在这种情况下，我想继续从checkpoint.i训练我的模型使用cifar-10示例并在cifar-10_train.py中做了一点改变，如下所示，它们几乎相同，除了我想要恢复检查点： 我用md替换了cifar-10。
"""
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os.path
import time
import numpy
import tensorflow.python.platform
from tensorflow.python.platform import gfile
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import md
"""
"""
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('train_dir', '/root/test/INT/tbc',
"""Directory where to write event logs """
"""and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 60000, # 55000 steps per epoch
"""Number of batches to run.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
tf.app.flags.DEFINE_string('pretrained_model_checkpoint_path', '/root/test/INT/',
"""If specified, restore this pretrained model """
"""before beginning any training.""")
def error_rate(predictions, labels):
"""Return the error rate based on dense predictions and 1-hot labels."""
return 100.0 - (
100.0 *
numpy.sum(numpy.argmax(predictions, 0) == numpy.argmax(labels, 0)) /
predictions.shape[0])
def train():
"""Train MD65500 for a number of steps."""
with tf.Graph().as_default():
# global_step = tf.Variable(0, trainable=False)
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0), trainable=False)
# Get images and labels for CIFAR-10.
images, labels = md.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logits = md.inference(images)
# Calculate loss.
loss = md.loss(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = md.train(loss, global_step)
# Predictions for the minibatch. there is no validation set or test set.
# train_prediction = tf.nn.softmax(logits)
train_prediction = logits
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
# sess = tf.Session(config=tf.ConfigProto(
# log_device_placement=FLAGS.log_device_placement))
# sess.run(init)
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
# sess.run(init)
if FLAGS.pretrained_model_checkpoint_path:
assert tf.gfile.Exists(FLAGS.pretrained_model_checkpoint_path)
# variables_to_restore = tf.get_collection(
# slim.variables.VARIABLES_TO_RESTORE)
variable_averages = tf.train.ExponentialMovingAverage(
md.MOVING_AVERAGE_DECAY)
variables_to_restore = {}
for v in tf.all_variables():
if v in tf.trainable_variables():
restore_name = variable_averages.average_name(v)
else:
restore_name = v.op.name
variables_to_restore[restore_name] = v
ckpt = tf.train.get_checkpoint_state(FLAGS.pretrained_model_checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
# global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
restorer = tf.train.Saver(variables_to_restore)
restorer.restore(sess, ckpt.model_checkpoint_path)
print('%s: Pre-trained model restored from %s' %
(datetime.now(), ckpt.model_checkpoint_path))
# print("variables_to_restore")
# print(variables_to_restore)
else:
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
graph_def=sess.graph) #####graph_def=sess.graph_def)
# tf.add_to_collection('train_op', train_op)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value, predictions = sess.run([train_op, loss, train_prediction])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 100 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
# print('Minibatch error: %.5f%%' % error_rate(predictions, labels))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
def main(argv=None): # pylint: disable=unused-argument
# md.maybe_download()
# if gfile.Exists(FLAGS.train_dir):
# gfile.DeleteRecursively(FLAGS.train_dir)
# gfile.MakeDirs(FLAGS.train_dir)
train()
if __name__ == '__main__':
tf.app.run()
当我运行代码时，错误如下：
[root@bogon md try]# pythonnew mdtbc_3.py
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
Filling queue with 4000 CIFAR images before starting to train. This will take a few minutes.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
name: GeForce GTX 980 Ti
major: 5 minor: 2 memoryClockRate (GHz) 1.228
pciBusID 0000:01:00.0
Total memory: 6.00GiB
Free memory: 5.78GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:755] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 980 Ti, pci bus id: 0000:01:00.0)
2016-08-30 17:12:48.883303: Pre-trained model restored from /root/test/INT/model.ckpt-59999
WARNING:tensorflow:When passing a `Graph` object, please use the `graph` named argument instead of `graph_def`.
Traceback (most recent call last):
File "mdtbc_3.py", line 195, in <module>
tf.app.run()
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 30, in run
sys.exit(main(sys.argv))
File "mdtbc_3.py", line 191, in main
train()
File "mdtbc_3.py", line 160, in train
_, loss_value, predictions = sess.run([train_op, loss, train_prediction])
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 340, in run
run_metadata_ptr)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 564, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 637, in _do_run
target_list, options, run_metadata)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 659, in _do_call
e.code)
tensorflow.python.framework.errors.FailedPreconditionError: Attempting to use uninitialized value conv2/weights
[[Node: conv2/weights/read = Identity[T=DT_FLOAT, _class=["loc:@conv2/weights"], _device="/job:localhost/replica:0/task:0/cpu:0"](conv2/weights)]]
Caused by op u'conv2/weights/read', defined at:
File "mdtbc_3.py", line 195, in <module>
tf.app.run()
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 30, in run
sys.exit(main(sys.argv))
File "mdtbc_3.py", line 191, in main
train()
File "mdtbc_3.py", line 77, in train
logits = md.inference(images)
File "/root/test/md try/md.py", line 272, in inference
stddev=0.1, wd=0.0)
File "/root/test/md try/md.py", line 114, in _variable_with_weight_decay
tf.truncated_normal_initializer(stddev=stddev))
File "/root/test/md try/md.py", line 93, in _variable_on_cpu
var = tf.get_variable(name, shape, initializer=initializer)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 339, in get_variable
collections=collections)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 262, in get_variable
collections=collections, caching_device=caching_device)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 158, in get_variable
dtype=variable_dtype)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 209, in __init__
dtype=dtype)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 318, in _init_from_args
self._snapshot = array_ops.identity(self._variable, name="read")
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 609, in identity
return _op_def_lib.apply_op("Identity", input=input, name=name)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
op_def=op_def)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2154, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/pythonnew/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1154, in __init__
self._traceback = _extract_stack()
当我取消注释第107行＆＃34; sess.run（init）＆＃34;它完美运行，但是初始化的模型，只是来自sctrach的新模型。我想从检查点恢复变量，并继续我的训练。我想恢复。
答案 0 :(得分：3)
如果没有其他代码方便，我会说以下部分存在问题：
for v in tf.all_variables():
if v in tf.trainable_variables():
restore_name = variable_averages.average_name(v)
else:
restore_name = v.op.name
variables_to_restore[restore_name] = v
因为您在此处指定要恢复的变量列表，但是您排除了一些变量（即可训练变量中的变量的v.op.name）。这将改变抛出错误的网络中的变量名称（再次，没有其余的代码，我真的不能说），s.t。一个（或多个）变量未正确恢复。两种方法（不是很复杂）可以帮助你：