我训练了一个模型并保存了它。现在,我想看看权重扰动如何影响它的精度,所以我需要修改保存在权重变量上的值,本质上就是给它添加一些噪声。问题是,在我加载它们之后,我无法为它们赋值。我使用tensorflow版本1.2.1.来训练和加载模型。这是我的代码:
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
这将产生以下输出:
^{pr2}$所以,我一直在尝试修改第一个(FF_NN/Model/hidden_layer_1/weights/变量:0)但这给了我一个错误:
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)
这给了我赋值操作中的一个错误:
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'
所有类似的问题都指出w1应该是tf.变量根据tf.global_variables()
的输出,这里似乎就是这样。在
下面的代码就可以了。使用get_variable的最佳方法
现在这个步骤行不通,这是tensorflowhttps://github.com/tensorflow/tensorflow/issues/1325中的一个bug
有效解决方案:
^{pr2}$您需要使用
tf.get_variable
或tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)[0]
获取底层变量对象Ishant指出,
get_variable
当前有一个作用域变量的错误,因此在修复之前,您需要使用get_collection
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