どのように私はこれを解決することができます： "RuntimeError：閉じられたセッションを使用しようとしました。

Question

私はテンソルボードコードを書いています。実行すると、「閉じたセッションを使用しようとしました」と言いました。誰かがこの問題を解決する方法を教えてくれますか？ここにコードがあります：

# coding=utf-8 
from color_1 import read_and_decode, get_batch, get_test_batch 
import cv2 
import os 
import time 
import math 
import numpy as np 
import tensorflow as tf 

max_steps=3000 
batch_size=128 
log_dir='/home/vrview/tensorflow/example/char/tfrecords' 
def variable_with_weight_loss(shape,stddev,w1): 
    var=tf.Variable(tf.truncated_normal(shape,stddev=stddev)) 
    if w1 is not None: 
    weight_loss=tf.multiply(tf.nn.l2_loss(var),w1,name='weight_loss') 
    tf.add_to_collection('losses',weight_loss)         
    return var 
with tf.name_scope('input'): 
    image_holder=tf.placeholder(tf.float32,[batch_size,56,56,3],name='x-input') 
    label_holder=tf.placeholder(tf.int32,[batch_size],name='y-input') 
with tf.name_scope('input_reshape'): 
    image_input=tf.reshape(image_holder,[-1,56,56,3]) 
    tf.summary.image('input',image_input,10) 

def variable_summaries(var): 
    with tf.name_scope('summaries'): 
    mean=tf.reduce_mean(var) 
    tf.summary.scalar('mean',mean) 
    with tf.name_scope('stddev'): 
     stddev=tf.sqrt(tf.reduce_mean(tf.square(var-mean))) 
    tf.summary.scalar('stddev',stddev) 
    tf.summary.scalar('max',tf.reduce_max(var)) 
    tf.summary.scalar('min',tf.reduce_min(var)) 
    tf.summary.histogram('histogram',var) 
def layer1(layer_name): 
    with tf.name_scope(layer_name): 
     with tf.name_scope('weight1'): 
      weight1=variable_with_weight_loss(shape=[5,5,3,64],stddev=5e-2,w1=0.0) 
      variable_summaries(weight1) 
     kernel1=tf.nn.conv2d(image_holder,weight1,[1,1,1,1],padding='SAME') 
     with tf.name_scope('biases'): 
      bias1=tf.Variable(tf.constant(0.0,shape=[64])) 
      variable_summaries(bias1) 
     conv1=tf.nn.relu(tf.nn.bias_add(kernel1,bias1)) 
     pool1=tf.nn.max_pool(conv1,ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME') 
     norm1=tf.nn.lrn(pool1,4,bias=1.0,alpha=0.001/9.0,beta=0.75) 
     return norm1 
def full_layer1(pool,name): 
    with tf.name_scope(name): 
     reshape=tf.reshape(pool,[batch_size,-1]) 
     dim=reshape.get_shape()[1].value 
     with tf.name_scope('weight3'): 
      weight3=variable_with_weight_loss(shape=[dim,384],stddev=0.04,w1=0.004) 
      variable_summaries(weight3) 
     with tf.name_scope('bias3'): 
      bias3=tf.Variable(tf.constant(0.1,shape=[384])) 
      variable_summaries(bias3) 
     with tf.name_scope('Wx_plus'): 
      preactivate1=tf.matmul(reshape,weight3)+bias3 
      tf.summary.histogram('pre_activations1',preactivate1) 
     activations=tf.nn.relu(preactivate1,name='activation') 
     tf.summary.histogram('activations',activations) 
     return activations 
def full_layer2(local,name): 
    with tf.name_scope('weight4'): 
     weight4=variable_with_weight_loss(shape=[384,192],stddev=0.04,w1=0.004) 
     variable_summaries(weight4) 
    with tf.name_scope('bias4'): 
     bias4=tf.Variable(tf.constant(0.1,shape=[192])) 
     variable_summaries(bias4) 
    with tf.name_scope('Wx_plus2'): 
     preactivate2 =tf.matmul(local,weight4)+bias4 
     tf.summary.histogram('pre_activations2', preactivate2) 
    activations = tf.nn.relu(preactivate2, name='activation') 
    tf.summary.histogram('activations', activations) 
    return activations 
def full_layer3(local,name): 
    with tf.name_scope('weight5'): 
     weight5=variable_with_weight_loss(shape=[192,10],stddev=1/192,w1=0.0) 
     variable_summaries(weight5) 
    with tf.name_scope('bias5'): 
     bias5=tf.Variable(tf.constant(0.0,shape=[10])) 
     variable_summaries(bias5) 

