Wiederherstellen des Modells in tensorflow, Meldung Variable Fehler nicht initialisierten Wert zu verwenden Versuch

Question

Ich habe mein Modell trainiert und gespeichert es mit

mit

saver = tf.train.Saver() 
    saver.save(sess, '/final_model', global_step = i) 

Dann wieder aufzubauen ich genau das gleiche Diagramm und versuchen, das Modell zur Wiederherstellung meiner zu reproduzieren Ergebnis, Wiederherstellung funktioniert, aber solange versuchen, auf einen beliebigen Wert der Netzwerkparameter oder Operationen zugreifen, gibt es mir einen Fehler sagen, versuchen, die nicht initialisierte Variable zu verwenden.

Nach dem Diagramm wieder aufzubauen, der Code, den ich wieder her zu stellen ist:

sess=tf.Session() 
    new_saver = tf.train.import_meta_graph('final_model-699.meta') 
    new_saver.restore(sess, tf.train.latest_checkpoint('./')) 

jedoch nach dem jeder von ihnen geben Sie mir einen Fehler zu versuchen, nicht initialisierten Variablen

print(sess.run(weights['hidden1'])) 
    print(sess.run(loss_f, feed_dict={x: train_x, y_: train_y})) 

Jede Idee zu verwenden, ?

Als einfaches Beispiel, hier ist die Ausbildung und Speichern des Modells:

train_x = np.random.rand(200,2) 
w= np.array([2,3]) 
train_y = np.dot(train_x, w) 
train_y = np.reshape(train_y, [200,1]) 
feature_dim = 2 
output_dim = 1 

x = tf.placeholder(tf.float32, [None, feature_dim]) 
y_ = tf.placeholder(tf.float32, [None, output_dim]) 
weights = { 
    'hidden1': tf.Variable(tf.random_normal([feature_dim, output_dim], stddev=1/np.sqrt(feature_dim))) 
}  

def network1(data): 
    output = tf.matmul(x, weights['hidden1']) 
    return output 
y = network1(x) 

loss_f = output_dim * tf.reduce_mean(tf.squared_difference(y, y_)) 
optimizer_f = tf.train.AdamOptimizer(1e-4).minimize(loss_f) 
saver = tf.train.Saver() 
sess = tf.Session() 
sess.run(tf.global_variables_initializer()) 
for i in range(10000): 
    batch_x = train_x 
    batch_y = train_y 
    sess.run(optimizer_f, feed_dict={x: batch_x, y_: batch_y}) 
    print(sess.run(loss_f, feed_dict={x: batch_x, y_: batch_y})) 
saver.save(sess,'./savedmodel/', global_step = i)  

Wiederherstellen und Wiedergeben der Folge

import scipy.io 
import numpy as np 
import tensorflow as tf 
import random 
train_x = np.random.rand(200,2) 
w= np.array([2,3]) 
train_y = np.dot(train_x, w) 
train_y = np.reshape(train_y, [200,1]) 
feature_dim = 2 
output_dim = 1 
x = tf.placeholder(tf.float32, [None, feature_dim]) 
y_ = tf.placeholder(tf.float32, [None, output_dim]) 


weights = { 
    'hidden1': tf.Variable(tf.random_normal([feature_dim, output_dim], stddev=1/np.sqrt(feature_dim))) 
} 

def network1(data): 
    output = tf.matmul(x, weights['hidden1']) 
    return output 

y = network1(x) 
loss_f = tf.reduce_mean(tf.squared_difference(y, y_)) 
optimizer_f = tf.train.AdamOptimizer(1e-4).minimize(loss_f) 
sess = tf.Session() 
saver = tf.train.import_meta_graph('./savedmodel/-9999.meta') 
saver.restore(sess, tf.train.latest_checkpoint('./savedmodel/')) 

print(sess.run(loss_f, feed_dict={x: train_x, y_: train_y})) 

Der Fehler:

FailedPreconditionErrorTraceback (most recent call last) 
<ipython-input-5-17910473afab> in <module>() 
----> 1 print(sess.run(loss_f, feed_dict={x: train_x, y_: train_y})) 

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc 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) 

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc 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 = [] 

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc 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, 

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc 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): 

FailedPreconditionError: Attempting to use uninitialized value Variable 
    [[Node: Variable/read = Identity[T=DT_FLOAT, _class=["loc:@Variable"], _device="/job:localhost/replica:0/task:0/gpu:0"](Variable)]] 
    [[Node: Mean/_15 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_7_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

Caused by op u'Variable/read', defined at: 
    File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main 
    "__main__", fname, loader, pkg_name) 
    File "/usr/lib/python2.7/runpy.py", line 72, in _run_code 
    exec code in run_globals 
    File "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py", line 16, in <module> 
    app.launch_new_instance() 
    File "/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py", line 658, in launch_instance 
    app.start() 
    File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py", line 477, in start 
    ioloop.IOLoop.instance().start() 
    File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 177, in start 
    super(ZMQIOLoop, self).start() 
    File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 888, in start 
    handler_func(fd_obj, events) 
    File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper 
    return fn(*args, **kwargs) 
    File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events 
    self._handle_recv() 
    File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv 
    self._run_callback(callback, msg) 
    File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback 
    callback(*args, **kwargs) 
    File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper 
    return fn(*args, **kwargs) 
    File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher 
    return self.dispatch_shell(stream, msg) 
    File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell 
    handler(stream, idents, msg) 
    File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request 
    user_expressions, allow_stdin) 
    File "/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute 
    res = shell.run_cell(code, store_history=store_history, silent=silent) 
    File "/usr/local/lib/python2.7/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell 
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) 
    File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell 
    interactivity=interactivity, compiler=compiler, result=result) 
    File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes 
    if self.run_code(code, result): 
    File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code 
    exec(code_obj, self.user_global_ns, self.user_ns) 
    File "<ipython-input-3-7d229041d9bb>", line 6, in <module> 
    'hidden1': tf.Variable(tf.random_normal([feature_dim, output_dim], stddev=1/np.sqrt(feature_dim))) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 197, in __init__ 
    expected_shape=expected_shape) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 316, in _init_from_args 
    self._snapshot = array_ops.identity(self._variable, name="read") 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1338, in identity 
    result = _op_def_lib.apply_op("Identity", input=input, name=name) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op 
    op_def=op_def) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2336, in create_op 
    original_op=self._default_original_op, op_def=op_def) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1228, in __init__ 
    self._traceback = _extract_stack() 

