如何使用张量流对MNIST数据进行逻辑回归编写汇总日志？

Question

我是tensorflow的新手和tensorboard的实施。这是我第一次使用tensorflow在MNIST数据上实现logistic regression的经验。我已经成功实现了对数据的逻辑回归，现在我正在尝试使用tf.summary .fileWriter将摘要记录到日志文件中。

这是影响summary参数的代码

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，我收到以下错误

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

这个错误指向Y的声明

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

你能帮我解决一下我做错了什么吗？

Answer 1

从错误消息看起来，您正在某种jupyter环境中运行代码。尝试重新启动内核/运行时并再次运行所有内容。在图形模式下运行代码两次在jupyter中不起作用。如果我在下面运行我的代码，第一次它不会返回任何错误，当我第二次运行它（没有重新启动内核/运行时）时，它会以与你的相同的方式崩溃。

我懒得在实际模型上检查它所以我的pred=y。 ;）但是下面的代码没有崩溃，所以你应该能够根据你的需要进行调整。我在Google Colab上测试过它。

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)