Question

我正在运行一个函数'train'，我正在尝试使用

保存我的模型

    save_path = saver.save(sess, model_path,global_step=1000)
    print("Model saved in file: %s" % save_path)

其中model_path是传递给函数'train'的参数（以及定义我的神经网络的其他几个参数）

    model_path="Models/LeNet"

我想保存不同参数集的模型以进行比较。因此，当我在循环中运行函数时，有时它，但有时我得到以下错误

 FailedPreconditionError (see above for traceback): Failed to rename: Models/LeNet-1000.data-00000-of-00001.tempstate4653798046453900544 to: Models/LeNet-1000.data-00000-of-00001 : The process cannot access the file because it is being used by another process.

 [[Node: save/SaveV2 = SaveV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/SaveV2/tensor_names, save/SaveV2/shape_and_slices, Model/b1, Model/b1/Adam, Model/b1/Adam_1, Model/b2, Model/b2/Adam, Model/b2/Adam_1, Model/b3, Model/b3/Adam, Model/b3/Adam_1, Model/b4, Model/b4/Adam, Model/b4/Adam_1, Model/b5, Model/b5/Adam, Model/b5/Adam_1, Model/w1, Model/w1/Adam, Model/w1/Adam_1, Model/w2, Model/w2/Adam, Model/w2/Adam_1, Model/w3, Model/w3/Adam, Model/w3/Adam_1, Model/w4, Model/w4/Adam, Model/w4/Adam_1, Model/w5, Model/w5/Adam, Model/w5/Adam_1, SGD/beta1_power, SGD/beta2_power)]]

我想知道为什么它会创建一个扩展名为“tempstate4653798046453900544”的文件以及为什么它说'进程无法访问该文件，因为它正被另一个进程使用'，即使我尝试关闭计算机中的所有进程，除了jupyter笔记本。请注意，此错误与'Tensorflow FailedPreconditionError不同，但所有变量都已初始化'。

我在这里附上我的火车功能：

def train(learning_rate, training_epochs, batch_size, display_step, alg, actFun, logs_path,model_path):

tf.reset_default_graph()

# tf Graph Input:  mnist data image of shape 28*28=784
x = tf.placeholder(tf.float32, [None, 784], name='InputData')
# 0-9 digits recognition,  10 classes
y = tf.placeholder(tf.float32, [None, 10], name='LabelData')

with tf.name_scope('Model'):
    # Model
    xin=tf.reshape(x,[-1,28,28,1]) # reshape the input image to 28x28 size
    pred = LeNet5_Model(xin,actFun)
with tf.name_scope('Loss'):
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred))  
    print ('cost',cost)
with tf.name_scope('SGD'):
    optimizer = alg(learning_rate).minimize(cost)

with tf.name_scope('Accuracy'):
    # Accuracy
    acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    acc = tf.reduce_mean(tf.cast(acc, tf.float32))

init = tf.global_variables_initializer()

tf.summary.scalar("Loss", cost)
tf.summary.scalar("Accuracy", acc)
merged_summary_op = tf.summary.merge_all()
saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(init)
    train_writer= tf.summary.FileWriter(logs_path+'/train', graph=tf.get_default_graph())
    validation_writer= tf.summary.FileWriter(logs_path+'/validation', graph=tf.get_default_graph())
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size, shuffle=True)
            _, c, acc_train, summary = sess.run([optimizer, cost, acc, merged_summary_op],
                                     feed_dict={x: batch_xs, y: batch_ys})
            train_writer.add_summary(summary, epoch * total_batch + i)
            avg_cost += c / total_batch
        accValid, summary_validation= sess.run([acc,merged_summary_op],feed_dict={x: X_validation, y: y_validation})
        validation_writer.add_summary(summary_validation, epoch)     
        if (epoch+1) % display_step == 0:
            print("Epoch: ", '%02d' % (epoch+1), "  ====> Loss=", "{:.6f}".format(avg_cost), "Training Accuracy=","{:.6f}".format(acc_train*100),"%", " Validation Accuracy=","{:.6f}".format(accValid*100),"%")   
    print("Optimization Finished!") 
    save_path = saver.save(sess, model_path,global_step=1000)
    print("Model saved in file: %s" % save_path)

在Tensorflow中保存模型时出现FailedPreconditionError

0 个答案: