1.eval()
print "v2 = ", v2.eval()
Here is what I get when I execute this code:
这是我执行此代码时得到的结果：
$ python tensor_save_named_vars.py
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4
v1 = 1
v2 = 2
Model saved in file: /tmp/model.ckpt
$ python tensor_restore.py
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4
Model restored.
v1 = 1
v2 = 2
$ python tensor_save_not_named_vars.py
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4
v1 = 1
v2 = 2
Model saved in file: /tmp/model.ckpt
$ python tensor_restore.py
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4
W tensorflow/core/common_runtime/executor.cc:1076] 0x7ff953881e40 Compute status: Not found: Tensor name "v2" not found in checkpoint files /tmp/model.ckpt
[[Node: save/restore_slice_1 = RestoreSlice[dt=DT_INT32, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/restore_slice_1/tensor_name, save/restore_slice_1/shape_and_slice)]]
W tensorflow/core/common_runtime/executor.cc:1076] 0x7ff953881e40 Compute status: Not found: Tensor name "v1" not found in checkpoint files /tmp/model.ckpt
[[Node: save/restore_slice = RestoreSlice[dt=DT_INT32, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/restore_slice/tensor_name, save/restore_slice/shape_and_slice)]]
Traceback (most recent call last):
File "tensor_restore.py", line 14, in <module>
saver.restore(sess, "/tmp/model.ckpt")
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 891, in restore
sess.run([self._restore_op_name], {self._filename_tensor_name: save_path})
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 368, in run
results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 444, in _do_run
e.code)
tensorflow.python.framework.errors.NotFoundError: Tensor name "v2" not found in checkpoint files /tmp/model.ckpt
[[Node: save/restore_slice_1 = RestoreSlice[dt=DT_INT32, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/restore_slice_1/tensor_name, save/restore_slice_1/shape_and_slice)]]
Caused by op u'save/restore_slice_1', defined at:
File "tensor_restore.py", line 8, in <module>
saver = tf.train.Saver()
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 713, in __init__
restore_sequentially=restore_sequentially)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 432, in build
filename_tensor, vars_to_save, restore_sequentially, reshape)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 191, in _AddRestoreOps
values = self.restore_op(filename_tensor, vs, preferred_shard)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 106, in restore_op
preferred_shard=preferred_shard)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/io_ops.py", line 189, in _restore_slice
preferred_shard, name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_io_ops.py", line 271, in _restore_slice
preferred_shard=preferred_shard, name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 664, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1834, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1043, in __init__
self._traceback = _extract_stack()
So perhaps the original code (see the external link above) could be modified to something like this:
所以也许原始代码（见上面的外部链接）可以修改成这样：
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
weight_var = tf.Variable(initial, name="weight_var")
return weight_var
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
bias_var = tf.Variable(initial, name="bias_var")
return bias_var
But then the question I have: is restoring weight_var and bias_var variables sufficient to implement the prediction? I did the training on the powerful machine with GPU and I would like to copy the model to the less powerful computer without GPU to run predictions.
但是我的问题是：恢复 weight_var 和 bias_var 变量是否足以实现预测？我在带有 GPU 的强大机器上进行了训练，我想将模型复制到没有 GPU 的功能较弱的计算机上来运行预测。
采纳答案by Tomas
I think I tried running the same code in order to recreate model structure and I was getting the error. I think it could be related to the fact that code described here isn't using named variables: http://nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow/
我想我尝试运行相同的代码以重新创建模型结构，但出现错误。我认为这可能与此处描述的代码未使用命名变量有关：http: //nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow/
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
So I did this experiment. I wrote two versions of the code (with and without named variables) to save the model and the code to restore the model.
所以我做了这个实验。我写了两个版本的代码（有和没有命名变量）来保存模型和恢复模型的代码。
tensor_save_named_vars.py:
tensor_save_named_vars.py：
import tensorflow as tf
# Create some variables.
v1 = tf.Variable(1, name="v1")
v2 = tf.Variable(2, name="v2")
# Add an op to initialize the variables.
init_op = tf.initialize_all_variables()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, initialize the variables, do some work, save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
print "v1 = ", v1.eval()
print "v2 = ", v2.eval()
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print "Model saved in file: ", save_path
tensor_save_not_named_vars.py:
tensor_save_not_named_vars.py：
import tensorflow as tf
# Create some variables.
v1 = tf.Variable(1)
v2 = tf.Variable(2)
# Add an op to initialize the variables.
init_op = tf.initialize_all_variables()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, initialize the variables, do some work, save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
print "v1 = ", v1.eval()
print "v2 = ", v2.eval()
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print "Model saved in file: ", save_path
tensor_restore.py:
张量_restore.py：
import tensorflow as tf
# Create some variables.
v1 = tf.Variable(0, name="v1")
v2 = tf.Variable(0, name="v2")
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
# Restore variables from disk.
saver.restore(sess, "/tmp/model.ckpt")
print "Model restored."
