无法分配值从图

我训练的模型，并将其保存加载张量变量。现在，我试图看看权重扰动如何影响其准确性，所以我需要修改保存在我的权重变量上的值，从而基本上为其添加一些噪声。问题是，我加载它们后无法为它们分配值。我正在使用tensorflow 1.2.1版来训练和加载模型。这里是我的代码：无法分配值从图

import tensorflow as tf 
tf.reset_default_graph() 
sess = tf.InteractiveSession() 
saver = tf.train.import_meta_graph('/scratch/pedro/TFModels/Checks_and_Logs/20170803_215828/beta_model-1.meta') 
print("Graph restored") 
saver.restore(sess, tf.train.latest_checkpoint('/scratch/pedro/TFModels/Checks_and_Logs/20170803_215828/')) 
print("Model restored") 
tf.global_variables() #prints the list of variables in the graph

这将产生以下的输出：

[<tf.Variable 'FF_NN/Model/hidden_layer_1/weights/Variable:0' shape=(3960, 256) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/hidden_layer_1/bias/bias_hidden_layer_1:0' shape=(256,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/hidden_layer_2/weights/Variable:0' shape=(256, 256) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/hidden_layer_2/bias/bias_hidden_layer_2:0' shape=(256,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/hidden_layer_3/weights/Variable:0' shape=(256, 256) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/hidden_layer_3/bias/bias_hidden_layer_3:0' shape=(256,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/output_layer/weights/Variable:0' shape=(256, 5) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/output_layer/bias/bias_output_layer:0' shape=(5,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/Variable:0' shape=() dtype=int32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/beta1_power:0' shape=() dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/beta2_power:0' shape=() dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_1/weights/Variable/Adam:0' shape=(3960, 256) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_1/weights/Variable/Adam_1:0' shape=(3960, 256) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_1/bias/bias_hidden_layer_1/Adam:0' shape=(256,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_1/bias/bias_hidden_layer_1/Adam_1:0' shape=(256,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_2/weights/Variable/Adam:0' shape=(256, 256) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_2/weights/Variable/Adam_1:0' shape=(256, 256) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_2/bias/bias_hidden_layer_2/Adam:0' shape=(256,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_2/bias/bias_hidden_layer_2/Adam_1:0' shape=(256,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_3/weights/Variable/Adam:0' shape=(256, 256) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_3/weights/Variable/Adam_1:0' shape=(256, 256) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_3/bias/bias_hidden_layer_3/Adam:0' shape=(256,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/hidden_layer_3/bias/bias_hidden_layer_3/Adam_1:0' shape=(256,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/output_layer/weights/Variable/Adam:0' shape=(256, 5) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/output_layer/weights/Variable/Adam_1:0' shape=(256, 5) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/output_layer/bias/bias_output_layer/Adam:0' shape=(5,) dtype=float32_ref>, 
<tf.Variable 'FF_NN/Model/Training/Optimizer/FF_NN/Model/output_layer/bias/bias_output_layer/Adam_1:0' shape=(5,) dtype=float32_ref>]

所以，我一直在试图修改的第一个（FF_NN /型号/ hidden_layer_1 /重量/变量：0）但给我一个错误：

x = data_train[:batch_size] 
y = data_train_labels[:batch_size] 
graph = tf.get_default_graph() 
data_train_tensor = graph.get_tensor_by_name("Train_Dataset:0") 
data_train_labels_onehot = graph.get_tensor_by_name("Train_Labels:0") 
acc_te = graph.get_tensor_by_name("Test_Data_Accuracy/Mean:0") 
acc_tr = graph.get_tensor_by_name("Train_Data_Accuracy/Mean:0") 


w1 = graph.get_tensor_by_name("FF_NN/Model/hidden_layer_1/weights/Variable:0") 
print('w1:\n', w1.eval()) 
training_acc, test_acc = sess.run([acc_tr, acc_te], feed_dict={data_train_tensor: x, data_train_labels_onehot: y}) 
print(test_acc) 

w1 = w1 + 50 
print('w1:\n', w1.eval()) 
sess.run(w1.assign(w1)) 
training_acc, test_acc, _ = sess.run([acc_tr, acc_te, w1], feed_dict={data_train_tensor: x, data_train_labels_onehot: y}) 
print(test_acc)

