https://github.com/cmgreen210/tensorflowdeepautoencoder我正在尝试在尝试还原模型的微调步骤后保存和还原模型，然后从模型中获取变量，它给了我这个错误，valueerror：变量autoencoder_variables/striges1不存在，或者不是使用TF创建的。.get_variable（）。您的意思是设置Reuse = none in varscope中吗？如果我将重复使用变成false，它会为权重和偏见创建一个新变量
这是我的代码还原模型。
def do_eval(sess, eval_correct,images_placeholder,labels_placeholder,
data_set):
true_count = 0 # Counts the number of correct predictions.
steps_per_epoch = data_set.num_examples // FLAGS.batch_size
num_examples = steps_per_epoch * FLAGS.batch_size
for step in range(steps_per_epoch):
feed_dict = fill_feed_dict(data_set,
images_placeholder,
labels_placeholder)
true_count += sess.run(eval_correct, feed_dict=feed_dict)
precision = true_count / num_examples
print(' Num examples: %d Num correct: %d Precision @ 1: %0.04f' %
(num_examples, true_count, precision))
def evaluation(logits, labels):
return tf.reduce_sum(tf.cast(correct, tf.int32))
def test_nets(self):
data = read_data_sets(FLAGS.data_dir)
ckpt = tf.train.get_checkpoint_state("model_sps_2017-08-
29_11:45:25")
sess = tf.InteractiveSession()
saver = tf.train.import_meta_graph('model_sps_2017-08-29_11:45:25/model.meta')
saver.restore(sess, ckpt.model_checkpoint_path)
with sess.as_default():
ae_shape = [784, 2000, 2000, 2000, 10]
ae = AutoEncoder(ae_shape, sess)
input_pl = tf.placeholder(tf.float32, shape=(FLAGS.batch_size,
FLAGS.image_pixels),name='input_pl')
sup_net = ae.supervised_net(input_pl)
data = read_data_sets(FLAGS.data_dir)
labels_placeholder = tf.placeholder(tf.int32,
shape=FLAGS.batch_size,
name='target_pl')
eval_correct = evaluation(sup_net, labels_placeholder)
do_eval(sess,
eval_correct,
input_pl,
labels_placeholder,
data.test)
这是我训练和保存监督模型的代码
def main_supervised(ae):
with ae.session.graph.as_default():
saver = tf.train.Saver()
sess = ae.session
input_pl = tf.placeholder(tf.float32, shape=(FLAGS.batch_size,
FLAGS.image_pixels),
name='input_pl')
logits = ae.supervised_net(input_pl)
data = read_data_sets(FLAGS.data_dir)
num_train = data.train.num_examples
labels_placeholder = tf.placeholder(tf.int32,
shape=FLAGS.batch_size,
name='target_pl')
loss = loss_supervised(logits, labels_placeholder)
train_op, global_step = training(loss, FLAGS.supervised_learning_rate)
eval_correct = evaluation(logits, labels_placeholder)
hist_summaries = [ae['biases{0}'.format(i + 1)]
for i in range(ae.num_hidden_layers + 1)]
hist_summaries.extend([ae['weights{0}'.format(i + 1)]
for i in range(ae.num_hidden_layers + 1)])
hist_summaries = [tf.summary.histogram(v.op.name + "_fine_tuning", v)
for v in hist_summaries]
summary_op = tf.summary.merge(hist_summaries)
summary_writer = tf.summary.FileWriter(FLAGS.summary_dir, sess.graph_def)
# tf.train.SummaryWriter(pjoin(FLAGS.summary_dir,
# 'fine_tuning'),
# graph_def=sess.graph_def,
# flush_secs=FLAGS.flush_secs)
vars_to_init = ae.get_variables_to_init(ae.num_hidden_layers + 1)
vars_to_init.append(global_step)
#sess.run(tf.initialize_variables(vars_to_init))
init = tf.initialize_all_variables()
sess.run(init)
steps = (num_train//FLAGS.batch_size)
for k in range(1):
for step in range(1):
start_time = time.time()
feed_dict = fill_feed_dict(data.train,
input_pl,
labels_placeholder)
_, loss_value = sess.run([train_op, loss],
feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % 1 == 0:
# Print status to stdout.
print('Step %d/%d: loss = %.2f (%.3f sec)' % (step, steps,loss_value, duration))
