Question

我对tensorflow和tensorboard的实现不熟悉。这是我第一次使用Tensorflow在MNIST数据上实现logistic regression的经验。我已经成功实现了数据的逻辑回归，现在我正尝试使用tf.summary .fileWriter将摘要记录到日志文件。

这是我的代码，会影响摘要参数

x = tf.placeholder(dtype=tf.float32, shape=(None, 784))
y = tf.placeholder(dtype=tf.float32, shape=(None, 10)) 

loss_op = tf.losses.mean_squared_error(y, pred)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

tf.summary.scalar("loss", loss_op)
tf.summary.scalar("training_accuracy", accuracy_op)
summary_op = tf.summary.merge_all()

这就是我训练模型的方式

with tf.Session() as sess:   
    sess.run(init)
    writer = tf.summary.FileWriter('./graphs', sess.graph)

    for iter in range(50):
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        _, loss, tr_acc,summary = sess.run([optimizer_op, loss_op, accuracy_op, summary_op], feed_dict={x: batch_x, y: batch_y})
        summary = sess.run(summary_op, feed_dict={x: batch_x, y: batch_y})
        writer.add_summary(summary, iter)

添加摘要行以获取合并摘要后，我遇到了以下错误


InvalidArgumentError (see above for traceback): 
You must feed a value for placeholder tensor 'Placeholder_37' 
with dtype float and shape [?,10]

此错误指向Y

的声明

y = tf.placeholder(dtype=tf.float32, shape=(None, 10))

您能帮我做错什么吗？

Answer 1

从错误消息中，您似乎正在某种jupyter环境中运行代码。尝试重新启动内核/运行时，然后再次运行所有内容。在jupyter中，以图形模式运行两次代码无法正常工作。如果我在下面运行代码，则第一次不会返回任何错误，而在第二次运行时（不重新启动内核/运行时），则崩溃的方式与您的崩溃相同。

我太懒了，无法检查实际模型，所以我pred=y。 ;）但是下面的代码不会崩溃，因此您应该能够使其适应您的需求。我已经在Google Colab中对其进行了测试。

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

x = tf.placeholder(dtype=tf.float32, shape=(None, 784), name='x-input')
y = tf.placeholder(dtype=tf.float32, shape=(None, 10), name='y-input')

pred = y
loss_op = tf.losses.mean_squared_error(y, pred)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.name_scope('summaries'):
  tf.summary.scalar("loss", loss_op, collections=["train_summary"])
  tf.summary.scalar("training_accuracy", accuracy_op, collections=["train_summary"])

with tf.Session() as sess:   
  summary_op = tf.summary.merge_all(key='train_summary')
  train_writer = tf.summary.FileWriter('./graphs', sess.graph)
  sess.run([tf.global_variables_initializer(),tf.local_variables_initializer()])

  for iter in range(50):
    batch_x, batch_y = mnist.train.next_batch(1)
    loss, acc, summary = sess.run([loss_op, accuracy_op, summary_op], feed_dict={x:batch_x, y:batch_y})
    train_writer.add_summary(summary, iter)

如何使用张量流编写摘要日志以对MNIST数据进行逻辑回归？

1 个答案: