我尝试使用tf.train.Saver保存和恢复张量流模型。我认为我已经正确完成了保存部分，但是当我尝试恢复模型时，我收到了这个错误：
2017-07-03 15:55:14.824767: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key train/beta2_power not found in checkpoint
2017-07-03 15:55:14.824796: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases-outputlayer not found in checkpoint
2017-07-03 15:55:14.825913: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases-outputlayer/Adam not found in checkpoint
2017-07-03 15:55:14.826588: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases-outputlayer/Adam_1 not found in checkpoint
2017-07-03 15:55:14.827369: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key train/beta1_power not found in checkpoint
2017-07-03 15:55:14.828101: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases1 not found in checkpoint
2017-07-03 15:55:14.828973: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases1/Adam not found in checkpoint
2017-07-03 15:55:14.829151: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases1/Adam_1 not found in checkpoint
2017-07-03 15:55:14.830308: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights5/Adam_1 not found in checkpoint
2017-07-03 15:55:14.830590: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases2 not found in checkpoint
2017-07-03 15:55:14.831279: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases2/Adam not found in checkpoint
2017-07-03 15:55:14.832268: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases2/Adam_1 not found in checkpoint
2017-07-03 15:55:14.832558: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights5/Adam not found in checkpoint
2017-07-03 15:55:14.833052: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases3 not found in checkpoint
2017-07-03 15:55:14.834195: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases3/Adam not found in checkpoint
2017-07-03 15:55:14.834228: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases3/Adam_1 not found in checkpoint
2017-07-03 15:55:14.834629: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases4 not found in checkpoint
2017-07-03 15:55:14.835986: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights5 not found in checkpoint
2017-07-03 15:55:14.836128: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases5 not found in checkpoint
2017-07-03 15:55:14.836423: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases4/Adam not found in checkpoint
2017-07-03 15:55:14.837906: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases4/Adam_1 not found in checkpoint
2017-07-03 15:55:14.838055: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights4/Adam_1 not found in checkpoint
2017-07-03 15:55:14.838388: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases5/Adam not found in checkpoint
2017-07-03 15:55:14.839666: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Biases5/Adam_1 not found in checkpoint
2017-07-03 15:55:14.840299: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights-outputlayer not found in checkpoint
2017-07-03 15:55:14.840774: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights4/Adam not found in checkpoint
2017-07-03 15:55:14.841568: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights-outputlayer/Adam not found in checkpoint
2017-07-03 15:55:14.842312: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights-outputlayer/Adam_1 not found in checkpoint
2017-07-03 15:55:14.842689: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights1 not found in checkpoint
2017-07-03 15:55:14.843789: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights4 not found in checkpoint
2017-07-03 15:55:14.844030: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights1/Adam not found in checkpoint
2017-07-03 15:55:14.844775: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights1/Adam_1 not found in checkpoint
2017-07-03 15:55:14.845580: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights2 not found in checkpoint
2017-07-03 15:55:14.845919: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights3/Adam_1 not found in checkpoint
2017-07-03 15:55:14.846800: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights2/Adam not found in checkpoint
2017-07-03 15:55:14.847101: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights2/Adam_1 not found in checkpoint
2017-07-03 15:55:14.847274: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights3 not found in checkpoint
2017-07-03 15:55:14.847467: W tensorflow/core/framework/op_kernel.cc:1152] Not found: Key Weights3/Adam not found in checkpoint
Traceback (most recent call last):
File "predict2.py", line 6, in <module>
saver.restore(sess,tf.train.latest_checkpoint('./'))
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1457, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 778, in run
run_metadata_ptr)
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 982, in _run
feed_dict_string, options, run_metadata)
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1032, in _do_run
target_list, options, run_metadata)
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: Key Biases-outputlayer not found in checkpoint
[[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]
