无法在tensorboard中使用summary.merge进行单独的培训和评估摘要

Question

我正在尝试使用张量板来观察卷积神经网络的学习。我对tf.summary.merge_all函数做的很好，可以创建一个合并的摘要。但是，我想跟踪培训和测试数据的准确性和损失。这篇文章很有用：Logging training and validation loss in tensorboard。为了使事情更容易处理，我想将我的摘要合并成两个合并的摘要，一个用于训练和一个用于验证（最终我会添加更多的东西，比如图像权重等）。我试图遵循来自张力板tf.summary.merge的描述。我无法做到这一点，我无法找到任何有用的例子来帮助我理解我的错在哪里。

with tf.name_scope('accuracy'): 
    correct_prediction = tf.equal(tf.argmax(y_logits, 1), tf.argmax(y, 1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float')) 
    tf.summary.scalar('accuracy', accuracy) 
    tf.summary.scalar('train_accuracy', accuracy) 

with tf.name_scope('Cost'): 
    cross_entropy = tf.reduce_mean(
     tf.nn.softmax_cross_entropy_with_logits(logits=y_logits, labels=y)) 
    opt = tf.train.AdamOptimizer() 
    optimizer = opt.minimize(cross_entropy) 
    grads = opt.compute_gradients(cross_entropy, [b_fc_loc2]) 
    tf.summary.scalar('cost', cross_entropy) 
    tf.summary.scalar('train_cost', cross_entropy) 


with tf.Session() as sess: 
    writer = tf.summary.FileWriter('./logs/mnistlogs/1f', sess.graph) 
    sess.run(tf.global_variables_initializer()) 
    merged = tf.summary.merge([cost, accuracy]) 

这将导致以下错误：

InvalidArgumentError (see above for traceback): Could not parse one of the summary inputs [[Node: Merge/MergeSummary = MergeSummary[N=2, _device="/job:localhost/replica:0/task:0/cpu:0"](Merge/MergeSummary/inputs_0, Merge/MergeSummary/inputs_1)]]

我想知道为什么会这样是不行的，我怎么能找到解决的办法，任何工作实施赞赏。

Answer 1

我想通了。在合并之前，我需要提供摘要名称。下面的代码解决了这个问题：

with tf.name_scope('Cost'): 
cross_entropy = tf.reduce_mean(
     tf.nn.softmax_cross_entropy_with_logits(logits=y_logits, labels=y)) 
opt = tf.train.AdamOptimizer(learning_rate=0.000003) 
optimizer = opt.minimize(cross_entropy) 
grads = opt.compute_gradients(cross_entropy, [b_fc_loc2]) 
cost_sum = tf.summary.scalar('val_cost', cross_entropy) 
training_cost_sum = tf.summary.scalar('train_cost', cross_entropy) 


with tf.name_scope('accuracy'): 
correct_prediction = tf.equal(tf.argmax(y_logits, 1), tf.argmax(y, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float')) 
train_accuracy = accuracy 
accuracy_sum = tf.summary.scalar('val_accuracy', accuracy) 
training_accuracy_sum = tf.summary.scalar('train_accuracy', accuracy) 


with tf.Session() as sess: 
writer = tf.summary.FileWriter('./logs/{}/{}'.format(session_name, run_num), sess.graph) 
sess.run(tf.global_variables_initializer()) 
train_merged = tf.summary.merge([training_accuracy_sum, training_cost_sum])