保存張量流模型：找不到變量

我試圖使用tf.train.Saver保存和恢復張量流模型。我想我已經做了正確的儲蓄部分，但是當我嘗試恢復模式，我得到這個錯誤：保存張量流模型：找不到變量

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)]]

我已經看了他們使用import_meta_graph，get_graph_by_tensor其他StackOverflow的問題，並restore加載回到張量流模型，我已經嘗試過將它適用於我的代碼，但是我一直在收到這些錯誤，它說它無法在檢查點中找到任何變量。

下面是代碼保存模型：

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)

這裏是我的代碼，試圖恢復模式（這是產生錯誤的代碼）：

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')

任何幫助將不勝感激。謝謝。

來源

2017-07-03 HS1300

不要認爲加載了正確的檢查點文件。檢查保存在您的/models/baseDNN目錄中的文件。它應該包含model.ckpt和model.ckpt.meta（或類似的東西）。然後指向正確的文件：

saver = tf.train.import_meta_graph('/path/baseDNN.meta') 
saver.restore(sess,'/path/baseDNN')

來源

2017-07-03 23:58:44

其實，我只看到這些文件：'checkpoint'，'baseDNN.index'，'baseDNN.meta'和'baseDNN.data 00000-的-00001'。 'models /'中沒有'/ baseDNN'子目錄。 – HS1300

除了'saver.save（sess，os.getcwd（）+'/ models/baseDNN）之外，還需要運行'saver.export_meta_graph（os.getcwd（）+'/ models/baseDNN.meta'）' 「）'？ – HS1300

是的，你可以試試它。 –

保存張量流模型：找不到變量

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