PROSAGA码农传奇-模型可视化-Tensorboard没有为Visualization Python提供所有变量输出

<div class =“post-text”itemprop =“text”>
  <P>
    你必须告诉TensorFlow你想跟踪你的损失。您只是将图表添加到编写器中。例如，您可以执行此操作来跟踪您的损失：
  </p>
   <pre>
    <code>
      loss = ... (your def)
tf.summary.scalar('MyLoss', loss)

# ... maybe add some other variables (you can also make histograms, images, etc. via tf.summary.historam(...))

summ = tf.summary.merge_all()

</code>
  </pre>
  <P>
    在您的会话中，您可以像创建一样创建编写器。然后，您必须评估摘要操作并将其添加到编写器。但是，您应该在训练循环之外创建编写器，因为您不希望每次迭代都有编写器。您在迭代中提供迭代作为参数
     <code>
      add_summary
    </code>
     方法。
  </p>
   <pre>
    <code>
      saver = tf.train.Saver()
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter("outputLogs", sess.graph)

for iteration in range(int(n_epochs*train_set_size/batch_size)):

x_batch, y_batch = get_next_batch(batch_size) # fetch the next training batch

[_, s] = sess.run([training_op, summ], feed_dict={X: x_batch, y: y_batch})

writer.add_summary(s, iteration)

if iteration % int(5*train_set_size/batch_size) == 0:
            mse_train = loss.eval(feed_dict={X: x_train, y: y_train}) 
            mse_valid = loss.eval(feed_dict={X: x_valid, y: y_valid}) 
            print('%.2f epochs: MSE train/valid = %.10f/%.10f'%(
                iteration*batch_size/train_set_size, mse_train, mse_valid))
            save_path = saver.save(sess, "models\\model"+str(iteration)+".ckpt")

</code>
  </pre>
  <P>
    您的培训代码应如下所示。
  </p>
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