无法为从图中加载的张量变量赋值 [英] Cannot assign value to tensor variable loaded from graph
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问题描述
我已经训练了一个模型并保存了它.现在，我正在尝试查看权重扰动如何影响其准确性，因此我需要修改保存在我的权重变量中的值，从而基本上向其添加一些噪音.问题是我无法在加载它们后为它们分配值.我正在使用 tensorflow 1.2.1 版来训练和加载模型.这是我的代码:
I've trained a model and saved it. Now, I'm trying to see how weights perturbations might affect its accuracy, so I need to modify the values saved on my weights variables essentially adding some noise to it. The problem is that I can't assign a value to them after I've loaded them. I'm using tensorflow version 1.2.1., to train and load the model. Here is my code:
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
这会产生以下输出:
[<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，我一直在尝试修改第一个 (FF_NN/Model/hidden_layer_1/weights/Variable:0) 但这给了我一个错误:
So, I've been trying to modify the first one ( FF_NN/Model/hidden_layer_1/weights/Variable:0) but that gives me an error:
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)
这让我在分配操作中出错:
This gives me an error in the assign operation:
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'
根据 tf.global_variables() 的输出，所有类似的问题都指出 w1 应该是 tf.Variable 类型，这里似乎就是这种情况.
All the similar questions point out to the fact that w1 should be a tf.Variable type and that seems to be the case here, according to the output of tf.global_variables().
推荐答案
以下代码将起作用.使用 get_variable
following code would work. best way to use get_variable
w1 = tf.get_variable("FF_NN/Model/hidden_layer_1/weights/Variable:0")
sess.run(tf.assign(w1, w1+50))
目前这一步不起作用，这是 tensorflow 中的一个错误 https://github.com/tensorflow/tensorflow/issues/1325
FOR now this step won't work, this is a bug in tensorflow https://github.com/tensorflow/tensorflow/issues/1325
一个可行的解决方案:
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))
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