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value Variable 
    [[Node: Variable/read = Identity[T=DT_FLOAT, _class=["loc:@Variable"], _device="/job:localhost/replica:0/task:0/gpu:0"](Variable)]] 
    [[Node: Mean/_15 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_7_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 


In [ ]: 

print(sess.run(weights['hidden1'])) 
In [ ]: 

gleichen Fehler für :

print(sess.run(weights['hidden1'])) 
​ 

Answer 1

Schließlich, was ich finde ist, dass scheint jede Variable oder Operationen, die wir später zugreifen möchten, einen Namen gegeben werden, so ist der Platzhalter, dann haben wir nicht die Notwendigkeit, den Graphen neu zu definieren. Das Folgende funktioniert schließlich, bitte geben Sie mir weitere Hinweise, wenn es noch einen einfacheren Weg gibt, dies zu tun.

import scipy.io 
import numpy as np 
import tensorflow as tf 
import random 

train_x = np.random.rand(200,2) 
w= np.array([2,3]) 
train_y = np.dot(train_x, w) 
train_y = np.reshape(train_y, [200,1]) 
feature_dim = 2 
output_dim = 1 

x = tf.placeholder(tf.float32, [None, feature_dim], name="input") 
y_ = tf.placeholder(tf.float32, [None, output_dim], name="output") 
weights = { 
    'hidden1': tf.Variable(tf.random_normal([feature_dim, output_dim], stddev=1/np.sqrt(feature_dim)), name="weights") 
}  

def network1(data): 
    output = tf.matmul(data, weights['hidden1']) 
    return output 

y = network1(x) 

loss_f = tf.reduce_mean(tf.squared_difference(y, y_), name="op_to_restore") 

optimizer_f = tf.train.AdamOptimizer(1e-4).minimize(loss_f) 
saver = tf.train.Saver() 
sess = tf.Session() 
sess.run(tf.global_variables_initializer()) 
for i in range(100): 
    batch_x = train_x 
    batch_y = train_y 
    sess.run(optimizer_f, feed_dict={x: batch_x, y_: batch_y}) 
    print(sess.run(loss_f, feed_dict={x: batch_x, y_: batch_y})) 
saver.save(sess,'./savedmodel/', global_step = i)  


with tf.Session() as sess: 
    saver = tf.train.import_meta_graph('./savedmodel/-99.meta') 
    saver.restore(sess,tf.train.latest_checkpoint('./savedmodel/')) 
    graph = tf.get_default_graph() 
    x = graph.get_tensor_by_name("input:0") 
    y_ = graph.get_tensor_by_name("output:0") 
    feed_dict={x: train_x, y_: train_y} 
    op_to_restore = graph.get_tensor_by_name("op_to_restore:0") 
    print(sess.run(op_to_restore, feed_dict)) 

Answer 2

Ran in ähnliche Ausgabe vor kurzem mit TF v1.5. Es scheint, dass Variablen und Ops zu einer globalen Sammlung hinzugefügt werden sollten, damit sie ordnungsgemäß wiederhergestellt werden können. Im Folgenden finden Sie zwei Schnipsel für MNIST-Datasets, die ein Modell trainieren, speichern und wiederherstellen.

Zug und beharren

import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 
import os 

train_dir = 'train' 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 

x = tf.placeholder(tf.float32, [None, 784], name='x') 
W = tf.Variable(tf.zeros([784, 10]), name='W') 
b = tf.Variable(tf.zeros([10]), name='b') 
y = tf.nn.softmax(tf.matmul(x, W) + b, name='y') 

saver = tf.train.Saver(tf.trainable_variables()) 
sess = tf.InteractiveSession() 

tf.add_to_collection('x', x) 
tf.add_to_collection('yt', yt) 
tf.add_to_collection('accuracy', accuracy) 

with tf.Session() as sess: 
    latest_checkpoint = tf.train.latest_checkpoint(train_dir) 
    meta_path = '%s.meta' % latest_checkpoint 

    saver = tf.train.import_meta_graph(meta_path) 
    saver.restore(sess, latest_checkpoint) 

    x = tf.get_collection('x')[0] 
    yt = tf.get_collection('yt')[0] 
    accuracy = tf.get_collection('accuracy')[0] 

    feed_dict={x: mnist.test.images, yt: mnist.test.labels} 

    print(sess.run(accuracy, feed_dict)) 

wiederherstellen und

import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 
import os 

train_dir = 'train' 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 

with tf.Session() as sess: 
    latest_checkpoint = tf.train.latest_checkpoint(train_dir) 
    meta_path = '%s.meta' % latest_checkpoint 

    saver = tf.train.import_meta_graph(meta_path) 
    saver.restore(sess, latest_checkpoint) 

    x = tf.get_collection('x')[0] 
    yt = tf.get_collection('yt')[0] 
    accuracy = tf.get_collection('accuracy')[0] 

    feed_dict={x: mnist.test.images, yt: mnist.test.labels} 

    print(sess.run(accuracy, feed_dict)) 

Hier bewerten ist ein Link zu verwandten TF documentation