尝试使用未初始化的值错误，即使我初始化局部和全局变量

Question

我正在尝试使用张量流指标来查找召回率和/或精度，这是我的代码：

print("Starting session...")
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
sess.run(train_init_op)
tot_acc = 0
print("Starting training...")
for i in range(num_epochs):
sample_counter = 0
l, _, acc = sess.run([loss, optimizer, accuracy], feed_dict={keep_prob: 0.7})
tot_acc += acc
print("Epoch: {}, loss: {:.3f}, training accuracy: {:.2f}%".format(i, l, acc * 100))
print("Average training set accuracy over {} epoch is {:.2f}%".format(num_epochs,
              (tot_acc / num_epochs) * 100))
print("Starting Validation...")
sess.run(valid_init_op)
tot_acc = 0
for i in range(valid_iters):
acc = sess.run([accuracy], feed_dict={keep_prob: 1.0})
tot_acc += acc[0]
print("Iter: {}, validation accuracy: {:.2f}%".format(i, acc[0] * 100))
print("Average validation set accuracy over {} iterations is {:.2f}%".format(valid_iters,
     (tot_acc / valid_iters) * 100))
sess.run(valid_init_op)
val_img, val_label = next_element
finalprediction = tf.argmax(train_predict, 1)
actualprediction = tf.argmax(val_label, 1)
confusion = tf.confusion_matrix(labels=actualprediction, predictions=finalprediction, 
num_classes=num_classes, dtype=tf.int32, name="Confusion_Matrix")
recall = tf.metrics.recall(labels=actualprediction, predictions=finalprediction, name="Recall")

cm = np.zeros([2,2], dtype=int)
rc = np.zeros([1,2], dtype=int)
for i in range(valid_iters):
while True:
try:
conf_matrix = sess.run(confusion, feed_dict={keep_prob: 1.0})
rec = sess.run(recall, feed_dict={keep_prob: 1.0})
cm += conf_matrix
rc += rec
except tf.errors.OutOfRangeError:
print("End of append.")
break
print("confusion matrix: ", cm)
print("Recall: ", rc)

但每次我得到Attempting to use uninitialized value，在谷歌搜索后，我在这里找到了一些答案： TensorFlow："尝试在变量初始化中使用未初始化的值" 在这里： 张量流变量初始化错误：尝试使用未初始化的值变量 我按照他们的建议做了同样的事情，但这些都没有解决我的问题？这是错误代码：

FailedPreconditionError: Attempting to use uninitialized value Recall_3/true_positives/count
[[Node: Recall_3/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@Recall_3/true_positives/AssignAdd"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](Recall_3/true_positives/count)]]

Answer 1

您的代码不可运行(它错过了一些变量/操作(，但正如您引用的答案中所解释的，您需要在初始化其中的变量之前首先定义您的图形。您可能会发现官方文档的这一部分很有用。

val_img, val_label = next_element
finalprediction = tf.argmax(train_predict, 1)
actualprediction = tf.argmax(val_label, 1)
confusion = tf.confusion_matrix(labels=actualprediction, predictions=finalprediction, 
num_classes=num_classes, dtype=tf.int32, name="Confusion_Matrix")
recall = tf.metrics.recall(labels=actualprediction, predictions=finalprediction, name="Recall")

print("Starting session...")
with tf.Session() as sess:
# Notice that the call to the variables initializer is made after the call to
# to tf.metrics.recall and therefore after the recall variables have been added to the graph.

sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
sess.run(train_init_op)
tot_acc = 0
print("Starting training...")
for i in range(num_epochs):
sample_counter = 0
l, _, acc = sess.run([loss, optimizer, accuracy], feed_dict={keep_prob: 0.7})
tot_acc += acc
print("Epoch: {}, loss: {:.3f}, training accuracy: {:.2f}%".format(i, l, acc * 100))
print("Average training set accuracy over {} epoch is {:.2f}%".format(num_epochs,
          (tot_acc / num_epochs) * 100))
print("Starting Validation...")
sess.run(valid_init_op)
tot_acc = 0
for i in range(valid_iters):
acc = sess.run([accuracy], feed_dict={keep_prob: 1.0})
tot_acc += acc[0]
print("Iter: {}, validation accuracy: {:.2f}%".format(i, acc[0] * 100))
print("Average validation set accuracy over {} iterations is {:.2f}%".format(valid_iters,
 (tot_acc / valid_iters) * 100))
sess.run(valid_init_op)
cm = np.zeros([2,2], dtype=int)
rc = np.zeros([1,2], dtype=int)
for i in range(valid_iters):
while True:
try:
conf_matrix = sess.run(confusion, feed_dict={keep_prob: 1.0})
rec = sess.run(recall, feed_dict={keep_prob: 1.0})
cm += conf_matrix
rc += rec
except tf.errors.OutOfRangeError:
print("End of append.")
break
print("confusion matrix: ", cm)
print("Recall: ", rc)