在存储会话时在张量流中出现错误“无变量可保存”

Question

I was trYing to save session in model so that i can use it later on but i am getting an error everytime. My code is like:

with tf.Session() as sess:
    sess.run(init)
    for j in range(3):
        for i in range(xtest.shape[0]):

            _, indices = sess.run(pred, feed_dict={x_train: xtrain, x_test: xtest[i,:]})
            pred_label = getMajorityPredictions(ytrain, indices) 
            actual_val = get_char( int( (ytest[i]).argmax() ) )

            # print("test: ", i, "prediction:       ", get_char(pred_label), "          actual:            ",   actual_val)
            # print(pred_label, actual_val, type(pred_label), type(actual_val), sep=" --> ")
            if get_char(pred_label) == actual_val:
                accuracy += 1/len(xtest)

            # print((i / (xtest.shape[0])) * 100)
            # os.system("cls")
                print("accuracy: ",accuracy)

    savedPath = saver.save(sess, "/tmp/model.ckpt")
    print("Model saved at: " ,savedPath)

and the error is like:

Traceback (most recent call last):
File "prac3.py", line 74, in <module>
    saver = tf.train.Saver()
File "C:\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 1239, in __init__
    self.build()
File "C:\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 1248, in build
    self._build(self._filename, build_save=True, build_restore=True)
File "C:\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 1272, in _build
    raise ValueError("No variables to save")
ValueError: No variables to save

Answer 1

The code you provided does not give much information on the error. You might need to check your previous code to see if you actually have an variables to be saved. You can check tf.global_variables() and see if the list is empty.

In addition, you might want to put an indent before the savedPath = saver.save(sess, "/tmp/model.ckpt") as you used with tf.Session as sess, so the session is actually closed when you are outside that block, then you'll face the problem of 'Attempting to use closed session'.

Answer 2

x_train = tf.placeholder(tf.float32, shape=[None, 4096])           
y_train = tf.placeholder(tf.float32, shape=[None, 62])
x_test = tf.placeholder(tf.float32, shape=[4096])           
y_test = tf.placeholder(tf.float32, shape=[None, 62])

l1_distance = tf.abs(tf.subtract(x_train, x_test))
dis_l1 = tf.reduce_sum(l1_distance, axis=1)
pred = tf.nn.top_k(tf.negative(dis_l1), k=5)

xtrain, ytrain = TRAIN_SIZE(2852)
xtest, ytest = TEST_SIZE(557)

init = tf.global_variables_initializer()
accuracy = 0
saver = tf.train.Saver()
# --------------------- to create model 
with tf.Session() as sess:
    sess.run(init)
    for j in range(3):
        for i in range(xtest.shape[0]):

            _, indices = sess.run(pred, feed_dict={x_train: xtrain, x_test: xtest[i,:]})
            pred_label = getMajorityPredictions(ytrain, indices) 
            actual_val = get_char( int( (ytest[i]).argmax() ) )

            # print("test: ", i, "prediction:       ", get_char(pred_label), "          actual:            ",   actual_val)
            # print(pred_label, actual_val, type(pred_label), type(actual_val), sep=" --> ")
            if get_char(pred_label) == actual_val:
                accuracy += 1/len(xtest)

            # print((i / (xtest.shape[0])) * 100)
            # os.system("cls")
                print("accuracy: ",accuracy)

    savedPath = saver.save(sess, "/tmp/model.ckpt")
    print("Model saved at: " ,savedPath)