ValueError: Variable train/tower_01/block_1/conv1_1/weights does not exist, or was not created with

爱被打了一巴掌 2023-03-06 03:58 17阅读 0赞

#### 报错 ####

ValueError: Variable train/tower_01/block_1/conv1_1/weights does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=tf.AUTO_REUSE in VarScope?

#### 报错信息 ####

---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    <ipython-input-40-e649874e1c91> in <module>()
          1 config=tf.ConfigProto(allow_soft_placement=True)
          2                       #log_device_placement=True) # substantial output b/c big model
    ----> 3 multi_vgg = Multi_VGG_Model(4, config=config)
          4 multi_vgg
    
    <ipython-input-39-7e0bd3c4d61e> in __init__(s, num_towers, config)
         17                                                 staircase=True)
         18                 s.opt = tf.train.AdamOptimizer(lr)
    ---> 19                 s.tower_grads = [s._build_tower('tower_{:02d}'.format(i), i==0) for i in range(num_towers)]
         20                 s.avg_gradients = process_gradient_list(s.tower_grads)
         21                 s.train = s.opt.apply_gradients(s.avg_gradients)
    
    <ipython-input-39-7e0bd3c4d61e> in <listcomp>(.0)
         17                                                 staircase=True)
         18                 s.opt = tf.train.AdamOptimizer(lr)
    ---> 19                 s.tower_grads = [s._build_tower('tower_{:02d}'.format(i), i==0) for i in range(num_towers)]
         20                 s.avg_gradients = process_gradient_list(s.tower_grads)
         21                 s.train = s.opt.apply_gradients(s.avg_gradients)
    
    <ipython-input-39-7e0bd3c4d61e> in _build_tower(s, name, first_tower, device)
         56                 print(s.image_batch)
         57                 print('11111111111111111')
    ---> 58                 logits = vgg_net(s.image_batch, 0.5)
         59                 print(logits)
         60 
    
    <ipython-input-37-ea9fbae6f8b8> in vgg_net(images, keep_prob)
         38     # 第一个块结构，包括两个conv3-64
         39     with tf.name_scope('block_1'):
    ---> 40         conv1_1 = conv_op(images, filter_size=3, channel_out=64, step=1, name='conv1_1')
         41         conv1_2 = conv_op(conv1_1, filter_size=3, channel_out=64, step=1, name='conv1_2')
         42         pool1 = maxPool_op(conv1_2, filter_size=2, step=2, name='pooling_1')
    
    <ipython-input-37-ea9fbae6f8b8> in conv_op(input_op, filter_size, channel_out, step, name)
         12         print(scope + 'weights')
         13         weights = tf.get_variable(shape=[filter_size, filter_size, channel_in, channel_out], dtype=tf.float32,
    ---> 14                                   initializer=xavier_initializer_conv2d(), name=scope + 'weights')
         15         biases = tf.Variable(tf.constant(value=0.0, shape=[channel_out], dtype=tf.float32),
         16                              trainable=True, name='biases')
    
    ~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in get_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)
       1494       constraint=constraint,
       1495       synchronization=synchronization,
    -> 1496       aggregation=aggregation)
       1497 
       1498 
    
    ~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in get_variable(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)
       1237           constraint=constraint,
       1238           synchronization=synchronization,
    -> 1239           aggregation=aggregation)
       1240 
       1241   def _get_partitioned_variable(self,
    
    ~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in get_variable(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)
        560           constraint=constraint,
        561           synchronization=synchronization,
    --> 562           aggregation=aggregation)
        563 
        564   def _get_partitioned_variable(self,
    
    ~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in _true_getter(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, constraint, synchronization, aggregation)
        512           constraint=constraint,
        513           synchronization=synchronization,
    --> 514           aggregation=aggregation)
        515 
        516     synchronization, aggregation, trainable = (
    
    ~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in _get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint, synchronization, aggregation)
        880       raise ValueError("Variable %s does not exist, or was not created with "
        881                        "tf.get_variable(). Did you mean to set "
    --> 882                        "reuse=tf.AUTO_REUSE in VarScope?" % name)
        883 
        884     # Create the tensor to initialize the variable with default value.
    
    ValueError: Variable train/tower_01/block_1/conv1_1/weights does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=tf.AUTO_REUSE in VarScope?

#### 解决方法思路 打印路径 ####

print(scope + 'weights')
    发现 train/tower_01/block_1/conv1_1/weights 这个路径不存在，然后溯源这个路径的出处，改成存在的路径就好了

发现 train/tower\_01/block\_1/conv1\_1/weights 这个路径不存在，然后溯源这个路径的出处，改成存在的路径就好了