    logits=tf.add(tf.matmul(local,weight5),bias5) 
    return logits 
def loss(logits,labels): 
    labels=tf.cast(labels,tf.int64) 
    with tf.name_scope('cross_entropy'): 
     cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(
     logits=logits,labels=labels,name ='cross_entropy_per_example') 
     cross_entropy_mean=tf.reduce_mean(cross_entropy,name='cross_entropy') 
     tf.add_to_collection('losses', cross_entropy_mean) 
    tf.summary.scalar('cross_entropy',cross_entropy_mean) 
    return tf.add_n(tf.get_collection('losses'),name='total_loss') 
norm1=layer1('layer1') 
pool2=layer2(norm1,'layer2') 
act1=full_layer1(pool2,'full_layer1') 
act2=full_layer2(act1,'full_layer2') 
act3=full_layer3(act2,'full_layer3') 

loss=loss(act3,label_holder) 
with tf.name_scope('train'): 
    train_op=tf.train.AdamOptimizer(1e-3).minimize(loss) 
top_k_op=tf.nn.in_top_k(act3,label_holder,1) 
def run(): 

    image, label = read_and_decode('train.tfrecords') 
    batch_image, batch_label = get_batch(image, label, batch_size=128, crop_size=56) 

    test_image, test_label = read_and_decode('val.tfrecords') 
    test_images, test_labels = get_test_batch(test_image, test_label, batch_size=128, crop_size=56) # batch 生成测试 

    def feed_dict(train): 
     if train: 
      x=image_batch 
      y=label_batch 
     else: 
      x=img_batch 
      y=lab_batch 
     return {image_holder:x,label_holder:y} 

    saver=tf.train.Saver() 
    num_examples = 10000 
    num_iter = int(math.ceil(num_examples/batch_size)) 
    true_count = 0 
    total_sample_count = num_iter * batch_size 

    init = tf.global_variables_initializer() 
    with tf.Session() as sess: 
     sess.run(init) 
     merged = tf.summary.merge_all() 
     train_writer = tf.summary.FileWriter(log_dir + '/train', sess.graph) 
     test_writer = tf.summary.FileWriter(log_dir + '/test') 
     coord = tf.train.Coordinator() 
     threads = tf.train.start_queue_runners(coord=coord) 

    for step in range(max_steps): 
     start_time = time.time() 
     image_batch, label_batch = sess.run([batch_image, batch_label]) 

     if step % 10 == 0: 

      run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) 
      run_metadata = tf.RunMetadata() 
      img_batch, lab_batch = sess.run([test_images, test_labels]) 
      summary,loss_value=sess.run([merged,loss],feed_dict=feed_dict(True)) 
      train_writer.add_run_metadata(run_metadata,'step%03d' % step) 
      train_writer.add_summary(summary, step) 

      saver.save(sess,log_dir+"/model.ckpt",step) 

    step = 0 
    while step < num_iter: 
     img_batch, lab_batch = sess.run([test_images, test_labels]) 
     predictions = sess.run([top_k_op], feed_dict=feed_dict(False)) 
     true_count += np.sum(predictions) 
     step += 1 
     test_writer.add_summary(predictions) 
     print('Predict:', predictions) 
    precision = true_count * 1.0/total_sample_count 
    print 'true_count:', true_count, 'total_sample_count:', total_sample_count 
    print 'precision:', precision 
    train_writer.close() 
    test_writer.close() 
if __name__=='__main__': 
    run() 

私はそれを実行すると、それは "閉じたセッションを使用しようとしました"と言いました。次のように入力してください：

File "/home/vrview/tensorflow/example/char/tfrecords/color2_board.py", line 238, in <module> 
    run() 
    File "/home/vrview/tensorflow/example/char/tfrecords/color2_board.py", line 207, in run 
    image_batch, label_batch = sess.run([batch_image, batch_label]) 
    File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 767, in run 
    run_metadata_ptr) 
    File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 903, in _run 
    raise RuntimeError('Attempted to use a closed Session.') 
RuntimeError: Attempted to use a closed Session. 

誰でも何が起こったのですか？あなたの助けに感謝します！

Answer 1

sessを使用するものは、with tf.Session() as sessの内部にある必要があります。あなたは、基本的には何が起こることはあなたはそれがスコープの外に出ると自動的にSESのオブジェクトを閉じwith tf.Session() as sessスコープのsess.run([batch_image, batch_label])外を呼び出すようにしようとしているということですfor step in range(max_steps):からtest_writer.close()

にすべてをインデントする必要があります。