我可以加载架构(" a"(，并以不同的架构(" b"(恢复单个特定的Tensorflow变量(" B"(我保存为" B"的单个变量。
这有效：
import tensorflow as tf
####################################################
# Architecture "A"
w1 = tf.Variable(tf.linspace(0.0, 0.5, 6), name="w1")
w2 = tf.Variable(tf.linspace(1.0, 5.0, 6), name="w2")
saver = tf.train.Saver({'w1':w1}) #<---------- Save only w1
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver.save(sess, './my_architecture')
tf.reset_default_graph()
####################################################
# Architecture "B"
w1 = tf.Variable(tf.linspace(10.0, 50.0, 6), name="w1")
w2 = tf.Variable(tf.linspace(100.0, 500.0, 6), name="w2")
saver = tf.train.Saver({'w1':w1})
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver.save(sess, './my_variable')
tf.reset_default_graph()
######################################################
with tf.Session() as sess:
# Loading the model structure from 'my_test_model.meta'
new_saver = tf.train.import_meta_graph('./my_architecture.meta')
# Loading the saved "w1" Variable
new_saver.restore(sess,'./my_variable')
这不仅有效；我只将saver = tf.train.Saver({'w1':w1})更改为saver = tf.train.Saver() 8行：
import tensorflow as tf
####################################################
# Architecture "A"
w1 = tf.Variable(tf.linspace(0.0, 0.5, 6), name="w1")
w2 = tf.Variable(tf.linspace(1.0, 5.0, 6), name="w2")
saver = tf.train.Saver() #<---------- Save everything
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver.save(sess, './my_architecture')
tf.reset_default_graph()
####################################################
# Architecture "B"
w1 = tf.Variable(tf.linspace(10.0, 50.0, 6), name="w1")
w2 = tf.Variable(tf.linspace(100.0, 500.0, 6), name="w2")
saver = tf.train.Saver({'w1':w1})
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver.save(sess, './my_variable')
tf.reset_default_graph()
######################################################
with tf.Session() as sess:
# Loading the model structure from 'my_test_model.meta'
new_saver = tf.train.import_meta_graph('./my_architecture.meta')
# Loading the saved "w1" Variable
new_saver.restore(sess,'./my_variable')
换句话说，如果在将架构保存为">
我得到此错误：
INFO:tensorflow:Restoring parameters from ./my_variable
---------------------------------------------------------------------------
NotFoundError Traceback (most recent call last)
/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1038 try:
-> 1039 return fn(*args)
1040 except errors.OpError as e:
/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1020 feed_dict, fetch_list, target_list,
-> 1021 status, run_metadata)
1022
/home/paul/anaconda3/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:
NotFoundError: Key w2 not found in checkpoint
[[Node: save/RestoreV2_1 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2_1/tensor_names, save/RestoreV2_1/shape_and_slices)]]
[[Node: save/RestoreV2/_3 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_11_save/RestoreV2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
During handling of the above exception, another exception occurred:
NotFoundError Traceback (most recent call last)
<ipython-input-1-bc6592a722bf> in <module>()
42
43 # Loading the saved "w1" Variable
---> 44 new_saver.restore(sess,'./my_variable')
45
46 # initialize_uninitialized_vars(sess)
/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py in restore(self, sess, save_path)
1455 logging.info("Restoring parameters from %s", save_path)
1456 sess.run(self.saver_def.restore_op_name,
-> 1457 {self.saver_def.filename_tensor_name: save_path})
1458
1459 @staticmethod
/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
776 try:
777 result = self._run(None, fetches, feed_dict, options_ptr,
--> 778 run_metadata_ptr)
779 if run_metadata:
780 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
980 if final_fetches or final_targets:
981 results = self._do_run(handle, final_targets, final_fetches,
--> 982 feed_dict_string, options, run_metadata)
983 else:
984 results = []
/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1030 if handle is None:
1031 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1032 target_list, options, run_metadata)
1033 else:
1034 return self._do_call(_prun_fn, self._session, handle, feed_dict,
/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1050 except KeyError:
1051 pass
-> 1052 raise type(e)(node_def, op, message)
1053
1054 def _extend_graph(self):
NotFoundError: Key w2 not found in checkpoint
