恢复张量流模型

Question

我正试图恢复TensorFlow模型。我举了一个例子： http://nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow/

在示例中的代码末尾，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)

创建了两个文件：checkpoint和model.ckpt。

在一个新的python文件（tomas_bees_predict.py）中，我有以下代码：

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.")

但是，当我执行代码时，会出现以下错误：

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:没有要保存的变量

是否有方法读取mode.ckpt文件并查看保存了哪些变量？ 或者也许有人可以根据上面描述的例子来帮助保存和恢复模型？

编辑1:

我想我试图运行相同的代码来重新创建模型结构，但我得到了错误。我认为这可能与下面描述的代码没有使用命名变量有关： 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)

所以我做了这个实验。我编写了两个版本的代码（带和不带命名变量）来保存模型和恢复模型。

名为“vars.py”的张量保存：

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:

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

张量恢复.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()

下面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()

因此，也许原始代码（见上面的外部链接）可以修改为如下内容：

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

但接下来我要问的问题是：恢复权重变量和偏差变量是否足以实现预测？我用GPU在功能强大的机器上进行了培训，我想把模型复制到没有GPU的功能较弱的计算机上运行预测。

Answer 1

这里有一个类似的问题：Tensorflow: how to save/restore a model? 在使用Saver对象还原权重之前，需要使用相同序列的TensorFlow API命令重新创建模型结构

这是次优的，请按照Github issue #696的步骤来简化

Answer 2

这个问题应该是由于双重创建同一网络时的名称作用域变量引起的。

发出命令：

tf.重置默认图形（）

在创建网络之前

Answer 3

如果发生这样的问题，请尝试重新启动内核，因为当前变量会覆盖导致它们之间冲突的上一个变量，因此它会显示notFoundError和出现的其他问题。

我遇到了同样的问题，重新启动内核对我很有用。 （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。）