print "v1 = ", v1.eval()
print "v2 = ", v2.eval()
Here is what I get when I execute this code:
这是我执行此代码时得到的结果：
$ python tensor_save_named_vars.py
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4
v1 = 1
v2 = 2
Model saved in file: /tmp/model.ckpt
$ python tensor_restore.py
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4
Model restored.
v1 = 1
v2 = 2
$ python tensor_save_not_named_vars.py
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4
v1 = 1
v2 = 2
Model saved in file: /tmp/model.ckpt
$ python tensor_restore.py
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4
W tensorflow/core/common_runtime/executor.cc:1076] 0x7ff953881e40 Compute status: Not found: Tensor name "v2" not found in checkpoint files /tmp/model.ckpt
[[Node: save/restore_slice_1 = RestoreSlice[dt=DT_INT32, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/restore_slice_1/tensor_name, save/restore_slice_1/shape_and_slice)]]
W tensorflow/core/common_runtime/executor.cc:1076] 0x7ff953881e40 Compute status: Not found: Tensor name "v1" not found in checkpoint files /tmp/model.ckpt
[[Node: save/restore_slice = RestoreSlice[dt=DT_INT32, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/restore_slice/tensor_name, save/restore_slice/shape_and_slice)]]
Traceback (most recent call last):
File "tensor_restore.py", line 14, in <module>
saver.restore(sess, "/tmp/model.ckpt")
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 891, in restore
sess.run([self._restore_op_name], {self._filename_tensor_name: save_path})
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 368, in run
results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 444, in _do_run
e.code)
tensorflow.python.framework.errors.NotFoundError: Tensor name "v2" not found in checkpoint files /tmp/model.ckpt
[[Node: save/restore_slice_1 = RestoreSlice[dt=DT_INT32, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/restore_slice_1/tensor_name, save/restore_slice_1/shape_and_slice)]]
Caused by op u'save/restore_slice_1', defined at:
File "tensor_restore.py", line 8, in <module>
saver = tf.train.Saver()
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 713, in __init__
restore_sequentially=restore_sequentially)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 432, in build
filename_tensor, vars_to_save, restore_sequentially, reshape)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 191, in _AddRestoreOps
values = self.restore_op(filename_tensor, vs, preferred_shard)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 106, in restore_op
preferred_shard=preferred_shard)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/io_ops.py", line 189, in _restore_slice
preferred_shard, name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_io_ops.py", line 271, in _restore_slice
preferred_shard=preferred_shard, name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 664, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1834, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1043, in __init__
self._traceback = _extract_stack()
So perhaps the original code (see the external link above) could be modified to something like this:
所以也许原始代码（见上面的外部链接）可以修改成这样：
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
weight_var = tf.Variable(initial, name="weight_var")
return weight_var
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
bias_var = tf.Variable(initial, name="bias_var")
return bias_var
But then the question I have: is restoring weight_var and bias_var variables sufficient to implement the prediction? I did the training on the powerful machine with GPU and I would like to copy the model to the less powerful computer without GPU to run predictions.
但是我的问题是：恢复 weight_var 和 bias_var 变量是否足以实现预测？我在带有 GPU 的强大机器上进行了训练，我想将模型复制到没有 GPU 的功能较弱的计算机上来运行预测。
回答by Yaroslav Bulatov
There's a similar question here: Tensorflow: how to save/restore a model?TLDR; you need to recreate model structure using same sequence of TensorFlow API commands before using Saver object to restore the weights
这里有一个类似的问题：Tensorflow: how to save/restore a model? TLDR；在使用 Saver 对象恢复权重之前，您需要使用相同的 TensorFlow API 命令序列重新创建模型结构
This is suboptimal, follow Github issue #696for progress on making this easier
这是次优的，请关注Github 问题 #696以获取更轻松的进展
回答by Cro
make sure the declaration of tf.train.Saver() is in with tf.Session() as sess
确保 tf.train.Saver() 的声明与 tf.Session() 作为 sess
回答by Leo
This issue should be caused by the name scope variants when double creating the same network.
这个问题应该是由双重创建相同网络时的名称范围变体引起的。
put the command:
把命令：
tf.reset_default_graph()
tf.reset_default_graph()
before creating the network
在创建网络之前
回答by Mahesh_Tripathi
If a problem like this occurs then try to restart your kernel as the current variable overwrites the previous causing conflict between them, thus it shows notFoundError and other issues come up.
如果出现这样的问题，请尝试重新启动内核，因为当前变量会覆盖之前导致它们之间发生冲突的变量，从而显示 notFoundError 和其他问题。
I encountered the same type of problem and restarting the kernel worked for me. (Caution: Try avoiding running your kernel multiple times as it can ruin your model file recreating variables that overwrite the existing one thus end up changing the original values.)
我遇到了同样类型的问题，重新启动内核对我有用。（注意：尽量避免多次运行内核，因为它会破坏模型文件，重新创建覆盖现有变量的变量，从而最终更改原始值。）