[[Node: save/RestoreV2_1 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2_1/tensor_names, save/RestoreV2_1/shape_and_slices)]]
[[Node: save/RestoreV2/_3 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_11_save/RestoreV2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
Caused by op 'save/RestoreV2_1', defined at:
File "/home/paul/anaconda3/lib/python3.5/runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
File "/home/paul/anaconda3/lib/python3.5/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/paul/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py", line 3, in <module>
app.launch_new_instance()
File "/home/paul/anaconda3/lib/python3.5/site-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/home/paul/anaconda3/lib/python3.5/site-packages/ipykernel/kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "/home/paul/anaconda3/lib/python3.5/site-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/home/paul/anaconda3/lib/python3.5/site-packages/tornado/ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "/home/paul/anaconda3/lib/python3.5/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/paul/anaconda3/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/home/paul/anaconda3/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/home/paul/anaconda3/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/home/paul/anaconda3/lib/python3.5/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/paul/anaconda3/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "/home/paul/anaconda3/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "/home/paul/anaconda3/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "/home/paul/anaconda3/lib/python3.5/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/home/paul/anaconda3/lib/python3.5/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/home/paul/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/home/paul/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2821, in run_ast_nodes
if self.run_code(code, result):
File "/home/paul/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-1-bc6592a722bf>", line 41, in <module>
new_saver = tf.train.import_meta_graph('./my_architecture.meta')
File "/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 1595, in import_meta_graph
**kwargs)
File "/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/framework/meta_graph.py", line 499, in import_scoped_meta_graph
producer_op_list=producer_op_list)
File "/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/framework/importer.py", line 308, in import_graph_def
op_def=op_def)
File "/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/paul/.local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1228, in __init__
self._traceback = _extract_stack()
NotFoundError (see above for traceback): Key w2 not found in checkpoint
[[Node: save/RestoreV2_1 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2_1/tensor_names, save/RestoreV2_1/shape_and_slices)]]
[[Node: save/RestoreV2/_3 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_11_save/RestoreV2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
默认情况下，通过导入Metagraph创建的Saver将尝试恢复该Metagraph中的所有变量(并会抱怨检查点中缺少的变量(。但是，可以根据另一个检查点过滤这些变量：
import tensorflow as tf
with tf.Graph().as_default():
####################################################
# Architecture "A"
w1 = tf.Variable(tf.linspace(0.0, 0.5, 6), name="w1")
w2 = tf.Variable(tf.linspace(1.0, 5.0, 6), name="w2")
saver = tf.train.Saver() #<---------- Save everything
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.save(sess, '/tmp/my_architecture')
with tf.Graph().as_default():
####################################################
# Architecture "B"
w1 = tf.Variable(tf.linspace(10.0, 50.0, 6), name="w1")
w2 = tf.Variable(tf.linspace(100.0, 500.0, 6), name="w2")
saver = tf.train.Saver({'w1':w1})
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.save(sess, '/tmp/my_variable')
restored_graph = tf.Graph()
with restored_graph.as_default():
tf.train.import_meta_graph('/tmp/my_architecture.meta')
vars_to_restore = [
restored_graph.get_tensor_by_name(var_name + ':0') for var_name, _
in tf.contrib.framework.list_variables('/tmp/my_variable')]
filtered_saver = tf.train.Saver(var_list=vars_to_restore)
with tf.Session() as sess:
# Restore w1 from Architecture "B" into the metagraph from Architecture "A"
filtered_saver.restore(sess,'/tmp/my_variable')
print(restored_graph.get_tensor_by_name('w1:0').eval())
打印：
[10. 18. 26. 34. 42. 50。]