这使我在分配操作中的错误：

w1: 
[[-0.0531723 0.73768502 0.14098917 ..., 1.67111528 0.2495033 
    0.20415793] 
[ 1.20964873 -0.99254322 -3.01407313 ..., 0.40427083 0.33289135 
    0.2326804 ] 
[ 0.70157909 -1.61257529 -0.59762233 ..., 0.20860809 -0.02733657 
    1.57942903] 
..., 
[ 1.23854971 -2.28062844 -1.01647282 ..., 1.18426156 0.65342903 
    -0.45519635] 
[ 1.02164841 -0.11143603 1.71673298 ..., -0.85511237 1.15535712 
    0.50917912] 
[-2.52524352 -0.04488864 0.66239733 ..., -0.45516238 -0.76003599 
    -1.2073245 ]] 
0.242335 
w1: 
[[ 49.94682693 50.73768616 50.1409874 ..., 51.67111588 50.24950409 
    50.20415878] 
[ 51.20964813 49.00745773 46.98592758 ..., 50.40427017 50.33288956 
    50.23268127] 
[ 50.70158005 48.38742447 49.40237808 ..., 50.20860672 49.97266388 
    51.57942963] 
..., 
[ 51.23854828 47.7193718 48.98352814 ..., 51.18426132 50.65342712 
    49.54480362] 
[ 51.02164841 49.88856506 51.71673203 ..., 49.14488602 51.15535736 
    50.50917816] 
[ 47.47475815 49.95511246 50.66239548 ..., 49.54483795 49.23996353 
    48.79267502]] 
--------------------------------------------------------------------------- 
AttributeError       Traceback (most recent call last) 
<ipython-input-34-da5d05216392> in <module>() 
    16 w1 = w1 +50 
    17 print('w1:\n', w1.eval()) 
---> 18 sess.run(w1.assign(w1)) 
    19 #print('w1:\n', w1.eval()) 
    20 training_acc, test_acc, _ = sess.run([acc_tr, acc_te, w1], feed_dict={data_train_tensor: x, data_train_labels_onehot: y}) 

AttributeError: 'Tensor' object has no attribute 'assign'

个

所有类似的问题指出一个事实，即W1应该是一个tf.Variable类型，这似乎是在这里的情况下，根据tf.global_variables()输出。

来源

2017-08-09 Pedro Silva

'w1'用于指向'tf.Variable'，但是后来你将它指向了'tf.add'张量，它没有'assign'方法 –

我明白你的意思，但我仍然不确定如何做到这一点。即使我做了'w2 = w1 + 50'，然后'w1.assign（w2）.eval（）'，我得到了同样的错误。 –

啊，对不起，没有仔细阅读......做'get_tensor_by_name'给你一个张量对象。一个tf.Variable包含张量对象和其他一些东西（比如assign方法）。你应该尝试（在'tf.get_collection即像（ 'train_op'）[0]'）使用集合得到恢复Python的变量对象 –

下面的代码会工作。使用get_variable

w1 = tf.get_variable("FF_NN/Model/hidden_layer_1/weights/Variable:0") 
sess.run(tf.assign(w1, w1+50))

现在这一步都不行最好的办法，这是tensorflow错误https://github.com/tensorflow/tensorflow/issues/1325

一个有效的解决方案：

w1 = [v for v in tf.global_variables() if v.name=="FF_NN/Model/hidden_layer_1/weights/Variable:0"][0] 
sess.run(tf.assign(w1, w1+50))

来源

2017-08-09 17:26:36

我无法用'tf.get_variable（）'读取w1。首先，我得到了没有形状集的错误，所以我设置了与原始形状相同的形状。然后，我得到这个错误 'ValueError异常：“FF_NN /型号/ hidden_layer_1 /重量/变量：0”是不是一个有效的范围name' 我删除了'：0'，然后得到了以下错误： 'ValueError异常：变量FF_NN/Model/hidden_layer_1/weights /变量已经存在，不允许。你是否想在VarScope中设置reuse = True？' 虽然我在'tf.get_variable'里看不到'reuse = True'选项。 –

所以这应该确实做我想要的。感谢您指出这是目前的一个错误，让我不那么偏执。 –

看到我更新的答案。 –

你需要让潜在的变量对象使用tf.get_variable或tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)[0]

Ishant提到get_variable目前有ab ug作用域变量，所以直到它被修复为止你需要使用get_collection

来源

2017-08-09 18:30:27

无法分配值从图

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