# Update the events file.
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
# summary_img_str = sess.run(
# tf.summary.image("training_images",
# tf.reshape(input_pl,
# (FLAGS.batch_size,
# FLAGS.image_size,
# FLAGS.image_size, 1)),
# max_outputs=10),
# feed_dict=feed_dict
# )
# summary_writer.add_summary(summary_img_str)
if (step + 1) % 1000 == 0 or (step + 1) == steps:
train_sum = do_eval_summary("training_error",
sess,
eval_correct,
input_pl,
labels_placeholder,
data.train)
val_sum = do_eval_summary("validation_error",
sess,
eval_correct,
input_pl,
labels_placeholder,
data.validation)
test_sum = do_eval_summary("test_error",
sess,
eval_correct,
input_pl,
labels_placeholder,
data.test)
summary_writer.add_summary(train_sum, step)
summary_writer.add_summary(val_sum, step)
summary_writer.add_summary(test_sum, step)
folder = "model_sps_"+str(strftime("%Y-%m-%d_%H:%M:%S", gmtime()))
os.mkdir(folder)
folder += "/model"
saver.save(sess, folder)
do_eval(sess,
eval_correct,
input_pl,
labels_placeholder,
data.test)
这是我如何设置变量并在autoencoder_variables
的范围上一个一个恢复它的方式def _restore_variables(self):
#print(tf.get_collection(tf.GraphKeys.VARIABLES, scope='autoencoder_variables'))
# v=tf.get_variable("autoencoder_variables/weights1", shape=(784, 2000))
# print(v)
with tf.variable_scope("autoencoder_variables",reuse=True ) as scope1:
#print(tf.get_collection(tf.GraphKeys.VARIABLES, scope="autoencoder_variables"))
#print(scope)
#tf.Variable 'autoencoder_variables/weights1:0' shape=(784, 2000)
for i in range(self.__num_hidden_layers + 1):
# Train weights
name_w = self._weights_str.format(i + 1)
w_shape = (self.__shape[i], self.__shape[i + 1])
self[name_w] = tf.get_variable(name_w,w_shape,trainable=False)
# Train biases
name_b = self._biases_str.format(i + 1)
b_shape = (self.__shape[i + 1],)
self[name_b] = tf.get_variable(name_b,b_shape)
if i < self.__num_hidden_layers:
# Hidden layer fixed weights (after pretraining before fine tuning)
self[name_w + "_fixed"] = tf.get_variable(name_w+ "_fixed",w_shape)
# Hidden layer fixed biases
self[name_b + "_fixed"] = tf.get_variable(name_b+ "_fixed",b_shape)
# Pretraining output training biases
name_b_out = self._biases_str.format(i + 1) + "_out"
b_shape = (self.__shape[i],)
self[name_b_out] = tf.get_variable(name_b_out,b_shape)
追溯错误：
File "run.py", line 47, in <module>
main()
File "run.py", line 40, in main
test.test_nets_1()
File "C:UserssimjsDownloadsTensorFlowDeepAutoencoder-masterTensorFlowDeepAutoencoder-mastercodeaeautoencoder_test.py", line 55, in test_nets_1
ae = AutoEncoder(ae_shape, sess)
File "C:UserssimjsDownloadsTensorFlowDeepAutoencoder-masterTensorFlowDeepAutoencoder-mastercodeaeautoencoder.py", line 41, in __init__
self._restore_variables()
File "C:UserssimjsDownloadsTensorFlowDeepAutoencoder-masterTensorFlowDeepAutoencoder-mastercodeaeautoencoder.py", line 98, in _restore_variables
self[name_w] = tf.get_variable(name_w,w_shape)
File "C:UserssimjsAnaconda3libsite-packagestensorflowpythonopsvariable_scope.py", line 1065, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "C:UserssimjsAnaconda3libsite-packagestensorflowpythonopsvariable_scope.py", line 962, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "C:UserssimjsAnaconda3libsite-packagestensorflowpythonopsvariable_scope.py", line 367, in get_variable
validate_shape=validate_shape, use_resource=use_resource)
File "C:UserssimjsAnaconda3libsite-packagestensorflowpythonopsvariable_scope.py", line 352, in _true_getter
use_resource=use_resource)
File "C:UserssimjsAnaconda3libsite-packagestensorflowpythonopsvariable_scope.py", line 682, in _get_single_variable
"VarScope?" % name)
ValueError: Variable autoencoder_variables/weights1 does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?
-
检查哪些变量未通过恢复线后写入
print(sess.run(tf.report_uninitialized_variables()))来初始化。 -
还要在还原和分析结果之前先尝试
sess.run(tf.global_variables_initializer())。