Caused by op u'save/RestoreV2', defined at:
File "predict2.py", line 5, in <module>
saver = tf.train.import_meta_graph(os.getcwd()+'/models/baseDNN.meta')
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1595, in import_meta_graph
**kwargs)
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/meta_graph.py", line 499, in import_scoped_meta_graph
producer_op_list=producer_op_list)
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/importer.py", line 308, in import_graph_def
op_def=op_def)
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1228, in __init__
self._traceback = _extract_stack()
NotFoundError (see above for traceback): Key Biases-outputlayer not found in checkpoint
[[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]
我查看了其他StackOverflow问题，他们使用import_meta_graph，get_graph_by_tensor和restore来加载张量流模型，我尝试将其调整到我的代码中，但我不断发现这些错误，它说它无法在检查点找到任何变量。
以下是保存模型的代码：
TRAIN_KEEP_PROB = 1.0
TEST_KEEP_PROB = 1.0
learning_rate = 0.0001
ne = 10
train = 100
test = 1
num_nodes = 250
len_puzzle = 80
n_nodes_hl1 = num_nodes # hidden layer 1
n_nodes_hl2 = num_nodes
n_nodes_hl3 = num_nodes
n_nodes_hl4 = num_nodes
n_nodes_hl5 = num_nodes
n_classes = 4
batch_size = 100 # load 100 features at a time
x = tf.placeholder('float',[None,TF_SHAPE],name="x_placeholder")
y = tf.placeholder('float',name='y_placeholder')
keep_prob = tf.placeholder('float',name='keep_prob_placeholder')
def neuralNet(data):
hl_1 = {'weights':tf.Variable(tf.random_normal([TF_SHAPE, n_nodes_hl1]),name='Weights1'),
'biases':tf.Variable(tf.random_normal([n_nodes_hl1]),name='Biases1')}
hl_2 = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2]),name='Weights2'),
'biases':tf.Variable(tf.random_normal([n_nodes_hl2]),name='Biases2')}
hl_3 = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3]),name='Weights3'),
'biases':tf.Variable(tf.random_normal([n_nodes_hl3]),name='Biases3')}
hl_4 = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_nodes_hl4]),name='Weights4'),
'biases':tf.Variable(tf.random_normal([n_nodes_hl4]),name='Biases4')}
hl_5 = {'weights':tf.Variable(tf.random_normal([n_nodes_hl4, n_nodes_hl5]),name='Weights5'),
'biases':tf.Variable(tf.random_normal([n_nodes_hl5]),name='Biases5')}
output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl5, n_classes]),name='Weights-outputlayer'),
'biases':tf.Variable(tf.random_normal([n_classes]),name='Biases-outputlayer')}
l1 = tf.add(tf.matmul(data, hl_1['weights']), hl_1['biases'])
l1 = tf.nn.sigmoid(l1,name='op1')
l2 = tf.add(tf.matmul(l1, hl_2['weights']), hl_2['biases'])
l2 = tf.nn.sigmoid(l2,name='op2')
l3 = tf.add(tf.matmul(l2, hl_3['weights']), hl_3['biases'])
l3 = tf.nn.sigmoid(l3,name='op3')
l4 = tf.add(tf.matmul(l3, hl_4['weights']), hl_4['biases'])
l4 = tf.nn.sigmoid(l4,name='op4')
l5 = tf.add(tf.matmul(l4, hl_5['weights']), hl_5['biases'])
l5 = tf.nn.sigmoid(l5,name='op5')
dropout = tf.nn.dropout(l5,keep_prob, name='op6')
ol = tf.add(tf.matmul(dropout, output_layer['weights']), output_layer['biases'], name='op7')
return ol
def train(x):
prediction = neuralNet(x)
#print prediction
with tf.name_scope('cross_entropy'):
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
tf.summary.scalar('cross_entropy',cost)
with tf.name_scope('train'):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) # learning rate = 0.001
with tf.name_scope('accuracy'):
correct = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct,'float'))
tf.summary.scalar('accuracy',accuracy)
# cycles of feed forward and backprop
num_epochs = ne
with tf.Session() as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
merged_summary = tf.summary.merge_all()
for epoch in range(num_epochs):
epoch_loss = 0
for i in range(int(real_X_9.shape[0])/batch_size):#mnist.train.num_examples/batch_size)): # X.shape[0]
randidx = np.random.choice(real_X_9.shape[0], batch_size, replace=False)
epoch_x,epoch_y = real_X_9[randidx,:],real_y_9[randidx,:] #mnist.train.next_batch(batch_size) # X,y
j,c = sess.run([optimizer,cost],feed_dict={x:epoch_x,y:epoch_y,keep_prob:TRAIN_KEEP_PROB})
if i == 0:
[ta] = sess.run([accuracy],feed_dict={x:epoch_x,y:epoch_y,keep_prob:TRAIN_KEEP_PROB})
print 'Train Accuracy', ta
epoch_loss += c
print '\n','Epoch', epoch + 1, 'completed out of', num_epochs, '\nLoss:',epoch_loss
saver.save(sess, os.getcwd()+'/models/baseDNN')
train(x)
这是我尝试恢复模型的代码（产生错误的代码）：
import tensorflow as tf
import os
sess = tf.Session()
saver = tf.train.import_meta_graph(os.getcwd()+'/models/baseDNN.meta')
saver.restore(sess,tf.train.latest_checkpoint('./'))
graph = tf.get_default_graph()
x = graph.get_tensor_by_name('x_placeholder:0')
y = graph.get_tensor_by_name('y_placeholder:0')
op1 = graph.get_tensor_by_name('op1:0')
op2 = graph.get_tensor_by_name('op2:0')
op3 = graph.get_tensor_by_name('op3:0')
op4 = graph.get_tensor_by_name('op4:0')
op5 = graph.get_tensor_by_name('op5:0')
op6 = graph.get_tensor_by_name('op6:0')
op7 = graph.get_tensor_by_name('op7:0')
非常感谢任何帮助。感谢。
答案 0 :(得分：1)
不要认为加载了正确的检查点文件。
检查/models/baseDNN目录中保存的文件。它应该包含model.ckpt和model.ckpt.meta（或类似的东西）。然后指向正确的文件：
saver = tf.train.import_meta_graph('/path/baseDNN.meta')
saver.restore(sess,'/path/baseDNN')